diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 136a830a9..1392d4851 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -20,7 +20,7 @@ repos:
               | docs/.*\.(svg|png)$                    # documentation images
             )
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.15.2"
+    rev: "v0.16.10"
     hooks:
       - id: ruff-check
         args: [--fix]
diff --git a/docs/swapi/figure_src/figure_utils.py b/docs/swapi/figure_src/figure_utils.py
index 9e9dc7b2a..84623c6c8 100644
--- a/docs/swapi/figure_src/figure_utils.py
+++ b/docs/swapi/figure_src/figure_utils.py
@@ -4,8 +4,9 @@
 import os
 import sys
 import time
+from collections.abc import Callable
 from pathlib import Path
-from typing import Callable, TypeVar
+from typing import TypeVar
 
 import numpy as np
 
@@ -42,14 +43,68 @@ def load_swapi_response() -> SwapiResponse:
 
 COARSE_SWEEP_VOLTAGES_MEAN_V = np.array(
     [
-        9895.52, 9088.69, 8348.80, 7667.55, 7042.16, 6469.31, 5941.77, 5457.31,
-        5013.22, 4603.65, 4230.77, 3886.92, 3569.16, 3278.72, 3011.13, 2766.25,
-        2539.54, 2333.83, 2144.24, 1969.31, 1808.74, 1660.86, 1525.75, 1401.82,
-        1287.58, 1182.24, 1085.15, 995.55, 914.31, 839.94, 771.70, 709.46,
-        651.59, 598.47, 549.91, 505.12, 463.89, 425.92, 391.18, 359.35,
-        329.94, 303.02, 278.25, 255.55, 234.77, 215.61, 197.95, 181.82,
-        167.04, 153.46, 140.91, 129.50, 118.91, 109.20, 100.30, 92.11,
-        84.61, 77.73, 71.40, 65.59, 60.23, 55.34,
+        9895.52,
+        9088.69,
+        8348.80,
+        7667.55,
+        7042.16,
+        6469.31,
+        5941.77,
+        5457.31,
+        5013.22,
+        4603.65,
+        4230.77,
+        3886.92,
+        3569.16,
+        3278.72,
+        3011.13,
+        2766.25,
+        2539.54,
+        2333.83,
+        2144.24,
+        1969.31,
+        1808.74,
+        1660.86,
+        1525.75,
+        1401.82,
+        1287.58,
+        1182.24,
+        1085.15,
+        995.55,
+        914.31,
+        839.94,
+        771.70,
+        709.46,
+        651.59,
+        598.47,
+        549.91,
+        505.12,
+        463.89,
+        425.92,
+        391.18,
+        359.35,
+        329.94,
+        303.02,
+        278.25,
+        255.55,
+        234.77,
+        215.61,
+        197.95,
+        181.82,
+        167.04,
+        153.46,
+        140.91,
+        129.50,
+        118.91,
+        109.20,
+        100.30,
+        92.11,
+        84.61,
+        77.73,
+        71.40,
+        65.59,
+        60.23,
+        55.34,
     ]
 )
 
diff --git a/docs/swapi/figure_src/plot_alpha_peak_finding.py b/docs/swapi/figure_src/plot_alpha_peak_finding.py
index 0b8446d58..6cf26ef11 100644
--- a/docs/swapi/figure_src/plot_alpha_peak_finding.py
+++ b/docs/swapi/figure_src/plot_alpha_peak_finding.py
@@ -34,6 +34,8 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 
+from figure_utils import FIGURES_DIR, load_swapi_response
+
 from imap_l3_processing.constants import (
     ALPHA_MASS_PER_CHARGE_M_P_PER_E,
     ALPHA_PARTICLE_MASS_KG,
@@ -70,7 +72,6 @@
 )
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
 from imap_l3_processing.utils import SpiceKernelTypes
-from figure_utils import FIGURES_DIR, load_swapi_response
 
 DATE_YYYYMMDD = "20260101"
 N_SWEEPS = 5
@@ -187,8 +188,12 @@ def _read_5_sweep_block(
 
 def _coarse_measurement_times_ns(epoch_ns: np.ndarray) -> np.ndarray:
     coarse_bins = np.arange(SWAPI_COARSE_SWEEP_BINS.start, SWAPI_COARSE_SWEEP_BINS.stop)
-    seconds_into_sweep = coarse_bins * SWAPI_BIN_PERIOD_S + SWAPI_LIVETIME_CENTER_OFFSET_S
-    return (epoch_ns[:, None] + seconds_into_sweep * ONE_SECOND_IN_NANOSECONDS).flatten()
+    seconds_into_sweep = (
+        coarse_bins * SWAPI_BIN_PERIOD_S + SWAPI_LIVETIME_CENTER_OFFSET_S
+    )
+    return (
+        epoch_ns[:, None] + seconds_into_sweep * ONE_SECOND_IN_NANOSECONDS
+    ).flatten()
 
 
 # --------------------------------------------------------------------------- #
@@ -261,9 +266,7 @@ def _plot_case(ax, swapi_response, cdf_path, mag_data, chunk_index):
         proton_moments=proton_moments,
         magnetic_field_direction=b_hat,
     )
-    alpha_velocity_rtn = np.array(
-        [c.nominal_value for c in alpha_moments.velocity_rtn]
-    )
+    alpha_velocity_rtn = np.array([c.nominal_value for c in alpha_moments.velocity_rtn])
     alpha_sw = SolarWindParams(
         density=alpha_moments.density.nominal_value,
         velocity_rtn=alpha_velocity_rtn,
diff --git a/docs/swapi/figure_src/plot_calibration_curves.py b/docs/swapi/figure_src/plot_calibration_curves.py
index ba0628877..e96022bea 100644
--- a/docs/swapi/figure_src/plot_calibration_curves.py
+++ b/docs/swapi/figure_src/plot_calibration_curves.py
@@ -17,9 +17,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from figure_utils import FIGURES_DIR, load_swapi_response
 
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
-from figure_utils import FIGURES_DIR, load_swapi_response
 
 _AREA_CSV = (
     Path(__file__).resolve().parents[3]
diff --git a/docs/swapi/figure_src/plot_collapsed_response_grid.py b/docs/swapi/figure_src/plot_collapsed_response_grid.py
index 53be77b27..7211d9a5a 100644
--- a/docs/swapi/figure_src/plot_collapsed_response_grid.py
+++ b/docs/swapi/figure_src/plot_collapsed_response_grid.py
@@ -23,16 +23,20 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from figure_utils import FIGURES_DIR, REPO_ROOT, load_swapi_response
 
+from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E
 from imap_l3_processing.swapi.l3a.science.pickup_ion.collapsed_response_grid import (
     build_collapsed_response_grid,
     solar_wind_frame_speed_range,
 )
-from figure_utils import FIGURES_DIR, REPO_ROOT, load_swapi_response
-from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E
 
 _REFERENCE_CSV_PATH = (
-    REPO_ROOT / "tests" / "test_data" / "swapi" / "collapsed_response_grid_reference.csv"
+    REPO_ROOT
+    / "tests"
+    / "test_data"
+    / "swapi"
+    / "collapsed_response_grid_reference.csv"
 )
 
 _ESA_VOLTAGE = 5000.0
diff --git a/docs/swapi/figure_src/plot_fit_accuracy.py b/docs/swapi/figure_src/plot_fit_accuracy.py
index 795583dee..5ec337861 100644
--- a/docs/swapi/figure_src/plot_fit_accuracy.py
+++ b/docs/swapi/figure_src/plot_fit_accuracy.py
@@ -29,19 +29,13 @@
 
 import types
 
+import matplotlib
 import numpy as np
 import pandas as pd
 from uncertainties import UFloat
-import matplotlib
 
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
-
-from imap_l3_processing.constants import (
-    PROTON_MASS_KG,
-    PROTON_MASS_PER_CHARGE_M_P_PER_E,
-)
-from imap_l3_processing.swapi.constants import SWAPI_LIVETIME_S
 from figure_utils import (
     COARSE_BIN_INDICES_IN_SWEEP,
     COARSE_SWEEP_VOLTAGES_MEAN_V,
@@ -50,20 +44,26 @@
     load_swapi_response,
     run_parallel_map,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
-    fit_solar_wind_proton_model,
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.calculate_initial_guess import (
-    calculate_initial_guess,
+from imap_l3_processing.swapi.constants import SWAPI_LIVETIME_S
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
     model_solar_wind_ideal_coincidence_rates,
 )
-from imap_l3_processing.swapi.response.deadtime import deadtime_factor
-from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
-    build_solar_wind_fit_context,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.calculate_initial_guess import (
+    calculate_initial_guess,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
+    fit_solar_wind_proton_model,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
 
 _N_SWEEPS = 5
 _N_BINS = len(COARSE_SWEEP_VOLTAGES_MEAN_V)
@@ -188,10 +188,11 @@ def _process_one(i):
         result = fit_solar_wind_proton_model(fit_ctx)
     except Exception as e:
         print(f"  case {i}: fit failed ({type(e).__name__}: {e}); flagging bad")
+        from uncertainties import ufloat
+
         from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
             ProtonSolarWindFitResult,
         )
-        from uncertainties import ufloat
 
         nan_uf = ufloat(float("nan"), float("nan"))
         result = ProtonSolarWindFitResult(
@@ -315,7 +316,7 @@ def _plot_results(data: pd.DataFrame) -> None:
             marker="o",
             zorder=2,
             label="Initial guess",
-            rasterized=True
+            rasterized=True,
         )
         ax.errorbar(
             truth[good],
@@ -344,7 +345,7 @@ def _plot_results(data: pd.DataFrame) -> None:
                 edgecolors="k",
                 linewidths=0.4,
                 zorder=4,
-                rasterized=True
+                rasterized=True,
             )
             ax.scatter(
                 truth[~good],
@@ -356,7 +357,7 @@ def _plot_results(data: pd.DataFrame) -> None:
                 edgecolors="k",
                 linewidths=0.4,
                 zorder=4,
-                rasterized=True
+                rasterized=True,
             )
 
         ax.set_xlabel(f"True {label}", fontsize=9)
diff --git a/docs/swapi/figure_src/plot_passband_boundaries.py b/docs/swapi/figure_src/plot_passband_boundaries.py
index 862d543ee..5187f9b2b 100644
--- a/docs/swapi/figure_src/plot_passband_boundaries.py
+++ b/docs/swapi/figure_src/plot_passband_boundaries.py
@@ -8,10 +8,10 @@
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
+from figure_utils import FIGURES_DIR, load_swapi_response
 
 from imap_l3_processing.swapi.response.passband_grid import PassbandGrid
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
-from figure_utils import FIGURES_DIR, load_swapi_response
 
 _ELEVATION_DISPLAY_LIMIT_DEG = 15.0
 _ACTIVE_ELEVATION_SAMPLE_COUNT = 300
@@ -85,9 +85,7 @@ def plot_region_panel(
         grid, grid.max_boundary, active_elevations, np.maximum
     )
 
-    image = draw_transmission_heatmap(
-        axis, grid.values, elevations, speed_ratios
-    )
+    image = draw_transmission_heatmap(axis, grid.values, elevations, speed_ratios)
     draw_integration_window_outline(
         axis, active_elevations, lower_speed_ratios, upper_speed_ratios
     )
diff --git a/docs/swapi/figure_src/plot_pui_cutoff_staircase.py b/docs/swapi/figure_src/plot_pui_cutoff_staircase.py
index 9d16ad2a4..a075e092d 100644
--- a/docs/swapi/figure_src/plot_pui_cutoff_staircase.py
+++ b/docs/swapi/figure_src/plot_pui_cutoff_staircase.py
@@ -34,10 +34,12 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-
 from figure_utils import FIGURES_DIR, load_swapi_response
 
-from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E, ONE_AU_IN_KM
+from imap_l3_processing.constants import (
+    HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E,
+    ONE_AU_IN_KM,
+)
 from imap_l3_processing.swapi.l3a.science.pickup_ion.calculate_coincidence_rate import (
     apply_partial_heaviside_at_cutoff,
     calculate_coincidence_rate,
@@ -53,7 +55,9 @@
     FittingParameters,
     VasyliunasSiscoeDistribution,
 )
-from imap_l3_processing.swapi.l3a.utils import velocity_components_to_angles_in_instrument_frame
+from imap_l3_processing.swapi.l3a.utils import (
+    velocity_components_to_angles_in_instrument_frame,
+)
 
 _SW_SPEED_KMS = 450.0
 _SW_AZIMUTH_DEG = 0.0
@@ -90,9 +94,13 @@ def _params(cutoff_speed_kms: float) -> FittingParameters:
 
 
 def main():
-    voltages = pd.read_csv(
-        REPO_ROOT / "tests" / "test_data" / "swapi" / "pui_count_rate_reference.csv"
-    ).iloc[:, 0].to_numpy()
+    voltages = (
+        pd.read_csv(
+            REPO_ROOT / "tests" / "test_data" / "swapi" / "pui_count_rate_reference.csv"
+        )
+        .iloc[:, 0]
+        .to_numpy()
+    )
     voltage_index = int(np.argmin(np.abs(voltages - _ESA_VOLTAGE_V)))
 
     swapi_response = load_swapi_response()
@@ -109,11 +117,13 @@ def main():
 
     azimuth_rad = np.radians(_SW_AZIMUTH_DEG)
     elevation_rad = np.radians(_SW_ELEVATION_DEG)
-    bulk_vec = _SW_SPEED_KMS * np.array([
-        -np.cos(elevation_rad) * np.sin(azimuth_rad),
-        -np.cos(elevation_rad) * np.cos(azimuth_rad),
-        -np.sin(elevation_rad),
-    ])
+    bulk_vec = _SW_SPEED_KMS * np.array(
+        [
+            -np.cos(elevation_rad) * np.sin(azimuth_rad),
+            -np.cos(elevation_rad) * np.cos(azimuth_rad),
+            -np.sin(elevation_rad),
+        ]
+    )
     bulk_per_bin = np.broadcast_to(bulk_vec, (1, voltages.size, 3)).copy()
 
     chunk_response = build_chunk_collapsed_response(
@@ -130,8 +140,10 @@ def main():
     bin_weights = chunk_response.bin_weights[0, voltage_index, :]
 
     # Fine-grid reference response for the same voltage step.
-    bulk_azimuth_deg, bulk_elevation_deg = velocity_components_to_angles_in_instrument_frame(
-        bulk_vec[0], bulk_vec[1], bulk_vec[2]
+    bulk_azimuth_deg, bulk_elevation_deg = (
+        velocity_components_to_angles_in_instrument_frame(
+            bulk_vec[0], bulk_vec[1], bulk_vec[2]
+        )
     )
     fine_grid = np.linspace(1.0, _GRID_MAX_KMS, _FINE_GRID_POINTS)
     fine_collapsed = build_collapsed_response_grid(
@@ -145,9 +157,7 @@ def main():
         bulk_elevation_deg,
         speed_in_sw_frame=fine_grid,
     )
-    fine_weights = (
-        fine_collapsed.values * fine_grid**2 * (fine_grid[1] - fine_grid[0])
-    )
+    fine_weights = fine_collapsed.values * fine_grid**2 * (fine_grid[1] - fine_grid[0])
 
     # Below the neutral-helium LUT support the distribution is physically zero;
     # the LUT extrapolation returns non-finite values there, so clamp them.
@@ -190,15 +200,26 @@ def sampled_distribution(grid, params, apply_cutoff):
     axis.axvline(grid_edges[0], color="0.85", lw=0.8, zorder=0, label="$v'$ grid edges")
 
     axis.plot(
-        _CUTOFF_SWEEP_KMS, rate_reference, "-", color="0.4", lw=1,
-        label=f"fine-grid reference",
+        _CUTOFF_SWEEP_KMS,
+        rate_reference,
+        "-",
+        color="0.4",
+        lw=1,
+        label="fine-grid reference",
     )
     axis.scatter(
-        _CUTOFF_SWEEP_KMS, rate_without, color="tab:red", s=10,
+        _CUTOFF_SWEEP_KMS,
+        rate_without,
+        color="tab:red",
+        s=10,
         label="no correction",
     )
     axis.scatter(
-        _CUTOFF_SWEEP_KMS, rate_with, color="tab:blue", s=10, marker='x',
+        _CUTOFF_SWEEP_KMS,
+        rate_with,
+        color="tab:blue",
+        s=10,
+        marker="x",
         label="with correction",
     )
 
@@ -207,7 +228,10 @@ def sampled_distribution(grid, params, apply_cutoff):
     axis.set_xlim(_CUTOFF_SWEEP_KMS[0], _CUTOFF_SWEEP_KMS[-1])
     axis.annotate(
         rf"grid $\Delta v' = {delta_v_prime:.2f}$ km/s",
-        xy=(0.02, 0.04), xycoords="axes fraction", fontsize=9, color="0.3",
+        xy=(0.02, 0.04),
+        xycoords="axes fraction",
+        fontsize=9,
+        color="0.3",
     )
     axis.grid(True, alpha=0.3)
     axis.legend(loc="upper left")
diff --git a/docs/swapi/figure_src/plot_pui_distribution.py b/docs/swapi/figure_src/plot_pui_distribution.py
index 79275342f..f8fc254cf 100644
--- a/docs/swapi/figure_src/plot_pui_distribution.py
+++ b/docs/swapi/figure_src/plot_pui_distribution.py
@@ -27,7 +27,6 @@
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
-
 from figure_utils import FIGURES_DIR
 
 from imap_l3_processing.constants import ONE_AU_IN_KM
@@ -92,7 +91,9 @@ def main():
         label=r"cutoff speed $v_b$",
     )
     axis.set_yscale("log")
-    axis.set_xlabel(r"Comoving speed $v' = \|\mathbf{v} - \mathbf{v}_\text{sw}\|$ [km/s]")
+    axis.set_xlabel(
+        r"Comoving speed $v' = \|\mathbf{v} - \mathbf{v}_\text{sw}\|$ [km/s]"
+    )
     axis.set_ylabel(r"$f_\text{PUI}(v')$ [s$^3$ km$^{-6}$]")
     axis.set_xlim(speed_in_sw_frame[0], speed_in_sw_frame[-1])
     axis.set_ylim(1e0, 1e4)
diff --git a/docs/swapi/figure_src/plot_pui_reference_comparison.py b/docs/swapi/figure_src/plot_pui_reference_comparison.py
index 9b32aaab5..e39a32b2a 100644
--- a/docs/swapi/figure_src/plot_pui_reference_comparison.py
+++ b/docs/swapi/figure_src/plot_pui_reference_comparison.py
@@ -27,8 +27,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-
 from figure_utils import FIGURES_DIR
+
 from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E
 
 _REFERENCE_CSV_PATH = (
@@ -59,6 +59,8 @@ def compute_production_rates(voltages_v: np.ndarray) -> np.ndarray:
     """Build the chunk-level collapsed response and call the in-development
     production kernel. Imports live inside the function so a broken module
     doesn't break the figure script at import time."""
+    from figure_utils import load_swapi_response
+
     from imap_l3_processing.constants import ONE_AU_IN_KM
     from imap_l3_processing.swapi.l3a.science.pickup_ion.calculate_coincidence_rate import (
         calculate_coincidence_rate,
@@ -73,7 +75,6 @@ def compute_production_rates(voltages_v: np.ndarray) -> np.ndarray:
         FittingParameters,
         VasyliunasSiscoeDistribution,
     )
-    from figure_utils import load_swapi_response
 
     fitting_params = FittingParameters(
         cooling_index=_COOLING_INDEX,
@@ -95,14 +96,14 @@ def compute_production_rates(voltages_v: np.ndarray) -> np.ndarray:
 
     sw_az_rad = np.radians(_SW_AZIMUTH_DEG)
     sw_el_rad = np.radians(_SW_ELEVATION_DEG)
-    bulk_vec = _SW_SPEED_KMS * np.array([
-        -np.cos(sw_el_rad) * np.sin(sw_az_rad),
-        -np.cos(sw_el_rad) * np.cos(sw_az_rad),
-        -np.sin(sw_el_rad),
-    ])
-    bulk_sw_per_bin = np.broadcast_to(
-        bulk_vec, (1, voltages_v.size, 3)
-    ).copy()
+    bulk_vec = _SW_SPEED_KMS * np.array(
+        [
+            -np.cos(sw_el_rad) * np.sin(sw_az_rad),
+            -np.cos(sw_el_rad) * np.cos(sw_az_rad),
+            -np.sin(sw_el_rad),
+        ]
+    )
+    bulk_sw_per_bin = np.broadcast_to(bulk_vec, (1, voltages_v.size, 3)).copy()
 
     swapi_response = load_swapi_response()
     swapi_response.warm_cache(voltages_v)
@@ -115,7 +116,9 @@ def compute_production_rates(voltages_v: np.ndarray) -> np.ndarray:
         min_speed_kms=min_speed_kms,
         central_effective_area_scale=_HELIUM_EFFICIENCY_RATIO,
     )
-    return calculate_coincidence_rate(chunk_response, vasyliunas_siscoe_distribution, fitting_params)[0]
+    return calculate_coincidence_rate(
+        chunk_response, vasyliunas_siscoe_distribution, fitting_params
+    )[0]
 
 
 def main():
@@ -133,11 +136,17 @@ def main():
     )
     sw_az_rad = np.radians(_SW_AZIMUTH_DEG)
     sw_el_rad = np.radians(_SW_ELEVATION_DEG)
-    bulk_vec = _SW_SPEED_KMS * np.array([
-        -np.cos(sw_el_rad) * np.sin(sw_az_rad),
-        -np.cos(sw_el_rad) * np.cos(sw_az_rad),
-        -np.sin(sw_el_rad),
-    ]) + 0.0  # normalize -0.0 to +0.0 for display
+    bulk_vec = (
+        _SW_SPEED_KMS
+        * np.array(
+            [
+                -np.cos(sw_el_rad) * np.sin(sw_az_rad),
+                -np.cos(sw_el_rad) * np.cos(sw_az_rad),
+                -np.sin(sw_el_rad),
+            ]
+        )
+        + 0.0
+    )  # normalize -0.0 to +0.0 for display
     axes[0].set_title(
         f"PUI He coincidence rate — m/q={_HELIUM_MASS_PER_CHARGE_M_P_PER_E:.0f}, "
         f"efficiency ratio={_HELIUM_EFFICIENCY_RATIO:.2f}\n"
@@ -165,7 +174,7 @@ def main():
             ".",
             color="red",
             label="Optimized",
-            markersize=4
+            markersize=4,
         )
         rel_err = np.abs(production_rate_hz - count_rate_hz) / count_rate_hz
         axes[1].semilogy(voltage_v, rel_err, ".", color="red")
diff --git a/docs/swapi/figure_src/plot_real_data_fit.py b/docs/swapi/figure_src/plot_real_data_fit.py
index 70c11f713..7024ce7e2 100644
--- a/docs/swapi/figure_src/plot_real_data_fit.py
+++ b/docs/swapi/figure_src/plot_real_data_fit.py
@@ -37,40 +37,40 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 
+import scipy.optimize
+from figure_utils import FIGURES_DIR, REPO_ROOT, load_swapi_response
+
 from imap_l3_processing.constants import (
     BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
     ONE_SECOND_IN_NANOSECONDS,
     PROTON_MASS_KG,
     PROTON_MASS_PER_CHARGE_M_P_PER_E,
 )
-import scipy.optimize
-
+from imap_l3_processing.swapi.constants import (
+    SWAPI_BIN_PERIOD_S,
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_FINE_SWEEP_BINS,
+    SWAPI_K_FACTOR,
+    SWAPI_L2_K_FACTOR,
+    SWAPI_LIVETIME_CENTER_OFFSET_S,
+    SWAPI_SCIENCE_BINS,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
     model_solar_wind_ideal_coincidence_rates,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
     fit_solar_wind_proton_model,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.l3a.utils import (
     get_spacecraft_velocity_rtn,
     get_swapi_geometry,
 )
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
-from imap_l3_processing.swapi.constants import (
-    SWAPI_BIN_PERIOD_S,
-    SWAPI_COARSE_SWEEP_BINS,
-    SWAPI_FINE_SWEEP_BINS,
-    SWAPI_K_FACTOR,
-    SWAPI_L2_K_FACTOR,
-    SWAPI_LIVETIME_CENTER_OFFSET_S,
-    SWAPI_SCIENCE_BINS,
-)
 from imap_l3_processing.utils import SpiceKernelTypes
-from figure_utils import FIGURES_DIR, REPO_ROOT, load_swapi_response
 
 _DOC_PATH = REPO_ROOT / "docs" / "swapi" / "proton-sw.md"
 _TABLE_BEGIN = "<!-- BEGIN: real_data_table"
@@ -120,22 +120,18 @@ def main():
 
     sc_velocity_rtn = get_spacecraft_velocity_rtn(chunk_center_ns)
     science_result["velocity_rtn_sun"] = (
-        np.array(
-            [c.nominal_value for c in science_result["fit_result"].velocity_rtn]
-        )
+        np.array([c.nominal_value for c in science_result["fit_result"].velocity_rtn])
         + sc_velocity_rtn
     )
     coarse_result["velocity_rtn_sun"] = (
-        np.array(
-            [c.nominal_value for c in coarse_result["fit_result"].velocity_rtn]
-        )
+        np.array([c.nominal_value for c in coarse_result["fit_result"].velocity_rtn])
         + sc_velocity_rtn
     )
 
     print("\n--- Fit results ---")
-    print(f"  Science bins (1..71, includes fine):")
+    print("  Science bins (1..71, includes fine):")
     _print_fit(science_result, sc_velocity_rtn)
-    print(f"  Coarse-only bins (1..62):")
+    print("  Coarse-only bins (1..62):")
     _print_fit(coarse_result, sc_velocity_rtn)
 
     print("\n--- Bootstrap σ on the all-bins fit (B=300, warm-LM, xtol=1e-3) ---")
@@ -283,7 +279,9 @@ def _read_5_sweep_block(
 def _measurement_times_ns(epoch_ns: np.ndarray, bin_slice: slice) -> np.ndarray:
     bins = np.arange(bin_slice.start, bin_slice.stop)
     seconds_into_sweep = bins * SWAPI_BIN_PERIOD_S + SWAPI_LIVETIME_CENTER_OFFSET_S
-    return (epoch_ns[:, None] + seconds_into_sweep * ONE_SECOND_IN_NANOSECONDS).flatten()
+    return (
+        epoch_ns[:, None] + seconds_into_sweep * ONE_SECOND_IN_NANOSECONDS
+    ).flatten()
 
 
 # --------------------------------------------------------------------------- #
@@ -561,9 +559,7 @@ def _model_rates_with_deadtime(
     )
     sw = SolarWindParams(
         density=fit_result.density.nominal_value,
-        velocity_rtn=np.array(
-            [c.nominal_value for c in fit_result.velocity_rtn]
-        ),
+        velocity_rtn=np.array([c.nominal_value for c in fit_result.velocity_rtn]),
         temperature=fit_result.temperature.nominal_value,
         mass=PROTON_MASS_KG,
     )
diff --git a/docs/swapi/figure_src/plot_spectra.py b/docs/swapi/figure_src/plot_spectra.py
index b32990886..2c4f25032 100644
--- a/docs/swapi/figure_src/plot_spectra.py
+++ b/docs/swapi/figure_src/plot_spectra.py
@@ -26,24 +26,23 @@
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
+from figure_utils import (
+    FIGURES_DIR,
+    load_swapi_response,
+    peak_esa_voltage_for_proton_bulk_speed,
+    velocity_rtn_from_swapi_angles,
+)
 
 from imap_l3_processing.constants import (
+    EV_TO_KELVIN,
     PROTON_MASS_KG,
     PROTON_MASS_PER_CHARGE_M_P_PER_E,
-    EV_TO_KELVIN,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
     calculate_integral,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from scripts.swapi.reference_integral import reference_integral_fixed_limits
-from figure_utils import (
-    FIGURES_DIR,
-    velocity_rtn_from_swapi_angles,
-    load_swapi_response,
-    peak_esa_voltage_for_proton_bulk_speed,
-)
-
 
 # (label, bulk_speed, T_K, bulk_azimuth, bulk_elevation, density)
 CASES = [
diff --git a/docs/swapi/figure_src/plot_uncertainty_mc.py b/docs/swapi/figure_src/plot_uncertainty_mc.py
index 3d11baac0..b31834e75 100644
--- a/docs/swapi/figure_src/plot_uncertainty_mc.py
+++ b/docs/swapi/figure_src/plot_uncertainty_mc.py
@@ -20,18 +20,12 @@
 
 import types
 
+import matplotlib
 import numpy as np
 import pandas as pd
-import matplotlib
 
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
-
-from imap_l3_processing.constants import (
-    PROTON_MASS_KG,
-    PROTON_MASS_PER_CHARGE_M_P_PER_E,
-)
-from imap_l3_processing.swapi.constants import SWAPI_LIVETIME_S
 from figure_utils import (
     COARSE_BIN_INDICES_IN_SWEEP,
     COARSE_SWEEP_VOLTAGES_MEAN_V,
@@ -40,13 +34,17 @@
     load_swapi_response,
     run_parallel_map,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.calculate_initial_guess import (
-    calculate_initial_guess,
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
-    _derive_uncertainties,
-    escape_local_minimum,
-    optimize_solar_wind_proton_params,
+from imap_l3_processing.swapi.constants import SWAPI_LIVETIME_S
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     LOG_DENSITY_IDX,
@@ -54,13 +52,15 @@
     VELOCITY_SLICE,
     SolarWindParams,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
-    model_solar_wind_ideal_coincidence_rates,
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.calculate_initial_guess import (
+    calculate_initial_guess,
 )
-from imap_l3_processing.swapi.response.deadtime import deadtime_factor
-from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
-    build_solar_wind_fit_context,
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
+    _derive_uncertainties,
+    escape_local_minimum,
+    optimize_solar_wind_proton_params,
 )
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
 
 _N_SWEEPS = 5
 _N_BINS = len(COARSE_SWEEP_VOLTAGES_MEAN_V)
diff --git a/docs/swapi/figure_src/plot_validation_scatter.py b/docs/swapi/figure_src/plot_validation_scatter.py
index ea117ff02..6e6f9c4b0 100644
--- a/docs/swapi/figure_src/plot_validation_scatter.py
+++ b/docs/swapi/figure_src/plot_validation_scatter.py
@@ -21,6 +21,14 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from figure_utils import (
+    ANCHOR_ROTATION_SWAPI_TO_RTN,
+    FIGURES_DIR,
+    REPO_ROOT,
+    load_swapi_response,
+    peak_esa_voltage_for_proton_bulk_speed,
+    run_parallel_map,
+)
 
 from imap_l3_processing.constants import (
     PROTON_MASS_KG,
@@ -31,15 +39,6 @@
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 
-from figure_utils import (
-    ANCHOR_ROTATION_SWAPI_TO_RTN,
-    FIGURES_DIR,
-    REPO_ROOT,
-    load_swapi_response,
-    peak_esa_voltage_for_proton_bulk_speed,
-    run_parallel_map,
-)
-
 _worker_state: types.SimpleNamespace | None = None
 
 _REFERENCE_INTEGRALS_PATH = (
@@ -123,8 +122,13 @@ def _plot_scatter(reference: np.ndarray, optimized: np.ndarray) -> None:
         zorder=4,
     )
     ax.plot(
-        line, line, color="black", linestyle="--", linewidth=0.8,
-        label="1:1", zorder=5,
+        line,
+        line,
+        color="black",
+        linestyle="--",
+        linewidth=0.8,
+        label="1:1",
+        zorder=5,
     )
 
     ax.set_xscale("log")
diff --git a/imap_l3_data_processor.py b/imap_l3_data_processor.py
index 3b7af7cfb..dc925df57 100644
--- a/imap_l3_data_processor.py
+++ b/imap_l3_data_processor.py
@@ -13,21 +13,36 @@
 from imap_l3_processing.codice.l3.hi.codice_hi_processor import CodiceHiProcessor
 from imap_l3_processing.codice.l3.lo.codice_lo_processor import CodiceLoProcessor
 from imap_l3_processing.glows.glows_processor import GlowsProcessor
-from imap_l3_processing.hi.hi_combined_initializer import HI_COMBINED_DESCRIPTORS, HiCombinedInitializer
+from imap_l3_processing.hi.hi_combined_initializer import (
+    HI_COMBINED_DESCRIPTORS,
+    HiCombinedInitializer,
+)
 from imap_l3_processing.hi.hi_processor import HiProcessor
-from imap_l3_processing.hi.hi_sp_initializer import HiSPInitializer, HI_SP_MAP_DESCRIPTORS
+from imap_l3_processing.hi.hi_sp_initializer import (
+    HI_SP_MAP_DESCRIPTORS,
+    HiSPInitializer,
+)
 from imap_l3_processing.hit.l3.hit_processor import HitProcessor
-from imap_l3_processing.lo.l3.lo_sp_initializer import LoSPInitializer, LO_SP_MAP_DESCRIPTORS
+from imap_l3_processing.lo.l3.lo_sp_initializer import (
+    LO_SP_MAP_DESCRIPTORS,
+    LoSPInitializer,
+)
 from imap_l3_processing.lo.lo_processor import LoProcessor
 from imap_l3_processing.maps.map_descriptors import parse_map_descriptor
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.swapi.swapi_processor import SwapiProcessor
 from imap_l3_processing.swe.swe_processor import SweProcessor
-from imap_l3_processing.ultra.ultra_combined_nsp_initializer import UltraCombinedNSPInitializer, \
-    ULTRA_COMBINED_NSP_DESCRIPTORS
+from imap_l3_processing.ultra.ultra_combined_nsp_initializer import (
+    ULTRA_COMBINED_NSP_DESCRIPTORS,
+    UltraCombinedNSPInitializer,
+)
 from imap_l3_processing.ultra.ultra_processor import UltraProcessor
-from imap_l3_processing.ultra.ultra_sp_initializer import UltraSPInitializer, ULTRA_45_DESCRIPTORS, \
-    ULTRA_90_DESCRIPTORS, ULTRA_COMBINED_SP_DESCRIPTORS
+from imap_l3_processing.ultra.ultra_sp_initializer import (
+    ULTRA_45_DESCRIPTORS,
+    ULTRA_90_DESCRIPTORS,
+    ULTRA_COMBINED_SP_DESCRIPTORS,
+    UltraSPInitializer,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -75,32 +90,68 @@ def imap_l3_processor():
         processing_input_collection.deserialize(args.dependency)
         version_map = VersionMap({}, Version(None, args.version))
     else:
-        processing_input_collection.deserialize(json.dumps(parsed_dependency["dependency"]))
-        version_map = VersionMap({k: Version(v["major_version"], v["minor_version"]) for k, v in parsed_dependency["version"].items()})
+        processing_input_collection.deserialize(
+            json.dumps(parsed_dependency["dependency"])
+        )
+        version_map = VersionMap(
+            {
+                k: Version(v["major_version"], v["minor_version"])
+                for k, v in parsed_dependency["version"].items()
+            }
+        )
 
     repointing_number = None
     if args.repointing is not None:
-        repointing_number_match = re.match(r"repoint(?P<repoint>\d{5})", args.repointing)
+        repointing_number_match = re.match(
+            r"repoint(?P<repoint>\d{5})", args.repointing
+        )
         if repointing_number_match is None:
             raise ValueError("Unexpected repointing number command line format!")
         repointing_number = int(repointing_number_match["repoint"])
 
     _furnish_spice_kernels(processing_input_collection)
-    input_dependency = InputMetadata(args.instrument,
-                                     args.data_level,
-                                     _convert_to_datetime(args.start_date),
-                                     _convert_to_datetime(args.end_date or args.start_date),
-                                     version_map, descriptor=args.descriptor, repointing=repointing_number)
-    if args.instrument in ["hi", "lo", "ultra"] and args.data_level == 'l3' and not parse_map_descriptor(
-            args.descriptor):
+    input_dependency = InputMetadata(
+        args.instrument,
+        args.data_level,
+        _convert_to_datetime(args.start_date),
+        _convert_to_datetime(args.end_date or args.start_date),
+        version_map,
+        descriptor=args.descriptor,
+        repointing=repointing_number,
+    )
+    if (
+        args.instrument in ["hi", "lo", "ultra"]
+        and args.data_level == "l3"
+        and not parse_map_descriptor(args.descriptor)
+    ):
         initializer_class, processor_class, descriptors = {
             ("hi", "sp-maps"): (HiSPInitializer, HiProcessor, HI_SP_MAP_DESCRIPTORS),
-            ("hi", "hic-maps"): (HiCombinedInitializer, HiProcessor, HI_COMBINED_DESCRIPTORS),
+            ("hi", "hic-maps"): (
+                HiCombinedInitializer,
+                HiProcessor,
+                HI_COMBINED_DESCRIPTORS,
+            ),
             ("lo", "all-maps"): (LoSPInitializer, LoProcessor, LO_SP_MAP_DESCRIPTORS),
-            ("ultra", "u45-maps"): (UltraSPInitializer, UltraProcessor, ULTRA_45_DESCRIPTORS),
-            ("ultra", "u90-maps"): (UltraSPInitializer, UltraProcessor, ULTRA_90_DESCRIPTORS),
-            ("ultra", "ulc-sp-maps"): (UltraSPInitializer, UltraProcessor, ULTRA_COMBINED_SP_DESCRIPTORS),
-            ("ultra", "ulc-nsp-maps"): (UltraCombinedNSPInitializer, UltraProcessor, ULTRA_COMBINED_NSP_DESCRIPTORS),
+            ("ultra", "u45-maps"): (
+                UltraSPInitializer,
+                UltraProcessor,
+                ULTRA_45_DESCRIPTORS,
+            ),
+            ("ultra", "u90-maps"): (
+                UltraSPInitializer,
+                UltraProcessor,
+                ULTRA_90_DESCRIPTORS,
+            ),
+            ("ultra", "ulc-sp-maps"): (
+                UltraSPInitializer,
+                UltraProcessor,
+                ULTRA_COMBINED_SP_DESCRIPTORS,
+            ),
+            ("ultra", "ulc-nsp-maps"): (
+                UltraCombinedNSPInitializer,
+                UltraProcessor,
+                ULTRA_COMBINED_NSP_DESCRIPTORS,
+            ),
         }[args.instrument, args.descriptor]
 
         initializer = initializer_class()
@@ -108,57 +159,75 @@ def imap_l3_processor():
         maps_to_produce = []
         for map_descriptor in descriptors:
             major_version_from_dependency = version_map.lookup(map_descriptor).major
-            maps_to_produce.extend(initializer.get_maps_that_should_be_produced(map_descriptor, major_version_from_dependency))
+            maps_to_produce.extend(
+                initializer.get_maps_that_should_be_produced(
+                    map_descriptor, major_version_from_dependency
+                )
+            )
 
-        logger.info(f"maps to produce {[m.input_metadata.descriptor for m in maps_to_produce]}")
+        logger.info(
+            f"maps to produce {[m.input_metadata.descriptor for m in maps_to_produce]}"
+        )
         if len(maps_to_produce) == 0:
-            logger.info(f"Did not find any maps to produce for instrument {args.instrument}")
+            logger.info(
+                f"Did not find any maps to produce for instrument {args.instrument}"
+            )
 
         for map_to_produce in maps_to_produce:
             logger.info(
-                f"Processing map {map_to_produce.input_metadata.descriptor}, {map_to_produce.input_metadata.start_date}")
+                f"Processing map {map_to_produce.input_metadata.descriptor}, {map_to_produce.input_metadata.start_date}"
+            )
             initializer.furnish_spice_dependencies(map_to_produce)
 
             try:
-                processor = processor_class(map_to_produce.processing_input_collection, map_to_produce.input_metadata)
+                processor = processor_class(
+                    map_to_produce.processing_input_collection,
+                    map_to_produce.input_metadata,
+                )
                 paths.extend(processor.process())
             except Exception as e:
                 logger.exception(
                     f"Failed to produce map {map_to_produce.input_metadata.descriptor}, {map_to_produce.input_metadata.start_date}!",
-                    exc_info=e)
-    elif args.instrument == 'swapi' and (args.data_level == 'l3a' or args.data_level == 'l3b'):
+                    exc_info=e,
+                )
+    elif args.instrument == "swapi" and (
+        args.data_level == "l3a" or args.data_level == "l3b"
+    ):
         processor = SwapiProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'glows' and args.data_level in ['l3a', 'l3b']:
+    elif args.instrument == "glows" and args.data_level in ["l3a", "l3b"]:
         processor = GlowsProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'swe' and args.data_level == 'l3':
+    elif args.instrument == "swe" and args.data_level == "l3":
         processor = SweProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'hit' and args.data_level == 'l3':
+    elif args.instrument == "hit" and args.data_level == "l3":
         processor = HitProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'hi' and args.data_level == 'l3':
+    elif args.instrument == "hi" and args.data_level == "l3":
         processor = HiProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'ultra' and args.data_level == 'l3':
+    elif args.instrument == "ultra" and args.data_level == "l3":
         processor = UltraProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'lo' and args.data_level == 'l3':
+    elif args.instrument == "lo" and args.data_level == "l3":
         processor = LoProcessor(processing_input_collection, input_dependency)
         paths = processor.process()
-    elif args.instrument == 'codice':
-        if args.descriptor.startswith("hi") and args.data_level in ['l3a', 'l3b']:
+    elif args.instrument == "codice":
+        if args.descriptor.startswith("hi") and args.data_level in ["l3a", "l3b"]:
             processor = CodiceHiProcessor(processing_input_collection, input_dependency)
             paths = processor.process()
-        elif args.descriptor.startswith("lo") and args.data_level in ['l3a', 'l3b']:
+        elif args.descriptor.startswith("lo") and args.data_level in ["l3a", "l3b"]:
             processor = CodiceLoProcessor(processing_input_collection, input_dependency)
             paths = processor.process()
         else:
-            raise NotImplementedError(f"Unknown descriptor '{args.descriptor}' for codice instrument")
+            raise NotImplementedError(
+                f"Unknown descriptor '{args.descriptor}' for codice instrument"
+            )
     else:
         raise NotImplementedError(
-            f'Level {args.data_level} data processing has not yet been implemented for {args.instrument}')
+            f"Level {args.data_level} data processing has not yet been implemented for {args.instrument}"
+        )
 
     if args.upload_to_sdc:
         exceptions = []
@@ -168,20 +237,23 @@ def imap_l3_processor():
             except Exception as e:
                 exceptions.append(e)
         if exceptions:
-            raise IOError(f"Failed to upload some files: {exceptions}")
+            raise OSError(f"Failed to upload some files: {exceptions}")
 
 
 def _furnish_spice_kernels(processing_input_collection):
-    spice_kernel_paths = processing_input_collection.get_file_paths(data_type='spice')
+    spice_kernel_paths = processing_input_collection.get_file_paths(data_type="spice")
     for kernel in spice_kernel_paths:
         kernel_path = imap_data_access.download(kernel)
         spiceypy.furnsh(str(kernel_path))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     with TemporaryDirectory() as dir:
-        logging.basicConfig(force=True, level=logging.INFO,
-                            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        logging.basicConfig(
+            force=True,
+            level=logging.INFO,
+            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        )
 
         try:
             imap_l3_processor()
diff --git a/imap_l3_processing/cdf/cdf_utils.py b/imap_l3_processing/cdf/cdf_utils.py
index 230b435e5..39490bf8d 100644
--- a/imap_l3_processing/cdf/cdf_utils.py
+++ b/imap_l3_processing/cdf/cdf_utils.py
@@ -14,11 +14,15 @@
 }
 
 
-def write_cdf(file_path: str, data: DataProduct, attribute_manager: ImapAttributeManager):
-    with CDF(file_path, '') as cdf:
+def write_cdf(
+    file_path: str, data: DataProduct, attribute_manager: ImapAttributeManager
+):
+    with CDF(file_path, "") as cdf:
         cdf.col_major(True)
         try:
-            global_attrs = attribute_manager.get_global_attributes(data.input_metadata.logical_source)
+            global_attrs = attribute_manager.get_global_attributes(
+                data.input_metadata.logical_source
+            )
         except KeyError:
             trimmed_source = re.sub(r"-\d+$", "-", data.input_metadata.logical_source)
             global_attrs = attribute_manager.get_global_attributes(trimmed_source)
@@ -27,9 +31,16 @@ def write_cdf(file_path: str, data: DataProduct, attribute_manager: ImapAttribut
             cdf.attrs[k] = v
 
         map_descriptor = None
-        if data.input_metadata.instrument in ("lo", "hi", "ultra", "glows",):
+        if data.input_metadata.instrument in (
+            "lo",
+            "hi",
+            "ultra",
+            "glows",
+        ):
             try:
-                map_descriptor = naming.MapDescriptor.from_string(data.input_metadata.descriptor)
+                map_descriptor = naming.MapDescriptor.from_string(
+                    data.input_metadata.descriptor
+                )
             except ValueError:
                 # Assume this is not actually a map
                 pass
@@ -54,48 +65,58 @@ def write_cdf(file_path: str, data: DataProduct, attribute_manager: ImapAttribut
                 data_array = None
             else:
                 target_float_dtype = _CDF_FLOAT_NUMPY_DTYPE.get(data_type.value)
-                if target_float_dtype is not None and np.issubdtype(data_array.dtype, np.floating):
+                if target_float_dtype is not None and np.issubdtype(
+                    data_array.dtype, np.floating
+                ):
                     data_array = data_array.astype(target_float_dtype)
 
-                if 'FILLVAL' in variable_attributes:
+                if "FILLVAL" in variable_attributes:
                     if np.issubdtype(data_array.dtype, np.floating):
                         data_array = np.ma.masked_invalid(data_array)
-                    data_array = np.ma.filled(data_array, variable_attributes['FILLVAL'])
+                    data_array = np.ma.filled(
+                        data_array, variable_attributes["FILLVAL"]
+                    )
                 else:
                     assert not np.ma.isMaskedArray(data_array)
 
             if (
-                    data_type
-                    not in (pycdf.const.CDF_TIME_TT2000, pycdf.const.CDF_EPOCH,
-                            pycdf.const.CDF_EPOCH16)
-                    and record_varying
+                data_type
+                not in (
+                    pycdf.const.CDF_TIME_TT2000,
+                    pycdf.const.CDF_EPOCH,
+                    pycdf.const.CDF_EPOCH16,
+                )
+                and record_varying
             ):
                 compress = pycdf.const.GZIP_COMPRESSION
                 compress_param = 7
             else:
                 compress = None
                 compress_param = None
-            cdf.new(var_name, data_array,
-                    recVary=record_varying,
-                    type=data_type,
-                    dims=dims,
-                    compress=compress,
-                    compress_param=compress_param)
+            cdf.new(
+                var_name,
+                data_array,
+                recVary=record_varying,
+                type=data_type,
+                dims=dims,
+                compress=compress,
+                compress_param=compress_param,
+            )
             for k, v in variable_attributes.items():
-                if v == '':
+                if v == "":
                     continue
-                if k in ['DATA_TYPE', 'RECORD_VARYING']:
+                if k in ["DATA_TYPE", "RECORD_VARYING"]:
                     continue
-                if k == 'FILLVAL' and data_type is not None:
+                if k == "FILLVAL" and data_type is not None:
                     cdf[var_name].attrs.new(k, v, data_type)
                 else:
                     cdf[var_name].attrs[k] = v
 
 
 def read_variable_and_mask_fill_values(var: pycdf.Var) -> np.ma.masked_array:
-    return np.ma.masked_equal(var[...], var.attrs['FILLVAL'])
+    return np.ma.masked_equal(var[...], var.attrs["FILLVAL"])
 
 
 def read_numeric_variable(var: pycdf.Var) -> np.ndarray:
     assert np.issubdtype(var.dtype, np.number)
-    return np.where(var[...] == var.attrs['FILLVAL'], np.nan, var[...])
+    return np.where(var[...] == var.attrs["FILLVAL"], np.nan, var[...])
diff --git a/imap_l3_processing/cdf/imap_attribute_manager.py b/imap_l3_processing/cdf/imap_attribute_manager.py
index 25c93e923..4dfe39b53 100644
--- a/imap_l3_processing/cdf/imap_attribute_manager.py
+++ b/imap_l3_processing/cdf/imap_attribute_manager.py
@@ -1,22 +1,31 @@
 from pathlib import Path
-from typing import Optional
 
 from sammi.cdf_attribute_manager import CdfAttributeManager
 
 
 class ImapAttributeManager(CdfAttributeManager):
     def __init__(self):
-        super().__init__(variable_schema_layers=[
-            Path(f'{Path(__file__).parent.resolve()}/config/imap_l3_variable_cdf_attrs_schema.yaml'),
-            Path(f'{Path(__file__).parent.resolve()}/config/default_variable_cdf_attrs_schema.yaml')
-        ],
-            global_schema_layers=[Path(__file__).parent / "config/default_global_cdf_attrs_schema.yaml"],
-            use_defaults=True)
-        self.config_folder_path = Path(f'{Path(__file__).parent.resolve()}/config')
-
-        self.load_global_attributes(self.config_folder_path / 'imap_default_global_cdf_attrs.yaml')
-
-    def try_load_global_metadata(self, logical_source: str) -> Optional[dict]:
+        super().__init__(
+            variable_schema_layers=[
+                Path(
+                    f"{Path(__file__).parent.resolve()}/config/imap_l3_variable_cdf_attrs_schema.yaml"
+                ),
+                Path(
+                    f"{Path(__file__).parent.resolve()}/config/default_variable_cdf_attrs_schema.yaml"
+                ),
+            ],
+            global_schema_layers=[
+                Path(__file__).parent / "config/default_global_cdf_attrs_schema.yaml"
+            ],
+            use_defaults=True,
+        )
+        self.config_folder_path = Path(f"{Path(__file__).parent.resolve()}/config")
+
+        self.load_global_attributes(
+            self.config_folder_path / "imap_default_global_cdf_attrs.yaml"
+        )
+
+    def try_load_global_metadata(self, logical_source: str) -> dict | None:
         try:
             return self.get_global_attributes(logical_source)
         except KeyError:
@@ -24,16 +33,23 @@ def try_load_global_metadata(self, logical_source: str) -> Optional[dict]:
 
     def add_instrument_attrs(self, instrument: str, level: str, descriptor: str):
         self._load_variable_attributes_if_file_exists(
-            self.config_folder_path / f"imap_{instrument}_{level}_variable_attrs.yaml")
+            self.config_folder_path / f"imap_{instrument}_{level}_variable_attrs.yaml"
+        )
         self._load_variable_attributes_if_file_exists(
-            self.config_folder_path / f"imap_{instrument}_{level}_{descriptor}_variable_attrs.yaml")
+            self.config_folder_path
+            / f"imap_{instrument}_{level}_{descriptor}_variable_attrs.yaml"
+        )
 
         self._load_global_attributes_if_file_exists(
-            self.config_folder_path / f"imap_{instrument}_global_cdf_attrs.yaml")
+            self.config_folder_path / f"imap_{instrument}_global_cdf_attrs.yaml"
+        )
         self._load_global_attributes_if_file_exists(
-            self.config_folder_path / f"imap_{instrument}_{level}_global_cdf_attrs.yaml")
+            self.config_folder_path / f"imap_{instrument}_{level}_global_cdf_attrs.yaml"
+        )
         self._load_global_attributes_if_file_exists(
-            self.config_folder_path / f"imap_{instrument}_{level}_{descriptor}_global_cdf_attrs.yaml")
+            self.config_folder_path
+            / f"imap_{instrument}_{level}_{descriptor}_global_cdf_attrs.yaml"
+        )
 
     def _load_variable_attributes_if_file_exists(self, path: Path):
         if path.exists():
diff --git a/imap_l3_processing/codice/l3/hi/codice_hi_processor.py b/imap_l3_processing/codice/l3/hi/codice_hi_processor.py
index fc9a48337..4e5200d62 100644
--- a/imap_l3_processing/codice/l3/hi/codice_hi_processor.py
+++ b/imap_l3_processing/codice/l3/hi/codice_hi_processor.py
@@ -4,40 +4,62 @@
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection
 
-from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import \
-    CodiceHiL3aDirectEventsDependencies
-from imap_l3_processing.codice.l3.hi.models import CodiceHiL3PitchAngleDataProduct
-from imap_l3_processing.codice.l3.hi.models import CodiceL3HiDirectEvents
-from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import CodicePitchAngleDependencies
-from imap_l3_processing.codice.l3.lo.constants import CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET
+from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import (
+    CodiceHiL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.hi.models import (
+    CodiceHiL3PitchAngleDataProduct,
+    CodiceL3HiDirectEvents,
+)
+from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import (
+    CodicePitchAngleDependencies,
+)
+from imap_l3_processing.codice.l3.lo.constants import (
+    CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET,
+)
 from imap_l3_processing.codice.quality_flags import CodiceL3Flags
-from imap_l3_processing.hit.l3.sectored_products.science.sectored_products_algorithms import \
-    get_sector_unit_vectors
+from imap_l3_processing.hit.l3.sectored_products.science.sectored_products_algorithms import (
+    get_sector_unit_vectors,
+)
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.pitch_angles import calculate_unit_vector, calculate_pitch_angle, calculate_gyrophase, \
-    rebin_by_pitch_angle_and_gyrophase
+from imap_l3_processing.pitch_angles import (
+    calculate_gyrophase,
+    calculate_pitch_angle,
+    calculate_unit_vector,
+    rebin_by_pitch_angle_and_gyrophase,
+)
 from imap_l3_processing.processor import Processor
 from imap_l3_processing.utils import save_data
 
 
 class CodiceHiProcessor(Processor):
-    def __init__(self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         super().__init__(dependencies, input_metadata)
 
     def process(self) -> list[Path]:
         if self.input_metadata.data_level == "l3a":
-            dependencies = CodiceHiL3aDirectEventsDependencies.fetch_dependencies(self.dependencies)
+            dependencies = CodiceHiL3aDirectEventsDependencies.fetch_dependencies(
+                self.dependencies
+            )
             data_product = self.process_l3a_direct_event(dependencies)
         elif self.input_metadata.data_level == "l3b":
-            dependencies = CodicePitchAngleDependencies.fetch_dependencies(self.dependencies)
+            dependencies = CodicePitchAngleDependencies.fetch_dependencies(
+                self.dependencies
+            )
             data_product = self.process_l3b(dependencies)
         else:
-            raise NotImplementedError(f"Unknown data level for CoDICE: {self.input_metadata.data_level}")
+            raise NotImplementedError(
+                f"Unknown data level for CoDICE: {self.input_metadata.data_level}"
+            )
 
         data_product.parent_file_names = self.get_parent_file_names()
         return [save_data(data_product)]
 
-    def process_l3a_direct_event(self, dependencies: CodiceHiL3aDirectEventsDependencies) -> CodiceL3HiDirectEvents:
+    def process_l3a_direct_event(
+        self, dependencies: CodiceHiL3aDirectEventsDependencies
+    ) -> CodiceL3HiDirectEvents:
         l2_data = dependencies.codice_l2_hi_data
         l1a_data = dependencies.codice_l1a_hi_data
         hi_mass_correction_lookup = dependencies.codice_hi_mass_correction_lut_ancillary
@@ -59,20 +81,18 @@ def process_l3a_direct_event(self, dependencies: CodiceHiL3aDirectEventsDependen
 
                 correction_factors[high_gain] = [
                     hi_mass_correction_lookup.lookup_correction_factor(
-                        ssd_id = ssd,
-                        energy_channel = energy,
-                        tof = tof_channel
+                        ssd_id=ssd, energy_channel=energy, tof=tof_channel
                     )
                     for ssd, energy, tof_channel in zip(
-                        ssd_id[high_gain],
-                        energy_channel[high_gain],
-                        tof[high_gain]
+                        ssd_id[high_gain], energy_channel[high_gain], tof[high_gain]
                     )
                 ]
 
                 incident_ion_energy[i, j, :npts] = l2_energy * correction_factors
-                estimated_mass[i, j, :npts] = incident_ion_energy[i, j, :npts] / l2_data.energy_per_nuc[i, j, :npts]
-
+                estimated_mass[i, j, :npts] = (
+                    incident_ion_energy[i, j, :npts]
+                    / l2_data.energy_per_nuc[i, j, :npts]
+                )
 
         return CodiceL3HiDirectEvents(
             input_metadata=self.input_metadata,
@@ -95,21 +115,36 @@ def process_l3a_direct_event(self, dependencies: CodiceHiL3aDirectEventsDependen
             spin_sector=l2_data.spin_sector,
         )
 
-    def process_l3b(self, dependencies: CodicePitchAngleDependencies) -> CodiceHiL3PitchAngleDataProduct:
+    def process_l3b(
+        self, dependencies: CodicePitchAngleDependencies
+    ) -> CodiceHiL3PitchAngleDataProduct:
         mag_data = dependencies.mag_data
         sectored_intensities = dependencies.codice_sectored_intensities_data
         epochs = dependencies.codice_sectored_intensities_data.epoch
         h_energy_bins = dependencies.codice_sectored_intensities_data.energy_h.shape[0]
-        he3he4_energy_bins = dependencies.codice_sectored_intensities_data.energy_he3he4.shape[0]
-        cno_energy_bins = dependencies.codice_sectored_intensities_data.energy_cno.shape[0]
-        fe_energy_bins = dependencies.codice_sectored_intensities_data.energy_fe.shape[0]
-        rebinned_mag_data = mag_data.rebin_to(sectored_intensities.epoch, sectored_intensities.epoch_delta_plus)
+        he3he4_energy_bins = (
+            dependencies.codice_sectored_intensities_data.energy_he3he4.shape[0]
+        )
+        cno_energy_bins = (
+            dependencies.codice_sectored_intensities_data.energy_cno.shape[0]
+        )
+        fe_energy_bins = dependencies.codice_sectored_intensities_data.energy_fe.shape[
+            0
+        ]
+        rebinned_mag_data = mag_data.rebin_to(
+            sectored_intensities.epoch, sectored_intensities.epoch_delta_plus
+        )
 
-        spin_angles_in_dps_elevation_by_spin_angle = (sectored_intensities.spin_angles.T + CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET) % 360
+        spin_angles_in_dps_elevation_by_spin_angle = (
+            sectored_intensities.spin_angles.T
+            + CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET
+        ) % 360
 
         mag_unit_vectors = calculate_unit_vector(rebinned_mag_data)
-        sector_unit = get_sector_unit_vectors(sectored_intensities.elevation_angle,
-                                              spin_angles_in_dps_elevation_by_spin_angle).transpose(1,0,2)
+        sector_unit = get_sector_unit_vectors(
+            sectored_intensities.elevation_angle,
+            spin_angles_in_dps_elevation_by_spin_angle,
+        ).transpose(1, 0, 2)
         sector_unit_vectors = calculate_unit_vector(sector_unit)
         particle_unit_vectors = -1 * sector_unit_vectors
 
@@ -117,27 +152,68 @@ def process_l3b(self, dependencies: CodicePitchAngleDependencies) -> CodiceHiL3P
         num_gyrophase_bins = 12
         pitch_angle_delta_value = (180 / num_pitch_angle_bins) / 2
         gyrophase_delta_value = (360 / num_gyrophase_bins) / 2
-        pitch_angle_bins = np.linspace(0, 180, num_pitch_angle_bins, endpoint=False) + pitch_angle_delta_value
-        gyrophase_bins = np.linspace(0, 360, num_gyrophase_bins, endpoint=False) + gyrophase_delta_value
+        pitch_angle_bins = (
+            np.linspace(0, 180, num_pitch_angle_bins, endpoint=False)
+            + pitch_angle_delta_value
+        )
+        gyrophase_bins = (
+            np.linspace(0, 360, num_gyrophase_bins, endpoint=False)
+            + gyrophase_delta_value
+        )
 
         h_pa_shape = (len(epochs), h_energy_bins, num_pitch_angle_bins)
         he3he4_pa_shape = (len(epochs), he3he4_energy_bins, num_pitch_angle_bins)
         cno_pa_shape = (len(epochs), cno_energy_bins, num_pitch_angle_bins)
         fe_pa_shape = (len(epochs), fe_energy_bins, num_pitch_angle_bins)
-        h_gyro_shape = (len(epochs), h_energy_bins, num_pitch_angle_bins, num_gyrophase_bins)
-        he3he4_gyro_shape = (len(epochs), he3he4_energy_bins, num_pitch_angle_bins, num_gyrophase_bins)
-        cno_gyro_shape = (len(epochs), cno_energy_bins, num_pitch_angle_bins, num_gyrophase_bins)
-        fe_gyro_shape = (len(epochs), fe_energy_bins, num_pitch_angle_bins, num_gyrophase_bins)
+        h_gyro_shape = (
+            len(epochs),
+            h_energy_bins,
+            num_pitch_angle_bins,
+            num_gyrophase_bins,
+        )
+        he3he4_gyro_shape = (
+            len(epochs),
+            he3he4_energy_bins,
+            num_pitch_angle_bins,
+            num_gyrophase_bins,
+        )
+        cno_gyro_shape = (
+            len(epochs),
+            cno_energy_bins,
+            num_pitch_angle_bins,
+            num_gyrophase_bins,
+        )
+        fe_gyro_shape = (
+            len(epochs),
+            fe_energy_bins,
+            num_pitch_angle_bins,
+            num_gyrophase_bins,
+        )
 
-        SpeciesIntensity = namedtuple("SpeciesIntensity",
-                                      ["l2_intensity", "intensity_by_pa", "intensity_by_pa_and_gyro"])
+        SpeciesIntensity = namedtuple(
+            "SpeciesIntensity",
+            ["l2_intensity", "intensity_by_pa", "intensity_by_pa_and_gyro"],
+        )
 
         # @formatter:off
         species_intensities: dict[str, SpeciesIntensity] = {
-            "h": SpeciesIntensity(sectored_intensities.h_intensities, *_create_pa_and_gyro_nan_arrays(h_pa_shape, h_gyro_shape)),
-            "he4": SpeciesIntensity(sectored_intensities.he3he4_intensities, *_create_pa_and_gyro_nan_arrays(he3he4_pa_shape, he3he4_gyro_shape)),
-            "o": SpeciesIntensity(sectored_intensities.cno_intensities, *_create_pa_and_gyro_nan_arrays(cno_pa_shape, cno_gyro_shape)),
-            "fe": SpeciesIntensity(sectored_intensities.fe_intensities,*_create_pa_and_gyro_nan_arrays(fe_pa_shape, fe_gyro_shape))}
+            "h": SpeciesIntensity(
+                sectored_intensities.h_intensities,
+                *_create_pa_and_gyro_nan_arrays(h_pa_shape, h_gyro_shape),
+            ),
+            "he4": SpeciesIntensity(
+                sectored_intensities.he3he4_intensities,
+                *_create_pa_and_gyro_nan_arrays(he3he4_pa_shape, he3he4_gyro_shape),
+            ),
+            "o": SpeciesIntensity(
+                sectored_intensities.cno_intensities,
+                *_create_pa_and_gyro_nan_arrays(cno_pa_shape, cno_gyro_shape),
+            ),
+            "fe": SpeciesIntensity(
+                sectored_intensities.fe_intensities,
+                *_create_pa_and_gyro_nan_arrays(fe_pa_shape, fe_gyro_shape),
+            ),
+        }
         # @formatter:on
 
         codice_flags = np.full(len(epochs), CodiceL3Flags.NONE)
@@ -145,8 +221,12 @@ def process_l3b(self, dependencies: CodicePitchAngleDependencies) -> CodiceHiL3P
             codice_flags |= CodiceL3Flags.PRELIMINARY_MAG
 
         for time_index in range(len(epochs)):
-            pitch_angles = calculate_pitch_angle(particle_unit_vectors, mag_unit_vectors[time_index])
-            gyrophases = calculate_gyrophase(particle_unit_vectors, mag_unit_vectors[time_index])
+            pitch_angles = calculate_pitch_angle(
+                particle_unit_vectors, mag_unit_vectors[time_index]
+            )
+            gyrophases = calculate_gyrophase(
+                particle_unit_vectors, mag_unit_vectors[time_index]
+            )
 
             for species, species_intensity in species_intensities.items():
                 intensity = species_intensity.l2_intensity[time_index]
@@ -154,13 +234,29 @@ def process_l3b(self, dependencies: CodicePitchAngleDependencies) -> CodiceHiL3P
                 intensities_delta_plus = np.full_like(intensity, 0)
                 intensities_delta_minus = np.full_like(intensity, 0)
 
-                rebinned_intensities_by_pa_and_gyro, _, _, rebinned_intensities_by_pa, _, _ = rebin_by_pitch_angle_and_gyrophase(
-                    intensity_data=intensity, intensity_delta_plus=intensities_delta_plus,
-                    intensity_delta_minus=intensities_delta_minus, pitch_angles=pitch_angles, gyrophases=gyrophases,
-                    number_of_pitch_angle_bins=num_pitch_angle_bins, number_of_gyrophase_bins=num_gyrophase_bins)
+                (
+                    rebinned_intensities_by_pa_and_gyro,
+                    _,
+                    _,
+                    rebinned_intensities_by_pa,
+                    _,
+                    _,
+                ) = rebin_by_pitch_angle_and_gyrophase(
+                    intensity_data=intensity,
+                    intensity_delta_plus=intensities_delta_plus,
+                    intensity_delta_minus=intensities_delta_minus,
+                    pitch_angles=pitch_angles,
+                    gyrophases=gyrophases,
+                    number_of_pitch_angle_bins=num_pitch_angle_bins,
+                    number_of_gyrophase_bins=num_gyrophase_bins,
+                )
 
-                species_intensity.intensity_by_pa[time_index] = rebinned_intensities_by_pa
-                species_intensity.intensity_by_pa_and_gyro[time_index] = rebinned_intensities_by_pa_and_gyro
+                species_intensity.intensity_by_pa[time_index] = (
+                    rebinned_intensities_by_pa
+                )
+                species_intensity.intensity_by_pa_and_gyro[time_index] = (
+                    rebinned_intensities_by_pa_and_gyro
+                )
 
         return CodiceHiL3PitchAngleDataProduct(
             input_metadata=self.input_metadata,
@@ -182,17 +278,29 @@ def process_l3b(self, dependencies: CodicePitchAngleDependencies) -> CodiceHiL3P
             pitch_angle_delta=np.repeat(pitch_angle_delta_value, num_pitch_angle_bins),
             gyrophase=gyrophase_bins,
             gyrophase_delta=np.repeat(gyrophase_delta_value, num_gyrophase_bins),
-            h_intensity_by_pitch_angle=species_intensities['h'].intensity_by_pa,
-            h_intensity_by_pitch_angle_and_gyrophase=species_intensities['h'].intensity_by_pa_and_gyro,
-            he3he4_intensity_by_pitch_angle=species_intensities['he4'].intensity_by_pa,
-            he3he4_intensity_by_pitch_angle_and_gyrophase=species_intensities['he4'].intensity_by_pa_and_gyro,
-            cno_intensity_by_pitch_angle=species_intensities['o'].intensity_by_pa,
-            cno_intensity_by_pitch_angle_and_gyrophase=species_intensities['o'].intensity_by_pa_and_gyro,
-            fe_intensity_by_pitch_angle=species_intensities['fe'].intensity_by_pa,
-            fe_intensity_by_pitch_angle_and_gyrophase=species_intensities['fe'].intensity_by_pa_and_gyro,
+            h_intensity_by_pitch_angle=species_intensities["h"].intensity_by_pa,
+            h_intensity_by_pitch_angle_and_gyrophase=species_intensities[
+                "h"
+            ].intensity_by_pa_and_gyro,
+            he3he4_intensity_by_pitch_angle=species_intensities["he4"].intensity_by_pa,
+            he3he4_intensity_by_pitch_angle_and_gyrophase=species_intensities[
+                "he4"
+            ].intensity_by_pa_and_gyro,
+            cno_intensity_by_pitch_angle=species_intensities["o"].intensity_by_pa,
+            cno_intensity_by_pitch_angle_and_gyrophase=species_intensities[
+                "o"
+            ].intensity_by_pa_and_gyro,
+            fe_intensity_by_pitch_angle=species_intensities["fe"].intensity_by_pa,
+            fe_intensity_by_pitch_angle_and_gyrophase=species_intensities[
+                "fe"
+            ].intensity_by_pa_and_gyro,
             codice_flags=codice_flags,
         )
 
 
-def _create_pa_and_gyro_nan_arrays(pitch_angle_shape, pitch_angle_and_gyrophase_shape) -> np.array:
-    return np.full(pitch_angle_shape, fill_value=np.nan), np.full(pitch_angle_and_gyrophase_shape, fill_value=np.nan)
+def _create_pa_and_gyro_nan_arrays(
+    pitch_angle_shape, pitch_angle_and_gyrophase_shape
+) -> np.array:
+    return np.full(pitch_angle_shape, fill_value=np.nan), np.full(
+        pitch_angle_and_gyrophase_shape, fill_value=np.nan
+    )
diff --git a/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_l3a_direct_events_dependencies.py b/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_l3a_direct_events_dependencies.py
index 621bd3d72..3de466f06 100644
--- a/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_l3a_direct_events_dependencies.py
+++ b/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_l3a_direct_events_dependencies.py
@@ -4,9 +4,13 @@
 from imap_data_access import download
 from imap_data_access.processing_input import ProcessingInputCollection
 
-from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_mass_correction_lookup_table import \
-    CodiceHiMassCorrectionLookupTable
-from imap_l3_processing.codice.l3.hi.models import CodiceL2HiDirectEventData, CodiceL1aHiDirectEvents
+from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_mass_correction_lookup_table import (
+    CodiceHiMassCorrectionLookupTable,
+)
+from imap_l3_processing.codice.l3.hi.models import (
+    CodiceL1aHiDirectEvents,
+    CodiceL2HiDirectEventData,
+)
 
 
 @dataclass
@@ -17,19 +21,46 @@ class CodiceHiL3aDirectEventsDependencies:
 
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
-        l2_hi_direct_events_file_paths = dependencies.get_file_paths("codice", "hi-direct-events", data_type="l2")
-        l1a_hi_direct_events_file_paths = dependencies.get_file_paths("codice", "hi-direct-events", data_type="l1a")
-        ancillary_file_paths = dependencies.get_file_paths("codice", "l3-hi-mass-correction-lut")
+        l2_hi_direct_events_file_paths = dependencies.get_file_paths(
+            "codice", "hi-direct-events", data_type="l2"
+        )
+        l1a_hi_direct_events_file_paths = dependencies.get_file_paths(
+            "codice", "hi-direct-events", data_type="l1a"
+        )
+        ancillary_file_paths = dependencies.get_file_paths(
+            "codice", "l3-hi-mass-correction-lut"
+        )
 
-        for download_location_file_path in [*l2_hi_direct_events_file_paths, *l1a_hi_direct_events_file_paths, *ancillary_file_paths]:
+        for download_location_file_path in [
+            *l2_hi_direct_events_file_paths,
+            *l1a_hi_direct_events_file_paths,
+            *ancillary_file_paths,
+        ]:
             download(download_location_file_path)
 
-        return cls.from_file_paths(l2_hi_direct_events_file_paths[0], l1a_hi_direct_events_file_paths[0], ancillary_file_paths[0])
+        return cls.from_file_paths(
+            l2_hi_direct_events_file_paths[0],
+            l1a_hi_direct_events_file_paths[0],
+            ancillary_file_paths[0],
+        )
 
     @classmethod
-    def from_file_paths(cls, codice_l2_hi_cdf: Path, codice_l1a_hi_cdf: Path, codice_hi_mass_correction_lut_ancillary: Path):
+    def from_file_paths(
+        cls,
+        codice_l2_hi_cdf: Path,
+        codice_l1a_hi_cdf: Path,
+        codice_hi_mass_correction_lut_ancillary: Path,
+    ):
         codice_l2_hi_data = CodiceL2HiDirectEventData.read_from_cdf(codice_l2_hi_cdf)
         codice_l1a_hi_data = CodiceL1aHiDirectEvents.read_from_cdf(codice_l1a_hi_cdf)
-        codice_hi_mass_correction_lut_ancillary = CodiceHiMassCorrectionLookupTable.from_file(codice_hi_mass_correction_lut_ancillary)
+        codice_hi_mass_correction_lut_ancillary = (
+            CodiceHiMassCorrectionLookupTable.from_file(
+                codice_hi_mass_correction_lut_ancillary
+            )
+        )
 
-        return cls(codice_l2_hi_data, codice_l1a_hi_data, codice_hi_mass_correction_lut_ancillary)
+        return cls(
+            codice_l2_hi_data,
+            codice_l1a_hi_data,
+            codice_hi_mass_correction_lut_ancillary,
+        )
diff --git a/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_mass_correction_lookup_table.py b/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_mass_correction_lookup_table.py
index df0c5292d..9471302f9 100644
--- a/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_mass_correction_lookup_table.py
+++ b/imap_l3_processing/codice/l3/hi/direct_event/codice_hi_mass_correction_lookup_table.py
@@ -6,7 +6,8 @@
 import numpy as np
 import pandas as pd
 
-SSD_SHEET_NAME_FORMAT = "ssd(\d+)"
+SSD_SHEET_NAME_FORMAT = r"ssd(\d+)"
+
 
 @dataclass
 class CodiceHiMassCorrectionLookupTable:
@@ -23,15 +24,21 @@ def from_file(cls, file_path: Path):
                 ssd = int(ssd_name_match.group(1))
                 data[ssd] = file[sheet_name].to_numpy()
             else:
-                logging.warning(f"Not using sheet {sheet_name} from {file_path.name} ancillary! Expected sheet names in the format: {SSD_SHEET_NAME_FORMAT}")
+                logging.warning(
+                    f"Not using sheet {sheet_name} from {file_path.name} ancillary! Expected sheet names in the format: {SSD_SHEET_NAME_FORMAT}"
+                )
 
-        assert len(data) > 0, f"Failed to read any correction factors from: {file_path.name}"
+        assert len(data) > 0, (
+            f"Failed to read any correction factors from: {file_path.name}"
+        )
 
         return cls(correction_factors=data)
 
-    def lookup_correction_factor(self, ssd_id: int, energy_channel: int, tof: int) -> float:
+    def lookup_correction_factor(
+        self, ssd_id: int, energy_channel: int, tof: int
+    ) -> float:
         data_for_ssd = self.correction_factors.get(ssd_id)
         if data_for_ssd is not None:
             return data_for_ssd[energy_channel, tof]
         else:
-            return 1.0
\ No newline at end of file
+            return 1.0
diff --git a/imap_l3_processing/codice/l3/hi/models.py b/imap_l3_processing/codice/l3/hi/models.py
index abae7993b..5cd5fd0c6 100644
--- a/imap_l3_processing/codice/l3/hi/models.py
+++ b/imap_l3_processing/codice/l3/hi/models.py
@@ -5,7 +5,10 @@
 from numpy import ndarray
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.cdf.cdf_utils import read_numeric_variable, read_variable_and_mask_fill_values
+from imap_l3_processing.cdf.cdf_utils import (
+    read_numeric_variable,
+    read_variable_and_mask_fill_values,
+)
 from imap_l3_processing.models import DataProduct, DataProductVariable
 
 CODICE_HI_NUM_L2_PRIORITIES = 6
@@ -39,20 +42,46 @@ def read_from_cdf(cls, filename):
         with CDF(str(filename)) as cdf:
             return cls(
                 epoch=cdf["epoch"][...],
-                epoch_delta_plus=cdf['epoch_delta_plus'][...],
-                data_quality=read_variable_and_mask_fill_values(cdf["data_quality"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                multi_flag=read_variable_and_mask_fill_values(cdf["multi_flag"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                number_of_events=read_variable_and_mask_fill_values(cdf["num_events"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                ssd_energy=read_variable_and_mask_fill_values(cdf["ssd_energy"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                ssd_id=read_variable_and_mask_fill_values(cdf["ssd_id"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                spin_angle=read_numeric_variable(cdf["spin_angle"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                spin_number=read_variable_and_mask_fill_values(cdf["spin_number"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                time_of_flight=read_numeric_variable(cdf["tof"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                type=read_variable_and_mask_fill_values(cdf["type"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                energy_per_nuc=read_numeric_variable(cdf["energy_per_nuc"])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                elevation_angle=read_numeric_variable(cdf[ELEVATION_ANGLE_VAR_NAME])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                gain=read_variable_and_mask_fill_values(cdf[GAIN_VAR_NAME])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
-                spin_sector=read_variable_and_mask_fill_values(cdf[SPIN_SECTOR_VAR_NAME])[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
+                epoch_delta_plus=cdf["epoch_delta_plus"][...],
+                data_quality=read_variable_and_mask_fill_values(cdf["data_quality"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                multi_flag=read_variable_and_mask_fill_values(cdf["multi_flag"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                number_of_events=read_variable_and_mask_fill_values(cdf["num_events"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                ssd_energy=read_variable_and_mask_fill_values(cdf["ssd_energy"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                ssd_id=read_variable_and_mask_fill_values(cdf["ssd_id"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                spin_angle=read_numeric_variable(cdf["spin_angle"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                spin_number=read_variable_and_mask_fill_values(cdf["spin_number"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                time_of_flight=read_numeric_variable(cdf["tof"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                type=read_variable_and_mask_fill_values(cdf["type"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                energy_per_nuc=read_numeric_variable(cdf["energy_per_nuc"])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                elevation_angle=read_numeric_variable(cdf[ELEVATION_ANGLE_VAR_NAME])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                gain=read_variable_and_mask_fill_values(cdf[GAIN_VAR_NAME])[
+                    :, :CODICE_HI_NUM_L2_PRIORITIES, ...
+                ],
+                spin_sector=read_variable_and_mask_fill_values(
+                    cdf[SPIN_SECTOR_VAR_NAME]
+                )[:, :CODICE_HI_NUM_L2_PRIORITIES, ...],
             )
         # @formatter:on
 
@@ -95,13 +124,21 @@ def read_from_cdf(cls, filename):
 GYROPHASE_VAR_NAME = "gyrophase"
 GYROPHASE_DELTA_VAR_NAME = "gyrophase_delta"
 H_INTENSITY_BY_PITCH_ANGLE_VAR_NAME = "h_intensity_by_pitch_angle"
-H_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = "h_intensity_by_pitch_angle_and_gyrophase"
+H_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = (
+    "h_intensity_by_pitch_angle_and_gyrophase"
+)
 HE3HE4_INTENSITY_BY_PITCH_ANGLE_VAR_NAME = "he3he4_intensity_by_pitch_angle"
-HE3HE4_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = "he3he4_intensity_by_pitch_angle_and_gyrophase"
+HE3HE4_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = (
+    "he3he4_intensity_by_pitch_angle_and_gyrophase"
+)
 CNO_INTENSITY_BY_PITCH_ANGLE_VAR_NAME = "cno_intensity_by_pitch_angle"
-CNO_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = "cno_intensity_by_pitch_angle_and_gyrophase"
+CNO_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = (
+    "cno_intensity_by_pitch_angle_and_gyrophase"
+)
 FE_INTENSITY_BY_PITCH_ANGLE_VAR_NAME = "fe_intensity_by_pitch_angle"
-FE_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = "fe_intensity_by_pitch_angle_and_gyrophase"
+FE_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME = (
+    "fe_intensity_by_pitch_angle_and_gyrophase"
+)
 ENERGY_H_LABEL_VAR_NAME = "energy_h_label"
 ENERGY_CNO_LABEL_VAR_NAME = "energy_cno_label"
 ENERGY_FE_LABEL_VAR_NAME = "energy_fe_label"
@@ -138,8 +175,12 @@ class CodiceL3HiDirectEvents(DataProduct):
     def __post_init__(self):
         self.priority_index = np.arange(CODICE_HI_NUM_L2_PRIORITIES)
         self.event_index = np.arange(self.ssd_id.shape[-1])
-        self.priority_index_label = np.array([str(i) for i in range(CODICE_HI_NUM_L2_PRIORITIES)])
-        self.event_index_label = np.array([str(i) for i in range(len(self.event_index))])
+        self.priority_index_label = np.array(
+            [str(i) for i in range(CODICE_HI_NUM_L2_PRIORITIES)]
+        )
+        self.event_index_label = np.array(
+            [str(i) for i in range(len(self.event_index))]
+        )
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
@@ -162,7 +203,9 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(SPIN_SECTOR_VAR_NAME, self.spin_sector),
             DataProductVariable(PRIORITY_INDEX_VAR_NAME, self.priority_index),
             DataProductVariable(EVENT_INDEX_VAR_NAME, self.event_index),
-            DataProductVariable(PRIORITY_INDEX_LABEL_VAR_NAME, self.priority_index_label),
+            DataProductVariable(
+                PRIORITY_INDEX_LABEL_VAR_NAME, self.priority_index_label
+            ),
             DataProductVariable(EVENT_INDEX_LABEL_VAR_NAME, self.event_index_label),
         ]
 
@@ -215,7 +258,10 @@ def __post_init__(self):
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_VAR_NAME, self.epoch),
-            DataProductVariable(EPOCH_DELTA_VAR_NAME, np.array([t.total_seconds() for t in self.epoch_delta]) * 1e9),
+            DataProductVariable(
+                EPOCH_DELTA_VAR_NAME,
+                np.array([t.total_seconds() for t in self.epoch_delta]) * 1e9,
+            ),
             DataProductVariable(ENERGY_H_VAR_NAME, self.energy_h),
             DataProductVariable(ENERGY_H_PLUS_VAR_NAME, self.energy_h_plus),
             DataProductVariable(ENERGY_H_MINUS_VAR_NAME, self.energy_h_minus),
@@ -232,18 +278,35 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(PITCH_ANGLE_DELTA_VAR_NAME, self.pitch_angle_delta),
             DataProductVariable(GYROPHASE_VAR_NAME, self.gyrophase),
             DataProductVariable(GYROPHASE_DELTA_VAR_NAME, self.gyrophase_delta),
-            DataProductVariable(H_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.h_intensity_by_pitch_angle),
-            DataProductVariable(H_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
-                                self.h_intensity_by_pitch_angle_and_gyrophase),
-            DataProductVariable(HE3HE4_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.he3he4_intensity_by_pitch_angle),
-            DataProductVariable(HE3HE4_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
-                                self.he3he4_intensity_by_pitch_angle_and_gyrophase),
-            DataProductVariable(CNO_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.cno_intensity_by_pitch_angle),
-            DataProductVariable(CNO_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
-                                self.cno_intensity_by_pitch_angle_and_gyrophase),
-            DataProductVariable(FE_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.fe_intensity_by_pitch_angle),
-            DataProductVariable(FE_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
-                                self.fe_intensity_by_pitch_angle_and_gyrophase),
+            DataProductVariable(
+                H_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.h_intensity_by_pitch_angle
+            ),
+            DataProductVariable(
+                H_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
+                self.h_intensity_by_pitch_angle_and_gyrophase,
+            ),
+            DataProductVariable(
+                HE3HE4_INTENSITY_BY_PITCH_ANGLE_VAR_NAME,
+                self.he3he4_intensity_by_pitch_angle,
+            ),
+            DataProductVariable(
+                HE3HE4_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
+                self.he3he4_intensity_by_pitch_angle_and_gyrophase,
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.cno_intensity_by_pitch_angle
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
+                self.cno_intensity_by_pitch_angle_and_gyrophase,
+            ),
+            DataProductVariable(
+                FE_INTENSITY_BY_PITCH_ANGLE_VAR_NAME, self.fe_intensity_by_pitch_angle
+            ),
+            DataProductVariable(
+                FE_INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_VAR_NAME,
+                self.fe_intensity_by_pitch_angle_and_gyrophase,
+            ),
             DataProductVariable(ENERGY_H_LABEL_VAR_NAME, self.energy_h_label),
             DataProductVariable(ENERGY_CNO_LABEL_VAR_NAME, self.energy_cno_label),
             DataProductVariable(ENERGY_FE_LABEL_VAR_NAME, self.energy_fe_label),
@@ -281,28 +344,32 @@ class CodiceHiL2SectoredIntensitiesData:
     @classmethod
     def read_from_cdf(cls, l2_sectored_intensities_cdf):
         with CDF(str(l2_sectored_intensities_cdf)) as cdf:
-            return cls(epoch=cdf["epoch"][...],
-                       epoch_delta_plus=np.array([timedelta(seconds=ns / 1e9) for ns in cdf["epoch_delta_plus"][...]]),
-                       spin_angles=cdf['spin_angle'][...],
-                       elevation_angle=cdf['elevation_angle'][...],
-                       data_quality=cdf['data_quality'][...],
-                       h_intensities=read_numeric_variable(cdf['h']),
-                       energy_h=cdf['energy_h'][...],
-                       energy_h_plus=cdf['energy_h_plus'][...],
-                       energy_h_minus=cdf['energy_h_minus'][...],
-                       cno_intensities=read_numeric_variable(cdf['cno']),
-                       energy_cno=cdf['energy_cno'][...],
-                       energy_cno_plus=cdf['energy_cno_plus'][...],
-                       energy_cno_minus=cdf['energy_cno_minus'][...],
-                       fe_intensities=read_numeric_variable(cdf['fe']),
-                       energy_fe=cdf['energy_fe'][...],
-                       energy_fe_plus=cdf['energy_fe_plus'][...],
-                       energy_fe_minus=cdf['energy_fe_minus'][...],
-                       he3he4_intensities=read_numeric_variable(cdf['he3he4']),
-                       energy_he3he4=cdf['energy_he3he4'][...],
-                       energy_he3he4_plus=cdf['energy_he3he4_plus'][...],
-                       energy_he3he4_minus=cdf['energy_he3he4_minus'][...],
-                       )
+            return cls(
+                epoch=cdf["epoch"][...],
+                epoch_delta_plus=np.array(
+                    [timedelta(seconds=ns / 1e9) for ns in cdf["epoch_delta_plus"][...]]
+                ),
+                spin_angles=cdf["spin_angle"][...],
+                elevation_angle=cdf["elevation_angle"][...],
+                data_quality=cdf["data_quality"][...],
+                h_intensities=read_numeric_variable(cdf["h"]),
+                energy_h=cdf["energy_h"][...],
+                energy_h_plus=cdf["energy_h_plus"][...],
+                energy_h_minus=cdf["energy_h_minus"][...],
+                cno_intensities=read_numeric_variable(cdf["cno"]),
+                energy_cno=cdf["energy_cno"][...],
+                energy_cno_plus=cdf["energy_cno_plus"][...],
+                energy_cno_minus=cdf["energy_cno_minus"][...],
+                fe_intensities=read_numeric_variable(cdf["fe"]),
+                energy_fe=cdf["energy_fe"][...],
+                energy_fe_plus=cdf["energy_fe_plus"][...],
+                energy_fe_minus=cdf["energy_fe_minus"][...],
+                he3he4_intensities=read_numeric_variable(cdf["he3he4"]),
+                energy_he3he4=cdf["energy_he3he4"][...],
+                energy_he3he4_plus=cdf["energy_he3he4_plus"][...],
+                energy_he3he4_minus=cdf["energy_he3he4_minus"][...],
+            )
+
 
 @dataclass
 class CodiceL1aHiDirectEvents:
@@ -316,5 +383,5 @@ def read_from_cdf(cls, l1a_hi_direct_events_cdf):
             return cls(
                 epoch=cdf["epoch"][...],
                 ssd_energy=cdf["ssd_energy"][...],
-                tof=cdf["tof"][...]
+                tof=cdf["tof"][...],
             )
diff --git a/imap_l3_processing/codice/l3/hi/pitch_angle/codice_pitch_angle_dependencies.py b/imap_l3_processing/codice/l3/hi/pitch_angle/codice_pitch_angle_dependencies.py
index 89709ac89..8209251d4 100644
--- a/imap_l3_processing/codice/l3/hi/pitch_angle/codice_pitch_angle_dependencies.py
+++ b/imap_l3_processing/codice/l3/hi/pitch_angle/codice_pitch_angle_dependencies.py
@@ -17,19 +17,34 @@ class CodicePitchAngleDependencies:
     @classmethod
     def fetch_dependencies(cls, input_collection: ProcessingInputCollection):
         codice_file_paths = input_collection.get_file_paths("codice", "hi-sectored")
-        mag_l2_paths = input_collection.get_file_paths("mag", data_type="l2", descriptor="norm-dsrf")
-        mag_l1d_paths = input_collection.get_file_paths("mag", data_type="l1d", descriptor="norm-dsrf")
+        mag_l2_paths = input_collection.get_file_paths(
+            "mag", data_type="l2", descriptor="norm-dsrf"
+        )
+        mag_l1d_paths = input_collection.get_file_paths(
+            "mag", data_type="l1d", descriptor="norm-dsrf"
+        )
         mag_dependency = [*mag_l2_paths, *mag_l1d_paths][0]
         mag_is_preliminary = len(mag_l2_paths) == 0
 
         for download_location_file_path in [*codice_file_paths, mag_dependency]:
             imap_data_access.download(download_location_file_path)
 
-        return cls.from_file_paths(mag_dependency, codice_file_paths[0], mag_is_preliminary=mag_is_preliminary)
+        return cls.from_file_paths(
+            mag_dependency, codice_file_paths[0], mag_is_preliminary=mag_is_preliminary
+        )
 
     @classmethod
-    def from_file_paths(cls, mag_file_path, codice_l2_sectored_intensities_path, mag_is_preliminary: bool = False):
+    def from_file_paths(
+        cls,
+        mag_file_path,
+        codice_l2_sectored_intensities_path,
+        mag_is_preliminary: bool = False,
+    ):
         mag_data = read_mag_data(mag_file_path)
-        sectored_intensities = CodiceHiL2SectoredIntensitiesData.read_from_cdf(codice_l2_sectored_intensities_path)
+        sectored_intensities = CodiceHiL2SectoredIntensitiesData.read_from_cdf(
+            codice_l2_sectored_intensities_path
+        )
 
-        return cls(mag_data, sectored_intensities, mag_is_preliminary=mag_is_preliminary)
+        return cls(
+            mag_data, sectored_intensities, mag_is_preliminary=mag_is_preliminary
+        )
diff --git a/imap_l3_processing/codice/l3/lo/codice_lo_l3a_direct_events_dependencies.py b/imap_l3_processing/codice/l3/lo/codice_lo_l3a_direct_events_dependencies.py
index bf6d11a60..f165ebc32 100644
--- a/imap_l3_processing/codice/l3/lo/codice_lo_l3a_direct_events_dependencies.py
+++ b/imap_l3_processing/codice/l3/lo/codice_lo_l3a_direct_events_dependencies.py
@@ -4,16 +4,23 @@
 from imap_data_access import download
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup, \
-    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import MassCoefficientLookup
-from imap_l3_processing.codice.l3.lo.models import CodiceLoL2DirectEventData, CodiceLoL1aSWPriorityRates, \
-    CodiceLoL1aNSWPriorityRates
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR,
+    EnergyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import (
+    MassCoefficientLookup,
+)
+from imap_l3_processing.codice.l3.lo.models import (
+    CodiceLoL1aNSWPriorityRates,
+    CodiceLoL1aSWPriorityRates,
+    CodiceLoL2DirectEventData,
+)
 
-SW_PRIORITY_DESCRIPTOR = 'lo-sw-priority'
-NSW_PRIORITY_DESCRIPTOR = 'lo-nsw-priority'
-DIRECT_EVENTS_DESCRIPTOR = 'lo-direct-events'
-MASS_COEFFICIENT_DESCRIPTOR = 'mass-coefficient-lookup'
+SW_PRIORITY_DESCRIPTOR = "lo-sw-priority"
+NSW_PRIORITY_DESCRIPTOR = "lo-nsw-priority"
+DIRECT_EVENTS_DESCRIPTOR = "lo-direct-events"
+MASS_COEFFICIENT_DESCRIPTOR = "mass-coefficient-lookup"
 
 
 @dataclass
@@ -31,14 +38,17 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         direct_events_paths = []
         for science_input in dependencies.get_science_inputs():
             match science_input:
-                case ScienceInput(data_type="l1a", source="codice",
-                                  descriptor=descriptor) if descriptor == SW_PRIORITY_DESCRIPTOR:
+                case ScienceInput(
+                    data_type="l1a", source="codice", descriptor=descriptor
+                ) if descriptor == SW_PRIORITY_DESCRIPTOR:
                     sw_priority_rates_paths.extend(science_input.filename_list)
-                case ScienceInput(data_type="l1a", source="codice",
-                                  descriptor=descriptor) if descriptor == NSW_PRIORITY_DESCRIPTOR:
+                case ScienceInput(
+                    data_type="l1a", source="codice", descriptor=descriptor
+                ) if descriptor == NSW_PRIORITY_DESCRIPTOR:
                     nsw_priority_rates_paths.extend(science_input.filename_list)
-                case ScienceInput(data_type="l2", source="codice",
-                                  descriptor=descriptor) if descriptor == DIRECT_EVENTS_DESCRIPTOR:
+                case ScienceInput(
+                    data_type="l2", source="codice", descriptor=descriptor
+                ) if descriptor == DIRECT_EVENTS_DESCRIPTOR:
                     direct_events_paths.extend(science_input.filename_list)
 
         assert len(sw_priority_rates_paths) == 1
@@ -48,38 +58,59 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         assert len(direct_events_paths) == 1
         direct_events_path = direct_events_paths[0]
 
-        [mass_coefficients_file_path] = dependencies.get_file_paths(source='codice',
-                                                                    descriptor=MASS_COEFFICIENT_DESCRIPTOR)
-        [energy_per_charge_file_path] = dependencies.get_file_paths(source='codice',
-                                                                    descriptor=ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR)
+        [mass_coefficients_file_path] = dependencies.get_file_paths(
+            source="codice", descriptor=MASS_COEFFICIENT_DESCRIPTOR
+        )
+        [energy_per_charge_file_path] = dependencies.get_file_paths(
+            source="codice", descriptor=ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR
+        )
 
         sw_priority_rates_downloaded_path = download(sw_priority_rates_path)
         nsw_priority_rates_downloaded_path = download(nsw_priority_rates_path)
         direct_events_downloaded_path = download(direct_events_path)
 
-        mass_coefficients_file_downloaded_path = download(mass_coefficients_file_path.name)
-        energy_per_charge_lookup_downloaded_path = download(energy_per_charge_file_path.name)
+        mass_coefficients_file_downloaded_path = download(
+            mass_coefficients_file_path.name
+        )
+        energy_per_charge_lookup_downloaded_path = download(
+            energy_per_charge_file_path.name
+        )
 
         return cls.from_file_paths(
             sw_priority_rates_downloaded_path,
             nsw_priority_rates_downloaded_path,
             direct_events_downloaded_path,
             mass_coefficients_file_downloaded_path,
-            energy_per_charge_lookup_downloaded_path
+            energy_per_charge_lookup_downloaded_path,
         )
 
     @classmethod
-    def from_file_paths(cls,
-                        sw_priority_rates_cdf: Path,
-                        nsw_priority_rates_cdf: Path,
-                        direct_event_path: Path,
-                        mass_coefficients_file_path: Path,
-                        esa_to_energy_per_charge_file_path: Path):
-        mass_coefficients_file_path = MassCoefficientLookup.read_from_csv(mass_coefficients_file_path)
-        sw_priority_rates = CodiceLoL1aSWPriorityRates.read_from_cdf(sw_priority_rates_cdf)
-        nsw_priority_rates = CodiceLoL1aNSWPriorityRates.read_from_cdf(nsw_priority_rates_cdf)
-        codice_l2_direct_events = CodiceLoL2DirectEventData.read_from_cdf(direct_event_path)
+    def from_file_paths(
+        cls,
+        sw_priority_rates_cdf: Path,
+        nsw_priority_rates_cdf: Path,
+        direct_event_path: Path,
+        mass_coefficients_file_path: Path,
+        esa_to_energy_per_charge_file_path: Path,
+    ):
+        mass_coefficients_file_path = MassCoefficientLookup.read_from_csv(
+            mass_coefficients_file_path
+        )
+        sw_priority_rates = CodiceLoL1aSWPriorityRates.read_from_cdf(
+            sw_priority_rates_cdf
+        )
+        nsw_priority_rates = CodiceLoL1aNSWPriorityRates.read_from_cdf(
+            nsw_priority_rates_cdf
+        )
+        codice_l2_direct_events = CodiceLoL2DirectEventData.read_from_cdf(
+            direct_event_path
+        )
         energy_lookup = EnergyLookup.read_from_csv(esa_to_energy_per_charge_file_path)
 
-        return cls(sw_priority_rates, nsw_priority_rates, codice_l2_direct_events, mass_coefficients_file_path,
-                   energy_lookup)
+        return cls(
+            sw_priority_rates,
+            nsw_priority_rates,
+            codice_l2_direct_events,
+            mass_coefficients_file_path,
+            energy_lookup,
+        )
diff --git a/imap_l3_processing/codice/l3/lo/codice_lo_l3a_partial_densities_dependencies.py b/imap_l3_processing/codice/l3/lo/codice_lo_l3a_partial_densities_dependencies.py
index 0bc002486..910cdc6c1 100644
--- a/imap_l3_processing/codice/l3/lo/codice_lo_l3a_partial_densities_dependencies.py
+++ b/imap_l3_processing/codice/l3/lo/codice_lo_l3a_partial_densities_dependencies.py
@@ -5,10 +5,12 @@
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
 from imap_l3_processing.codice.l3.lo.models import CodiceLoL2SWSpeciesData
-from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import MassPerChargeLookup
+from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import (
+    MassPerChargeLookup,
+)
 
-SW_SPECIES_DESCRIPTOR = 'lo-sw-species'
-MASS_PER_CHARGE_DESCRIPTOR = 'mass-per-charge'
+SW_SPECIES_DESCRIPTOR = "lo-sw-species"
+MASS_PER_CHARGE_DESCRIPTOR = "mass-per-charge"
 
 
 @dataclass
@@ -22,28 +24,39 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
 
         for science_input in dependencies.get_science_inputs():
             match science_input:
-                case ScienceInput(data_type="l2", source="codice",
-                                  descriptor=descriptor) if descriptor == SW_SPECIES_DESCRIPTOR:
+                case ScienceInput(
+                    data_type="l2", source="codice", descriptor=descriptor
+                ) if descriptor == SW_SPECIES_DESCRIPTOR:
                     sectored_intensities_file_paths.extend(science_input.filename_list)
 
         assert len(sectored_intensities_file_paths) == 1
         sectored_intensities_file_path = sectored_intensities_file_paths[0]
 
-        mass_per_charge_ancillary_file_paths = dependencies.get_file_paths(source='codice',
-                                                                           descriptor=MASS_PER_CHARGE_DESCRIPTOR)
+        mass_per_charge_ancillary_file_paths = dependencies.get_file_paths(
+            source="codice", descriptor=MASS_PER_CHARGE_DESCRIPTOR
+        )
         assert len(mass_per_charge_ancillary_file_paths) == 1
-        mass_per_charge_ancillary_file_path = mass_per_charge_ancillary_file_paths[0].name
-
-        sectored_intensities_downloaded_path = imap_data_access.download(sectored_intensities_file_path)
-        mass_per_charge_ancillary_file_downloaded_path = imap_data_access.download(mass_per_charge_ancillary_file_path)
-
-        return cls.from_file_paths(sectored_intensities_downloaded_path, mass_per_charge_ancillary_file_downloaded_path)
+        mass_per_charge_ancillary_file_path = mass_per_charge_ancillary_file_paths[
+            0
+        ].name
+
+        sectored_intensities_downloaded_path = imap_data_access.download(
+            sectored_intensities_file_path
+        )
+        mass_per_charge_ancillary_file_downloaded_path = imap_data_access.download(
+            mass_per_charge_ancillary_file_path
+        )
+
+        return cls.from_file_paths(
+            sectored_intensities_downloaded_path,
+            mass_per_charge_ancillary_file_downloaded_path,
+        )
 
     @classmethod
-    def from_file_paths(cls, codice_l2_lo_cdf: Path,
-                        mass_per_charge_lookup_path: Path):
-        mass_per_charge_lookup = MassPerChargeLookup.read_from_file(mass_per_charge_lookup_path)
+    def from_file_paths(cls, codice_l2_lo_cdf: Path, mass_per_charge_lookup_path: Path):
+        mass_per_charge_lookup = MassPerChargeLookup.read_from_file(
+            mass_per_charge_lookup_path
+        )
         codice_l2_lo_data = CodiceLoL2SWSpeciesData.read_from_cdf(codice_l2_lo_cdf)
 
-        return cls(codice_l2_lo_data,
-                   mass_per_charge_lookup)
+        return cls(codice_l2_lo_data, mass_per_charge_lookup)
diff --git a/imap_l3_processing/codice/l3/lo/codice_lo_l3a_ratios_dependencies.py b/imap_l3_processing/codice/l3/lo/codice_lo_l3a_ratios_dependencies.py
index f4600b007..89df62b0f 100644
--- a/imap_l3_processing/codice/l3/lo/codice_lo_l3a_ratios_dependencies.py
+++ b/imap_l3_processing/codice/l3/lo/codice_lo_l3a_ratios_dependencies.py
@@ -6,7 +6,7 @@
 
 from imap_l3_processing.codice.l3.lo.models import CodiceLoPartialDensityData
 
-PARTIAL_DENSITY_DESCRIPTOR = 'lo-partial-densities'
+PARTIAL_DENSITY_DESCRIPTOR = "lo-partial-densities"
 
 
 @dataclass
@@ -15,7 +15,9 @@ class CodiceLoL3aRatiosDependencies:
 
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
-        input_file_paths = dependencies.get_file_paths(source='codice', descriptor=PARTIAL_DENSITY_DESCRIPTOR)
+        input_file_paths = dependencies.get_file_paths(
+            source="codice", descriptor=PARTIAL_DENSITY_DESCRIPTOR
+        )
         assert len(input_file_paths) == 1
         input_file_name = input_file_paths[0].name
 
@@ -25,6 +27,8 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
 
     @classmethod
     def from_file_paths(cls, codice_l2_lo_cdf: Path | str):
-        partial_density_data = CodiceLoPartialDensityData.read_from_cdf(codice_l2_lo_cdf)
+        partial_density_data = CodiceLoPartialDensityData.read_from_cdf(
+            codice_l2_lo_cdf
+        )
 
         return cls(partial_density_data)
diff --git a/imap_l3_processing/codice/l3/lo/codice_lo_l3b_3d_distributions_dependencies.py b/imap_l3_processing/codice/l3/lo/codice_lo_l3b_3d_distributions_dependencies.py
index d4dcf61b5..87c237511 100644
--- a/imap_l3_processing/codice/l3/lo/codice_lo_l3b_3d_distributions_dependencies.py
+++ b/imap_l3_processing/codice/l3/lo/codice_lo_l3b_3d_distributions_dependencies.py
@@ -4,12 +4,22 @@
 import imap_data_access
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import DIRECT_EVENTS_DESCRIPTOR
-from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import EfficiencyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup, \
-    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR
-from imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup import GeometricFactorLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import (
+    DIRECT_EVENTS_DESCRIPTOR,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import (
+    EfficiencyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR,
+    EnergyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup import (
+    GeometricFactorLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import (
+    MassSpeciesBinLookup,
+)
 from imap_l3_processing.codice.l3.lo.models import CodiceLoDirectEventData
 
 MASS_SPECIES_BIN_LOOKUP_DESCRIPTOR = "lo-mass-species-bin-lookup"
@@ -27,58 +37,80 @@ class CodiceLoL3b3dDistributionsDependencies:
     species: str
 
     @classmethod
-    def fetch_dependencies(cls, processing_input_collection: ProcessingInputCollection, species: str):
+    def fetch_dependencies(
+        cls, processing_input_collection: ProcessingInputCollection, species: str
+    ):
         l3a_direct_event_paths = []
         for input in processing_input_collection.get_science_inputs():
             match input:
-                case ScienceInput(data_type="l3a", source="codice", descriptor=descriptor) \
-                    if descriptor == DIRECT_EVENTS_DESCRIPTOR:
+                case ScienceInput(
+                    data_type="l3a", source="codice", descriptor=descriptor
+                ) if descriptor == DIRECT_EVENTS_DESCRIPTOR:
                     l3a_direct_event_paths.extend(input.filename_list)
 
         [direct_events_path] = l3a_direct_event_paths
         [mass_species_bin_path] = processing_input_collection.get_file_paths(
-            source='codice',
+            source="codice",
             descriptor=MASS_SPECIES_BIN_LOOKUP_DESCRIPTOR,
         )
         [geometric_factor_path] = processing_input_collection.get_file_paths(
-            source='codice',
+            source="codice",
             descriptor=GEOMETRIC_FACTOR_LOOKUP_DESCRIPTOR,
         )
         [efficiency_factor_path] = processing_input_collection.get_file_paths(
-            source='codice',
+            source="codice",
             descriptor=EFFICIENCY_FACTOR_LOOKUP_DESCRIPTOR,
         )
         [esa_to_energy_per_charge_path] = processing_input_collection.get_file_paths(
-            source='codice',
+            source="codice",
             descriptor=ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR,
         )
 
         direct_events_downloaded_path = imap_data_access.download(direct_events_path)
-        mass_species_bin_downloaded_path = imap_data_access.download(mass_species_bin_path.name)
-        geometric_factor_downloaded_path = imap_data_access.download(geometric_factor_path.name)
-        efficiency_factor_downloaded_path = imap_data_access.download(efficiency_factor_path.name)
-        esa_to_energy_per_charge_downloaded_path = imap_data_access.download(esa_to_energy_per_charge_path.name)
+        mass_species_bin_downloaded_path = imap_data_access.download(
+            mass_species_bin_path.name
+        )
+        geometric_factor_downloaded_path = imap_data_access.download(
+            geometric_factor_path.name
+        )
+        efficiency_factor_downloaded_path = imap_data_access.download(
+            efficiency_factor_path.name
+        )
+        esa_to_energy_per_charge_downloaded_path = imap_data_access.download(
+            esa_to_energy_per_charge_path.name
+        )
 
-        return cls.from_file_paths(l3a_file_path=direct_events_downloaded_path,
-                                   mass_species_bin_lut=mass_species_bin_downloaded_path,
-                                   geometric_factors_lut=geometric_factor_downloaded_path,
-                                   efficiency_factors_lut=efficiency_factor_downloaded_path,
-                                   energy_per_charge_lut=esa_to_energy_per_charge_downloaded_path,
-                                   species=species)
+        return cls.from_file_paths(
+            l3a_file_path=direct_events_downloaded_path,
+            mass_species_bin_lut=mass_species_bin_downloaded_path,
+            geometric_factors_lut=geometric_factor_downloaded_path,
+            efficiency_factors_lut=efficiency_factor_downloaded_path,
+            energy_per_charge_lut=esa_to_energy_per_charge_downloaded_path,
+            species=species,
+        )
 
     @classmethod
-    def from_file_paths(cls, l3a_file_path: Path,
-                        mass_species_bin_lut: Path,
-                        geometric_factors_lut: Path,
-                        efficiency_factors_lut: Path,
-                        energy_per_charge_lut: Path,
-                        species: str):
-        mass_species_bin_lookup = MassSpeciesBinLookup.read_from_csv(mass_species_bin_lut)
+    def from_file_paths(
+        cls,
+        l3a_file_path: Path,
+        mass_species_bin_lut: Path,
+        geometric_factors_lut: Path,
+        efficiency_factors_lut: Path,
+        energy_per_charge_lut: Path,
+        species: str,
+    ):
+        mass_species_bin_lookup = MassSpeciesBinLookup.read_from_csv(
+            mass_species_bin_lut
+        )
         return cls(
             l3a_direct_event_data=CodiceLoDirectEventData.read_from_cdf(l3a_file_path),
             mass_species_bin_lookup=mass_species_bin_lookup,
-            geometric_factors_lookup=GeometricFactorLookup.read_from_csv(geometric_factors_lut),
-            efficiency_factors_lut=EfficiencyLookup.read_from_csv(efficiency_factors_lut, species),
+            geometric_factors_lookup=GeometricFactorLookup.read_from_csv(
+                geometric_factors_lut
+            ),
+            efficiency_factors_lut=EfficiencyLookup.read_from_csv(
+                efficiency_factors_lut, species
+            ),
             energy_per_charge_lut=EnergyLookup.read_from_csv(energy_per_charge_lut),
-            species=species
+            species=species,
         )
diff --git a/imap_l3_processing/codice/l3/lo/codice_lo_processor.py b/imap_l3_processing/codice/l3/lo/codice_lo_processor.py
index 547a91d44..32645897e 100644
--- a/imap_l3_processing/codice/l3/lo/codice_lo_processor.py
+++ b/imap_l3_processing/codice/l3/lo/codice_lo_processor.py
@@ -2,60 +2,98 @@
 
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection
-from imap_processing.spice.geometry import get_spacecraft_to_instrument_spin_phase_offset, SpiceFrame
-
-from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import \
-    CodiceLoL3b3dDistributionsDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import CodiceLoL3aDirectEventsDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import \
-    CodiceLoL3aPartialDensitiesDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import CodiceLoL3aRatiosDependencies
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import SpinAngleLookup, \
-    PositionToElevationLookup
-from imap_l3_processing.codice.l3.lo.models import CodiceLoL3aPartialDensityDataProduct, \
-    CodiceLoL3aDirectEventDataProduct, CodiceLoPartialDensityData, CodiceLoL3aRatiosDataProduct, \
-    CodiceLoL3ChargeStateDistributionsDataProduct, CodiceLoL3b3dDistributionDataProduct
-from imap_l3_processing.codice.l3.lo.science.codice_lo_calculations import calculate_partial_densities, \
-    calculate_mass, calculate_mass_per_charge,convert_counts_to_intensity, \
-    rebin_to_counts_by_species_elevation_and_spin_sector, combine_priorities_for_species_and_calculate_count_uncertainty, \
-    calculate_normalization_factor, lookup_normalization_per_event, rebin_3d_distribution_azimuth_to_elevation
+from imap_processing.spice.geometry import (
+    SpiceFrame,
+    get_spacecraft_to_instrument_spin_phase_offset,
+)
+
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import (
+    CodiceLoL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import (
+    CodiceLoL3aPartialDensitiesDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import (
+    CodiceLoL3aRatiosDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import (
+    CodiceLoL3b3dDistributionsDependencies,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    PositionToElevationLookup,
+    SpinAngleLookup,
+)
+from imap_l3_processing.codice.l3.lo.models import (
+    CodiceLoL3aDirectEventDataProduct,
+    CodiceLoL3aPartialDensityDataProduct,
+    CodiceLoL3aRatiosDataProduct,
+    CodiceLoL3b3dDistributionDataProduct,
+    CodiceLoL3ChargeStateDistributionsDataProduct,
+    CodiceLoPartialDensityData,
+)
+from imap_l3_processing.codice.l3.lo.science.codice_lo_calculations import (
+    calculate_mass,
+    calculate_mass_per_charge,
+    calculate_normalization_factor,
+    calculate_partial_densities,
+    combine_priorities_for_species_and_calculate_count_uncertainty,
+    convert_counts_to_intensity,
+    lookup_normalization_per_event,
+    rebin_3d_distribution_azimuth_to_elevation,
+    rebin_to_counts_by_species_elevation_and_spin_sector,
+)
 from imap_l3_processing.data_utils import safe_divide
 from imap_l3_processing.models import InputMetadata
 from imap_l3_processing.processor import Processor
 from imap_l3_processing.utils import save_data
 
-PriorityRate = namedtuple('PriorityRate', ('epoch', 'energy_table', 'priority_count'))
+PriorityRate = namedtuple("PriorityRate", ("epoch", "energy_table", "priority_count"))
 
 
 class CodiceLoProcessor(Processor):
-    def __init__(self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         super().__init__(dependencies, input_metadata)
 
     def process(self):
         if self.input_metadata.descriptor == "lo-partial-densities":
-            dependencies = CodiceLoL3aPartialDensitiesDependencies.fetch_dependencies(self.dependencies)
+            dependencies = CodiceLoL3aPartialDensitiesDependencies.fetch_dependencies(
+                self.dependencies
+            )
             data_product = self.process_l3a_partial_densities(dependencies)
         elif self.input_metadata.descriptor == "lo-direct-events":
-            dependencies = CodiceLoL3aDirectEventsDependencies.fetch_dependencies(self.dependencies)
+            dependencies = CodiceLoL3aDirectEventsDependencies.fetch_dependencies(
+                self.dependencies
+            )
             data_product = self.process_l3a_direct_event_data_product(dependencies)
         elif self.input_metadata.descriptor == "lo-sw-ratios":
-            dependencies = CodiceLoL3aRatiosDependencies.fetch_dependencies(self.dependencies)
+            dependencies = CodiceLoL3aRatiosDependencies.fetch_dependencies(
+                self.dependencies
+            )
             data_product = self.process_l3a_ratios(dependencies)
         elif self.input_metadata.descriptor == "lo-sw-charge-state-distributions":
-            dependencies = CodiceLoL3aRatiosDependencies.fetch_dependencies(self.dependencies)
+            dependencies = CodiceLoL3aRatiosDependencies.fetch_dependencies(
+                self.dependencies
+            )
             data_product = self.process_l3a_charge_state_distributions(dependencies)
         elif "3d-distribution" in self.input_metadata.descriptor:
-            species = self.input_metadata.descriptor.split('-')[1]
-            dependencies = CodiceLoL3b3dDistributionsDependencies.fetch_dependencies(self.dependencies, species)
+            species = self.input_metadata.descriptor.split("-")[1]
+            dependencies = CodiceLoL3b3dDistributionsDependencies.fetch_dependencies(
+                self.dependencies, species
+            )
             data_product = self.process_l3b_3d_distribution_product(dependencies)
         else:
             raise NotImplementedError(
-                f"Unknown data level and descriptor for CoDICE: {self.input_metadata.data_level}, {self.input_metadata.descriptor}")
+                f"Unknown data level and descriptor for CoDICE: {self.input_metadata.data_level}, {self.input_metadata.descriptor}"
+            )
 
         data_product.parent_file_names = self.get_parent_file_names()
         return [save_data(data_product)]
 
-    def process_l3a_ratios(self, dependencies: CodiceLoL3aRatiosDependencies) -> CodiceLoL3aRatiosDataProduct:
+    def process_l3a_ratios(
+        self, dependencies: CodiceLoL3aRatiosDependencies
+    ) -> CodiceLoL3aRatiosDataProduct:
         input_data = dependencies.partial_density_data
         c4 = _average_over_block(input_data.cplus4_partial_density, 3)
         c5 = _average_over_block(input_data.cplus5_partial_density, 3)
@@ -81,8 +119,9 @@ def process_l3a_ratios(self, dependencies: CodiceLoL3aRatiosDependencies) -> Cod
             felo_to_fehi_ratio=safe_divide(feloq, fehiq),
         )
 
-    def process_l3a_charge_state_distributions(self,
-                                               dependencies: CodiceLoL3aRatiosDependencies) -> CodiceLoL3ChargeStateDistributionsDataProduct:
+    def process_l3a_charge_state_distributions(
+        self, dependencies: CodiceLoL3aRatiosDependencies
+    ) -> CodiceLoL3ChargeStateDistributionsDataProduct:
 
         o5 = dependencies.partial_density_data.oplus5_partial_density
         o6 = dependencies.partial_density_data.oplus6_partial_density
@@ -92,21 +131,31 @@ def process_l3a_charge_state_distributions(self,
         c5 = dependencies.partial_density_data.cplus5_partial_density
         c6 = dependencies.partial_density_data.cplus6_partial_density
 
-        o_densities = _average_over_block(np.column_stack((o5, o6, o7, o8)), block_size=3)
-        o_distribution = safe_divide(o_densities, np.sum(o_densities, axis=1, keepdims=True))
+        o_densities = _average_over_block(
+            np.column_stack((o5, o6, o7, o8)), block_size=3
+        )
+        o_distribution = safe_divide(
+            o_densities, np.sum(o_densities, axis=1, keepdims=True)
+        )
 
         c_densities = _average_over_block(np.column_stack((c4, c5, c6)), block_size=3)
-        c_distribution = safe_divide(c_densities, np.sum(c_densities, axis=1, keepdims=True))
+        c_distribution = safe_divide(
+            c_densities, np.sum(c_densities, axis=1, keepdims=True)
+        )
 
         return CodiceLoL3ChargeStateDistributionsDataProduct(
             self.input_metadata,
             epoch=_average_dates_over_block(dependencies.partial_density_data.epoch, 3),
-            epoch_delta=_sum_over_block(dependencies.partial_density_data.epoch_delta, 3),
+            epoch_delta=_sum_over_block(
+                dependencies.partial_density_data.epoch_delta, 3
+            ),
             oxygen_charge_state_distribution=o_distribution,
-            carbon_charge_state_distribution=c_distribution
+            carbon_charge_state_distribution=c_distribution,
         )
 
-    def process_l3a_partial_densities(self, dependencies: CodiceLoL3aPartialDensitiesDependencies):
+    def process_l3a_partial_densities(
+        self, dependencies: CodiceLoL3aPartialDensitiesDependencies
+    ):
         codice_lo_l2_data = dependencies.codice_l2_lo_data
         mass_per_charge_lookup = dependencies.mass_per_charge_lookup
         h_plus_partial_density = calculate_partial_densities(
@@ -114,42 +163,71 @@ def process_l3a_partial_densities(self, dependencies: CodiceLoL3aPartialDensitie
             codice_lo_l2_data.energy_per_charge,
             mass_per_charge_lookup.hplus,
         )
-        heplusplus_partial_density = calculate_partial_densities(codice_lo_l2_data.heplusplus,
-                                                                 codice_lo_l2_data.energy_per_charge,
-                                                                 mass_per_charge_lookup.heplusplus)
-        cplus4_partial_density = calculate_partial_densities(codice_lo_l2_data.cplus4,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.cplus4)
-        cplus5_partial_density = calculate_partial_densities(codice_lo_l2_data.cplus5,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.cplus5)
-        cplus6_partial_density = calculate_partial_densities(codice_lo_l2_data.cplus6,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.cplus6)
-        oplus5_partial_density = calculate_partial_densities(codice_lo_l2_data.oplus5,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.oplus5)
-        oplus6_partial_density = calculate_partial_densities(codice_lo_l2_data.oplus6,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.oplus6)
-        oplus7_partial_density = calculate_partial_densities(codice_lo_l2_data.oplus7,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.oplus7)
-        oplus8_partial_density = calculate_partial_densities(codice_lo_l2_data.oplus8,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.oplus8)
-        ne_partial_density = calculate_partial_densities(codice_lo_l2_data.ne, codice_lo_l2_data.energy_per_charge,
-                                                         mass_per_charge_lookup.ne)
-        mg_partial_density = calculate_partial_densities(codice_lo_l2_data.mg, codice_lo_l2_data.energy_per_charge,
-                                                         mass_per_charge_lookup.mg)
-        si_partial_density = calculate_partial_densities(codice_lo_l2_data.si, codice_lo_l2_data.energy_per_charge,
-                                                         mass_per_charge_lookup.si)
-        fe_loq_partial_density = calculate_partial_densities(codice_lo_l2_data.fe_loq,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.fe_loq)
-        fe_hiq_partial_density = calculate_partial_densities(codice_lo_l2_data.fe_hiq,
-                                                             codice_lo_l2_data.energy_per_charge,
-                                                             mass_per_charge_lookup.fe_hiq)
+        heplusplus_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.heplusplus,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.heplusplus,
+        )
+        cplus4_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.cplus4,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.cplus4,
+        )
+        cplus5_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.cplus5,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.cplus5,
+        )
+        cplus6_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.cplus6,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.cplus6,
+        )
+        oplus5_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.oplus5,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.oplus5,
+        )
+        oplus6_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.oplus6,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.oplus6,
+        )
+        oplus7_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.oplus7,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.oplus7,
+        )
+        oplus8_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.oplus8,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.oplus8,
+        )
+        ne_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.ne,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.ne,
+        )
+        mg_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.mg,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.mg,
+        )
+        si_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.si,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.si,
+        )
+        fe_loq_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.fe_loq,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.fe_loq,
+        )
+        fe_hiq_partial_density = calculate_partial_densities(
+            codice_lo_l2_data.fe_hiq,
+            codice_lo_l2_data.energy_per_charge,
+            mass_per_charge_lookup.fe_hiq,
+        )
 
         return CodiceLoL3aPartialDensityDataProduct(
             input_metadata=self.input_metadata,
@@ -170,15 +248,18 @@ def process_l3a_partial_densities(self, dependencies: CodiceLoL3aPartialDensitie
                 si_partial_density=si_partial_density,
                 fe_loq_partial_density=fe_loq_partial_density,
                 fe_hiq_partial_density=fe_hiq_partial_density,
-            )
+            ),
         )
 
     def process_l3a_direct_event_data_product(
-            self,
-            dependencies: CodiceLoL3aDirectEventsDependencies
+        self, dependencies: CodiceLoL3aDirectEventsDependencies
     ) -> CodiceLoL3aDirectEventDataProduct:
-        codice_sw_priority_counts_l1a_data = dependencies.codice_lo_l1a_sw_priority_rates
-        codice_nsw_priority_counts_l1a_data = dependencies.codice_lo_l1a_nsw_priority_rates
+        codice_sw_priority_counts_l1a_data = (
+            dependencies.codice_lo_l1a_sw_priority_rates
+        )
+        codice_nsw_priority_counts_l1a_data = (
+            dependencies.codice_lo_l1a_nsw_priority_rates
+        )
         codice_direct_events = dependencies.codice_l2_direct_events
         esa_energy_per_charge_lookup = dependencies.energy_lookup
 
@@ -186,8 +267,14 @@ def process_l3a_direct_event_data_product(
 
         spin_angle_lut = SpinAngleLookup()
 
-        mass_per_charge = calculate_mass_per_charge(codice_direct_events.energy_per_charge, codice_direct_events.tof)
-        mass = calculate_mass(codice_direct_events.apd_energy, codice_direct_events.tof, mass_coefficient_lookup)
+        mass_per_charge = calculate_mass_per_charge(
+            codice_direct_events.energy_per_charge, codice_direct_events.tof
+        )
+        mass = calculate_mass(
+            codice_direct_events.apd_energy,
+            codice_direct_events.tof,
+            mass_coefficient_lookup,
+        )
 
         direct_event_epochs = codice_direct_events.epoch
 
@@ -209,49 +296,84 @@ def process_l3a_direct_event_data_product(
             axis=1,
         )
         sw_aligned, sw_missing = _align_priority_counts_variable_to_direct_event_epochs(
-            sw_priority_counts, codice_sw_priority_counts_l1a_data.epoch, direct_event_epochs
+            sw_priority_counts,
+            codice_sw_priority_counts_l1a_data.epoch,
+            direct_event_epochs,
         )
-        nsw_aligned, nsw_missing = _align_priority_counts_variable_to_direct_event_epochs(
-            nsw_priority_counts, codice_nsw_priority_counts_l1a_data.epoch, direct_event_epochs
+        nsw_aligned, nsw_missing = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                nsw_priority_counts,
+                codice_nsw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        acquisition_time_per_esa_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.acquisition_time_per_esa_step, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        acquisition_time_per_esa_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.acquisition_time_per_esa_step,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        rgfo_half_spin_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.rgfo_half_spin, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        rgfo_half_spin_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.rgfo_half_spin,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        rgfo_spin_sector_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.rgfo_spin_sector, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        rgfo_spin_sector_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.rgfo_spin_sector,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
+        )
+        rgfo_esa_step_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.rgfo_esa_step,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        rgfo_esa_step_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.rgfo_esa_step, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        half_spin_per_esa_step_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.half_spin_per_esa_step,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        half_spin_per_esa_step_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.half_spin_per_esa_step, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        nso_spin_sector_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.nso_spin_sector,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        nso_spin_sector_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.nso_spin_sector, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        nso_esa_step_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.nso_esa_step,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        nso_esa_step_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.nso_esa_step, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        nso_half_spin_aligned, _ = (
+            _align_priority_counts_variable_to_direct_event_epochs(
+                codice_sw_priority_counts_l1a_data.nso_half_spin,
+                codice_sw_priority_counts_l1a_data.epoch,
+                direct_event_epochs,
+            )
         )
-        nso_half_spin_aligned, _ = _align_priority_counts_variable_to_direct_event_epochs(
-            codice_sw_priority_counts_l1a_data.nso_half_spin, codice_sw_priority_counts_l1a_data.epoch,
-            direct_event_epochs
+        stacked_priorities = np.concatenate(
+            [np.ma.filled(sw_aligned, np.nan), np.ma.filled(nsw_aligned, np.nan)],
+            axis=1,
         )
-        stacked_priorities = np.concatenate([
-            np.ma.filled(sw_aligned, np.nan), np.ma.filled(nsw_aligned, np.nan)], axis=1)
 
-        normalization = calculate_normalization_factor(stacked_priorities, codice_direct_events.num_events,
-                                                       codice_direct_events.energy_step,
-                                                       codice_direct_events.spin_sector)
+        normalization = calculate_normalization_factor(
+            stacked_priorities,
+            codice_direct_events.num_events,
+            codice_direct_events.energy_step,
+            codice_direct_events.spin_sector,
+        )
         normalization_per_event = lookup_normalization_per_event(
             normalization,
             codice_direct_events.num_events,
@@ -262,7 +384,8 @@ def process_l3a_direct_event_data_product(
         num_sw_priorities = sw_aligned.shape[1]
         num_nsw_priorities = nsw_aligned.shape[1]
         priority_missing_mask = np.zeros(
-            (len(direct_event_epochs), num_sw_priorities + num_nsw_priorities), dtype=bool
+            (len(direct_event_epochs), num_sw_priorities + num_nsw_priorities),
+            dtype=bool,
         )
         priority_missing_mask[sw_missing, :num_sw_priorities] = True
         priority_missing_mask[nsw_missing, num_sw_priorities:] = True
@@ -306,39 +429,68 @@ def process_l3a_direct_event_data_product(
             type=codice_direct_events.type,
         )
 
-    def process_l3b_3d_distribution_product(self, dependencies: CodiceLoL3b3dDistributionsDependencies):
+    def process_l3b_3d_distribution_product(
+        self, dependencies: CodiceLoL3b3dDistributionsDependencies
+    ):
         mass_species_bin_lookup = dependencies.mass_species_bin_lookup
         position_elevation_lut = PositionToElevationLookup()
         energy_lut = dependencies.energy_per_charge_lut
         geometric_factor_lut = dependencies.geometric_factors_lookup
 
         counts_3d_data = rebin_to_counts_by_species_elevation_and_spin_sector(
-            direct_event_data=dependencies.l3a_direct_event_data, mass_species_bin_lookup=mass_species_bin_lookup)
+            direct_event_data=dependencies.l3a_direct_event_data,
+            mass_species_bin_lookup=mass_species_bin_lookup,
+        )
 
         species_index = mass_species_bin_lookup.get_species_index(dependencies.species)
         counts_for_species = counts_3d_data[species_index]
-        normalized_counts, counts_uncertainty = combine_priorities_for_species_and_calculate_count_uncertainty(counts_for_species)
+        normalized_counts, counts_uncertainty = (
+            combine_priorities_for_species_and_calculate_count_uncertainty(
+                counts_for_species
+            )
+        )
         geometric_factors = geometric_factor_lut.get_geometric_factors(
-            dependencies.l3a_direct_event_data.rgfo_half_spin, dependencies.l3a_direct_event_data.rgfo_spin_sector,
-            dependencies.l3a_direct_event_data.rgfo_esa_step, dependencies.l3a_direct_event_data.half_spin_per_esa_step,
-            self.input_metadata.start_date.date()
-        )
-        intensities = convert_counts_to_intensity(normalized_counts, dependencies.l3a_direct_event_data.acquisition_time_per_esa_step,
-                                                      dependencies.energy_per_charge_lut,
-                                                      dependencies.efficiency_factors_lut,
-                                                      geometric_factors)
-        intensities_uncertainty = convert_counts_to_intensity(counts_uncertainty, dependencies.l3a_direct_event_data.acquisition_time_per_esa_step,
-                                                      dependencies.energy_per_charge_lut,
-                                                      dependencies.efficiency_factors_lut,
-                                                      geometric_factors)
-
-        rebinned_intensity = rebin_3d_distribution_azimuth_to_elevation(intensities, np.arange(1, 25), position_elevation_lut,
-                                                               dependencies.l3a_direct_event_data.half_spin_per_esa_step)
-
-        rebinned_intensity_uncertainty = rebin_3d_distribution_azimuth_to_elevation(intensities_uncertainty, np.arange(1, 25), position_elevation_lut,
-                                                               dependencies.l3a_direct_event_data.half_spin_per_esa_step)
-        offset_degrees = get_spacecraft_to_instrument_spin_phase_offset(SpiceFrame.IMAP_CODICE)*360 - 180
-        spin_angle_bins_in_dps = (dependencies.l3a_direct_event_data.spin_angle_bin + offset_degrees) % 360
+            dependencies.l3a_direct_event_data.rgfo_half_spin,
+            dependencies.l3a_direct_event_data.rgfo_spin_sector,
+            dependencies.l3a_direct_event_data.rgfo_esa_step,
+            dependencies.l3a_direct_event_data.half_spin_per_esa_step,
+            self.input_metadata.start_date.date(),
+        )
+        intensities = convert_counts_to_intensity(
+            normalized_counts,
+            dependencies.l3a_direct_event_data.acquisition_time_per_esa_step,
+            dependencies.energy_per_charge_lut,
+            dependencies.efficiency_factors_lut,
+            geometric_factors,
+        )
+        intensities_uncertainty = convert_counts_to_intensity(
+            counts_uncertainty,
+            dependencies.l3a_direct_event_data.acquisition_time_per_esa_step,
+            dependencies.energy_per_charge_lut,
+            dependencies.efficiency_factors_lut,
+            geometric_factors,
+        )
+
+        rebinned_intensity = rebin_3d_distribution_azimuth_to_elevation(
+            intensities,
+            np.arange(1, 25),
+            position_elevation_lut,
+            dependencies.l3a_direct_event_data.half_spin_per_esa_step,
+        )
+
+        rebinned_intensity_uncertainty = rebin_3d_distribution_azimuth_to_elevation(
+            intensities_uncertainty,
+            np.arange(1, 25),
+            position_elevation_lut,
+            dependencies.l3a_direct_event_data.half_spin_per_esa_step,
+        )
+        offset_degrees = (
+            get_spacecraft_to_instrument_spin_phase_offset(SpiceFrame.IMAP_CODICE) * 360
+            - 180
+        )
+        spin_angle_bins_in_dps = (
+            dependencies.l3a_direct_event_data.spin_angle_bin + offset_degrees
+        ) % 360
         return CodiceLoL3b3dDistributionDataProduct(
             input_metadata=self.input_metadata,
             epoch=dependencies.l3a_direct_event_data.epoch,
@@ -356,14 +508,16 @@ def process_l3b_3d_distribution_product(self, dependencies: CodiceLoL3b3dDistrib
             rgfo_esa_step=dependencies.l3a_direct_event_data.rgfo_esa_step,
             rgfo_spin_sector=dependencies.l3a_direct_event_data.rgfo_spin_sector,
             rgfo_half_spin=dependencies.l3a_direct_event_data.rgfo_half_spin,
-            half_spin_per_esa_step=np.flip(dependencies.l3a_direct_event_data.half_spin_per_esa_step),
+            half_spin_per_esa_step=np.flip(
+                dependencies.l3a_direct_event_data.half_spin_per_esa_step
+            ),
         )
 
 
 def _align_priority_counts_variable_to_direct_event_epochs(
-        priority_variable: np.ndarray,
-        priority_epochs: np.ndarray,
-        direct_event_epochs: np.ndarray,
+    priority_variable: np.ndarray,
+    priority_epochs: np.ndarray,
+    direct_event_epochs: np.ndarray,
 ) -> tuple[np.ndarray, np.ndarray]:
     num_direct_event_epochs = len(direct_event_epochs)
     aligned_shape = (num_direct_event_epochs,) + priority_variable.shape[1:]
@@ -378,14 +532,32 @@ def _align_priority_counts_variable_to_direct_event_epochs(
 
 
 def _average_over_block(data_array: np.ndarray, block_size: int):
-    return np.array([data_array[i:i + block_size].mean(axis=0) for i in range(0, len(data_array), block_size)])
+    return np.array(
+        [
+            data_array[i : i + block_size].mean(axis=0)
+            for i in range(0, len(data_array), block_size)
+        ]
+    )
 
 
 def _sum_over_block(data_array: np.ndarray, block_size: int):
-    return np.array([data_array[i:i + block_size].sum() for i in range(0, len(data_array), block_size)])
+    return np.array(
+        [
+            data_array[i : i + block_size].sum()
+            for i in range(0, len(data_array), block_size)
+        ]
+    )
 
 
 def _average_dates_over_block(data_array: np.ndarray, block_size: int):
-    dates_as_int = data_array.astype('datetime64[us]').astype('int64')
-    return np.array([dates_as_int[i:i + block_size].mean() for i in range(0, len(dates_as_int), block_size)]).astype(
-        "datetime64[us]").astype('O')
+    dates_as_int = data_array.astype("datetime64[us]").astype("int64")
+    return (
+        np.array(
+            [
+                dates_as_int[i : i + block_size].mean()
+                for i in range(0, len(dates_as_int), block_size)
+            ]
+        )
+        .astype("datetime64[us]")
+        .astype("O")
+    )
diff --git a/imap_l3_processing/codice/l3/lo/constants.py b/imap_l3_processing/codice/l3/lo/constants.py
index 47edd09a6..38a2bf777 100644
--- a/imap_l3_processing/codice/l3/lo/constants.py
+++ b/imap_l3_processing/codice/l3/lo/constants.py
@@ -18,4 +18,4 @@
 CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET = 46.0
 
 CODICE_SECOND_ERA = date(2025, 11, 24)
-CODICE_THIRD_ERA = date(2026, 1, 29)
\ No newline at end of file
+CODICE_THIRD_ERA = date(2026, 1, 29)
diff --git a/imap_l3_processing/codice/l3/lo/direct_events/science/angle_lookup.py b/imap_l3_processing/codice/l3/lo/direct_events/science/angle_lookup.py
index 517ba36d4..d4ca3e163 100644
--- a/imap_l3_processing/codice/l3/lo/direct_events/science/angle_lookup.py
+++ b/imap_l3_processing/codice/l3/lo/direct_events/science/angle_lookup.py
@@ -10,6 +10,7 @@ class SpinAngleLookup:
     baseline: np.ndarray
 
     offset = 270
+
     def __init__(self):
         num_bins = 24
         self.baseline = np.linspace(0, 360, num_bins, endpoint=False)
@@ -42,7 +43,33 @@ def __init__(self):
         self.lower_bin_edges = self.bin_centers - self.bin_deltas
 
         self.elevation_indices_by_apd = np.array(
-            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
+            [
+                0,
+                1,
+                2,
+                3,
+                4,
+                5,
+                6,
+                7,
+                8,
+                9,
+                10,
+                11,
+                12,
+                11,
+                10,
+                9,
+                8,
+                7,
+                6,
+                5,
+                4,
+                3,
+                2,
+                1,
+            ]
+        )
 
     @property
     def num_bins(self):
diff --git a/imap_l3_processing/codice/l3/lo/direct_events/science/efficiency_lookup.py b/imap_l3_processing/codice/l3/lo/direct_events/science/efficiency_lookup.py
index b37496077..cc98fe550 100644
--- a/imap_l3_processing/codice/l3/lo/direct_events/science/efficiency_lookup.py
+++ b/imap_l3_processing/codice/l3/lo/direct_events/science/efficiency_lookup.py
@@ -3,13 +3,14 @@
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TypeVar
-import pandas as pd
 
 import numpy as np
+import pandas as pd
 
 POSITION = TypeVar("POSITION")
 ENERGIES = TypeVar("ENERGIES")
 
+
 @dataclass
 class EfficiencyLookup:
     efficiency_data: np.ndarray[(ENERGIES, POSITION)]
@@ -17,9 +18,11 @@ class EfficiencyLookup:
     @classmethod
     def read_from_csv(cls, path: Path, species: str) -> EfficiencyLookup:
         df = pd.read_csv(path)
-        efficiency_data = (df[(df['species'] == species) & (df['product'] == 'sw')]
-                           .sort_values('esa_step')
-                           .drop(['species', 'product', 'esa_step'], axis=1)
-                           .to_numpy())
+        efficiency_data = (
+            df[(df["species"] == species) & (df["product"] == "sw")]
+            .sort_values("esa_step")
+            .drop(["species", "product", "esa_step"], axis=1)
+            .to_numpy()
+        )
 
         return cls(efficiency_data=efficiency_data)
diff --git a/imap_l3_processing/codice/l3/lo/direct_events/science/energy_lookup.py b/imap_l3_processing/codice/l3/lo/direct_events/science/energy_lookup.py
index a4951e697..2742b4b9e 100644
--- a/imap_l3_processing/codice/l3/lo/direct_events/science/energy_lookup.py
+++ b/imap_l3_processing/codice/l3/lo/direct_events/science/energy_lookup.py
@@ -14,11 +14,15 @@ class EnergyLookup:
 
     @classmethod
     def read_from_csv(cls, path_to_csv: Path):
-        energy_lowers, energy_centers, energy_uppers = np.loadtxt(path_to_csv, delimiter=',', skiprows=1, usecols=(4, 5, 6)).T
+        energy_lowers, energy_centers, energy_uppers = np.loadtxt(
+            path_to_csv, delimiter=",", skiprows=1, usecols=(4, 5, 6)
+        ).T
 
-        return cls(bin_centers=energy_centers,
-                   delta_minus=energy_centers - energy_lowers,
-                   delta_plus=energy_uppers - energy_centers)
+        return cls(
+            bin_centers=energy_centers,
+            delta_minus=energy_centers - energy_lowers,
+            delta_plus=energy_uppers - energy_centers,
+        )
 
     @property
     def num_bins(self):
diff --git a/imap_l3_processing/codice/l3/lo/direct_events/science/geometric_factor_lookup.py b/imap_l3_processing/codice/l3/lo/direct_events/science/geometric_factor_lookup.py
index b3ac43f59..7cb24c826 100644
--- a/imap_l3_processing/codice/l3/lo/direct_events/science/geometric_factor_lookup.py
+++ b/imap_l3_processing/codice/l3/lo/direct_events/science/geometric_factor_lookup.py
@@ -1,61 +1,110 @@
 from dataclasses import dataclass, field
-from datetime import datetime, date
+from datetime import date
 from pathlib import Path
 from typing import TypeVar
 
 import numpy as np
 import pandas as pd
 
-from imap_l3_processing.codice.l3.lo.constants import CODICE_LO_NUM_SPIN_SECTORS, CODICE_LO_NUM_ESA_STEPS, \
-    CODICE_LO_NUM_AZIMUTH_BINS, CODICE_SECOND_ERA, CODICE_THIRD_ERA
+from imap_l3_processing.codice.l3.lo.constants import (
+    CODICE_LO_NUM_AZIMUTH_BINS,
+    CODICE_LO_NUM_ESA_STEPS,
+    CODICE_LO_NUM_SPIN_SECTORS,
+    CODICE_SECOND_ERA,
+    CODICE_THIRD_ERA,
+)
 
 
 def _half_spin_to_esa_step_lookup():
-    return np.array([
-        0, 1, 2, 3, 5, 7, 9, 11, 14, 17, 20, 23, 27, 31, 35, 39, 44, 49, 54, 59, 64, 69, 74, 79, 85, 91, 97, 103, 109,
-        115, 121, 127
-    ])
-
-
-POSITION = TypeVar('POSITION')
-ESA_STEP = TypeVar('ESA_STEP')
+    return np.array(
+        [
+            0,
+            1,
+            2,
+            3,
+            5,
+            7,
+            9,
+            11,
+            14,
+            17,
+            20,
+            23,
+            27,
+            31,
+            35,
+            39,
+            44,
+            49,
+            54,
+            59,
+            64,
+            69,
+            74,
+            79,
+            85,
+            91,
+            97,
+            103,
+            109,
+            115,
+            121,
+            127,
+        ]
+    )
+
+
+POSITION = TypeVar("POSITION")
+ESA_STEP = TypeVar("ESA_STEP")
 
 
 @dataclass
 class GeometricFactorLookup:
     _full_factor: np.ndarray[(ESA_STEP, POSITION)]
     _reduced_factor: np.ndarray[(ESA_STEP, POSITION)]
-    _esa_step_end_index: np.ndarray = field(default_factory=_half_spin_to_esa_step_lookup)
+    _esa_step_end_index: np.ndarray = field(
+        default_factory=_half_spin_to_esa_step_lookup
+    )
 
     @classmethod
     def read_from_csv(cls, filepath: Path):
-        df = (pd.read_csv(filepath)
-              .sort_values(['mode', 'esa_step'])
-              .groupby('mode'))
+        df = pd.read_csv(filepath).sort_values(["mode", "esa_step"]).groupby("mode")
 
-        full = df.get_group('full').drop(['mode', 'esa_step'], axis=1).to_numpy()
-        reduced = df.get_group('reduced').drop(['mode', 'esa_step'], axis=1).to_numpy()
+        full = df.get_group("full").drop(["mode", "esa_step"], axis=1).to_numpy()
+        reduced = df.get_group("reduced").drop(["mode", "esa_step"], axis=1).to_numpy()
 
         return cls(full, reduced)
 
-    def get_geometric_factors(self,
-                              rgfo_half_spin: np.ma.masked_array,
-                              rgfo_spin_sector: np.ma.masked_array,
-                              rgfo_esa_step: np.ma.masked_array,
-                              half_spin: np.ma.masked_array,
-                              epoch: date,
-                              ) -> np.ndarray:
-
-        full_shape = (rgfo_half_spin.shape[0], CODICE_LO_NUM_ESA_STEPS, CODICE_LO_NUM_SPIN_SECTORS, CODICE_LO_NUM_AZIMUTH_BINS)
-        reduced_factor_full_shape = np.broadcast_to(self._reduced_factor[None, :, None, :], full_shape)
-        full_factor_full_shape = np.broadcast_to(self._full_factor[None, :, None, :], full_shape)
+    def get_geometric_factors(
+        self,
+        rgfo_half_spin: np.ma.masked_array,
+        rgfo_spin_sector: np.ma.masked_array,
+        rgfo_esa_step: np.ma.masked_array,
+        half_spin: np.ma.masked_array,
+        epoch: date,
+    ) -> np.ndarray:
+
+        full_shape = (
+            rgfo_half_spin.shape[0],
+            CODICE_LO_NUM_ESA_STEPS,
+            CODICE_LO_NUM_SPIN_SECTORS,
+            CODICE_LO_NUM_AZIMUTH_BINS,
+        )
+        reduced_factor_full_shape = np.broadcast_to(
+            self._reduced_factor[None, :, None, :], full_shape
+        )
+        full_factor_full_shape = np.broadcast_to(
+            self._full_factor[None, :, None, :], full_shape
+        )
 
         if epoch < CODICE_SECOND_ERA:
             use_reduced = self._rgfo_half_spin_compare_only(rgfo_half_spin, half_spin)
         elif epoch < CODICE_THIRD_ERA:
             use_reduced = False
         else:
-            use_reduced = self._is_past_rgfo(rgfo_half_spin, rgfo_spin_sector, rgfo_esa_step, half_spin)
+            use_reduced = self._is_past_rgfo(
+                rgfo_half_spin, rgfo_spin_sector, rgfo_esa_step, half_spin
+            )
         return np.where(
             use_reduced,
             reduced_factor_full_shape,
@@ -64,27 +113,28 @@ def get_geometric_factors(self,
 
     @staticmethod
     def _rgfo_half_spin_compare_only(
-                                        rgfo_half_spin: np.ma.masked_array,
-                                        half_spin: np.ma.masked_array
-                                     ):
+        rgfo_half_spin: np.ma.masked_array, half_spin: np.ma.masked_array
+    ):
         rgfo_half_spin_full_shape = rgfo_half_spin[:, None, None, None]
         half_spin_full_shape = half_spin[:, :, None, None]
         return half_spin_full_shape > rgfo_half_spin_full_shape
 
-
     @staticmethod
-    def _is_past_rgfo(rgfo_half_spin: np.ma.masked_array,
-                      rgfo_spin_sector: np.ma.masked_array,
-                      rgfo_esa_step: np.ma.masked_array,
-                      half_spin: np.ma.masked_array,
-                      ) -> np.ndarray:
+    def _is_past_rgfo(
+        rgfo_half_spin: np.ma.masked_array,
+        rgfo_spin_sector: np.ma.masked_array,
+        rgfo_esa_step: np.ma.masked_array,
+        half_spin: np.ma.masked_array,
+    ) -> np.ndarray:
         rgfo_half_spin_e = rgfo_half_spin[:, None, None, None]
         rgfo_esa_step_e = rgfo_esa_step[:, None, None, None]
         rgfo_spin_sector_mod = (rgfo_spin_sector % 12)[:, None, None, None]
 
         half_spin_e = half_spin[:, :, None, None]
         esa_step_axis = np.arange(CODICE_LO_NUM_ESA_STEPS)[None, :, None, None]
-        spin_sector_axis_mod = np.arange(CODICE_LO_NUM_SPIN_SECTORS)[None, None, :, None] % 12
+        spin_sector_axis_mod = (
+            np.arange(CODICE_LO_NUM_SPIN_SECTORS)[None, None, :, None] % 12
+        )
 
         half_spin_past = half_spin_e > rgfo_half_spin_e
         half_spin_match = half_spin_e == rgfo_half_spin_e
@@ -93,7 +143,7 @@ def _is_past_rgfo(rgfo_half_spin: np.ma.masked_array,
         esa_step_past = esa_step_axis > rgfo_esa_step_e
 
         return (
-                half_spin_past
-                | (half_spin_match & spin_sector_past)
-                | (half_spin_match & spin_sector_match & esa_step_past)
+            half_spin_past
+            | (half_spin_match & spin_sector_past)
+            | (half_spin_match & spin_sector_match & esa_step_past)
         )
diff --git a/imap_l3_processing/codice/l3/lo/direct_events/science/mass_coefficient_lookup.py b/imap_l3_processing/codice/l3/lo/direct_events/science/mass_coefficient_lookup.py
index 91685aa24..9feb8c0ef 100644
--- a/imap_l3_processing/codice/l3/lo/direct_events/science/mass_coefficient_lookup.py
+++ b/imap_l3_processing/codice/l3/lo/direct_events/science/mass_coefficient_lookup.py
@@ -13,5 +13,5 @@ def __getitem__(self, item):
 
     @classmethod
     def read_from_csv(cls, path: Path):
-        mass_coefficients = np.loadtxt(path, dtype=float, delimiter=',')
+        mass_coefficients = np.loadtxt(path, dtype=float, delimiter=",")
         return cls(mass_coefficients)
diff --git a/imap_l3_processing/codice/l3/lo/direct_events/science/mass_species_bin_lookup.py b/imap_l3_processing/codice/l3/lo/direct_events/science/mass_species_bin_lookup.py
index d667cadd9..d74247597 100644
--- a/imap_l3_processing/codice/l3/lo/direct_events/science/mass_species_bin_lookup.py
+++ b/imap_l3_processing/codice/l3/lo/direct_events/science/mass_species_bin_lookup.py
@@ -1,6 +1,5 @@
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional
 
 import numpy as np
 
@@ -15,7 +14,9 @@ class MassSpeciesBinLookup:
     def read_from_csv(cls, path: Path | str):
         loaded_csv = np.genfromtxt(path, delimiter=",", dtype=None, skip_header=1)
         range_data = list(zip(*loaded_csv))
-        [species, mpq_min, mpq_max, mass_min, mass_max] = [list(column) for column in range_data]
+        [species, mpq_min, mpq_max, mass_min, mass_max] = [
+            list(column) for column in range_data
+        ]
 
         return cls(
             species=species,
@@ -23,11 +24,14 @@ def read_from_csv(cls, path: Path | str):
             mass_ranges=list(zip(mass_min, mass_max)),
         )
 
-    def get_species(self, mass: float, mass_per_charge: float) -> Optional[str]:
+    def get_species(self, mass: float, mass_per_charge: float) -> str | None:
         for i in range(len(self.species)):
             lower_mass, upper_mass = self.mass_ranges[i]
             lower_mass_per_charge, upper_mass_per_charge = self.mass_per_charge[i]
-            if lower_mass <= mass < upper_mass and lower_mass_per_charge <= mass_per_charge < upper_mass_per_charge:
+            if (
+                lower_mass <= mass < upper_mass
+                and lower_mass_per_charge <= mass_per_charge < upper_mass_per_charge
+            ):
                 return self.species[i]
 
     def get_species_index(self, species: str) -> int:
diff --git a/imap_l3_processing/codice/l3/lo/models.py b/imap_l3_processing/codice/l3/lo/models.py
index 4f0d94810..559147b0b 100644
--- a/imap_l3_processing/codice/l3/lo/models.py
+++ b/imap_l3_processing/codice/l3/lo/models.py
@@ -3,15 +3,16 @@
 from collections import namedtuple
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import TypeVar
 
 import numpy as np
 from numpy import ndarray
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.cdf.cdf_utils import read_numeric_variable, read_variable_and_mask_fill_values
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
-from imap_l3_processing.models import DataProductVariable, DataProduct
+from imap_l3_processing.cdf.cdf_utils import (
+    read_numeric_variable,
+    read_variable_and_mask_fill_values,
+)
+from imap_l3_processing.models import DataProduct, DataProductVariable
 
 CODICE_LO_L2_NUM_PRIORITIES = 7
 
@@ -89,7 +90,9 @@ def read_from_cdf(cls, l2_lo_sw_species_cdf: Path):
             )
 
 
-EnergyAndSpinAngle = namedtuple(typename="EnergyAndSpinAngle", field_names=["energy", "spin_angle"])
+EnergyAndSpinAngle = namedtuple(
+    typename="EnergyAndSpinAngle", field_names=["energy", "spin_angle"]
+)
 
 
 @dataclass
@@ -119,26 +122,48 @@ def read_from_cdf(cls, l2_direct_event_cdf: Path):
                 epoch=cdf["epoch"][...],
                 epoch_delta_plus=cdf["epoch_delta_plus"][...],
                 epoch_delta_minus=cdf["epoch_delta_minus"][...],
-                apd_energy=read_variable_and_mask_fill_values(cdf["apd_energy"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                gain=read_variable_and_mask_fill_values(cdf["gain"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                apd_id=read_variable_and_mask_fill_values(cdf["apd_id"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                data_quality=read_variable_and_mask_fill_values(cdf["data_quality"])[:, :CODICE_LO_L2_NUM_PRIORITIES,
-                             ...],
-                energy_step=read_variable_and_mask_fill_values(cdf["energy_step"])[:, :CODICE_LO_L2_NUM_PRIORITIES,
-                            ...],
-                energy_per_charge=read_variable_and_mask_fill_values(cdf["energy_per_charge"])[:,
-                                  :CODICE_LO_L2_NUM_PRIORITIES,
-                                  ...],
-                multi_flag=read_variable_and_mask_fill_values(cdf["multi_flag"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                num_events=read_variable_and_mask_fill_values(cdf["num_events"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                spin_angle=read_variable_and_mask_fill_values(cdf["spin_angle"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                spin_sector=read_variable_and_mask_fill_values(cdf["spin_sector"])[:, :CODICE_LO_L2_NUM_PRIORITIES,
-                            ...].astype(int),
-                elevation_angle=read_variable_and_mask_fill_values(cdf["elevation_angle"])[:,
-                                :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                tof=read_variable_and_mask_fill_values(cdf["tof"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                type=read_variable_and_mask_fill_values(cdf["type"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
-                position=read_variable_and_mask_fill_values(cdf["position"])[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
+                apd_energy=read_variable_and_mask_fill_values(cdf["apd_energy"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                gain=read_variable_and_mask_fill_values(cdf["gain"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                apd_id=read_variable_and_mask_fill_values(cdf["apd_id"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                data_quality=read_variable_and_mask_fill_values(cdf["data_quality"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                energy_step=read_variable_and_mask_fill_values(cdf["energy_step"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                energy_per_charge=read_variable_and_mask_fill_values(
+                    cdf["energy_per_charge"]
+                )[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
+                multi_flag=read_variable_and_mask_fill_values(cdf["multi_flag"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                num_events=read_variable_and_mask_fill_values(cdf["num_events"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                spin_angle=read_variable_and_mask_fill_values(cdf["spin_angle"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                spin_sector=read_variable_and_mask_fill_values(cdf["spin_sector"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ].astype(int),
+                elevation_angle=read_variable_and_mask_fill_values(
+                    cdf["elevation_angle"]
+                )[:, :CODICE_LO_L2_NUM_PRIORITIES, ...],
+                tof=read_variable_and_mask_fill_values(cdf["tof"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                type=read_variable_and_mask_fill_values(cdf["type"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
+                position=read_variable_and_mask_fill_values(cdf["position"])[
+                    :, :CODICE_LO_L2_NUM_PRIORITIES, ...
+                ],
             )
             result.tof = np.ma.masked_less(result.tof, 0)
             return result
@@ -178,10 +203,16 @@ def read_from_cdf(cls, cdf_path: Path):
                 epoch_delta_minus=cdf["epoch_delta_minus"][...],
                 acquisition_time_per_esa_step=cdf["acquisition_time_per_esa_step"][...],
                 spin_sector_index=cdf["spin_sector"][...],
-                rgfo_half_spin=read_variable_and_mask_fill_values(cdf["rgfo_half_spin"]),
+                rgfo_half_spin=read_variable_and_mask_fill_values(
+                    cdf["rgfo_half_spin"]
+                ),
                 nso_half_spin=read_variable_and_mask_fill_values(cdf["nso_half_spin"]),
-                sw_bias_gain_mode=read_variable_and_mask_fill_values(cdf["sw_bias_gain_mode"]),
-                st_bias_gain_mode=read_variable_and_mask_fill_values(cdf["st_bias_gain_mode"]),
+                sw_bias_gain_mode=read_variable_and_mask_fill_values(
+                    cdf["sw_bias_gain_mode"]
+                ),
+                st_bias_gain_mode=read_variable_and_mask_fill_values(
+                    cdf["st_bias_gain_mode"]
+                ),
                 data_quality=read_variable_and_mask_fill_values(cdf["data_quality"]),
                 spin_period=read_numeric_variable(cdf["spin_period"]),
                 p0_tcrs=read_variable_and_mask_fill_values(cdf["p0_tcrs"]),
@@ -189,10 +220,16 @@ def read_from_cdf(cls, cdf_path: Path):
                 p2_heplusplus=read_variable_and_mask_fill_values(cdf["p2_heplusplus"]),
                 p3_heavies=read_variable_and_mask_fill_values(cdf["p3_heavies"]),
                 p4_dcrs=read_variable_and_mask_fill_values(cdf["p4_dcrs"]),
-                half_spin_per_esa_step=read_variable_and_mask_fill_values(cdf["half_spin_per_esa_step"]),
-                rgfo_spin_sector=read_variable_and_mask_fill_values(cdf["rgfo_spin_sector"]),
+                half_spin_per_esa_step=read_variable_and_mask_fill_values(
+                    cdf["half_spin_per_esa_step"]
+                ),
+                rgfo_spin_sector=read_variable_and_mask_fill_values(
+                    cdf["rgfo_spin_sector"]
+                ),
                 rgfo_esa_step=read_variable_and_mask_fill_values(cdf["rgfo_esa_step"]),
-                nso_spin_sector=read_variable_and_mask_fill_values(cdf["nso_spin_sector"]),
+                nso_spin_sector=read_variable_and_mask_fill_values(
+                    cdf["nso_spin_sector"]
+                ),
                 nso_esa_step=read_variable_and_mask_fill_values(cdf["nso_esa_step"]),
                 esa_step=read_variable_and_mask_fill_values(cdf["esa_step"]),
             )
@@ -223,13 +260,21 @@ def read_from_cdf(cls, cdf_path: Path):
                 epoch_delta_minus=cdf["epoch_delta_minus"][...],
                 acquisition_time_per_esa_step=cdf["acquisition_time_per_esa_step"][...],
                 spin_sector_index=cdf["spin_sector"][...],
-                rgfo_half_spin=read_variable_and_mask_fill_values(cdf["rgfo_half_spin"]),
+                rgfo_half_spin=read_variable_and_mask_fill_values(
+                    cdf["rgfo_half_spin"]
+                ),
                 data_quality=read_variable_and_mask_fill_values(cdf["data_quality"]),
                 p5_heavies=read_variable_and_mask_fill_values(cdf["p5_heavies"]),
-                p6_hplus_heplusplus=read_variable_and_mask_fill_values(cdf["p6_hplus_heplusplus"]),
+                p6_hplus_heplusplus=read_variable_and_mask_fill_values(
+                    cdf["p6_hplus_heplusplus"]
+                ),
                 nso_half_spin=read_variable_and_mask_fill_values(cdf["nso_half_spin"]),
-                sw_bias_gain_mode=read_variable_and_mask_fill_values(cdf["sw_bias_gain_mode"]),
-                st_bias_gain_mode=read_variable_and_mask_fill_values(cdf["st_bias_gain_mode"]),
+                sw_bias_gain_mode=read_variable_and_mask_fill_values(
+                    cdf["sw_bias_gain_mode"]
+                ),
+                st_bias_gain_mode=read_variable_and_mask_fill_values(
+                    cdf["st_bias_gain_mode"]
+                ),
                 spin_period=read_numeric_variable(cdf["spin_period"]),
             )
 
@@ -259,20 +304,48 @@ def read_from_cdf(cls, cdf_path: Path | str):
             return cls(
                 epoch=cdf[EPOCH_VAR_NAME][...],
                 epoch_delta=cdf[EPOCH_DELTA_VAR_NAME][...],
-                hplus_partial_density=read_numeric_variable(cdf[H_PARTIAL_DENSITY_VAR_NAME]),
-                heplusplus_partial_density=read_numeric_variable(cdf[HE_PARTIAL_DENSITY_VAR_NAME]),
-                cplus4_partial_density=read_numeric_variable(cdf[C4_PARTIAL_DENSITY_VAR_NAME]),
-                cplus5_partial_density=read_numeric_variable(cdf[C5_PARTIAL_DENSITY_VAR_NAME]),
-                cplus6_partial_density=read_numeric_variable(cdf[C6_PARTIAL_DENSITY_VAR_NAME]),
-                oplus5_partial_density=read_numeric_variable(cdf[O5_PARTIAL_DENSITY_VAR_NAME]),
-                oplus6_partial_density=read_numeric_variable(cdf[O6_PARTIAL_DENSITY_VAR_NAME]),
-                oplus7_partial_density=read_numeric_variable(cdf[O7_PARTIAL_DENSITY_VAR_NAME]),
-                oplus8_partial_density=read_numeric_variable(cdf[O8_PARTIAL_DENSITY_VAR_NAME]),
-                ne_partial_density=read_numeric_variable(cdf[NE_PARTIAL_DENSITY_VAR_NAME]),
-                mg_partial_density=read_numeric_variable(cdf[MG_PARTIAL_DENSITY_VAR_NAME]),
-                si_partial_density=read_numeric_variable(cdf[SI_PARTIAL_DENSITY_VAR_NAME]),
-                fe_loq_partial_density=read_numeric_variable(cdf[FE_LOW_PARTIAL_DENSITY_VAR_NAME]),
-                fe_hiq_partial_density=read_numeric_variable(cdf[FE_HIGH_PARTIAL_DENSITY_VAR_NAME]),
+                hplus_partial_density=read_numeric_variable(
+                    cdf[H_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                heplusplus_partial_density=read_numeric_variable(
+                    cdf[HE_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                cplus4_partial_density=read_numeric_variable(
+                    cdf[C4_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                cplus5_partial_density=read_numeric_variable(
+                    cdf[C5_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                cplus6_partial_density=read_numeric_variable(
+                    cdf[C6_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                oplus5_partial_density=read_numeric_variable(
+                    cdf[O5_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                oplus6_partial_density=read_numeric_variable(
+                    cdf[O6_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                oplus7_partial_density=read_numeric_variable(
+                    cdf[O7_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                oplus8_partial_density=read_numeric_variable(
+                    cdf[O8_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                ne_partial_density=read_numeric_variable(
+                    cdf[NE_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                mg_partial_density=read_numeric_variable(
+                    cdf[MG_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                si_partial_density=read_numeric_variable(
+                    cdf[SI_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                fe_loq_partial_density=read_numeric_variable(
+                    cdf[FE_LOW_PARTIAL_DENSITY_VAR_NAME]
+                ),
+                fe_hiq_partial_density=read_numeric_variable(
+                    cdf[FE_HIGH_PARTIAL_DENSITY_VAR_NAME]
+                ),
             )
 
 
@@ -284,24 +357,52 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_VAR_NAME, self.data.epoch),
             DataProductVariable(EPOCH_DELTA_VAR_NAME, self.data.epoch_delta),
-            DataProductVariable(H_PARTIAL_DENSITY_VAR_NAME, self.data.hplus_partial_density),
-            DataProductVariable(HE_PARTIAL_DENSITY_VAR_NAME, self.data.heplusplus_partial_density),
-            DataProductVariable(C4_PARTIAL_DENSITY_VAR_NAME, self.data.cplus4_partial_density),
-            DataProductVariable(C5_PARTIAL_DENSITY_VAR_NAME, self.data.cplus5_partial_density),
-            DataProductVariable(C6_PARTIAL_DENSITY_VAR_NAME, self.data.cplus6_partial_density),
-            DataProductVariable(O5_PARTIAL_DENSITY_VAR_NAME, self.data.oplus5_partial_density),
-            DataProductVariable(O6_PARTIAL_DENSITY_VAR_NAME, self.data.oplus6_partial_density),
-            DataProductVariable(O7_PARTIAL_DENSITY_VAR_NAME, self.data.oplus7_partial_density),
-            DataProductVariable(O8_PARTIAL_DENSITY_VAR_NAME, self.data.oplus8_partial_density),
-            DataProductVariable(NE_PARTIAL_DENSITY_VAR_NAME, self.data.ne_partial_density),
-            DataProductVariable(MG_PARTIAL_DENSITY_VAR_NAME, self.data.mg_partial_density),
-            DataProductVariable(SI_PARTIAL_DENSITY_VAR_NAME, self.data.si_partial_density),
-            DataProductVariable(FE_LOW_PARTIAL_DENSITY_VAR_NAME, self.data.fe_loq_partial_density),
-            DataProductVariable(FE_HIGH_PARTIAL_DENSITY_VAR_NAME, self.data.fe_hiq_partial_density)
+            DataProductVariable(
+                H_PARTIAL_DENSITY_VAR_NAME, self.data.hplus_partial_density
+            ),
+            DataProductVariable(
+                HE_PARTIAL_DENSITY_VAR_NAME, self.data.heplusplus_partial_density
+            ),
+            DataProductVariable(
+                C4_PARTIAL_DENSITY_VAR_NAME, self.data.cplus4_partial_density
+            ),
+            DataProductVariable(
+                C5_PARTIAL_DENSITY_VAR_NAME, self.data.cplus5_partial_density
+            ),
+            DataProductVariable(
+                C6_PARTIAL_DENSITY_VAR_NAME, self.data.cplus6_partial_density
+            ),
+            DataProductVariable(
+                O5_PARTIAL_DENSITY_VAR_NAME, self.data.oplus5_partial_density
+            ),
+            DataProductVariable(
+                O6_PARTIAL_DENSITY_VAR_NAME, self.data.oplus6_partial_density
+            ),
+            DataProductVariable(
+                O7_PARTIAL_DENSITY_VAR_NAME, self.data.oplus7_partial_density
+            ),
+            DataProductVariable(
+                O8_PARTIAL_DENSITY_VAR_NAME, self.data.oplus8_partial_density
+            ),
+            DataProductVariable(
+                NE_PARTIAL_DENSITY_VAR_NAME, self.data.ne_partial_density
+            ),
+            DataProductVariable(
+                MG_PARTIAL_DENSITY_VAR_NAME, self.data.mg_partial_density
+            ),
+            DataProductVariable(
+                SI_PARTIAL_DENSITY_VAR_NAME, self.data.si_partial_density
+            ),
+            DataProductVariable(
+                FE_LOW_PARTIAL_DENSITY_VAR_NAME, self.data.fe_loq_partial_density
+            ),
+            DataProductVariable(
+                FE_HIGH_PARTIAL_DENSITY_VAR_NAME, self.data.fe_hiq_partial_density
+            ),
         ]
 
 
-ACQUISITION_TIME_PER_ESA_STEP_VAR_NAME = 'acquisition_time_per_esa_step'
+ACQUISITION_TIME_PER_ESA_STEP_VAR_NAME = "acquisition_time_per_esa_step"
 EVENT_INDEX_VAR_NAME = "event_index"
 SPIN_ANGLE_DELTA_BIN_VAR_NAME = "spin_angle_bin_delta"
 SPIN_ANGLE_BIN_VAR_NAME = "spin_angle_bin"
@@ -333,10 +434,10 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
 RGFO_SPIN_SECTOR_VAR_NAME = "rgfo_spin_sector"
 RGFO_HALF_SPIN_VAR_NAME = "rgfo_half_spin"
 RGFO_ESA_STEP_VAR_NAME = "rgfo_esa_step"
-NSO_HALF_SPIN_NAME = 'nso_half_spin'
+NSO_HALF_SPIN_NAME = "nso_half_spin"
 NSO_SPIN_SECTOR_VAR_NAME = "nso_spin_sector"
 NSO_ESA_STEP_VAR_NAME = "nso_esa_step"
-RGFO_HALF_SPIN_NAME = 'rgfo_half_spin'
+RGFO_HALF_SPIN_NAME = "rgfo_half_spin"
 ESA_STEP_VAR_NAME = "esa_step"
 ENERGY_PER_CHARGE_VAR_NAME = "energy_per_charge"
 TYPE_VAR_NAME = "type"
@@ -378,29 +479,49 @@ def read_from_cdf(cls, cdf_path: Path | str):
             return cls(
                 epoch=cdf[EPOCH_VAR_NAME][...],
                 epoch_delta=cdf[EPOCH_DELTA_VAR_NAME][...],
-                acquisition_time_per_esa_step=read_numeric_variable(cdf[ACQUISITION_TIME_PER_ESA_STEP_VAR_NAME]),
+                acquisition_time_per_esa_step=read_numeric_variable(
+                    cdf[ACQUISITION_TIME_PER_ESA_STEP_VAR_NAME]
+                ),
                 apd_energy=read_numeric_variable(cdf[APD_ENERGY_VAR_NAME]),
                 apd_id=read_variable_and_mask_fill_values(cdf[APD_ID_VAR_NAME]),
                 data_quality=cdf[DATA_QUALITY_VAR_NAME][...],
                 elevation=read_numeric_variable(cdf[ELEVATION_VAR_NAME]),
-                energy_per_charge=read_numeric_variable(cdf[ENERGY_PER_CHARGE_VAR_NAME]),
-                energy_step=read_variable_and_mask_fill_values(cdf[ENERGY_STEP_VAR_NAME]),
+                energy_per_charge=read_numeric_variable(
+                    cdf[ENERGY_PER_CHARGE_VAR_NAME]
+                ),
+                energy_step=read_variable_and_mask_fill_values(
+                    cdf[ENERGY_STEP_VAR_NAME]
+                ),
                 gain=read_variable_and_mask_fill_values(cdf[GAIN_VAR_NAME]),
                 mass=read_numeric_variable(cdf[MASS_VAR_NAME]),
                 mass_per_charge=read_numeric_variable(cdf[MASS_PER_CHARGE_VAR_NAME]),
                 multi_flag=read_variable_and_mask_fill_values(cdf[MULTI_FLAG_VAR_NAME]),
                 normalization=read_numeric_variable(cdf[NORMALIZATION_VAR_NAME]),
-                normalization_per_event=read_numeric_variable(cdf[NORMALIZATION_PER_EVENT_VAR_NAME]),
+                normalization_per_event=read_numeric_variable(
+                    cdf[NORMALIZATION_PER_EVENT_VAR_NAME]
+                ),
                 num_events=read_variable_and_mask_fill_values(cdf[NUM_EVENTS_VAR_NAME]),
                 position=read_variable_and_mask_fill_values(cdf[POSITION_VAR_NAME]),
                 spin_angle=read_numeric_variable(cdf[SPIN_ANGLE_VAR_NAME]),
                 spin_angle_bin=read_numeric_variable(cdf[SPIN_ANGLE_BIN_VAR_NAME]),
-                spin_angle_bin_delta=read_numeric_variable(cdf[SPIN_ANGLE_DELTA_BIN_VAR_NAME]),
-                spin_sector=read_variable_and_mask_fill_values(cdf[SPIN_SECTOR_VAR_NAME]),
-                half_spin_per_esa_step=read_variable_and_mask_fill_values(cdf[HALF_SPIN_PER_ESA_STEP_VAR_NAME]),
-                rgfo_half_spin=read_variable_and_mask_fill_values(cdf[RGFO_HALF_SPIN_VAR_NAME]),
-                rgfo_spin_sector=read_variable_and_mask_fill_values(cdf[RGFO_SPIN_SECTOR_VAR_NAME]),
-                rgfo_esa_step=read_variable_and_mask_fill_values(cdf[RGFO_ESA_STEP_VAR_NAME]),
+                spin_angle_bin_delta=read_numeric_variable(
+                    cdf[SPIN_ANGLE_DELTA_BIN_VAR_NAME]
+                ),
+                spin_sector=read_variable_and_mask_fill_values(
+                    cdf[SPIN_SECTOR_VAR_NAME]
+                ),
+                half_spin_per_esa_step=read_variable_and_mask_fill_values(
+                    cdf[HALF_SPIN_PER_ESA_STEP_VAR_NAME]
+                ),
+                rgfo_half_spin=read_variable_and_mask_fill_values(
+                    cdf[RGFO_HALF_SPIN_VAR_NAME]
+                ),
+                rgfo_spin_sector=read_variable_and_mask_fill_values(
+                    cdf[RGFO_SPIN_SECTOR_VAR_NAME]
+                ),
+                rgfo_esa_step=read_variable_and_mask_fill_values(
+                    cdf[RGFO_ESA_STEP_VAR_NAME]
+                ),
                 tof=read_variable_and_mask_fill_values(cdf[TOF_VAR_NAME]),
                 type=read_variable_and_mask_fill_values(cdf[TYPE_VAR_NAME]),
             )
@@ -437,14 +558,21 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_VAR_NAME, self.epoch),
             DataProductVariable(EPOCH_DELTA_VAR_NAME, self.epoch_delta),
-            DataProductVariable(ACQUISITION_TIME_PER_ESA_STEP_VAR_NAME, self.acquisition_time_per_esa_step),
+            DataProductVariable(
+                ACQUISITION_TIME_PER_ESA_STEP_VAR_NAME,
+                self.acquisition_time_per_esa_step,
+            ),
             DataProductVariable(APD_ENERGY_VAR_NAME, self.apd_energy),
             DataProductVariable(APD_ID_VAR_NAME, self.apd_id),
             DataProductVariable(DATA_QUALITY_VAR_NAME, self.data_quality),
             DataProductVariable(ELEVATION_VAR_NAME, self.elevation),
             DataProductVariable(ENERGY_BIN_VAR_NAME, self.energy_bin),
-            DataProductVariable(ENERGY_BIN_DELTA_MINUS_VAR_NAME, self.energy_bin_delta_minus),
-            DataProductVariable(ENERGY_BIN_DELTA_PLUS_VAR_NAME, self.energy_bin_delta_plus),
+            DataProductVariable(
+                ENERGY_BIN_DELTA_MINUS_VAR_NAME, self.energy_bin_delta_minus
+            ),
+            DataProductVariable(
+                ENERGY_BIN_DELTA_PLUS_VAR_NAME, self.energy_bin_delta_plus
+            ),
             DataProductVariable(ENERGY_BIN_LABEL_VAR_NAME, self.energy_bin_label),
             DataProductVariable(ENERGY_PER_CHARGE_VAR_NAME, self.energy_per_charge),
             DataProductVariable(ENERGY_STEP_VAR_NAME, self.energy_step),
@@ -452,26 +580,36 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(EVENT_INDEX_VAR_NAME, self.event_index),
             DataProductVariable(EVENT_INDEX_LABEL_VAR_NAME, self.event_index_label),
             DataProductVariable(GAIN_VAR_NAME, self.gain),
-            DataProductVariable(HALF_SPIN_PER_ESA_STEP_VAR_NAME, self.half_spin_per_esa_step),
+            DataProductVariable(
+                HALF_SPIN_PER_ESA_STEP_VAR_NAME, self.half_spin_per_esa_step
+            ),
             DataProductVariable(MASS_VAR_NAME, self.mass),
             DataProductVariable(MASS_PER_CHARGE_VAR_NAME, self.mass_per_charge),
             DataProductVariable(MULTI_FLAG_VAR_NAME, self.multi_flag),
             DataProductVariable(NORMALIZATION_VAR_NAME, self.normalization),
-            DataProductVariable(NORMALIZATION_PER_EVENT_VAR_NAME, self.normalization_per_event),
+            DataProductVariable(
+                NORMALIZATION_PER_EVENT_VAR_NAME, self.normalization_per_event
+            ),
             DataProductVariable(NSO_ESA_STEP_VAR_NAME, self.nso_esa_step),
             DataProductVariable(NSO_SPIN_SECTOR_VAR_NAME, self.nso_spin_sector),
             DataProductVariable(NSO_HALF_SPIN_NAME, self.nso_half_spin),
             DataProductVariable(NUM_EVENTS_VAR_NAME, self.num_events),
             DataProductVariable(POSITION_VAR_NAME, self.position),
             DataProductVariable(PRIORITY_INDEX_VAR_NAME, self.priority_index),
-            DataProductVariable(PRIORITY_INDEX_LABEL_VAR_NAME, self.priority_index_label),
+            DataProductVariable(
+                PRIORITY_INDEX_LABEL_VAR_NAME, self.priority_index_label
+            ),
             DataProductVariable(RGFO_ESA_STEP_VAR_NAME, self.rgfo_esa_step),
             DataProductVariable(RGFO_SPIN_SECTOR_VAR_NAME, self.rgfo_spin_sector),
             DataProductVariable(RGFO_HALF_SPIN_NAME, self.rgfo_half_spin),
             DataProductVariable(SPIN_ANGLE_VAR_NAME, self.spin_angle),
             DataProductVariable(SPIN_ANGLE_BIN_VAR_NAME, self.spin_angle_bin),
-            DataProductVariable(SPIN_ANGLE_DELTA_BIN_VAR_NAME, self.spin_angle_bin_delta),
-            DataProductVariable(SPIN_ANGLE_BIN_LABEL_VAR_NAME, self.spin_angle_bin_label),
+            DataProductVariable(
+                SPIN_ANGLE_DELTA_BIN_VAR_NAME, self.spin_angle_bin_delta
+            ),
+            DataProductVariable(
+                SPIN_ANGLE_BIN_LABEL_VAR_NAME, self.spin_angle_bin_label
+            ),
             DataProductVariable(SPIN_SECTOR_VAR_NAME, self.spin_sector),
             DataProductVariable(TOF_VAR_NAME, self.tof),
             DataProductVariable(TYPE_VAR_NAME, self.type),
@@ -509,7 +647,7 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(C6_TO_C5_RATIO_VAR_NAME, self.c6_to_c5_ratio),
             DataProductVariable(C6_TO_C4_RATIO_VAR_NAME, self.c6_to_c4_ratio),
             DataProductVariable(O7_TO_O6_RATIO_VAR_NAME, self.o7_to_o6_ratio),
-            DataProductVariable(FELO_TO_FEHI_RATIO_VAR_NAME, self.felo_to_fehi_ratio)
+            DataProductVariable(FELO_TO_FEHI_RATIO_VAR_NAME, self.felo_to_fehi_ratio),
         ]
 
 
@@ -524,12 +662,19 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable("epoch", self.epoch),
             DataProductVariable("epoch_delta", self.epoch_delta),
-            DataProductVariable("oxygen_charge_state_distribution", self.oxygen_charge_state_distribution),
-            DataProductVariable("carbon_charge_state_distribution", self.carbon_charge_state_distribution),
+            DataProductVariable(
+                "oxygen_charge_state_distribution",
+                self.oxygen_charge_state_distribution,
+            ),
+            DataProductVariable(
+                "carbon_charge_state_distribution",
+                self.carbon_charge_state_distribution,
+            ),
             DataProductVariable("oxygen_charge_state", np.array([5, 6, 7, 8])),
             DataProductVariable("carbon_charge_state", np.array([4, 5, 6])),
         ]
 
+
 ENERGY_VAR_NAME = "energy"
 ENERGY_DELTA_PLUS_VAR_NAME = "energy_delta_plus"
 ENERGY_DELTA_MINUS_VAR_NAME = "energy_delta_minus"
@@ -574,10 +719,16 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(self.species, self.species_data),
             DataProductVariable(ENERGY_LABEL_VAR_NAME, self.energy.astype(str)),
             DataProductVariable(SPIN_ANGLE_LABEL_VAR_NAME, self.spin_angle.astype(str)),
-            DataProductVariable(ELEVATION_ANGLE_LABEL_VAR_NAME, self.elevation.astype(str)),
-            DataProductVariable(f"{self.species}_stat_uncert", self.species_data_stat_uncert),
+            DataProductVariable(
+                ELEVATION_ANGLE_LABEL_VAR_NAME, self.elevation.astype(str)
+            ),
+            DataProductVariable(
+                f"{self.species}_stat_uncert", self.species_data_stat_uncert
+            ),
             DataProductVariable(RGFO_ESA_STEP_VAR_NAME, self.rgfo_esa_step),
             DataProductVariable(RGFO_SPIN_SECTOR_VAR_NAME, self.rgfo_spin_sector),
             DataProductVariable(RGFO_HALF_SPIN_VAR_NAME, self.rgfo_half_spin),
-            DataProductVariable(HALF_SPIN_PER_ESA_STEP_VAR_NAME, self.half_spin_per_esa_step),
+            DataProductVariable(
+                HALF_SPIN_PER_ESA_STEP_VAR_NAME, self.half_spin_per_esa_step
+            ),
         ]
diff --git a/imap_l3_processing/codice/l3/lo/science/codice_lo_calculations.py b/imap_l3_processing/codice/l3/lo/science/codice_lo_calculations.py
index 4d9a3d375..149e40c83 100644
--- a/imap_l3_processing/codice/l3/lo/science/codice_lo_calculations.py
+++ b/imap_l3_processing/codice/l3/lo/science/codice_lo_calculations.py
@@ -4,62 +4,127 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.constants import AZIMUTH_STEP_SIZE, ELEVATION_STEP_SIZE, ENERGY_STEP_SIZE, \
-    ENERGY_LOST_IN_CARBON_FOIL, POST_ACCELERATION_VOLTAGE_IN_KV, CONVERSION_CONSTANT_K, CODICE_LO_NUM_AZIMUTH_BINS, \
-    CONSTANT_C_FROM_INSTRUMENT_TEAM, CODICE_LO_NUM_ESA_STEPS, CODICE_LO_NUM_SPIN_SECTORS
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import PositionToElevationLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import EfficiencyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import MassCoefficientLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
-from imap_l3_processing.codice.l3.lo.models import EnergyAndSpinAngle, CodiceLoDirectEventData
-
-
-def calculate_partial_densities(intensities: np.ndarray, esa_steps: np.ndarray, mass_per_charge: float):
+from imap_l3_processing.codice.l3.lo.constants import (
+    AZIMUTH_STEP_SIZE,
+    CODICE_LO_NUM_AZIMUTH_BINS,
+    CODICE_LO_NUM_ESA_STEPS,
+    CODICE_LO_NUM_SPIN_SECTORS,
+    CONSTANT_C_FROM_INSTRUMENT_TEAM,
+    CONVERSION_CONSTANT_K,
+    ELEVATION_STEP_SIZE,
+    ENERGY_LOST_IN_CARBON_FOIL,
+    ENERGY_STEP_SIZE,
+    POST_ACCELERATION_VOLTAGE_IN_KV,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    PositionToElevationLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import (
+    EfficiencyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    EnergyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import (
+    MassCoefficientLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import (
+    MassSpeciesBinLookup,
+)
+from imap_l3_processing.codice.l3.lo.models import (
+    CodiceLoDirectEventData,
+)
+
+
+def calculate_partial_densities(
+    intensities: np.ndarray, esa_steps: np.ndarray, mass_per_charge: float
+):
     return np.nansum(
-        CONSTANT_C_FROM_INSTRUMENT_TEAM * AZIMUTH_STEP_SIZE * ELEVATION_STEP_SIZE * ENERGY_STEP_SIZE * intensities * np.sqrt(
-            esa_steps[np.newaxis, :, np.newaxis]) * np.sqrt(mass_per_charge), axis=(1, 2))
-
-
-def calculate_total_number_of_events(priority_rate_variable: np.ndarray, acquisition_time: np.ndarray) -> np.ndarray[
-    int]:
+        CONSTANT_C_FROM_INSTRUMENT_TEAM
+        * AZIMUTH_STEP_SIZE
+        * ELEVATION_STEP_SIZE
+        * ENERGY_STEP_SIZE
+        * intensities
+        * np.sqrt(esa_steps[np.newaxis, :, np.newaxis])
+        * np.sqrt(mass_per_charge),
+        axis=(1, 2),
+    )
+
+
+def calculate_total_number_of_events(
+    priority_rate_variable: np.ndarray, acquisition_time: np.ndarray
+) -> np.ndarray[int]:
     acquisition_time_in_seconds = acquisition_time / 1_000_000
-    counts = np.multiply(priority_rate_variable, acquisition_time_in_seconds[np.newaxis, :, np.newaxis])
+    counts = np.multiply(
+        priority_rate_variable, acquisition_time_in_seconds[np.newaxis, :, np.newaxis]
+    )
     return np.sum(counts, axis=(1, 2), dtype=int)
 
 
-def calculate_mass(apd_energy: np.ndarray, tof: np.ndarray, mass_coefficients: MassCoefficientLookup) -> np.ndarray:
+def calculate_mass(
+    apd_energy: np.ndarray, tof: np.ndarray, mass_coefficients: MassCoefficientLookup
+) -> np.ndarray:
     energy = np.log(apd_energy)
     tof = np.log(tof)
 
-    mass_calculation = mass_coefficients[0] + (mass_coefficients[1] * energy) + (mass_coefficients[2] * tof) + (
-            mass_coefficients[3] * energy * tof) + (mass_coefficients[4] * np.power(energy, 2)) + (
-                               mass_coefficients[5] * np.power(tof, 3))
-    return np.e ** mass_calculation
-
-
-def calculate_mass_per_charge(energy_per_charge: np.ndarray, tof: np.ndarray) -> np.ndarray:
-    return (energy_per_charge + POST_ACCELERATION_VOLTAGE_IN_KV - ENERGY_LOST_IN_CARBON_FOIL) * (
-            tof ** 2) * CONVERSION_CONSTANT_K
-
-
-def rebin_direct_events_for_normalization(num_events: np.ndarray, spin_sector: np.ndarray, energy_step: np.ndarray,
-                                          num_spin_sectors: int, num_energies: int) -> np.ndarray:
-    base_counts = rebin_direct_events_by_energy_and_spin_sector(num_events, spin_sector, energy_step, num_spin_sectors,
-                                                                num_energies)
+    mass_calculation = (
+        mass_coefficients[0]
+        + (mass_coefficients[1] * energy)
+        + (mass_coefficients[2] * tof)
+        + (mass_coefficients[3] * energy * tof)
+        + (mass_coefficients[4] * np.power(energy, 2))
+        + (mass_coefficients[5] * np.power(tof, 3))
+    )
+    return np.e**mass_calculation
+
+
+def calculate_mass_per_charge(
+    energy_per_charge: np.ndarray, tof: np.ndarray
+) -> np.ndarray:
+    return (
+        (
+            energy_per_charge
+            + POST_ACCELERATION_VOLTAGE_IN_KV
+            - ENERGY_LOST_IN_CARBON_FOIL
+        )
+        * (tof**2)
+        * CONVERSION_CONSTANT_K
+    )
+
+
+def rebin_direct_events_for_normalization(
+    num_events: np.ndarray,
+    spin_sector: np.ndarray,
+    energy_step: np.ndarray,
+    num_spin_sectors: int,
+    num_energies: int,
+) -> np.ndarray:
+    base_counts = rebin_direct_events_by_energy_and_spin_sector(
+        num_events, spin_sector, energy_step, num_spin_sectors, num_energies
+    )
     half_spin = num_spin_sectors // 2
     result = np.zeros_like(base_counts)
-    result[:, :, :, 0:half_spin] = base_counts[:, :, :, 0:half_spin] + base_counts[:, :, :, half_spin:num_spin_sectors]
+    result[:, :, :, 0:half_spin] = (
+        base_counts[:, :, :, 0:half_spin]
+        + base_counts[:, :, :, half_spin:num_spin_sectors]
+    )
     result[:, :, :, half_spin:num_spin_sectors] = result[:, :, :, 0:half_spin]
     return result
 
 
-def rebin_direct_events_by_energy_and_spin_sector(num_events: np.ndarray, spin_sector: np.ndarray,
-                                                  energy_step: np.ndarray, num_spin_sectors: int, num_energies: int) -> np.ndarray:
+def rebin_direct_events_by_energy_and_spin_sector(
+    num_events: np.ndarray,
+    spin_sector: np.ndarray,
+    energy_step: np.ndarray,
+    num_spin_sectors: int,
+    num_energies: int,
+) -> np.ndarray:
     num_epochs = num_events.shape[0]
     num_priorities = num_events.shape[1]
 
-    rebinned_output = np.zeros((num_epochs, num_priorities, num_energies, num_spin_sectors))
+    rebinned_output = np.zeros(
+        (num_epochs, num_priorities, num_energies, num_spin_sectors)
+    )
 
     for time_index in range(num_epochs):
         for priority_index in range(num_priorities):
@@ -77,17 +142,26 @@ def rebin_direct_events_by_energy_and_spin_sector(num_events: np.ndarray, spin_s
             valid_spin_sectors = spin_sectors[valid_events_mask]
             valid_energy_indices = energy_indices[valid_events_mask]
 
-            np.add.at(rebinned_output[time_index, priority_index], (valid_energy_indices, valid_spin_sectors), 1)
+            np.add.at(
+                rebinned_output[time_index, priority_index],
+                (valid_energy_indices, valid_spin_sectors),
+                1,
+            )
     return rebinned_output
 
 
-def calculate_normalization_factor(priority_counts: np.ndarray, num_events: np.ndarray, energy_steps: np.ndarray,
-                                   spin_sectors: np.ndarray) -> np.ndarray:
+def calculate_normalization_factor(
+    priority_counts: np.ndarray,
+    num_events: np.ndarray,
+    energy_steps: np.ndarray,
+    spin_sectors: np.ndarray,
+) -> np.ndarray:
     numerator = np.concatenate((priority_counts, priority_counts), axis=3)
     num_energies = priority_counts.shape[2]
     num_spin_sectors = 2 * priority_counts.shape[3]
-    denominator = rebin_direct_events_for_normalization(num_events, spin_sectors, energy_steps, num_spin_sectors,
-                                                        num_energies)
+    denominator = rebin_direct_events_for_normalization(
+        num_events, spin_sectors, energy_steps, num_spin_sectors, num_energies
+    )
 
     division_result = np.zeros(numerator.shape, dtype=float)
     np.divide(numerator, denominator, out=division_result, where=denominator != 0)
@@ -99,8 +173,12 @@ def calculate_normalization_factor(priority_counts: np.ndarray, num_events: np.n
     return output
 
 
-def lookup_normalization_per_event(normalization: np.ndarray, num_events: np.ndarray, energy_steps: np.ndarray,
-                                   spin_sectors: np.ndarray) -> np.ndarray:
+def lookup_normalization_per_event(
+    normalization: np.ndarray,
+    num_events: np.ndarray,
+    energy_steps: np.ndarray,
+    spin_sectors: np.ndarray,
+) -> np.ndarray:
     results = np.full(spin_sectors.shape, np.nan)
     for (epoch, priority), count in np.ma.ndenumerate(num_events, compressed=True):
         energy_step = energy_steps[epoch, priority, :count]
@@ -111,13 +189,16 @@ def lookup_normalization_per_event(normalization: np.ndarray, num_events: np.nda
 
         good_events = ~(bad_spin_sectors | bad_energy_steps)
         results[epoch, priority, :count][good_events] = normalization[
-            epoch, priority, energy_step[good_events], spin_sector[good_events]]
+            epoch, priority, energy_step[good_events], spin_sector[good_events]
+        ]
 
     return results
 
 
-def rebin_to_counts_by_species_elevation_and_spin_sector(direct_event_data: CodiceLoDirectEventData,
-                                                         mass_species_bin_lookup: MassSpeciesBinLookup) -> np.ndarray:
+def rebin_to_counts_by_species_elevation_and_spin_sector(
+    direct_event_data: CodiceLoDirectEventData,
+    mass_species_bin_lookup: MassSpeciesBinLookup,
+) -> np.ndarray:
     mass = direct_event_data.mass
     mass_per_charge = direct_event_data.mass_per_charge
     spin_sector = direct_event_data.spin_sector
@@ -128,8 +209,17 @@ def rebin_to_counts_by_species_elevation_and_spin_sector(direct_event_data: Codi
     num_epochs = mass.shape[0]
     num_priorities = mass.shape[1]
 
-    output = np.full((mass_species_bin_lookup.get_num_species(), num_epochs, num_priorities,
-                      CODICE_LO_NUM_ESA_STEPS, CODICE_LO_NUM_SPIN_SECTORS, CODICE_LO_NUM_AZIMUTH_BINS), 0.0)
+    output = np.full(
+        (
+            mass_species_bin_lookup.get_num_species(),
+            num_epochs,
+            num_priorities,
+            CODICE_LO_NUM_ESA_STEPS,
+            CODICE_LO_NUM_SPIN_SECTORS,
+            CODICE_LO_NUM_AZIMUTH_BINS,
+        ),
+        0.0,
+    )
 
     for epoch_i in range(num_epochs):
         for priority_i in range(num_priorities):
@@ -148,16 +238,22 @@ def rebin_to_counts_by_species_elevation_and_spin_sector(direct_event_data: Codi
                 if apd_id_of_event < 1 or apd_id_of_event > CODICE_LO_NUM_AZIMUTH_BINS:
                     continue
 
-                species = mass_species_bin_lookup.get_species(mass[indices_of_event],
-                                                              mass_per_charge[indices_of_event])
+                species = mass_species_bin_lookup.get_species(
+                    mass[indices_of_event], mass_per_charge[indices_of_event]
+                )
                 if species is not None:
                     energy_i = energy_step[indices_of_event]
                     species_i = mass_species_bin_lookup.get_species_index(species)
                     spin_sector_i = spin_sector[indices_of_event]
                     apd_id_i = apd_id_of_event - 1
                     output[
-                        species_i, epoch_i, priority_i, energy_i, spin_sector_i, apd_id_i] += \
-                        direct_event_data.normalization_per_event[indices_of_event]
+                        species_i,
+                        epoch_i,
+                        priority_i,
+                        energy_i,
+                        spin_sector_i,
+                        apd_id_i,
+                    ] += direct_event_data.normalization_per_event[indices_of_event]
 
     return output
 
@@ -171,15 +267,18 @@ def rebin_to_counts_by_species_elevation_and_spin_sector(direct_event_data: Codi
 
 
 def combine_priorities_for_species_and_calculate_count_uncertainty(
-        counts: np.ndarray[(EPOCH, PRIORITY, POSITION, SPIN_ANGLE, ENERGY)]) -> tuple[np.ndarray, np.ndarray]:
+    counts: np.ndarray[(EPOCH, PRIORITY, POSITION, SPIN_ANGLE, ENERGY)],
+) -> tuple[np.ndarray, np.ndarray]:
     combined_counts = np.sum(counts, axis=1)
     return combined_counts, np.sqrt(combined_counts)
 
 
-def rebin_3d_distribution_azimuth_to_elevation(intensity_data: np.ndarray,
-                                               azimuths: np.ndarray,
-                                               position_to_elevation_lut: PositionToElevationLookup,
-                                               half_spin) -> np.ndarray:
+def rebin_3d_distribution_azimuth_to_elevation(
+    intensity_data: np.ndarray,
+    azimuths: np.ndarray,
+    position_to_elevation_lut: PositionToElevationLookup,
+    half_spin,
+) -> np.ndarray:
     num_epochs = intensity_data.shape[0]
     num_elevations = len(position_to_elevation_lut.bin_centers)
     num_spin_angles = intensity_data.shape[2]
@@ -202,15 +301,27 @@ def rebin_3d_distribution_azimuth_to_elevation(intensity_data: np.ndarray,
     return rebinned
 
 
-def convert_counts_to_intensity(counts: np.ndarray,
-                                acquisition_times:  np.ndarray[(EPOCH, ENERGY,)],
-                                energy_per_charge: EnergyLookup,
-                                efficiency_lookup: EfficiencyLookup,
-                                geometric_factor: np.ndarray[(EPOCH, ENERGY, SPIN_ANGLE, POSITION)]) -> np.ndarray:
+def convert_counts_to_intensity(
+    counts: np.ndarray,
+    acquisition_times: np.ndarray[
+        (
+            EPOCH,
+            ENERGY,
+        )
+    ],
+    energy_per_charge: EnergyLookup,
+    efficiency_lookup: EfficiencyLookup,
+    geometric_factor: np.ndarray[(EPOCH, ENERGY, SPIN_ANGLE, POSITION)],
+) -> np.ndarray:
     count_rates = counts / acquisition_times[:, :, np.newaxis, np.newaxis]
-    reshaped_efficiency_data = efficiency_lookup.efficiency_data[np.newaxis, :, np.newaxis, :]
-    denominator = geometric_factor * energy_per_charge.bin_centers[np.newaxis, :, np.newaxis,
-                                     np.newaxis] * reshaped_efficiency_data
+    reshaped_efficiency_data = efficiency_lookup.efficiency_data[
+        np.newaxis, :, np.newaxis, :
+    ]
+    denominator = (
+        geometric_factor
+        * energy_per_charge.bin_centers[np.newaxis, :, np.newaxis, np.newaxis]
+        * reshaped_efficiency_data
+    )
     intensities = count_rates / denominator
     return intensities
 
diff --git a/imap_l3_processing/codice/quality_flags.py b/imap_l3_processing/codice/quality_flags.py
index f4133dcf5..d3c430368 100644
--- a/imap_l3_processing/codice/quality_flags.py
+++ b/imap_l3_processing/codice/quality_flags.py
@@ -1,6 +1,6 @@
-from imap_processing.quality_flags import FlagNameMixin, CommonFlags
+from imap_processing.quality_flags import CommonFlags, FlagNameMixin
 
 
 class CodiceL3Flags(FlagNameMixin):
     NONE = CommonFlags.NONE
-    PRELIMINARY_MAG = 2 ** 2
+    PRELIMINARY_MAG = 2**2
diff --git a/imap_l3_processing/constants.py b/imap_l3_processing/constants.py
index 8a7d25a37..f8bf6eed7 100644
--- a/imap_l3_processing/constants.py
+++ b/imap_l3_processing/constants.py
@@ -14,14 +14,22 @@
 HE_PUI_PARTICLE_MASS_KG = 6.64556828337e-27
 
 PROTON_CHARGE_OVER_MASS_C_PER_KG = PROTON_CHARGE_COULOMBS / PROTON_MASS_KG
-ALPHA_CHARGE_OVER_MASS_C_PER_KG = ALPHA_PARTICLE_CHARGE_COULOMBS / ALPHA_PARTICLE_MASS_KG
+ALPHA_CHARGE_OVER_MASS_C_PER_KG = (
+    ALPHA_PARTICLE_CHARGE_COULOMBS / ALPHA_PARTICLE_MASS_KG
+)
 
 PROTON_MASS_PER_CHARGE_M_P_PER_E = 1.0
-ALPHA_MASS_PER_CHARGE_M_P_PER_E = (ALPHA_PARTICLE_MASS_KG / PROTON_MASS_KG) / (ALPHA_PARTICLE_CHARGE_COULOMBS / PROTON_CHARGE_COULOMBS)
-HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E = (HE_PUI_PARTICLE_MASS_KG / PROTON_MASS_KG) / (PUI_PARTICLE_CHARGE_COULOMBS / PROTON_CHARGE_COULOMBS)
+ALPHA_MASS_PER_CHARGE_M_P_PER_E = (ALPHA_PARTICLE_MASS_KG / PROTON_MASS_KG) / (
+    ALPHA_PARTICLE_CHARGE_COULOMBS / PROTON_CHARGE_COULOMBS
+)
+HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E = (
+    HE_PUI_PARTICLE_MASS_KG / PROTON_MASS_KG
+) / (PUI_PARTICLE_CHARGE_COULOMBS / PROTON_CHARGE_COULOMBS)
 
 BOLTZMANN_CONSTANT_JOULES_PER_KELVIN = 1.380_649e-23
-EV_TO_KELVIN = PROTON_CHARGE_COULOMBS / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN  # ≈ 11604.52 K/eV
+EV_TO_KELVIN = (
+    PROTON_CHARGE_COULOMBS / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
+)  # ≈ 11604.52 K/eV
 
 ONE_SECOND_IN_MICROSECONDS = 1_000_000
 ONE_SECOND_IN_NANOSECONDS = 1_000_000_000
@@ -32,7 +40,9 @@
 
 CARRINGTON_ROTATION_IN_DAYS = 27.2753
 CARRINGTON_ROTATION_IN_SECONDS = CARRINGTON_ROTATION_IN_DAYS * SECONDS_PER_DAY
-CARRINGTON_ROTATION_IN_NANOSECONDS = CARRINGTON_ROTATION_IN_SECONDS * ONE_SECOND_IN_NANOSECONDS
+CARRINGTON_ROTATION_IN_NANOSECONDS = (
+    CARRINGTON_ROTATION_IN_SECONDS * ONE_SECOND_IN_NANOSECONDS
+)
 
 METERS_PER_KILOMETER = 1_000
 CENTIMETERS_PER_METER = 100
@@ -42,8 +52,8 @@
 
 TT2000_EPOCH = datetime(2000, 1, 1, 11, 58, 55, 816)
 
-FLOAT4_FILL_VALUE = -1.0E31
-FLOAT8_FILL_VALUE = -1.0E31
+FLOAT4_FILL_VALUE = -1.0e31
+FLOAT8_FILL_VALUE = -1.0e31
 INT1_FILL_VALUE = -128
 INT2_FILL_VALUE = -32768
 INT4_FILL_VALUE = -2147483648
@@ -52,4 +62,4 @@
 UNSIGNED_INT4_FILL_VALUE = 4294967295
 INT8_FILL_VALUE = -9223372036854775808
 
-TTJ2000_EPOCH = datetime.fromisoformat("2000-01-01T11:58:55.816")
\ No newline at end of file
+TTJ2000_EPOCH = datetime.fromisoformat("2000-01-01T11:58:55.816")
diff --git a/imap_l3_processing/data_utils.py b/imap_l3_processing/data_utils.py
index a02101f32..ab096f38f 100644
--- a/imap_l3_processing/data_utils.py
+++ b/imap_l3_processing/data_utils.py
@@ -1,10 +1,14 @@
-from datetime import datetime, timedelta
+from datetime import timedelta
 
 import numpy as np
 
 
-def rebin(from_epoch: np.ndarray[float], from_data: np.ndarray[float], to_epoch: np.ndarray[float],
-          to_epoch_delta: np.ndarray[float]) -> np.ndarray[float]:
+def rebin(
+    from_epoch: np.ndarray[float],
+    from_data: np.ndarray[float],
+    to_epoch: np.ndarray[float],
+    to_epoch_delta: np.ndarray[float],
+) -> np.ndarray[float]:
     output_shape = to_epoch.shape + from_data.shape[1:]
     vector_sums = np.zeros(shape=output_shape, dtype=float)
     vector_counts = np.zeros(shape=output_shape, dtype=float)
@@ -23,17 +27,22 @@ def rebin(from_epoch: np.ndarray[float], from_data: np.ndarray[float], to_epoch:
             vector_counts[i] += 1
             current_epoch, current_vec = next(input_data_iter, (None, None))
 
-    return np.divide(vector_sums, vector_counts, out=np.full_like(vector_sums, fill_value=np.nan),
-                     where=vector_counts != 0)
+    return np.divide(
+        vector_sums,
+        vector_counts,
+        out=np.full_like(vector_sums, fill_value=np.nan),
+        where=vector_counts != 0,
+    )
 
 
 class NearestInterpolator:
-    def __init__(self,
-                 from_epoch: np.ndarray,
-                 from_data: np.ndarray,
-                 to_epoch: np.ndarray,
-                 maximum_distance: timedelta | np.timedelta64
-                 ):
+    def __init__(
+        self,
+        from_epoch: np.ndarray,
+        from_data: np.ndarray,
+        to_epoch: np.ndarray,
+        maximum_distance: timedelta | np.timedelta64,
+    ):
         if len(from_data.shape) == 1:
             from_data = from_data.reshape(-1, 1)
         from_data_nan_mask = np.any(np.isnan(from_data), axis=1)
@@ -49,13 +58,17 @@ def __init__(self,
         right_delta = np.abs(from_epoch_as_dt64[right_indices] - to_epoch_as_dt64)
         left_delta = np.abs(from_epoch_as_dt64[left_indices] - to_epoch_as_dt64)
 
-        self.best_indices = np.where(right_delta < left_delta, right_indices, left_indices)
+        self.best_indices = np.where(
+            right_delta < left_delta, right_indices, left_indices
+        )
 
         min_deltas = np.abs(from_epoch_as_dt64[self.best_indices] - to_epoch_as_dt64)
         self.min_outside_range = min_deltas > maximum_distance
 
     def interpolate_data(self) -> np.ndarray:
-        closest_data = self.only_valid_from_data[self.best_indices].astype(float, copy=True)
+        closest_data = self.only_valid_from_data[self.best_indices].astype(
+            float, copy=True
+        )
         closest_data[self.min_outside_range] = np.nan
         return closest_data
 
diff --git a/imap_l3_processing/glows/descriptors.py b/imap_l3_processing/glows/descriptors.py
index 47a605345..75d1f4661 100644
--- a/imap_l3_processing/glows/descriptors.py
+++ b/imap_l3_processing/glows/descriptors.py
@@ -7,13 +7,13 @@
 GLOWS_L3A_DESCRIPTOR = "hist"
 GLOWS_L3B_DESCRIPTOR = "ion-rate-profile"
 GLOWS_L3C_DESCRIPTOR = "sw-profile"
-GLOWS_L3D_DESCRIPTOR = 'solar-hist'
+GLOWS_L3D_DESCRIPTOR = "solar-hist"
 
 WAW_HELIOION_DESCRIPTOR = "WawHelioIonMP"
 BAD_DAYS_LIST_DESCRIPTOR = "bad-days-list"
 UV_ANISOTROPY_1CR_DESCRIPTOR = "uv-anisotropy-1CR"
 
-PLASMA_SPEED_DESCRIPTOR = 'plasma-speed-2026d'
+PLASMA_SPEED_DESCRIPTOR = "plasma-speed-2026d"
 PROTON_DENSITY_DESCRIPTOR = "proton-density-2026d"
 UV_ANISOTROPY_DESCRIPTOR = "uv-anisotropy-2026d"
 PHOTOION_DESCRIPTOR = "photoion-2026d"
@@ -21,9 +21,9 @@
 ELECTRON_DENSITY_DESCRIPTOR = "electron-density-2026d"
 PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR = "pipeline-settings-l3bcde"
 
-GLOWS_L3E_HI_45_DESCRIPTOR = 'survival-probability-hi-45'
-GLOWS_L3E_HI_90_DESCRIPTOR = 'survival-probability-hi-90'
-GLOWS_L3E_LO_DESCRIPTOR = 'survival-probability-lo'
+GLOWS_L3E_HI_45_DESCRIPTOR = "survival-probability-hi-45"
+GLOWS_L3E_HI_90_DESCRIPTOR = "survival-probability-hi-90"
+GLOWS_L3E_LO_DESCRIPTOR = "survival-probability-lo"
 GLOWS_L3E_ULTRA_SF_DESCRIPTOR = "survival-probability-ul-sf"
 GLOWS_L3E_ULTRA_HF_DESCRIPTOR = "survival-probability-ul-hf"
 
@@ -37,7 +37,7 @@
     GLOWS_L3E_HI_90_DESCRIPTOR,
     GLOWS_L3E_LO_DESCRIPTOR,
     GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
-    GLOWS_L3E_ULTRA_HF_DESCRIPTOR
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
 ]
 
 GLOWS_L3E_DESCRIPTORS = [
@@ -46,4 +46,4 @@
     GLOWS_L3E_LO_DESCRIPTOR,
     GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
     GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
-]
\ No newline at end of file
+]
diff --git a/imap_l3_processing/glows/glows_processor.py b/imap_l3_processing/glows/glows_processor.py
index e72f52930..8be075c47 100644
--- a/imap_l3_processing/glows/glows_processor.py
+++ b/imap_l3_processing/glows/glows_processor.py
@@ -8,40 +8,70 @@
 from datetime import datetime, timedelta
 from pathlib import Path
 from subprocess import run
-from typing import Optional
-from zipfile import ZipFile, ZIP_DEFLATED
+from zipfile import ZIP_DEFLATED, ZipFile
 
 import numpy as np
-from imap_data_access.file_validation import generate_imap_file_path, ScienceFilePath, AncillaryFilePath, Version
+from imap_data_access.file_validation import (
+    AncillaryFilePath,
+    ScienceFilePath,
+    Version,
+    generate_imap_file_path,
+)
 from imap_data_access.processing_input import ProcessingInputCollection
 
 from imap_l3_processing.constants import TEMP_CDF_FOLDER_PATH
-from imap_l3_processing.glows.descriptors import GLOWS_L3A_DESCRIPTOR, GLOWS_L3E_ULTRA_SF_DESCRIPTOR, \
-    GLOWS_L3E_ULTRA_HF_DESCRIPTOR, GLOWS_L3D_DESCRIPTOR, GLOWS_L3B_DESCRIPTOR, GLOWS_L3C_DESCRIPTOR, \
-    GLOWS_L3E_LO_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3A_DESCRIPTOR,
+    GLOWS_L3B_DESCRIPTOR,
+    GLOWS_L3C_DESCRIPTOR,
+    GLOWS_L3D_DESCRIPTOR,
+    GLOWS_L3E_LO_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3a.glows_l3a_dependencies import GlowsL3ADependencies
 from imap_l3_processing.glows.l3a.glows_toolkit.l3a_data import L3aData
 from imap_l3_processing.glows.l3a.models import GlowsL3LightCurve
 from imap_l3_processing.glows.l3a.utils import create_glows_l3a_from_dictionary
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
-from imap_l3_processing.glows.l3bc.glows_l3bc_initializer import GlowsL3BCInitializer, GlowsL3BCInitializerData
-from imap_l3_processing.glows.l3bc.models import GlowsL3BIonizationRate, GlowsL3CSolarWind, GlowsL3BCProcessorOutput, \
-    ExternalDependencies, read_pipeline_settings
+from imap_l3_processing.glows.l3bc.glows_l3bc_initializer import (
+    GlowsL3BCInitializer,
+    GlowsL3BCInitializerData,
+)
+from imap_l3_processing.glows.l3bc.models import (
+    ExternalDependencies,
+    GlowsL3BCProcessorOutput,
+    GlowsL3BIonizationRate,
+    GlowsL3CSolarWind,
+)
 from imap_l3_processing.glows.l3bc.science.filter_out_bad_days import filter_l3a_files
 from imap_l3_processing.glows.l3bc.science.generate_l3bc import generate_l3bc
 from imap_l3_processing.glows.l3bc.utils import get_pointing_date_range
 from imap_l3_processing.glows.l3d.glows_l3d_dependencies import GlowsL3DDependencies
 from imap_l3_processing.glows.l3d.glows_l3d_initializer import GlowsL3DInitializer
 from imap_l3_processing.glows.l3d.models import GlowsL3DProcessorOutput
-from imap_l3_processing.glows.l3d.utils import create_glows_l3b_json_file_from_cdf, create_glows_l3c_json_file_from_cdf, \
-    PATH_TO_L3D_TOOLKIT, convert_json_to_l3d_data_product, get_parent_file_names_from_l3d_json, rename_l3d_text_outputs
+from imap_l3_processing.glows.l3d.utils import (
+    PATH_TO_L3D_TOOLKIT,
+    convert_json_to_l3d_data_product,
+    create_glows_l3b_json_file_from_cdf,
+    create_glows_l3c_json_file_from_cdf,
+    get_parent_file_names_from_l3d_json,
+    rename_l3d_text_outputs,
+)
 from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eSpacecraftInfo
 from imap_l3_processing.glows.l3e.glows_l3e_hi_model import GlowsL3EHiData
-from imap_l3_processing.glows.l3e.glows_l3e_initializer import GlowsL3EInitializer, GlowsL3EInitializerOutput
+from imap_l3_processing.glows.l3e.glows_l3e_initializer import (
+    GlowsL3EInitializer,
+    GlowsL3EInitializerOutput,
+)
 from imap_l3_processing.glows.l3e.glows_l3e_lo_model import GlowsL3ELoData
 from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import GlowsL3EUltraData
-from imap_l3_processing.glows.l3e.glows_l3e_utils import determine_call_args_for_l3e_executable, get_lo_pivot_angles, \
-    compute_glows_flags_for_repoint, determine_spacecraft_info_using_predict_if_needed
+from imap_l3_processing.glows.l3e.glows_l3e_utils import (
+    compute_glows_flags_for_repoint,
+    determine_call_args_for_l3e_executable,
+    determine_spacecraft_info_using_predict_if_needed,
+    get_lo_pivot_angles,
+)
 from imap_l3_processing.glows.l3e.reprocess_info import fetch_reprocess_info
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.processor import Processor
@@ -51,12 +81,16 @@
 
 
 class GlowsProcessor(Processor):
-    def __init__(self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         super().__init__(dependencies, input_metadata)
 
     def process(self):
         if self.input_metadata.data_level == "l3a":
-            l3a_dependencies = GlowsL3ADependencies.fetch_dependencies(self.dependencies)
+            l3a_dependencies = GlowsL3ADependencies.fetch_dependencies(
+                self.dependencies
+            )
 
             products = []
             l3a_output = self.process_l3a(l3a_dependencies)
@@ -66,14 +100,22 @@ def process(self):
             return products
         elif self.input_metadata.data_level == "l3b":
             products_list = []
-            l3bc_major_version = self.input_metadata.version.lookup(GLOWS_L3B_DESCRIPTOR).major
-            l3bc_initializer_data: GlowsL3BCInitializerData = GlowsL3BCInitializer.get_crs_to_process(self.dependencies, l3bc_major_version)
+            l3bc_major_version = self.input_metadata.version.lookup(
+                GLOWS_L3B_DESCRIPTOR
+            ).major
+            l3bc_initializer_data: GlowsL3BCInitializerData = (
+                GlowsL3BCInitializer.get_crs_to_process(
+                    self.dependencies, l3bc_major_version
+                )
+            )
 
             if len(l3bc_initializer_data.l3bc_dependencies) > 0:
                 logger.info("Found CRs to Process L3BC:")
                 for dep in l3bc_initializer_data.l3bc_dependencies:
                     l3a_file_names = [l3a_d["filename"] for l3a_d in dep.l3a_data]
-                    logger.info(f"\t{dep.carrington_rotation_number}, {dep.version}: {l3a_file_names}")
+                    logger.info(
+                        f"\t{dep.carrington_rotation_number}, {dep.version}: {l3a_file_names}"
+                    )
             else:
                 logger.info("No CRs to process for B/C")
 
@@ -82,13 +124,32 @@ def process(self):
 
             reprocess_info = fetch_reprocess_info(self.dependencies)
 
-            l3d_major_version = self.input_metadata.version.lookup(GLOWS_L3D_DESCRIPTOR).major
-            l3bs = list({**l3bc_initializer_data.l3bs_by_cr, **glows_l3bc_output_data.l3bs_by_cr}.values())
-            l3cs = list({**l3bc_initializer_data.l3cs_by_cr, **glows_l3bc_output_data.l3cs_by_cr}.values())
-            l3d_initializer_result = GlowsL3DInitializer.should_process_l3d(l3bc_initializer_data.external_dependencies,
-                                                                            l3bs, l3cs, reprocess_info, l3d_major_version)
+            l3d_major_version = self.input_metadata.version.lookup(
+                GLOWS_L3D_DESCRIPTOR
+            ).major
+            l3bs = list(
+                {
+                    **l3bc_initializer_data.l3bs_by_cr,
+                    **glows_l3bc_output_data.l3bs_by_cr,
+                }.values()
+            )
+            l3cs = list(
+                {
+                    **l3bc_initializer_data.l3cs_by_cr,
+                    **glows_l3bc_output_data.l3cs_by_cr,
+                }.values()
+            )
+            l3d_initializer_result = GlowsL3DInitializer.should_process_l3d(
+                l3bc_initializer_data.external_dependencies,
+                l3bs,
+                l3cs,
+                reprocess_info,
+                l3d_major_version,
+            )
             if l3d_initializer_result is None:
-                logger.info("No inputs to L3d have changed. Skipping processing of L3d and L3e!")
+                logger.info(
+                    "No inputs to L3d have changed. Skipping processing of L3d and L3e!"
+                )
                 return products_list
 
             version_number, glows_l3d_dependency, old_l3d = l3d_initializer_result
@@ -99,8 +160,12 @@ def process(self):
                 logger.info("Not enough L3B/C data to produce L3d.")
                 return products_list
 
-            logger.info(f"Finished processing L3d up to CR: {process_l3d_result.last_processed_cr}")
-            logger.info(f"Saved L3d CDF output to: {process_l3d_result.l3d_cdf_file_path}")
+            logger.info(
+                f"Finished processing L3d up to CR: {process_l3d_result.last_processed_cr}"
+            )
+            logger.info(
+                f"Saved L3d CDF output to: {process_l3d_result.l3d_cdf_file_path}"
+            )
             for txt_file in process_l3d_result.l3d_text_file_paths:
                 logger.info(f"Saved L3d text file output to: {txt_file}")
 
@@ -113,92 +178,155 @@ def process(self):
             )
 
             if l3e_initializer_output is not None:
-                logger.info(f"Processing L3e for repointings: {l3e_initializer_output.repointings.repointing_numbers}")
-                products_list.extend([*process_l3d_result.l3d_text_file_paths, process_l3d_result.l3d_cdf_file_path])
+                logger.info(
+                    f"Processing L3e for repointings: {l3e_initializer_output.repointings.repointing_numbers}"
+                )
+                products_list.extend(
+                    [
+                        *process_l3d_result.l3d_text_file_paths,
+                        process_l3d_result.l3d_cdf_file_path,
+                    ]
+                )
                 l3e_products = process_l3e(l3e_initializer_output)
                 products_list.extend(l3e_products)
             else:
-                logger.info(f"There are no changes between: {old_l3d} and the newly produced L3d. Not uploading L3d or processing L3e!")
+                logger.info(
+                    f"There are no changes between: {old_l3d} and the newly produced L3d. Not uploading L3d or processing L3e!"
+                )
 
             return products_list
 
-    def process_l3a(self, dependencies: GlowsL3ADependencies) -> Optional[GlowsL3LightCurve]:
+    def process_l3a(
+        self, dependencies: GlowsL3ADependencies
+    ) -> GlowsL3LightCurve | None:
         data = dependencies.data
         l3_data = L3aData(dependencies.ancillary_files)
         l3_data.process_l2_data_file(data)
         l3_data.generate_l3a_data(dependencies.ancillary_files)
-        if len(l3_data.data['daily_lightcurve']['exposure_times']) != 0:
-            data_with_spin_angle = self.add_spin_angle_delta(l3_data.data, dependencies.ancillary_files)
-            return create_glows_l3a_from_dictionary(data_with_spin_angle,
-                                                replace(self.input_metadata, descriptor=GLOWS_L3A_DESCRIPTOR))
+        if len(l3_data.data["daily_lightcurve"]["exposure_times"]) != 0:
+            data_with_spin_angle = self.add_spin_angle_delta(
+                l3_data.data, dependencies.ancillary_files
+            )
+            return create_glows_l3a_from_dictionary(
+                data_with_spin_angle,
+                replace(self.input_metadata, descriptor=GLOWS_L3A_DESCRIPTOR),
+            )
         return None
 
     @staticmethod
     def add_spin_angle_delta(data: dict, ancillary_files: dict) -> dict:
-        with open(ancillary_files['settings']) as f:
+        with open(ancillary_files["settings"]) as f:
             settings_file = json.load(f)
-            number_of_bins = settings_file['l3a_nominal_number_of_bins']
+            number_of_bins = settings_file["l3a_nominal_number_of_bins"]
 
         delta = 360 / number_of_bins / 2
-        data['daily_lightcurve']['spin_angle_delta'] = np.full_like(data['daily_lightcurve']['spin_angle'], delta)
+        data["daily_lightcurve"]["spin_angle_delta"] = np.full_like(
+            data["daily_lightcurve"]["spin_angle"], delta
+        )
 
         return data
 
     @staticmethod
-    def archive_dependencies(l3bc_deps: GlowsL3BCDependencies, external_dependencies: ExternalDependencies) -> Path:
+    def archive_dependencies(
+        l3bc_deps: GlowsL3BCDependencies, external_dependencies: ExternalDependencies
+    ) -> Path:
         start_date = l3bc_deps.start_date.strftime("%Y%m%d")
-        minor_version_str = str(Version(None,l3bc_deps.version.minor))
-        zip_path = TEMP_CDF_FOLDER_PATH / f"imap_glows_l3b-archive_{start_date}_{minor_version_str}.zip"
+        minor_version_str = str(Version(None, l3bc_deps.version.minor))
+        zip_path = (
+            TEMP_CDF_FOLDER_PATH
+            / f"imap_glows_l3b-archive_{start_date}_{minor_version_str}.zip"
+        )
         json_filename = "cr_to_process.json"
         with ZipFile(zip_path, "w", ZIP_DEFLATED) as file:
-            file.write(external_dependencies.lyman_alpha_path, "lyman_alpha_composite.nc")
+            file.write(
+                external_dependencies.lyman_alpha_path, "lyman_alpha_composite.nc"
+            )
             file.write(external_dependencies.omni2_data_path, "omni2_all_years.dat")
             file.write(external_dependencies.f107_index_file_path, "f107_fluxtable.txt")
-            cr = {"cr_rotation_number": l3bc_deps.carrington_rotation_number,
-                  "l3a_paths": [l3a['filename'] for l3a in l3bc_deps.l3a_data],
-                  "cr_start_date": str(l3bc_deps.start_date),
-                  "cr_end_date": str(l3bc_deps.end_date),
-                  "bad_days_list": l3bc_deps.ancillary_files['bad_days_list'].name,
-                  "pipeline_settings": l3bc_deps.ancillary_files['pipeline_settings'].name,
-                  "waw_helioion_mp": l3bc_deps.ancillary_files['WawHelioIonMP_parameters'].name,
-                  "uv_anisotropy": l3bc_deps.ancillary_files['uv_anisotropy'].name,
-                  "repointing_file": l3bc_deps.repointing_file_path.name,
-                  }
+            cr = {
+                "cr_rotation_number": l3bc_deps.carrington_rotation_number,
+                "l3a_paths": [l3a["filename"] for l3a in l3bc_deps.l3a_data],
+                "cr_start_date": str(l3bc_deps.start_date),
+                "cr_end_date": str(l3bc_deps.end_date),
+                "bad_days_list": l3bc_deps.ancillary_files["bad_days_list"].name,
+                "pipeline_settings": l3bc_deps.ancillary_files[
+                    "pipeline_settings"
+                ].name,
+                "waw_helioion_mp": l3bc_deps.ancillary_files[
+                    "WawHelioIonMP_parameters"
+                ].name,
+                "uv_anisotropy": l3bc_deps.ancillary_files["uv_anisotropy"].name,
+                "repointing_file": l3bc_deps.repointing_file_path.name,
+            }
             json_string = json.dumps(cr)
             file.writestr(json_filename, json_string)
         return zip_path
 
+
 def process_l3bc(processor, initializer_data: GlowsL3BCInitializerData):
     l3bs_by_cr = {}
     l3cs_by_cr = {}
     data_products = []
     for dependency in initializer_data.l3bc_dependencies:
         logger.info(f"Processing L3BC for CR: {dependency.carrington_rotation_number}")
-        zip_path = GlowsProcessor.archive_dependencies(l3bc_deps=dependency,
-                                                       external_dependencies=initializer_data.external_dependencies)
+        zip_path = GlowsProcessor.archive_dependencies(
+            l3bc_deps=dependency,
+            external_dependencies=initializer_data.external_dependencies,
+        )
         logger.info(f"Archived L3BC Dependencies to {zip_path}")
 
-        filtered_days = filter_l3a_files(dependency.l3a_data, dependency.ancillary_files['bad_days_list'],
-                                         dependency.carrington_rotation_number)
-        with SwallowExceptionAndLog(f"Exception caught in L3BC science code, skipping CR {dependency.carrington_rotation_number}") as manager:
-            dependency_filtering_out_bad_days = replace(dependency, l3a_data=filtered_days)
+        filtered_days = filter_l3a_files(
+            dependency.l3a_data,
+            dependency.ancillary_files["bad_days_list"],
+            dependency.carrington_rotation_number,
+        )
+        with SwallowExceptionAndLog(
+            f"Exception caught in L3BC science code, skipping CR {dependency.carrington_rotation_number}"
+        ) as manager:
+            dependency_filtering_out_bad_days = replace(
+                dependency, l3a_data=filtered_days
+            )
             l3b_data, l3c_data = generate_l3bc(dependency_filtering_out_bad_days)
         if manager.exception_caught:
             continue
 
-        l3b_metadata = InputMetadata("glows", "l3b", dependency.start_date, dependency.end_date,
-                                     VersionMap({GLOWS_L3B_DESCRIPTOR: dependency.version}), GLOWS_L3B_DESCRIPTOR)
-        l3c_metadata = InputMetadata("glows", "l3c", dependency.start_date, dependency.end_date,
-                                     VersionMap({GLOWS_L3C_DESCRIPTOR: dependency.version}), GLOWS_L3C_DESCRIPTOR)
+        l3b_metadata = InputMetadata(
+            "glows",
+            "l3b",
+            dependency.start_date,
+            dependency.end_date,
+            VersionMap({GLOWS_L3B_DESCRIPTOR: dependency.version}),
+            GLOWS_L3B_DESCRIPTOR,
+        )
+        l3c_metadata = InputMetadata(
+            "glows",
+            "l3c",
+            dependency.start_date,
+            dependency.end_date,
+            VersionMap({GLOWS_L3C_DESCRIPTOR: dependency.version}),
+            GLOWS_L3C_DESCRIPTOR,
+        )
 
-        l3b_data_product = GlowsL3BIonizationRate.from_instrument_team_dictionary(l3b_data, l3b_metadata)
-        l3c_data_product = GlowsL3CSolarWind.from_instrument_team_dictionary(l3c_data, l3c_metadata)
+        l3b_data_product = GlowsL3BIonizationRate.from_instrument_team_dictionary(
+            l3b_data, l3b_metadata
+        )
+        l3c_data_product = GlowsL3CSolarWind.from_instrument_team_dictionary(
+            l3c_data, l3c_metadata
+        )
 
-        l3b_data_product.parent_file_names += processor.get_parent_file_names([*dependency.l3a_file_names, zip_path])
-        l3b_cdf = save_data(l3b_data_product, cr_number=dependency.carrington_rotation_number)
+        l3b_data_product.parent_file_names += processor.get_parent_file_names(
+            [*dependency.l3a_file_names, zip_path]
+        )
+        l3b_cdf = save_data(
+            l3b_data_product, cr_number=dependency.carrington_rotation_number
+        )
 
-        l3c_data_product.parent_file_names += processor.get_parent_file_names([zip_path, Path(l3b_cdf)])
-        l3c_cdf = save_data(l3c_data_product, cr_number=dependency.carrington_rotation_number)
+        l3c_data_product.parent_file_names += processor.get_parent_file_names(
+            [zip_path, Path(l3b_cdf)]
+        )
+        l3c_cdf = save_data(
+            l3c_data_product, cr_number=dependency.carrington_rotation_number
+        )
 
         logger.info(f"Finished processing CR: {dependency.carrington_rotation_number}")
         for path in [l3b_cdf, l3c_cdf]:
@@ -212,9 +340,10 @@ def process_l3bc(processor, initializer_data: GlowsL3BCInitializerData):
         l3bs_by_cr=l3bs_by_cr, l3cs_by_cr=l3cs_by_cr, data_products=data_products
     )
 
+
 def process_l3d(
     dependencies: GlowsL3DDependencies, version: Version
-) -> Optional[GlowsL3DProcessorOutput]:
+) -> GlowsL3DProcessorOutput | None:
 
     [create_glows_l3b_json_file_from_cdf(l3b) for l3b in dependencies.l3b_file_paths]
     [create_glows_l3c_json_file_from_cdf(l3c) for l3c in dependencies.l3c_file_paths]
@@ -254,98 +383,212 @@ def process_l3d(
         logger.warning("generate_l3d.py error: %s /// %s", e.output, e.stderr)
         raise
     if output.stdout:
-        last_processed_cr = int(output.stdout.split('= ')[-1])
+        last_processed_cr = int(output.stdout.split("= ")[-1])
 
     if last_processed_cr:
         output_text_files = []
-        for text_file in os.listdir(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt'):
+        for text_file in os.listdir(PATH_TO_L3D_TOOLKIT / "data_l3d_txt"):
             if str(last_processed_cr) in text_file:
-                output_text_files.append(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt' / text_file)
+                output_text_files.append(
+                    PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / text_file
+                )
 
-        txt_files_with_correct_version = rename_l3d_text_outputs(output_text_files, str(Version(None, version.minor)))
+        txt_files_with_correct_version = rename_l3d_text_outputs(
+            output_text_files, str(Version(None, version.minor))
+        )
 
         for txt_file in txt_files_with_correct_version:
-            shutil.copy(txt_file, generate_imap_file_path(txt_file.name).construct_path())
+            shutil.copy(
+                txt_file, generate_imap_file_path(txt_file.name).construct_path()
+            )
 
-        file_name = f'imap_glows_l3d_solar-params-history_19470303-cr0{last_processed_cr}_v00.json'
+        file_name = f"imap_glows_l3d_solar-params-history_19470303-cr0{last_processed_cr}_v00.json"
 
         start_date = datetime(1947, 3, 3)
-        data_product_metadata = InputMetadata(instrument="glows", data_level="l3d", descriptor=GLOWS_L3D_DESCRIPTOR,
-                                              start_date=start_date, end_date=start_date, version=VersionMap({GLOWS_L3D_DESCRIPTOR: version}))
-        parent_file_names = get_parent_file_names_from_l3d_json(PATH_TO_L3D_TOOLKIT / 'data_l3d')
+        data_product_metadata = InputMetadata(
+            instrument="glows",
+            data_level="l3d",
+            descriptor=GLOWS_L3D_DESCRIPTOR,
+            start_date=start_date,
+            end_date=start_date,
+            version=VersionMap({GLOWS_L3D_DESCRIPTOR: version}),
+        )
+        parent_file_names = get_parent_file_names_from_l3d_json(
+            PATH_TO_L3D_TOOLKIT / "data_l3d"
+        )
 
-        l3d_data_product = convert_json_to_l3d_data_product(PATH_TO_L3D_TOOLKIT / 'data_l3d' / file_name,
-                                                            data_product_metadata, parent_file_names)
+        l3d_data_product = convert_json_to_l3d_data_product(
+            PATH_TO_L3D_TOOLKIT / "data_l3d" / file_name,
+            data_product_metadata,
+            parent_file_names,
+        )
         l3d_data_product_path = save_data(l3d_data_product, cr_number=last_processed_cr)
 
-        return GlowsL3DProcessorOutput(l3d_data_product_path, txt_files_with_correct_version, last_processed_cr)
+        return GlowsL3DProcessorOutput(
+            l3d_data_product_path, txt_files_with_correct_version, last_processed_cr
+        )
     return None
 
+
 def process_l3e(initializer_data: GlowsL3EInitializerOutput):
     products_list = []
 
-    lo_pivot_angles = get_lo_pivot_angles(initializer_data.repointings.repointing_numbers)
+    lo_pivot_angles = get_lo_pivot_angles(
+        initializer_data.repointings.repointing_numbers
+    )
     for repointing in initializer_data.repointings.repointing_numbers:
-        with SwallowExceptionAndLog(f"Exception encountered when processing L3e for repointing {repointing}"):
+        with SwallowExceptionAndLog(
+            f"Exception encountered when processing L3e for repointing {repointing}"
+        ):
             start_repointing, end_repointing = get_pointing_date_range(repointing)
             epoch_delta: timedelta = (end_repointing - start_repointing) / 2
             repointing_midpoint = start_repointing + epoch_delta
-            glows_flags = compute_glows_flags_for_repoint(initializer_data.l3d_cdf_path, repointing_midpoint)
-            spacecraft_info, predict_flag, kernel_names = determine_spacecraft_info_using_predict_if_needed(repointing_midpoint, initializer_data.metakernel_with_predict_ephem, initializer_data.metakernel_without_predict_ephem)
+            glows_flags = compute_glows_flags_for_repoint(
+                initializer_data.l3d_cdf_path, repointing_midpoint
+            )
+            spacecraft_info, predict_flag, kernel_names = (
+                determine_spacecraft_info_using_predict_if_needed(
+                    repointing_midpoint,
+                    initializer_data.metakernel_with_predict_ephem,
+                    initializer_data.metakernel_without_predict_ephem,
+                )
+            )
             glows_flags |= predict_flag
 
-            with SwallowExceptionAndLog(f"Exception encountered when processing L3e lo for repointing {repointing}"):
+            with SwallowExceptionAndLog(
+                f"Exception encountered when processing L3e lo for repointing {repointing}"
+            ):
                 if repointing in initializer_data.repointings.lo_repointings:
-                    lo_parent_file_names = initializer_data.dependencies.get_lo_parents() + kernel_names
+                    lo_parent_file_names = (
+                        initializer_data.dependencies.get_lo_parents() + kernel_names
+                    )
                     pivot_info = lo_pivot_angles[repointing]
                     if pivot_info.parent_filename is not None:
-                        lo_parent_file_names = lo_parent_file_names + [pivot_info.parent_filename]
+                        lo_parent_file_names = lo_parent_file_names + [
+                            pivot_info.parent_filename
+                        ]
                     lo_version = initializer_data.repointings.lo_repointings[repointing]
-                    products_list.extend(process_l3e_lo(lo_parent_file_names, repointing, start_repointing, epoch_delta, pivot_info.pivot_angle, lo_version, glows_flags, spacecraft_info))
+                    products_list.extend(
+                        process_l3e_lo(
+                            lo_parent_file_names,
+                            repointing,
+                            start_repointing,
+                            epoch_delta,
+                            pivot_info.pivot_angle,
+                            lo_version,
+                            glows_flags,
+                            spacecraft_info,
+                        )
+                    )
 
-            with SwallowExceptionAndLog(f"Exception encountered when processing L3e hi-90 for repointing {repointing}"):
+            with SwallowExceptionAndLog(
+                f"Exception encountered when processing L3e hi-90 for repointing {repointing}"
+            ):
                 if repointing in initializer_data.repointings.hi_90_repointings:
-                    hi_parent_file_names = initializer_data.dependencies.get_hi_parents() + kernel_names
-                    hi_90_version = initializer_data.repointings.hi_90_repointings[repointing]
-                    products_list.extend(process_l3e_hi(hi_parent_file_names, repointing, start_repointing, epoch_delta, 90, hi_90_version, glows_flags, spacecraft_info))
+                    hi_parent_file_names = (
+                        initializer_data.dependencies.get_hi_parents() + kernel_names
+                    )
+                    hi_90_version = initializer_data.repointings.hi_90_repointings[
+                        repointing
+                    ]
+                    products_list.extend(
+                        process_l3e_hi(
+                            hi_parent_file_names,
+                            repointing,
+                            start_repointing,
+                            epoch_delta,
+                            90,
+                            hi_90_version,
+                            glows_flags,
+                            spacecraft_info,
+                        )
+                    )
 
-            with SwallowExceptionAndLog(f"Exception encountered when processing L3e hi-45 for repointing {repointing}"):
+            with SwallowExceptionAndLog(
+                f"Exception encountered when processing L3e hi-45 for repointing {repointing}"
+            ):
                 if repointing in initializer_data.repointings.hi_45_repointings:
-                    hi_parent_file_names = initializer_data.dependencies.get_hi_parents() + kernel_names
-                    hi_45_version = initializer_data.repointings.hi_45_repointings[repointing]
-                    products_list.extend(process_l3e_hi(hi_parent_file_names, repointing, start_repointing, epoch_delta, 135, hi_45_version, glows_flags, spacecraft_info))
-
-            ul_parent_file_names = initializer_data.dependencies.get_ul_parents() + kernel_names
+                    hi_parent_file_names = (
+                        initializer_data.dependencies.get_hi_parents() + kernel_names
+                    )
+                    hi_45_version = initializer_data.repointings.hi_45_repointings[
+                        repointing
+                    ]
+                    products_list.extend(
+                        process_l3e_hi(
+                            hi_parent_file_names,
+                            repointing,
+                            start_repointing,
+                            epoch_delta,
+                            135,
+                            hi_45_version,
+                            glows_flags,
+                            spacecraft_info,
+                        )
+                    )
+
+            ul_parent_file_names = (
+                initializer_data.dependencies.get_ul_parents() + kernel_names
+            )
 
             with SwallowExceptionAndLog(
-                    f"Exception encountered when processing L3e ultra SF for repointing {repointing}"):
+                f"Exception encountered when processing L3e ultra SF for repointing {repointing}"
+            ):
                 if repointing in initializer_data.repointings.ultra_sf_repointings:
-                    ul_sf_version = initializer_data.repointings.ultra_sf_repointings[repointing]
+                    ul_sf_version = initializer_data.repointings.ultra_sf_repointings[
+                        repointing
+                    ]
                     products_list.extend(
-                        process_l3e_ul_sf(ul_parent_file_names, repointing, start_repointing, epoch_delta, ul_sf_version, glows_flags, spacecraft_info))
+                        process_l3e_ul_sf(
+                            ul_parent_file_names,
+                            repointing,
+                            start_repointing,
+                            epoch_delta,
+                            ul_sf_version,
+                            glows_flags,
+                            spacecraft_info,
+                        )
+                    )
 
             with SwallowExceptionAndLog(
-                    f"Exception encountered when processing L3e ultra HF for repointing {repointing}"):
+                f"Exception encountered when processing L3e ultra HF for repointing {repointing}"
+            ):
                 if repointing in initializer_data.repointings.ultra_hf_repointings:
-                    ul_hf_version = initializer_data.repointings.ultra_hf_repointings[repointing]
+                    ul_hf_version = initializer_data.repointings.ultra_hf_repointings[
+                        repointing
+                    ]
                     products_list.extend(
-                        process_l3e_ul_hf(ul_parent_file_names, repointing, start_repointing, epoch_delta, ul_hf_version, glows_flags, spacecraft_info))
+                        process_l3e_ul_hf(
+                            ul_parent_file_names,
+                            repointing,
+                            start_repointing,
+                            epoch_delta,
+                            ul_hf_version,
+                            glows_flags,
+                            spacecraft_info,
+                        )
+                    )
 
     return products_list
 
+
 def process_l3e_lo(
-        parent_file_names: list[str],
-        repointing: int,
-        repointing_start: datetime,
-        epoch_delta: timedelta,
-        elongation_value: float,
-        version: Version,
-        glows_flags: int,
-        spacecraft_info: GlowsL3eSpacecraftInfo
+    parent_file_names: list[str],
+    repointing: int,
+    repointing_start: datetime,
+    epoch_delta: timedelta,
+    elongation_value: float,
+    version: Version,
+    glows_flags: int,
+    spacecraft_info: GlowsL3eSpacecraftInfo,
 ) -> list[Path]:
     repointing_midpoint = repointing_start + epoch_delta
-    l3e_args = determine_call_args_for_l3e_executable(repointing_start, repointing_midpoint, float(elongation_value),
-                                                      spacecraft_info=spacecraft_info)
+    l3e_args = determine_call_args_for_l3e_executable(
+        repointing_start,
+        repointing_midpoint,
+        float(elongation_value),
+        spacecraft_info=spacecraft_info,
+    )
     call_args = l3e_args.to_argument_list()
 
     logger.info(f"Processing L3e Lo, calling survProbLo with {call_args}")
@@ -364,11 +607,19 @@ def process_l3e_lo(
 
     elongation_value_as_int = int(elongation_value)
     elongation_in_filename = f"{elongation_value_as_int}."
-    elongation_in_filename += "0" * (5-len(elongation_in_filename))
+    elongation_in_filename += "0" * (5 - len(elongation_in_filename))
 
-    output_path = Path(f'probSur.Imap.Lo_{l3e_args.formatted_date}_{l3e_args.decimal_date[:8]}_{elongation_in_filename}.dat')
-    lo_data = GlowsL3ELoData.convert_dat_to_glows_l3e_lo_product(input_metadata, output_path,
-                                                                 repointing_midpoint, epoch_delta, elongation_value_as_int, l3e_args)
+    output_path = Path(
+        f"probSur.Imap.Lo_{l3e_args.formatted_date}_{l3e_args.decimal_date[:8]}_{elongation_in_filename}.dat"
+    )
+    lo_data = GlowsL3ELoData.convert_dat_to_glows_l3e_lo_product(
+        input_metadata,
+        output_path,
+        repointing_midpoint,
+        epoch_delta,
+        elongation_value_as_int,
+        l3e_args,
+    )
 
     lo_data.parent_file_names = parent_file_names
     lo_data.glows_flags = np.array([glows_flags], dtype=np.uint16)
@@ -382,7 +633,7 @@ def process_l3e_lo(
         start_time=cdf_science_file_path.start_date,
         repointing=repointing,
         version=str(Version(None, version.minor)),
-        extension="dat"
+        extension="dat",
     ).construct_path()
 
     shutil.move(output_path, new_dat_path)
@@ -390,11 +641,19 @@ def process_l3e_lo(
     return [lo_cdf, new_dat_path]
 
 
-def process_l3e_ul_sf(parent_file_names: list[str], repointing: int, repointing_start: datetime, epoch_delta: timedelta,
-                      version: Version, glows_flags: int, spacecraft_info: GlowsL3eSpacecraftInfo) -> list[Path]:
+def process_l3e_ul_sf(
+    parent_file_names: list[str],
+    repointing: int,
+    repointing_start: datetime,
+    epoch_delta: timedelta,
+    version: Version,
+    glows_flags: int,
+    spacecraft_info: GlowsL3eSpacecraftInfo,
+) -> list[Path]:
     repointing_midpoint = repointing_start + epoch_delta
-    call_args_object = determine_call_args_for_l3e_executable(repointing_start, repointing_midpoint, 30,
-                                                              spacecraft_info=spacecraft_info)
+    call_args_object = determine_call_args_for_l3e_executable(
+        repointing_start, repointing_midpoint, 30, spacecraft_info=spacecraft_info
+    )
     call_args = call_args_object.to_argument_list()
 
     logger.info(f"Processing L3e Ultra, calling survProbUltra with {call_args}")
@@ -411,9 +670,10 @@ def process_l3e_ul_sf(parent_file_names: list[str], repointing: int, repointing_
         repointing=repointing,
     )
 
-    output_path = Path(f'probSur.Imap.Ul_{call_args[0]}_{call_args[1][:8]}.dat')
-    ul_data = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(input_metadata, output_path,
-                                                                    repointing_midpoint, epoch_delta, call_args_object)
+    output_path = Path(f"probSur.Imap.Ul_{call_args[0]}_{call_args[1][:8]}.dat")
+    ul_data = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(
+        input_metadata, output_path, repointing_midpoint, epoch_delta, call_args_object
+    )
 
     ul_data.parent_file_names = parent_file_names
     ul_data.glows_flags = np.array([glows_flags], dtype=np.uint16)
@@ -428,7 +688,7 @@ def process_l3e_ul_sf(parent_file_names: list[str], repointing: int, repointing_
         start_time=cdf_science_file_path.start_date,
         repointing=repointing,
         version=str(Version(None, version.minor)),
-        extension="dat"
+        extension="dat",
     ).construct_path()
 
     shutil.move(output_path, new_dat_path)
@@ -436,17 +696,26 @@ def process_l3e_ul_sf(parent_file_names: list[str], repointing: int, repointing_
     return [ul_cdf, new_dat_path]
 
 
-def process_l3e_ul_hf(parent_file_names: list[str], repointing: int, repointing_start: datetime, epoch_delta: timedelta,
-                      version: Version, glows_flags: int, spacecraft_info: GlowsL3eSpacecraftInfo) -> list[Path]:
+def process_l3e_ul_hf(
+    parent_file_names: list[str],
+    repointing: int,
+    repointing_start: datetime,
+    epoch_delta: timedelta,
+    version: Version,
+    glows_flags: int,
+    spacecraft_info: GlowsL3eSpacecraftInfo,
+) -> list[Path]:
     repointing_midpoint = repointing_start + epoch_delta
-    call_args_object = determine_call_args_for_l3e_executable(repointing_start, repointing_midpoint, 30,
-                                                              spacecraft_info=spacecraft_info)
+    call_args_object = determine_call_args_for_l3e_executable(
+        repointing_start, repointing_midpoint, 30, spacecraft_info=spacecraft_info
+    )
 
-    call_args_object.spacecraft_info = replace(call_args_object.spacecraft_info,
-                               spacecraft_velocity_x=0,
-                               spacecraft_velocity_y=0,
-                               spacecraft_velocity_z=0,
-                               )
+    call_args_object.spacecraft_info = replace(
+        call_args_object.spacecraft_info,
+        spacecraft_velocity_x=0,
+        spacecraft_velocity_y=0,
+        spacecraft_velocity_z=0,
+    )
 
     call_args = call_args_object.to_argument_list()
     logger.info(f"Processing L3e Ultra, calling survProbUltra with {call_args}")
@@ -462,9 +731,10 @@ def process_l3e_ul_hf(parent_file_names: list[str], repointing: int, repointing_
         repointing=repointing,
     )
 
-    output_path = Path(f'probSur.Imap.Ul.V0_{call_args[0]}_{call_args[1][:8]}.dat')
-    ul_data = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(input_metadata, output_path,
-                                                                    repointing_midpoint, epoch_delta, call_args_object)
+    output_path = Path(f"probSur.Imap.Ul.V0_{call_args[0]}_{call_args[1][:8]}.dat")
+    ul_data = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(
+        input_metadata, output_path, repointing_midpoint, epoch_delta, call_args_object
+    )
 
     ul_data.parent_file_names = parent_file_names
     ul_data.glows_flags = np.array([glows_flags], dtype=np.uint16)
@@ -479,36 +749,54 @@ def process_l3e_ul_hf(parent_file_names: list[str], repointing: int, repointing_
         start_time=cdf_science_file_path.start_date,
         repointing=repointing,
         version=str(Version(None, version.minor)),
-        extension="dat"
+        extension="dat",
     ).construct_path()
 
     shutil.move(output_path, new_dat_path)
 
     return [ul_cdf, new_dat_path]
 
-def process_l3e_hi(parent_file_names: list[str], repointing: int, repointing_start: datetime, epoch_delta: timedelta, elongation: int, version: Version, glows_flags: int, spacecraft_info: GlowsL3eSpacecraftInfo) -> list[Path]:
+
+def process_l3e_hi(
+    parent_file_names: list[str],
+    repointing: int,
+    repointing_start: datetime,
+    epoch_delta: timedelta,
+    elongation: int,
+    version: Version,
+    glows_flags: int,
+    spacecraft_info: GlowsL3eSpacecraftInfo,
+) -> list[Path]:
     repointing_midpoint = repointing_start + epoch_delta
-    l3e_hi_args = determine_call_args_for_l3e_executable(repointing_start, repointing_midpoint, elongation,
-                                                         spacecraft_info=spacecraft_info)
+    l3e_hi_args = determine_call_args_for_l3e_executable(
+        repointing_start,
+        repointing_midpoint,
+        elongation,
+        spacecraft_info=spacecraft_info,
+    )
     call_args = l3e_hi_args.to_argument_list()
 
     logger.info(f"Processing L3e Hi, calling survProbHi with {call_args}")
 
     run(["./survProbHi"] + call_args)
 
-    descriptor = f'survival-probability-hi-{180-elongation}'
-    input_metadata = InputMetadata(instrument='glows', descriptor=descriptor,
-                                   version=VersionMap({descriptor: version}), start_date=repointing_start, end_date=repointing_start + epoch_delta * 2,
-                                   repointing=repointing, data_level='l3e')
+    descriptor = f"survival-probability-hi-{180 - elongation}"
+    input_metadata = InputMetadata(
+        instrument="glows",
+        descriptor=descriptor,
+        version=VersionMap({descriptor: version}),
+        start_date=repointing_start,
+        end_date=repointing_start + epoch_delta * 2,
+        repointing=repointing,
+        data_level="l3e",
+    )
 
-    output_path = Path(f'probSur.Imap.Hi_{call_args[0]}_{call_args[1][:8]}_{call_args[-1][:5]}.dat')
+    output_path = Path(
+        f"probSur.Imap.Hi_{call_args[0]}_{call_args[1][:8]}_{call_args[-1][:5]}.dat"
+    )
 
     hi_data = GlowsL3EHiData.convert_dat_to_glows_l3e_hi_product(
-        input_metadata,
-        output_path,
-        repointing_midpoint,
-        epoch_delta,
-        l3e_hi_args
+        input_metadata, output_path, repointing_midpoint, epoch_delta, l3e_hi_args
     )
     hi_data.parent_file_names = parent_file_names
     hi_data.glows_flags = np.array([glows_flags], dtype=np.uint16)
@@ -522,7 +810,7 @@ def process_l3e_hi(parent_file_names: list[str], repointing: int, repointing_sta
         start_time=cdf_science_file_path.start_date,
         repointing=repointing,
         version=str(Version(None, version.minor)),
-        extension="dat"
+        extension="dat",
     ).construct_path()
 
     shutil.move(output_path, new_dat_path)
@@ -544,4 +832,4 @@ def __exit__(self, exc_type, exc_val, exc_tb) -> bool:
         elif exc_type is not None:
             logger.exception(self.message, exc_info=exc_val)
             self.exception_caught = True
-        return True
\ No newline at end of file
+        return True
diff --git a/imap_l3_processing/glows/l3a/glows_l3a_dependencies.py b/imap_l3_processing/glows/l3a/glows_l3a_dependencies.py
index 660d27488..5162197ec 100644
--- a/imap_l3_processing/glows/l3a/glows_l3a_dependencies.py
+++ b/imap_l3_processing/glows/l3a/glows_l3a_dependencies.py
@@ -5,9 +5,13 @@
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.glows.descriptors import GLOWS_L2_DESCRIPTOR, GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR, \
-    GLOWS_CALIBRATION_DATA_DESCRIPTOR, \
-    GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR, GLOWS_PIPELINE_SETTINGS_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_CALIBRATION_DATA_DESCRIPTOR,
+    GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR,
+    GLOWS_L2_DESCRIPTOR,
+    GLOWS_PIPELINE_SETTINGS_DESCRIPTOR,
+    GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3a.models import GlowsL2Data
 from imap_l3_processing.glows.l3a.utils import read_l2_glows_data
 
@@ -20,8 +24,12 @@ class GlowsL3ADependencies:
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         science_inputs: list[ScienceInput] = dependencies.get_science_inputs()
-        desired_input = next(science_input for science_input in science_inputs
-                             if science_input.descriptor == GLOWS_L2_DESCRIPTOR and science_input.source == 'glows')
+        desired_input = next(
+            science_input
+            for science_input in science_inputs
+            if science_input.descriptor == GLOWS_L2_DESCRIPTOR
+            and science_input.source == "glows"
+        )
 
         l2_cdf_file_path: Path = desired_input.imap_file_paths[0].construct_path()
 
@@ -29,17 +37,26 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
 
         cdf = CDF(str(l2_cdf))
         l2_glows_data = read_l2_glows_data(cdf)
-        calibration_dependency = dependencies.get_file_paths(source='glows',
-                                                             descriptor=GLOWS_CALIBRATION_DATA_DESCRIPTOR)
-        time_dependent_background_dependency = dependencies.get_file_paths(source='glows',
-                                                                           descriptor=GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR)
-        extra_heliospheric_background_dependency = dependencies.get_file_paths(source='glows',
-                                                                               descriptor=GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR)
-        settings_dependency = dependencies.get_file_paths(source='glows', descriptor=GLOWS_PIPELINE_SETTINGS_DESCRIPTOR)
+        calibration_dependency = dependencies.get_file_paths(
+            source="glows", descriptor=GLOWS_CALIBRATION_DATA_DESCRIPTOR
+        )
+        time_dependent_background_dependency = dependencies.get_file_paths(
+            source="glows", descriptor=GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR
+        )
+        extra_heliospheric_background_dependency = dependencies.get_file_paths(
+            source="glows", descriptor=GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR
+        )
+        settings_dependency = dependencies.get_file_paths(
+            source="glows", descriptor=GLOWS_PIPELINE_SETTINGS_DESCRIPTOR
+        )
 
         calibration_path = download(calibration_dependency[0])
-        extra_heliospheric_background = download(extra_heliospheric_background_dependency[0])
-        time_dependent_background_path = download(time_dependent_background_dependency[0])
+        extra_heliospheric_background = download(
+            extra_heliospheric_background_dependency[0]
+        )
+        time_dependent_background_path = download(
+            time_dependent_background_dependency[0]
+        )
         settings_path = download(settings_dependency[0])
 
         ancillary_files = {
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/ccsds.py b/imap_l3_processing/glows/l3a/glows_toolkit/ccsds.py
index eea07bce0..97d519117 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/ccsds.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/ccsds.py
@@ -3,7 +3,8 @@
 Class for CCSDS headers
 """
 
-class CCSDSHeader():
+
+class CCSDSHeader:
     """
     Class for CCSDS headers
     """
@@ -25,55 +26,73 @@ def deserialize(self, reader):
         aux = reader.read_uint16_be()
         if reader.overrun:
             return
-        bit_seq_as_string = format(aux, 'b').zfill(16)
-        #print('first', bit_seq_as_string)
-        self.data['ccsds_ver'] = int(bit_seq_as_string[:3], 2)
-        assert self.data['ccsds_ver'] == 0, \
-            'Field ccsds_ver in CCSDS header has incorrect value' + ' %d' % self.data['ccsds_ver']
-        self.data['pkt_type'] = bool(int(bit_seq_as_string[3]))
-        self.data['sec_header_flag'] = bool(int(bit_seq_as_string[4]))
-        self.data['apid'] = int(bit_seq_as_string[5:], 2)
+        bit_seq_as_string = format(aux, "b").zfill(16)
+        # print('first', bit_seq_as_string)
+        self.data["ccsds_ver"] = int(bit_seq_as_string[:3], 2)
+        assert self.data["ccsds_ver"] == 0, (
+            "Field ccsds_ver in CCSDS header has incorrect value"
+            + " %d" % self.data["ccsds_ver"]
+        )
+        self.data["pkt_type"] = bool(int(bit_seq_as_string[3]))
+        self.data["sec_header_flag"] = bool(int(bit_seq_as_string[4]))
+        self.data["apid"] = int(bit_seq_as_string[5:], 2)
 
-        #print(bit_seq_as_string, end=' ')
-        assert self.data['apid'] in [0x5a1, 0x5a2, 0x5a3, 0x5a4, 0x5a5, 0x5a8, 0x5a9, 0x5b1, 0x5c8, 0x5c9], \
-          'error deserialize(): wrong apid %d 0x%x' % (self.data['apid'], self.data['apid'])
+        # print(bit_seq_as_string, end=' ')
+        assert self.data["apid"] in [
+            0x5A1,
+            0x5A2,
+            0x5A3,
+            0x5A4,
+            0x5A5,
+            0x5A8,
+            0x5A9,
+            0x5B1,
+            0x5C8,
+            0x5C9,
+        ], "error deserialize(): wrong apid %d 0x%x" % (
+            self.data["apid"],
+            self.data["apid"],
+        )
 
         aux = reader.read_uint16_be()
-        bit_seq_as_string = format(aux, 'b').zfill(16)
-        #print('second', bit_seq_as_string)
-        self.data['grouping_flag'] = int(bit_seq_as_string[:2], 2)
-        self.data['seq_count'] = int(bit_seq_as_string[2:], 2)
+        bit_seq_as_string = format(aux, "b").zfill(16)
+        # print('second', bit_seq_as_string)
+        self.data["grouping_flag"] = int(bit_seq_as_string[:2], 2)
+        self.data["seq_count"] = int(bit_seq_as_string[2:], 2)
 
-        #print(bit_seq_as_string, end=' ')
+        # print(bit_seq_as_string, end=' ')
 
-        self.data['packet_length'] = reader.read_uint16_be()
-        #print('third', format(self.data['packet_length'], 'b').zfill(16))
+        self.data["packet_length"] = reader.read_uint16_be()
+        # print('third', format(self.data['packet_length'], 'b').zfill(16))
 
-        #print(self.data['apid'], self.data['packet_length'], 0x5a1)
+        # print(self.data['apid'], self.data['packet_length'], 0x5a1)
 
-        #bit_seq_as_string = format(self.data['packet_length'], 'b').zfill(16)
-        #print(bit_seq_as_string)
+        # bit_seq_as_string = format(self.data['packet_length'], 'b').zfill(16)
+        # print(bit_seq_as_string)
 
-#        if self.data['packet_length'] == 289:
-#          self.data['packet_length'] = 288
-#        if self.data['apid'] == 0x5c8 and self.data['packet_length'] == 289:
-#          print('error deserialize(): bad histogram', self.data['apid'], self.data['packet_length'])
-#          exit()
+        #        if self.data['packet_length'] == 289:
+        #          self.data['packet_length'] = 288
+        #        if self.data['apid'] == 0x5c8 and self.data['packet_length'] == 289:
+        #          print('error deserialize(): bad histogram', self.data['apid'], self.data['packet_length'])
+        #          exit()
 
         if not reader.overrun:
-            assert self.data['packet_length'] >= 3 and self.data['packet_length'] <= 4089, \
-                'Field packet_length in CCSDS header has incorrect value' + \
-                ' %d' % self.data['packet_length']
+            assert (
+                self.data["packet_length"] >= 3 and self.data["packet_length"] <= 4089
+            ), (
+                "Field packet_length in CCSDS header has incorrect value"
+                + " %d" % self.data["packet_length"]
+            )
 
         # SCLK/MET field is expected to be included in every CCSDS packet for GLOWS so it is
         # safe to always read it
-        self.data['sclk_seconds'] = reader.read_uint32_be()
-        #print(self.data['sclk_seconds'])
+        self.data["sclk_seconds"] = reader.read_uint32_be()
+        # print(self.data['sclk_seconds'])
 
     def print_data_keys(self):
         """
         Print data keys for CCSDS header
         """
-        print('\n\nCCSDS header keys:')
+        print("\n\nCCSDS header keys:")
         for key in self.data:
             print(key)
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/constants.py b/imap_l3_processing/glows/l3a/glows_toolkit/constants.py
index f2f348aa7..598e8dfff 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/constants.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/constants.py
@@ -3,25 +3,25 @@
 Constants (broad-sense understood) are grouped together for convenience in this file
 """
 
-VERSION = "0.3" # version of ground processing software
+VERSION = "0.3"  # version of ground processing software
 
 # ancillary input expected from SDC (based on general IMAP telemetry)
 ANC_INPUT_FROM_SDC = {
     # only one file is here now but in the real pipeline there will be probably several files
-    'anc_data_file_name': 'data_ancillary/imap_l1_anc_sc_Merged_2010-2030_mockByGlowsTeam001.csv'
+    "anc_data_file_name": "data_ancillary/imap_l1_anc_sc_Merged_2010-2030_mockByGlowsTeam001.csv"
 }
 
 # dictionary with names of ancillary input files expected from IMAP/GLOWS Instrument Team
 ANC_INPUT_FROM_INSTRUMENT_TEAM = {
-    'conversion_table_for_anc_data': 'data_ancillary/imap_glows_conversion_table_for_anc_data_v002.json',
-    'calibration_data': 'data_ancillary/imap_glows_calibration_data_v002.dat',
-    'settings': 'data_ancillary/imap_glows_pipeline_settings_v002.json',
-    'map_of_uv_sources': 'data_ancillary/imap_glows_map_of_uv_sources_v002.dat',
-    'map_of_excluded_regions': 'data_ancillary/imap_glows_map_of_excluded_regions_v002.dat',
-    'exclusions_by_instr_team': 'data_ancillary/imap_glows_exclusions_by_instr_team_v002.dat',
-    'suspected_transients': 'data_ancillary/imap_glows_suspected_transients_v002.dat',
-    'extra_heliospheric_bckgrd': 'data_ancillary/imap_glows_map_of_extra_helio_bckgrd_v001.dat',
-    'time_dependent_bckgrd': 'data_ancillary/imap_glows_time_dep_bckgrd_v001.dat'
+    "conversion_table_for_anc_data": "data_ancillary/imap_glows_conversion_table_for_anc_data_v002.json",
+    "calibration_data": "data_ancillary/imap_glows_calibration_data_v002.dat",
+    "settings": "data_ancillary/imap_glows_pipeline_settings_v002.json",
+    "map_of_uv_sources": "data_ancillary/imap_glows_map_of_uv_sources_v002.dat",
+    "map_of_excluded_regions": "data_ancillary/imap_glows_map_of_excluded_regions_v002.dat",
+    "exclusions_by_instr_team": "data_ancillary/imap_glows_exclusions_by_instr_team_v002.dat",
+    "suspected_transients": "data_ancillary/imap_glows_suspected_transients_v002.dat",
+    "extra_heliospheric_bckgrd": "data_ancillary/imap_glows_map_of_extra_helio_bckgrd_v001.dat",
+    "time_dependent_bckgrd": "data_ancillary/imap_glows_time_dep_bckgrd_v001.dat",
 }
 
 # subsecond limit for GLOWS clock (and consequently also onboard-interpolated IMAP clock)
@@ -34,14 +34,14 @@
 # numbers indicate bitwise left_shift for a given field
 L1B_L2_HIST_FLAG_FIELDS = {
     # bad-angle bits (related to individual bins)
-    'is_close_to_uv_source': 0,
-    'is_inside_excluded_region': 1,
-    'is_excluded_by_instr_team': 2,
-    'is_suspected_transient': 3
+    "is_close_to_uv_source": 0,
+    "is_inside_excluded_region": 1,
+    "is_excluded_by_instr_team": 2,
+    "is_suspected_transient": 3,
 }
 
 # instrument emulator sometimes does not set the imap_start_time properly, then we have to use
 # glows_start_time, MAIN_TIME_FIELD was defined to switch quickly between imap_start_time and
 # glows_start_time for some places in the code where correct values of imap_start_time are crucial
 # TMP_SOLUTION, NEEDS TO BE CHANGED WHEN imap_start_time will be correct
-MAIN_TIME_FIELD = 'imap_start_time'
+MAIN_TIME_FIELD = "imap_start_time"
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/constants_arch20241201.py b/imap_l3_processing/glows/l3a/glows_toolkit/constants_arch20241201.py
index 86532562d..348ba6088 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/constants_arch20241201.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/constants_arch20241201.py
@@ -3,24 +3,23 @@
 Constants (broad-sense understood) are grouped together for convenience in this file
 """
 
-VERSION = "0.2" # version of ground processing software
+VERSION = "0.2"  # version of ground processing software
 
 # ancillary input expected from SDC (based on general IMAP telemetry)
 ANC_INPUT_FROM_SDC = {
     # only one file is here now but in the real pipeline there will be probably several files
-    'anc_data_file_name': 'data_ancillary/imap_l1_anc_sc_Merged_2010-2030_mockByGlowsTeam001.csv'
+    "anc_data_file_name": "data_ancillary/imap_l1_anc_sc_Merged_2010-2030_mockByGlowsTeam001.csv"
 }
 
 # ancillary input expected from IMAP/GLOWS Instrument Team
 ANC_INPUT_FROM_INSTRUMENT_TEAM = {
-    'conversion_table_for_anc_data':
-        'data_ancillary/imap_glows_conversion_table_for_anc_data_v001.json',
-    'calibration_data': 'data_ancillary/imap_glows_calibration_data_v001.json',
-    'settings': 'data_ancillary/imap_glows_pipeline_settings_v001.json',
-    'map_of_uv_sources': 'data_ancillary/imap_glows_map_of_uv_sources_v001.dat',
-    'map_of_excluded_regions': 'data_ancillary/imap_glows_map_of_excluded_regions_v001.dat',
-    'exclusions_by_instr_team': 'data_ancillary/imap_glows_exclusions_by_instr_team_v001.dat',
-    'suspected_transients': 'data_ancillary/imap_glows_suspected_transients_v001.dat'
+    "conversion_table_for_anc_data": "data_ancillary/imap_glows_conversion_table_for_anc_data_v001.json",
+    "calibration_data": "data_ancillary/imap_glows_calibration_data_v001.json",
+    "settings": "data_ancillary/imap_glows_pipeline_settings_v001.json",
+    "map_of_uv_sources": "data_ancillary/imap_glows_map_of_uv_sources_v001.dat",
+    "map_of_excluded_regions": "data_ancillary/imap_glows_map_of_excluded_regions_v001.dat",
+    "exclusions_by_instr_team": "data_ancillary/imap_glows_exclusions_by_instr_team_v001.dat",
+    "suspected_transients": "data_ancillary/imap_glows_suspected_transients_v001.dat",
 }
 
 # subsecond limit for GLOWS clock (and consequently also onboard-interpolated IMAP clock)
@@ -33,17 +32,17 @@
 # numbers indicate bitwise left_shift for a given field
 L1B_L2_HIST_FLAG_FIELDS = {
     # bad-angle bits (related to individual bins)
-    'is_close_to_uv_source': 0,
-    'is_inside_excluded_region': 1,
-    'is_excluded_by_instr_team': 2,
-    'is_suspected_transient': 3
+    "is_close_to_uv_source": 0,
+    "is_inside_excluded_region": 1,
+    "is_excluded_by_instr_team": 2,
+    "is_suspected_transient": 3,
 }
 
 # instrument emulator sometimes does not set the imap_start_time properly, then we have to use
 # glows_start_time, MAIN_TIME_FIELD was defined to switch quickly between imap_start_time and
 # glows_start_time for some places in the code where correct values of imap_start_time are crucial
 # TMP_SOLUTION, NEEDS TO BE CHANGED WHEN imap_start_time will be correct
-MAIN_TIME_FIELD = 'imap_start_time'
+MAIN_TIME_FIELD = "imap_start_time"
 
 # number of bins for helioglow lightcurve at L3
 L3_N_BINS = 90
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/direct_event.py b/imap_l3_processing/glows/l3a/glows_toolkit/direct_event.py
index 3eea1dd07..927987d89 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/direct_event.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/direct_event.py
@@ -1,36 +1,46 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
+
 import struct
 from dataclasses import dataclass
+
 from .constants import SUBSECOND_LIMIT
 
+
 @dataclass(frozen=True)
 class DirectEvent:
     """
     DirectEvent() class for IMAP/GLOWS
     """
+
     seconds: int
     subseconds: int
     impulse_length: int
     multi_event: bool = False
 
     # based on deserialize() in glows_appsw-master/tooling/science/models/direct_event.py by KPLabs
-    def build_event_from_uncompressed_data(raw) -> 'DirectEvent':
+    def build_event_from_uncompressed_data(raw) -> "DirectEvent":
         """
         Build direct event from raw binary 8-byte array assuming that it contains uncompressed timestamps
         """
         assert len(raw) == 8
-        values = struct.unpack('>II', raw)
-        return DirectEvent( # see glows_appsw/-/blob/master/libs/science/data/include/science/science_direct_event.h (bits 22,23 are not used)
+        values = struct.unpack(">II", raw)
+        return DirectEvent(  # see glows_appsw/-/blob/master/libs/science/data/include/science/science_direct_event.h (bits 22,23 are not used)
             seconds=values[0],
-            subseconds=values[1] & 0x1FFFFF, # subsecond encoding on the least significant 21 bits
-            impulse_length=(values[1] >> 24) & 0xFF, # first byte encodes the impulse length
-            multi_event=bool((values[1] >> 23) & 0b1), # KPLabs says it is set by FPGA and currently not used by AppSW at all
+            subseconds=values[1]
+            & 0x1FFFFF,  # subsecond encoding on the least significant 21 bits
+            impulse_length=(values[1] >> 24)
+            & 0xFF,  # first byte encodes the impulse length
+            multi_event=bool(
+                (values[1] >> 23) & 0b1
+            ),  # KPLabs says it is set by FPGA and currently not used by AppSW at all
         )
 
     # based on  _build_event() in glows_appsw-master/tooling/science/models/direct_event_file_decompressor.py by KPLabs
-    def build_event_from_compressed_data(diff: int, length: int, current_event: 'DirectEvent') -> 'DirectEvent':
+    def build_event_from_compressed_data(
+        diff: int, length: int, current_event: "DirectEvent"
+    ) -> "DirectEvent":
         """
         Build direct event assuming that it contains timestamps compressed as timedeltas
         """
@@ -38,8 +48,13 @@ def build_event_from_compressed_data(diff: int, length: int, current_event: 'Dir
         seconds = current_event.seconds
 
         if subseconds >= SUBSECOND_LIMIT:
-            full_seconds = int(subseconds/SUBSECOND_LIMIT)
+            full_seconds = int(subseconds / SUBSECOND_LIMIT)
             seconds += full_seconds
-            subseconds -= full_seconds*SUBSECOND_LIMIT
+            subseconds -= full_seconds * SUBSECOND_LIMIT
 
-        return DirectEvent(seconds=seconds, subseconds=subseconds, impulse_length=length, multi_event=False)
+        return DirectEvent(
+            seconds=seconds,
+            subseconds=subseconds,
+            impulse_length=length,
+            multi_event=False,
+        )
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/funcs.py b/imap_l3_processing/glows/l3a/glows_toolkit/funcs.py
index 44c30d31b..0aee09fcb 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/funcs.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/funcs.py
@@ -2,10 +2,14 @@
 Author: Marek Strumik, maro at cbk.waw.pl
 Various functions that are not methods to classes
 """
+
 import datetime
+
 import numpy as np
-from .imap_time import SpacecraftTime
+
 from .constants import SUBSECOND_LIMIT
+from .imap_time import SpacecraftTime
+
 
 def calibration_factor(calibration_file_name, date_string):
     """
@@ -22,6 +26,7 @@ def calibration_factor(calibration_file_name, date_string):
     idx = np.nonzero(date_array <= np.datetime64(date_string))[0][-1]
     return cps_per_R_array[idx]
 
+
 def check_if_contains_actual_data(file_name):
     """
     Check if there is any actual data in a file apart from header
@@ -29,17 +34,18 @@ def check_if_contains_actual_data(file_name):
     Args:
         file_name: name of file to be checked
     """
-    f = open(file_name, 'r')
+    f = open(file_name, "r")
     line = f.readline()
     is_data_in_file = False
     while line:
-        if line.lstrip()[0] != '#' and len(line.lstrip()) > 0:
+        if line.lstrip()[0] != "#" and len(line.lstrip()) > 0:
             is_data_in_file = True
             break
         line = f.readline()
     f.close()
     return is_data_in_file
 
+
 def decode_ancillary_parameter(name, decoding_table, encoded_value):
     """
     Decode parameters collected onboard, which are encoded as int at
@@ -49,17 +55,18 @@ def decode_ancillary_parameter(name, decoding_table, encoded_value):
         decoding_table: dictionary created from JSON file provided by GLOWS Instrument Team
         l1a_data: L1a data provided as dictionary
     """
-    params = decoding_table[name] # extract decoding params for a given parameter name
+    params = decoding_table[name]  # extract decoding params for a given parameter name
 
     # compute parameters A and B
-    p_a = (2**params['n_bits'] - 1)/(params['max'] - params['min'])
-    p_b = -params['min']*p_a
+    p_a = (2 ** params["n_bits"] - 1) / (params["max"] - params["min"])
+    p_b = -params["min"] * p_a
 
     # decode int-encoded value
-    decoded_value = (encoded_value-p_b)/p_a
+    decoded_value = (encoded_value - p_b) / p_a
 
     return decoded_value
 
+
 def decode_ancillary_parameters_avg_stddev(name, decoding_table, l1a_data):
     """
     Decode parameters collected onboard (average and variance), which are encoded as int at
@@ -70,22 +77,23 @@ def decode_ancillary_parameters_avg_stddev(name, decoding_table, l1a_data):
         decoding_table: dictionary created from JSON file provided by GLOWS Instrument Team
         l1a_data: L1a data provided as dictionary
     """
-    params = decoding_table[name] # extract decoding params for a given parameter name
+    params = decoding_table[name]  # extract decoding params for a given parameter name
 
     # compute parameters A and B from Sec. "On-board encoding and ground decoding of measured quantities"
     # in the algorithm document
-    p_a = (2**params['n_bits'] - 1)/(params['max'] - params['min'])
-    p_b = -params['min']*p_a
+    p_a = (2 ** params["n_bits"] - 1) / (params["max"] - params["min"])
+    p_b = -params["min"] * p_a
 
     # decode int values for the average and variance
-    average = (l1a_data[name + '_average']-p_b)/p_a
-    variance = l1a_data[name + '_variance']/(p_a*p_a)
+    average = (l1a_data[name + "_average"] - p_b) / p_a
+    variance = l1a_data[name + "_variance"] / (p_a * p_a)
 
     # compute standard deviation
     std_deviation = np.sqrt(variance)
 
     return average, std_deviation
 
+
 def flags_deserialize(raw):
     """
     Convert raw-int-number-encoded GLOWS flags  to a dictionary
@@ -93,20 +101,21 @@ def flags_deserialize(raw):
     Args:
         raw: flags encoded as bit fields of an integer
     """
-    assert raw < 65536, 'Argument of flags_deserialize() is wrong'
+    assert raw < 65536, "Argument of flags_deserialize() is wrong"
     flags = {}
-    flags['is_pps_missing'] = bool((raw >> 0) & 1)
-    flags['is_time_status_missing'] = bool((raw >> 1) & 1)
-    flags['is_phase_missing'] = bool((raw >> 2) & 1)
-    flags['is_spin_period_missing'] = bool((raw >> 3) & 1)
-    flags['is_overexposed'] = bool((raw >> 4) & 1)
-    flags['is_direct_event_non_monotonic'] = bool((raw >> 5) & 1)
-    flags['is_night'] = bool((raw >> 6) & 1)
-    flags['is_hv_test_in_progress'] = bool((raw >> 7) & 1)
-    flags['is_test_pulse_in_progress'] = bool((raw >> 8) & 1)
-    flags['is_memory_error_detected'] = bool((raw >> 9) & 1)
+    flags["is_pps_missing"] = bool((raw >> 0) & 1)
+    flags["is_time_status_missing"] = bool((raw >> 1) & 1)
+    flags["is_phase_missing"] = bool((raw >> 2) & 1)
+    flags["is_spin_period_missing"] = bool((raw >> 3) & 1)
+    flags["is_overexposed"] = bool((raw >> 4) & 1)
+    flags["is_direct_event_non_monotonic"] = bool((raw >> 5) & 1)
+    flags["is_night"] = bool((raw >> 6) & 1)
+    flags["is_hv_test_in_progress"] = bool((raw >> 7) & 1)
+    flags["is_test_pulse_in_progress"] = bool((raw >> 8) & 1)
+    flags["is_memory_error_detected"] = bool((raw >> 9) & 1)
     return flags
 
+
 def read_l0_data(pkts_file_name):
     """
     Read L0 data as an input data for L1a
@@ -114,11 +123,12 @@ def read_l0_data(pkts_file_name):
         pkts_file_name: name of of the file with CCSDS packets
     """
     # open binary pkts file containing CCSDS packets with histograms
-    file_handler = open('%s' % pkts_file_name, mode='rb')
+    file_handler = open("%s" % pkts_file_name, mode="rb")
     # read binary file with histograms (CCSDS headers are assumed to be included)
     histogram_telemetry_packets = file_handler.read()
     file_handler.close()
-    return histogram_telemetry_packets # byte array
+    return histogram_telemetry_packets  # byte array
+
 
 def time_sec_subsec_to_utc(seconds: int, subseconds: int) -> datetime:
     """
@@ -128,8 +138,9 @@ def time_sec_subsec_to_utc(seconds: int, subseconds: int) -> datetime:
         subseconds:
     """
     result = SpacecraftTime.spacecraft_epoch_to_date(seconds)
-    result = result + datetime.timedelta(microseconds=subseconds/2)
-    return result # datatime object
+    result = result + datetime.timedelta(microseconds=subseconds / 2)
+    return result  # datatime object
+
 
 def time_sec_subsec_to_float64(seconds: int, subseconds: int):
     """
@@ -141,5 +152,5 @@ def time_sec_subsec_to_float64(seconds: int, subseconds: int):
         subseconds:
     """
     # python default floating-point type is 64-bit double precision
-    result = seconds + subseconds/SUBSECOND_LIMIT
+    result = seconds + subseconds / SUBSECOND_LIMIT
     return result
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/geom_fun_libr_box/__init__.py b/imap_l3_processing/glows/l3a/glows_toolkit/geom_fun_libr_box/__init__.py
index a1aa58757..a94a5eb57 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/geom_fun_libr_box/__init__.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/geom_fun_libr_box/__init__.py
@@ -5,8 +5,10 @@
 Function names and functionality are intentionally possibly similar to geomFunLibrBox
 Functions are listed in alphabetical order
 """
+
 import numpy as np
 
+
 def angle_coord(lon_1, lat_1, lon_2, lat_2):
     """
     Compute angle between two directions lon_1,lat_1 and lon_2,lat_2 (longitude l and latitude b)
@@ -44,6 +46,7 @@ def angle_coord(lon_1, lat_1, lon_2, lat_2):
     angle = angle_vec(v_1, v_2)
     return angle
 
+
 def angle_vec(vec1, vec2):
     """
     Compute angle between two vectors vec1 and vec2
@@ -78,37 +81,42 @@ def angle_vec(vec1, vec2):
       [   0.   40.   80.  120.  160.  160.  120.   80.   40.    0.]
     """
     if np.shape(vec1) != np.shape(vec2):
-        raise Exception('angle_vec() error: np.shape(vec1)!=np.shape(vec2)')
+        raise Exception("angle_vec() error: np.shape(vec1)!=np.shape(vec2)")
     if vec1.ndim == 1:
         if np.shape(vec1)[0] != 3:
-            raise Exception('angle_vec() error: np.shape(vec1)[0]!=3')
+            raise Exception("angle_vec() error: np.shape(vec1)[0]!=3")
         r_1 = np.sqrt(np.dot(vec1, vec1))
         r_2 = np.sqrt(np.dot(vec2, vec2))
-        cos = np.dot(vec1, vec2)/(r_1*r_2)
+        cos = np.dot(vec1, vec2) / (r_1 * r_2)
         # handle a case where cos is shifted below -1 due to numerical errors
-        if cos < -1.0 and np.fabs(cos+1.0) < 1.0e-14:
+        if cos < -1.0 and np.fabs(cos + 1.0) < 1.0e-14:
             cos = -1.0
         # handle a case where cos is shifted above 1 due to numerical errors
-        if cos > 1.0 and np.fabs(cos-1.0) < 1.0e-14:
+        if cos > 1.0 and np.fabs(cos - 1.0) < 1.0e-14:
             cos = 1.0
         if r_1 != 0.0 and r_2 != 0.0:
             return np.arccos(cos)
         return 0.0
 
     if np.shape(vec1)[1] != 3:
-        raise Exception('angle_vec() error: np.shape(vec1)[1]!=3')
+        raise Exception("angle_vec() error: np.shape(vec1)[1]!=3")
     r_1 = np.sqrt((vec1 * vec1).sum(axis=1))
     r_2 = np.sqrt((vec2 * vec2).sum(axis=1))
     idxs0 = np.nonzero(np.logical_and(r_1 != 0.0, r_2 != 0.0))
-    cos = (vec1[idxs0[0], :] * vec2[idxs0[0], :]).sum(axis=1)/(r_1[idxs0[0]]*r_2[idxs0[0]])
-    idxs = np.nonzero(np.logical_and(cos < -1.0, np.fabs(cos+1.0) < 1.0e-14))
-    cos[idxs[0]] = -1.0 # just in case if cos is shifted below -1 due to numerical errors
-    idxs = np.nonzero(np.logical_and(cos > 1.0, np.fabs(cos-1.0) < 1.0e-14))
-    cos[idxs[0]] = 1.0 # just in case if cos is shifted above 1 due to numerical errors
+    cos = (vec1[idxs0[0], :] * vec2[idxs0[0], :]).sum(axis=1) / (
+        r_1[idxs0[0]] * r_2[idxs0[0]]
+    )
+    idxs = np.nonzero(np.logical_and(cos < -1.0, np.fabs(cos + 1.0) < 1.0e-14))
+    cos[
+        idxs[0]
+    ] = -1.0  # just in case if cos is shifted below -1 due to numerical errors
+    idxs = np.nonzero(np.logical_and(cos > 1.0, np.fabs(cos - 1.0) < 1.0e-14))
+    cos[idxs[0]] = 1.0  # just in case if cos is shifted above 1 due to numerical errors
     angle = np.zeros((np.shape(vec1)[0]), dtype=float)
     angle[idxs0[0]] = np.arccos(cos)
     return angle
 
+
 def get_spher_coord_rad(vec):
     """
     Compute radius r, longitude l and latitude b for a vector vec
@@ -159,23 +167,28 @@ def get_spher_coord_rad(vec):
         if vec_m == 0.0:
             lat = 0.0
         else:
-            lat = np.arcsin(vec[2]/vec_m) # compute latitude
+            lat = np.arcsin(vec[2] / vec_m)  # compute latitude
         # compute longitude
         if vec[0] == 0.0 and vec[1] == 0.0:
             lon = 0.0
         else:
-            lon = np.arctan2(vec[1], vec[0])%(2*np.pi)
-        return np.array([vec_m, lon, lat]) # return one array similarly as in Mathematica
+            lon = np.arctan2(vec[1], vec[0]) % (2 * np.pi)
+        return np.array(
+            [vec_m, lon, lat]
+        )  # return one array similarly as in Mathematica
     if np.shape(vec)[1] != 3:
         raise Exception("get_spher_coord_rad() error: np.shape(vec)[1]!=3")
     vec_m = np.sqrt((vec * vec).sum(axis=1))
     idxs = np.nonzero(vec_m != 0.0)
     lat = np.zeros((len(vec_m)), float)
-    lat[idxs[0]] = np.arcsin(vec[idxs[0], 2]/vec_m[idxs[0]]) # compute latitude
-    lon = np.arctan2(vec[:, 1], vec[:, 0])%(2*np.pi) # compute longitude
+    lat[idxs[0]] = np.arcsin(vec[idxs[0], 2] / vec_m[idxs[0]])  # compute latitude
+    lon = np.arctan2(vec[:, 1], vec[:, 0]) % (2 * np.pi)  # compute longitude
     idxs = np.nonzero(np.logical_and(vec[:, 0] == 0.0, vec[:, 1] == 0.0))
-    lon[idxs[0]] = 0.0 # set lon=0 if vec[:,0]==0.0 and vec[:,1]==0.0
-    return np.column_stack([vec_m, lon, lat]) # return one array similarly as in Mathematica
+    lon[idxs[0]] = 0.0  # set lon=0 if vec[:,0]==0.0 and vec[:,1]==0.0
+    return np.column_stack(
+        [vec_m, lon, lat]
+    )  # return one array similarly as in Mathematica
+
 
 def lb2spin(l_0, b_0, soff, l_q, b_q):
     """
@@ -231,11 +244,12 @@ def lb2spin(l_0, b_0, soff, l_q, b_q):
        [  40.  270.]]
     """
     tht = angle_coord(l_0, b_0, l_q, b_q)
-    spin = (-position_angle(l_q, b_q, l_0, b_0)-soff)%(2.*np.pi)
+    spin = (-position_angle(l_q, b_q, l_0, b_0) - soff) % (2.0 * np.pi)
     if np.isscalar(l_0):
         return np.array([tht, spin])
     return np.column_stack([tht, spin])
 
+
 def make_co_vec(l_q, b_q):
     """
     Compute vectors for given values of longitude l_q and co-latitude b_q
@@ -279,8 +293,13 @@ def make_co_vec(l_q, b_q):
     if np.shape(l_q) != np.shape(b_q):
         raise Exception("make_co_vec() error: np.shape(l_q)!=np.shape(b_q)")
     if np.isscalar(l_q):
-        return np.array([np.cos(l_q)*np.sin(b_q), np.sin(l_q)*np.sin(b_q), np.cos(b_q)])
-    return np.column_stack([np.cos(l_q)*np.sin(b_q), np.sin(l_q)*np.sin(b_q), np.cos(b_q)])
+        return np.array(
+            [np.cos(l_q) * np.sin(b_q), np.sin(l_q) * np.sin(b_q), np.cos(b_q)]
+        )
+    return np.column_stack(
+        [np.cos(l_q) * np.sin(b_q), np.sin(l_q) * np.sin(b_q), np.cos(b_q)]
+    )
+
 
 def make_vec(l_q, b_q):
     """
@@ -325,8 +344,13 @@ def make_vec(l_q, b_q):
     if np.shape(l_q) != np.shape(b_q):
         raise Exception("make_vec() error: np.shape(l_q)!=np.shape(b_q)")
     if np.isscalar(l_q):
-        return np.array([np.cos(l_q)*np.cos(b_q), np.sin(l_q)*np.cos(b_q), np.sin(b_q)])
-    return np.column_stack([np.cos(l_q)*np.cos(b_q), np.sin(l_q)*np.cos(b_q), np.sin(b_q)])
+        return np.array(
+            [np.cos(l_q) * np.cos(b_q), np.sin(l_q) * np.cos(b_q), np.sin(b_q)]
+        )
+    return np.column_stack(
+        [np.cos(l_q) * np.cos(b_q), np.sin(l_q) * np.cos(b_q), np.sin(b_q)]
+    )
+
 
 def position_angle(a_1, d_1, a_2, d_2):
     """
@@ -368,10 +392,13 @@ def position_angle(a_1, d_1, a_2, d_2):
       [  0.           6.60406655  15.06329832  25.50555026  37.6074435
         50.43084061  62.72683044  73.55865702  82.60570856  90.        ]
     """
-    delta_a = a_1-a_2
-    pos_a = np.arctan2(np.sin(delta_a), np.cos(d_2)*np.tan(d_1)-np.sin(d_2)*np.cos(delta_a))
+    delta_a = a_1 - a_2
+    pos_a = np.arctan2(
+        np.sin(delta_a), np.cos(d_2) * np.tan(d_1) - np.sin(d_2) * np.cos(delta_a)
+    )
     return pos_a
 
+
 def rot_two_vec_frame(l_c, b_c, l_x, b_x, l_q, b_q):
     """
     rotate (l_q,b_q) to a frame centered at (l_c,b_c) and x-axis coaligned with (l_c,b_c),(l_x,b_x)
@@ -390,17 +417,18 @@ def rot_two_vec_frame(l_c, b_c, l_x, b_x, l_q, b_q):
     else:
         pos_ang = lb2spin(l_c, b_c, 0.0, l_x, b_x)[:, 1]
     # compute rotation matrix
-    rot_mtrx = rzc_ryb_rza(-(2.0*np.pi-l_c), -(b_c-0.5*np.pi), pos_ang+np.pi)
-    vec0 = make_vec(l_q, b_q) # temporary vectors to rotate
+    rot_mtrx = rzc_ryb_rza(-(2.0 * np.pi - l_c), -(b_c - 0.5 * np.pi), pos_ang + np.pi)
+    vec0 = make_vec(l_q, b_q)  # temporary vectors to rotate
     if np.isscalar(l_q):
-        vec = np.matmul(rot_mtrx, vec0) # rotate vec0
+        vec = np.matmul(rot_mtrx, vec0)  # rotate vec0
     else:
-        vec = np.einsum('lij,lj->li', rot_mtrx, vec0)
+        vec = np.einsum("lij,lj->li", rot_mtrx, vec0)
     rlb = get_spher_coord_rad(vec)
     if np.isscalar(l_q):
         return np.array([-rlb[1], rlb[2]])
     return np.column_stack([-rlb[:, 1], rlb[:, 2]])
 
+
 def rot_x(ang):
     """
     Construct a rotation matrix that rotates vectors by the angle ang about
@@ -441,21 +469,26 @@ def rot_x(ang):
         [  0.00000000e+00  -1.00000000e+00   6.12323400e-17]]]
     """
     if np.isscalar(ang):
-        rot_mtrx = [[1.0, 0.0, 0.0],
-                    [0.0, np.cos(ang), np.sin(ang)],
-                    [0.0, -np.sin(ang), np.cos(ang)]]
+        rot_mtrx = [
+            [1.0, 0.0, 0.0],
+            [0.0, np.cos(ang), np.sin(ang)],
+            [0.0, -np.sin(ang), np.cos(ang)],
+        ]
         rot_mtrx = np.array(rot_mtrx)
     else:
         n_ang = len(ang)
         zers = np.zeros((n_ang), float)
         ones = np.ones((n_ang), float)
-        rot_mtrx = [[ones, zers, zers],
-                    [zers, np.cos(ang), np.sin(ang)],
-                    [zers, -np.sin(ang), np.cos(ang)]]
+        rot_mtrx = [
+            [ones, zers, zers],
+            [zers, np.cos(ang), np.sin(ang)],
+            [zers, -np.sin(ang), np.cos(ang)],
+        ]
         rot_mtrx = np.array(rot_mtrx)
         rot_mtrx = rot_mtrx.transpose(2, 0, 1)
     return rot_mtrx
 
+
 def rot_y(ang):
     """
     Construct a rotation matrix that rotates vectors by the angle ang about
@@ -496,21 +529,26 @@ def rot_y(ang):
         [  1.00000000e+00   0.00000000e+00   6.12323400e-17]]]
     """
     if np.isscalar(ang):
-        rot_mtrx = [[np.cos(ang), 0.0, -np.sin(ang)],
-                    [0.0, 1.0, 0.0],
-                    [np.sin(ang), 0.0, np.cos(ang)]]
+        rot_mtrx = [
+            [np.cos(ang), 0.0, -np.sin(ang)],
+            [0.0, 1.0, 0.0],
+            [np.sin(ang), 0.0, np.cos(ang)],
+        ]
         rot_mtrx = np.array(rot_mtrx)
     else:
         n_ang = len(ang)
         zers = np.zeros((n_ang), float)
         ones = np.ones((n_ang), float)
-        rot_mtrx = [[np.cos(ang), zers, -np.sin(ang)],
-                    [zers, ones, zers],
-                    [np.sin(ang), zers, np.cos(ang)]]
+        rot_mtrx = [
+            [np.cos(ang), zers, -np.sin(ang)],
+            [zers, ones, zers],
+            [np.sin(ang), zers, np.cos(ang)],
+        ]
         rot_mtrx = np.array(rot_mtrx)
         rot_mtrx = rot_mtrx.transpose(2, 0, 1)
     return rot_mtrx
 
+
 def rot_z(ang):
     """
     Construct a rotation matrix that rotates vectors by the angle ang about
@@ -551,21 +589,26 @@ def rot_z(ang):
         [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]]
     """
     if np.isscalar(ang):
-        rot_mtrx = [[np.cos(ang), np.sin(ang), 0.0],
-                    [-np.sin(ang), np.cos(ang), 0.0],
-                    [0.0, 0.0, 1.0]]
+        rot_mtrx = [
+            [np.cos(ang), np.sin(ang), 0.0],
+            [-np.sin(ang), np.cos(ang), 0.0],
+            [0.0, 0.0, 1.0],
+        ]
         rot_mtrx = np.array(rot_mtrx)
     else:
         n_ang = len(ang)
         zers = np.zeros((n_ang), float)
         ones = np.ones((n_ang), float)
-        rot_mtrx = [[np.cos(ang), np.sin(ang), zers],
-                    [-np.sin(ang), np.cos(ang), zers],
-                    [zers, zers, ones]]
+        rot_mtrx = [
+            [np.cos(ang), np.sin(ang), zers],
+            [-np.sin(ang), np.cos(ang), zers],
+            [zers, zers, ones],
+        ]
         rot_mtrx = np.array(rot_mtrx)
         rot_mtrx = rot_mtrx.transpose(2, 0, 1)
     return rot_mtrx
 
+
 def rxc_ryb_rza(ang_a, ang_b, ang_c):
     """
     Construct a rotation matrix that rotates vectors by the
@@ -595,6 +638,7 @@ def rxc_ryb_rza(ang_a, ang_b, ang_c):
     rot_mtrx = np.matmul(rot_x(ang_c), np.matmul(rot_y(ang_b), rot_z(ang_a)))
     return rot_mtrx
 
+
 def rxc_rzb_rxa(ang_a, ang_b, ang_c):
     """
     Construct a rotation matrix that rotates vectors by the
@@ -624,6 +668,7 @@ def rxc_rzb_rxa(ang_a, ang_b, ang_c):
     rot_mtrx = np.matmul(rot_x(ang_c), np.matmul(rot_z(ang_b), rot_x(ang_a)))
     return rot_mtrx
 
+
 def rzb_rya(ang_a, ang_b):
     """
     Construct a rotation matrix that rotates vectors by the
@@ -650,6 +695,7 @@ def rzb_rya(ang_a, ang_b):
     rot_mtrx = np.matmul(rot_z(ang_b), rot_y(ang_a))
     return rot_mtrx
 
+
 def rzc_ryb_rza(ang_a, ang_b, ang_c):
     """
     Construct a rotation matrix that rotates vectors by the
@@ -698,6 +744,7 @@ def rzc_ryb_rza(ang_a, ang_b, ang_c):
     rot_mtrx = np.matmul(rot_z(ang_c), np.matmul(rot_y(ang_b), rot_z(ang_a)))
     return rot_mtrx
 
+
 def spin2lb(l_0, b_0, spin_ang, spin_off, tht):
     """
     Computes longitude,latitude (l,b) for a given spin angle spin_ang.
@@ -749,18 +796,23 @@ def spin2lb(l_0, b_0, spin_ang, spin_off, tht):
        [  1.00000000e+02  -4.00000000e+01]
        [  1.40000000e+02  -4.07782511e-15]]
     """
-    rot_mtrx = rzc_ryb_rza(-spin_ang-spin_off, b_0-0.5*np.pi, -l_0) # compute rotation matrix
+    rot_mtrx = rzc_ryb_rza(
+        -spin_ang - spin_off, b_0 - 0.5 * np.pi, -l_0
+    )  # compute rotation matrix
     if np.isscalar(tht):
-        vec0 = np.array([-np.sin(tht), 0.0, np.cos(tht)]) # temporary vector to rotate
-        vec = np.matmul(rot_mtrx, vec0) # rotate vec0
+        vec0 = np.array([-np.sin(tht), 0.0, np.cos(tht)])  # temporary vector to rotate
+        vec = np.matmul(rot_mtrx, vec0)  # rotate vec0
     else:
-        vec0 = np.column_stack([-np.sin(tht), np.zeros((len(tht)), dtype=float), np.cos(tht)])
-        vec = np.einsum('lij,lj->li', rot_mtrx, vec0)
+        vec0 = np.column_stack(
+            [-np.sin(tht), np.zeros((len(tht)), dtype=float), np.cos(tht)]
+        )
+        vec = np.einsum("lij,lj->li", rot_mtrx, vec0)
     rlb = get_spher_coord_rad(vec)
     if np.isscalar(l_0):
         return np.array([rlb[1], rlb[2]])
     return np.column_stack([rlb[:, 1], rlb[:, 2]])
 
+
 def spin2lb_test_on_grid(spin_off, tht):
     """
     Test functions spin2lb() and lb2spin() for l_0,b_0,spin values on a grid of 11x11x11 cases
@@ -790,12 +842,12 @@ def spin2lb_test_on_grid(spin_off, tht):
     # create a grid of cases
     lin_spc_1 = np.linspace(0.0, 360.0, 11)
     lin_spc_2 = np.linspace(-90.0, 90.0, 11)
-#    l_0, b_0, s_0 = np.radians(np.mgrid[0.0:360.0:11*1j, -90.0:90.0:11*1j, 0.0:360.0:11*1j])
+    #    l_0, b_0, s_0 = np.radians(np.mgrid[0.0:360.0:11*1j, -90.0:90.0:11*1j, 0.0:360.0:11*1j])
     l_0, b_0, s_0 = np.radians(np.meshgrid(lin_spc_1, lin_spc_2, lin_spc_1))
     l_0 = l_0.flatten()
     b_0 = b_0.flatten()
     s_0 = s_0.flatten()
-    tht_0 = tht*np.ones_like(s_0)
+    tht_0 = tht * np.ones_like(s_0)
 
     lon_lat = spin2lb(l_0, b_0, s_0, spin_off, tht_0)
     tht1s = lb2spin(l_0, b_0, spin_off, lon_lat[:, 0], lon_lat[:, 1])
@@ -805,23 +857,24 @@ def spin2lb_test_on_grid(spin_off, tht):
     ist = 0
 
     # check theta
-    idxs = np.nonzero(np.fabs(tht_1-tht_0) > 1.0e-14)
+    idxs = np.nonzero(np.fabs(tht_1 - tht_0) > 1.0e-14)
     if np.shape(idxs[0])[0] > 0:
         ist = 1
 
     # check spin
-    err_s = np.fabs(spin_ang-s_0)
-    idxs = np.nonzero(np.fabs(err_s-2.0*np.pi) < 1.0e-14)
-    err_s[idxs] = err_s[idxs]-2.0*np.pi
+    err_s = np.fabs(spin_ang - s_0)
+    idxs = np.nonzero(np.fabs(err_s - 2.0 * np.pi) < 1.0e-14)
+    err_s[idxs] = err_s[idxs] - 2.0 * np.pi
     if err_s.max() > 1.0e-14:
         ist = 1
 
     if ist == 0:
-        print('spin2lb_test_on_grid(tht=%g,spin_off=%g): passed' % (tht, spin_off))
+        print("spin2lb_test_on_grid(tht=%g,spin_off=%g): passed" % (tht, spin_off))
         return 1
-    print('spin2lb_test_on_grid(tht=%g,spin_off=%g): failed' % (tht, spin_off))
+    print("spin2lb_test_on_grid(tht=%g,spin_off=%g): failed" % (tht, spin_off))
     return 0
 
+
 def spin2lb_test_on_text_file(filename):
     """
     Test spin2lb() and lb2spin() using content of a text file
@@ -848,7 +901,9 @@ def spin2lb_test_on_text_file(filename):
       1
     """
     # load a text file with testing cases
-    l_0, b_0, s_0, spin_off, tht, l_q, b_q = np.radians(np.loadtxt(filename, unpack=True))
+    l_0, b_0, s_0, spin_off, tht, l_q, b_q = np.radians(
+        np.loadtxt(filename, unpack=True)
+    )
 
     # back-and-forth transforms
     ltbt = spin2lb(l_0, b_0, s_0, spin_off, tht)
@@ -859,30 +914,30 @@ def spin2lb_test_on_text_file(filename):
     ist = 0
 
     # check longitude
-    idxs = np.nonzero(np.fabs(l_q-ltbt[:, 0]) > 1.0e-14)
+    idxs = np.nonzero(np.fabs(l_q - ltbt[:, 0]) > 1.0e-14)
     if np.shape(idxs[0])[0] > 0:
         ist = 1
 
     # check latitude
-    idxs = np.nonzero(np.fabs(b_q-ltbt[:, 1]) > 1.0e-14)
+    idxs = np.nonzero(np.fabs(b_q - ltbt[:, 1]) > 1.0e-14)
     if np.shape(idxs[0])[0] > 0:
         ist = 1
 
     # check theta
-    idxs = np.nonzero(np.fabs(tht-thtt) > 1.0e-14)
+    idxs = np.nonzero(np.fabs(tht - thtt) > 1.0e-14)
     if np.shape(idxs[0])[0] > 0:
         ist = 1
 
     # check spin
-    err_s = np.fabs(s_t-s_0)
-    idxs = np.nonzero(np.fabs(err_s-2.0*np.pi) < 1.0e-14)
-    err_s[idxs] = err_s[idxs]-2.0*np.pi
+    err_s = np.fabs(s_t - s_0)
+    idxs = np.nonzero(np.fabs(err_s - 2.0 * np.pi) < 1.0e-14)
+    err_s[idxs] = err_s[idxs] - 2.0 * np.pi
     idxs = np.nonzero(err_s > 1.0e-14)
     if np.shape(idxs[0])[0] > 0:
         ist = 1
 
     if ist == 0:
-        print('spin2lb_test_on_text_file(\'%s\'): passed' % filename)
+        print("spin2lb_test_on_text_file('%s'): passed" % filename)
         return 1
-    print('spin2lb_test_on_text_file(\'%s\'): failed' % filename)
+    print("spin2lb_test_on_text_file('%s'): failed" % filename)
     return 0
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/imap_time.py b/imap_l3_processing/glows/l3a/glows_toolkit/imap_time.py
index 3bde4a168..15b0d5195 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/imap_time.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/imap_time.py
@@ -1,12 +1,15 @@
 """@package docstring
 Author: KPLabs with some modifications by Marek Strumik, maro at cbk.waw.pl
 """
+
 from datetime import datetime, timedelta
 
-class SpacecraftTime():
+
+class SpacecraftTime:
     """
     SpacecraftTime() class for epoch-datatime conversions
     """
+
     @staticmethod
     def date_to_spacecraft_epoch(time_tag: datetime) -> int:
         """Get number of seconds after 1 January 2010, midnight
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_direct_events.py b/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_direct_events.py
index 5b129933a..789274c9b 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_direct_events.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_direct_events.py
@@ -2,10 +2,12 @@
 Author: Marek Strumik, maro at cbk.waw.pl
 """
 
-class L0DataDirectEvents():
+
+class L0DataDirectEvents:
     """
     L0DataDirectEvents class for GLOWS-L0-direct-events data
     """
+
     def __init__(self):
         """
         Constructor for this class
@@ -21,38 +23,41 @@ def deserialize(self, reader, packet_length):
         Args:
             reader: Reader-class object needed for reading binary data stream
         """
-        self.data['imap_start_time_second'] = reader.read_uint32_be()
-        self.data['number_of_de_packets'] = reader.read_uint16_be()
-        self.data['seq_num_of_de_packet'] = reader.read_uint16_be()
+        self.data["imap_start_time_second"] = reader.read_uint32_be()
+        self.data["number_of_de_packets"] = reader.read_uint16_be()
+        self.data["seq_num_of_de_packet"] = reader.read_uint16_be()
 
         # TMP_SOLUTION
         # read 4078 bytes if zero-padding is used
-        #self.data['de_data'] = bytearray(reader.read_buffer(4078))
+        # self.data['de_data'] = bytearray(reader.read_buffer(4078))
 
         # TMP_SOLUTION
         # read packet_length-with-correction bytes if zero-padding is not used
         # + 1 see definition of CCSDS header, -4 sclk, -4 - 2 - 2 if for the three reads above
         packet_length_correction = 1 - 4 - 4 - 2 - 2
-        assert (packet_length + packet_length_correction) > 0, \
-            'wrong packet length for direct-event packet'
-        self.data['de_data'] = bytearray(reader.read_buffer(packet_length + packet_length_correction))
+        assert (packet_length + packet_length_correction) > 0, (
+            "wrong packet length for direct-event packet"
+        )
+        self.data["de_data"] = bytearray(
+            reader.read_buffer(packet_length + packet_length_correction)
+        )
 
         # DEBUG TEST
-        #print()
-        #print(len(self.data['de_data']))
-        #import binascii
-        #print(binascii.hexlify(self.data['de_data']))
-        #print(' '.join(format(x, '02x') for x in self.data['de_data'][:40]))
-        #print(' '.join(format(x, '02x') for x in self.data['de_data'][40:]))
-        #if len(self.data['de_data']) != 40:
+        # print()
+        # print(len(self.data['de_data']))
+        # import binascii
+        # print(binascii.hexlify(self.data['de_data']))
+        # print(' '.join(format(x, '02x') for x in self.data['de_data'][:40]))
+        # print(' '.join(format(x, '02x') for x in self.data['de_data'][40:]))
+        # if len(self.data['de_data']) != 40:
         #  print('\nerror: len(self.data[\'de_data\']) != 40')
         #  exit()
-        #return self.data
+        # return self.data
 
     def print_data_keys(self):
         """
         Print data keys for L0 data
         """
-        print('\n\nL0 data keys:')
+        print("\n\nL0 data keys:")
         for key in self.data:
             print(key)
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_histogram.py b/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_histogram.py
index a8863eb74..933046cea 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_histogram.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l0_data_histogram.py
@@ -2,10 +2,12 @@
 Author: Marek Strumik, maro at cbk.waw.pl
 """
 
-class L0DataHistogram():
+
+class L0DataHistogram:
     """
     L0DataHistogram class (based on HistogramPayload by KPLabs) for GLOWS-L0-histogram data
     """
+
     def __init__(self):
         """
         Constructor for this class
@@ -22,49 +24,50 @@ def deserialize(self, reader):
             reader: Reader-class object needed for reading binary data stream
         """
         # start reading the data
-        self.data['first_spin_id_in_block'] = reader.read_uint32_be()
-        self.data['diff_spin_id_in_block'] = reader.read_uint16_be()
-        self.data['histogram_validity_flags'] = reader.read_uint16_be()
-        self.data['software_version'] = reader.read_uint24_be() # 11
+        self.data["first_spin_id_in_block"] = reader.read_uint32_be()
+        self.data["diff_spin_id_in_block"] = reader.read_uint16_be()
+        self.data["histogram_validity_flags"] = reader.read_uint16_be()
+        self.data["software_version"] = reader.read_uint24_be()  # 11
 
         # IMAP time
-        self.data['imap_start_time_second'] = reader.read_uint32_be()
-        self.data['imap_start_time_subsecond'] = reader.read_uint24_be()
-        self.data['imap_diff_second'] = reader.read_uint16_be()
-        self.data['imap_diff_subsecond'] = reader.read_uint24_be() # 23
+        self.data["imap_start_time_second"] = reader.read_uint32_be()
+        self.data["imap_start_time_subsecond"] = reader.read_uint24_be()
+        self.data["imap_diff_second"] = reader.read_uint16_be()
+        self.data["imap_diff_subsecond"] = reader.read_uint24_be()  # 23
 
         # GLOWS time
-        self.data['glows_start_time_second'] = reader.read_uint32_be()
-        self.data['glows_start_time_subsecond'] = reader.read_uint24_be()
-        self.data['glows_diff_second'] = reader.read_uint16_be()
-        self.data['glows_diff_subsecond'] = reader.read_uint24_be() # 35
+        self.data["glows_start_time_second"] = reader.read_uint32_be()
+        self.data["glows_start_time_subsecond"] = reader.read_uint24_be()
+        self.data["glows_diff_second"] = reader.read_uint16_be()
+        self.data["glows_diff_subsecond"] = reader.read_uint24_be()  # 35
 
         # histogram parameters
-        self.data['number_of_spins_per_block'] = reader.read_uint8() + 1
-        self.data['number_of_bins_per_histogram'] = reader.read_uint16_be() # 38
+        self.data["number_of_spins_per_block"] = reader.read_uint8() + 1
+        self.data["number_of_bins_per_histogram"] = reader.read_uint16_be()  # 38
 
         # ancillary parameters collected onboard
-        self.data['filter_temperature_average'] = reader.read_uint8()
-        self.data['filter_temperature_variance'] = reader.read_uint16_be()
-        self.data['hv_voltage_average'] = reader.read_uint16_be()
-        self.data['hv_voltage_variance'] = reader.read_uint32_be()
-        self.data['spin_period_average'] = reader.read_uint16_be()
-        self.data['spin_period_variance'] = reader.read_uint32_be()
-        self.data['pulse_length_average'] = reader.read_uint8()
-        self.data['pulse_length_variance'] = reader.read_uint16_be() # 56
+        self.data["filter_temperature_average"] = reader.read_uint8()
+        self.data["filter_temperature_variance"] = reader.read_uint16_be()
+        self.data["hv_voltage_average"] = reader.read_uint16_be()
+        self.data["hv_voltage_variance"] = reader.read_uint32_be()
+        self.data["spin_period_average"] = reader.read_uint16_be()
+        self.data["spin_period_variance"] = reader.read_uint32_be()
+        self.data["pulse_length_average"] = reader.read_uint8()
+        self.data["pulse_length_variance"] = reader.read_uint16_be()  # 56
 
         # TMP_SOLUTION
         # in TLM_GLX_2023_06_22.xls a new field (number of events in histogram) was introduced
         # the line below needs to be commented out, if earlier versions of mock data are used
-        self.data['number_of_events'] = reader.read_uint32_be()
+        self.data["number_of_events"] = reader.read_uint32_be()
 
         # histogram array
-        self.data['histogram'] = bytearray(reader.read_buffer(
-            self.data['number_of_bins_per_histogram']))
+        self.data["histogram"] = bytearray(
+            reader.read_buffer(self.data["number_of_bins_per_histogram"])
+        )
 
         # additional padding when odd number of bins?
-        if self.data['number_of_bins_per_histogram'] % 2 == 1:
-          aux = reader.read_uint8()
+        if self.data["number_of_bins_per_histogram"] % 2 == 1:
+            aux = reader.read_uint8()
 
         return self.data
 
@@ -72,6 +75,6 @@ def print_data_keys(self):
         """
         Print data keys for L0 data
         """
-        print('\n\nL0 data keys:')
+        print("\n\nL0 data keys:")
         for key in self.data:
             print(key)
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_direct_events.py b/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_direct_events.py
index ed6c9b454..3aea9e2be 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_direct_events.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_direct_events.py
@@ -1,17 +1,18 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
-import json
-import os
-import struct
+
 import copy
+import json
+
 import numpy as np
-from .constants import VERSION, SUBSECOND_LIMIT
-from .time import Time
-from .funcs import time_sec_subsec_to_utc
+
+from .constants import SUBSECOND_LIMIT, VERSION
 from .direct_event import DirectEvent
+from .funcs import time_sec_subsec_to_utc
 
-class L1aDataDirectEvents():
+
+class L1aDataDirectEvents:
     """
     L1aDataHistogram() class for GLOWS-L1a-histogram data
     """
@@ -35,153 +36,217 @@ def create_l1a_from_l0_data(self, l0_data, pkts_file_name: str, packet_number: i
         # this is supposed to carry information about errors to be passed up at return
         error_messages = []
 
-        self.data['header'] = {
-            'ground_software_version': VERSION,
-            'pkts_file_name': pkts_file_name,
+        self.data["header"] = {
+            "ground_software_version": VERSION,
+            "pkts_file_name": pkts_file_name,
             # note: packet number is seq_count (per apid!) field in CCSDS header
-            'seq_count_in_pkts_file': packet_number
+            "seq_count_in_pkts_file": packet_number,
         }
 
-        self.data['imap_start_time_seconds'] = l0_data['imap_start_time_second']
-        self.data['number_of_de_packets'] = l0_data['number_of_de_packets']
-        self.data['seq_num_of_de_packet'] = l0_data['seq_num_of_de_packet']
-        data = l0_data['de_data'] # data buffer which may contain (data_every_second + DE) data or only DE data
+        self.data["imap_start_time_seconds"] = l0_data["imap_start_time_second"]
+        self.data["number_of_de_packets"] = l0_data["number_of_de_packets"]
+        self.data["seq_num_of_de_packet"] = l0_data["seq_num_of_de_packet"]
+        data = l0_data[
+            "de_data"
+        ]  # data buffer which may contain (data_every_second + DE) data or only DE data
 
         # we assume that multi-part DE packets have been merged together before execution of this method
         # in the merged packet self.data['seq_num_of_de_packet'] should be set to 0
-        assert self.data['seq_num_of_de_packet'] == 0
+        assert self.data["seq_num_of_de_packet"] == 0
 
         # sanity check, we should have:
         # >= 51B = 40 (data_every_second) + 8 (first direct event uncompressed) + 3 (the smallest timedelta + pulse_length)
         # or
         # 40 B if there is only data_every_second structure and no direct events
-        #assert len(data) >= 51 or len(data) == 40 # commented out -> see conditions checked later
+        # assert len(data) >= 51 or len(data) == 40 # commented out -> see conditions checked later
 
         # read data_every_second structure
         # based on Jupyter notebook sent in an email by K. Ber on Sep 20, 2023
         # corrected (missing fields filter_temperature and hv_voltage) by looking at
         # glows_appsw-master/tooling/science/models/direct_event_file_model.py:42
-        self.data['data_every_second'] = {}
-        self.data['data_every_second']['imap_sclk_last_pps'] = int.from_bytes(data[0:4], "big")
-        self.data['data_every_second']['glows_sclk_last_pps'] = int.from_bytes(data[4:8], "big")
-        self.data['data_every_second']['glows_ssclk_last_pps'] = int.from_bytes(data[8:12], "big")
-        self.data['data_every_second']['imap_sclk_next_pps'] = int.from_bytes(data[12:16], "big")
-        self.data['data_every_second']['catbed_heater_active'] = False if int(data[16]) == 0 else True
-        self.data['data_every_second']['spin_period_valid'] = False if int(data[17]) == 0 else True
-        self.data['data_every_second']['spin_phase_at_next_pps_valid'] = False if int(data[18]) == 0 else True
-        self.data['data_every_second']['spin_period_source'] = False if int(data[19]) == 0 else True
-        self.data['data_every_second']['spin_period'] = int.from_bytes(data[20:22], "big")
-        self.data['data_every_second']['spin_phase_at_next_pps'] = int.from_bytes(data[22:24], "big")
-        self.data['data_every_second']['number_of_completed_spins'] = int.from_bytes(data[24:28], "big")
-        self.data['data_every_second']['filter_temperature'] = int.from_bytes(data[28:30], "big")
-        self.data['data_every_second']['hv_voltage'] = int.from_bytes(data[30:32], "big")
-        self.data['data_every_second']['glows_time_on_pps_valid'] = False if int(data[32]) == 0 else True
-        self.data['data_every_second']['time_status_valid'] = False if int(data[33]) == 0 else True
-        self.data['data_every_second']['housekeeping_valid'] = False if int(data[34]) == 0 else True
-        self.data['data_every_second']['is_pps_autogenerated'] = False if int(data[35]) == 0 else True
-        self.data['data_every_second']['hv_test_in_progress'] = False if int(data[36]) == 0 else True
-        self.data['data_every_second']['pulse_test_in_progress'] = False if int(data[37]) == 0 else True
-        self.data['data_every_second']['memory_error_detected'] = False if int(data[38]) == 0 else True
+        self.data["data_every_second"] = {}
+        self.data["data_every_second"]["imap_sclk_last_pps"] = int.from_bytes(
+            data[0:4], "big"
+        )
+        self.data["data_every_second"]["glows_sclk_last_pps"] = int.from_bytes(
+            data[4:8], "big"
+        )
+        self.data["data_every_second"]["glows_ssclk_last_pps"] = int.from_bytes(
+            data[8:12], "big"
+        )
+        self.data["data_every_second"]["imap_sclk_next_pps"] = int.from_bytes(
+            data[12:16], "big"
+        )
+        self.data["data_every_second"]["catbed_heater_active"] = (
+            False if int(data[16]) == 0 else True
+        )
+        self.data["data_every_second"]["spin_period_valid"] = (
+            False if int(data[17]) == 0 else True
+        )
+        self.data["data_every_second"]["spin_phase_at_next_pps_valid"] = (
+            False if int(data[18]) == 0 else True
+        )
+        self.data["data_every_second"]["spin_period_source"] = (
+            False if int(data[19]) == 0 else True
+        )
+        self.data["data_every_second"]["spin_period"] = int.from_bytes(
+            data[20:22], "big"
+        )
+        self.data["data_every_second"]["spin_phase_at_next_pps"] = int.from_bytes(
+            data[22:24], "big"
+        )
+        self.data["data_every_second"]["number_of_completed_spins"] = int.from_bytes(
+            data[24:28], "big"
+        )
+        self.data["data_every_second"]["filter_temperature"] = int.from_bytes(
+            data[28:30], "big"
+        )
+        self.data["data_every_second"]["hv_voltage"] = int.from_bytes(
+            data[30:32], "big"
+        )
+        self.data["data_every_second"]["glows_time_on_pps_valid"] = (
+            False if int(data[32]) == 0 else True
+        )
+        self.data["data_every_second"]["time_status_valid"] = (
+            False if int(data[33]) == 0 else True
+        )
+        self.data["data_every_second"]["housekeeping_valid"] = (
+            False if int(data[34]) == 0 else True
+        )
+        self.data["data_every_second"]["is_pps_autogenerated"] = (
+            False if int(data[35]) == 0 else True
+        )
+        self.data["data_every_second"]["hv_test_in_progress"] = (
+            False if int(data[36]) == 0 else True
+        )
+        self.data["data_every_second"]["pulse_test_in_progress"] = (
+            False if int(data[37]) == 0 else True
+        )
+        self.data["data_every_second"]["memory_error_detected"] = (
+            False if int(data[38]) == 0 else True
+        )
         # NOTE: there is one-byte padding in the C structure of data_every_seconds, so it has 40 bytes in total
 
         # DEBUG TEST
-        #print(self.data)
-        #exit()
-        #print('\nMET', self.data['imap_start_time_seconds'])
-        #print('XXX', self.data['data_every_second']['imap_sclk_last_pps'])
-        #exit()
+        # print(self.data)
+        # exit()
+        # print('\nMET', self.data['imap_start_time_seconds'])
+        # print('XXX', self.data['data_every_second']['imap_sclk_last_pps'])
+        # exit()
 
         # sanity check
-        #assert self.data['imap_start_time_seconds'] == self.data['data_every_second']['imap_sclk_last_pps'], \
+        # assert self.data['imap_start_time_seconds'] == self.data['data_every_second']['imap_sclk_last_pps'], \
         #  'error create_l1a_from_l0_data(): imap_start_time_seconds=%d != imap_sclk_last_pps=%d' % \
         #  (self.data['imap_start_time_seconds'], self.data['data_every_second']['imap_sclk_last_pps'])
 
         # check if there are any direct events included in the current packet or it contains only data_every_second
         if len(data) == 40:
-          return error_messages
+            return error_messages
 
         # sanity check, the first direct event must be uncompressed 8-byte structure
-        assert len(data) >= 48, \
-          'error create_l1a_from_l0_data(): len(data)=%d < 48' % len(data)
+        assert len(data) >= 48, (
+            "error create_l1a_from_l0_data(): len(data)=%d < 48" % len(data)
+        )
 
         # read the first direct event, which is always uncompressed (i.e., not timedelta but full timestamp)
         current_event = DirectEvent.build_event_from_uncompressed_data(data[40:48])
         self.direct_events.append(current_event)
 
         # sanity check, the first-direct-event sclk must be within +1 second from data_every_second sclk
-        #assert self.direct_events[0].seconds <= self.data['data_every_second']['glows_sclk_last_pps']+1, \
+        # assert self.direct_events[0].seconds <= self.data['data_every_second']['glows_sclk_last_pps']+1, \
         #    'error create_l1a_from_l0_data(): sclk %d %d' % (self.direct_events[0].seconds, self.data['data_every_second']['glows_sclk_last_pps'])
 
         # TMP SOLUTION: this if was included instead of the assert above, because in the tests performed in the PTB, some
         # packets had incorrect sclk in the first direct event included; when the bug is fixed by the GLOWS SW team this if
         # should be replaced by the assert above
-        if self.direct_events[0].seconds > self.data['data_every_second']['glows_sclk_last_pps']+1:
-            print('\n### data_every_second ###')
-            print(self.data['data_every_second'])
-            print('---')
+        if (
+            self.direct_events[0].seconds
+            > self.data["data_every_second"]["glows_sclk_last_pps"] + 1
+        ):
+            print("\n### data_every_second ###")
+            print(self.data["data_every_second"])
+            print("---")
             print(self.direct_events)
-            print('###')
-            error_messages.append('DE: de[0].sec %d > glows_sclk_last_pps+1 %d' % (self.direct_events[0].seconds, self.data['data_every_second']['glows_sclk_last_pps']+1))
+            print("###")
+            error_messages.append(
+                "DE: de[0].sec %d > glows_sclk_last_pps+1 %d"
+                % (
+                    self.direct_events[0].seconds,
+                    self.data["data_every_second"]["glows_sclk_last_pps"] + 1,
+                )
+            )
 
         # sanity check, KPLabs says that multi event field is actually not processed by AppSW, so it is expected to be False
         # TMP_SOLUTION this assert was commented out but this needs to be discussed with GLOWS FSW team
-        #assert self.direct_events[0].multi_event != True, \
+        # assert self.direct_events[0].multi_event != True, \
         #    'error create_l1a_from_l0_data(): multi event occured'
 
         # check if there are any compressed direct events included in the current packet
         if len(data) <= 48:
-          return error_messages
+            return error_messages
 
         # pack the rest of the binary stream into a separate variable to be processed later
         binary_stream_of_timedeltas = data[48:]
 
         # a test snippet
-        #current_event = DirectEvent(54232337, 1997808, 6, False)
-        #binary_stream_of_timedeltas = bytearray.fromhex("033b8511061e7bf0") + bytearray.fromhex("876106") + bytearray.fromhex("E7620687") + bytearray.fromhex("00")
+        # current_event = DirectEvent(54232337, 1997808, 6, False)
+        # binary_stream_of_timedeltas = bytearray.fromhex("033b8511061e7bf0") + bytearray.fromhex("876106") + bytearray.fromhex("E7620687") + bytearray.fromhex("00")
 
         # convert direct events in the form of compressed timedeltas (in a binary stream) into DirectEvent objects
         # based on glows_appsw-master/tooling/science/models/direct_event_file_decompressor.py by KPLabs
         i = 0
-        while i < len(binary_stream_of_timedeltas)-1:
+        while i < len(binary_stream_of_timedeltas) - 1:
             first_byte = int(binary_stream_of_timedeltas[i])
             i += 1
             oldest_diff = first_byte & 0x3F
             marker = first_byte >> 6
 
-            if marker == 0x00 and i <= len(binary_stream_of_timedeltas)-7: # uncompressed time stamp (8-bytes)
-                rest_bytes = binary_stream_of_timedeltas[i:i+7]
+            if (
+                marker == 0x00 and i <= len(binary_stream_of_timedeltas) - 7
+            ):  # uncompressed time stamp (8-bytes)
+                rest_bytes = binary_stream_of_timedeltas[i : i + 7]
                 i += 7
                 part = bytearray([oldest_diff])
                 part.extend(rest_bytes)
                 current_event = DirectEvent.build_event_from_uncompressed_data(part)
 
-            elif marker == 0x2 and i < len(binary_stream_of_timedeltas)-1: # 2-byte compression of timedelta
+            elif (
+                marker == 0x2 and i < len(binary_stream_of_timedeltas) - 1
+            ):  # 2-byte compression of timedelta
                 rest = int(binary_stream_of_timedeltas[i])
                 i += 1
                 diff = oldest_diff << 8 | rest
                 length = int(binary_stream_of_timedeltas[i])
                 i += 1
-                current_event = DirectEvent.build_event_from_compressed_data(diff, length, current_event)
-
-            elif marker == 0x3 and i < len(binary_stream_of_timedeltas)-2: # 3-byte compression of timedelta
-                rest = int.from_bytes(binary_stream_of_timedeltas[i:i+2], "big")
+                current_event = DirectEvent.build_event_from_compressed_data(
+                    diff, length, current_event
+                )
+
+            elif (
+                marker == 0x3 and i < len(binary_stream_of_timedeltas) - 2
+            ):  # 3-byte compression of timedelta
+                rest = int.from_bytes(binary_stream_of_timedeltas[i : i + 2], "big")
                 i += 2
                 diff = oldest_diff << 16 | rest
                 length = int(binary_stream_of_timedeltas[i])
                 i += 1
-                current_event = DirectEvent.build_event_from_compressed_data(diff, length, current_event)
-
-            else: # wrong-marker or hitting-the-buffer-end case
-#                print()
-#                pass
-#                raise Exception('error create_l1a_from_l0_data(): unexpected marker %d or end of data stream %d %d' % \
-#                                (marker, i, len(binary_stream_of_timedeltas)))
-                error_messages.append('DE: direct event decompressing failed at byte %d' % (i-1))
+                current_event = DirectEvent.build_event_from_compressed_data(
+                    diff, length, current_event
+                )
+
+            else:  # wrong-marker or hitting-the-buffer-end case
+                #                print()
+                #                pass
+                #                raise Exception('error create_l1a_from_l0_data(): unexpected marker %d or end of data stream %d %d' % \
+                #                                (marker, i, len(binary_stream_of_timedeltas)))
+                error_messages.append(
+                    "DE: direct event decompressing failed at byte %d" % (i - 1)
+                )
                 break
 
             # sanity check
             if current_event.subseconds >= SUBSECOND_LIMIT:
-                error_messages.append('DE: de[0].subsec >= SUBSECOND_LIMIT')
+                error_messages.append("DE: de[0].subsec >= SUBSECOND_LIMIT")
                 break
 
             self.direct_events.append(current_event)
@@ -190,13 +255,16 @@ def create_l1a_from_l0_data(self, l0_data, pkts_file_name: str, packet_number: i
     def generate_file_name(self, extension):
         # file names should be tagged with utc time as suggested in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        start_time_utc = time_sec_subsec_to_utc(self.data['imap_start_time_seconds'], 0
-                                               ).strftime('%Y%m%d%H%M%S')
+        start_time_utc = time_sec_subsec_to_utc(
+            self.data["imap_start_time_seconds"], 0
+        ).strftime("%Y%m%d%H%M%S")
 
         # the output file name is constructed using a convention defined in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        file_name = 'data_l1a_direct_events/imap_glows_l1a_%s_orbX_modX_p_v00.%s' % \
-                    (start_time_utc, extension)
+        file_name = "data_l1a_direct_events/imap_glows_l1a_%s_orbX_modX_p_v00.%s" % (
+            start_time_utc,
+            extension,
+        )
 
         return file_name
 
@@ -209,7 +277,7 @@ def read_from_file(self, file_name):
         # TMP_SOLUTION currently JSON and text format are used, it needs to be changed to CDF when python-cdf
         # modules for IMAP will be provided by SDC
         self.read_from_json_file(file_name)
-        self.read_direct_events(file_name.replace('.json', '.dat'))
+        self.read_direct_events(file_name.replace(".json", ".dat"))
 
     def read_from_json_file(self, file_name):
         """
@@ -217,11 +285,11 @@ def read_from_json_file(self, file_name):
         Args:
             file_name: name of file with L1a data
         """
-        file_handler = open(file_name, 'r')
+        file_handler = open(file_name, "r")
         self.data = json.load(file_handler)
 
         # additional field to keep track where data come from
-        self.data['l1a_file_name'] = file_name
+        self.data["l1a_file_name"] = file_name
         file_handler.close()
 
     def read_direct_events(self, file_name):
@@ -233,7 +301,7 @@ def read_direct_events(self, file_name):
         self.direct_events = np.loadtxt(file_name, dtype=int)
 
         # sanity check
-        #assert np.shape(self.direct_events)[1] == 3
+        # assert np.shape(self.direct_events)[1] == 3
 
     def save_data_to_file(self):
         """
@@ -251,20 +319,20 @@ def save_data_to_json_file(self):
         """
         # generate JSON content as string from self.data dictionary
         data = copy.deepcopy(self.data)
-        del data['seq_num_of_de_packet']
+        del data["seq_num_of_de_packet"]
         json_content = json.dumps(data, indent=4, default=vars)
 
-        file_name = self.generate_file_name('json')
+        file_name = self.generate_file_name("json")
 
         # sanity check
         # TMP_SOLUTION due to problems with FSW this needed to be commented out, but
         # it seems to be ok to check this when the problems will be fixed
-        #if os.path.exists(file_name) and self.data['number_of_de_packets'] == 1:
+        # if os.path.exists(file_name) and self.data['number_of_de_packets'] == 1:
         #    print('L1a direct-event file already exists, while it should not', file_name)
         #    print(self.data['number_of_de_packets'], self.data['seq_num_of_de_packet'])
         #    exit()
 
-        file_handler = open(file_name, 'w')
+        file_handler = open(file_name, "w")
         print(json_content, file=file_handler)
         file_handler.close()
 
@@ -274,8 +342,10 @@ def save_direct_events(self):
         Output file name is set automatically here (TBC if it should be method argument)
         """
 
-        file_name = self.generate_file_name('dat')
-        file_handler = open(file_name, 'w')
-        for i, x in enumerate(self.direct_events):
-            file_handler.write('%d %7d %3d\n' % (x.seconds, x.subseconds, x.impulse_length))
+        file_name = self.generate_file_name("dat")
+        file_handler = open(file_name, "w")
+        file_handler.writelines(
+            "%d %7d %3d\n" % (x.seconds, x.subseconds, x.impulse_length)
+            for i, x in enumerate(self.direct_events)
+        )
         file_handler.close()
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_histogram.py b/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_histogram.py
index 8b016de2d..fb025636a 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_histogram.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l1a_data_histogram.py
@@ -1,13 +1,17 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
+
 import json
+
 import numpy as np
-from .constants import VERSION, MAIN_TIME_FIELD
-from .time import Time
+
+from .constants import MAIN_TIME_FIELD, VERSION
 from .funcs import time_sec_subsec_to_utc
+from .time import Time
+
 
-class L1aDataHistogram():
+class L1aDataHistogram:
     """
     L1aDataHistogram() class for GLOWS-L1a-histogram data
     """
@@ -30,58 +34,69 @@ def create_l1a_from_l0_data(self, l0_data, pkts_file_name: str, packet_number: i
         # this is supposed to carry information about errors to be passed up at return
         error_messages = []
 
-        self.data['first_spin_id_in_block'] = l0_data['first_spin_id_in_block']
-        self.data['last_spin_id_in_block'] = self.data['first_spin_id_in_block'] + \
-                                              l0_data['diff_spin_id_in_block']
+        self.data["first_spin_id_in_block"] = l0_data["first_spin_id_in_block"]
+        self.data["last_spin_id_in_block"] = (
+            self.data["first_spin_id_in_block"] + l0_data["diff_spin_id_in_block"]
+        )
 
         # sanity check
         # TMP_SOLUTION: instrument emulator does not set l0_data['diff_spin_id_in_block'] properly
         # thus we temporarily comment out this assert
-        #assert l0_data['diff_spin_id_in_block'] == l0_data['number_of_spins_per_block'], \
+        # assert l0_data['diff_spin_id_in_block'] == l0_data['number_of_spins_per_block'], \
         #    'Inconsistency between spin-numbering field l0_data[\'diff_spin_id_in_block\'] and' + \
         #    'histogram-parameter field l0_data[\'number_of_spins_per_block\']'
 
-        self.data['block_header'] = {
-            'flight_software_version': l0_data['software_version'],
-            'ground_software_version': VERSION,
-            'pkts_file_name': pkts_file_name,
+        self.data["block_header"] = {
+            "flight_software_version": l0_data["software_version"],
+            "ground_software_version": VERSION,
+            "pkts_file_name": pkts_file_name,
             # note: packet number is seq_count (per apid!) field in CCSDS header
-            'seq_count_in_pkts_file': packet_number
+            "seq_count_in_pkts_file": packet_number,
         }
 
         # times for L1a are kept in the same format as for L0 (i.e., using seconds and subseconds)
-        self.data['glows_start_time'] = Time(l0_data['glows_start_time_second'],
-                                             l0_data['glows_start_time_subsecond'])
-        self.data['glows_end_time_offset'] = Time(l0_data['glows_diff_second'],
-                                                  l0_data['glows_diff_subsecond'])
-        self.data['imap_start_time'] = Time(l0_data['imap_start_time_second'],
-                                            l0_data['imap_start_time_subsecond'])
-        self.data['imap_end_time_offset'] = Time(l0_data['imap_diff_second'],
-                                                 l0_data['imap_diff_subsecond'])
-
-        self.data['number_of_spins_per_block'] = l0_data['number_of_spins_per_block']
-        self.data['number_of_bins_per_histogram'] = l0_data['number_of_bins_per_histogram']
-        self.data['histogram'] = l0_data['histogram']
+        self.data["glows_start_time"] = Time(
+            l0_data["glows_start_time_second"], l0_data["glows_start_time_subsecond"]
+        )
+        self.data["glows_end_time_offset"] = Time(
+            l0_data["glows_diff_second"], l0_data["glows_diff_subsecond"]
+        )
+        self.data["imap_start_time"] = Time(
+            l0_data["imap_start_time_second"], l0_data["imap_start_time_subsecond"]
+        )
+        self.data["imap_end_time_offset"] = Time(
+            l0_data["imap_diff_second"], l0_data["imap_diff_subsecond"]
+        )
+
+        self.data["number_of_spins_per_block"] = l0_data["number_of_spins_per_block"]
+        self.data["number_of_bins_per_histogram"] = l0_data[
+            "number_of_bins_per_histogram"
+        ]
+        self.data["histogram"] = l0_data["histogram"]
         # TMP_SOLUTION
-        self.data['number_of_events'] = l0_data['number_of_events']
+        self.data["number_of_events"] = l0_data["number_of_events"]
 
         # sanity check
-        if self.data['number_of_events'] != np.sum(self.data['histogram']):
-          error_messages.append('HIST: number of events is not equal to the sum over histogram %d %d' % \
-                                (self.data['number_of_events'], np.sum(self.data['histogram'])))
-
-        self.data['filter_temperature_average'] = l0_data['filter_temperature_average']
-        self.data['filter_temperature_variance'] = l0_data['filter_temperature_variance']
-        self.data['hv_voltage_average'] = l0_data['hv_voltage_average']
-        self.data['hv_voltage_variance'] = l0_data['hv_voltage_variance']
-        self.data['spin_period_average'] = l0_data['spin_period_average']
-        self.data['spin_period_variance'] = l0_data['spin_period_variance']
-        self.data['pulse_length_average'] = l0_data['pulse_length_average']
-        self.data['pulse_length_variance'] = l0_data['pulse_length_variance']
-
-        self.data['flags'] = {}
-        self.data['flags']['flags_set_onboard'] = l0_data['histogram_validity_flags']
-        self.data['flags']['is_generated_on_ground'] = False
+        if self.data["number_of_events"] != np.sum(self.data["histogram"]):
+            error_messages.append(
+                "HIST: number of events is not equal to the sum over histogram %d %d"
+                % (self.data["number_of_events"], np.sum(self.data["histogram"]))
+            )
+
+        self.data["filter_temperature_average"] = l0_data["filter_temperature_average"]
+        self.data["filter_temperature_variance"] = l0_data[
+            "filter_temperature_variance"
+        ]
+        self.data["hv_voltage_average"] = l0_data["hv_voltage_average"]
+        self.data["hv_voltage_variance"] = l0_data["hv_voltage_variance"]
+        self.data["spin_period_average"] = l0_data["spin_period_average"]
+        self.data["spin_period_variance"] = l0_data["spin_period_variance"]
+        self.data["pulse_length_average"] = l0_data["pulse_length_average"]
+        self.data["pulse_length_variance"] = l0_data["pulse_length_variance"]
+
+        self.data["flags"] = {}
+        self.data["flags"]["flags_set_onboard"] = l0_data["histogram_validity_flags"]
+        self.data["flags"]["is_generated_on_ground"] = False
 
         return error_messages
 
@@ -101,15 +116,16 @@ def read_from_json_file(self, file_name):
         Args:
             file_name: name of file with L1a data
         """
-        file_handler = open(file_name, 'r')
+        file_handler = open(file_name, "r")
         self.data = json.load(file_handler)
 
         # hexadecimal values saved as strings require special treatment to make them integers
-        self.data['flags']['flags_set_onboard'] = int(self.data['flags']['flags_set_onboard'],
-                                                      base=16)
+        self.data["flags"]["flags_set_onboard"] = int(
+            self.data["flags"]["flags_set_onboard"], base=16
+        )
 
         # additional field to keep track where data come from
-        self.data['l1a_file_name'] = file_name
+        self.data["l1a_file_name"] = file_name
         file_handler.close()
 
     def save_data_to_file(self):
@@ -127,28 +143,29 @@ def save_data_to_json_file(self):
         """
         # histogram needs to be temporarily converted to string, because we want to force that it
         # is saved as one line (i.e., without \n after each element) by json.dumps()
-        histogram_tmp = self.data['histogram']
-        flag_tmp = self.data['flags']['flags_set_onboard']
-        self.data['histogram'] = '[' + ', '.join(map(str, list(histogram_tmp))) + ']'
-        self.data['flags']['flags_set_onboard'] = '0x' + format(flag_tmp, 'x').zfill(4)
+        histogram_tmp = self.data["histogram"]
+        flag_tmp = self.data["flags"]["flags_set_onboard"]
+        self.data["histogram"] = "[" + ", ".join(map(str, list(histogram_tmp))) + "]"
+        self.data["flags"]["flags_set_onboard"] = "0x" + format(flag_tmp, "x").zfill(4)
         json_content = json.dumps(self.data, indent=4, default=vars)
-        self.data['flags']['flags_set_onboard'] = flag_tmp
-        self.data['histogram'] = histogram_tmp
+        self.data["flags"]["flags_set_onboard"] = flag_tmp
+        self.data["histogram"] = histogram_tmp
         # modify json_content to make histogram string be seen finally as a number array
-        json_content = json_content.replace('"[', '[').replace(']"', ']')
+        json_content = json_content.replace('"[', "[").replace(']"', "]")
 
         # file names should be tagged with utc time as suggested in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        start_time_utc = time_sec_subsec_to_utc(self.data[MAIN_TIME_FIELD].seconds,
-                                                self.data[MAIN_TIME_FIELD].subseconds
-                                               ).strftime('%Y%m%d%H%M%S')
+        start_time_utc = time_sec_subsec_to_utc(
+            self.data[MAIN_TIME_FIELD].seconds, self.data[MAIN_TIME_FIELD].subseconds
+        ).strftime("%Y%m%d%H%M%S")
 
         # the output file name is constructed using a convention defined in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        file_name = 'data_l1a_histograms/imap_glows_l1a_%s_orbX_modX_p_v00.json' % \
-                    (start_time_utc)
+        file_name = "data_l1a_histograms/imap_glows_l1a_%s_orbX_modX_p_v00.json" % (
+            start_time_utc
+        )
 
-        file_handler = open(file_name, 'w')
+        file_handler = open(file_name, "w")
         print(json_content, file=file_handler)
         file_handler.close()
 
@@ -157,15 +174,22 @@ def total_counts_file_init(self, tag):
         Initialize a file for storing total count numbers for L1a histograms
         This kind of information is needed when L1b is generated (for one of the filters)
         """
-        file_handler = open('data_l1a_histograms/total_counts_file.dat', 'w')
-        file_handler.write('%s %s\n' % (tag, self.data['block_header']['pkts_file_name']))
+        file_handler = open("data_l1a_histograms/total_counts_file.dat", "w")
+        file_handler.write(
+            "%s %s\n" % (tag, self.data["block_header"]["pkts_file_name"])
+        )
         file_handler.close()
 
     def total_counts_file_update(self):
         """
         Update a file for storing total counts numbers for L1a histograms
         """
-        file_handler = open('data_l1a_histograms/total_counts_file.dat', 'a')
-        file_handler.write('%d %d\n' % (self.data['block_header']['seq_count_in_pkts_file'],
-                                        int(np.sum(self.data['histogram']))))
+        file_handler = open("data_l1a_histograms/total_counts_file.dat", "a")
+        file_handler.write(
+            "%d %d\n"
+            % (
+                self.data["block_header"]["seq_count_in_pkts_file"],
+                int(np.sum(self.data["histogram"])),
+            )
+        )
         file_handler.close()
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_direct_events.py b/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_direct_events.py
index da392f90d..3ae28d96f 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_direct_events.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_direct_events.py
@@ -1,13 +1,21 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
-import json
+
 import copy
+import json
+
 import numpy as np
-from .constants import VERSION, SUBSECOND_LIMIT
-from .funcs import decode_ancillary_parameter, time_sec_subsec_to_utc, time_sec_subsec_to_float64
 
-class L1bDataDirectEvents():
+from .constants import SUBSECOND_LIMIT, VERSION
+from .funcs import (
+    decode_ancillary_parameter,
+    time_sec_subsec_to_float64,
+    time_sec_subsec_to_utc,
+)
+
+
+class L1bDataDirectEvents:
     """
     L1bDataDirectEvents() class for GLOWS-L1b-direct-event data
     """
@@ -18,20 +26,23 @@ def __init__(self):
         """
         self.data = {}
 
-    def _block_header_add_list_of_anc_files(self, anc_input_from_sdc, anc_input_from_instr_team):
+    def _block_header_add_list_of_anc_files(
+        self, anc_input_from_sdc, anc_input_from_instr_team
+    ):
         """
         Add list of used ancillary files to block header
         Args:
             anc_input_from_sdc: ancillary input provided by IMAP SDC (general telemetry)
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
         """
-        self.data['header']['ancillary_data_files'] = []
-        for key in ['conversion_table_for_anc_data']:
-            self.data['header']['ancillary_data_files'].append(anc_input_from_instr_team[key])
-        #for key in anc_input_from_sdc:
+        self.data["header"]["ancillary_data_files"] = []
+        for key in ["conversion_table_for_anc_data"]:
+            self.data["header"]["ancillary_data_files"].append(
+                anc_input_from_instr_team[key]
+            )
+        # for key in anc_input_from_sdc:
         #    self.data['block_header']['ancillary_data_files'].append(anc_input_from_sdc[key])
 
-
     def _convert_ancillary_parameters(self, l1a_data, anc_input_from_instr_team):
         """
         Convert ancillary parameters collected onboard from integer values to physcial
@@ -42,21 +53,29 @@ def _convert_ancillary_parameters(self, l1a_data, anc_input_from_instr_team):
         """
 
         # load conversion/decoding table for ancillary parameters collected onboard
-        file_handler = open(anc_input_from_instr_team['conversion_table_for_anc_data'], 'r')
+        file_handler = open(
+            anc_input_from_instr_team["conversion_table_for_anc_data"], "r"
+        )
         conversion_table = json.load(file_handler)
         file_handler.close()
 
         # ancillary parameters collected onboard that need to be decoded
-        self.data['filter_temperature'] = decode_ancillary_parameter('filter_temperature',
-            conversion_table, l1a_data['filter_temperature'])
-        self.data['hv_voltage'] = decode_ancillary_parameter('hv_voltage',
-            conversion_table, l1a_data['hv_voltage'])
-        self.data['spin_period'] = decode_ancillary_parameter('spin_period',
-            conversion_table, l1a_data['spin_period'])
-        self.data['spin_phase_at_next_pps'] = decode_ancillary_parameter('spin_phase',
-            conversion_table, l1a_data['spin_phase_at_next_pps'])
-
-    def create_l1b_from_l1a_data(self, l1a, anc_input_from_sdc, anc_input_from_instr_team):
+        self.data["filter_temperature"] = decode_ancillary_parameter(
+            "filter_temperature", conversion_table, l1a_data["filter_temperature"]
+        )
+        self.data["hv_voltage"] = decode_ancillary_parameter(
+            "hv_voltage", conversion_table, l1a_data["hv_voltage"]
+        )
+        self.data["spin_period"] = decode_ancillary_parameter(
+            "spin_period", conversion_table, l1a_data["spin_period"]
+        )
+        self.data["spin_phase_at_next_pps"] = decode_ancillary_parameter(
+            "spin_phase", conversion_table, l1a_data["spin_phase_at_next_pps"]
+        )
+
+    def create_l1b_from_l1a_data(
+        self, l1a, anc_input_from_sdc, anc_input_from_instr_team
+    ):
         """
         Sets values of L1b fields using L1a data read from a file
         Args:
@@ -64,71 +83,95 @@ def create_l1b_from_l1a_data(self, l1a, anc_input_from_sdc, anc_input_from_instr
             anc_input_from_sdc: ancillary input provided by IMAP SDC (general telemetry)
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
         """
-        self.data['header'] = {
-            'ground_software_version': VERSION,
-            'pkts_file_name': l1a.data['header']['pkts_file_name'],
-            'seq_count_in_pkts_file': l1a.data['header']['seq_count_in_pkts_file'],
-            'l1a_file_name': l1a.data['l1a_file_name']
+        self.data["header"] = {
+            "ground_software_version": VERSION,
+            "pkts_file_name": l1a.data["header"]["pkts_file_name"],
+            "seq_count_in_pkts_file": l1a.data["header"]["seq_count_in_pkts_file"],
+            "l1a_file_name": l1a.data["l1a_file_name"],
         }
 
         # add list of used ancillary files to block_header
-        self._block_header_add_list_of_anc_files(anc_input_from_sdc, anc_input_from_instr_team)
+        self._block_header_add_list_of_anc_files(
+            anc_input_from_sdc, anc_input_from_instr_team
+        )
 
-        #self.data['unique_identifier'] = \
+        # self.data['unique_identifier'] = \
         #    time_sec_subsec_to_utc(l1a.data['data_every_second']['imap_sclk_last_pps'],
         #                           0).strftime('%Y-%m-%dT%H:%M:%S')
 
         # TMP_SOLUTION: for now we use imap_start_time_seconds for unique_identifier
         # becasue FSW has a bug that leads to a discrepancy between ['imap_start_time_seconds']
         # and ['data_every_second']['imap_sclk_last_pps']
-        self.data['unique_identifier'] = \
-            time_sec_subsec_to_utc(l1a.data['imap_start_time_seconds'],
-                                   0).strftime('%Y-%m-%dT%H:%M:%S')
+        self.data["unique_identifier"] = time_sec_subsec_to_utc(
+            l1a.data["imap_start_time_seconds"], 0
+        ).strftime("%Y-%m-%dT%H:%M:%S")
 
         # set IMAP and GLOWS time fields
         self.set_times(l1a.data)
 
         # set number of completed spins
-        self.data['number_of_completed_spins'] = \
-            l1a.data['data_every_second']['number_of_completed_spins']
+        self.data["number_of_completed_spins"] = l1a.data["data_every_second"][
+            "number_of_completed_spins"
+        ]
 
         # convert ancillary parameters collected onboard to physical units
-        self._convert_ancillary_parameters(l1a.data['data_every_second'], anc_input_from_instr_team)
+        self._convert_ancillary_parameters(
+            l1a.data["data_every_second"], anc_input_from_instr_team
+        )
 
         # add flags set onboard to self.data['flags']
-        self.data['flags'] = {}
-        for key in ['catbed_heater_active', 'spin_period_valid', 'spin_phase_at_next_pps_valid',
-                    'spin_period_source', 'glows_time_on_pps_valid', 'time_status_valid',
-                    'housekeeping_valid', 'is_pps_autogenerated', 'hv_test_in_progress',
-                    'pulse_test_in_progress', 'memory_error_detected']:
-            if key[:3] != 'is_':
-                self.data['flags']['is_' + key] = l1a.data['data_every_second'][key]
+        self.data["flags"] = {}
+        for key in [
+            "catbed_heater_active",
+            "spin_period_valid",
+            "spin_phase_at_next_pps_valid",
+            "spin_period_source",
+            "glows_time_on_pps_valid",
+            "time_status_valid",
+            "housekeeping_valid",
+            "is_pps_autogenerated",
+            "hv_test_in_progress",
+            "pulse_test_in_progress",
+            "memory_error_detected",
+        ]:
+            if key[:3] != "is_":
+                self.data["flags"]["is_" + key] = l1a.data["data_every_second"][key]
             else:
-                self.data['flags'][key] = l1a.data['data_every_second'][key]
+                self.data["flags"][key] = l1a.data["data_every_second"][key]
 
         if np.shape(l1a.direct_events)[0] > 0:
-            if l1a.direct_events.ndim > 1: # this is supposed to check if we have more than one DE
-                self.data['direct_event_glows_times'] = (l1a.direct_events[:, 0] + \
-                    l1a.direct_events[:, 1]/SUBSECOND_LIMIT).astype(float)
+            if (
+                l1a.direct_events.ndim > 1
+            ):  # this is supposed to check if we have more than one DE
+                self.data["direct_event_glows_times"] = (
+                    l1a.direct_events[:, 0] + l1a.direct_events[:, 1] / SUBSECOND_LIMIT
+                ).astype(float)
             else:
-                self.data['direct_event_glows_times'] = (l1a.direct_events[0] + \
-                    l1a.direct_events[1]/SUBSECOND_LIMIT).astype(float)
+                self.data["direct_event_glows_times"] = (
+                    l1a.direct_events[0] + l1a.direct_events[1] / SUBSECOND_LIMIT
+                ).astype(float)
 
             # load conversion/decoding table for ancillary parameters collected onboard
-            file_handler = open(anc_input_from_instr_team['conversion_table_for_anc_data'], 'r')
+            file_handler = open(
+                anc_input_from_instr_team["conversion_table_for_anc_data"], "r"
+            )
             conversion_table = json.load(file_handler)
             file_handler.close()
 
             # convert pulse lengths into physical units
-            if l1a.direct_events.ndim > 1: # this is supposed to check if we have more than one DE
-                self.data['direct_event_pulse_lengths'] = decode_ancillary_parameter('pulse_length',
-                    conversion_table, l1a.direct_events[:, 2])
+            if (
+                l1a.direct_events.ndim > 1
+            ):  # this is supposed to check if we have more than one DE
+                self.data["direct_event_pulse_lengths"] = decode_ancillary_parameter(
+                    "pulse_length", conversion_table, l1a.direct_events[:, 2]
+                )
             else:
-                self.data['direct_event_pulse_lengths'] = decode_ancillary_parameter('pulse_length',
-                    conversion_table, l1a.direct_events[2])
+                self.data["direct_event_pulse_lengths"] = decode_ancillary_parameter(
+                    "pulse_length", conversion_table, l1a.direct_events[2]
+                )
         else:
-            self.data['direct_event_glows_times'] = np.array([])
-            self.data['direct_event_pulse_lengths'] = np.array([])
+            self.data["direct_event_glows_times"] = np.array([])
+            self.data["direct_event_pulse_lengths"] = np.array([])
 
     def save_data_to_file(self):
         """
@@ -145,49 +188,56 @@ def _save_data_to_json_file(self):
         """
         # define formats for printing values to JSON file
         fmt = {
-            'arrays': {
-                'direct_event_glows_times': '%.7f',
-                'direct_event_pulse_lengths': '%d',
+            "arrays": {
+                "direct_event_glows_times": "%.7f",
+                "direct_event_pulse_lengths": "%d",
             },
-            'scalar variables': {
-                'glows_time_last_pps': '%.7f',
-                'imap_time_last_pps': '%.7f',
-                'imap_time_next_pps': '%.7f',
-                'filter_temperature': '%.2f',
-                'hv_voltage': '%.1f',
-                'spin_period': '%.7f',
-                'spin_phase_at_next_pps': '%.7f',
+            "scalar variables": {
+                "glows_time_last_pps": "%.7f",
+                "imap_time_last_pps": "%.7f",
+                "imap_time_next_pps": "%.7f",
+                "filter_temperature": "%.2f",
+                "hv_voltage": "%.1f",
+                "spin_period": "%.7f",
+                "spin_phase_at_next_pps": "%.7f",
             },
         }
 
         # create local copy for printing purposes
         data = copy.deepcopy(self.data)
 
-        for key in fmt['arrays'].keys():
-            if type(data[key]) == np.ndarray: # check if we have more than one direct event
-                data[key] = '[' + ', '.join(map(lambda x: fmt['arrays'][key] % x, list(data[key]))) + ']'
+        for key in fmt["arrays"].keys():
+            if (
+                type(data[key]) == np.ndarray
+            ):  # check if we have more than one direct event
+                data[key] = (
+                    "["
+                    + ", ".join(map(lambda x: fmt["arrays"][key] % x, list(data[key])))
+                    + "]"
+                )
             else:
-                data[key] = '[' + fmt['arrays'][key] % data[key] + ']'
-        for key in fmt['scalar variables'].keys():
-            data[key] = '|'+ fmt['scalar variables'][key] % data[key] +'|'
+                data[key] = "[" + fmt["arrays"][key] % data[key] + "]"
+        for key in fmt["scalar variables"].keys():
+            data[key] = "|" + fmt["scalar variables"][key] % data[key] + "|"
 
         # generate JSON content to be saved in output file
         json_content = json.dumps(data, indent=4, default=vars)
 
         # some corrections to get numbers back
-        json_content = json_content.replace('"[', '[').replace(']"', ']')
-        json_content = json_content.replace('|"', '').replace('"|', '')
-        #json_content = json_content.replace('\\"', '"')
+        json_content = json_content.replace('"[', "[").replace(']"', "]")
+        json_content = json_content.replace('|"', "").replace('"|', "")
+        # json_content = json_content.replace('\\"', '"')
 
         # start_time_utc string for output file name, based on unique_block_identifier
-        start_time_utc = ''.join(filter(str.isdigit, self.data['unique_identifier']))
+        start_time_utc = "".join(filter(str.isdigit, self.data["unique_identifier"]))
 
         # the output file name is constructed using a convention defined in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        file_name = 'data_l1b_direct_events/imap_glows_l1b_%s_orbX_modX_p_v00.json' % \
-                    (start_time_utc)
+        file_name = "data_l1b_direct_events/imap_glows_l1b_%s_orbX_modX_p_v00.json" % (
+            start_time_utc
+        )
 
-        file_handler = open(file_name, 'w')
+        file_handler = open(file_name, "w")
         print(json_content, file=file_handler)
         file_handler.close()
 
@@ -197,10 +247,13 @@ def set_times(self, l1a_data):
         Args:
             l1a_data: L1a data provided in the form of data field of L1aDataDirectEvents object
         """
-        self.data['imap_time_last_pps'] = \
-            time_sec_subsec_to_float64(l1a_data['data_every_second']['imap_sclk_last_pps'], 0)
-        self.data['imap_time_next_pps'] = \
-            time_sec_subsec_to_float64(l1a_data['data_every_second']['imap_sclk_next_pps'], 0)
-        self.data['glows_time_last_pps'] = \
-            time_sec_subsec_to_float64(l1a_data['data_every_second']['glows_sclk_last_pps'],
-                                       l1a_data['data_every_second']['glows_ssclk_last_pps'])
+        self.data["imap_time_last_pps"] = time_sec_subsec_to_float64(
+            l1a_data["data_every_second"]["imap_sclk_last_pps"], 0
+        )
+        self.data["imap_time_next_pps"] = time_sec_subsec_to_float64(
+            l1a_data["data_every_second"]["imap_sclk_next_pps"], 0
+        )
+        self.data["glows_time_last_pps"] = time_sec_subsec_to_float64(
+            l1a_data["data_every_second"]["glows_sclk_last_pps"],
+            l1a_data["data_every_second"]["glows_ssclk_last_pps"],
+        )
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_histogram.py b/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_histogram.py
index e20ff9a5e..0443684bb 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_histogram.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l1b_data_histogram.py
@@ -1,19 +1,32 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
-import json
+
 import copy
-import sys
+import json
+
 import numpy as np
 import pandas as pd
-from astropy.coordinates import SkyCoord
 from astropy import units as u
-from .constants import VERSION, SCAN_CIRCLE_ANGULAR_RADIUS, L1B_L2_HIST_FLAG_FIELDS, MAIN_TIME_FIELD
-from .funcs import time_sec_subsec_to_utc, time_sec_subsec_to_float64, flags_deserialize, \
-    decode_ancillary_parameters_avg_stddev, check_if_contains_actual_data
+from astropy.coordinates import SkyCoord
+
+from .constants import (
+    L1B_L2_HIST_FLAG_FIELDS,
+    MAIN_TIME_FIELD,
+    SCAN_CIRCLE_ANGULAR_RADIUS,
+    VERSION,
+)
+from .funcs import (
+    check_if_contains_actual_data,
+    decode_ancillary_parameters_avg_stddev,
+    flags_deserialize,
+    time_sec_subsec_to_float64,
+    time_sec_subsec_to_utc,
+)
 from .geom_fun_libr_box import spin2lb
 
-class L1bDataHistogram():
+
+class L1bDataHistogram:
     """
     L1bDataHistogram() class for GLOWS-L1b-histogram data
     """
@@ -28,18 +41,24 @@ def __init__(self):
         self.block_start_time = None
         self.block_end_time = None
 
-    def _block_header_add_list_of_anc_files(self, anc_input_from_sdc, anc_input_from_instr_team):
+    def _block_header_add_list_of_anc_files(
+        self, anc_input_from_sdc, anc_input_from_instr_team
+    ):
         """
         Add list of used ancillary files to block header
         Args:
             anc_input_from_sdc: ancillary input provided by IMAP SDC (general telemetry)
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
         """
-        self.data['block_header']['ancillary_data_files'] = []
+        self.data["block_header"]["ancillary_data_files"] = []
         for key in anc_input_from_instr_team:
-            self.data['block_header']['ancillary_data_files'].append(anc_input_from_instr_team[key])
+            self.data["block_header"]["ancillary_data_files"].append(
+                anc_input_from_instr_team[key]
+            )
         for key in anc_input_from_sdc:
-            self.data['block_header']['ancillary_data_files'].append(anc_input_from_sdc[key])
+            self.data["block_header"]["ancillary_data_files"].append(
+                anc_input_from_sdc[key]
+            )
 
     def _compute_spin_period_ground_estimate(self, anc_data):
         """
@@ -51,13 +70,16 @@ def _compute_spin_period_ground_estimate(self, anc_data):
             anc_data: Pandas Dataframe with ancillary data from CSV file
         """
         # extract data frames: last before block start and first after block end
-        df_1 = anc_data.loc[anc_data['imap_time'] <= self.block_start_time].iloc[-1, :]
-        df_2 = anc_data.loc[anc_data['imap_time'] >= self.block_end_time].iloc[0, :]
+        df_1 = anc_data.loc[anc_data["imap_time"] <= self.block_start_time].iloc[-1, :]
+        df_2 = anc_data.loc[anc_data["imap_time"] >= self.block_end_time].iloc[0, :]
 
         # generate synthetic series with spin-period data (assumed to arrive every 0.1 s)
         time = np.arange(self.block_start_time, self.block_end_time, 0.1)
-        data = np.interp(time, [df_1['imap_time'], df_2['imap_time']],
-                         [df_1['spin_period'], df_2['spin_period']])
+        data = np.interp(
+            time,
+            [df_1["imap_time"], df_2["imap_time"]],
+            [df_1["spin_period"], df_2["spin_period"]],
+        )
 
         # compute spin-period average and standard deviation over block
         average = np.mean(data)
@@ -74,13 +96,16 @@ def _compute_position_angle(self, anc_data):
             anc_data: Pandas Dataframe with ancillary data from CSV file
         """
         # extract data frames: last before block start and first after block end
-        df_1 = anc_data.loc[anc_data['imap_time'] <= self.block_start_time].iloc[-1, :]
-        df_2 = anc_data.loc[anc_data['imap_time'] >= self.block_end_time].iloc[0, :]
+        df_1 = anc_data.loc[anc_data["imap_time"] <= self.block_start_time].iloc[-1, :]
+        df_2 = anc_data.loc[anc_data["imap_time"] >= self.block_end_time].iloc[0, :]
 
         # generate synthetic series with position-angle data (assumed to arrive every 0.1 s)
         time = np.arange(self.block_start_time, self.block_end_time, 0.1)
-        data = np.interp(time, [df_1['imap_time'], df_2['imap_time']],
-                         [df_1['position_angle'], df_2['position_angle']])
+        data = np.interp(
+            time,
+            [df_1["imap_time"], df_2["imap_time"]],
+            [df_1["position_angle"], df_2["position_angle"]],
+        )
 
         # compute position-angle average and standard deviation over block
         average = np.mean(data)
@@ -97,23 +122,31 @@ def _compute_spin_axis_orientation(self, anc_data):
             anc_data: Pandas Dataframe with ancillary data from CSV file
         """
         # extract data frames: last before block start and first after block end
-        df_1 = anc_data.loc[anc_data['imap_time'] <= self.block_start_time].iloc[-1, :]
-        df_2 = anc_data.loc[anc_data['imap_time'] >= self.block_end_time].iloc[0, :]
+        df_1 = anc_data.loc[anc_data["imap_time"] <= self.block_start_time].iloc[-1, :]
+        df_2 = anc_data.loc[anc_data["imap_time"] >= self.block_end_time].iloc[0, :]
 
         # generate synthetic series with spin-axis-orientation data (assumed to arrive every 0.1 s)
         time = np.arange(self.block_start_time, self.block_end_time, 0.1)
-        data_lon = np.interp(time, [df_1['imap_time'], df_2['imap_time']],
-                             [df_1['spin_ax_lon'], df_2['spin_ax_lon']])
-        data_lat = np.interp(time, [df_1['imap_time'], df_2['imap_time']],
-                             [df_1['spin_ax_lat'], df_2['spin_ax_lat']])
+        data_lon = np.interp(
+            time,
+            [df_1["imap_time"], df_2["imap_time"]],
+            [df_1["spin_ax_lon"], df_2["spin_ax_lon"]],
+        )
+        data_lat = np.interp(
+            time,
+            [df_1["imap_time"], df_2["imap_time"]],
+            [df_1["spin_ax_lat"], df_2["spin_ax_lat"]],
+        )
 
         # compute spin-axis-orientation average and standard deviation over block
         average_lon = np.mean(data_lon)
         std_deviation_lon = np.std(data_lon)
         average_lat = np.mean(data_lat)
         std_deviation_lat = np.std(data_lat)
-        return {'lon': average_lon, 'lat': average_lat}, \
-               {'lon': std_deviation_lon, 'lat': std_deviation_lat}
+        return {"lon": average_lon, "lat": average_lat}, {
+            "lon": std_deviation_lon,
+            "lat": std_deviation_lat,
+        }
 
     def _compute_spacecraft_location(self, anc_data):
         """
@@ -125,20 +158,29 @@ def _compute_spacecraft_location(self, anc_data):
             anc_data: Pandas Dataframe with ancillary data from CSV file
         """
         # extract data frames: last before block start and first after block end
-        df_1 = anc_data.loc[anc_data['imap_time'] <= self.block_start_time].iloc[-1, :]
-        df_2 = anc_data.loc[anc_data['imap_time'] >= self.block_end_time].iloc[0, :]
+        df_1 = anc_data.loc[anc_data["imap_time"] <= self.block_start_time].iloc[-1, :]
+        df_2 = anc_data.loc[anc_data["imap_time"] >= self.block_end_time].iloc[0, :]
 
         # generate synthetic series with spacecraft location data (assumed to arrive every 0.1 s)
         time = np.arange(self.block_start_time, self.block_end_time, 0.1)
-        data_x = np.interp(time, [df_1['imap_time'], df_2['imap_time']], [df_1['x'], df_2['x']])
-        data_y = np.interp(time, [df_1['imap_time'], df_2['imap_time']], [df_1['y'], df_2['y']])
-        data_z = np.interp(time, [df_1['imap_time'], df_2['imap_time']], [df_1['z'], df_2['z']])
+        data_x = np.interp(
+            time, [df_1["imap_time"], df_2["imap_time"]], [df_1["x"], df_2["x"]]
+        )
+        data_y = np.interp(
+            time, [df_1["imap_time"], df_2["imap_time"]], [df_1["y"], df_2["y"]]
+        )
+        data_z = np.interp(
+            time, [df_1["imap_time"], df_2["imap_time"]], [df_1["z"], df_2["z"]]
+        )
 
         # compute spacecraft-location average and standard deviation over block
         average = [np.mean(data_x), np.mean(data_y), np.mean(data_z)]
         std_deviation = [np.std(data_x), np.std(data_y), np.std(data_z)]
-        return {'x': average[0], 'y': average[1], 'z': average[2]}, \
-               {'x': std_deviation[0], 'y': std_deviation[1], 'z': std_deviation[2]}
+        return {"x": average[0], "y": average[1], "z": average[2]}, {
+            "x": std_deviation[0],
+            "y": std_deviation[1],
+            "z": std_deviation[2],
+        }
 
     def _compute_spacecraft_velocity(self, anc_data):
         """
@@ -150,41 +192,56 @@ def _compute_spacecraft_velocity(self, anc_data):
             anc_data: Pandas Dataframe with ancillary data from CSV file
         """
         # extract data frames: last before block start and first after block end
-        df_1 = anc_data.loc[anc_data['imap_time'] <= self.block_start_time].iloc[-1, :]
-        df_2 = anc_data.loc[anc_data['imap_time'] >= self.block_end_time].iloc[0, :]
+        df_1 = anc_data.loc[anc_data["imap_time"] <= self.block_start_time].iloc[-1, :]
+        df_2 = anc_data.loc[anc_data["imap_time"] >= self.block_end_time].iloc[0, :]
 
         # generate synthetic series with spacecraft velocity data (assumed to arrive every 0.1 s)
         time = np.arange(self.block_start_time, self.block_end_time, 0.1)
-        data_vx = np.interp(time, [df_1['imap_time'], df_2['imap_time']], [df_1['vx'], df_2['vx']])
-        data_vy = np.interp(time, [df_1['imap_time'], df_2['imap_time']], [df_1['vy'], df_2['vy']])
-        data_vz = np.interp(time, [df_1['imap_time'], df_2['imap_time']], [df_1['vz'], df_2['vz']])
+        data_vx = np.interp(
+            time, [df_1["imap_time"], df_2["imap_time"]], [df_1["vx"], df_2["vx"]]
+        )
+        data_vy = np.interp(
+            time, [df_1["imap_time"], df_2["imap_time"]], [df_1["vy"], df_2["vy"]]
+        )
+        data_vz = np.interp(
+            time, [df_1["imap_time"], df_2["imap_time"]], [df_1["vz"], df_2["vz"]]
+        )
 
         # compute spacecraft-velocity average and standard deviation over block
         average = [np.mean(data_vx), np.mean(data_vy), np.mean(data_vz)]
         std_deviation = [np.std(data_vx), np.std(data_vy), np.std(data_vz)]
-        return {'x': average[0], 'y': average[1], 'z': average[2]}, \
-               {'x': std_deviation[0], 'y': std_deviation[1], 'z': std_deviation[2]}
+        return {"x": average[0], "y": average[1], "z": average[2]}, {
+            "x": std_deviation[0],
+            "y": std_deviation[1],
+            "z": std_deviation[2],
+        }
 
     def _compute_histogram_bins_ecl_coords(self):
         """
         Compute ecliptic longitude and latitude for histogram bin centers
         """
-        n_bins = self.data['number_of_bins_per_histogram'] # just for convenience
+        n_bins = self.data["number_of_bins_per_histogram"]  # just for convenience
 
         # generate spin angle values (for histogram bin centers) measured from the northernmost
         # point of the scanning circle
-        spin_angle_from_north = (self.data['imap_spin_angle_bin_cntr'] - \
-                                 self.data['position_angle_offset_average'] + \
-                                 360.0) % 360.0
+        spin_angle_from_north = (
+            self.data["imap_spin_angle_bin_cntr"]
+            - self.data["position_angle_offset_average"]
+            + 360.0
+        ) % 360.0
 
         # compute ecliptic longitude and latitude of histogram bin centers
         histogram_bins_ecl_coords = spin2lb(
-            np.radians(self.data['spin_axis_orientation_average']['lon'])*np.ones(n_bins, float),
-            np.radians(self.data['spin_axis_orientation_average']['lat'])*np.ones(n_bins, float),
-            np.radians(spin_angle_from_north), 0.0,
-            np.radians(SCAN_CIRCLE_ANGULAR_RADIUS)*np.ones(n_bins, float))
+            np.radians(self.data["spin_axis_orientation_average"]["lon"])
+            * np.ones(n_bins, float),
+            np.radians(self.data["spin_axis_orientation_average"]["lat"])
+            * np.ones(n_bins, float),
+            np.radians(spin_angle_from_north),
+            0.0,
+            np.radians(SCAN_CIRCLE_ANGULAR_RADIUS) * np.ones(n_bins, float),
+        )
 
-        return np.degrees(histogram_bins_ecl_coords) # in deg units
+        return np.degrees(histogram_bins_ecl_coords)  # in deg units
 
     def _convert_ancillary_parameters(self, l1a_data, anc_input_from_instr_team):
         """
@@ -196,21 +253,38 @@ def _convert_ancillary_parameters(self, l1a_data, anc_input_from_instr_team):
         """
 
         # load conversion/decoding table for ancillary parameters collected onboard
-        file_handler = open(anc_input_from_instr_team['conversion_table_for_anc_data'], 'r')
+        file_handler = open(
+            anc_input_from_instr_team["conversion_table_for_anc_data"], "r"
+        )
         conversion_table = json.load(file_handler)
         file_handler.close()
 
         # ancillary parameters collected onboard that need to be decoded
-        self.data['filter_temperature_average'], self.data['filter_temperature_std_dev'] = \
-            decode_ancillary_parameters_avg_stddev('filter_temperature', conversion_table, l1a_data)
-        self.data['hv_voltage_average'], self.data['hv_voltage_std_dev'] = \
-            decode_ancillary_parameters_avg_stddev('hv_voltage', conversion_table, l1a_data)
-        self.data['spin_period_average'], self.data['spin_period_std_dev'] = \
-            decode_ancillary_parameters_avg_stddev('spin_period', conversion_table, l1a_data)
-        self.data['pulse_length_average'], self.data['pulse_length_std_dev'] = \
-            decode_ancillary_parameters_avg_stddev('pulse_length', conversion_table, l1a_data)
-
-    def create_l1b_from_l1a_data(self, l1a_data, anc_input_from_sdc, anc_input_from_instr_team):
+        (
+            self.data["filter_temperature_average"],
+            self.data["filter_temperature_std_dev"],
+        ) = decode_ancillary_parameters_avg_stddev(
+            "filter_temperature", conversion_table, l1a_data
+        )
+        self.data["hv_voltage_average"], self.data["hv_voltage_std_dev"] = (
+            decode_ancillary_parameters_avg_stddev(
+                "hv_voltage", conversion_table, l1a_data
+            )
+        )
+        self.data["spin_period_average"], self.data["spin_period_std_dev"] = (
+            decode_ancillary_parameters_avg_stddev(
+                "spin_period", conversion_table, l1a_data
+            )
+        )
+        self.data["pulse_length_average"], self.data["pulse_length_std_dev"] = (
+            decode_ancillary_parameters_avg_stddev(
+                "pulse_length", conversion_table, l1a_data
+            )
+        )
+
+    def create_l1b_from_l1a_data(
+        self, l1a_data, anc_input_from_sdc, anc_input_from_instr_team
+    ):
         """
         Sets values of L1b fields using L1a data read from a file
         Args:
@@ -218,51 +292,66 @@ def create_l1b_from_l1a_data(self, l1a_data, anc_input_from_sdc, anc_input_from_
             anc_input_from_sdc: ancillary input provided by IMAP SDC (general telemetry)
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
         """
-        self.data['block_header'] = {
-            'flight_software_version': l1a_data['block_header']['flight_software_version'],
-            'ground_software_version': VERSION,
-            'pkts_file_name': l1a_data['block_header']['pkts_file_name'],
-            'seq_count_in_pkts_file': l1a_data['block_header']['seq_count_in_pkts_file'],
-            'l1a_file_name': l1a_data['l1a_file_name']
+        self.data["block_header"] = {
+            "flight_software_version": l1a_data["block_header"][
+                "flight_software_version"
+            ],
+            "ground_software_version": VERSION,
+            "pkts_file_name": l1a_data["block_header"]["pkts_file_name"],
+            "seq_count_in_pkts_file": l1a_data["block_header"][
+                "seq_count_in_pkts_file"
+            ],
+            "l1a_file_name": l1a_data["l1a_file_name"],
         }
 
         # add list of used ancillary files to block_header
-        self._block_header_add_list_of_anc_files(anc_input_from_sdc, anc_input_from_instr_team)
+        self._block_header_add_list_of_anc_files(
+            anc_input_from_sdc, anc_input_from_instr_team
+        )
 
-        self.data['unique_block_identifier'] = \
-            time_sec_subsec_to_utc(l1a_data[MAIN_TIME_FIELD]['seconds'],
-                                   l1a_data[MAIN_TIME_FIELD]['subseconds']
-                                  ).strftime('%Y-%m-%dT%H:%M:%S') # TBC: cmp with l1a identifier
+        self.data["unique_block_identifier"] = time_sec_subsec_to_utc(
+            l1a_data[MAIN_TIME_FIELD]["seconds"],
+            l1a_data[MAIN_TIME_FIELD]["subseconds"],
+        ).strftime("%Y-%m-%dT%H:%M:%S")  # TBC: cmp with l1a identifier
 
         # set IMAP and GLOWS time fields
         self.set_times(l1a_data)
 
         # set histogram-related quantities
-        self.data['number_of_spins_per_block'] = l1a_data['number_of_spins_per_block']
-        self.data['number_of_bins_per_histogram'] = l1a_data['number_of_bins_per_histogram']
+        self.data["number_of_spins_per_block"] = l1a_data["number_of_spins_per_block"]
+        self.data["number_of_bins_per_histogram"] = l1a_data[
+            "number_of_bins_per_histogram"
+        ]
 
         # set histogram
-        self.data['histogram'] = l1a_data['histogram']
-        self.data['number_of_events'] = l1a_data['number_of_events']
+        self.data["histogram"] = l1a_data["histogram"]
+        self.data["number_of_events"] = l1a_data["number_of_events"]
 
         # set spin angle values for bin centers
-        spin_offset_first_bin = 0.5*360.0/self.data['number_of_bins_per_histogram']
-        self.data['imap_spin_angle_bin_cntr'] = np.array( \
-            np.array(range(self.data['number_of_bins_per_histogram']))*360.0/ \
-            self.data['number_of_bins_per_histogram'] + spin_offset_first_bin).tolist()
+        spin_offset_first_bin = 0.5 * 360.0 / self.data["number_of_bins_per_histogram"]
+        self.data["imap_spin_angle_bin_cntr"] = np.array(
+            np.array(range(self.data["number_of_bins_per_histogram"]))
+            * 360.0
+            / self.data["number_of_bins_per_histogram"]
+            + spin_offset_first_bin
+        ).tolist()
 
         # this is to make the key 'histogram_flag_array' to be seen immediately after 'histogram'
-        self.data['histogram_flag_array'] = None
+        self.data["histogram_flag_array"] = None
 
         # dictionary for some additional debugging data
-        self.data['debugging_data'] = {}
+        self.data["debugging_data"] = {}
 
         # set block_start_time and block_end_time for funcs invoked in process_anc_input_from_sdc()
         self.block_start_time = self.data[MAIN_TIME_FIELD]
-        if MAIN_TIME_FIELD == 'imap_start_time':
-            self.block_end_time = self.block_start_time + self.data['imap_end_time_offset']
-        if MAIN_TIME_FIELD == 'glows_start_time':
-            self.block_end_time = self.block_start_time + self.data['glows_end_time_offset']
+        if MAIN_TIME_FIELD == "imap_start_time":
+            self.block_end_time = (
+                self.block_start_time + self.data["imap_end_time_offset"]
+            )
+        if MAIN_TIME_FIELD == "glows_start_time":
+            self.block_end_time = (
+                self.block_start_time + self.data["glows_end_time_offset"]
+            )
 
         # convert ancillary parameters collected onboard to physical units
         self._convert_ancillary_parameters(l1a_data, anc_input_from_instr_team)
@@ -271,26 +360,28 @@ def create_l1b_from_l1a_data(self, l1a_data, anc_input_from_sdc, anc_input_from_
         self._process_anc_input_from_sdc(anc_input_from_sdc)
 
         # deserialize flags generated onboard and save them as self.data['flags'] dictionary
-        self.data['flags'] = flags_deserialize(l1a_data['flags']['flags_set_onboard'])
+        self.data["flags"] = flags_deserialize(l1a_data["flags"]["flags_set_onboard"])
 
         # add ground-processing flags to self.data['flags']
-        for key in l1a_data['flags']:
-            if key != 'flags_set_onboard':
-                self.data['flags'][key] = l1a_data['flags'][key]
+        for key in l1a_data["flags"]:
+            if key != "flags_set_onboard":
+                self.data["flags"][key] = l1a_data["flags"][key]
 
         # initialize histogram bin flags
-        self.data['histogram_flag_array'] = np.zeros_like(self.data['histogram'], np.int16)
+        self.data["histogram_flag_array"] = np.zeros_like(
+            self.data["histogram"], np.int16
+        )
 
         # apply filters defined by the IMAP/GLOWS Instrument Team
         self._filters(anc_input_from_instr_team)
 
         # move 'debuging_data' dictionary to the end of self.data dictionary
-        aux = self.data['debugging_data']
-        self.data.pop('debugging_data')
-        self.data['debugging_data'] = aux
+        aux = self.data["debugging_data"]
+        self.data.pop("debugging_data")
+        self.data["debugging_data"] = aux
 
         # uncomment the line below to remove 'debugging_data' from JSON
-        del self.data['debugging_data']
+        del self.data["debugging_data"]
 
     def _filters(self, anc_input_from_instr_team):
         """
@@ -299,7 +390,7 @@ def _filters(self, anc_input_from_instr_team):
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
         """
         # read filter settings
-        file_handler = open(anc_input_from_instr_team['settings'], 'r')
+        file_handler = open(anc_input_from_instr_team["settings"], "r")
         settings = json.load(file_handler)
         file_handler.close()
 
@@ -310,49 +401,61 @@ def _filters(self, anc_input_from_instr_team):
         self._set_histogram_flag_array_excl_regions(anc_input_from_instr_team, settings)
 
         # set excluded-by-instrument-team bit in histogram flag array
-        self._set_histogram_flag_array_by_instr_team(anc_input_from_instr_team, 'exclusions_by_instr_team')
+        self._set_histogram_flag_array_by_instr_team(
+            anc_input_from_instr_team, "exclusions_by_instr_team"
+        )
 
         # set suspected_transient bit in histogram flag array
-        self._set_histogram_flag_array_by_instr_team(anc_input_from_instr_team, 'suspected_transients')
+        self._set_histogram_flag_array_by_instr_team(
+            anc_input_from_instr_team, "suspected_transients"
+        )
 
         # apply filter based on daily statistical error and set related flag
         self._flag_based_on_daily_statistical_error(settings)
 
         # apply filter based on filter-temperature spread and set related flag
-        self._flag_compare_with_threshold('filter_temperature_std_dev',
-                                          settings['filter_based_on_temperature_std_dev']
-                                          ['std_dev_threshold__celsius_deg'],
-                                          'is_temperature_std_dev_beyond_threshold')
+        self._flag_compare_with_threshold(
+            "filter_temperature_std_dev",
+            settings["filter_based_on_temperature_std_dev"][
+                "std_dev_threshold__celsius_deg"
+            ],
+            "is_temperature_std_dev_beyond_threshold",
+        )
 
         # apply filter based on HV-voltage spread and set related flag
-        self._flag_compare_with_threshold('hv_voltage_std_dev',
-                                          settings['filter_based_on_hv_voltage_std_dev']
-                                          ['std_dev_threshold__volt'],
-                                          'is_hv_voltage_std_dev_beyond_threshold')
+        self._flag_compare_with_threshold(
+            "hv_voltage_std_dev",
+            settings["filter_based_on_hv_voltage_std_dev"]["std_dev_threshold__volt"],
+            "is_hv_voltage_std_dev_beyond_threshold",
+        )
 
         # apply filter based on spin-period spread and set related flag
-        self._flag_compare_with_threshold('spin_period_std_dev',
-                                          settings['filter_based_on_spin_period_std_dev']
-                                          ['std_dev_threshold__sec'],
-                                          'is_spin_period_std_dev_beyond_threshold')
+        self._flag_compare_with_threshold(
+            "spin_period_std_dev",
+            settings["filter_based_on_spin_period_std_dev"]["std_dev_threshold__sec"],
+            "is_spin_period_std_dev_beyond_threshold",
+        )
 
         # apply filter based on pulse-length spread and set related flag
-        self._flag_compare_with_threshold('pulse_length_std_dev',
-                                          settings['filter_based_on_pulse_length_std_dev']
-                                          ['std_dev_threshold__usec'],
-                                          'is_pulse_length_std_dev_beyond_threshold')
+        self._flag_compare_with_threshold(
+            "pulse_length_std_dev",
+            settings["filter_based_on_pulse_length_std_dev"]["std_dev_threshold__usec"],
+            "is_pulse_length_std_dev_beyond_threshold",
+        )
 
         # apply filter based on relative difference between onboard and ground spin periods
         self._flag_relative_difference_vs_threshold(
-            'spin_period_average',
-            'spin_period_ground_average',
-            settings['filter_based_on_comparison_of_spin_periods']
-            ['relative_difference_threshold'],
-            'is_spin_period_difference_beyond_threshold')
+            "spin_period_average",
+            "spin_period_ground_average",
+            settings["filter_based_on_comparison_of_spin_periods"][
+                "relative_difference_threshold"
+            ],
+            "is_spin_period_difference_beyond_threshold",
+        )
 
         # set a fancy pattern 0x5555 in histogram_flag_array
         # this is for testing purposes, uncomment only if know exactly what you are doing
-        #self._set_histogram_flag_array_test_pattern()
+        # self._set_histogram_flag_array_test_pattern()
 
     def _flag_based_on_daily_statistical_error(self, settings):
         """
@@ -361,25 +464,37 @@ def _flag_based_on_daily_statistical_error(self, settings):
         Args:
             settings: filter settings from a file provided by IMAP/GLOWS Instrument Team
         """
-        n_lo = settings['filter_based_on_daily_statistical_error']['n_sigma_threshold_lower']
-        n_up = settings['filter_based_on_daily_statistical_error']['n_sigma_threshold_upper']
+        n_lo = settings["filter_based_on_daily_statistical_error"][
+            "n_sigma_threshold_lower"
+        ]
+        n_up = settings["filter_based_on_daily_statistical_error"][
+            "n_sigma_threshold_upper"
+        ]
         if n_lo < 0 or n_up < 0:
             # negative value means that filter is turned off, so we can immediately set the flag
-            self.data['flags']['is_beyond_daily_statistical_error'] = False
+            self.data["flags"]["is_beyond_daily_statistical_error"] = False
         else:
             # filter is turned on, so we need to compare the total number of counts in the current
             # histogram with thresholds and set the flag accordingly
-            total_counts = np.sum(self.data['histogram'])
-            self.data['debugging_data']['total_counts_in_histogram'] = int(total_counts)
-            total_counts_daily_average, total_counts_daily_std_dev = \
+            total_counts = np.sum(self.data["histogram"])
+            self.data["debugging_data"]["total_counts_in_histogram"] = int(total_counts)
+            total_counts_daily_average, total_counts_daily_std_dev = (
                 self.total_counts_daily_statistics()
-            self.data['debugging_data']['total_counts_daily_average'] = total_counts_daily_average
-            self.data['debugging_data']['total_counts_daily_std_dev'] = total_counts_daily_std_dev
-            if total_counts_daily_average - n_lo*total_counts_daily_std_dev <= total_counts <= \
-               total_counts_daily_average + n_up*total_counts_daily_std_dev:
-                self.data['flags']['is_beyond_daily_statistical_error'] = False
+            )
+            self.data["debugging_data"]["total_counts_daily_average"] = (
+                total_counts_daily_average
+            )
+            self.data["debugging_data"]["total_counts_daily_std_dev"] = (
+                total_counts_daily_std_dev
+            )
+            if (
+                total_counts_daily_average - n_lo * total_counts_daily_std_dev
+                <= total_counts
+                <= total_counts_daily_average + n_up * total_counts_daily_std_dev
+            ):
+                self.data["flags"]["is_beyond_daily_statistical_error"] = False
             else:
-                self.data['flags']['is_beyond_daily_statistical_error'] = True
+                self.data["flags"]["is_beyond_daily_statistical_error"] = True
 
     def _flag_compare_with_threshold(self, parameter_name, threshold, flag_name):
         """
@@ -391,17 +506,18 @@ def _flag_compare_with_threshold(self, parameter_name, threshold, flag_name):
         """
         if threshold < 0.0:
             # negative value means that filter is turned off, so we can immediately set the flag
-            self.data['flags'][flag_name] = False
+            self.data["flags"][flag_name] = False
         else:
             # filter is turned on, so we need to compare the parameter value in the current
             # histogram with threshold and set the flag accordingly
             if self.data[parameter_name] < threshold:
-                self.data['flags'][flag_name] = False
+                self.data["flags"][flag_name] = False
             else:
-                self.data['flags'][flag_name] = True
+                self.data["flags"][flag_name] = True
 
-    def _flag_relative_difference_vs_threshold(self, parameter_1_name, parameter_2_name, threshold,
-                                               flag_name):
+    def _flag_relative_difference_vs_threshold(
+        self, parameter_1_name, parameter_2_name, threshold, flag_name
+    ):
         """
         Set flag based on comparison of relative difference of two parameters vs. threshold,
             set True if beyond threshold
@@ -413,19 +529,20 @@ def _flag_relative_difference_vs_threshold(self, parameter_1_name, parameter_2_n
         """
         if threshold < 0.0:
             # negative value means that filter is turned off, so we can immediately set the flag
-            self.data['flags'][flag_name] = False
+            self.data["flags"][flag_name] = False
         else:
             # filter is turned on, so we need to compare the relative difference in the current
             # histogram with threshold and set the flag accordingly
-            relative_difference = np.fabs(self.data[parameter_1_name] - \
-                                          self.data[parameter_2_name])/ \
-                                          (self.data[parameter_1_name] + \
-                                           self.data[parameter_2_name])
-            self.data['debugging_data']['spin_period_relative_difference'] = relative_difference
+            relative_difference = np.fabs(
+                self.data[parameter_1_name] - self.data[parameter_2_name]
+            ) / (self.data[parameter_1_name] + self.data[parameter_2_name])
+            self.data["debugging_data"]["spin_period_relative_difference"] = (
+                relative_difference
+            )
             if relative_difference < threshold:
-                self.data['flags'][flag_name] = False
+                self.data["flags"][flag_name] = False
             else:
-                self.data['flags'][flag_name] = True
+                self.data["flags"][flag_name] = True
 
     def _process_anc_input_from_sdc(self, anc_input_from_sdc):
         """
@@ -436,7 +553,9 @@ def _process_anc_input_from_sdc(self, anc_input_from_sdc):
 
         # read ancillary variables provided by SDC (IMAP general telemetry)
         # note that finally different CSV files can be used for different quantities
-        anc_data = pd.read_csv(anc_input_from_sdc['anc_data_file_name'], sep=' ', comment="#")
+        anc_data = pd.read_csv(
+            anc_input_from_sdc["anc_data_file_name"], sep=" ", comment="#"
+        )
         # TMP_SOLUTION: we use here a csv file with mock ancillary data generated by
         # GLOWS Team using generate_anc_file.py script
         # the following procedures invoked below:
@@ -447,16 +566,26 @@ def _process_anc_input_from_sdc(self, anc_input_from_sdc):
         # e.g., it is expected that finally there will be several anc_data files instead
         # of one containing all required data
 
-        self.data['spin_period_ground_average'], self.data['spin_period_ground_std_dev'] = \
-            self._compute_spin_period_ground_estimate(anc_data)
-        self.data['position_angle_offset_average'], self.data['position_angle_offset_std_dev'] = \
-            self._compute_position_angle(anc_data)
-        self.data['spin_axis_orientation_average'], self.data['spin_axis_orientation_std_dev'] = \
-            self._compute_spin_axis_orientation(anc_data)
-        self.data['spacecraft_location_average'], self.data['spacecraft_location_std_dev'] = \
-            self._compute_spacecraft_location(anc_data)
-        self.data['spacecraft_velocity_average'], self.data['spacecraft_velocity_std_dev'] = \
-            self._compute_spacecraft_velocity(anc_data)
+        (
+            self.data["spin_period_ground_average"],
+            self.data["spin_period_ground_std_dev"],
+        ) = self._compute_spin_period_ground_estimate(anc_data)
+        (
+            self.data["position_angle_offset_average"],
+            self.data["position_angle_offset_std_dev"],
+        ) = self._compute_position_angle(anc_data)
+        (
+            self.data["spin_axis_orientation_average"],
+            self.data["spin_axis_orientation_std_dev"],
+        ) = self._compute_spin_axis_orientation(anc_data)
+        (
+            self.data["spacecraft_location_average"],
+            self.data["spacecraft_location_std_dev"],
+        ) = self._compute_spacecraft_location(anc_data)
+        (
+            self.data["spacecraft_velocity_average"],
+            self.data["spacecraft_velocity_std_dev"],
+        ) = self._compute_spacecraft_velocity(anc_data)
 
     def read_from_file(self, file_name):
         """
@@ -474,16 +603,17 @@ def _read_from_json_file(self, file_name):
         Args:
             file_name: name of of file with L1b data
         """
-        file_handler = open(file_name, 'r')
+        file_handler = open(file_name, "r")
         self.data = json.load(file_handler)
         file_handler.close()
 
         # hexadecimal values saved as strings require special treatment to make them integers
-        self.data['histogram_flag_array'] = \
-            [int(x, base=16) for x in self.data['histogram_flag_array']]
+        self.data["histogram_flag_array"] = [
+            int(x, base=16) for x in self.data["histogram_flag_array"]
+        ]
 
         # additional field to keep track where data come from
-        self.data['l1b_file_name'] = file_name
+        self.data["l1b_file_name"] = file_name
 
     def save_data_to_file(self):
         """
@@ -500,38 +630,38 @@ def _save_data_to_json_file(self):
         """
         # define formats for printing values to JSON file
         fmt = {
-            'arrays': {
-                'histogram': '%d',
-                'imap_spin_angle_bin_cntr': '%.2f',
+            "arrays": {
+                "histogram": "%d",
+                "imap_spin_angle_bin_cntr": "%.2f",
+            },
+            "scalar variables": {
+                "glows_start_time": "%.7f",
+                "glows_end_time_offset": "%.7f",
+                "imap_start_time": "%.7f",
+                "imap_end_time_offset": "%.7f",
+                "filter_temperature_average": "%.2f",
+                "filter_temperature_std_dev": "%.3e",
+                "hv_voltage_average": "%.1f",
+                "hv_voltage_std_dev": "%.3e",
+                "spin_period_average": "%.7f",
+                "spin_period_std_dev": "%.3e",
+                "spin_period_ground_average": "%.7f",
+                "spin_period_ground_std_dev": "%.3e",
+                "pulse_length_average": "%.3e",
+                "pulse_length_std_dev": "%.3e",
+                "position_angle_offset_average": "%.3f",
+                "position_angle_offset_std_dev": "%.3e",
             },
-            'scalar variables': {
-                'glows_start_time': '%.7f',
-                'glows_end_time_offset': '%.7f',
-                'imap_start_time': '%.7f',
-                'imap_end_time_offset': '%.7f',
-                'filter_temperature_average': '%.2f',
-                'filter_temperature_std_dev': '%.3e',
-                'hv_voltage_average': '%.1f',
-                'hv_voltage_std_dev': '%.3e',
-                'spin_period_average': '%.7f',
-                'spin_period_std_dev': '%.3e',
-                'spin_period_ground_average': '%.7f',
-                'spin_period_ground_std_dev': '%.3e',
-                'pulse_length_average': '%.3e',
-                'pulse_length_std_dev': '%.3e',
-                'position_angle_offset_average': '%.3f',
-                'position_angle_offset_std_dev': '%.3e'
+            "lon lat variables": {
+                "spin_axis_orientation_average": "%.3f",
+                "spin_axis_orientation_std_dev": "%.3e",
             },
-            'lon lat variables': {
-                'spin_axis_orientation_average': '%.3f',
-                'spin_axis_orientation_std_dev': '%.3e'
+            "vector variables": {
+                "spacecraft_location_average": "%.1f",
+                "spacecraft_location_std_dev": "%.3e",
+                "spacecraft_velocity_average": "%.3f",
+                "spacecraft_velocity_std_dev": "%.3e",
             },
-            'vector variables': {
-                'spacecraft_location_average': '%.1f',
-                'spacecraft_location_std_dev': '%.3e',
-                'spacecraft_velocity_average': '%.3f',
-                'spacecraft_velocity_std_dev': '%.3e'
-            }
         }
 
         # create local copy for printing purposes
@@ -541,68 +671,98 @@ def _save_data_to_json_file(self):
         # them to be saved as one-liners (i.e., without \n after each element) or we want them to
         # be printed with a specific format by json.dumps()
         # so we convert variables to strings as defined in fmt
-        for key in fmt['arrays'].keys():
-            data[key] = '[' + ', '.join(map(lambda x: fmt['arrays'][key] % x, list(data[key]))) + ']'
-        for key in fmt['scalar variables'].keys():
-            data[key] = '|'+ fmt['scalar variables'][key] % data[key] +'|'
-        for key in fmt['lon lat variables'].keys():
-            for sec_key in ['lon', 'lat']:
-                data[key][sec_key] = '|'+ fmt['lon lat variables'][key] % data[key][sec_key] +'|'
-        for key in fmt['vector variables'].keys():
-            for sec_key in ['x', 'y', 'z']:
-                data[key][sec_key] = '|'+ fmt['vector variables'][key] % data[key][sec_key] +'|'
+        for key in fmt["arrays"].keys():
+            data[key] = (
+                "["
+                + ", ".join(map(lambda x: fmt["arrays"][key] % x, list(data[key])))
+                + "]"
+            )
+        for key in fmt["scalar variables"].keys():
+            data[key] = "|" + fmt["scalar variables"][key] % data[key] + "|"
+        for key in fmt["lon lat variables"].keys():
+            for sec_key in ["lon", "lat"]:
+                data[key][sec_key] = (
+                    "|" + fmt["lon lat variables"][key] % data[key][sec_key] + "|"
+                )
+        for key in fmt["vector variables"].keys():
+            for sec_key in ["x", "y", "z"]:
+                data[key][sec_key] = (
+                    "|" + fmt["vector variables"][key] % data[key][sec_key] + "|"
+                )
 
         # histogram_flag_array requires special hexadecimal treatment
-        data['histogram_flag_array'] = '[' + \
-            ', '.join(map(lambda x: '"0x' + format(x, 'x').zfill(4) +'"',
-            list(data['histogram_flag_array']))) + ']'
+        data["histogram_flag_array"] = (
+            "["
+            + ", ".join(
+                map(
+                    lambda x: '"0x' + format(x, "x").zfill(4) + '"',
+                    list(data["histogram_flag_array"]),
+                )
+            )
+            + "]"
+        )
 
         # generate JSON content to be saved in output file
         json_content = json.dumps(data, indent=4, default=vars)
 
         # some corrections to get numbers back
-        json_content = json_content.replace('"[', '[').replace(']"', ']')
-        json_content = json_content.replace('|"', '').replace('"|', '')
+        json_content = json_content.replace('"[', "[").replace(']"', "]")
+        json_content = json_content.replace('|"', "").replace('"|', "")
         json_content = json_content.replace('\\"', '"')
 
-        if 1==2:
+        if 1 == 2:
             # some fields need to be temporarily converted to string arrays, because we want to force
             # them to be saved as one-liners (i.e., without \n after each element) by json.dumps()
-            spin_angle_tmp = self.data['imap_spin_angle_bin_cntr']
-            histogram_tmp = self.data['histogram']
-            flags_tmp = self.data['histogram_flag_array']
-            self.data['histogram'] = '[' + ', '.join(map(str, list(histogram_tmp))) + ']'
-            self.data['imap_spin_angle_bin_cntr'] = \
-                '[' + ', '.join(map(lambda x: '%.2f' % x, list(spin_angle_tmp))) + ']'
-            self.data['histogram_flag_array'] = '[' + \
-                ', '.join(map(lambda x: '"0x' + format(x, 'x').zfill(4) +'"', list(flags_tmp))) + ']'
+            spin_angle_tmp = self.data["imap_spin_angle_bin_cntr"]
+            histogram_tmp = self.data["histogram"]
+            flags_tmp = self.data["histogram_flag_array"]
+            self.data["histogram"] = (
+                "[" + ", ".join(map(str, list(histogram_tmp))) + "]"
+            )
+            self.data["imap_spin_angle_bin_cntr"] = (
+                "[" + ", ".join(map(lambda x: "%.2f" % x, list(spin_angle_tmp))) + "]"
+            )
+            self.data["histogram_flag_array"] = (
+                "["
+                + ", ".join(
+                    map(
+                        lambda x: '"0x' + format(x, "x").zfill(4) + '"', list(flags_tmp)
+                    )
+                )
+                + "]"
+            )
             json_content = json.dumps(self.data, indent=4, default=vars)
-            self.data['imap_spin_angle_bin_cntr'] = spin_angle_tmp
-            self.data['histogram'] = histogram_tmp
-            self.data['histogram_flag_array'] = flags_tmp
+            self.data["imap_spin_angle_bin_cntr"] = spin_angle_tmp
+            self.data["histogram"] = histogram_tmp
+            self.data["histogram_flag_array"] = flags_tmp
             # modify json_content to make string arrays be seen finally as number arrays
-            json_content = json_content.replace('"[', '[').replace(']"', ']')
+            json_content = json_content.replace('"[', "[").replace(']"', "]")
             json_content = json_content.replace('\\"', '"')
 
         # start_time_utc string for output file name, based on unique_block_identifier
-        start_time_utc = ''.join(filter(str.isdigit, self.data['unique_block_identifier']))
+        start_time_utc = "".join(
+            filter(str.isdigit, self.data["unique_block_identifier"])
+        )
 
         # the output file name is constructed using a convention defined in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        file_name = 'data_l1b_histograms/imap_glows_l1b_%s_orbX_modX_p_v00.json' % \
-                    (start_time_utc)
+        file_name = "data_l1b_histograms/imap_glows_l1b_%s_orbX_modX_p_v00.json" % (
+            start_time_utc
+        )
 
         # save data
-        file_handler = open(file_name, 'w')
+        file_handler = open(file_name, "w")
         print(json_content, file=file_handler)
         file_handler.close()
 
         # TO_BE_REMOVED append file name to l1b list needed for the l1b-to-l2 processing
-        #file_handler = open('l1b_list.dat', 'a')
-        #print(file_name, file=file_handler)
-        #file_handler.close()
+        # file_handler = open('l1b_list.dat', 'a')
+        # print(file_name, file=file_handler)
+        # file_handler.close()
 
-    def _set_histogram_flag_array_by_instr_team(self, anc_input_from_instr_team, type_of_list):
+    def _set_histogram_flag_array_by_instr_team(
+        self, anc_input_from_instr_team, type_of_list
+    ):
         """
         Set histogram flag array using lists defined by IMAP/GLOWS Instrument Team
             bin indexes in the list are marked by True
@@ -610,31 +770,40 @@ def _set_histogram_flag_array_by_instr_team(self, anc_input_from_instr_team, typ
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
             type_of_list: 'exclusions_by_instr_team' or 'suspected_transients'
         """
-        file_handler = open(anc_input_from_instr_team[type_of_list], 'r')
+        file_handler = open(anc_input_from_instr_team[type_of_list], "r")
         line = file_handler.readline()
         while line:
-            if line[0] != '#':
+            if line[0] != "#":
                 # sanity check
-                assert line[:2] == '20' and line[10] == 'T', \
-                    'Bad identifier ' + line[:20] + ' in ' + \
-                    anc_input_from_instr_team[type_of_list]
-                identifier = line.rstrip().split(' ')[0]
-                flags = line.rstrip().split(' ')[1]
+                assert line[:2] == "20" and line[10] == "T", (
+                    "Bad identifier "
+                    + line[:20]
+                    + " in "
+                    + anc_input_from_instr_team[type_of_list]
+                )
+                identifier = line.rstrip().split(" ")[0]
+                flags = line.rstrip().split(" ")[1]
 
                 # sanity check
                 # this check turned out to be inappropriate because number of bins is not always the same
-                #assert len(flags) == self.data['number_of_bins_per_histogram'], \
+                # assert len(flags) == self.data['number_of_bins_per_histogram'], \
                 #    'Bad length of mask_array for identifier ' + identifier + ' in ' + \
                 #    anc_input_from_instr_team[type_of_list]
 
-                if identifier == self.data['unique_block_identifier']:
+                if identifier == self.data["unique_block_identifier"]:
                     flags = np.array(list(flags)).astype(bool)
-#                    print(np.shape(flags))
-                    if type_of_list == 'exclusions_by_instr_team':
-                        flags = np.left_shift(flags, L1B_L2_HIST_FLAG_FIELDS['is_excluded_by_instr_team'])
-                    if type_of_list == 'suspected_transients':
-                        flags = np.left_shift(flags, L1B_L2_HIST_FLAG_FIELDS['is_suspected_transient'])
-                    self.data['histogram_flag_array'] = self.data['histogram_flag_array'] | flags
+                    #                    print(np.shape(flags))
+                    if type_of_list == "exclusions_by_instr_team":
+                        flags = np.left_shift(
+                            flags, L1B_L2_HIST_FLAG_FIELDS["is_excluded_by_instr_team"]
+                        )
+                    if type_of_list == "suspected_transients":
+                        flags = np.left_shift(
+                            flags, L1B_L2_HIST_FLAG_FIELDS["is_suspected_transient"]
+                        )
+                    self.data["histogram_flag_array"] = (
+                        self.data["histogram_flag_array"] | flags
+                    )
             line = file_handler.readline()
         file_handler.close()
 
@@ -645,26 +814,36 @@ def _set_histogram_flag_array_excl_by_instr_team(self, anc_input_from_instr_team
         Args:
             anc_input_from_instr_team: ancillary input provided by IMAP/GLOWS Instrument Team
         """
-        file_handler = open(anc_input_from_instr_team['exclusions_by_instr_team'], 'r')
+        file_handler = open(anc_input_from_instr_team["exclusions_by_instr_team"], "r")
         line = file_handler.readline()
         while line:
-            if line[0] != '#':
+            if line[0] != "#":
                 # sanity check
-                assert line[:2] == '20' and line[10] == '_', \
-                    'Bad identifier ' + line[:20] + ' in ' + \
-                    anc_input_from_instr_team['exclusions_by_instr_team']
-                identifier = line.rstrip().split(' ')[0]
-                flags = line.rstrip().split(' ')[1]
+                assert line[:2] == "20" and line[10] == "_", (
+                    "Bad identifier "
+                    + line[:20]
+                    + " in "
+                    + anc_input_from_instr_team["exclusions_by_instr_team"]
+                )
+                identifier = line.rstrip().split(" ")[0]
+                flags = line.rstrip().split(" ")[1]
 
                 # sanity check
-                assert len(flags) == self.data['number_of_bins_per_histogram'], \
-                    'Bad length of mask_array for identifier ' + identifier + ' in ' + \
-                    anc_input_from_instr_team['exclusions_by_instr_team']
-
-                if identifier == self.data['unique_block_identifier']:
+                assert len(flags) == self.data["number_of_bins_per_histogram"], (
+                    "Bad length of mask_array for identifier "
+                    + identifier
+                    + " in "
+                    + anc_input_from_instr_team["exclusions_by_instr_team"]
+                )
+
+                if identifier == self.data["unique_block_identifier"]:
                     flags = np.array(list(flags)).astype(bool)
-                    flags = np.left_shift(flags, L1B_L2_HIST_FLAG_FIELDS['is_excluded_by_instr_team'])
-                    self.data['histogram_flag_array'] = self.data['histogram_flag_array'] | flags
+                    flags = np.left_shift(
+                        flags, L1B_L2_HIST_FLAG_FIELDS["is_excluded_by_instr_team"]
+                    )
+                    self.data["histogram_flag_array"] = (
+                        self.data["histogram_flag_array"] | flags
+                    )
             line = file_handler.readline()
         file_handler.close()
 
@@ -677,46 +856,68 @@ def _set_histogram_flag_array_uv_sources(self, anc_input_from_instr_team, settin
             settings: filter settings
         """
 
-        if check_if_contains_actual_data(anc_input_from_instr_team['map_of_uv_sources']):
+        if check_if_contains_actual_data(
+            anc_input_from_instr_team["map_of_uv_sources"]
+        ):
             # read ecliptic coords and angular radiuses for the masking of bright uv point sources
-            uv_sources_coords_and_radiuses = np.loadtxt(anc_input_from_instr_team['map_of_uv_sources'],
-                                               usecols=[1, 2, 3])
+            uv_sources_coords_and_radiuses = np.loadtxt(
+                anc_input_from_instr_team["map_of_uv_sources"], usecols=[1, 2, 3]
+            )
 
             # compute ecliptic coords (lon, lat) for histogram bin centers
             histogram_bins_ecl_coords = self._compute_histogram_bins_ecl_coords()
 
-            flag = np.zeros_like(self.data['histogram'], bool)
+            flag = np.zeros_like(self.data["histogram"], bool)
 
             # a radius_bins grouping is used below for code speed up, because otherwise we would need to use
             # coord1.separation(coord2) from astropy, which turned out to be slow in tests
-            n_radius_bins = 10 # actually used bins, see +2 in np.linspace() argument below
-            radius_bins = np.linspace(uv_sources_coords_and_radiuses[:, 2].min()-1.0e-6,
-                                      uv_sources_coords_and_radiuses[:, 2].max()+1.0e-6, n_radius_bins+2)
+            n_radius_bins = (
+                10  # actually used bins, see +2 in np.linspace() argument below
+            )
+            radius_bins = np.linspace(
+                uv_sources_coords_and_radiuses[:, 2].min() - 1.0e-6,
+                uv_sources_coords_and_radiuses[:, 2].max() + 1.0e-6,
+                n_radius_bins + 2,
+            )
             # digitizing/binning into radius_bins
-            radius_idxs = np.digitize(uv_sources_coords_and_radiuses[:, 2], bins=radius_bins)
+            radius_idxs = np.digitize(
+                uv_sources_coords_and_radiuses[:, 2], bins=radius_bins
+            )
 
-            for i in range(1, n_radius_bins+2): # looping over all radius_bins groups
+            for i in range(1, n_radius_bins + 2):  # looping over all radius_bins groups
                 rejection_radius = radius_bins[i]
                 idxs = np.nonzero(radius_idxs == i)[0]
-                if len(idxs) > 0: # check if we have any uv sources to check in the current group
-                    match_catalog = SkyCoord(uv_sources_coords_and_radiuses[idxs, 0], uv_sources_coords_and_radiuses[idxs, 1],
-                                             frame='geocentrictrueecliptic', unit='deg')
-                    search_catalog = SkyCoord(histogram_bins_ecl_coords[:, 0],
-                                              histogram_bins_ecl_coords[:, 1],
-                                              frame='geocentrictrueecliptic', unit='deg')
+                if (
+                    len(idxs) > 0
+                ):  # check if we have any uv sources to check in the current group
+                    match_catalog = SkyCoord(
+                        uv_sources_coords_and_radiuses[idxs, 0],
+                        uv_sources_coords_and_radiuses[idxs, 1],
+                        frame="geocentrictrueecliptic",
+                        unit="deg",
+                    )
+                    search_catalog = SkyCoord(
+                        histogram_bins_ecl_coords[:, 0],
+                        histogram_bins_ecl_coords[:, 1],
+                        frame="geocentrictrueecliptic",
+                        unit="deg",
+                    )
 
                     # find bin vs. map point pairs that are closer than rejection_radius
-                    idxs_match, __, __, __ = \
-                        match_catalog.search_around_sky(search_catalog, rejection_radius*u.deg)
+                    idxs_match, __, __, __ = match_catalog.search_around_sky(
+                        search_catalog, rejection_radius * u.deg
+                    )
                     # find indexes of bins that are too close to map points
                     idxs_to_reject = np.unique(idxs_match)
                     # set histogram bin flags
                     flag[idxs_to_reject] = True
 
-            flag = np.left_shift(flag, L1B_L2_HIST_FLAG_FIELDS['is_close_to_uv_source'])
-            self.data['histogram_flag_array'] = self.data['histogram_flag_array'] | flag
+            flag = np.left_shift(flag, L1B_L2_HIST_FLAG_FIELDS["is_close_to_uv_source"])
+            self.data["histogram_flag_array"] = self.data["histogram_flag_array"] | flag
 
-    def _set_histogram_flag_array_excl_regions(self, anc_input_from_instr_team, settings):
+    def _set_histogram_flag_array_excl_regions(
+        self, anc_input_from_instr_team, settings
+    ):
         """
         Set histogram flag array using a map of excluded regions, close-to-map-points bins are
             denoted by True
@@ -725,18 +926,23 @@ def _set_histogram_flag_array_excl_regions(self, anc_input_from_instr_team, sett
             settings: filter settings
         """
 
-        if check_if_contains_actual_data(anc_input_from_instr_team['map_of_excluded_regions']):
+        if check_if_contains_actual_data(
+            anc_input_from_instr_team["map_of_excluded_regions"]
+        ):
             # read ecliptic coords of points defining excluded regions
-            excluded_pts_coords = np.loadtxt(anc_input_from_instr_team['map_of_excluded_regions'])
+            excluded_pts_coords = np.loadtxt(
+                anc_input_from_instr_team["map_of_excluded_regions"]
+            )
 
             # set angular radius below which bins are too close to map points
-            rejection_radius = settings['filter_based_on_maps'] \
-                                       ['angular_radius_for_excl_regions__deg']
+            rejection_radius = settings["filter_based_on_maps"][
+                "angular_radius_for_excl_regions__deg"
+            ]
 
             if rejection_radius <= 0.0:
                 # negative or zero value of rejection_radius means that the filter is turned off and we
                 # can immediately set False value in the histogram_flag_array for all bins
-                flag = np.zeros_like(self.data['histogram'], bool)
+                flag = np.zeros_like(self.data["histogram"], bool)
             else:
                 # positive value of rejection_radius means that the filter is turned on and we must
                 # compare bin positions in the sky with map-point positions to set True/False values
@@ -745,23 +951,33 @@ def _set_histogram_flag_array_excl_regions(self, anc_input_from_instr_team, sett
                 # compute ecliptic coords (lon, lat) for histogram bin centers
                 histogram_bins_ecl_coords = self._compute_histogram_bins_ecl_coords()
 
-                search_catalog = SkyCoord(excluded_pts_coords[:, 0], excluded_pts_coords[:, 1],
-                                          frame='geocentrictrueecliptic', unit='deg')
-                match_catalog = SkyCoord(histogram_bins_ecl_coords[:, 0],
-                                         histogram_bins_ecl_coords[:, 1],
-                                         frame='geocentrictrueecliptic', unit='deg')
+                search_catalog = SkyCoord(
+                    excluded_pts_coords[:, 0],
+                    excluded_pts_coords[:, 1],
+                    frame="geocentrictrueecliptic",
+                    unit="deg",
+                )
+                match_catalog = SkyCoord(
+                    histogram_bins_ecl_coords[:, 0],
+                    histogram_bins_ecl_coords[:, 1],
+                    frame="geocentrictrueecliptic",
+                    unit="deg",
+                )
 
                 # find bin vs. map point pairs that are closer than rejection_radius
-                __, idxs_match, __, __ = \
-                    match_catalog.search_around_sky(search_catalog, rejection_radius*u.deg)
+                __, idxs_match, __, __ = match_catalog.search_around_sky(
+                    search_catalog, rejection_radius * u.deg
+                )
                 # find indexes of bins that are too close to map points
                 idxs_to_reject = np.unique(idxs_match)
                 # set histogram bin flags
-                flag = np.zeros_like(self.data['histogram'], bool)
+                flag = np.zeros_like(self.data["histogram"], bool)
                 flag[idxs_to_reject] = True
 
-            flag = np.left_shift(flag, L1B_L2_HIST_FLAG_FIELDS['is_inside_excluded_region'])
-            self.data['histogram_flag_array'] = self.data['histogram_flag_array'] | flag
+            flag = np.left_shift(
+                flag, L1B_L2_HIST_FLAG_FIELDS["is_inside_excluded_region"]
+            )
+            self.data["histogram_flag_array"] = self.data["histogram_flag_array"] | flag
 
     def _set_histogram_flag_array_test_pattern(self):
         """
@@ -769,11 +985,11 @@ def _set_histogram_flag_array_test_pattern(self):
         """
         for i in range(15):
             if i % 2 == 0:
-                flag = np.ones_like(self.data['histogram'], bool)
+                flag = np.ones_like(self.data["histogram"], bool)
             else:
-                flag = np.zeros_like(self.data['histogram'], bool)
+                flag = np.zeros_like(self.data["histogram"], bool)
             flag = np.left_shift(flag, i)
-            self.data['histogram_flag_array'] = self.data['histogram_flag_array'] | flag
+            self.data["histogram_flag_array"] = self.data["histogram_flag_array"] | flag
 
     def set_times(self, l1a_data):
         """
@@ -781,32 +997,42 @@ def set_times(self, l1a_data):
         Args:
             l1a_data: L1a data provided in the form of data field of L1aDataHistogram object
         """
-        self.data['glows_start_time'] = \
-            time_sec_subsec_to_float64(l1a_data['glows_start_time']['seconds'],
-                                       l1a_data['glows_start_time']['subseconds'])
-        self.data['glows_end_time_offset'] = \
-            time_sec_subsec_to_float64(l1a_data['glows_end_time_offset']['seconds'],
-                                       l1a_data['glows_end_time_offset']['subseconds'])
-        self.data['imap_start_time'] = \
-            time_sec_subsec_to_float64(l1a_data['imap_start_time']['seconds'],
-                                       l1a_data['imap_start_time']['subseconds'])
-        self.data['imap_end_time_offset'] = \
-            time_sec_subsec_to_float64(l1a_data['imap_end_time_offset']['seconds'],
-                                       l1a_data['imap_end_time_offset']['subseconds'])
+        self.data["glows_start_time"] = time_sec_subsec_to_float64(
+            l1a_data["glows_start_time"]["seconds"],
+            l1a_data["glows_start_time"]["subseconds"],
+        )
+        self.data["glows_end_time_offset"] = time_sec_subsec_to_float64(
+            l1a_data["glows_end_time_offset"]["seconds"],
+            l1a_data["glows_end_time_offset"]["subseconds"],
+        )
+        self.data["imap_start_time"] = time_sec_subsec_to_float64(
+            l1a_data["imap_start_time"]["seconds"],
+            l1a_data["imap_start_time"]["subseconds"],
+        )
+        self.data["imap_end_time_offset"] = time_sec_subsec_to_float64(
+            l1a_data["imap_end_time_offset"]["seconds"],
+            l1a_data["imap_end_time_offset"]["subseconds"],
+        )
 
     def total_counts_daily_statistics(self):
         """
         Compute daily average for the total number of counts in L1a histograms
         """
-        file_name = 'data_l1a_histograms/total_counts_file.dat'
-        file_handler = open(file_name, 'r')
+        file_name = "data_l1a_histograms/total_counts_file.dat"
+        file_handler = open(file_name, "r")
         line = file_handler.readline()
 
         # sanity check
-        assert line.rstrip().split(' ')[1] == self.data['block_header']['pkts_file_name'], \
-            'Packets file name ' + line.rstrip().split(' ')[1] + ' in ' + file_name + \
-            ' is different from currently processed packets file ' + \
-            self.data['block_header']['pkts_file_name']
+        assert (
+            line.rstrip().split(" ")[1] == self.data["block_header"]["pkts_file_name"]
+        ), (
+            "Packets file name "
+            + line.rstrip().split(" ")[1]
+            + " in "
+            + file_name
+            + " is different from currently processed packets file "
+            + self.data["block_header"]["pkts_file_name"]
+        )
 
         # the first column of data array below: 'seq_count_in_pkts_file'
         # the second column: total counts in a L1a histogram
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l2_data_histogram.py b/imap_l3_processing/glows/l3a/glows_toolkit/l2_data_histogram.py
index dc7222f7a..5d9ba8ae0 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l2_data_histogram.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l2_data_histogram.py
@@ -1,16 +1,18 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
-import json
+
 import copy
+import json
+
 import numpy as np
-import time
-import sys
-from .constants import VERSION, L1B_L2_HIST_FLAG_FIELDS, SCAN_CIRCLE_ANGULAR_RADIUS
-from .funcs import time_sec_subsec_to_utc, calibration_factor
+
+from .constants import L1B_L2_HIST_FLAG_FIELDS, SCAN_CIRCLE_ANGULAR_RADIUS, VERSION
+from .funcs import calibration_factor, time_sec_subsec_to_utc
 from .geom_fun_libr_box import spin2lb
 
-class L2DataHistogram():
+
+class L2DataHistogram:
     """
     L2DataHistogram() class for GLOWS-L2-histogram data
     """
@@ -21,57 +23,74 @@ def __init__(self, anc_input_from_instr_team):
         """
         # self.data is a dictionary to be saved as L2 data
         self.data = {}
-        self.data['header'] = {}
-        self.data['identifier'] = None
-        self.data['start_time'] = None
-        self.data['end_time'] = None
-        self.data['daily_lightcurve'] = {}
+        self.data["header"] = {}
+        self.data["identifier"] = None
+        self.data["start_time"] = None
+        self.data["end_time"] = None
+        self.data["daily_lightcurve"] = {}
 
         # self.var is for various variables needed for computations, but not to be saved in L2 output file
         self.var = {}
 
         # initialize counters
-        self.var['l1b_file_counter_all'] = 0 # all l1b files processed for a given day
-        self.var['l1b_file_counter_good_time'] = 0 # only good-time files for a given day
+        self.var["l1b_file_counter_all"] = 0  # all l1b files processed for a given day
+        self.var["l1b_file_counter_good_time"] = (
+            0  # only good-time files for a given day
+        )
 
         # read pipeline settings to be used for L2
-        file_handler = open(anc_input_from_instr_team['settings'], 'r')
-        self.var['settings'] = json.load(file_handler)
+        file_handler = open(anc_input_from_instr_team["settings"], "r")
+        self.var["settings"] = json.load(file_handler)
         file_handler.close()
 
         # sanity check for bad-angle flags (all L1B_L2_HIST_FLAG_FIELDS flags must be included in
         # self.var['settings'] in the correct order)
-        list_1 = list(self.var['settings']['active_bad_angle_flags'].keys())
+        list_1 = list(self.var["settings"]["active_bad_angle_flags"].keys())
         list_2 = list(L1B_L2_HIST_FLAG_FIELDS.keys())
-        assert len(list_1) == len(list_2), \
-            'Different numbers of keys in self.var[\'settings\'][\'active_bad_angle_flags\'] ' + \
-            'and in L1B_L2_HIST_FLAG_FIELDS'
+        assert len(list_1) == len(list_2), (
+            "Different numbers of keys in self.var['settings']['active_bad_angle_flags'] "
+            + "and in L1B_L2_HIST_FLAG_FIELDS"
+        )
         for i, __ in enumerate(list_1):
-            assert list_1[i] == list_2[i], \
-                'Different keys "' + list_1[i] + '" and "' + list_2[i] + \
-                '" in self.var[\'settings\'][\'active_bad_angle_flags\'] and L1B_L2_HIST_FLAG_FIELDS'
+            assert __ == list_2[i], (
+                'Different keys "'
+                + __
+                + '" and "'
+                + list_2[i]
+                + "\" in self.var['settings']['active_bad_angle_flags'] and L1B_L2_HIST_FLAG_FIELDS"
+            )
 
         # define a structure to keep track of occurences of bad-time flags
-        self.var['bad_time_flag_occurences'] = {}
-        for key in self.var['settings']['active_bad_time_flags'].keys():
-            self.var['bad_time_flag_occurences'][key] = 0
+        self.var["bad_time_flag_occurences"] = {}
+        for key in self.var["settings"]["active_bad_time_flags"].keys():
+            self.var["bad_time_flag_occurences"][key] = 0
 
         # define a bad-time mask which is used to decide which L1b files are accepted
         # as a whole during generation of L2 data product
-        self.var['bad_time_mask'] = \
-            ''.join(['%d' % self.var['settings']['active_bad_time_flags'][key]
-                     for key in self.var['settings']['active_bad_time_flags']])
-        self.var['bad_time_mask'] = '0b' + self.var['bad_time_mask'][::-1]
+        self.var["bad_time_mask"] = "".join(
+            [
+                "%d" % self.var["settings"]["active_bad_time_flags"][key]
+                for key in self.var["settings"]["active_bad_time_flags"]
+            ]
+        )
+        self.var["bad_time_mask"] = "0b" + self.var["bad_time_mask"][::-1]
 
         # define a bad-angle mask which is used to decide which bins of L1b histograms are accepted
         # during generation of L2 data product
-        self.var['bad_angle_mask'] = \
-            ''.join(['%d' % self.var['settings']['active_bad_angle_flags'][key]
-                     for key in self.var['settings']['active_bad_angle_flags']])
-        self.var['bad_angle_mask'] = '0b' + self.var['bad_angle_mask'][::-1]
-
-        self.var['is_night_for_previous_l1b'] = -1 # stores is-night-flag state from the previously processed L1b file
-        self.var['is_night_counter'] = 0 # counter of L1b histograms for which is_night flag has been raised
+        self.var["bad_angle_mask"] = "".join(
+            [
+                "%d" % self.var["settings"]["active_bad_angle_flags"][key]
+                for key in self.var["settings"]["active_bad_angle_flags"]
+            ]
+        )
+        self.var["bad_angle_mask"] = "0b" + self.var["bad_angle_mask"][::-1]
+
+        self.var[
+            "is_night_for_previous_l1b"
+        ] = -1  # stores is-night-flag state from the previously processed L1b file
+        self.var["is_night_counter"] = (
+            0  # counter of L1b histograms for which is_night flag has been raised
+        )
 
     def _averages_accumulate(self, l1b_data):
         """
@@ -80,15 +99,17 @@ def _averages_accumulate(self, l1b_data):
         Args:
             l1b_data: L1b data provided as data field of L1bDataHistogram object
         """
-        for key in self.var['l1b_averages'].keys():
-            if key == 'counter':
+        for key in self.var["l1b_averages"].keys():
+            if key == "counter":
                 continue
-            if isinstance(self.var['l1b_averages'][key], dict):
-                for second_key in self.var['l1b_averages'][key].keys():
-                    self.var['l1b_averages'][key][second_key].append(l1b_data[key][second_key])
+            if isinstance(self.var["l1b_averages"][key], dict):
+                for second_key in self.var["l1b_averages"][key].keys():
+                    self.var["l1b_averages"][key][second_key].append(
+                        l1b_data[key][second_key]
+                    )
             else:
-                self.var['l1b_averages'][key].append(l1b_data[key])
-        self.var['l1b_averages']['counter'] += 1
+                self.var["l1b_averages"][key].append(l1b_data[key])
+        self.var["l1b_averages"]["counter"] += 1
 
     def _averages_init(self, l1b_data):
         """
@@ -99,16 +120,16 @@ def _averages_init(self, l1b_data):
         """
 
         # generate dictionary for computing averages over GOOD TIMES for L1b-file ancillary parameters
-        self.var['l1b_averages'] = {}
+        self.var["l1b_averages"] = {}
         for key in l1b_data.keys():
-            if key.endswith('average'):
+            if key.endswith("average"):
                 if isinstance(l1b_data[key], dict):
-                    self.var['l1b_averages'][key] = {}
+                    self.var["l1b_averages"][key] = {}
                     for second_key in l1b_data[key].keys():
-                        self.var['l1b_averages'][key][second_key] = []
+                        self.var["l1b_averages"][key][second_key] = []
                 else:
-                    self.var['l1b_averages'][key] = []
-        self.var['l1b_averages']['counter'] = 0
+                    self.var["l1b_averages"][key] = []
+        self.var["l1b_averages"]["counter"] = 0
 
     def _check_l1b_bad_time_flags(self, l1b_data):
         """
@@ -120,37 +141,47 @@ def _check_l1b_bad_time_flags(self, l1b_data):
         """
         # sanity check, all bad-time flags in L1b file must be included in self.var['settings']
         # in the correct order, otherwise we can assume that an error occurred
-        list_1 = list(self.var['settings']['active_bad_time_flags'].keys())
-        list_2 = list(l1b_data['flags'].keys())
-        assert len(list_1) == len(list_2), \
-            'Different number of keys in self.var[\'settings\'][\'active_bad_time_flags\'] ' + \
-            'and in l1b_data[\'flags\']'
+        list_1 = list(self.var["settings"]["active_bad_time_flags"].keys())
+        list_2 = list(l1b_data["flags"].keys())
+        assert len(list_1) == len(list_2), (
+            "Different number of keys in self.var['settings']['active_bad_time_flags'] "
+            + "and in l1b_data['flags']"
+        )
         for i, __ in enumerate(list_1):
-            assert list_1[i] == list_2[i], \
-                'Different keys "' + list_1[i] + '" and "' + list_2[i] + \
-                '" in self.var[\'settings\'][\'active_bad_time_flags\'] and l1b_data[\'flags\']'
+            assert __ == list_2[i], (
+                'Different keys "'
+                + __
+                + '" and "'
+                + list_2[i]
+                + "\" in self.var['settings']['active_bad_time_flags'] and l1b_data['flags']"
+            )
 
         # set bad-time-flag bits to compare it with self.var['bad_time_mask']
-        l1b_bad_time_flags = ''.join(['%d' % l1b_data['flags'][key] for key in l1b_data['flags']])
-        l1b_bad_time_flags = '0b' + l1b_bad_time_flags[::-1]
+        l1b_bad_time_flags = "".join(
+            ["%d" % l1b_data["flags"][key] for key in l1b_data["flags"]]
+        )
+        l1b_bad_time_flags = "0b" + l1b_bad_time_flags[::-1]
 
         # compare bad_time_flags for L1b with mask
-        cmp_result = int(l1b_bad_time_flags, 2) & int(self.var['bad_time_mask'], 2)
+        cmp_result = int(l1b_bad_time_flags, 2) & int(self.var["bad_time_mask"], 2)
 
         # if self.var['bad_time_mask'] has inactive is_night field we have to account for
         # self.var['is_night_counter'] and correct the cmp_result
-        if cmp_result == 0 and \
-           self.var['is_night_counter'] >= self.var['settings']['number_of_good_histograms_at_night']:
+        if (
+            cmp_result == 0
+            and self.var["is_night_counter"]
+            >= self.var["settings"]["number_of_good_histograms_at_night"]
+        ):
             cmp_result = 1
 
-        if l1b_data['flags']['is_night'] == True:
-            print('(night', self.var['is_night_counter'], cmp_result, end=') ')
+        if l1b_data["flags"]["is_night"] == True:
+            print("(night", self.var["is_night_counter"], cmp_result, end=") ")
 
         if cmp_result != 0:
-            print('-> bad time')
+            print("-> bad time")
             return False
 
-        print('-> good time')
+        print("-> good time")
         return True
 
     def _compute_anc_parms_averages_and_spreads(self):
@@ -158,23 +189,26 @@ def _compute_anc_parms_averages_and_spreads(self):
         Compute averages and spread (std dev) for accumulated L1b data. Mean value is computed over
             all accumulated L1b averages. Spread is computed as standard deviation of block averages.
         """
-        if self.var['l1b_averages']['counter'] == 0: # there is nothing to compute
+        if self.var["l1b_averages"]["counter"] == 0:  # there is nothing to compute
             return
 
-        for key in self.var['l1b_averages'].keys():
-            spread_key = '_'.join(key.split('_')[:-1]) + '_std_dev'
-            if key == 'counter':
+        for key in self.var["l1b_averages"].keys():
+            spread_key = "_".join(key.split("_")[:-1]) + "_std_dev"
+            if key == "counter":
                 continue
-            if isinstance(self.var['l1b_averages'][key], dict):
+            if isinstance(self.var["l1b_averages"][key], dict):
                 self.data[key] = {}
                 self.data[spread_key] = {}
-                for second_key in self.var['l1b_averages'][key].keys():
-                    self.data[key][second_key] = np.mean(self.var['l1b_averages'][key][second_key])
-                    self.data[spread_key][second_key] = \
-                        np.std(self.var['l1b_averages'][key][second_key])
+                for second_key in self.var["l1b_averages"][key].keys():
+                    self.data[key][second_key] = np.mean(
+                        self.var["l1b_averages"][key][second_key]
+                    )
+                    self.data[spread_key][second_key] = np.std(
+                        self.var["l1b_averages"][key][second_key]
+                    )
             else:
-                self.data[key] = np.mean(self.var['l1b_averages'][key])
-                self.data[spread_key] = np.std(self.var['l1b_averages'][key])
+                self.data[key] = np.mean(self.var["l1b_averages"][key])
+                self.data[spread_key] = np.std(self.var["l1b_averages"][key])
 
     def generate_l2_data(self, anc_input_from_instr_team):
         """
@@ -184,91 +218,122 @@ def generate_l2_data(self, anc_input_from_instr_team):
                 and MOC see ANC_INPUT_FROM_INSTRUMENT_TEAM in constants.py
         """
 
-        self.data['header']['number_of_l1b_files_used'] = self.var['l1b_file_counter_good_time']
-        self.data['header']['number_of_all_l1b_files'] = self.var['l1b_file_counter_all']
-
-        if self.data['header']['number_of_l1b_files_used'] > 0:
-            self.data['start_time'] = \
-                time_sec_subsec_to_utc(int(self.var['l1b_start_time']),
-                                       0).strftime('%Y-%m-%d %H:%M:%S')
-            self.data['end_time'] = \
-                time_sec_subsec_to_utc(int(self.var['l1b_end_time']),
-                                       0).strftime('%Y-%m-%d %H:%M:%S')
+        self.data["header"]["number_of_l1b_files_used"] = self.var[
+            "l1b_file_counter_good_time"
+        ]
+        self.data["header"]["number_of_all_l1b_files"] = self.var[
+            "l1b_file_counter_all"
+        ]
+
+        if self.data["header"]["number_of_l1b_files_used"] > 0:
+            self.data["start_time"] = time_sec_subsec_to_utc(
+                int(self.var["l1b_start_time"]), 0
+            ).strftime("%Y-%m-%d %H:%M:%S")
+            self.data["end_time"] = time_sec_subsec_to_utc(
+                int(self.var["l1b_end_time"]), 0
+            ).strftime("%Y-%m-%d %H:%M:%S")
         else:
             # good times have not been found
-            self.data['start_time'] = \
-                time_sec_subsec_to_utc(int(self.var['l1b_start_time_all']),
-                                       0).strftime('%Y-%m-%d %H:%M:%S')
-            self.data['end_time'] = \
-                time_sec_subsec_to_utc(int(self.var['l1b_end_time_all']),
-                                       0).strftime('%Y-%m-%d %H:%M:%S')
-            self.data['daily_lightcurve']['exposure_times'] = \
-                np.zeros(self.var['number_of_bins_per_histogram'], float)
-            self.var['raw_histogram'] = \
-                np.zeros(self.var['number_of_bins_per_histogram'], int)
-            self.data['daily_lightcurve']['photon_flux'] = \
-                np.zeros(self.var['number_of_bins_per_histogram'], float)
-            self.data['daily_lightcurve']['flux_uncertainties'] = \
-                np.zeros(self.var['number_of_bins_per_histogram'], float)
+            self.data["start_time"] = time_sec_subsec_to_utc(
+                int(self.var["l1b_start_time_all"]), 0
+            ).strftime("%Y-%m-%d %H:%M:%S")
+            self.data["end_time"] = time_sec_subsec_to_utc(
+                int(self.var["l1b_end_time_all"]), 0
+            ).strftime("%Y-%m-%d %H:%M:%S")
+            self.data["daily_lightcurve"]["exposure_times"] = np.zeros(
+                self.var["number_of_bins_per_histogram"], float
+            )
+            self.var["raw_histogram"] = np.zeros(
+                self.var["number_of_bins_per_histogram"], int
+            )
+            self.data["daily_lightcurve"]["photon_flux"] = np.zeros(
+                self.var["number_of_bins_per_histogram"], float
+            )
+            self.data["daily_lightcurve"]["flux_uncertainties"] = np.zeros(
+                self.var["number_of_bins_per_histogram"], float
+            )
 
         # compute cps from raw histogram and exposure times
-        idxs = np.nonzero(self.data['daily_lightcurve']['exposure_times'] > 0.0)[0]
-        self.data['daily_lightcurve']['photon_flux'][idxs] = \
-            self.var['raw_histogram'][idxs]/self.data['daily_lightcurve']['exposure_times'][idxs]
+        idxs = np.nonzero(self.data["daily_lightcurve"]["exposure_times"] > 0.0)[0]
+        self.data["daily_lightcurve"]["photon_flux"][idxs] = (
+            self.var["raw_histogram"][idxs]
+            / self.data["daily_lightcurve"]["exposure_times"][idxs]
+        )
 
         # add raw histogram counts
-        self.data['daily_lightcurve']['raw_histogram'] = self.var['raw_histogram']
+        self.data["daily_lightcurve"]["raw_histogram"] = self.var["raw_histogram"]
 
         # compute uncertainties
-        self.var['raw_uncertainties'] = np.sqrt(self.var['raw_histogram'])
-        self.data['daily_lightcurve']['flux_uncertainties'][idxs] = \
-            self.var['raw_uncertainties'][idxs]/ \
-            self.data['daily_lightcurve']['exposure_times'][idxs]
+        self.var["raw_uncertainties"] = np.sqrt(self.var["raw_histogram"])
+        self.data["daily_lightcurve"]["flux_uncertainties"][idxs] = (
+            self.var["raw_uncertainties"][idxs]
+            / self.data["daily_lightcurve"]["exposure_times"][idxs]
+        )
 
-        if self.data['header']['number_of_l1b_files_used'] > 0:
+        if self.data["header"]["number_of_l1b_files_used"] > 0:
             # compute averages and spreads for parameters for L2
             self._compute_anc_parms_averages_and_spreads()
 
             # generate spin angle values (for histogram bin centers) measured from the northernmost
             # point of the scanning circle
-            spin_angle_from_north = (self.data['daily_lightcurve']['spin_angle'] - \
-                                     self.data['position_angle_offset_average'] + 360.0) % 360.0
+            spin_angle_from_north = (
+                self.data["daily_lightcurve"]["spin_angle"]
+                - self.data["position_angle_offset_average"]
+                + 360.0
+            ) % 360.0
 
             # redefine the spin angle, for L2 it is not the imap spin angle anymore but angle measured
             # from the northernmost part of the scanning circle
-            self.data['daily_lightcurve']['spin_angle'] = spin_angle_from_north
+            self.data["daily_lightcurve"]["spin_angle"] = spin_angle_from_north
 
             # find the index of the smallest value of spin_angle_from_north, it indicates the shift
             # value for rolling elements of self.data['daily_lightcurve'] arrays
             idx = np.argsort(spin_angle_from_north)[0]
 
             # we have the shift value, so we can roll all arrays in self.data['daily_lightcurve']
-            for key in self.data['daily_lightcurve'].keys():
-                self.data['daily_lightcurve'][key] = np.roll(self.data['daily_lightcurve'][key], -idx)
+            for key in self.data["daily_lightcurve"].keys():
+                self.data["daily_lightcurve"][key] = np.roll(
+                    self.data["daily_lightcurve"][key], -idx
+                )
 
             # compute ecliptic longitude and latitude of histogram bin centers
-            ones = np.ones(len(self.data['daily_lightcurve']['spin_angle']), float)
-            histogram_bins_ecl_coords = np.degrees(spin2lb(
-                np.radians(self.data['spin_axis_orientation_average']['lon'])*ones,
-                np.radians(self.data['spin_axis_orientation_average']['lat'])*ones,
-                np.radians(self.data['daily_lightcurve']['spin_angle']), 0.0,
-                np.radians(SCAN_CIRCLE_ANGULAR_RADIUS)*ones))
-            self.data['daily_lightcurve']['ecliptic_lon'] = histogram_bins_ecl_coords[:, 0]
-            self.data['daily_lightcurve']['ecliptic_lat'] = histogram_bins_ecl_coords[:, 1]
+            ones = np.ones(len(self.data["daily_lightcurve"]["spin_angle"]), float)
+            histogram_bins_ecl_coords = np.degrees(
+                spin2lb(
+                    np.radians(self.data["spin_axis_orientation_average"]["lon"])
+                    * ones,
+                    np.radians(self.data["spin_axis_orientation_average"]["lat"])
+                    * ones,
+                    np.radians(self.data["daily_lightcurve"]["spin_angle"]),
+                    0.0,
+                    np.radians(SCAN_CIRCLE_ANGULAR_RADIUS) * ones,
+                )
+            )
+            self.data["daily_lightcurve"]["ecliptic_lon"] = histogram_bins_ecl_coords[
+                :, 0
+            ]
+            self.data["daily_lightcurve"]["ecliptic_lat"] = histogram_bins_ecl_coords[
+                :, 1
+            ]
 
         # apply calibration to get helioglow intensity in physical units of Rayleigh
-        cps_per_R = calibration_factor(anc_input_from_instr_team['calibration_data'],
-            self.data['start_time'].replace(' ', 'T'))
-        self.data['daily_lightcurve']['photon_flux'] = \
-            self.data['daily_lightcurve']['photon_flux'] / cps_per_R
-        self.data['daily_lightcurve']['flux_uncertainties'] = \
-            self.data['daily_lightcurve']['flux_uncertainties'] / cps_per_R
+        cps_per_R = calibration_factor(
+            anc_input_from_instr_team["calibration_data"],
+            self.data["start_time"].replace(" ", "T"),
+        )
+        self.data["daily_lightcurve"]["photon_flux"] = (
+            self.data["daily_lightcurve"]["photon_flux"] / cps_per_R
+        )
+        self.data["daily_lightcurve"]["flux_uncertainties"] = (
+            self.data["daily_lightcurve"]["flux_uncertainties"] / cps_per_R
+        )
 
         # add the number of bins as a key to be saved in L2 output file
-        self.data['daily_lightcurve']['number_of_bins'] = \
-            len(self.data['daily_lightcurve']['spin_angle'])
+        self.data["daily_lightcurve"]["number_of_bins"] = len(
+            self.data["daily_lightcurve"]["spin_angle"]
+        )
 
-        self.data['bad_time_flag_occurences'] = self.var['bad_time_flag_occurences']
+        self.data["bad_time_flag_occurences"] = self.var["bad_time_flag_occurences"]
 
     def is_new_observational_day(self, l1b_data):
         """
@@ -280,14 +345,19 @@ def is_new_observational_day(self, l1b_data):
             l1b_data: L1b data provided in the form of data field of L1bDataHistogram object
         """
 
-        is_new_observ_day = False # init
+        is_new_observ_day = False  # init
 
         # check if is_night flag at L1b goes from True to False, if so then we have a new observational day
-        if self.var['is_night_for_previous_l1b'] >= 0: # we are at the second (or later) l1b file
-            if self.var['is_night_for_previous_l1b'] == 1 and l1b_data['flags']['is_night'] == False: # new observational day
+        if (
+            self.var["is_night_for_previous_l1b"] >= 0
+        ):  # we are at the second (or later) l1b file
+            if (
+                self.var["is_night_for_previous_l1b"] == 1
+                and l1b_data["flags"]["is_night"] == False
+            ):  # new observational day
                 is_new_observ_day = True
 
-        self.var['is_night_for_previous_l1b'] = l1b_data['flags']['is_night']
+        self.var["is_night_for_previous_l1b"] = l1b_data["flags"]["is_night"]
         return is_new_observ_day
 
     def process_l1b_data_files(self, l1b_data, pointing_counter):
@@ -297,88 +367,109 @@ def process_l1b_data_files(self, l1b_data, pointing_counter):
             l1b_data: L1b data provided in the form of data field of L1bDataHistogram object
         """
 
-        if self.var['l1b_file_counter_all'] == 0:
+        if self.var["l1b_file_counter_all"] == 0:
             # the start time of the first L1b file
-            self.var['l1b_start_time_all'] = l1b_data['imap_start_time']
+            self.var["l1b_start_time_all"] = l1b_data["imap_start_time"]
 
             # generate spin-angle values for L2 for which spin=0 is measured from the
             # northernmost point of lightcurve, however it must have the same resolution as
             # l1b histograms so we can use L1b imap_spin_angle values for angular-grid generation
-            self.data['daily_lightcurve']['spin_angle'] = l1b_data['imap_spin_angle_bin_cntr']
-        self.var['l1b_end_time_all'] = l1b_data['imap_start_time'] + l1b_data['imap_end_time_offset']
-        self.var['number_of_bins_per_histogram'] = l1b_data['number_of_bins_per_histogram']
+            self.data["daily_lightcurve"]["spin_angle"] = l1b_data[
+                "imap_spin_angle_bin_cntr"
+            ]
+        self.var["l1b_end_time_all"] = (
+            l1b_data["imap_start_time"] + l1b_data["imap_end_time_offset"]
+        )
+        self.var["number_of_bins_per_histogram"] = l1b_data[
+            "number_of_bins_per_histogram"
+        ]
 
         # increment all-l1b-files counter
-        self.var['l1b_file_counter_all'] += 1
+        self.var["l1b_file_counter_all"] += 1
 
-        for key in self.var['settings']['active_bad_time_flags'].keys():
-            if l1b_data['flags'][key]:
-                self.var['bad_time_flag_occurences'][key] += 1
+        for key in self.var["settings"]["active_bad_time_flags"].keys():
+            if l1b_data["flags"][key]:
+                self.var["bad_time_flag_occurences"][key] += 1
 
         if self._check_l1b_bad_time_flags(l1b_data):
-
-            if self.var['l1b_file_counter_good_time'] <= 0:
+            if self.var["l1b_file_counter_good_time"] <= 0:
                 # things to do when we have the first good-time L1b data file
-                self.data['header'] = {
-                    'flight_software_version': l1b_data['block_header']['flight_software_version'],
-                    'ground_software_version': VERSION,
-                    'pkts_file_name': l1b_data['block_header']['pkts_file_name'],
-                    'ancillary_data_files': l1b_data['block_header']['ancillary_data_files']
+                self.data["header"] = {
+                    "flight_software_version": l1b_data["block_header"][
+                        "flight_software_version"
+                    ],
+                    "ground_software_version": VERSION,
+                    "pkts_file_name": l1b_data["block_header"]["pkts_file_name"],
+                    "ancillary_data_files": l1b_data["block_header"][
+                        "ancillary_data_files"
+                    ],
                 }
 
                 # we are here at the first good-time L1b file, so we set the current state of
                 # the pointing counter as an unique identifier in L2
-                self.data['identifier'] = pointing_counter
+                self.data["identifier"] = pointing_counter
 
                 # the start time of the first good-time L1b file is needed for start time of L2
-                self.var['l1b_start_time'] = l1b_data['imap_start_time']
+                self.var["l1b_start_time"] = l1b_data["imap_start_time"]
 
                 # initialize dictionary for computing averages over L1b files
                 self._averages_init(l1b_data)
 
                 # initialize daily helioglow-flux-lightcurve related stuff
-                self.data['daily_lightcurve']['photon_flux'] = \
-                    np.zeros_like(self.data['daily_lightcurve']['spin_angle'], float)
-                self.var['raw_histogram'] = \
-                    np.zeros_like(self.data['daily_lightcurve']['spin_angle'], int)
-                self.data['daily_lightcurve']['exposure_times'] = \
-                    np.zeros_like(self.data['daily_lightcurve']['spin_angle'], float)
-                self.data['daily_lightcurve']['flux_uncertainties'] = \
-                    np.zeros_like(self.data['daily_lightcurve']['spin_angle'], float)
-                self.data['daily_lightcurve']['histogram_flag_array'] = \
-                    np.zeros_like(self.data['daily_lightcurve']['spin_angle'], np.int16)
+                self.data["daily_lightcurve"]["photon_flux"] = np.zeros_like(
+                    self.data["daily_lightcurve"]["spin_angle"], float
+                )
+                self.var["raw_histogram"] = np.zeros_like(
+                    self.data["daily_lightcurve"]["spin_angle"], int
+                )
+                self.data["daily_lightcurve"]["exposure_times"] = np.zeros_like(
+                    self.data["daily_lightcurve"]["spin_angle"], float
+                )
+                self.data["daily_lightcurve"]["flux_uncertainties"] = np.zeros_like(
+                    self.data["daily_lightcurve"]["spin_angle"], float
+                )
+                self.data["daily_lightcurve"]["histogram_flag_array"] = np.zeros_like(
+                    self.data["daily_lightcurve"]["spin_angle"], np.int16
+                )
 
             # keep track of the last end_time for good-time L1b files, it is needed for
             # the end time of L2 file
-            self.var['l1b_end_time'] = l1b_data['imap_start_time'] + l1b_data['imap_end_time_offset']
+            self.var["l1b_end_time"] = (
+                l1b_data["imap_start_time"] + l1b_data["imap_end_time_offset"]
+            )
 
             # update dictionary for computing averages over L1b files
             self._averages_accumulate(l1b_data)
 
             # generate bad-angle mask for L2, if any L1b file has bitfield set to True, L2 does too
-            self.data['daily_lightcurve']['histogram_flag_array'] = \
-                np.bitwise_and(int(self.var['bad_angle_mask'], 2),
-                np.bitwise_or(self.data['daily_lightcurve']['histogram_flag_array'],
-                              l1b_data['histogram_flag_array']))
+            self.data["daily_lightcurve"]["histogram_flag_array"] = np.bitwise_and(
+                int(self.var["bad_angle_mask"], 2),
+                np.bitwise_or(
+                    self.data["daily_lightcurve"]["histogram_flag_array"],
+                    l1b_data["histogram_flag_array"],
+                ),
+            )
 
             # compute the exposure time per bin for currently processed L1b-data file
             # we must do it here because it can vary from one block to another
-            l1b_exposure_time_per_bin = l1b_data['spin_period_average'] * \
-                                        l1b_data['number_of_spins_per_block'] / \
-                                        l1b_data['number_of_bins_per_histogram']
+            l1b_exposure_time_per_bin = (
+                l1b_data["spin_period_average"]
+                * l1b_data["number_of_spins_per_block"]
+                / l1b_data["number_of_bins_per_histogram"]
+            )
 
             # update raw L2 histogram and exposure times, note that block-to-block variations of the
             # position angle offset are not taken into account here, but if they will turn to be
             # significant we must include them somehow here
-            self.var['raw_histogram'] += np.array(l1b_data['histogram'])
-            self.data['daily_lightcurve']['exposure_times'] += l1b_exposure_time_per_bin
+            self.var["raw_histogram"] += np.array(l1b_data["histogram"])
+            self.data["daily_lightcurve"]["exposure_times"] += l1b_exposure_time_per_bin
 
             # increment good-time counter for L1b files
-            self.var['l1b_file_counter_good_time'] += 1
+            self.var["l1b_file_counter_good_time"] += 1
 
-        if l1b_data['flags']['is_night'] == True:
+        if l1b_data["flags"]["is_night"] == True:
             # increment is-night-flag-raised counter for L1b files
-            self.var['is_night_counter'] += 1
+            self.var["is_night_counter"] += 1
 
     def read_from_file(self, file_name):
         """
@@ -396,16 +487,18 @@ def _read_from_json_file(self, file_name):
         Args:
             file_name: name of of file with L1b data
         """
-        file_handler = open(file_name, 'r')
+        file_handler = open(file_name, "r")
         self.data = json.load(file_handler)
         file_handler.close()
 
         # hexadecimal values saved as strings require special treatment to make them integers
-        self.data['daily_lightcurve']['histogram_flag_array'] = \
-            [int(x, base=16) for x in self.data['daily_lightcurve']['histogram_flag_array']]
+        self.data["daily_lightcurve"]["histogram_flag_array"] = [
+            int(x, base=16)
+            for x in self.data["daily_lightcurve"]["histogram_flag_array"]
+        ]
 
         # additional field to keep track where data come from
-        self.data['l2_file_name'] = file_name
+        self.data["l2_file_name"] = file_name
 
     def save_data_to_file(self):
         """
@@ -415,8 +508,8 @@ def save_data_to_file(self):
         # modules for IMAP will be provided by SDC
         self._save_data_to_json_file()
 
-        #sys.stdout.flush()
-        #time.sleep(5)
+        # sys.stdout.flush()
+        # time.sleep(5)
 
     def _save_data_to_json_file(self):
         """
@@ -425,39 +518,39 @@ def _save_data_to_json_file(self):
         """
         # define formats for printing values to JSON file
         fmt = {
-            'daily_lightcurve': {
-                'spin_angle': '%.3f',
-                'photon_flux': '%.1f',
-                'raw_histogram': '%d',
-                'exposure_times': '%.3e',
-                'flux_uncertainties': '%.3e',
-                'ecliptic_lon': '%.3f',
-                'ecliptic_lat': '%.3f'
+            "daily_lightcurve": {
+                "spin_angle": "%.3f",
+                "photon_flux": "%.1f",
+                "raw_histogram": "%d",
+                "exposure_times": "%.3e",
+                "flux_uncertainties": "%.3e",
+                "ecliptic_lon": "%.3f",
+                "ecliptic_lat": "%.3f",
+            },
+            "scalar variables": {
+                "filter_temperature_average": "%.2f",
+                "filter_temperature_std_dev": "%.3e",
+                "hv_voltage_average": "%.1f",
+                "hv_voltage_std_dev": "%.3e",
+                "spin_period_average": "%.7f",
+                "spin_period_std_dev": "%.3e",
+                "spin_period_ground_average": "%.7f",
+                "spin_period_ground_std_dev": "%.3e",
+                "pulse_length_average": "%.3e",
+                "pulse_length_std_dev": "%.3e",
+                "position_angle_offset_average": "%.3f",
+                "position_angle_offset_std_dev": "%.3e",
             },
-            'scalar variables': {
-                'filter_temperature_average': '%.2f',
-                'filter_temperature_std_dev': '%.3e',
-                'hv_voltage_average': '%.1f',
-                'hv_voltage_std_dev': '%.3e',
-                'spin_period_average': '%.7f',
-                'spin_period_std_dev': '%.3e',
-                'spin_period_ground_average': '%.7f',
-                'spin_period_ground_std_dev': '%.3e',
-                'pulse_length_average': '%.3e',
-                'pulse_length_std_dev': '%.3e',
-                'position_angle_offset_average': '%.3f',
-                'position_angle_offset_std_dev': '%.3e'
+            "lon lat variables": {
+                "spin_axis_orientation_average": "%.3f",
+                "spin_axis_orientation_std_dev": "%.3e",
             },
-            'lon lat variables': {
-                'spin_axis_orientation_average': '%.3f',
-                'spin_axis_orientation_std_dev': '%.3e'
+            "vector variables": {
+                "spacecraft_location_average": "%.1f",
+                "spacecraft_location_std_dev": "%.3e",
+                "spacecraft_velocity_average": "%.3f",
+                "spacecraft_velocity_std_dev": "%.3e",
             },
-            'vector variables': {
-                'spacecraft_location_average': '%.1f',
-                'spacecraft_location_std_dev': '%.3e',
-                'spacecraft_velocity_average': '%.3f',
-                'spacecraft_velocity_std_dev': '%.3e'
-            }
         }
 
         # create local copy for printing purposes
@@ -467,37 +560,57 @@ def _save_data_to_json_file(self):
         # them to be saved as one-liners (i.e., without \n after each element) or we want them to
         # be printed with a specific format by json.dumps()
         # so we convert variables to strings as defined in fmt
-        for key in fmt['daily_lightcurve'].keys():
-            data['daily_lightcurve'][key] = \
-                '[' + ', '.join(map(lambda x: fmt['daily_lightcurve'][key] % x,
-                                list(data['daily_lightcurve'][key]))) + ']'
-        for key in fmt['scalar variables'].keys():
-            data[key] = '|'+ fmt['scalar variables'][key] % data[key] +'|'
-        for key in fmt['lon lat variables'].keys():
-            for sec_key in ['lon', 'lat']:
-                data[key][sec_key] = '|'+ fmt['lon lat variables'][key] % data[key][sec_key] +'|'
-        for key in fmt['vector variables'].keys():
-            for sec_key in ['x', 'y', 'z']:
-                data[key][sec_key] = '|'+ fmt['vector variables'][key] % data[key][sec_key] +'|'
+        for key in fmt["daily_lightcurve"].keys():
+            data["daily_lightcurve"][key] = (
+                "["
+                + ", ".join(
+                    map(
+                        lambda x: fmt["daily_lightcurve"][key] % x,
+                        list(data["daily_lightcurve"][key]),
+                    )
+                )
+                + "]"
+            )
+        for key in fmt["scalar variables"].keys():
+            data[key] = "|" + fmt["scalar variables"][key] % data[key] + "|"
+        for key in fmt["lon lat variables"].keys():
+            for sec_key in ["lon", "lat"]:
+                data[key][sec_key] = (
+                    "|" + fmt["lon lat variables"][key] % data[key][sec_key] + "|"
+                )
+        for key in fmt["vector variables"].keys():
+            for sec_key in ["x", "y", "z"]:
+                data[key][sec_key] = (
+                    "|" + fmt["vector variables"][key] % data[key][sec_key] + "|"
+                )
 
         # histogram_flag_array requires special hexadecimal treatment
-        data['daily_lightcurve']['histogram_flag_array'] = '[' + \
-            ', '.join(map(lambda x: '"0x' + format(x, 'x').zfill(4) +'"',
-            list(data['daily_lightcurve']['histogram_flag_array']))) + ']'
+        data["daily_lightcurve"]["histogram_flag_array"] = (
+            "["
+            + ", ".join(
+                map(
+                    lambda x: '"0x' + format(x, "x").zfill(4) + '"',
+                    list(data["daily_lightcurve"]["histogram_flag_array"]),
+                )
+            )
+            + "]"
+        )
 
         # generate JSON content to be saved in output file
         json_content = json.dumps(data, indent=4, default=vars)
 
         # some corrections to get numbers back
-        json_content = json_content.replace('"[', '[').replace(']"', ']')
-        json_content = json_content.replace('|"', '').replace('"|', '')
+        json_content = json_content.replace('"[', "[").replace(']"', "]")
+        json_content = json_content.replace('|"', "").replace('"|', "")
         json_content = json_content.replace('\\"', '"')
 
         # the output file name is constructed using a convention defined in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
-        date_utc = ''.join(filter(str.isdigit, self.data['start_time']))
-        file_name = 'data_l2_histograms/imap_glows_l2_%s_orbX_modX_p_v00.json' % date_utc
+        date_utc = "".join(filter(str.isdigit, self.data["start_time"]))
+        file_name = (
+            "data_l2_histograms/imap_glows_l2_%s_orbX_modX_p_v00.json" % date_utc
+        )
 
-        file_handler = open(file_name, 'w')
+        file_handler = open(file_name, "w")
         print(json_content, file=file_handler)
         file_handler.close()
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/l3a_data.py b/imap_l3_processing/glows/l3a/glows_toolkit/l3a_data.py
index 451788084..33c4f4068 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/l3a_data.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/l3a_data.py
@@ -1,6 +1,7 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
+
 import copy
 import json
 
@@ -8,12 +9,12 @@
 from astropy import units as u
 from astropy_healpix import HEALPix
 
-from .constants import VERSION, SCAN_CIRCLE_ANGULAR_RADIUS
+from .constants import SCAN_CIRCLE_ANGULAR_RADIUS, VERSION
 from .funcs import calibration_factor, check_if_contains_actual_data
 from .geom_fun_libr_box import spin2lb
 
 
-class L3aData():
+class L3aData:
     """
     L3aData() class for GLOWS-L3a data
     """
@@ -24,20 +25,20 @@ def __init__(self, anc_input_from_instr_team):
         """
         # self.data is a dictionary to be saved as L2 data
         self.data = {}
-        self.data['header'] = {}
-        self.data['identifier'] = None
-        self.data['start_time'] = None
-        self.data['end_time'] = None
-        self.data['daily_lightcurve'] = {}
+        self.data["header"] = {}
+        self.data["identifier"] = None
+        self.data["start_time"] = None
+        self.data["end_time"] = None
+        self.data["daily_lightcurve"] = {}
 
         # self.var is for various variables needed for computations, but not to be saved in L3a output file
         self.var = {}
 
         # read pipeline settings to be used for L3a
-        file_handler = open(anc_input_from_instr_team['settings'], 'r')
+        file_handler = open(anc_input_from_instr_team["settings"], "r")
         settings = json.load(file_handler)
         file_handler.close()
-        self.var['nominal_number_of_bins'] = settings['l3a_nominal_number_of_bins']
+        self.var["nominal_number_of_bins"] = settings["l3a_nominal_number_of_bins"]
 
     def _compute_extra_heliospheric_bckgrd(self, anc_input_from_instr_team):
         """
@@ -48,14 +49,17 @@ def _compute_extra_heliospheric_bckgrd(self, anc_input_from_instr_team):
                 see ANC_INPUT_FROM_INSTRUMENT_TEAM in constants.py
         """
 
-        if check_if_contains_actual_data(anc_input_from_instr_team['extra_heliospheric_bckgrd']):
-
-            fh = open(anc_input_from_instr_team['extra_heliospheric_bckgrd'], 'r')
+        if check_if_contains_actual_data(
+            anc_input_from_instr_team["extra_heliospheric_bckgrd"]
+        ):
+            fh = open(anc_input_from_instr_team["extra_heliospheric_bckgrd"], "r")
             for i in range(3):
                 line = fh.readline()
             # sanity check
-            if line.split()[4] != 'NSIDE':
-                raise Exception('error _compute_extra_heliospheric_bckgrd(): NSIDE string not found')
+            if line.split()[4] != "NSIDE":
+                raise Exception(
+                    "error _compute_extra_heliospheric_bckgrd(): NSIDE string not found"
+                )
             # read NSIDE parameter for healpix grid
             nside = int(line.split()[6])
             # read ecliptic coords and background values span on a healpix grid
@@ -63,23 +67,30 @@ def _compute_extra_heliospheric_bckgrd(self, anc_input_from_instr_team):
             fh.close()
 
             # generate Healpix grid internally here for a sanity check performed later
-            ahp = HEALPix(nside=nside, order='ring')
+            ahp = HEALPix(nside=nside, order="ring")
             lon, lat = ahp.healpix_to_lonlat(range(ahp.npix))
             lon = np.degrees(lon.value)
             lat = np.degrees(lat.value)
 
             # sanity check for the Healpix grid read from anc_input_from_instr_team['extra_heliospheric_bckgrd']
             # it should match the Healpix grid generated internally, otherwise raise error
-            if np.fabs(lon - lon_lat_bckgrd[:, 0]).max() > 1.0e-6 or \
-                    np.fabs(lat - lon_lat_bckgrd[:, 1]).max() > 1.0e-6:
-                raise Exception('error _compute_extra_heliospheric_bckgrd(): ' + \
-                                'healpix grid mismatch (NSIDE wrong? NESTED ordering instead of RING?)')
+            if (
+                np.fabs(lon - lon_lat_bckgrd[:, 0]).max() > 1.0e-6
+                or np.fabs(lat - lon_lat_bckgrd[:, 1]).max() > 1.0e-6
+            ):
+                raise Exception(
+                    "error _compute_extra_heliospheric_bckgrd(): "
+                    + "healpix grid mismatch (NSIDE wrong? NESTED ordering instead of RING?)"
+                )
 
             # interpolate the background estimate from the healpix map into the centers of the scanning circle bins
-            self.data['daily_lightcurve']['extra_heliospheric_bckgrd'] = \
-                ahp.interpolate_bilinear_lonlat(self.data['daily_lightcurve']['ecliptic_lon'] * u.deg,
-                                                self.data['daily_lightcurve']['ecliptic_lat'] * u.deg,
-                                                lon_lat_bckgrd[:, 2])
+            self.data["daily_lightcurve"]["extra_heliospheric_bckgrd"] = (
+                ahp.interpolate_bilinear_lonlat(
+                    self.data["daily_lightcurve"]["ecliptic_lon"] * u.deg,
+                    self.data["daily_lightcurve"]["ecliptic_lat"] * u.deg,
+                    lon_lat_bckgrd[:, 2],
+                )
+            )
 
     def _compute_time_dependent_bckgrd(self, anc_input_from_instr_team):
         """
@@ -90,54 +101,85 @@ def _compute_time_dependent_bckgrd(self, anc_input_from_instr_team):
                 see ANC_INPUT_FROM_INSTRUMENT_TEAM in constants.py
         """
 
-        if check_if_contains_actual_data(anc_input_from_instr_team['time_dependent_bckgrd']):
-
+        if check_if_contains_actual_data(
+            anc_input_from_instr_team["time_dependent_bckgrd"]
+        ):
             # read the size of the background-estimate array
-            fh = open(anc_input_from_instr_team['time_dependent_bckgrd'], 'r')
+            fh = open(anc_input_from_instr_team["time_dependent_bckgrd"], "r")
             for i in range(2):
                 line = fh.readline()
             # sanity check
-            if line.split()[5] != 'array' or line.split()[6] != 'size' or line.split()[7] != '=':
+            if (
+                line.split()[5] != "array"
+                or line.split()[6] != "size"
+                or line.split()[7] != "="
+            ):
                 raise Exception(
-                    'error _compute_time_dependent_bckgrd(): input file header does not contain expected keywords (1)')
+                    "error _compute_time_dependent_bckgrd(): input file header does not contain expected keywords (1)"
+                )
             # read the number of bins
             bckgrd_array_size = int(line.split()[8])
             # sanity check
             if bckgrd_array_size != 3600:
                 raise Exception(
-                    'error _compute_time_dependent_bckgrd(): array size in input file header is not equal to 3600')
+                    "error _compute_time_dependent_bckgrd(): array size in input file header is not equal to 3600"
+                )
 
             # read the array of spin angles for which the background was estimated
             line = fh.readline()
             # sanity check
-            if line.split()[1] != 'spin' or line.split()[2] != 'angle' or line.split()[3] != 'grid:':
+            if (
+                line.split()[1] != "spin"
+                or line.split()[2] != "angle"
+                or line.split()[3] != "grid:"
+            ):
                 raise Exception(
-                    'error _compute_time_dependent_bckgrd(): input file header does not contain expected keywords (2)')
-            bckgrd_array_spin_angle = np.fromstring(line[18:], dtype=float, sep=' ')
+                    "error _compute_time_dependent_bckgrd(): input file header does not contain expected keywords (2)"
+                )
+            bckgrd_array_spin_angle = np.fromstring(line[18:], dtype=float, sep=" ")
             fh.close()
 
             # read L3a lightcurve start dates and array of corrections
-            identifiers = np.loadtxt(anc_input_from_instr_team['time_dependent_bckgrd'], usecols=[0], dtype=str)
-            start_dates = np.loadtxt(anc_input_from_instr_team['time_dependent_bckgrd'], usecols=[1], dtype=str, ndmin=1)
-            bckgrd_arrays = np.loadtxt(anc_input_from_instr_team['time_dependent_bckgrd'],
-                                       usecols=range(2, bckgrd_array_size + 2), ndmin=2)
-            idx = np.nonzero(self.data['start_time'].replace(' ', 'T') == start_dates)[0]
-            print(' tdbckgrd idx:', idx, '  ', end='')
+            identifiers = np.loadtxt(
+                anc_input_from_instr_team["time_dependent_bckgrd"],
+                usecols=[0],
+                dtype=str,
+            )
+            start_dates = np.loadtxt(
+                anc_input_from_instr_team["time_dependent_bckgrd"],
+                usecols=[1],
+                dtype=str,
+                ndmin=1,
+            )
+            bckgrd_arrays = np.loadtxt(
+                anc_input_from_instr_team["time_dependent_bckgrd"],
+                usecols=range(2, bckgrd_array_size + 2),
+                ndmin=2,
+            )
+            idx = np.nonzero(self.data["start_time"].replace(" ", "T") == start_dates)[
+                0
+            ]
+            print(" tdbckgrd idx:", idx, "  ", end="")
             if idx.size == 0:
                 # corrections has not been found for the current date, so we set zeros
-                self.data['daily_lightcurve']['time_dependent_bckgrd'] = \
-                    np.zeros_like(self.data['daily_lightcurve']['spin_angle'], float)
+                self.data["daily_lightcurve"]["time_dependent_bckgrd"] = np.zeros_like(
+                    self.data["daily_lightcurve"]["spin_angle"], float
+                )
             else:
                 idx = idx[0]
                 if idx.size != 1:
                     raise Exception(
-                        'error _compute_time_dependent_bckgrd(): not-found or multiple-definition error for ' +
-                        'the start date %s' % self.data['start_time'].replace(' ', 'T'))
+                        "error _compute_time_dependent_bckgrd(): not-found or multiple-definition error for "
+                        + "the start date %s"
+                        % self.data["start_time"].replace(" ", "T")
+                    )
 
                 # interpolate the background estimate from the bckgrd_array into the centers of the scanning circle bins
-                self.data['daily_lightcurve']['time_dependent_bckgrd'] = \
-                    np.interp(self.data['daily_lightcurve']['spin_angle'], bckgrd_array_spin_angle,
-                              bckgrd_arrays[idx, :])
+                self.data["daily_lightcurve"]["time_dependent_bckgrd"] = np.interp(
+                    self.data["daily_lightcurve"]["spin_angle"],
+                    bckgrd_array_spin_angle,
+                    bckgrd_arrays[idx, :],
+                )
 
     def generate_l3a_data(self, anc_input_from_instr_team):
         """
@@ -148,39 +190,57 @@ def generate_l3a_data(self, anc_input_from_instr_team):
         """
 
         # compute count rate for L3a
-        self.var['count_rate'] = np.zeros_like(self.data['daily_lightcurve']['raw_histogram'], float)
-        good_angle_idxs = np.nonzero(np.array(self.data['daily_lightcurve']['exposure_times']) > 0)[0]
-        self.var['count_rate'][good_angle_idxs] = \
-            self.data['daily_lightcurve']['raw_histogram'][good_angle_idxs] / \
-            self.data['daily_lightcurve']['exposure_times'][good_angle_idxs]
+        self.var["count_rate"] = np.zeros_like(
+            self.data["daily_lightcurve"]["raw_histogram"], float
+        )
+        good_angle_idxs = np.nonzero(
+            np.array(self.data["daily_lightcurve"]["exposure_times"]) > 0
+        )[0]
+        self.var["count_rate"][good_angle_idxs] = (
+            self.data["daily_lightcurve"]["raw_histogram"][good_angle_idxs]
+            / self.data["daily_lightcurve"]["exposure_times"][good_angle_idxs]
+        )
 
         # compute calibration factor
-        cps_per_R = calibration_factor(anc_input_from_instr_team['calibration_data'],
-                                       self.data['start_time'].replace(' ', 'T'))
-        print(' cps/R:', cps_per_R, '  ', end='')
+        cps_per_R = calibration_factor(
+            anc_input_from_instr_team["calibration_data"],
+            self.data["start_time"].replace(" ", "T"),
+        )
+        print(" cps/R:", cps_per_R, "  ", end="")
 
         # convert count rate to photon flux
-        self.data['daily_lightcurve']['photon_flux'] = self.var['count_rate'] / cps_per_R
+        self.data["daily_lightcurve"]["photon_flux"] = (
+            self.var["count_rate"] / cps_per_R
+        )
 
         # compute uncertainties
-        self.var['raw_uncertainties'] = np.sqrt(self.data['daily_lightcurve']['raw_histogram'])
-        self.data['daily_lightcurve']['flux_uncertainties'] = np.zeros_like(
-            self.data['daily_lightcurve']['raw_histogram'], float)
-        self.data['daily_lightcurve']['flux_uncertainties'][good_angle_idxs] = \
-            self.var['raw_uncertainties'][good_angle_idxs] / \
-            np.array(self.data['daily_lightcurve']['exposure_times'])[good_angle_idxs]
-        self.data['daily_lightcurve']['flux_uncertainties'] = \
-            self.data['daily_lightcurve']['flux_uncertainties'] / cps_per_R
+        self.var["raw_uncertainties"] = np.sqrt(
+            self.data["daily_lightcurve"]["raw_histogram"]
+        )
+        self.data["daily_lightcurve"]["flux_uncertainties"] = np.zeros_like(
+            self.data["daily_lightcurve"]["raw_histogram"], float
+        )
+        self.data["daily_lightcurve"]["flux_uncertainties"][good_angle_idxs] = (
+            self.var["raw_uncertainties"][good_angle_idxs]
+            / np.array(self.data["daily_lightcurve"]["exposure_times"])[good_angle_idxs]
+        )
+        self.data["daily_lightcurve"]["flux_uncertainties"] = (
+            self.data["daily_lightcurve"]["flux_uncertainties"] / cps_per_R
+        )
 
         # compute ecliptic longitude and latitude of histogram bin centers
-        ones = np.ones_like(self.data['daily_lightcurve']['spin_angle'], float)
-        histogram_bins_ecl_coords = np.degrees(spin2lb(
-            np.radians(self.data['spin_axis_orientation_average']['lon']) * ones,
-            np.radians(self.data['spin_axis_orientation_average']['lat']) * ones,
-            np.radians(self.data['daily_lightcurve']['spin_angle']), 0.0,
-            np.radians(SCAN_CIRCLE_ANGULAR_RADIUS) * ones))
-        self.data['daily_lightcurve']['ecliptic_lon'] = histogram_bins_ecl_coords[:, 0]
-        self.data['daily_lightcurve']['ecliptic_lat'] = histogram_bins_ecl_coords[:, 1]
+        ones = np.ones_like(self.data["daily_lightcurve"]["spin_angle"], float)
+        histogram_bins_ecl_coords = np.degrees(
+            spin2lb(
+                np.radians(self.data["spin_axis_orientation_average"]["lon"]) * ones,
+                np.radians(self.data["spin_axis_orientation_average"]["lat"]) * ones,
+                np.radians(self.data["daily_lightcurve"]["spin_angle"]),
+                0.0,
+                np.radians(SCAN_CIRCLE_ANGULAR_RADIUS) * ones,
+            )
+        )
+        self.data["daily_lightcurve"]["ecliptic_lon"] = histogram_bins_ecl_coords[:, 0]
+        self.data["daily_lightcurve"]["ecliptic_lat"] = histogram_bins_ecl_coords[:, 1]
 
         # compute an estimate of extra-heliospheric background
         self._compute_extra_heliospheric_bckgrd(anc_input_from_instr_team)
@@ -192,10 +252,11 @@ def generate_l3a_data(self, anc_input_from_instr_team):
         self._remove_masked_bins()
 
         # add the number of bins as a key to be saved in L3a output file
-        self.data['daily_lightcurve']['number_of_bins'] = \
-            len(self.data['daily_lightcurve']['spin_angle'])
+        self.data["daily_lightcurve"]["number_of_bins"] = len(
+            self.data["daily_lightcurve"]["spin_angle"]
+        )
 
-        print('...  done')
+        print("...  done")
 
     def process_l2_data_file(self, l2_data):
         """
@@ -205,62 +266,97 @@ def process_l2_data_file(self, l2_data):
         """
 
         # prepare quality_flag_array for L2, where 0 means bad data and 1 means good data
-        l2_quality_flag_array = np.ones_like(l2_data['daily_lightcurve']['histogram_flag_array'], dtype=int)
-        bad_angle_idxs = np.nonzero(np.array(l2_data['daily_lightcurve']['histogram_flag_array']) > 0)[0]
+        l2_quality_flag_array = np.ones_like(
+            l2_data["daily_lightcurve"]["histogram_flag_array"], dtype=int
+        )
+        bad_angle_idxs = np.nonzero(
+            np.array(l2_data["daily_lightcurve"]["histogram_flag_array"]) > 0
+        )[0]
         l2_quality_flag_array[bad_angle_idxs] = 0
 
         # copy ancillary date from L2 to L3a - PART 1
-        for key in ['identifier', 'start_time', 'end_time', 'filter_temperature_average', 'filter_temperature_std_dev',
-                    'hv_voltage_average', 'hv_voltage_std_dev', 'spin_period_average', 'spin_period_std_dev',
-                    'spin_period_ground_average', 'spin_period_ground_std_dev', 'pulse_length_average',
-                    'pulse_length_std_dev',
-                    'position_angle_offset_average', 'position_angle_offset_std_dev']:
+        for key in [
+            "identifier",
+            "start_time",
+            "end_time",
+            "filter_temperature_average",
+            "filter_temperature_std_dev",
+            "hv_voltage_average",
+            "hv_voltage_std_dev",
+            "spin_period_average",
+            "spin_period_std_dev",
+            "spin_period_ground_average",
+            "spin_period_ground_std_dev",
+            "pulse_length_average",
+            "pulse_length_std_dev",
+            "position_angle_offset_average",
+            "position_angle_offset_std_dev",
+        ]:
             self.data[key] = l2_data[key]
 
         # copy ancillary date from L2 to L3a - PART 2
-        for key1 in ['spin_axis_orientation_average', 'spin_axis_orientation_std_dev']:
+        for key1 in ["spin_axis_orientation_average", "spin_axis_orientation_std_dev"]:
             self.data[key1] = {}
-            for key2 in ['lon', 'lat']:
+            for key2 in ["lon", "lat"]:
                 self.data[key1][key2] = l2_data[key1][key2]
 
         # copy ancillary date from L2 to L3a - PART 3
-        for key1 in ['spacecraft_location_average', 'spacecraft_location_std_dev',
-                     'spacecraft_velocity_average', 'spacecraft_velocity_std_dev']:
+        for key1 in [
+            "spacecraft_location_average",
+            "spacecraft_location_std_dev",
+            "spacecraft_velocity_average",
+            "spacecraft_velocity_std_dev",
+        ]:
             self.data[key1] = {}
-            for key2 in ['x', 'y', 'z']:
+            for key2 in ["x", "y", "z"]:
                 self.data[key1][key2] = l2_data[key1][key2]
 
         # generate the nominal spin angle grid for L3a
-        bin_width = 360.0 / self.var['nominal_number_of_bins']
-        self.data['daily_lightcurve']['spin_angle'] = np.linspace(0.5 * bin_width,
-                                                                  360.0 - 0.5 * bin_width,
-                                                                  self.var['nominal_number_of_bins'])
+        bin_width = 360.0 / self.var["nominal_number_of_bins"]
+        self.data["daily_lightcurve"]["spin_angle"] = np.linspace(
+            0.5 * bin_width, 360.0 - 0.5 * bin_width, self.var["nominal_number_of_bins"]
+        )
 
         # compute raw counts and exposure times for low-resolution L3a histogram
-        self.data['daily_lightcurve']['raw_histogram'] = []
-        self.data['daily_lightcurve']['exposure_times'] = []
-        for i in range(self.var['nominal_number_of_bins']):
+        self.data["daily_lightcurve"]["raw_histogram"] = []
+        self.data["daily_lightcurve"]["exposure_times"] = []
+        for i in range(self.var["nominal_number_of_bins"]):
             # find indexes of l2 bins falling into i-th l3a bin
-            idxs = np.nonzero(np.logical_and(
-                l2_data['daily_lightcurve']['spin_angle'] > self.data['daily_lightcurve']['spin_angle'][
-                    i] - 0.5 * bin_width,
-                l2_data['daily_lightcurve']['spin_angle'] <= self.data['daily_lightcurve']['spin_angle'][
-                    i] + 0.5 * bin_width))[0]
+            idxs = np.nonzero(
+                np.logical_and(
+                    l2_data["daily_lightcurve"]["spin_angle"]
+                    > self.data["daily_lightcurve"]["spin_angle"][i] - 0.5 * bin_width,
+                    l2_data["daily_lightcurve"]["spin_angle"]
+                    <= self.data["daily_lightcurve"]["spin_angle"][i] + 0.5 * bin_width,
+                )
+            )[0]
             # compute raw counts
-            self.data['daily_lightcurve']['raw_histogram'].append(
-                np.sum(np.array(l2_data['daily_lightcurve']['raw_histogram'])[idxs] * l2_quality_flag_array[idxs]))
+            self.data["daily_lightcurve"]["raw_histogram"].append(
+                np.sum(
+                    np.array(l2_data["daily_lightcurve"]["raw_histogram"])[idxs]
+                    * l2_quality_flag_array[idxs]
+                )
+            )
             # compute exposure times
-            self.data['daily_lightcurve']['exposure_times'].append(
-                np.sum(np.array(l2_data['daily_lightcurve']['exposure_times'])[idxs] * l2_quality_flag_array[idxs]))
-        self.data['daily_lightcurve']['raw_histogram'] = np.array(self.data['daily_lightcurve']['raw_histogram'])
-        self.data['daily_lightcurve']['exposure_times'] = np.array(self.data['daily_lightcurve']['exposure_times'])
-
-        self.data['header'] = {
-            'flight_software_version': l2_data['header']['flight_software_version'],
-            'ground_software_version': VERSION,
-            'pkts_file_name': l2_data['header']['pkts_file_name'],
-            'ancillary_data_files': l2_data['header']['ancillary_data_files'],
-            'l2_input_file_name': l2_data['l2_file_name']
+            self.data["daily_lightcurve"]["exposure_times"].append(
+                np.sum(
+                    np.array(l2_data["daily_lightcurve"]["exposure_times"])[idxs]
+                    * l2_quality_flag_array[idxs]
+                )
+            )
+        self.data["daily_lightcurve"]["raw_histogram"] = np.array(
+            self.data["daily_lightcurve"]["raw_histogram"]
+        )
+        self.data["daily_lightcurve"]["exposure_times"] = np.array(
+            self.data["daily_lightcurve"]["exposure_times"]
+        )
+
+        self.data["header"] = {
+            "flight_software_version": l2_data["header"]["flight_software_version"],
+            "ground_software_version": VERSION,
+            "pkts_file_name": l2_data["header"]["pkts_file_name"],
+            "ancillary_data_files": l2_data["header"]["ancillary_data_files"],
+            "l2_input_file_name": l2_data["l2_file_name"],
         }
 
     def read_from_file(self, file_name):
@@ -279,20 +375,24 @@ def _read_from_json_file(self, file_name):
         Args:
             file_name: name of of file with L1b data
         """
-        file_handler = open(file_name, 'r')
+        file_handler = open(file_name, "r")
         self.data = json.load(file_handler)
         file_handler.close()
 
         # additional field to keep track where data come from
-        self.data['l3a_file_name'] = file_name
+        self.data["l3a_file_name"] = file_name
 
     def _remove_masked_bins(self):
         """
         Remove masked bins from L3a lightcurve
         """
-        good_angle_idxs = np.nonzero(np.array(self.data['daily_lightcurve']['exposure_times']) > 0)[0]
-        for key in self.data['daily_lightcurve'].keys():
-            self.data['daily_lightcurve'][key] = self.data['daily_lightcurve'][key][good_angle_idxs]
+        good_angle_idxs = np.nonzero(
+            np.array(self.data["daily_lightcurve"]["exposure_times"]) > 0
+        )[0]
+        for key in self.data["daily_lightcurve"].keys():
+            self.data["daily_lightcurve"][key] = self.data["daily_lightcurve"][key][
+                good_angle_idxs
+            ]
 
     def save_data_to_file(self):
         """
@@ -309,41 +409,41 @@ def _save_data_to_json_file(self):
         """
         # define formats for printing values to JSON file
         fmt = {
-            'daily_lightcurve': {
-                'spin_angle': '%.3f',
-                'photon_flux': '%.1f',
-                'raw_histogram': '%d',
-                'extra_heliospheric_bckgrd': '%.2f',
-                'time_dependent_bckgrd': '%.2f',
-                'exposure_times': '%.3e',
-                'flux_uncertainties': '%.3e',
-                'ecliptic_lon': '%.3f',
-                'ecliptic_lat': '%.3f'
+            "daily_lightcurve": {
+                "spin_angle": "%.3f",
+                "photon_flux": "%.1f",
+                "raw_histogram": "%d",
+                "extra_heliospheric_bckgrd": "%.2f",
+                "time_dependent_bckgrd": "%.2f",
+                "exposure_times": "%.3e",
+                "flux_uncertainties": "%.3e",
+                "ecliptic_lon": "%.3f",
+                "ecliptic_lat": "%.3f",
+            },
+            "scalar variables": {
+                "filter_temperature_average": "%.2f",
+                "filter_temperature_std_dev": "%.3e",
+                "hv_voltage_average": "%.1f",
+                "hv_voltage_std_dev": "%.3e",
+                "spin_period_average": "%.7f",
+                "spin_period_std_dev": "%.3e",
+                "spin_period_ground_average": "%.7f",
+                "spin_period_ground_std_dev": "%.3e",
+                "pulse_length_average": "%.3e",
+                "pulse_length_std_dev": "%.3e",
+                "position_angle_offset_average": "%.3f",
+                "position_angle_offset_std_dev": "%.3e",
             },
-            'scalar variables': {
-                'filter_temperature_average': '%.2f',
-                'filter_temperature_std_dev': '%.3e',
-                'hv_voltage_average': '%.1f',
-                'hv_voltage_std_dev': '%.3e',
-                'spin_period_average': '%.7f',
-                'spin_period_std_dev': '%.3e',
-                'spin_period_ground_average': '%.7f',
-                'spin_period_ground_std_dev': '%.3e',
-                'pulse_length_average': '%.3e',
-                'pulse_length_std_dev': '%.3e',
-                'position_angle_offset_average': '%.3f',
-                'position_angle_offset_std_dev': '%.3e'
+            "lon lat variables": {
+                "spin_axis_orientation_average": "%.3f",
+                "spin_axis_orientation_std_dev": "%.3e",
             },
-            'lon lat variables': {
-                'spin_axis_orientation_average': '%.3f',
-                'spin_axis_orientation_std_dev': '%.3e'
+            "vector variables": {
+                "spacecraft_location_average": "%.1f",
+                "spacecraft_location_std_dev": "%.3e",
+                "spacecraft_velocity_average": "%.3f",
+                "spacecraft_velocity_std_dev": "%.3e",
             },
-            'vector variables': {
-                'spacecraft_location_average': '%.1f',
-                'spacecraft_location_std_dev': '%.3e',
-                'spacecraft_velocity_average': '%.3f',
-                'spacecraft_velocity_std_dev': '%.3e'
-            }
         }
 
         # create local copy for printing purposes
@@ -353,33 +453,44 @@ def _save_data_to_json_file(self):
         # them to be saved as one-liners (i.e., without \n after each element) or we want them to
         # be printed with a specific format by json.dumps()
         # so we convert variables to strings as defined in fmt
-        for key in fmt['daily_lightcurve'].keys():
-            data['daily_lightcurve'][key] = \
-                '[' + ', '.join(map(lambda x: fmt['daily_lightcurve'][key] % x,
-                                    list(data['daily_lightcurve'][key]))) + ']'
-        for key in fmt['scalar variables'].keys():
-            data[key] = '|' + fmt['scalar variables'][key] % data[key] + '|'
-        for key in fmt['lon lat variables'].keys():
-            for sec_key in ['lon', 'lat']:
-                data[key][sec_key] = '|' + fmt['lon lat variables'][key] % data[key][sec_key] + '|'
-        for key in fmt['vector variables'].keys():
-            for sec_key in ['x', 'y', 'z']:
-                data[key][sec_key] = '|' + fmt['vector variables'][key] % data[key][sec_key] + '|'
+        for key in fmt["daily_lightcurve"].keys():
+            data["daily_lightcurve"][key] = (
+                "["
+                + ", ".join(
+                    map(
+                        lambda x: fmt["daily_lightcurve"][key] % x,
+                        list(data["daily_lightcurve"][key]),
+                    )
+                )
+                + "]"
+            )
+        for key in fmt["scalar variables"].keys():
+            data[key] = "|" + fmt["scalar variables"][key] % data[key] + "|"
+        for key in fmt["lon lat variables"].keys():
+            for sec_key in ["lon", "lat"]:
+                data[key][sec_key] = (
+                    "|" + fmt["lon lat variables"][key] % data[key][sec_key] + "|"
+                )
+        for key in fmt["vector variables"].keys():
+            for sec_key in ["x", "y", "z"]:
+                data[key][sec_key] = (
+                    "|" + fmt["vector variables"][key] % data[key][sec_key] + "|"
+                )
 
         # generate JSON content to be saved in output file
         json_content = json.dumps(data, indent=4, default=vars)
 
         # some corrections to get numbers back
-        json_content = json_content.replace('"[', '[').replace(']"', ']')
-        json_content = json_content.replace('|"', '').replace('"|', '')
+        json_content = json_content.replace('"[', "[").replace(']"', "]")
+        json_content = json_content.replace('|"', "").replace('"|', "")
         json_content = json_content.replace('\\"', '"')
 
         # the output file name is constructed using a convention defined in Sec. 1.4.2 of
         # "IMAP SDC to Instrument Team ICD"
         # date_utc = ''.join(filter(str.isdigit, self.data['identifier']))
-        date_utc = ''.join(filter(str.isdigit, self.data['start_time']))
-        file_name = 'data_l3a/imap_glows_l3a_%s_orbX_modX_p_v00.json' % date_utc
+        date_utc = "".join(filter(str.isdigit, self.data["start_time"]))
+        file_name = "data_l3a/imap_glows_l3a_%s_orbX_modX_p_v00.json" % date_utc
 
-        file_handler = open(file_name, 'w')
+        file_handler = open(file_name, "w")
         print(json_content, file=file_handler)
         file_handler.close()
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/reader.py b/imap_l3_processing/glows/l3a/glows_toolkit/reader.py
index ee13298fc..a0941dfc6 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/reader.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/reader.py
@@ -1,15 +1,17 @@
 """@package docstring
 Author: KPLabs with some modifications by Marek Strumik, maro at cbk.waw.pl
 """
+
 import struct
-from typing import Any, cast, Tuple, Union
+from typing import Any, cast
+
 
 class Reader:
     """
     Reader() class for reading binary stream with GLOWS data
     """
 
-    def __init__(self, buffer: Union[memoryview, bytes]):
+    def __init__(self, buffer: memoryview | bytes):
         """
         Constructor for this class, sets init values of its fields
         """
@@ -19,18 +21,18 @@ def __init__(self, buffer: Union[memoryview, bytes]):
 
     @property
     def overrun(self) -> bool:
-        """ overrun property """
+        """overrun property"""
         return self._overrun
 
     @property
     def remaining_size(self) -> int:
-        """ remaining_size property"""
+        """remaining_size property"""
         if self._overrun:
             return 0
         return len(self._buffer) - self._offset
 
     def _read_part(self, length: int) -> bytes:
-        """ read bytes from binary stream """
+        """read bytes from binary stream"""
         if self._overrun:
             return bytes([0] * length)
 
@@ -38,33 +40,33 @@ def _read_part(self, length: int) -> bytes:
             self._overrun = True
             return bytes([0] * length)
 
-        part = self._buffer[self._offset:self._offset + length]
+        part = self._buffer[self._offset : self._offset + length]
         self._offset += length
         return part
 
-    def read(self, pack_format: str) -> Tuple[Any, ...]:
-        """ read bytes according to pack_format """
+    def read(self, pack_format: str) -> tuple[Any, ...]:
+        """read bytes according to pack_format"""
         length = struct.calcsize(pack_format)
         part = self._read_part(length)
         return struct.unpack(pack_format, part)
 
     def read_uint8(self) -> int:
-        """ read one byte as uint """
-        return cast(int, self.read('<B')[0])
+        """read one byte as uint"""
+        return cast(int, self.read("<B")[0])
 
     def read_uint16_be(self) -> int:
-        """ read 2 bytes as big-endian uint """
-        return cast(int, self.read('>H')[0])
+        """read 2 bytes as big-endian uint"""
+        return cast(int, self.read(">H")[0])
 
     def read_uint24_be(self) -> int:
-        """ read 3 bytes as big-endian uint """
-        high, low = cast(Tuple[int, int], self.read('>BH'))
+        """read 3 bytes as big-endian uint"""
+        high, low = cast(tuple[int, int], self.read(">BH"))
         return high << 16 | low
 
     def read_uint32_be(self) -> int:
-        """ read 4 bytes as big-endian uint """
-        return cast(int, self.read('>L')[0])
+        """read 4 bytes as big-endian uint"""
+        return cast(int, self.read(">L")[0])
 
     def read_buffer(self, size: int) -> bytes:
-        """ read bytes from binary stream """
+        """read bytes from binary stream"""
         return self._read_part(size)
diff --git a/imap_l3_processing/glows/l3a/glows_toolkit/time.py b/imap_l3_processing/glows/l3a/glows_toolkit/time.py
index cb471709e..0863c336f 100644
--- a/imap_l3_processing/glows/l3a/glows_toolkit/time.py
+++ b/imap_l3_processing/glows/l3a/glows_toolkit/time.py
@@ -1,12 +1,15 @@
 """@package docstring
 Author: Marek Strumik, maro at cbk.waw.pl
 """
+
 from dataclasses import dataclass
 
+
 @dataclass(frozen=True)
 class Time:
     """
     Time() class for IMAP and GLOWS times (with subseconds)
     """
+
     seconds: int
     subseconds: int
diff --git a/imap_l3_processing/glows/l3a/models.py b/imap_l3_processing/glows/l3a/models.py
index b4d75894d..64108452b 100644
--- a/imap_l3_processing/glows/l3a/models.py
+++ b/imap_l3_processing/glows/l3a/models.py
@@ -7,11 +7,11 @@
 from imap_l3_processing.models import DataProduct, DataProductVariable
 
 REPOINTING_CDF_VAR_NAME = "identifier"
-PHOTON_FLUX_CDF_VAR_NAME = 'photon_flux'
-PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME = 'photon_flux_uncertainty'
-RAW_HISTOGRAM_CDF_VAR_NAME = 'raw_histogram'
-EXPOSURE_TIMES_CDF_VAR_NAME = 'exposure_times'
-NUM_OF_BINS_CDF_VAR_NAME = 'number_of_bins'
+PHOTON_FLUX_CDF_VAR_NAME = "photon_flux"
+PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME = "photon_flux_uncertainty"
+RAW_HISTOGRAM_CDF_VAR_NAME = "raw_histogram"
+EXPOSURE_TIMES_CDF_VAR_NAME = "exposure_times"
+NUM_OF_BINS_CDF_VAR_NAME = "number_of_bins"
 EPOCH_CDF_VAR_NAME = "epoch"
 EPOCH_DELTA_CDF_VAR_NAME = "epoch_delta"
 START_TIME_CDF_VAR_NAME = "start_time"
@@ -142,7 +142,9 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(REPOINTING_CDF_VAR_NAME, [self.identifier]),
             DataProductVariable(PHOTON_FLUX_CDF_VAR_NAME, self.photon_flux),
-            DataProductVariable(PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME, self.photon_flux_uncertainty),
+            DataProductVariable(
+                PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME, self.photon_flux_uncertainty
+            ),
             DataProductVariable(RAW_HISTOGRAM_CDF_VAR_NAME, self.raw_histogram),
             DataProductVariable(EXPOSURE_TIMES_CDF_VAR_NAME, self.exposure_times),
             DataProductVariable(NUM_OF_BINS_CDF_VAR_NAME, self.number_of_bins),
@@ -154,28 +156,75 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(SPIN_ANGLE_DELTA_CDF_VAR_NAME, self.spin_angle_delta),
             DataProductVariable(LATITUDE_CDF_VAR_NAME, self.latitude),
             DataProductVariable(LONGITUDE_CDF_VAR_NAME, self.longitude),
-            DataProductVariable(EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME, self.extra_heliospheric_background),
-            DataProductVariable(TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME, self.time_dependent_background),
-
-            DataProductVariable(FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME, self.filter_temperature_average),
-            DataProductVariable(FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME, self.filter_temperature_std_dev),
-            DataProductVariable(HV_VOLTAGE_AVERAGE_CDF_VAR_NAME, self.hv_voltage_average),
-            DataProductVariable(HV_VOLTAGE_STD_DEV_CDF_VAR_NAME, self.hv_voltage_std_dev),
-            DataProductVariable(SPIN_PERIOD_AVERAGE_CDF_VAR_NAME, self.spin_period_average),
-            DataProductVariable(SPIN_PERIOD_STD_DEV_CDF_VAR_NAME, self.spin_period_std_dev),
-            DataProductVariable(SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME, self.spin_period_ground_average),
-            DataProductVariable(SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME, self.spin_period_ground_std_dev),
-            DataProductVariable(PULSE_LENGTH_AVERAGE_CDF_VAR_NAME, self.pulse_length_average),
-            DataProductVariable(PULSE_LENGTH_STD_DEV_CDF_VAR_NAME, self.pulse_length_std_dev),
-            DataProductVariable(POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME, self.position_angle_offset_average),
-            DataProductVariable(POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME, self.position_angle_offset_std_dev),
-            DataProductVariable(SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME, self.spin_axis_orientation_average),
-            DataProductVariable(SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME, self.spin_axis_orientation_std_dev),
-            DataProductVariable(SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME, self.spacecraft_location_average),
-            DataProductVariable(SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME, self.spacecraft_location_std_dev),
-            DataProductVariable(SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME, self.spacecraft_velocity_average),
-            DataProductVariable(SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME, self.spacecraft_velocity_std_dev),
-
+            DataProductVariable(
+                EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME,
+                self.extra_heliospheric_background,
+            ),
+            DataProductVariable(
+                TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME, self.time_dependent_background
+            ),
+            DataProductVariable(
+                FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME, self.filter_temperature_average
+            ),
+            DataProductVariable(
+                FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME, self.filter_temperature_std_dev
+            ),
+            DataProductVariable(
+                HV_VOLTAGE_AVERAGE_CDF_VAR_NAME, self.hv_voltage_average
+            ),
+            DataProductVariable(
+                HV_VOLTAGE_STD_DEV_CDF_VAR_NAME, self.hv_voltage_std_dev
+            ),
+            DataProductVariable(
+                SPIN_PERIOD_AVERAGE_CDF_VAR_NAME, self.spin_period_average
+            ),
+            DataProductVariable(
+                SPIN_PERIOD_STD_DEV_CDF_VAR_NAME, self.spin_period_std_dev
+            ),
+            DataProductVariable(
+                SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME, self.spin_period_ground_average
+            ),
+            DataProductVariable(
+                SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME, self.spin_period_ground_std_dev
+            ),
+            DataProductVariable(
+                PULSE_LENGTH_AVERAGE_CDF_VAR_NAME, self.pulse_length_average
+            ),
+            DataProductVariable(
+                PULSE_LENGTH_STD_DEV_CDF_VAR_NAME, self.pulse_length_std_dev
+            ),
+            DataProductVariable(
+                POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME,
+                self.position_angle_offset_average,
+            ),
+            DataProductVariable(
+                POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME,
+                self.position_angle_offset_std_dev,
+            ),
+            DataProductVariable(
+                SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME,
+                self.spin_axis_orientation_average,
+            ),
+            DataProductVariable(
+                SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME,
+                self.spin_axis_orientation_std_dev,
+            ),
+            DataProductVariable(
+                SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME,
+                self.spacecraft_location_average,
+            ),
+            DataProductVariable(
+                SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME,
+                self.spacecraft_location_std_dev,
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME,
+                self.spacecraft_velocity_average,
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME,
+                self.spacecraft_velocity_std_dev,
+            ),
             DataProductVariable("lon_lat", np.arange(2)),
             DataProductVariable("lon_lat_labels", ["lon", "lat"]),
             DataProductVariable("x_y_z", np.arange(3)),
diff --git a/imap_l3_processing/glows/l3a/science/bad_angle_flag_configuration.py b/imap_l3_processing/glows/l3a/science/bad_angle_flag_configuration.py
index 399eda955..21235890c 100644
--- a/imap_l3_processing/glows/l3a/science/bad_angle_flag_configuration.py
+++ b/imap_l3_processing/glows/l3a/science/bad_angle_flag_configuration.py
@@ -1,7 +1,6 @@
 import json
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Union
 
 import numpy as np
 
@@ -14,15 +13,19 @@ class BadAngleFlagConfiguration:
     mask_suspected_transient: bool
 
     def evaluate_flags(self, flags: np.ndarray):
-        configuration = np.array([self.mask_close_to_uv_source,
-                                  self.mask_inside_excluded_region,
-                                  self.mask_excluded_by_instr_team,
-                                  self.mask_suspected_transient]).reshape(4, 1)
+        configuration = np.array(
+            [
+                self.mask_close_to_uv_source,
+                self.mask_inside_excluded_region,
+                self.mask_excluded_by_instr_team,
+                self.mask_suspected_transient,
+            ]
+        ).reshape(4, 1)
 
         return np.any(np.logical_and(configuration, flags), axis=-2)
 
     @classmethod
-    def from_file(cls, file_path: Union[Path, str]):
+    def from_file(cls, file_path: Path | str):
         with open(file_path) as f:
             config = json.load(f)
 
diff --git a/imap_l3_processing/glows/l3a/science/calculate_daily_lightcurve.py b/imap_l3_processing/glows/l3a/science/calculate_daily_lightcurve.py
index f5189f1c6..c368f7f21 100644
--- a/imap_l3_processing/glows/l3a/science/calculate_daily_lightcurve.py
+++ b/imap_l3_processing/glows/l3a/science/calculate_daily_lightcurve.py
@@ -1,21 +1,29 @@
 import numpy as np
 from numpy import ndarray
 
-from imap_l3_processing.glows.l3a.science.bad_angle_flag_configuration import BadAngleFlagConfiguration
-from imap_l3_processing.glows.l3a.science.time_independent_background_lookup_table import \
-    TimeIndependentBackgroundLookupTable
-
-
-def rebin_lightcurve(time_independent_background_table: TimeIndependentBackgroundLookupTable,
-                     bad_angle_flag_configuration: BadAngleFlagConfiguration,
-                     photon_flux: np.ndarray,
-                     latitudes: np.ndarray,
-                     longitudes: np.ndarray,
-                     flags: np.ndarray,
-                     exposure_times: np.ndarray, output_size: int, background: ndarray[float]
-                     ) -> tuple[np.ndarray, np.ndarray]:
-    photon_flux_minus_time_independent_background = photon_flux - time_independent_background_table.lookup(
-        lat=latitudes, lon=longitudes)
+from imap_l3_processing.glows.l3a.science.bad_angle_flag_configuration import (
+    BadAngleFlagConfiguration,
+)
+from imap_l3_processing.glows.l3a.science.time_independent_background_lookup_table import (
+    TimeIndependentBackgroundLookupTable,
+)
+
+
+def rebin_lightcurve(
+    time_independent_background_table: TimeIndependentBackgroundLookupTable,
+    bad_angle_flag_configuration: BadAngleFlagConfiguration,
+    photon_flux: np.ndarray,
+    latitudes: np.ndarray,
+    longitudes: np.ndarray,
+    flags: np.ndarray,
+    exposure_times: np.ndarray,
+    output_size: int,
+    background: ndarray[float],
+) -> tuple[np.ndarray, np.ndarray]:
+    photon_flux_minus_time_independent_background = (
+        photon_flux
+        - time_independent_background_table.lookup(lat=latitudes, lon=longitudes)
+    )
 
     masked = bad_angle_flag_configuration.evaluate_flags(flags)
     included = np.logical_not(masked)
@@ -24,13 +32,21 @@ def rebin_lightcurve(time_independent_background_table: TimeIndependentBackgroun
 
     exposure_times_rebinned = np.sum(exposure_time_by_bins, axis=-1)
 
-    filtered_photon_flux = photon_flux_minus_time_independent_background * filtered_exposure_times
+    filtered_photon_flux = (
+        photon_flux_minus_time_independent_background * filtered_exposure_times
+    )
     split_by_bins = np.reshape(filtered_photon_flux, (output_size, -1))
 
-    rebinned_photon_flux = np.full(shape=(output_size,), fill_value=np.nan, dtype=filtered_photon_flux.dtype)
+    rebinned_photon_flux = np.full(
+        shape=(output_size,), fill_value=np.nan, dtype=filtered_photon_flux.dtype
+    )
 
-    np.divide(np.sum(split_by_bins, axis=-1), exposure_times_rebinned, out=rebinned_photon_flux,
-              where=exposure_times_rebinned != 0)
+    np.divide(
+        np.sum(split_by_bins, axis=-1),
+        exposure_times_rebinned,
+        out=rebinned_photon_flux,
+        where=exposure_times_rebinned != 0,
+    )
 
     rebinned_photon_flux = rebinned_photon_flux - background
     return rebinned_photon_flux, exposure_times_rebinned
diff --git a/imap_l3_processing/glows/l3a/science/time_independent_background_lookup_table.py b/imap_l3_processing/glows/l3a/science/time_independent_background_lookup_table.py
index 632b4c094..78467b436 100644
--- a/imap_l3_processing/glows/l3a/science/time_independent_background_lookup_table.py
+++ b/imap_l3_processing/glows/l3a/science/time_independent_background_lookup_table.py
@@ -1,11 +1,12 @@
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Union, TypeVar
+from typing import TypeVar
 
 import numpy as np
 import scipy.interpolate
+from typing_extensions import Self
 
-T = TypeVar('T', bound='TimeIndependentBackgroundLookupTable')
+T = TypeVar("T", bound="TimeIndependentBackgroundLookupTable")
 
 
 @dataclass
@@ -14,16 +15,22 @@ class TimeIndependentBackgroundLookupTable:
     latitudes: np.ndarray[float]
     background_values: np.ndarray[float]
 
-    def lookup(self, lat: Union[np.ndarray[float], float], lon: Union[np.ndarray[float], float]) -> Union[
-        np.ndarray[float], float]:
+    def lookup(
+        self, lat: np.ndarray[float] | float, lon: np.ndarray[float] | float
+    ) -> np.ndarray[float] | float:
         wrapped_lon = np.mod(lon, 360)
-        background_extended = np.append(self.background_values, self.background_values[:, :1], axis=1)
+        background_extended = np.append(
+            self.background_values, self.background_values[:, :1], axis=1
+        )
         longitudes_extended = np.append(self.longitudes, self.longitudes[0] + 360)
-        return scipy.interpolate.interpn((self.latitudes, longitudes_extended), background_extended,
-                                         np.column_stack([lat, wrapped_lon]))
+        return scipy.interpolate.interpn(
+            (self.latitudes, longitudes_extended),
+            background_extended,
+            np.column_stack([lat, wrapped_lon]),
+        )
 
     @classmethod
-    def from_file(cls: type[T], path: Path) -> T:
+    def from_file(cls, path: Path) -> Self:
         values = np.loadtxt(path)
         numlat, numlon = values.shape
         longitudes = np.linspace(0, 360, numlon, endpoint=False)
diff --git a/imap_l3_processing/glows/l3a/utils.py b/imap_l3_processing/glows/l3a/utils.py
index 77b2d9d0c..6ab8c89ec 100644
--- a/imap_l3_processing/glows/l3a/utils.py
+++ b/imap_l3_processing/glows/l3a/utils.py
@@ -6,8 +6,14 @@
 from imap_data_access import AncillaryFilePath
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.glows.l3a.models import GlowsL2Data, GlowsL2LightCurve, GlowsLatLon, GlowsL3LightCurve, XYZ, \
-    GlowsL2Header
+from imap_l3_processing.glows.l3a.models import (
+    XYZ,
+    GlowsL2Data,
+    GlowsL2Header,
+    GlowsL2LightCurve,
+    GlowsL3LightCurve,
+    GlowsLatLon,
+)
 from imap_l3_processing.models import InputMetadata, Instrument
 
 MAX_SPIN_ANGLE_BINS = 90
@@ -18,13 +24,13 @@ def _pad_lightcurve(values) -> np.ndarray:
     values = np.asarray(values)
     if len(values) > MAX_SPIN_ANGLE_BINS:
         raise ValueError(
-                f"GLOWS L3a lightcurve bins ({len(values)}) exceed "
-                f"the allowed maximum ({MAX_SPIN_ANGLE_BINS})."
+            f"GLOWS L3a lightcurve bins ({len(values)}) exceed "
+            f"the allowed maximum ({MAX_SPIN_ANGLE_BINS})."
         )
 
     if np.issubdtype(values.dtype, np.integer):
         padded_values = np.ma.masked_all(MAX_SPIN_ANGLE_BINS, dtype=values.dtype)
-        padded_values[:len(values)] = values
+        padded_values[: len(values)] = values
         return padded_values.reshape(1, -1)
 
     return np.pad(
@@ -35,32 +41,48 @@ def _pad_lightcurve(values) -> np.ndarray:
 
 
 def read_l2_glows_data(cdf: CDF) -> GlowsL2Data:
-    assert 1 == cdf['photon_flux'].shape[0], "Level 2 file should have only one histogram"
-
-    light_curve = GlowsL2LightCurve(photon_flux=cdf['photon_flux'][0],
-                                    spin_angle=cdf['spin_angle'][0],
-                                    histogram_flag_array=cdf['histogram_flag_array'][0],
-                                    exposure_times=cdf['exposure_times'][0],
-                                    flux_uncertainties=cdf['flux_uncertainties'][0],
-                                    raw_histogram=cdf['raw_histograms'][0],
-                                    ecliptic_lon=cdf['ecliptic_lon'][0],
-                                    ecliptic_lat=cdf['ecliptic_lat'][0])
-    spin_axis_average = GlowsLatLon(lon=cdf['spin_axis_orientation_average'][0, 0],
-                                    lat=cdf['spin_axis_orientation_average'][0, 1])
-    spin_axis_std_dev = GlowsLatLon(lon=cdf['spin_axis_orientation_std_dev'][0, 0],
-                                    lat=cdf['spin_axis_orientation_std_dev'][0, 1])
-    spacecraft_location_average = XYZ(x=cdf['spacecraft_location_average'][0, 0],
-                                      y=cdf['spacecraft_location_average'][0, 1],
-                                      z=cdf['spacecraft_location_average'][0, 2], )
-    spacecraft_location_std_dev = XYZ(x=cdf['spacecraft_location_std_dev'][0, 0],
-                                      y=cdf['spacecraft_location_std_dev'][0, 1],
-                                      z=cdf['spacecraft_location_std_dev'][0, 2], )
-    spacecraft_velocity_average = XYZ(x=cdf['spacecraft_velocity_average'][0, 0],
-                                      y=cdf['spacecraft_velocity_average'][0, 1],
-                                      z=cdf['spacecraft_velocity_average'][0, 2], )
-    spacecraft_velocity_std_dev = XYZ(x=cdf['spacecraft_velocity_std_dev'][0, 0],
-                                      y=cdf['spacecraft_velocity_std_dev'][0, 1],
-                                      z=cdf['spacecraft_velocity_std_dev'][0, 2], )
+    assert 1 == cdf["photon_flux"].shape[0], (
+        "Level 2 file should have only one histogram"
+    )
+
+    light_curve = GlowsL2LightCurve(
+        photon_flux=cdf["photon_flux"][0],
+        spin_angle=cdf["spin_angle"][0],
+        histogram_flag_array=cdf["histogram_flag_array"][0],
+        exposure_times=cdf["exposure_times"][0],
+        flux_uncertainties=cdf["flux_uncertainties"][0],
+        raw_histogram=cdf["raw_histograms"][0],
+        ecliptic_lon=cdf["ecliptic_lon"][0],
+        ecliptic_lat=cdf["ecliptic_lat"][0],
+    )
+    spin_axis_average = GlowsLatLon(
+        lon=cdf["spin_axis_orientation_average"][0, 0],
+        lat=cdf["spin_axis_orientation_average"][0, 1],
+    )
+    spin_axis_std_dev = GlowsLatLon(
+        lon=cdf["spin_axis_orientation_std_dev"][0, 0],
+        lat=cdf["spin_axis_orientation_std_dev"][0, 1],
+    )
+    spacecraft_location_average = XYZ(
+        x=cdf["spacecraft_location_average"][0, 0],
+        y=cdf["spacecraft_location_average"][0, 1],
+        z=cdf["spacecraft_location_average"][0, 2],
+    )
+    spacecraft_location_std_dev = XYZ(
+        x=cdf["spacecraft_location_std_dev"][0, 0],
+        y=cdf["spacecraft_location_std_dev"][0, 1],
+        z=cdf["spacecraft_location_std_dev"][0, 2],
+    )
+    spacecraft_velocity_average = XYZ(
+        x=cdf["spacecraft_velocity_average"][0, 0],
+        y=cdf["spacecraft_velocity_average"][0, 1],
+        z=cdf["spacecraft_velocity_average"][0, 2],
+    )
+    spacecraft_velocity_std_dev = XYZ(
+        x=cdf["spacecraft_velocity_std_dev"][0, 0],
+        y=cdf["spacecraft_velocity_std_dev"][0, 1],
+        z=cdf["spacecraft_velocity_std_dev"][0, 2],
+    )
 
     ancillary_files = []
     for file in list(cdf.attrs["Parents"][...]):
@@ -71,43 +93,46 @@ def read_l2_glows_data(cdf: CDF) -> GlowsL2Data:
         except AncillaryFilePath.InvalidImapFileError:
             pass
 
-    return GlowsL2Data(identifier=cdf['identifier'][0],
-                       start_time=cdf['start_time'][0],
-                       end_time=cdf['end_time'][0],
-                       daily_lightcurve=light_curve,
-                       number_of_bins=cdf['number_of_bins'][0],
-                       spin_axis_orientation_average=spin_axis_average,
-                       spin_axis_orientation_std_dev=spin_axis_std_dev,
-                       filter_temperature_average=cdf['filter_temperature_average'][0],
-                       filter_temperature_std_dev=cdf['filter_temperature_std_dev'][0],
-                       hv_voltage_average=cdf['hv_voltage_average'][0],
-                       hv_voltage_std_dev=cdf['hv_voltage_std_dev'][0],
-                       spin_period_average=cdf['spin_period_average'][0],
-                       spin_period_std_dev=cdf['spin_period_std_dev'][0],
-                       spin_period_ground_average=cdf['spin_period_ground_average'][0],
-                       spin_period_ground_std_dev=cdf['spin_period_ground_std_dev'][0],
-                       pulse_length_average=cdf['pulse_length_average'][0],
-                       pulse_length_std_dev=cdf['pulse_length_std_dev'][0],
-                       position_angle_offset_average=cdf['position_angle_offset_average'][0],
-                       position_angle_offset_std_dev=cdf['position_angle_offset_std_dev'][0],
-                       spacecraft_location_average=spacecraft_location_average,
-                       spacecraft_location_std_dev=spacecraft_location_std_dev,
-                       spacecraft_velocity_average=spacecraft_velocity_average,
-                       spacecraft_velocity_std_dev=spacecraft_velocity_std_dev,
-                       header=GlowsL2Header(
-                           flight_software_version=str(cdf.attrs["flight_software_version"][0]),
-                           pkts_file_name=cdf.attrs["pkts_file_name"][0],
-                           ancillary_data_files=ancillary_files,
-                       ),
-                       l2_file_name=Path(cdf.pathname.decode('utf-8')).name
-                       )
+    return GlowsL2Data(
+        identifier=cdf["identifier"][0],
+        start_time=cdf["start_time"][0],
+        end_time=cdf["end_time"][0],
+        daily_lightcurve=light_curve,
+        number_of_bins=cdf["number_of_bins"][0],
+        spin_axis_orientation_average=spin_axis_average,
+        spin_axis_orientation_std_dev=spin_axis_std_dev,
+        filter_temperature_average=cdf["filter_temperature_average"][0],
+        filter_temperature_std_dev=cdf["filter_temperature_std_dev"][0],
+        hv_voltage_average=cdf["hv_voltage_average"][0],
+        hv_voltage_std_dev=cdf["hv_voltage_std_dev"][0],
+        spin_period_average=cdf["spin_period_average"][0],
+        spin_period_std_dev=cdf["spin_period_std_dev"][0],
+        spin_period_ground_average=cdf["spin_period_ground_average"][0],
+        spin_period_ground_std_dev=cdf["spin_period_ground_std_dev"][0],
+        pulse_length_average=cdf["pulse_length_average"][0],
+        pulse_length_std_dev=cdf["pulse_length_std_dev"][0],
+        position_angle_offset_average=cdf["position_angle_offset_average"][0],
+        position_angle_offset_std_dev=cdf["position_angle_offset_std_dev"][0],
+        spacecraft_location_average=spacecraft_location_average,
+        spacecraft_location_std_dev=spacecraft_location_std_dev,
+        spacecraft_velocity_average=spacecraft_velocity_average,
+        spacecraft_velocity_std_dev=spacecraft_velocity_std_dev,
+        header=GlowsL2Header(
+            flight_software_version=str(cdf.attrs["flight_software_version"][0]),
+            pkts_file_name=cdf.attrs["pkts_file_name"][0],
+            ancillary_data_files=ancillary_files,
+        ),
+        l2_file_name=Path(cdf.pathname.decode("utf-8")).name,
+    )
 
 
 def _read_xyz(cdf: CDF, variable_name: str) -> XYZ:
-    return {k: cdf[f'{variable_name}_{k}'][0] for k in "xyz"}
+    return {k: cdf[f"{variable_name}_{k}"][0] for k in "xyz"}
 
 
-def create_glows_l3a_from_dictionary(data: dict, input_metadata: InputMetadata) -> GlowsL3LightCurve:
+def create_glows_l3a_from_dictionary(
+    data: dict, input_metadata: InputMetadata
+) -> GlowsL3LightCurve:
     start_time = datetime.fromisoformat(data["start_time"])
     end_time = datetime.fromisoformat(data["end_time"])
     total_time = end_time - start_time
@@ -121,15 +146,21 @@ def create_glows_l3a_from_dictionary(data: dict, input_metadata: InputMetadata)
         start_time=data["start_time"],
         end_time=data["end_time"],
         photon_flux=_pad_lightcurve(data["daily_lightcurve"]["photon_flux"]),
-        photon_flux_uncertainty=_pad_lightcurve(data["daily_lightcurve"]["flux_uncertainties"]),
+        photon_flux_uncertainty=_pad_lightcurve(
+            data["daily_lightcurve"]["flux_uncertainties"]
+        ),
         raw_histogram=_pad_lightcurve(data["daily_lightcurve"]["raw_histogram"]),
         exposure_times=_pad_lightcurve(data["daily_lightcurve"]["exposure_times"]),
         spin_angle=_pad_lightcurve(data["daily_lightcurve"]["spin_angle"]),
         spin_angle_delta=_pad_lightcurve(data["daily_lightcurve"]["spin_angle_delta"]),
         latitude=_pad_lightcurve(data["daily_lightcurve"]["ecliptic_lat"]),
         longitude=_pad_lightcurve(data["daily_lightcurve"]["ecliptic_lon"]),
-        extra_heliospheric_background=_pad_lightcurve(data["daily_lightcurve"]["extra_heliospheric_bckgrd"]),
-        time_dependent_background=_pad_lightcurve(data["daily_lightcurve"]["time_dependent_bckgrd"]),
+        extra_heliospheric_background=_pad_lightcurve(
+            data["daily_lightcurve"]["extra_heliospheric_bckgrd"]
+        ),
+        time_dependent_background=_pad_lightcurve(
+            data["daily_lightcurve"]["time_dependent_bckgrd"]
+        ),
         filter_temperature_average=np.array([data["filter_temperature_average"]]),
         filter_temperature_std_dev=np.array([data["filter_temperature_std_dev"]]),
         hv_voltage_average=np.array([data["hv_voltage_average"]]),
@@ -142,77 +173,83 @@ def create_glows_l3a_from_dictionary(data: dict, input_metadata: InputMetadata)
         pulse_length_std_dev=np.array([data["pulse_length_std_dev"]]),
         position_angle_offset_average=np.array([data["position_angle_offset_average"]]),
         position_angle_offset_std_dev=np.array([data["position_angle_offset_std_dev"]]),
-        spin_axis_orientation_average=get_lon_lat(data["spin_axis_orientation_average"]),
-        spin_axis_orientation_std_dev=get_lon_lat(data["spin_axis_orientation_std_dev"]),
+        spin_axis_orientation_average=get_lon_lat(
+            data["spin_axis_orientation_average"]
+        ),
+        spin_axis_orientation_std_dev=get_lon_lat(
+            data["spin_axis_orientation_std_dev"]
+        ),
         spacecraft_location_average=get_xyz(data["spacecraft_location_average"]),
         spacecraft_location_std_dev=get_xyz(data["spacecraft_location_std_dev"]),
         spacecraft_velocity_average=get_xyz(data["spacecraft_velocity_average"]),
         spacecraft_velocity_std_dev=get_xyz(data["spacecraft_velocity_std_dev"]),
-        number_of_bins=np.array([data['daily_lightcurve']['number_of_bins']]),
+        number_of_bins=np.array([data["daily_lightcurve"]["number_of_bins"]]),
     )
 
 
 def create_glows_l3a_dictionary_from_cdf(cdf_file_path: Path) -> dict:
     cdf = CDF(str(cdf_file_path))
-    time_delta = timedelta(seconds=cdf['epoch_delta'][0] / 1e9)
-    start_time = cdf['epoch'][0] - time_delta
-    end_time = cdf['epoch'][0] + time_delta
-    valid_bin_count = cdf['number_of_bins'][0]
+    time_delta = timedelta(seconds=cdf["epoch_delta"][0] / 1e9)
+    start_time = cdf["epoch"][0] - time_delta
+    end_time = cdf["epoch"][0] + time_delta
+    valid_bin_count = cdf["number_of_bins"][0]
     return {
-        'filename': f'{os.path.basename(cdf_file_path)}',
-        'start_time': start_time.strftime("%Y-%m-%d %H:%M:%S"),
-        'end_time': end_time.strftime("%Y-%m-%d %H:%M:%S"),
-        'daily_lightcurve': {
-            'ecliptic_lat': cdf['ecliptic_lat'][0][:valid_bin_count],
-            'ecliptic_lon': cdf['ecliptic_lon'][0][:valid_bin_count],
-            'exposure_times': cdf['exposure_times'][0][:valid_bin_count],
-            'photon_flux': cdf['photon_flux'][0][:valid_bin_count],
-            'flux_uncertainties': cdf['photon_flux_uncertainty'][0][:valid_bin_count],
-            'extra_heliospheric_bckgrd': cdf['extra_heliospheric_bckgrd'][0][:valid_bin_count],
-            'time_dependent_bckgrd': cdf['time_dependent_bckgrd'][0][:valid_bin_count],
-            'spin_angle': cdf['spin_angle'][0][:valid_bin_count],
-            'raw_histogram': cdf['raw_histogram'][0][:valid_bin_count],
-            'number_of_bins': valid_bin_count
+        "filename": f"{os.path.basename(cdf_file_path)}",
+        "start_time": start_time.strftime("%Y-%m-%d %H:%M:%S"),
+        "end_time": end_time.strftime("%Y-%m-%d %H:%M:%S"),
+        "daily_lightcurve": {
+            "ecliptic_lat": cdf["ecliptic_lat"][0][:valid_bin_count],
+            "ecliptic_lon": cdf["ecliptic_lon"][0][:valid_bin_count],
+            "exposure_times": cdf["exposure_times"][0][:valid_bin_count],
+            "photon_flux": cdf["photon_flux"][0][:valid_bin_count],
+            "flux_uncertainties": cdf["photon_flux_uncertainty"][0][:valid_bin_count],
+            "extra_heliospheric_bckgrd": cdf["extra_heliospheric_bckgrd"][0][
+                :valid_bin_count
+            ],
+            "time_dependent_bckgrd": cdf["time_dependent_bckgrd"][0][:valid_bin_count],
+            "spin_angle": cdf["spin_angle"][0][:valid_bin_count],
+            "raw_histogram": cdf["raw_histogram"][0][:valid_bin_count],
+            "number_of_bins": valid_bin_count,
         },
-        'filter_temperature_average': cdf['filter_temperature_average'][0],
-        'filter_temperature_std_dev': cdf['filter_temperature_std_dev'][0],
-        'hv_voltage_average': cdf['hv_voltage_average'][0],
-        'hv_voltage_std_dev': cdf['hv_voltage_std_dev'][0],
-        'spin_period_average': cdf['spin_period_average'][0],
-        'spin_period_std_dev': cdf['spin_period_std_dev'][0],
-        'spin_period_ground_average': cdf['spin_period_ground_average'][0],
-        'spin_period_ground_std_dev': cdf['spin_period_ground_std_dev'][0],
-        'pulse_length_average': cdf['pulse_length_average'][0],
-        'pulse_length_std_dev': cdf['pulse_length_std_dev'][0],
-        'position_angle_offset_average': cdf['position_angle_offset_average'][0],
-        'position_angle_offset_std_dev': cdf['position_angle_offset_std_dev'][0],
-        'spin_axis_orientation_average': {
-            'lon': cdf['spin_axis_orientation_average'][0][0],
-            'lat': cdf['spin_axis_orientation_average'][0][1],
+        "filter_temperature_average": cdf["filter_temperature_average"][0],
+        "filter_temperature_std_dev": cdf["filter_temperature_std_dev"][0],
+        "hv_voltage_average": cdf["hv_voltage_average"][0],
+        "hv_voltage_std_dev": cdf["hv_voltage_std_dev"][0],
+        "spin_period_average": cdf["spin_period_average"][0],
+        "spin_period_std_dev": cdf["spin_period_std_dev"][0],
+        "spin_period_ground_average": cdf["spin_period_ground_average"][0],
+        "spin_period_ground_std_dev": cdf["spin_period_ground_std_dev"][0],
+        "pulse_length_average": cdf["pulse_length_average"][0],
+        "pulse_length_std_dev": cdf["pulse_length_std_dev"][0],
+        "position_angle_offset_average": cdf["position_angle_offset_average"][0],
+        "position_angle_offset_std_dev": cdf["position_angle_offset_std_dev"][0],
+        "spin_axis_orientation_average": {
+            "lon": cdf["spin_axis_orientation_average"][0][0],
+            "lat": cdf["spin_axis_orientation_average"][0][1],
         },
-        'spin_axis_orientation_std_dev': {
-            'lon': cdf['spin_axis_orientation_std_dev'][0][0],
-            'lat': cdf['spin_axis_orientation_std_dev'][0][1],
+        "spin_axis_orientation_std_dev": {
+            "lon": cdf["spin_axis_orientation_std_dev"][0][0],
+            "lat": cdf["spin_axis_orientation_std_dev"][0][1],
         },
-        'spacecraft_location_average': {
-            'x': cdf['spacecraft_location_average'][0][0],
-            'y': cdf['spacecraft_location_average'][0][1],
-            'z': cdf['spacecraft_location_average'][0][2],
+        "spacecraft_location_average": {
+            "x": cdf["spacecraft_location_average"][0][0],
+            "y": cdf["spacecraft_location_average"][0][1],
+            "z": cdf["spacecraft_location_average"][0][2],
         },
-        'spacecraft_location_std_dev': {
-            'x': cdf['spacecraft_location_std_dev'][0][0],
-            'y': cdf['spacecraft_location_std_dev'][0][1],
-            'z': cdf['spacecraft_location_std_dev'][0][2],
+        "spacecraft_location_std_dev": {
+            "x": cdf["spacecraft_location_std_dev"][0][0],
+            "y": cdf["spacecraft_location_std_dev"][0][1],
+            "z": cdf["spacecraft_location_std_dev"][0][2],
         },
-        'spacecraft_velocity_average': {
-            'x': cdf['spacecraft_velocity_average'][0][0],
-            'y': cdf['spacecraft_velocity_average'][0][1],
-            'z': cdf['spacecraft_velocity_average'][0][2],
+        "spacecraft_velocity_average": {
+            "x": cdf["spacecraft_velocity_average"][0][0],
+            "y": cdf["spacecraft_velocity_average"][0][1],
+            "z": cdf["spacecraft_velocity_average"][0][2],
         },
-        'spacecraft_velocity_std_dev': {
-            'x': cdf['spacecraft_velocity_std_dev'][0][0],
-            'y': cdf['spacecraft_velocity_std_dev'][0][1],
-            'z': cdf['spacecraft_velocity_std_dev'][0][2],
+        "spacecraft_velocity_std_dev": {
+            "x": cdf["spacecraft_velocity_std_dev"][0][0],
+            "y": cdf["spacecraft_velocity_std_dev"][0][1],
+            "z": cdf["spacecraft_velocity_std_dev"][0][2],
         },
     }
 
diff --git a/imap_l3_processing/glows/l3bc/cannot_process_carrington_rotation_error.py b/imap_l3_processing/glows/l3bc/cannot_process_carrington_rotation_error.py
index 476061749..6eb626984 100644
--- a/imap_l3_processing/glows/l3bc/cannot_process_carrington_rotation_error.py
+++ b/imap_l3_processing/glows/l3bc/cannot_process_carrington_rotation_error.py
@@ -1,4 +1,3 @@
 class CannotProcessCarringtonRotationError(Exception):
-
     def __init__(self, message):
         super().__init__(message)
diff --git a/imap_l3_processing/glows/l3bc/dependency_validator.py b/imap_l3_processing/glows/l3bc/dependency_validator.py
index bdf58db2e..335f74012 100644
--- a/imap_l3_processing/glows/l3bc/dependency_validator.py
+++ b/imap_l3_processing/glows/l3bc/dependency_validator.py
@@ -3,7 +3,6 @@
 
 import netCDF4
 import numpy as np
-from astropy.time import Time, TimeDelta
 from numpy import ndarray
 
 YEAR_COLUMN = 0
@@ -15,41 +14,62 @@
 SPEED_COLUMN_2 = 8
 
 
-def validate_omni2_dependency(cr_start_date: datetime,
-                              cr_end_date_exclusive: datetime, file_path: Path) -> bool:
-    omni_data: ndarray = np.loadtxt(file_path, usecols=(0, 1, 2, 5, 23, 24, 27, 30, 31, 34))
+def validate_omni2_dependency(
+    cr_start_date: datetime, cr_end_date_exclusive: datetime, file_path: Path
+) -> bool:
+    omni_data: ndarray = np.loadtxt(
+        file_path, usecols=(0, 1, 2, 5, 23, 24, 27, 30, 31, 34)
+    )
 
     row_day_keys = omni_data[:, YEAR_COLUMN] * 1000 + omni_data[:, DOY_COLUMN]
     start_key = cr_start_date.year * 1000 + cr_start_date.timetuple().tm_yday
-    end_key = cr_end_date_exclusive.year * 1000 + cr_end_date_exclusive.timetuple().tm_yday
+    end_key = (
+        cr_end_date_exclusive.year * 1000 + cr_end_date_exclusive.timetuple().tm_yday
+    )
 
     start_idx = np.searchsorted(row_day_keys, start_key)
     end_idx = np.searchsorted(row_day_keys, end_key)
     omni_data = omni_data[start_idx:end_idx]
 
     for data_point in omni_data:
-        mask_fill_density_rows = np.all(data_point[[DENSITY_COLUMN_1, DENSITY_COLUMN_2]] < 999.9)
-        mask_fill_speed_rows = np.all(data_point[[SPEED_COLUMN_1, SPEED_COLUMN_2]] < 9999)
+        mask_fill_density_rows = np.all(
+            data_point[[DENSITY_COLUMN_1, DENSITY_COLUMN_2]] < 999.9
+        )
+        mask_fill_speed_rows = np.all(
+            data_point[[SPEED_COLUMN_1, SPEED_COLUMN_2]] < 9999
+        )
 
         if np.all([mask_fill_density_rows, mask_fill_speed_rows]):
             return True
 
     return False
 
-def validate_dependencies(cr_start_date: datetime, cr_end_date: datetime, buffer: timedelta,
-                          omni2_file_path: Path,
-                          f107_index_path: Path, lyman_alpha_path: Path) -> bool:
-    omni_condition = validate_omni2_dependency(cr_start_date, cr_end_date, omni2_file_path)
 
-    f107_condition = validate_f107_fluxtable_dependency(cr_end_date, buffer,
-                                                        f107_index_path)
-    lyman_alpha_condition = validate_lyman_alpha_dependency(cr_end_date, buffer, lyman_alpha_path)
+def validate_dependencies(
+    cr_start_date: datetime,
+    cr_end_date: datetime,
+    buffer: timedelta,
+    omni2_file_path: Path,
+    f107_index_path: Path,
+    lyman_alpha_path: Path,
+) -> bool:
+    omni_condition = validate_omni2_dependency(
+        cr_start_date, cr_end_date, omni2_file_path
+    )
+
+    f107_condition = validate_f107_fluxtable_dependency(
+        cr_end_date, buffer, f107_index_path
+    )
+    lyman_alpha_condition = validate_lyman_alpha_dependency(
+        cr_end_date, buffer, lyman_alpha_path
+    )
 
     return omni_condition and f107_condition and lyman_alpha_condition
 
 
-def validate_f107_fluxtable_dependency(end_date: datetime,
-                                       buffer: timedelta, file_path: Path) -> bool:
+def validate_f107_fluxtable_dependency(
+    end_date: datetime, buffer: timedelta, file_path: Path
+) -> bool:
     f107_data = np.loadtxt(file_path, dtype=str)
     f107_data = f107_data[2:]
     last_date = f107_data[-1][0]
@@ -57,10 +77,12 @@ def validate_f107_fluxtable_dependency(end_date: datetime,
     return datetime.strptime(last_date, "%Y%m%d") >= end_date + buffer
 
 
-def validate_lyman_alpha_dependency(end_date: datetime, buffer: timedelta, file_path: Path) -> bool:
+def validate_lyman_alpha_dependency(
+    end_date: datetime, buffer: timedelta, file_path: Path
+) -> bool:
     cdf = netCDF4.Dataset(file_path)
 
-    isoformat_time = cdf.time_coverage_end.replace('Z', '+00:00')
+    isoformat_time = cdf.time_coverage_end.replace("Z", "+00:00")
 
     datetime_from_iso = datetime.fromisoformat(isoformat_time).replace(tzinfo=None)
 
diff --git a/imap_l3_processing/glows/l3bc/glows_l3bc_dependencies.py b/imap_l3_processing/glows/l3bc/glows_l3bc_dependencies.py
index c71c7236b..78c07edd5 100644
--- a/imap_l3_processing/glows/l3bc/glows_l3bc_dependencies.py
+++ b/imap_l3_processing/glows/l3bc/glows_l3bc_dependencies.py
@@ -22,21 +22,34 @@ class GlowsL3BCDependencies:
 
     @property
     def l3a_file_names(self):
-        return [l3a['filename'] for l3a in self.l3a_data]
+        return [l3a["filename"] for l3a in self.l3a_data]
 
     @classmethod
-    def download_from_cr_to_process(cls, cr_to_process: CRToProcess, version: Version,
-                                    external_dependencies: ExternalDependencies, repointing_file_path: Path):
+    def download_from_cr_to_process(
+        cls,
+        cr_to_process: CRToProcess,
+        version: Version,
+        external_dependencies: ExternalDependencies,
+        repointing_file_path: Path,
+    ):
         external_files = {
-            'f107_raw_data': external_dependencies.f107_index_file_path,
-            'omni_raw_data': external_dependencies.omni2_data_path
+            "f107_raw_data": external_dependencies.f107_index_file_path,
+            "omni_raw_data": external_dependencies.omni2_data_path,
         }
 
         ancillary_files = {
-            'uv_anisotropy': imap_data_access.download(cr_to_process.uv_anisotropy_file_name),
-            'WawHelioIonMP_parameters': imap_data_access.download(cr_to_process.waw_helio_ion_mp_file_name),
-            'bad_days_list': imap_data_access.download(cr_to_process.bad_days_list_file_name),
-            'pipeline_settings': imap_data_access.download(cr_to_process.pipeline_settings_file_name),
+            "uv_anisotropy": imap_data_access.download(
+                cr_to_process.uv_anisotropy_file_name
+            ),
+            "WawHelioIonMP_parameters": imap_data_access.download(
+                cr_to_process.waw_helio_ion_mp_file_name
+            ),
+            "bad_days_list": imap_data_access.download(
+                cr_to_process.bad_days_list_file_name
+            ),
+            "pipeline_settings": imap_data_access.download(
+                cr_to_process.pipeline_settings_file_name
+            ),
         }
 
         l3a_data = []
@@ -52,5 +65,5 @@ def download_from_cr_to_process(cls, cr_to_process: CRToProcess, version: Versio
             l3a_data=l3a_data,
             external_files=external_files,
             ancillary_files=ancillary_files,
-            repointing_file_path=repointing_file_path
+            repointing_file_path=repointing_file_path,
         )
diff --git a/imap_l3_processing/glows/l3bc/glows_l3bc_initializer.py b/imap_l3_processing/glows/l3bc/glows_l3bc_initializer.py
index 337ee2450..53c2a21f7 100644
--- a/imap_l3_processing/glows/l3bc/glows_l3bc_initializer.py
+++ b/imap_l3_processing/glows/l3bc/glows_l3bc_initializer.py
@@ -2,20 +2,28 @@
 from collections import defaultdict
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional
 
 import imap_data_access
-from imap_data_access import ScienceFilePath, ProcessingInputCollection, RepointInput
+from imap_data_access import ProcessingInputCollection, RepointInput, ScienceFilePath
 from imap_data_access.file_validation import Version
 from imap_processing.spice.repoint import set_global_repoint_table_paths
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.glows.descriptors import BAD_DAYS_LIST_DESCRIPTOR, WAW_HELIOION_DESCRIPTOR, \
-    UV_ANISOTROPY_1CR_DESCRIPTOR, PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR, GLOWS_L3B_DESCRIPTOR, GLOWS_L3C_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    BAD_DAYS_LIST_DESCRIPTOR,
+    GLOWS_L3B_DESCRIPTOR,
+    GLOWS_L3C_DESCRIPTOR,
+    PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR,
+    UV_ANISOTROPY_1CR_DESCRIPTOR,
+    WAW_HELIOION_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
 from imap_l3_processing.glows.l3bc.models import CRToProcess, ExternalDependencies
-from imap_l3_processing.glows.l3bc.utils import get_date_range_of_cr, get_best_ancillary, \
-    get_cr_for_date_time
+from imap_l3_processing.glows.l3bc.utils import (
+    get_best_ancillary,
+    get_cr_for_date_time,
+    get_date_range_of_cr,
+)
 from imap_l3_processing.utils import read_cdf_parents
 
 logger = logging.getLogger(__name__)
@@ -38,32 +46,60 @@ class GlowsL3BCInitializerData:
 
 class GlowsL3BCInitializer:
     @staticmethod
-    def get_crs_to_process(dependencies: ProcessingInputCollection, major_version_to_process: int) -> GlowsL3BCInitializerData:
+    def get_crs_to_process(
+        dependencies: ProcessingInputCollection, major_version_to_process: int
+    ) -> GlowsL3BCInitializerData:
         [repoint_file] = dependencies.get_file_paths(data_type=RepointInput.data_type)
         repoint_downloaded_path = imap_data_access.download(repoint_file)
         set_global_repoint_table_paths([repoint_downloaded_path])
 
-        l3a_query_results = imap_data_access.query(instrument="glows", data_level="l3a", descriptor="hist",
-                                                   version="latest")
-        l3a_files_names = [Path(l3a_query_result["file_path"]).name for l3a_query_result in l3a_query_results]
+        l3a_query_results = imap_data_access.query(
+            instrument="glows", data_level="l3a", descriptor="hist", version="latest"
+        )
+        l3a_files_names = [
+            Path(l3a_query_result["file_path"]).name
+            for l3a_query_result in l3a_query_results
+        ]
         cr_to_l3a_file_names = GlowsL3BCInitializer.group_l3a_by_cr(l3a_files_names)
 
-        l3b_query_result = imap_data_access.query(instrument="glows", data_level="l3b",
-                                                  descriptor=GLOWS_L3B_DESCRIPTOR, version="latest")
-        l3c_query_result = imap_data_access.query(instrument="glows", data_level="l3c", descriptor=GLOWS_L3C_DESCRIPTOR,
-                                                  version="latest")
-
-        l3bs_by_cr = {int(result['cr']): Path(result["file_path"]).name for result in l3b_query_result}
-        l3cs_by_cr = {int(result['cr']): Path(result["file_path"]).name for result in l3c_query_result}
+        l3b_query_result = imap_data_access.query(
+            instrument="glows",
+            data_level="l3b",
+            descriptor=GLOWS_L3B_DESCRIPTOR,
+            version="latest",
+        )
+        l3c_query_result = imap_data_access.query(
+            instrument="glows",
+            data_level="l3c",
+            descriptor=GLOWS_L3C_DESCRIPTOR,
+            version="latest",
+        )
 
-        uv_anisotropy_query_result = imap_data_access.query(table="ancillary", instrument="glows",
-                                                            descriptor=UV_ANISOTROPY_1CR_DESCRIPTOR)
-        waw_helio_ion_mp_query_result = imap_data_access.query(table="ancillary", instrument="glows",
-                                                               descriptor=WAW_HELIOION_DESCRIPTOR)
-        bad_days_list_query_result = imap_data_access.query(table="ancillary", instrument="glows",
-                                                            descriptor=BAD_DAYS_LIST_DESCRIPTOR)
-        pipeline_settings_query_result = imap_data_access.query(table="ancillary", instrument="glows",
-                                                                descriptor=PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR)
+        l3bs_by_cr = {
+            int(result["cr"]): Path(result["file_path"]).name
+            for result in l3b_query_result
+        }
+        l3cs_by_cr = {
+            int(result["cr"]): Path(result["file_path"]).name
+            for result in l3c_query_result
+        }
+
+        uv_anisotropy_query_result = imap_data_access.query(
+            table="ancillary",
+            instrument="glows",
+            descriptor=UV_ANISOTROPY_1CR_DESCRIPTOR,
+        )
+        waw_helio_ion_mp_query_result = imap_data_access.query(
+            table="ancillary", instrument="glows", descriptor=WAW_HELIOION_DESCRIPTOR
+        )
+        bad_days_list_query_result = imap_data_access.query(
+            table="ancillary", instrument="glows", descriptor=BAD_DAYS_LIST_DESCRIPTOR
+        )
+        pipeline_settings_query_result = imap_data_access.query(
+            table="ancillary",
+            instrument="glows",
+            descriptor=PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR,
+        )
 
         logger.info("Downloading external dependencies...")
 
@@ -71,16 +107,23 @@ def get_crs_to_process(dependencies: ProcessingInputCollection, major_version_to
 
         logger.info("Finished downloading external dependencies")
 
-        if not all([external_dependencies.f107_index_file_path, external_dependencies.omni2_data_path,
-                    external_dependencies.lyman_alpha_path]):
-            logger.info(f"Found issues with external dependencies, returning {external_dependencies}")
+        if not all(
+            [
+                external_dependencies.f107_index_file_path,
+                external_dependencies.omni2_data_path,
+                external_dependencies.lyman_alpha_path,
+            ]
+        ):
+            logger.info(
+                f"Found issues with external dependencies, returning {external_dependencies}"
+            )
 
             return GlowsL3BCInitializerData(
                 external_dependencies=external_dependencies,
                 l3bc_dependencies=[],
                 l3bs_by_cr=l3bs_by_cr,
                 l3cs_by_cr=l3cs_by_cr,
-                repoint_file_path=repoint_downloaded_path
+                repoint_file_path=repoint_downloaded_path,
             )
 
         all_l3bc_dependencies = []
@@ -89,11 +132,18 @@ def get_crs_to_process(dependencies: ProcessingInputCollection, major_version_to
             cr_start_date, cr_end_date = get_date_range_of_cr(cr_number)
 
             ancillaries = {
-                "uv-anisotropy-1CR": get_best_ancillary(cr_start_date, cr_end_date, uv_anisotropy_query_result),
-                "WawHelioIonMP": get_best_ancillary(cr_start_date, cr_end_date, waw_helio_ion_mp_query_result),
-                "bad-days-list": get_best_ancillary(cr_start_date, cr_end_date, bad_days_list_query_result),
-                "pipeline-settings-l3bcde": get_best_ancillary(cr_start_date, cr_end_date,
-                                                               pipeline_settings_query_result),
+                "uv-anisotropy-1CR": get_best_ancillary(
+                    cr_start_date, cr_end_date, uv_anisotropy_query_result
+                ),
+                "WawHelioIonMP": get_best_ancillary(
+                    cr_start_date, cr_end_date, waw_helio_ion_mp_query_result
+                ),
+                "bad-days-list": get_best_ancillary(
+                    cr_start_date, cr_end_date, bad_days_list_query_result
+                ),
+                "pipeline-settings-l3bcde": get_best_ancillary(
+                    cr_start_date, cr_end_date, pipeline_settings_query_result
+                ),
             }
 
             if all(ancillaries.values()):
@@ -105,14 +155,23 @@ def get_crs_to_process(dependencies: ProcessingInputCollection, major_version_to
                     ancillaries["pipeline-settings-l3bcde"],
                     cr_start_date,
                     cr_end_date,
-                    cr_number
+                    cr_number,
                 )
 
-                if version := GlowsL3BCInitializer.should_process_cr_candidate(cr_candidate, l3bs_by_cr,
-                                                                               external_dependencies, major_version_to_process):
-                    l3bc_dependencies = GlowsL3BCDependencies.download_from_cr_to_process(cr_candidate, version,
-                                                                                          external_dependencies,
-                                                                                          repoint_downloaded_path)
+                if version := GlowsL3BCInitializer.should_process_cr_candidate(
+                    cr_candidate,
+                    l3bs_by_cr,
+                    external_dependencies,
+                    major_version_to_process,
+                ):
+                    l3bc_dependencies = (
+                        GlowsL3BCDependencies.download_from_cr_to_process(
+                            cr_candidate,
+                            version,
+                            external_dependencies,
+                            repoint_downloaded_path,
+                        )
+                    )
                     all_l3bc_dependencies.append(l3bc_dependencies)
                 else:
                     logger.info(f"decided not to process {cr_candidate}")
@@ -124,18 +183,26 @@ def get_crs_to_process(dependencies: ProcessingInputCollection, major_version_to
             l3bc_dependencies=all_l3bc_dependencies,
             l3bs_by_cr=l3bs_by_cr,
             l3cs_by_cr=l3cs_by_cr,
-            repoint_file_path=repoint_downloaded_path
+            repoint_file_path=repoint_downloaded_path,
         )
 
     @staticmethod
-    def should_process_cr_candidate(cr_candidate: CRToProcess, l3bs_by_cr: dict[int, str],
-                                    external_dependencies: ExternalDependencies, major_version: int|None) -> Optional[Version]:
+    def should_process_cr_candidate(
+        cr_candidate: CRToProcess,
+        l3bs_by_cr: dict[int, str],
+        external_dependencies: ExternalDependencies,
+        major_version: int | None,
+    ) -> Version | None:
         if not cr_candidate.buffer_time_has_elapsed_since_cr():
-            logger.warning(f"Not enough time has elapsed for cr {cr_candidate.cr_rotation_number}")
+            logger.warning(
+                f"Not enough time has elapsed for cr {cr_candidate.cr_rotation_number}"
+            )
             return None
 
         if not cr_candidate.has_valid_external_dependencies(external_dependencies):
-            logger.warning(f"Invalid external dependencies for {cr_candidate.cr_rotation_number}")
+            logger.warning(
+                f"Invalid external dependencies for {cr_candidate.cr_rotation_number}"
+            )
             return None
 
         match l3bs_by_cr.get(cr_candidate.cr_rotation_number):
@@ -144,11 +211,17 @@ def should_process_cr_candidate(cr_candidate: CRToProcess, l3bs_by_cr: dict[int,
             case l3b_file_name:
                 l3b_parents = read_cdf_parents(l3b_file_name)
                 existing_path = ScienceFilePath(l3b_file_name)
-                existing_version = Version(existing_path.major_version, existing_path.minor_version)
+                existing_version = Version(
+                    existing_path.major_version, existing_path.minor_version
+                )
                 major_version_updated = major_version != existing_version.major
-                inputs_changed = not cr_candidate.pipeline_dependency_file_names().issubset(l3b_parents)
+                inputs_changed = (
+                    not cr_candidate.pipeline_dependency_file_names().issubset(
+                        l3b_parents
+                    )
+                )
                 if major_version_updated or inputs_changed:
-                    return Version(major_version, existing_version.minor+1)
+                    return Version(major_version, existing_version.minor + 1)
         return None
 
     @staticmethod
@@ -157,7 +230,7 @@ def group_l3a_by_cr(l3a_file_names: list[str]) -> dict[int, set[str]]:
         for l3a_file_name in l3a_file_names:
             path = imap_data_access.download(l3a_file_name)
             with CDF(str(path)) as cdf:
-                epoch = cdf['epoch'][0]
+                epoch = cdf["epoch"][0]
 
             cr_number = get_cr_for_date_time(epoch)
             grouped_l3a_by_cr[cr_number].add(l3a_file_name)
diff --git a/imap_l3_processing/glows/l3bc/l3bc_toolkit/constants.py b/imap_l3_processing/glows/l3bc/l3bc_toolkit/constants.py
index 827b4c06c..0082d96da 100644
--- a/imap_l3_processing/glows/l3bc/l3bc_toolkit/constants.py
+++ b/imap_l3_processing/glows/l3bc/l3bc_toolkit/constants.py
@@ -5,33 +5,34 @@
 
 import astropy.units as u
 
-VERSION = "3.3" # version of ground processing software
+VERSION = "3.3"  # version of ground processing software
 
 # dictionary with names of ancillary input files expected from IMAP/GLOWS Instrument Team
 ANC_INPUT_FROM_INSTRUMENT_TEAM = {
-    'WawHelioIonMP_parameters': 'data_ancillary/imap_glows_WawHelioIonMP_20251113_v007.json',
-    'bad_day_list': 'data_ancillary/imap_glows_bad-days-list_20251113_v001.dat',
-    'pipeline_settings': 'data_ancillary/imap_glows_pipeline-settings-l3bcde_20251113_v005.json',
-    'uv_anisotropy' : 'data_ancillary/imap_glows_uv-anisotropy-1CR_20251113_v002.json',
-    'WawHelioIon' : {
-        'speed': 'data_ancillary/imap_glows_plasma-speed-2026d_20251113_v003.dat',
-        'p-dens': 'data_ancillary/imap_glows_proton-density-2026d_20251113_v003.dat',
-        'uv-anis': 'data_ancillary/imap_glows_uv-anisotropy-2026d_20251113_v003.dat',
-        'phion': 'data_ancillary/imap_glows_photoion-2026d_20251113_v003.dat',
-        'lya': 'data_ancillary/imap_glows_lya-2026d_20251113_v003.dat',
-        'e-dens': 'data_ancillary/imap_glows_electron-density-2026d_20251113_v003.dat'
-    }}
+    "WawHelioIonMP_parameters": "data_ancillary/imap_glows_WawHelioIonMP_20251113_v007.json",
+    "bad_day_list": "data_ancillary/imap_glows_bad-days-list_20251113_v001.dat",
+    "pipeline_settings": "data_ancillary/imap_glows_pipeline-settings-l3bcde_20251113_v005.json",
+    "uv_anisotropy": "data_ancillary/imap_glows_uv-anisotropy-1CR_20251113_v002.json",
+    "WawHelioIon": {
+        "speed": "data_ancillary/imap_glows_plasma-speed-2026d_20251113_v003.dat",
+        "p-dens": "data_ancillary/imap_glows_proton-density-2026d_20251113_v003.dat",
+        "uv-anis": "data_ancillary/imap_glows_uv-anisotropy-2026d_20251113_v003.dat",
+        "phion": "data_ancillary/imap_glows_photoion-2026d_20251113_v003.dat",
+        "lya": "data_ancillary/imap_glows_lya-2026d_20251113_v003.dat",
+        "e-dens": "data_ancillary/imap_glows_electron-density-2026d_20251113_v003.dat",
+    },
+}
 
-EXT_DEPENDENCIES={
-    'f107_raw_data': 'external_dependencies/f107_fluxtable.txt',
-    'omni_raw_data': 'external_dependencies/omni2_all_years.dat',
-    'lya_raw_data': 'external_dependencies/lyman_alpha_composite.nc'
+EXT_DEPENDENCIES = {
+    "f107_raw_data": "external_dependencies/f107_fluxtable.txt",
+    "omni_raw_data": "external_dependencies/omni2_all_years.dat",
+    "lya_raw_data": "external_dependencies/lyman_alpha_composite.nc",
 }
 
-PHISICAL_CONSTANTS={
-    'm_alpha' : 6.6446573357e-27*u.kg,
-    'jd_carrington_first' : 2.398167329*10**6,
-    'carrington_time' : 27.2753
+PHISICAL_CONSTANTS = {
+    "m_alpha": 6.6446573357e-27 * u.kg,
+    "jd_carrington_first": 2.398167329 * 10**6,
+    "carrington_time": 27.2753,
 }
 
-CONST_ALPHA_TO_PROTON=0.04
+CONST_ALPHA_TO_PROTON = 0.04
diff --git a/imap_l3_processing/glows/l3bc/l3bc_toolkit/funcs.py b/imap_l3_processing/glows/l3bc/l3bc_toolkit/funcs.py
index e669c40f5..2caf64ce7 100644
--- a/imap_l3_processing/glows/l3bc/l3bc_toolkit/funcs.py
+++ b/imap_l3_processing/glows/l3bc/l3bc_toolkit/funcs.py
@@ -1,27 +1,30 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska (ikowalska@cbk.waw.pl)
 Functions used in L3a->L3b processing that are not class methods
-'''
+"""
 
-import numpy as np
-import astropy.units as u
-import astropy.constants as const
 import json
+import logging
+
+import astropy.constants as const
+import astropy.units as u
 import netCDF4
-from astropy.time import Time
+import numpy as np
 from astropy.coordinates import get_sun
+from astropy.time import Time
 
-from imap_l3_processing.glows.quality_flags import NOMINAL_ALPHA_PROTON_RATIO_VALUE
-from .constants import PHISICAL_CONSTANTS, CONST_ALPHA_TO_PROTON
+from .constants import CONST_ALPHA_TO_PROTON, PHISICAL_CONSTANTS
 
-import logging
 logging.basicConfig(level=logging.ERROR)
 
+
 ##########################
 # Exceptions
 ##########################
 class ArrayShapeError(Exception):
     pass
+
+
 class OutOfLimitsError(Exception):
     pass
 
@@ -41,87 +44,109 @@ def base_funcs_def(i, sin_or_cos):
     """
 
     if i == 0:
+
         def fun(angle):
-            return 1.0/(2.0*np.sqrt(np.pi))*np.ones_like(angle)
+            return 1.0 / (2.0 * np.sqrt(np.pi)) * np.ones_like(angle)
+
         return fun
-    if sin_or_cos == 's':
+    if sin_or_cos == "s":
+
         def fun_s(angle):
-            return np.sin(i*angle)/np.sqrt(np.pi)
+            return np.sin(i * angle) / np.sqrt(np.pi)
+
         return fun_s
-    if sin_or_cos == 'c':
+    if sin_or_cos == "c":
+
         def fun_c(angle):
-            return np.cos(i*angle)/np.sqrt(np.pi)
+            return np.cos(i * angle) / np.sqrt(np.pi)
+
         return fun_c
     # TODO: maybe we need some error if sin_or_cos is anything beside 'c' or 's'
-    return 'None'
+    return "None"
+
 
-def calculate_lya_monthly(t,flux):
-   '''
-   Calculates data that are averaged over each Carrington rotation period. Time is mean value of decimal year in each period.
-   '''
-   t_carr,flux_carr=resize_by_carr(t,flux)
-   t_mean=Time(np.array([np.mean(t.decimalyear) for t in t_carr]),format='decimalyear')
-   flux_mean=np.array([f.mean() for f in flux_carr])
+def calculate_lya_monthly(t, flux):
+    """
+    Calculates data that are averaged over each Carrington rotation period. Time is mean value of decimal year in each period.
+    """
+    t_carr, flux_carr = resize_by_carr(t, flux)
+    t_mean = Time(
+        np.array([np.mean(t.decimalyear) for t in t_carr]), format="decimalyear"
+    )
+    flux_mean = np.array([f.mean() for f in flux_carr])
+
+    return t_mean, flux_mean
 
-   return t_mean,flux_mean
 
 def calculate_mean_date(fn_list):
-    daily_dates=[]
+    daily_dates = []
     for f in fn_list:
-        l3a=read_json(f)
-        daily_dates.append(l3a['start_time'])
-    date_mean=Time(daily_dates).mean()
+        l3a = read_json(f)
+        daily_dates.append(l3a["start_time"])
+    date_mean = Time(daily_dates).mean()
     return date_mean
 
-def calculate_sw_energy_flux(n,v,p):
-        '''
-        Solar wind energy flux that is latitudinal invariant
-        Le Chat et al. 2012a
-        Parameters must be given as a object with unit
 
-        Parameters:
-        --------------
-        n - protons number density
-        v - plasma speed
-        p - concentration of the alpha particles wrt to the protons
+def calculate_sw_energy_flux(n, v, p):
+    """
+    Solar wind energy flux that is latitudinal invariant
+    Le Chat et al. 2012a
+    Parameters must be given as a object with unit
+
+    Parameters:
+    --------------
+    n - protons number density
+    v - plasma speed
+    p - concentration of the alpha particles wrt to the protons
+
+    Output:
+    ------------
+    energy_flux: energy flux at 1au in the ecliptic plane (invariant)
+    """
+    energy_flux = (
+        n
+        * (const.m_p + p * PHISICAL_CONSTANTS["m_alpha"])
+        * v
+        * (0.5 * v**2 + const.GM_sun / const.R_sun)
+    )
 
-        Output:
-        ------------
-        energy_flux: energy flux at 1au in the ecliptic plane (invariant)
-        '''
-        energy_flux=n*(const.m_p+p*PHISICAL_CONSTANTS['m_alpha'])*v*(0.5*v**2+const.GM_sun/const.R_sun)
+    return energy_flux.to("erg.s-1.cm-2")
 
-        return energy_flux.to('erg.s-1.cm-2')
 
 def carrington(jd):
-    '''
+    """
     Converts time in Julian Day format into Carrington Rotation number
-    '''
-    ans=(jd-PHISICAL_CONSTANTS['jd_carrington_first'])/PHISICAL_CONSTANTS['carrington_time']+1
+    """
+    ans = (jd - PHISICAL_CONSTANTS["jd_carrington_first"]) / PHISICAL_CONSTANTS[
+        "carrington_time"
+    ] + 1
     return ans
 
+
 def check_nan(data):
-    '''
+    """
     Checks if there are any NaNs in the output data. If so, raises Exception and data product is not produced
 
     Parameters:
     --------------
     data - dictionary with the data product
-    '''
+    """
     for key in data:
-            if np.any((np.isnan(np.array(data[key])))):
-                raise Exception("There are NaN in the output data")
+        if np.any(np.isnan(np.array(data[key]))):
+            raise Exception("There are NaN in the output data")
+
 
 def cr_from_l3b_fn(fn):
-    '''
+    """
     Extracts CR from the L3b filename. This may change if the naming convention will change
-    '''
-    cr=fn.split('_')[5]
+    """
+    cr = fn.split("_")[5]
 
     return cr
 
+
 def cross_section_cx_LS(E):
-    '''
+    """
     Lindsay&Stebbings 2005
     Returns cross_section as a astro.unit object
 
@@ -133,14 +158,15 @@ def cross_section_cx_LS(E):
     ------------
     sigma: cross-section as a astropy.unit object
 
-    '''
-    EkeV=E.to('keV').value
-    out=1e-16*(1-np.exp(-67.3/EkeV))**4.5*(4.15-0.531*np.log(EkeV))**2
-    sigma=out*u.cm*u.cm
+    """
+    EkeV = E.to("keV").value
+    out = 1e-16 * (1 - np.exp(-67.3 / EkeV)) ** 4.5 * (4.15 - 0.531 * np.log(EkeV)) ** 2
+    sigma = out * u.cm * u.cm
     return sigma
 
+
 def cross_section_cx(E):
-    '''
+    """
     Swaczyna et al. 2025
     https://doi.org/10.3847/1538-4365/adaf17
     Returns cross_section as a astro.unit object
@@ -153,24 +179,29 @@ def cross_section_cx(E):
     ------------
     sigma: cross-section as a astropy.unit object
 
-    '''
+    """
 
     # Parameters
-    A1=4.8569
-    A2=21.906
-    A3=31.487
-    A4=0.12018
-    A5=4.1402E-6
-    A6=3.7524
-    A7=8.8476E-12
-    A8=6.1091
-
-    EkeV=E.to('keV').value
-    out=(1e-16)*(A1*np.log(A2/EkeV+A3))/(1+A4*EkeV+A5*EkeV**A6+A7*EkeV**A8)
-    sigma=out*u.cm*u.cm
+    A1 = 4.8569
+    A2 = 21.906
+    A3 = 31.487
+    A4 = 0.12018
+    A5 = 4.1402e-6
+    A6 = 3.7524
+    A7 = 8.8476e-12
+    A8 = 6.1091
+
+    EkeV = E.to("keV").value
+    out = (
+        (1e-16)
+        * (A1 * np.log(A2 / EkeV + A3))
+        / (1 + A4 * EkeV + A5 * EkeV**A6 + A7 * EkeV**A8)
+    )
+    sigma = out * u.cm * u.cm
 
     return sigma
 
+
 def derive_sin_cos(order):
     """
     equivalent of Mathematica's deriveSinCos function
@@ -184,135 +215,147 @@ def derive_sin_cos(order):
     """
 
     funcs = []
-    for i in range(order+1):
+    for i in range(order + 1):
         if i == 0:
-            funcs.append(base_funcs_def(i, 'None'))
+            funcs.append(base_funcs_def(i, "None"))
         else:
-            funcs.append(base_funcs_def(i, 's'))
-            funcs.append(base_funcs_def(i, 'c'))
+            funcs.append(base_funcs_def(i, "s"))
+            funcs.append(base_funcs_def(i, "c"))
     return funcs
 
-def f107_daily_data(t,flux):
-        '''
-        Returns adjusted F10.7 flux with daily cadence. In the case of multiple measurements during a day,
-        the closest to the noon is chosen
-
-        Parameters:
-        ------------
-        t:    time array of raw data (astropy.time)
-        flux: F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
-
-        Output:
-        ------------
-        t_out: daily time array (astropy.time)
-        flux_out: daily F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
-        '''
-        idx_noon,idx_dup=select_noon(t)        # select one measurment per day
-        idx_no0=np.where(flux[idx_noon]>0)[0]
-        t_out=t[idx_noon[idx_no0]]
-        flux_out=flux[idx_noon[idx_no0]]
-        return t_out,flux_out
+
+def f107_daily_data(t, flux):
+    """
+    Returns adjusted F10.7 flux with daily cadence. In the case of multiple measurements during a day,
+    the closest to the noon is chosen
+
+    Parameters:
+    ------------
+    t:    time array of raw data (astropy.time)
+    flux: F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
+
+    Output:
+    ------------
+    t_out: daily time array (astropy.time)
+    flux_out: daily F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
+    """
+    idx_noon, idx_dup = select_noon(t)  # select one measurment per day
+    idx_no0 = np.where(flux[idx_noon] > 0)[0]
+    t_out = t[idx_noon[idx_no0]]
+    flux_out = flux[idx_noon[idx_no0]]
+    return t_out, flux_out
+
 
 def find_CR_idx(CR, CR_list):
-    '''
+    """
     finds index of the value in the CR_list that is closest to the CR and the next idx
     it corresponds to the two L3bc files: close to current CR and the next one (for interpolation)
-    '''
-    idx_CR = np.abs(np.array(CR_list)-int(CR)).argmin()
-   # OLD idx_read=[idx_CR-1,idx_CR,idx_CR+1]
-    idx_read=[idx_CR,idx_CR+1]
+    """
+    idx_CR = np.abs(np.array(CR_list) - int(CR)).argmin()
+    # OLD idx_read=[idx_CR-1,idx_CR,idx_CR+1]
+    idx_read = [idx_CR, idx_CR + 1]
     return idx_read
 
+
 def generate_cr_lya(CR, data_lya):
-    '''
+    """
     calculates Lyman-alpha composite irradiance interpolated on the center of a given CR
-    '''
+    """
 
-    t_CR = Time(jd_fm_Carrington(CR),format='jd')
+    t_CR = Time(jd_fm_Carrington(CR), format="jd")
 
     CR_list = [int(carrington(t.jd)) for t in data_lya[0]]
     idx_read = find_CR_idx(CR, CR_list)
 
     t_lya = np.array([data_lya[0][i].jd for i in idx_read])
     lya = np.array([data_lya[1][i] for i in idx_read])
-    lya_CR = np.interp(t_CR.jd,t_lya,lya)
+    lya_CR = np.interp(t_CR.jd, t_lya, lya)
 
     return lya_CR
 
 
-
 def get_spher_coord_rad(vec):
-  """
-  Compute radius r, longitude l and latitude b for a vector vec
-
-  Parameters
-  ----------
-    vec: 1D or 2D numpy array
-      input vector(s)
-
-  Returns
-  -------
-    if vec is an 1D numpy array, return 1D numpy array rlb (see below)
-    if vec is a 2D numpy array, assume that its rows represent vectors and return 2D array
-      rlb (see below), where every row of rlb corresponds to every row of vec
-
-    rlb: 1D or 2D numpy array
-      if 1D then rlb=[r,l,b] where: r - magnitude of vector vec, l - longitude of the direction
-        pointed by vec in radians, b - latitude of the direction pointed by vec in radians
-      if 2D then columns of the array contain r,l,b
-
-  Examples
-  --------
-    >>> import geom_fun_libr_box as gflb
-    >>> import numpy as np
-    >>> rlb=gflb.get_spher_coord_rad(np.array([0,1,1]))
-    >>> print(rlb[0],np.degrees(rlb[1]),np.degrees(rlb[2]))
-    1.41421356237 90.0 45.0
-    >>> n=10
-    >>> phi=np.radians(np.linspace(0,360,n))
-    >>> tht=np.radians(np.linspace(-90,90,n))
-    >>> vecs=np.column_stack([np.cos(phi)*np.cos(tht),np.sin(phi)*np.cos(tht),np.sin(tht)])
-    >>> print(gflb.get_spher_coord_rad(vecs))
-    [[ 1.          0.         -1.57079633]
-     [ 1.          0.6981317  -1.22173048]
-     [ 1.          1.3962634  -0.87266463]
-     [ 1.          2.0943951  -0.52359878]
-     [ 1.          2.7925268  -0.17453293]
-     [ 1.          3.4906585   0.17453293]
-     [ 1.          4.1887902   0.52359878]
-     [ 1.          4.88692191  0.87266463]
-     [ 1.          5.58505361  1.22173048]
-     [ 1.          6.28318531  1.57079633]]
-  """
-  if vec.ndim==1:
-    if np.shape(vec)[0]!=3:
-      raise "get_spher_coord_rad() error: np.shape(vec)[0]!=3"
-    r=np.sqrt(np.dot(vec,vec))
-    if r==0.0: b=0.0;
-    else: b=np.arcsin(vec[2]/r) # compute latitude
-    # compute longitude
-    if vec[0]==0.0 and vec[1]==0.0: l=0.0
-    else: l=np.arctan2(vec[1],vec[0])%(2*np.pi)
-    return np.array([r,l,b]) # return one array similarly as in Mathematica
-  else:
-    if np.shape(vec)[1]!=3:
-      raise "get_spher_coord_rad() error: np.shape(vec)[1]!=3"
-    r=np.sqrt((vec * vec).sum(axis=1))
-    idxs=np.nonzero(r!=0.0)
-    b=np.zeros((len(r)),np.float64)
-    b[idxs[0]]=np.arcsin(vec[idxs[0],2]/r[idxs[0]]) # compute latitude
-    l=np.arctan2(vec[:,1],vec[:,0])%(2*np.pi) # compute longitude
-    idxs=np.nonzero(np.logical_and(vec[:,0]==0.0,vec[:,1]==0.0))
-    l[idxs[0]]=0.0 # set l=0 if vec[:,0]==0.0 and vec[:,1]==0.0
-    return np.column_stack([r,l,b]) # return one array similarly as in Mathematica
+    """
+    Compute radius r, longitude l and latitude b for a vector vec
+
+    Parameters
+    ----------
+      vec: 1D or 2D numpy array
+        input vector(s)
+
+    Returns
+    -------
+      if vec is an 1D numpy array, return 1D numpy array rlb (see below)
+      if vec is a 2D numpy array, assume that its rows represent vectors and return 2D array
+        rlb (see below), where every row of rlb corresponds to every row of vec
+
+      rlb: 1D or 2D numpy array
+        if 1D then rlb=[r,l,b] where: r - magnitude of vector vec, l - longitude of the direction
+          pointed by vec in radians, b - latitude of the direction pointed by vec in radians
+        if 2D then columns of the array contain r,l,b
+
+    Examples
+    --------
+      >>> import geom_fun_libr_box as gflb
+      >>> import numpy as np
+      >>> rlb=gflb.get_spher_coord_rad(np.array([0,1,1]))
+      >>> print(rlb[0],np.degrees(rlb[1]),np.degrees(rlb[2]))
+      1.41421356237 90.0 45.0
+      >>> n=10
+      >>> phi=np.radians(np.linspace(0,360,n))
+      >>> tht=np.radians(np.linspace(-90,90,n))
+      >>> vecs=np.column_stack([np.cos(phi)*np.cos(tht),np.sin(phi)*np.cos(tht),np.sin(tht)])
+      >>> print(gflb.get_spher_coord_rad(vecs))
+      [[ 1.          0.         -1.57079633]
+       [ 1.          0.6981317  -1.22173048]
+       [ 1.          1.3962634  -0.87266463]
+       [ 1.          2.0943951  -0.52359878]
+       [ 1.          2.7925268  -0.17453293]
+       [ 1.          3.4906585   0.17453293]
+       [ 1.          4.1887902   0.52359878]
+       [ 1.          4.88692191  0.87266463]
+       [ 1.          5.58505361  1.22173048]
+       [ 1.          6.28318531  1.57079633]]
+    """
+    if vec.ndim == 1:
+        if np.shape(vec)[0] != 3:
+            raise "get_spher_coord_rad() error: np.shape(vec)[0]!=3"
+        r = np.sqrt(np.dot(vec, vec))
+        if r == 0.0:
+            b = 0.0
+        else:
+            b = np.arcsin(vec[2] / r)  # compute latitude
+        # compute longitude
+        if vec[0] == 0.0 and vec[1] == 0.0:
+            l = 0.0
+        else:
+            l = np.arctan2(vec[1], vec[0]) % (2 * np.pi)
+        return np.array([r, l, b])  # return one array similarly as in Mathematica
+    else:
+        if np.shape(vec)[1] != 3:
+            raise "get_spher_coord_rad() error: np.shape(vec)[1]!=3"
+        r = np.sqrt((vec * vec).sum(axis=1))
+        idxs = np.nonzero(r != 0.0)
+        b = np.zeros((len(r)), np.float64)
+        b[idxs[0]] = np.arcsin(vec[idxs[0], 2] / r[idxs[0]])  # compute latitude
+        l = np.arctan2(vec[:, 1], vec[:, 0]) % (2 * np.pi)  # compute longitude
+        idxs = np.nonzero(np.logical_and(vec[:, 0] == 0.0, vec[:, 1] == 0.0))
+        l[idxs[0]] = 0.0  # set l=0 if vec[:,0]==0.0 and vec[:,1]==0.0
+        return np.column_stack(
+            [r, l, b]
+        )  # return one array similarly as in Mathematica
+
 
 def jd_fm_Carrington(carr):
-    '''
+    """
     calculates time in format of Julian Days for a given Carrington Rotation number
-    '''
-    ans=(carr-1)*PHISICAL_CONSTANTS['carrington_time']+PHISICAL_CONSTANTS['jd_carrington_first']
+    """
+    ans = (carr - 1) * PHISICAL_CONSTANTS["carrington_time"] + PHISICAL_CONSTANTS[
+        "jd_carrington_first"
+    ]
     return ans
 
+
 def make_lsq_matrix(psi, base_funcs):
     """
     make data matrix and vector for least-squares method in the matrix form
@@ -332,52 +375,56 @@ def make_lsq_matrix(psi, base_funcs):
         i = i + 1
     return lsq_mtrx
 
+
 def process_omni_param(omni_raw, cr_grid, param_settings):
-    '''
+    """
     Processes OMNI2 raw data to obtain averaged solar wind speed, density and alpha-particles abundance
-    '''
+    """
 
     # select from the raw data columns relevant for a given parameter
-    param_raw=omni_raw[:,param_settings['column_numbers']]
+    param_raw = omni_raw[:, param_settings["column_numbers"]]
 
     # remove gaps where there was no data available based on coded value
-    param=param_raw[param_raw[:,3]<param_settings['gap_marker']]
-    date=time_from_yday(param)
+    param = param_raw[param_raw[:, 3] < param_settings["gap_marker"]]
+    date = time_from_yday(param)
 
-    date_cr=carrington(date.jd)
+    date_cr = carrington(date.jd)
 
     # Scale parameter to 1au. OMNI Observations are done at different distance and we need to scale proton density
-    if param_settings['scale']: param[:,3]=scale_density(date,param[:,3])
+    if param_settings["scale"]:
+        param[:, 3] = scale_density(date, param[:, 3])
 
-    param_v=param[:,3]  # values of the parameter
+    param_v = param[:, 3]  # values of the parameter
 
     # fill alpha abudance from the end of the measurements to the last point in cr_grid by constant value 0.04 based on Ulysses data McComas et al. 2000
     original_last_date = date_cr[-1]
     used_nominal_per_cr = np.full(len(cr_grid), False)
-    if param_settings['const_if_empty'] and original_last_date < cr_grid[-1]:
-        date_cr=np.append(date_cr,cr_grid[-1])
+    if param_settings["const_if_empty"] and original_last_date < cr_grid[-1]:
+        date_cr = np.append(date_cr, cr_grid[-1])
         used_nominal_per_cr = cr_grid > original_last_date
-        param_v=np.append(param_v,CONST_ALPHA_TO_PROTON)
+        param_v = np.append(param_v, CONST_ALPHA_TO_PROTON)
 
     # split array into 1-Carrington chunks
-    param_cr, idx_param=np.unique(np.floor(date_cr), return_index=True,axis=0)
-    param_s=np.split(param_v,idx_param)[1:]
+    param_cr, idx_param = np.unique(np.floor(date_cr), return_index=True, axis=0)
+    param_s = np.split(param_v, idx_param)[1:]
 
     # calculate averaged over 1 Carrington time and parameter value
-    param_value=np.array([p.mean() for p in param_s])
+    param_value = np.array([p.mean() for p in param_s])
 
     # interpolate into cr_grid
-    if np.logical_and(param_cr[0]<=cr_grid[0], param_cr[-1]>=cr_grid[-1]):
-        param_value_interp=np.interp(cr_grid,param_cr,param_value)
+    if np.logical_and(param_cr[0] <= cr_grid[0], param_cr[-1] >= cr_grid[-1]):
+        param_value_interp = np.interp(cr_grid, param_cr, param_value)
     else:
-        logging.info('There are gaps in the OMNI2 data that cannot be filled by the interpolation')
-        raise Exception('OMNI Error: not enough data for interpolation')
+        logging.info(
+            "There are gaps in the OMNI2 data that cannot be filled by the interpolation"
+        )
+        raise Exception("OMNI Error: not enough data for interpolation")
 
     return param_value_interp, used_nominal_per_cr
 
 
 def read_f107_raw_data(fn):
-    '''
+    """
     Read raw F10.7 data from file
 
     Parameters:
@@ -388,61 +435,76 @@ def read_f107_raw_data(fn):
     ------------
     t0:  time array (astropy.time)
     flux_adjusted: F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
-    '''
+    """
 
     data = np.loadtxt(fn)
 
-    jd=data[:,2]                  # time of observation in JD
-    flux_adjusted=data[:,5]       # adjusted do 1AU flux in sfu 1sfu=10^(-22) W/m^2/Hz (taking into accont that Sun-Earth distance is changing)
-    t0=Time(jd,format='jd')       # time converted to astropy.time format
+    jd = data[:, 2]  # time of observation in JD
+    flux_adjusted = data[
+        :, 5
+    ]  # adjusted do 1AU flux in sfu 1sfu=10^(-22) W/m^2/Hz (taking into accont that Sun-Earth distance is changing)
+    t0 = Time(jd, format="jd")  # time converted to astropy.time format
+
+    return t0, flux_adjusted
 
-    return t0,flux_adjusted
 
-def read_hdr_txt(fn,N):
-    '''
+def read_hdr_txt(fn, N):
+    """
     Reads header from the text file and preapers it to the further analysis
-    '''
+    """
 
     # open file and read header
     with open(fn) as file:
-        hdr=[next(file) for _ in range(N)]
+        hdr = [next(file) for _ in range(N)]
 
     # remove hash
-    hdr_str=remove_hash(hdr)
+    hdr_str = remove_hash(hdr)
     return hdr_str
 
+
 def read_lya_data(fn):
-    '''
+    """
     reads daily Lyman-alpha composite irradiance and calculates monthly averages
-    '''
-    t_lya,flux_lya,_= read_lya_raw_data(fn)
-    t_lya_m,flux_lya_m = calculate_lya_monthly(t_lya,flux_lya)
-    return [t_lya_m,flux_lya_m]
+    """
+    t_lya, flux_lya, _ = read_lya_raw_data(fn)
+    t_lya_m, flux_lya_m = calculate_lya_monthly(t_lya, flux_lya)
+    return [t_lya_m, flux_lya_m]
+
 
 def read_lya_raw_data(filename):
-    '''
+    """
     Reads *.nc file with composite Lyman-alpha solar irradiance (from LAPS)
     Returns daily product adjusted to 1au. Flux is in ph/(cm^2 s^2)
-    '''
-
-    data=netCDF4.Dataset(filename)
-
-    convert_to_ph=data.variables['convert_to_photons'][0].data # Multiplicative factor to convert irr_121 from W/m^2 to ph/(cm^2 s^2) [ph / (cm^2 s^2)] / (W / m^2)
-    t_d1947=data.variables['time'][:].data                     # time of observation [days since 1947-01-01 12:00:00 UTC]
-    flux0=data.variables['irr_121'][:].data                    # Averaged daily irradiance in band from 121.0 to 122.0 nm at 1 AU [W/m^2]
-    flux=flux0*convert_to_ph                                   # Flux in ph/(cm^2 s^2)
-    flux_uncert=data.variables['irr_121_uncertainty'][:].data  # Uncertainty (1 standard deviation) of average daily irradiance [W/m^2]
+    """
 
-    t0=Time('1947-01-01 12:00')
+    data = netCDF4.Dataset(filename)
+
+    convert_to_ph = data.variables[
+        "convert_to_photons"
+    ][
+        0
+    ].data  # Multiplicative factor to convert irr_121 from W/m^2 to ph/(cm^2 s^2) [ph / (cm^2 s^2)] / (W / m^2)
+    t_d1947 = data.variables["time"][
+        :
+    ].data  # time of observation [days since 1947-01-01 12:00:00 UTC]
+    flux0 = data.variables["irr_121"][
+        :
+    ].data  # Averaged daily irradiance in band from 121.0 to 122.0 nm at 1 AU [W/m^2]
+    flux = flux0 * convert_to_ph  # Flux in ph/(cm^2 s^2)
+    flux_uncert = data.variables["irr_121_uncertainty"][
+        :
+    ].data  # Uncertainty (1 standard deviation) of average daily irradiance [W/m^2]
+
+    t0 = Time("1947-01-01 12:00")
 
     # convertion to jd and adding days since 1947-01-01 - it is more accurate than simple adding days to date
-    t=Time(t0.jd+t_d1947,format='jd')
+    t = Time(t0.jd + t_d1947, format="jd")
 
     return t, flux, flux_uncert
 
 
-def read_raw_OMNI_data(ext_dependencies,t_window=None):
-    '''
+def read_raw_OMNI_data(ext_dependencies, t_window=None):
+    """
     Reads 3 types of data from OMNI2 database: plasma speed, proton density, and alpha-particles abundance in the ecliptic plane
 
     Parameters:
@@ -451,48 +513,55 @@ def read_raw_OMNI_data(ext_dependencies,t_window=None):
         list of file names with external dependencies
     t_window: list [astropy.time, astropy.time]
         time window of the data that are needed
-    '''
+    """
     # Read OMNI2 data (hour resolution)
     # year, DOY, hour, S/C Id, proton_dens, plasma_speed, alpha_abundance, sigma proton_dens, sigma plasma_speed, sigma alpha_abundance
-    omni_raw=np.loadtxt(ext_dependencies['omni_raw_data'],usecols=(0,1,2,5,23,24,27,30,31,34))
-    omni_raw_date=time_from_yday(omni_raw[:,(0,1,2)])
-    if t_window==None: out=omni_raw
+    omni_raw = np.loadtxt(
+        ext_dependencies["omni_raw_data"], usecols=(0, 1, 2, 5, 23, 24, 27, 30, 31, 34)
+    )
+    omni_raw_date = time_from_yday(omni_raw[:, (0, 1, 2)])
+    if t_window == None:
+        out = omni_raw
     else:
-        idx_ini=np.abs(omni_raw_date-t_window[0]).argmin()
-        idx_fin=np.abs(omni_raw_date-t_window[1]).argmin()
+        idx_ini = np.abs(omni_raw_date - t_window[0]).argmin()
+        idx_fin = np.abs(omni_raw_date - t_window[1]).argmin()
 
-        out=omni_raw[idx_ini:idx_fin]
+        out = omni_raw[idx_ini:idx_fin]
     return out
 
+
 def read_json(fn):
-    '''
+    """
     Opens, reads and returns the content of the json file
-    '''
-    fp = open(fn, 'r')
+    """
+    fp = open(fn, "r")
     data = json.load(fp)
     fp.close()
     return data
 
+
 def remove_hash(hdr):
-    '''
+    """
     Removes # from the header that is read from the text files and returns string that will
     be used to generate another header
-    '''
-    hdr_str=[l[2:] for l in hdr]
+    """
+    hdr_str = [l[2:] for l in hdr]
     return hdr_str
 
-def resize_by_carr(t,flux):
-   '''
-   Reshaping flux array in such a way that each element is a list of fluxes from the same carrington rotation number
-   '''
-   t_carr=carrington(t.jd)
-   n_carr=np.array([int(t_carr[i]) for i in np.arange(len(t_carr))])
-   flux_carr_sort=np.split(flux,np.unique(n_carr,return_index=True)[1][1:])
-   t_carr_sort=np.split(t,np.unique(n_carr,return_index=True)[1][1:])
-   return t_carr_sort,flux_carr_sort
+
+def resize_by_carr(t, flux):
+    """
+    Reshaping flux array in such a way that each element is a list of fluxes from the same carrington rotation number
+    """
+    t_carr = carrington(t.jd)
+    n_carr = np.array([int(t_carr[i]) for i in np.arange(len(t_carr))])
+    flux_carr_sort = np.split(flux, np.unique(n_carr, return_index=True)[1][1:])
+    t_carr_sort = np.split(t, np.unique(n_carr, return_index=True)[1][1:])
+    return t_carr_sort, flux_carr_sort
+
 
 def scale_density(date, dens):
-    '''
+    """
     Scaling density measurements that are taken at different distance than 1au.
     We need solar wind parameters ajusted to 1au.
     Proton density decreases as 1/r^2
@@ -503,21 +572,22 @@ def scale_density(date, dens):
         time of the measurement
     dens : 1D float vector
         measured density [#/cm^3]
-    '''
+    """
 
     # Sun-Earth distance at given moment
-    d_sun=get_sun(date).distance
+    d_sun = get_sun(date).distance
 
     # Sun-L1 distance
-    r_obs=d_sun*(1-(const.M_earth/(3*const.M_sun))**(1./3))
+    r_obs = d_sun * (1 - (const.M_earth / (3 * const.M_sun)) ** (1.0 / 3))
 
     # density ajustment
-    dens_1au=dens*(r_obs/u.au)**2
+    dens_1au = dens * (r_obs / u.au) ** 2
 
     return dens_1au
 
-def select_data_window(data,t_window):
-    '''
+
+def select_data_window(data, t_window):
+    """
     Selects data whitin a given time window
 
     Parameters:
@@ -527,15 +597,16 @@ def select_data_window(data,t_window):
     t_ini: astropy.time
     t_end: astropy.time
 
-    '''
-    idx=np.where(np.logical_and(data[0]>=t_window[0],data[0]<=t_window[1]))[0]
-    t=data[0][idx]
-    data=data[1][idx]
+    """
+    idx = np.where(np.logical_and(data[0] >= t_window[0], data[0] <= t_window[1]))[0]
+    t = data[0][idx]
+    data = data[1][idx]
+
+    return [t, data]
 
-    return [t,data]
 
 def select_noon(t_pen):
-    '''
+    """
     Select the dates that are closest to the local noon
 
      Parameters:
@@ -547,119 +618,203 @@ def select_noon(t_pen):
         1. index list of all data taken at noon (20 UT) or the closest one if there is no data at noon
         2. a list of indexes of data taken on the same day (if there is only one measurement then this
             element is just 1 element list np. [[1],[2,3,4],[6]]
-    '''
-    #TODO it should be done in a dynamical way - it should determine how many measurements are in a given day
-    index=np.array([0])
-    index_duplicate=[]
-    i=0
-    while i<(len(t_pen)-3):
-
-        if (t_pen[i].yday[:8]==t_pen[i+1].yday[:8]):
-
-            if(t_pen[i].yday[:8]==t_pen[i+2].yday[:8]):
-                if(t_pen[i].yday[:8]==t_pen[i+3].yday[:8]):
-                    h=np.array([int(t_pen[i].yday[9:11]),int(t_pen[i+1].yday[9:11]),int(t_pen[i+2].yday[9:11]),int(t_pen[i+3].yday[9:11])])
-                    k=i+np.abs(h-20).argmin()   # index of the hour that is near 20UT (local noon)
-                    d=[i,i+1,i+2,i+3]
-                    i=i+4
+    """
+    # TODO it should be done in a dynamical way - it should determine how many measurements are in a given day
+    index = np.array([0])
+    index_duplicate = []
+    i = 0
+    while i < (len(t_pen) - 3):
+        if t_pen[i].yday[:8] == t_pen[i + 1].yday[:8]:
+            if t_pen[i].yday[:8] == t_pen[i + 2].yday[:8]:
+                if t_pen[i].yday[:8] == t_pen[i + 3].yday[:8]:
+                    h = np.array(
+                        [
+                            int(t_pen[i].yday[9:11]),
+                            int(t_pen[i + 1].yday[9:11]),
+                            int(t_pen[i + 2].yday[9:11]),
+                            int(t_pen[i + 3].yday[9:11]),
+                        ]
+                    )
+                    k = (
+                        i + np.abs(h - 20).argmin()
+                    )  # index of the hour that is near 20UT (local noon)
+                    d = [i, i + 1, i + 2, i + 3]
+                    i = i + 4
                 else:
-                    h=np.array([int(t_pen[i].yday[9:11]),int(t_pen[i+1].yday[9:11]),int(t_pen[i+2].yday[9:11])])
-                    k=i+np.abs(h-20).argmin()   # index of the hour that is near 20UT (local noon)
-                    d=[i,i+1,i+2]
-                    i=i+3
+                    h = np.array(
+                        [
+                            int(t_pen[i].yday[9:11]),
+                            int(t_pen[i + 1].yday[9:11]),
+                            int(t_pen[i + 2].yday[9:11]),
+                        ]
+                    )
+                    k = (
+                        i + np.abs(h - 20).argmin()
+                    )  # index of the hour that is near 20UT (local noon)
+                    d = [i, i + 1, i + 2]
+                    i = i + 3
             else:
-                h=np.array([int(t_pen[i].yday[9:11]),int(t_pen[i+1].yday[9:11])])
-                k=i+np.abs(h-20).argmin()   # index of the hour that is near 20UT (local noon)
-                d=[i,i+1]
-            i=i+2
+                h = np.array([int(t_pen[i].yday[9:11]), int(t_pen[i + 1].yday[9:11])])
+                k = (
+                    i + np.abs(h - 20).argmin()
+                )  # index of the hour that is near 20UT (local noon)
+                d = [i, i + 1]
+            i = i + 2
         else:
-            k=i
-            d=[i]
-            i=i+1
-        index=np.append(index,k)
+            k = i
+            d = [i]
+            i = i + 1
+        index = np.append(index, k)
         index_duplicate.append(d)
 
-    return index[1:],index_duplicate
+    return index[1:], index_duplicate
+
 
 def test_WawHelioInMP_file(model):
-    '''
+    """
     test if json file has what it should inside
-    '''
-
-    vg_points_number=model['header']['vg_points_number']
-    hion_filtration=model['params_hion']['hion_filtration']
-    hion_order=model['params_hion']['hion_order']
-
-    hion_const_0=np.array(model['params_hion']['hion_const']).shape[0]
-    hion_base=np.array(model['params_hion']['hion_base'])
-    hion_base_0=np.array(model['params_hion']['hion_base']).shape[0]
-    hion_base_1=np.array(model['params_hion']['hion_base']).shape[1]
-
-    if vg_points_number!=len(model['vg_points']):
-        logging.error('vg_points_number is incorrect.\nIt is '+str(vg_points_number)
-                      +'\nIt should be '+str(len(model['vg_points'])))
-        raise ArrayShapeError('Array shape error: Array shape is incorrect')
-
-    elif hion_const_0!=(hion_order-1-hion_filtration):
-            logging.error('hion_const shape is incorrect.\nIt is '+str(hion_const_0)
-                          +'\nIt should be '+str(hion_order-1-hion_filtration))
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
-
-    elif np.logical_or(hion_base_0!=hion_order-1,hion_base_1!=hion_order+1):
-            logging.error('hion_base shape is incorrect.\nIt is '+str(hion_base.shape)
-                          +'\nIt should be ('+str(hion_order-1)+','+str(hion_order+1)+')')
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
-
-
-    for k in np.arange(model['header']['vg_points_number']):
-
-        lcrv_filtration=model['vg_points'][k]['parameters']['lcrv_filtration']
-        lcrv_order=model['vg_points'][k]['parameters']['lcrv_order']
-
-        model_const_0=np.array(model['vg_points'][k]['parameters']['model_const']).shape[0]
-        model_matrix=np.array(model['vg_points'][k]['parameters']['model_matrix'])
-        model_matrix_0=np.array(model['vg_points'][k]['parameters']['model_matrix']).shape[0]
-        model_matrix_1=np.array(model['vg_points'][k]['parameters']['model_matrix']).shape[1]
-
-        lcrv_Trmatrix=np.array(model['vg_points'][k]['parameters']['lcrv_Trmatrix'])
-        lcrv_Trmatrix_0=np.array(model['vg_points'][k]['parameters']['lcrv_Trmatrix']).shape[0]
-        lcrv_Trmatrix_1=np.array(model['vg_points'][k]['parameters']['lcrv_Trmatrix']).shape[1]
-
-        if model_const_0!=hion_filtration:
-            logging.error('model_const shape is incorrect.\nIt is '+str(model_const_0)
-                        +'\nIt should be '+str(hion_filtration))
-            raise ArrayShapeError('Array shape error: Array shape is incorrect')
+    """
 
-        elif np.logical_or(model_matrix_0!=hion_filtration,model_matrix_1!=lcrv_filtration):
-            logging.error('model_matrix shape is incorrect.\nIt is '+str(model_matrix.shape)
-                          +'\nIt should be ('+str(hion_filtration)+','+str(lcrv_filtration)+')')
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
+    vg_points_number = model["header"]["vg_points_number"]
+    hion_filtration = model["params_hion"]["hion_filtration"]
+    hion_order = model["params_hion"]["hion_order"]
+
+    hion_const_0 = np.array(model["params_hion"]["hion_const"]).shape[0]
+    hion_base = np.array(model["params_hion"]["hion_base"])
+    hion_base_0 = np.array(model["params_hion"]["hion_base"]).shape[0]
+    hion_base_1 = np.array(model["params_hion"]["hion_base"]).shape[1]
+
+    if vg_points_number != len(model["vg_points"]):
+        logging.error(
+            "vg_points_number is incorrect.\nIt is "
+            + str(vg_points_number)
+            + "\nIt should be "
+            + str(len(model["vg_points"]))
+        )
+        raise ArrayShapeError("Array shape error: Array shape is incorrect")
+
+    elif hion_const_0 != (hion_order - 1 - hion_filtration):
+        logging.error(
+            "hion_const shape is incorrect.\nIt is "
+            + str(hion_const_0)
+            + "\nIt should be "
+            + str(hion_order - 1 - hion_filtration)
+        )
+        raise ArrayShapeError("Array shape error: Array shape is incorect")
+
+    elif np.logical_or(hion_base_0 != hion_order - 1, hion_base_1 != hion_order + 1):
+        logging.error(
+            "hion_base shape is incorrect.\nIt is "
+            + str(hion_base.shape)
+            + "\nIt should be ("
+            + str(hion_order - 1)
+            + ","
+            + str(hion_order + 1)
+            + ")"
+        )
+        raise ArrayShapeError("Array shape error: Array shape is incorect")
+
+    for k in np.arange(model["header"]["vg_points_number"]):
+        lcrv_filtration = model["vg_points"][k]["parameters"]["lcrv_filtration"]
+        lcrv_order = model["vg_points"][k]["parameters"]["lcrv_order"]
+
+        model_const_0 = np.array(
+            model["vg_points"][k]["parameters"]["model_const"]
+        ).shape[0]
+        model_matrix = np.array(model["vg_points"][k]["parameters"]["model_matrix"])
+        model_matrix_0 = np.array(
+            model["vg_points"][k]["parameters"]["model_matrix"]
+        ).shape[0]
+        model_matrix_1 = np.array(
+            model["vg_points"][k]["parameters"]["model_matrix"]
+        ).shape[1]
+
+        lcrv_Trmatrix = np.array(model["vg_points"][k]["parameters"]["lcrv_Trmatrix"])
+        lcrv_Trmatrix_0 = np.array(
+            model["vg_points"][k]["parameters"]["lcrv_Trmatrix"]
+        ).shape[0]
+        lcrv_Trmatrix_1 = np.array(
+            model["vg_points"][k]["parameters"]["lcrv_Trmatrix"]
+        ).shape[1]
+
+        if model_const_0 != hion_filtration:
+            logging.error(
+                "model_const shape is incorrect.\nIt is "
+                + str(model_const_0)
+                + "\nIt should be "
+                + str(hion_filtration)
+            )
+            raise ArrayShapeError("Array shape error: Array shape is incorrect")
+
+        elif np.logical_or(
+            model_matrix_0 != hion_filtration, model_matrix_1 != lcrv_filtration
+        ):
+            logging.error(
+                "model_matrix shape is incorrect.\nIt is "
+                + str(model_matrix.shape)
+                + "\nIt should be ("
+                + str(hion_filtration)
+                + ","
+                + str(lcrv_filtration)
+                + ")"
+            )
+            raise ArrayShapeError("Array shape error: Array shape is incorect")
+
+        elif np.logical_or(
+            lcrv_Trmatrix_0 != 2 * lcrv_order + 1, lcrv_Trmatrix_1 != 2 * lcrv_order + 1
+        ):
+            logging.error(
+                "lcrv_Trmatrix shape is incorrect.\nIt is "
+                + str(lcrv_Trmatrix.shape)
+                + "\nIt should be ("
+                + str(2 * lcrv_order + 1)
+                + ","
+                + str(2 * lcrv_order + 1)
+                + ")"
+            )
+            raise ArrayShapeError("Array shape error: Array shape is incorect")
 
-        elif np.logical_or(lcrv_Trmatrix_0!=2*lcrv_order+1,lcrv_Trmatrix_1!=2*lcrv_order+1):
-            logging.error('lcrv_Trmatrix shape is incorrect.\nIt is '+str(lcrv_Trmatrix.shape)
-                          +'\nIt should be ('+str(2*lcrv_order+1)+','+str(2*lcrv_order+1)+')')
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
 
 def time_string_from_l3a_fn(fn):
-    '''
+    """
     Extracts date from the L3a filename. This may change if the naming convention will change
-    '''
-    fn_string=fn.split('_')[4]
-    time_string=fn_string[0:4]+'-'+fn_string[4:6]+'-'+fn_string[6:8]+' '+fn_string[8:10]+':'+fn_string[10:12]+':'+fn_string[12:14]
+    """
+    fn_string = fn.split("_")[4]
+    time_string = (
+        fn_string[0:4]
+        + "-"
+        + fn_string[4:6]
+        + "-"
+        + fn_string[6:8]
+        + " "
+        + fn_string[8:10]
+        + ":"
+        + fn_string[10:12]
+        + ":"
+        + fn_string[12:14]
+    )
     return time_string
 
+
 def time_from_l3a(fn):
-    data_l3a=read_json(fn)
-    mean_time=Time(data_l3a['start_time'])+0.5*(Time(data_l3a['end_time'])-Time(data_l3a['start_time']))
-    return [mean_time, Time(data_l3a['start_time']), Time(data_l3a['end_time'])]
+    data_l3a = read_json(fn)
+    mean_time = Time(data_l3a["start_time"]) + 0.5 * (
+        Time(data_l3a["end_time"]) - Time(data_l3a["start_time"])
+    )
+    return [mean_time, Time(data_l3a["start_time"]), Time(data_l3a["end_time"])]
+
 
 def time_from_yday(yday_list):
-        string = [f'{int(yday_list[i, 0])}:{int(yday_list[i, 1]):03d}:{int(yday_list[i, 2]):02d}:00' for i in range(len(yday_list))]
-        date=Time(string,format='yday')
-        return date
+    string = [
+        f"{int(yday_list[i, 0])}:{int(yday_list[i, 1]):03d}:{int(yday_list[i, 2]):02d}:00"
+        for i in range(len(yday_list))
+    ]
+    date = Time(string, format="yday")
+    return date
+
 
 def v2E(v):
-    '''
+    """
     Speed to kinetic energy transformation for hydrogen
 
     Parameters:
@@ -669,8 +824,8 @@ def v2E(v):
     Output:
     ------------
     E: energy as an astropy.unit object
-    '''
+    """
 
-    m_H=1.6733e-24*u.g
-    E=(0.5*m_H*v**2).to('eV')
+    m_H = 1.6733e-24 * u.g
+    E = (0.5 * m_H * v**2).to("eV")
     return E
diff --git a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_CarringtonIonRate.py b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_CarringtonIonRate.py
index 76c8db678..f2df1dc99 100644
--- a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_CarringtonIonRate.py
+++ b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_CarringtonIonRate.py
@@ -1,7 +1,7 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska (ikowalska@cbk.waw.pl)
 Carrington averaged ionization rate class
-'''
+"""
 
 import json
 import logging
@@ -16,9 +16,10 @@
 
 logging.basicConfig(level=logging.ERROR)
 
+
 @dataclass
-class CarringtonIonizationRate():
-    '''
+class CarringtonIonizationRate:
+    """
     Class for a GLOWS Carrington averaged ionization rate profiles
     Contains original daily ionization rate profiles, averaged ionization rate profile
     charge exchange and photoionization profiles
@@ -87,47 +88,47 @@ class CarringtonIonizationRate():
         Read file with UV latitudinal anisotrpy factor averaged over 1 Carrington rotation period
     save_to_file(self,fn)
         saves L3b structure to file
-    '''
+    """
 
-    def __init__(self, anc_input_from_instr_team,ext_dependencies):
+    def __init__(self, anc_input_from_instr_team, ext_dependencies):
         """
         Class constructor
         """
-        self.header={
-            'software_version': VERSION,
-            'filename': None,
-            'ancillary_data_files': anc_input_from_instr_team,
-            'external_dependeciens': ext_dependencies['f107_raw_data'],
+        self.header = {
+            "software_version": VERSION,
+            "filename": None,
+            "ancillary_data_files": anc_input_from_instr_team,
+            "external_dependeciens": ext_dependencies["f107_raw_data"],
         }
 
         # read pipeline settings to be used for L3b
-        self.settings = fun.read_json(anc_input_from_instr_team['pipeline_settings'])
+        self.settings = fun.read_json(anc_input_from_instr_team["pipeline_settings"])
 
-        self.sw_ecliptic={}
-        self.sw_ecliptic['mean_speed']=None
-        self.sw_ecliptic['mean_proton_density']=None
+        self.sw_ecliptic = {}
+        self.sw_ecliptic["mean_speed"] = None
+        self.sw_ecliptic["mean_proton_density"] = None
 
         # All parameters will be read from L3a file or computed later on
-        self.daily_ion_rate={}
-        self.daily_ion_rate['date']=None
-        self.daily_ion_rate['ion_rate']=None
-
-        self.carr_ion_rate={}
-        self.carr_ion_rate['date']=None
-        self.carr_ion_rate['CR']=None
-        self.carr_ion_rate['ion_grid']=self.settings['ion_rate_grid']
-        self.carr_ion_rate['ion_rate']=None
-        self.carr_ion_rate['cx_rate']=None
-        self.carr_ion_rate['ph_rate']=None
-        self.carr_ion_rate['ion_rate_uncert']=None
-        self.carr_ion_rate['cx_rate_uncert']=None
-        self.carr_ion_rate['ph_rate_uncert']=None
-
-        self.uv_anisotropy=None
-        self.uv_anisotropy_flag=None
-
-    def _anchor_to_ecliptic(self,carr_ion_rate):
-        '''
+        self.daily_ion_rate = {}
+        self.daily_ion_rate["date"] = None
+        self.daily_ion_rate["ion_rate"] = None
+
+        self.carr_ion_rate = {}
+        self.carr_ion_rate["date"] = None
+        self.carr_ion_rate["CR"] = None
+        self.carr_ion_rate["ion_grid"] = self.settings["ion_rate_grid"]
+        self.carr_ion_rate["ion_rate"] = None
+        self.carr_ion_rate["cx_rate"] = None
+        self.carr_ion_rate["ph_rate"] = None
+        self.carr_ion_rate["ion_rate_uncert"] = None
+        self.carr_ion_rate["cx_rate_uncert"] = None
+        self.carr_ion_rate["ph_rate_uncert"] = None
+
+        self.uv_anisotropy = None
+        self.uv_anisotropy_flag = None
+
+    def _anchor_to_ecliptic(self, carr_ion_rate):
+        """
         normalizes the profile in such a way that the value in its central bin
         matches the average ionization rate calculated based on the measured parameters
         of the solar wind in the ecliptic plane
@@ -136,120 +137,162 @@ def _anchor_to_ecliptic(self,carr_ion_rate):
         ---------------
         carr_ion_rate: mean ionization rate profile calculated by taking average value of all daily ionization rate profiles within considered CR
 
-        '''
-        idx_middle=int(len(self.carr_ion_rate['ion_grid'])/2)    # middle bin (equator) in the latitudinal grid
-        carr_ion_rate0=carr_ion_rate[idx_middle]
-
-        ph_rate0=self.carr_ion_rate['ph_rate'][idx_middle]    # ph_rate at latitude=0
-        v0=self.sw_ecliptic['mean_speed']*u.km/u.s
-        n0=self.sw_ecliptic['mean_proton_density']/(u.cm)**3
-        sigma0=fun.cross_section_cx(fun.v2E(v0))
+        """
+        idx_middle = int(
+            len(self.carr_ion_rate["ion_grid"]) / 2
+        )  # middle bin (equator) in the latitudinal grid
+        carr_ion_rate0 = carr_ion_rate[idx_middle]
 
-        cx_rate0=(v0*sigma0*n0).to('1/s').value
+        ph_rate0 = self.carr_ion_rate["ph_rate"][idx_middle]  # ph_rate at latitude=0
+        v0 = self.sw_ecliptic["mean_speed"] * u.km / u.s
+        n0 = self.sw_ecliptic["mean_proton_density"] / (u.cm) ** 3
+        sigma0 = fun.cross_section_cx(fun.v2E(v0))
 
-        ion_rate0=ph_rate0+cx_rate0
+        cx_rate0 = (v0 * sigma0 * n0).to("1/s").value
 
-        self.carr_ion_rate['ion_rate']=carr_ion_rate/carr_ion_rate0*ion_rate0
+        ion_rate0 = ph_rate0 + cx_rate0
 
+        self.carr_ion_rate["ion_rate"] = carr_ion_rate / carr_ion_rate0 * ion_rate0
 
     def calculate_averaged_profile(self):
-        '''
+        """
         Mean value of the ionization profile and time. Average taken bin-by-bin on the latitudinal grid.
-        '''
+        """
 
-        carr_ion_rate=np.mean(self.daily_ion_rate['ion_rate'],axis=0)
+        carr_ion_rate = np.mean(self.daily_ion_rate["ion_rate"], axis=0)
 
         # Anchore averaged ionization rate profile to the solar wind measurements in the ecliptic plane
         self._anchor_to_ecliptic(carr_ion_rate)
-        self.carr_ion_rate['date']=np.mean(self.daily_ion_rate['date'])
+        self.carr_ion_rate["date"] = np.mean(self.daily_ion_rate["date"])
 
         # Statistical uncertinity
-        if len(self.daily_ion_rate['ion_rate'])>1:
-            self.carr_ion_rate['ion_rate_uncert']=np.std(self.daily_ion_rate['ion_rate'],axis=0,ddof=1)
-        else: self.carr_ion_rate['ion_rate_uncert']=-1.0E31*np.ones_like(self.carr_ion_rate['ion_rate'])
-
-
+        if len(self.daily_ion_rate["ion_rate"]) > 1:
+            self.carr_ion_rate["ion_rate_uncert"] = np.std(
+                self.daily_ion_rate["ion_rate"], axis=0, ddof=1
+            )
+        else:
+            self.carr_ion_rate["ion_rate_uncert"] = -1.0e31 * np.ones_like(
+                self.carr_ion_rate["ion_rate"]
+            )
 
     def calculate_charge_exchange(self):
-        '''
+        """
         Charge exchange rate for a stationary atom aproximation assuming that total ionization rate is a sum
         of photoionization and charge exchange rate.
-        '''
+        """
 
         # ionization rate obtained from the light curve is a sum of charge exchange rate and photoionization rate
-        self.carr_ion_rate['cx_rate']=self.carr_ion_rate['ion_rate']-self.carr_ion_rate['ph_rate']
+        self.carr_ion_rate["cx_rate"] = (
+            self.carr_ion_rate["ion_rate"] - self.carr_ion_rate["ph_rate"]
+        )
 
         # statistical uncertainty
-        if len(self.carr_ion_rate['cx_rate'])>1:
-            self.carr_ion_rate['cx_rate_uncert']=self.carr_ion_rate['ion_rate_uncert']+self.carr_ion_rate['ph_rate_uncert']
+        if len(self.carr_ion_rate["cx_rate"]) > 1:
+            self.carr_ion_rate["cx_rate_uncert"] = (
+                self.carr_ion_rate["ion_rate_uncert"]
+                + self.carr_ion_rate["ph_rate_uncert"]
+            )
         else:
-            self.carr_ion_rate['cx_rate_uncert']=-1.0E31*np.ones_like(self.carr_ion_rate['cx_rate'])
+            self.carr_ion_rate["cx_rate_uncert"] = -1.0e31 * np.ones_like(
+                self.carr_ion_rate["cx_rate"]
+            )
 
-    def calculate_photoion(self, anc_input_from_instr_team,ext_dependencies):
-        '''
+    def calculate_photoion(self, anc_input_from_instr_team, ext_dependencies):
+        """
         Photoionization rate at 1au averaged over a time window
         Based on daily photoionization calculated from F10.7 using WawHelioIon method
-        '''
+        """
 
         # raw data from the same period as available daily profiles
-        t_ini=self.daily_ion_rate['date'][0]
-        t_end=self.daily_ion_rate['date'][-1]
-        t_mid=t_ini+(t_end-t_ini)/2
+        t_ini = self.daily_ion_rate["date"][0]
+        t_end = self.daily_ion_rate["date"][-1]
+        t_mid = t_ini + (t_end - t_ini) / 2
 
         # read F10.7 file (the raw data from LASP)
-        t_raw,flux_raw=fun.read_f107_raw_data(ext_dependencies['f107_raw_data'])
-        t_daily, flux_daily0=fun.f107_daily_data(t_raw,flux_raw) # zmiana
+        t_raw, flux_raw = fun.read_f107_raw_data(ext_dependencies["f107_raw_data"])
+        t_daily, flux_daily0 = fun.f107_daily_data(t_raw, flux_raw)  # zmiana
 
         # select data from the window
-        #data_window=fun.select_data_window([t_raw,flux_raw],[t_ini,t_end])  zmiana
-        data_window=fun.select_data_window([t_daily,flux_daily0],[t_ini,t_end])
+        # data_window=fun.select_data_window([t_raw,flux_raw],[t_ini,t_end])  zmiana
+        data_window = fun.select_data_window([t_daily, flux_daily0], [t_ini, t_end])
 
         # check if there are at least 2 data points in the window
-        photoion_daily=[]
+        photoion_daily = []
 
-        if len(data_window[0])>2:
-            flux_daily=data_window[1]
+        if len(data_window[0]) > 2:
+            flux_daily = data_window[1]
             # apply correlation between photoion and F10.7
-            photoion_daily=self._calculate_photoion_from_f107(flux_daily)
+            photoion_daily = self._calculate_photoion_from_f107(flux_daily)
 
             # average daily photoionization
-            photoion_mean=photoion_daily.mean()
-            photoion_std=np.std(photoion_daily,ddof=1)
+            photoion_mean = photoion_daily.mean()
+            photoion_std = np.std(photoion_daily, ddof=1)
 
         else:
             # if there are less than 2 data points in the time window, we want to interpolate
             # between mean values of the previous and next CR
-            t_ini_next=Time(fun.jd_fm_Carrington(self.carr_ion_rate['CR']+1),format='jd')
-            t_end_next=Time(fun.jd_fm_Carrington(self.carr_ion_rate['CR']+2),format='jd')
-            data_window_next=fun.select_data_window([t_raw,flux_raw],[t_ini_next,t_end_next])
-            if len(data_window_next[0])>2:
+            t_ini_next = Time(
+                fun.jd_fm_Carrington(self.carr_ion_rate["CR"] + 1), format="jd"
+            )
+            t_end_next = Time(
+                fun.jd_fm_Carrington(self.carr_ion_rate["CR"] + 2), format="jd"
+            )
+            data_window_next = fun.select_data_window(
+                [t_raw, flux_raw], [t_ini_next, t_end_next]
+            )
+            if len(data_window_next[0]) > 2:
                 # if there are at least data points in the next CR, then we can interpolate
-                t_ini_prev=Time(fun.jd_fm_Carrington(self.carr_ion_rate['CR']-1),format='jd')
-                t_end_prev=Time(fun.jd_fm_Carrington(self.carr_ion_rate['CR']),format='jd')
-                data_window_prev=fun.select_data_window([t_raw,flux_raw],[t_ini_prev,t_end_prev])
-
-                data_next_mean=[data_window_next[0].mean(),data_window_next[1].mean()]
-                data_prev_mean=[data_window_prev[0].mean(),data_window_prev[1].mean()]
-
-                data_mean=[t_mid,np.interp(t_mid.jd,np.array([data_next_mean[0].jd,data_prev_mean[0].jd]),np.array([data_next_mean[1],data_prev_mean[1]]))]
-
-                photoion_mean=self._calculate_photoion_from_f107(data_mean[1])
+                t_ini_prev = Time(
+                    fun.jd_fm_Carrington(self.carr_ion_rate["CR"] - 1), format="jd"
+                )
+                t_end_prev = Time(
+                    fun.jd_fm_Carrington(self.carr_ion_rate["CR"]), format="jd"
+                )
+                data_window_prev = fun.select_data_window(
+                    [t_raw, flux_raw], [t_ini_prev, t_end_prev]
+                )
+
+                data_next_mean = [
+                    data_window_next[0].mean(),
+                    data_window_next[1].mean(),
+                ]
+                data_prev_mean = [
+                    data_window_prev[0].mean(),
+                    data_window_prev[1].mean(),
+                ]
+
+                data_mean = [
+                    t_mid,
+                    np.interp(
+                        t_mid.jd,
+                        np.array([data_next_mean[0].jd, data_prev_mean[0].jd]),
+                        np.array([data_next_mean[1], data_prev_mean[1]]),
+                    ),
+                ]
+
+                photoion_mean = self._calculate_photoion_from_f107(data_mean[1])
             else:
                 # if there are no data in the next CR, then we need to wait for another CR
-                raise Exception("There are no data to interpolate F10.7 for CR=%d",self.carr_ion_rate['CR'])
-
+                raise Exception(
+                    "There are no data to interpolate F10.7 for CR=%d",
+                    self.carr_ion_rate["CR"],
+                )
 
         # including latitudinal anisotropy factor
-        self._read_uv_anisotrpy(anc_input_from_instr_team['uv_anisotropy'])
-        self.carr_ion_rate['ph_rate']=photoion_mean*self.uv_anisotropy
+        self._read_uv_anisotrpy(anc_input_from_instr_team["uv_anisotropy"])
+        self.carr_ion_rate["ph_rate"] = photoion_mean * self.uv_anisotropy
 
-        if len(photoion_daily)>1:
-            self.carr_ion_rate['ph_rate_uncert']=photoion_std*np.ones_like(self.carr_ion_rate['ph_rate'])
+        if len(photoion_daily) > 1:
+            self.carr_ion_rate["ph_rate_uncert"] = photoion_std * np.ones_like(
+                self.carr_ion_rate["ph_rate"]
+            )
         else:
-            self.carr_ion_rate['ph_rate_uncert']=-1.0E31*np.ones_like(self.carr_ion_rate['ph_rate'])
+            self.carr_ion_rate["ph_rate_uncert"] = -1.0e31 * np.ones_like(
+                self.carr_ion_rate["ph_rate"]
+            )
 
-    def _calculate_photoion_from_f107(self,flux):
-        '''
+    def _calculate_photoion_from_f107(self, flux):
+        """
         Correlation between photoionization rate and F10.7 (sokol_bzowski:14a, sokol_etal:20a)
 
         Parameters:
@@ -260,50 +303,56 @@ def _calculate_photoion_from_f107(self,flux):
         ------------
         photoion: the value of the photoionization at 1au on ecliptic calculated from F10.7
 
-        '''
-        photoion=-2.9819e-8+2.416e-8*flux**0.4017
+        """
+        photoion = -2.9819e-8 + 2.416e-8 * flux**0.4017
         return photoion
 
-    def _read_uv_anisotrpy(self,fn):
-        '''
+    def _read_uv_anisotrpy(self, fn):
+        """
         Read file with UV latitudinal anisotrpy factor averaged over 1 Carrington rotation period.
         File will be delivered by the instrument team
 
         Parameters:
         --------------
         fn:    file name (str)
-        '''
+        """
 
         anisotropy = fun.read_json(fn)
 
-        self.uv_anisotropy = np.array(anisotropy['anisotropy_factor'])
-        self.uv_anisotropy_flag = anisotropy['flag']
+        self.uv_anisotropy = np.array(anisotropy["anisotropy_factor"])
+        self.uv_anisotropy_flag = anisotropy["flag"]
 
     def get_dict(self):
 
         # Dictionary with the ionization rate profiles (total, photoionization and charge exchange) that will be saved in json file
         output = {}
-        output['header'] = self.header
-
-        output['date'] = self.carr_ion_rate['date'].iso
-        output['CR'] = int(self.carr_ion_rate['CR'])
-
-        output['ion_rate_profile'] = {}
-        output['ion_rate_profile']['lat_grid'] = self.carr_ion_rate['ion_grid']
-        output['ion_rate_profile']['sum_rate'] = self.carr_ion_rate['ion_rate'].tolist()
-        output['ion_rate_profile']['ph_rate'] = self.carr_ion_rate['ph_rate'].tolist()
-        output['ion_rate_profile']['cx_rate'] = self.carr_ion_rate['cx_rate'].tolist()
-        output['ion_rate_profile']['sum_uncert'] = self.carr_ion_rate['ion_rate_uncert'].tolist()
-        output['ion_rate_profile']['ph_uncert'] = self.carr_ion_rate['ph_rate_uncert'].tolist()
-        output['ion_rate_profile']['cx_uncert'] = self.carr_ion_rate['cx_rate_uncert'].tolist()
-
-        output['uv_anisotropy_factor'] = self.uv_anisotropy.tolist()
-        output['uv_anisotropy_flag'] = self.uv_anisotropy_flag
+        output["header"] = self.header
+
+        output["date"] = self.carr_ion_rate["date"].iso
+        output["CR"] = int(self.carr_ion_rate["CR"])
+
+        output["ion_rate_profile"] = {}
+        output["ion_rate_profile"]["lat_grid"] = self.carr_ion_rate["ion_grid"]
+        output["ion_rate_profile"]["sum_rate"] = self.carr_ion_rate["ion_rate"].tolist()
+        output["ion_rate_profile"]["ph_rate"] = self.carr_ion_rate["ph_rate"].tolist()
+        output["ion_rate_profile"]["cx_rate"] = self.carr_ion_rate["cx_rate"].tolist()
+        output["ion_rate_profile"]["sum_uncert"] = self.carr_ion_rate[
+            "ion_rate_uncert"
+        ].tolist()
+        output["ion_rate_profile"]["ph_uncert"] = self.carr_ion_rate[
+            "ph_rate_uncert"
+        ].tolist()
+        output["ion_rate_profile"]["cx_uncert"] = self.carr_ion_rate[
+            "cx_rate_uncert"
+        ].tolist()
+
+        output["uv_anisotropy_factor"] = self.uv_anisotropy.tolist()
+        output["uv_anisotropy_flag"] = self.uv_anisotropy_flag
 
         return output
 
-    def save_to_file(self,fn):
-        '''
+    def save_to_file(self, fn):
+        """
         Carrington ionization rate file is an official GLOWS L3b data product
         Temporarly it is in json format, but in production code it should be in CDF
 
@@ -312,33 +361,38 @@ def save_to_file(self,fn):
         fn: str
             name of the output file
 
-        '''
+        """
 
         # Dictionary with the ionization rate profiles (total, photoionization and charge exchange) that will be saved in json file
-        output={}
-        output['header']=self.header
-
-        output['date']=self.carr_ion_rate['date'].iso
-        output['CR']=int(self.carr_ion_rate['CR'])
-
-        output['ion_rate_profile']={}
-        output['ion_rate_profile']['lat_grid']=self.carr_ion_rate['ion_grid']
-        output['ion_rate_profile']['sum_rate']=self.carr_ion_rate['ion_rate'].tolist()
-        output['ion_rate_profile']['ph_rate']=self.carr_ion_rate['ph_rate'].tolist()
-        output['ion_rate_profile']['cx_rate']=self.carr_ion_rate['cx_rate'].tolist()
-        output['ion_rate_profile']['sum_uncert']=self.carr_ion_rate['ion_rate_uncert'].tolist()
-        output['ion_rate_profile']['ph_uncert']=self.carr_ion_rate['ph_rate_uncert'].tolist()
-        output['ion_rate_profile']['cx_uncert']=self.carr_ion_rate['cx_rate_uncert'].tolist()
-
-        output['uv_anisotropy_factor']=self.uv_anisotropy.tolist()
-        output['uv_anisotropy_flag'] = self.uv_anisotropy_flag
+        output = {}
+        output["header"] = self.header
+
+        output["date"] = self.carr_ion_rate["date"].iso
+        output["CR"] = int(self.carr_ion_rate["CR"])
+
+        output["ion_rate_profile"] = {}
+        output["ion_rate_profile"]["lat_grid"] = self.carr_ion_rate["ion_grid"]
+        output["ion_rate_profile"]["sum_rate"] = self.carr_ion_rate["ion_rate"].tolist()
+        output["ion_rate_profile"]["ph_rate"] = self.carr_ion_rate["ph_rate"].tolist()
+        output["ion_rate_profile"]["cx_rate"] = self.carr_ion_rate["cx_rate"].tolist()
+        output["ion_rate_profile"]["sum_uncert"] = self.carr_ion_rate[
+            "ion_rate_uncert"
+        ].tolist()
+        output["ion_rate_profile"]["ph_uncert"] = self.carr_ion_rate[
+            "ph_rate_uncert"
+        ].tolist()
+        output["ion_rate_profile"]["cx_uncert"] = self.carr_ion_rate[
+            "cx_rate_uncert"
+        ].tolist()
+
+        output["uv_anisotropy_factor"] = self.uv_anisotropy.tolist()
+        output["uv_anisotropy_flag"] = self.uv_anisotropy_flag
 
         # Check validity of the data
-        fun.check_nan(output['ion_rate_profile'])
+        fun.check_nan(output["ion_rate_profile"])
 
         json_content = json.dumps(output, indent=3)
 
-        output_fp = open(fn, 'w')
+        output_fp = open(fn, "w")
         print(json_content, file=output_fp)
         output_fp.close()
-
diff --git a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_DailyIonRate.py b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_DailyIonRate.py
index 0c8de8087..6ffae811e 100644
--- a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_DailyIonRate.py
+++ b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3b_DailyIonRate.py
@@ -1,7 +1,7 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska (ikowalska@cbk.waw.pl)
 Daily ionization rate class
-'''
+"""
 
 import json
 import logging
@@ -11,7 +11,7 @@
 from astropy import units as u
 from astropy.coordinates import SkyCoord
 from astropy.time import Time
-from sklearn.linear_model import LinearRegression, Lasso, Ridge
+from sklearn.linear_model import Lasso, LinearRegression, Ridge
 
 from . import funcs as fun
 from .constants import VERSION
@@ -35,8 +35,8 @@ class WawHelioIonMPError(Exception):
 
 
 @dataclass
-class DailyIonizationRate():
-    '''
+class DailyIonizationRate:
+    """
     Class for a GLOWS daily ionization rate
     Contains original light curve, parameters used to convert light curve into
     ionization profile and ionization rate profile
@@ -151,7 +151,7 @@ class DailyIonizationRate():
     _total_ion_params_from_lcrv_params()
         calculates ionization rate Legendre decomposition coeffiecients from the the lightcurve decomposed coeffiecients
 
-    '''
+    """
 
     def __init__(self, anc_input_from_instr_team):
         """
@@ -159,62 +159,64 @@ def __init__(self, anc_input_from_instr_team):
         """
 
         self.header = {
-            'software_version': VERSION,
-            'ancillary_data_files': anc_input_from_instr_team['WawHelioIonMP_parameters'],
-            'l3a_input_filename': None
+            "software_version": VERSION,
+            "ancillary_data_files": anc_input_from_instr_team[
+                "WawHelioIonMP_parameters"
+            ],
+            "l3a_input_filename": None,
         }
 
         # read pipeline settings to be used for L3b
-        self.settings = fun.read_json(anc_input_from_instr_team['pipeline_settings'])
+        self.settings = fun.read_json(anc_input_from_instr_team["pipeline_settings"])
 
         # All parameters will be read from L3a file or computed later on
         self.spacecraft_location = None
 
         self.lcrv = {}
-        self.lcrv['date'] = None
-        self.lcrv['ecl_lon'] = None
-        self.lcrv['sc_coord'] = None
-        self.lcrv['spin_angle'] = None
-        self.lcrv['photon_flux'] = None
-        self.lcrv['flux_uncertainties'] = None
-        self.lcrv['bkg_galaxy'] = None
-        self.lcrv['bkg_time'] = None
-        self.lcrv['glow_flux'] = None
-        self.lcrv['glow_flux_swan'] = None
-        self.lcrv['decomposition_coeff'] = None
+        self.lcrv["date"] = None
+        self.lcrv["ecl_lon"] = None
+        self.lcrv["sc_coord"] = None
+        self.lcrv["spin_angle"] = None
+        self.lcrv["photon_flux"] = None
+        self.lcrv["flux_uncertainties"] = None
+        self.lcrv["bkg_galaxy"] = None
+        self.lcrv["bkg_time"] = None
+        self.lcrv["glow_flux"] = None
+        self.lcrv["glow_flux_swan"] = None
+        self.lcrv["decomposition_coeff"] = None
 
         self.model = {}
-        self.model['params_hion'] = {}
-        self.model['params_hion']['hion_order'] = None
-        self.model['params_hion']['hion_filtration'] = None
-        self.model['params_hion']['hion_const'] = None
-        self.model['params_hion']['hion_base'] = None
-        self.model['vg_points'] = {}
-        self.model['vg_points']['pos_name'] = None
-        self.model['vg_points']['ecl_lon'] = None
-        self.model['vg_points']['ecl_lon_ini'] = None
-        self.model['vg_points']['ecl_lon_fin'] = None
-        self.model['vg_points']['parameters'] = {}
-        self.model['vg_points']['parameters']['lcrv_order'] = None
-        self.model['vg_points']['parameters']['lcrv_filtration'] = None
-        self.model['vg_points']['parameters']['lcrv_Tmatrix'] = None
-        self.model['vg_points']['parameters']['lcrv_const'] = None
-        self.model['vg_points']['parameters']['lcrv_matrix'] = None
+        self.model["params_hion"] = {}
+        self.model["params_hion"]["hion_order"] = None
+        self.model["params_hion"]["hion_filtration"] = None
+        self.model["params_hion"]["hion_const"] = None
+        self.model["params_hion"]["hion_base"] = None
+        self.model["vg_points"] = {}
+        self.model["vg_points"]["pos_name"] = None
+        self.model["vg_points"]["ecl_lon"] = None
+        self.model["vg_points"]["ecl_lon_ini"] = None
+        self.model["vg_points"]["ecl_lon_fin"] = None
+        self.model["vg_points"]["parameters"] = {}
+        self.model["vg_points"]["parameters"]["lcrv_order"] = None
+        self.model["vg_points"]["parameters"]["lcrv_filtration"] = None
+        self.model["vg_points"]["parameters"]["lcrv_Tmatrix"] = None
+        self.model["vg_points"]["parameters"]["lcrv_const"] = None
+        self.model["vg_points"]["parameters"]["lcrv_matrix"] = None
 
         self.ion = {}
-        self.ion['ion_grid'] = self.settings['ion_rate_grid']
-        self.ion['ion_rate'] = None
-        self.ion['decomposition_coeff'] = None
+        self.ion["ion_grid"] = self.settings["ion_rate_grid"]
+        self.ion["ion_rate"] = None
+        self.ion["decomposition_coeff"] = None
 
     def calculate_daily_ionization_rate(self):
-        '''
+        """
         Main method that calculates daily ionization rate profile step-by-step
-        '''
+        """
 
         # subtract background
         self._subtract_background()
         # rescale to SWAN
-        self._rescale_to_SWAN(self.settings['swan_scaling_factor'])
+        self._rescale_to_SWAN(self.settings["swan_scaling_factor"])
         # decompose  lightcurve into coeffiecents vector
         self._decompose_light_curve()
         # calculate function that describes ionization rate profile
@@ -223,109 +225,141 @@ def calculate_daily_ionization_rate(self):
         self._calculate_ion_profile(ion_profile_fun)
 
     def _calculate_ion_profile(self, ion_profile_fun):
-        '''
+        """
         Generating tabular profile on ionization grid defined in pipeline settings
 
         Parameters
         -----------
         ion_profile_fun
 
-        '''
+        """
 
         bins = np.arange(-90, 100, 10)  # latitude in deg
         # sin, not cos at it is in Legendre's because in astronomy latitude is meaured
         # from the equator, not from z axis as it is in mathematics
         ion = ion_profile_fun(np.sin(np.radians(bins))) / 1e6
-        self.ion['ion_grid'] = bins
-        self.ion['ion_rate'] = ion
+        self.ion["ion_grid"] = bins
+        self.ion["ion_rate"] = ion
 
     def _decompose_light_curve(self):
-        '''
+        """
         Decomposition GLOWS light curve rescaled to SWAN units into sin, cos series
         As a result, the list of coefficients is calculated
-        '''
-        order = self.model['vg_points']['parameters']['lcrv_order']
-        uncert = self.lcrv['flux_uncertainties']
-        lightcurve = self.lcrv['glow_flux_swan']
-        method = self.settings['lcrv_approximation_method']
-        alpha = self.settings['lcrv_approximation_alpha']
+        """
+        order = self.model["vg_points"]["parameters"]["lcrv_order"]
+        uncert = self.lcrv["flux_uncertainties"]
+        lightcurve = self.lcrv["glow_flux_swan"]
+        method = self.settings["lcrv_approximation_method"]
+        alpha = self.settings["lcrv_approximation_alpha"]
 
         # define basis functions
         base_funcs = fun.derive_sin_cos(order)
         # compute matrix for the least-squares method
-        lsq_mtrx = fun.make_lsq_matrix(np.radians(self.lcrv['spin_angle']), base_funcs)
+        lsq_mtrx = fun.make_lsq_matrix(np.radians(self.lcrv["spin_angle"]), base_funcs)
         # fit the linear regression model
 
-        if method == 'LinearRegression':
-            lin_model = LinearRegression(fit_intercept=False).fit(lsq_mtrx, lightcurve,
-                                                                  sample_weight=1.0 / (uncert * uncert))
-        elif method == 'Lasso':
-            lin_model = Lasso(alpha=alpha, fit_intercept=False).fit(lsq_mtrx, lightcurve,
-                                                                    sample_weight=1.0 / (uncert * uncert))
-        elif method == 'Ridge':
-            lin_model = Ridge(alpha=alpha, fit_intercept=False).fit(lsq_mtrx, lightcurve,
-                                                                    sample_weight=1.0 / (uncert * uncert))
+        if method == "LinearRegression":
+            lin_model = LinearRegression(fit_intercept=False).fit(
+                lsq_mtrx, lightcurve, sample_weight=1.0 / (uncert * uncert)
+            )
+        elif method == "Lasso":
+            lin_model = Lasso(alpha=alpha, fit_intercept=False).fit(
+                lsq_mtrx, lightcurve, sample_weight=1.0 / (uncert * uncert)
+            )
+        elif method == "Ridge":
+            lin_model = Ridge(alpha=alpha, fit_intercept=False).fit(
+                lsq_mtrx, lightcurve, sample_weight=1.0 / (uncert * uncert)
+            )
         else:
-            raise Exception('Unknown regression method. Use LinearRegression, Lasso or Ridge')
+            raise Exception(
+                "Unknown regression method. Use LinearRegression, Lasso or Ridge"
+            )
 
-        self.lcrv['decomposition_coeff'] = lin_model.coef_
+        self.lcrv["decomposition_coeff"] = lin_model.coef_
 
     def _find_vg_ecl(self, model):
-        '''
+        """
         find the index of the vg_point that is closest to the lightcurve, but whitin defined boundaries.
 
         Parameters
         -----------
         model : dict
             Structure read from the WawHelioIon-MP json file
-        '''
+        """
 
-        model_ecl_bins = np.array([[model['vg_points'][i]['ecl_longIni'], model['vg_points'][i]['ecl_longFin']] for i in
-                                   range(len(model['vg_points']))])
-        k = self.settings['no_vg_point_flag']  # if there is no bin in vg_points main loop should be interupted
+        model_ecl_bins = np.array(
+            [
+                [
+                    model["vg_points"][i]["ecl_longIni"],
+                    model["vg_points"][i]["ecl_longFin"],
+                ]
+                for i in range(len(model["vg_points"]))
+            ]
+        )
+        k = self.settings[
+            "no_vg_point_flag"
+        ]  # if there is no bin in vg_points main loop should be interupted
         for i, ecl_bin in enumerate(model_ecl_bins):
             ini, fin = ecl_bin
             # Case when we are not crossing 0-360
             if ini <= fin:
-                if ini <= self.lcrv['ecl_lon'] <= fin: k = i
+                if ini <= self.lcrv["ecl_lon"] <= fin:
+                    k = i
             # Case when we are crossing 0-360
             else:
-                if np.logical_or(self.lcrv['ecl_lon'] >= ini, self.lcrv['ecl_lon'] <= fin): k = i
+                if np.logical_or(
+                    self.lcrv["ecl_lon"] >= ini, self.lcrv["ecl_lon"] <= fin
+                ):
+                    k = i
         return k
 
     def read_l3a(self, data_l3a: dict):
-        '''
+        """
         Read light curve from the L3a json file
 
         Parameters
         -----------
         filename : str
             Path to the L3a data product file
-        '''
+        """
         # Open and load json file with L3a data
 
-        self.spacecraft_location = list(data_l3a['spacecraft_location_average'].values())
-        self.lcrv['ecl_lon'] = np.degrees(fun.get_spher_coord_rad(np.array(self.spacecraft_location))[1])
-        self.lcrv['date'] = Time(data_l3a['start_time']) + 0.5 * (
-                Time(data_l3a['end_time']) - Time(data_l3a['start_time']))
-        self.lcrv['sc_coords'] = SkyCoord(lon=data_l3a['spin_axis_orientation_average']['lon'],
-                                          lat=data_l3a['spin_axis_orientation_average']['lat'],
-                                          unit=u.deg,
-                                          frame='heliocentrictrueecliptic')  # scaning circle centre coordinates
-        self.lcrv['spin_angle'] = np.array(
-            data_l3a['daily_lightcurve']['spin_angle'])  # spin angle grid [masked bins are missing]
-        self.lcrv['photon_flux'] = np.array(data_l3a['daily_lightcurve']['photon_flux'])  # signal in physical units
-        self.lcrv['flux_uncertainties'] = np.array(
-            data_l3a['daily_lightcurve']['flux_uncertainties'])  # measurements uncertaintie
-        self.lcrv['bkg_galaxy'] = np.array(
-            data_l3a['daily_lightcurve']['extra_heliospheric_bckgrd'])  # constant extra-galactic background
-        self.lcrv['bkg_time'] = np.array(
-            data_l3a['daily_lightcurve']['time_dependent_bckgrd'])  # time-dependend background
-        self.lcrv['glow_flux'] = self.lcrv['photon_flux'] - self.lcrv['bkg_galaxy'] - self.lcrv[
-            'bkg_time']  # glow flux in physical units
+        self.spacecraft_location = list(
+            data_l3a["spacecraft_location_average"].values()
+        )
+        self.lcrv["ecl_lon"] = np.degrees(
+            fun.get_spher_coord_rad(np.array(self.spacecraft_location))[1]
+        )
+        self.lcrv["date"] = Time(data_l3a["start_time"]) + 0.5 * (
+            Time(data_l3a["end_time"]) - Time(data_l3a["start_time"])
+        )
+        self.lcrv["sc_coords"] = SkyCoord(
+            lon=data_l3a["spin_axis_orientation_average"]["lon"],
+            lat=data_l3a["spin_axis_orientation_average"]["lat"],
+            unit=u.deg,
+            frame="heliocentrictrueecliptic",
+        )  # scaning circle centre coordinates
+        self.lcrv["spin_angle"] = np.array(
+            data_l3a["daily_lightcurve"]["spin_angle"]
+        )  # spin angle grid [masked bins are missing]
+        self.lcrv["photon_flux"] = np.array(
+            data_l3a["daily_lightcurve"]["photon_flux"]
+        )  # signal in physical units
+        self.lcrv["flux_uncertainties"] = np.array(
+            data_l3a["daily_lightcurve"]["flux_uncertainties"]
+        )  # measurements uncertaintie
+        self.lcrv["bkg_galaxy"] = np.array(
+            data_l3a["daily_lightcurve"]["extra_heliospheric_bckgrd"]
+        )  # constant extra-galactic background
+        self.lcrv["bkg_time"] = np.array(
+            data_l3a["daily_lightcurve"]["time_dependent_bckgrd"]
+        )  # time-dependend background
+        self.lcrv["glow_flux"] = (
+            self.lcrv["photon_flux"] - self.lcrv["bkg_galaxy"] - self.lcrv["bkg_time"]
+        )  # glow flux in physical units
 
     def read_model_params(self, anc_input_from_instr_team):
-        '''
+        """
         Read WawHelioIon-MP parameters
 
         Parameters:
@@ -337,34 +371,34 @@ def read_model_params(self, anc_input_from_instr_team):
         Output:
         returns flag that is non 0 if light curve can't be converted to the ionization rate due to the gap in
         WawHelioIon-MP
-        '''
+        """
 
         # read file
-        model = fun.read_json(anc_input_from_instr_team['WawHelioIonMP_parameters'])
+        model = fun.read_json(anc_input_from_instr_team["WawHelioIonMP_parameters"])
 
         # find vantage point in the WawHelioIon-MP file that isvalid to analyse current light curve
         vg_idx = self._find_vg_ecl(model)
-        if vg_idx == self.settings['no_vg_point_flag']:
+        if vg_idx == self.settings["no_vg_point_flag"]:
             # Light curve can not be converted to the ionization rate profile - WawHelioIon-MP could not be train in that part of the orbit
-            return self.settings['no_vg_point_flag']
+            return self.settings["no_vg_point_flag"]
 
-        self.model['params_hion'] = model['params_hion']
-        self.model['vg_points'] = model['vg_points'][vg_idx]
+        self.model["params_hion"] = model["params_hion"]
+        self.model["vg_points"] = model["vg_points"][vg_idx]
         return 0
 
     def _rescale_to_SWAN(self, sf):
-        '''
+        """
         GLOWS ligth curve without background needs to be rescaled to the SWAN units, because WaWHelioIon-MP was tuned on SWAN data
 
         Parameters
         -----------
         sf : vector
             coefficients of the linear scaling function
-        '''
-        self.lcrv['glow_flux_swan'] = self.lcrv['glow_flux'] * sf[0] + sf[1]
+        """
+        self.lcrv["glow_flux_swan"] = self.lcrv["glow_flux"] * sf[0] + sf[1]
 
     def save_to_file(self, fn):
-        '''
+        """
         Daily ionization rate files are not official GLOWS data product.
         They are needed to calculate average profile, so they will be calculated but not saved to a files.
         This method is for testing purposes
@@ -373,64 +407,72 @@ def save_to_file(self, fn):
         -----------
         fn : str
             Path to the daily L3b file
-        '''
+        """
 
         # Dictionary with the daily ionization rate profile that will be saved in json file
         output = {}
-        output['header'] = self.header
+        output["header"] = self.header
 
-        output['data'] = {}
-        output['data']['date'] = self.lcrv['date'].iso
-        output['data']['ion_grid'] = self.ion['ion_grid'].tolist()
-        output['data']['ion_rate'] = self.ion['ion_rate'].tolist()
+        output["data"] = {}
+        output["data"]["date"] = self.lcrv["date"].iso
+        output["data"]["ion_grid"] = self.ion["ion_grid"].tolist()
+        output["data"]["ion_rate"] = self.ion["ion_rate"].tolist()
 
         json_content = json.dumps(output, indent=3)
 
-        output_fp = open(fn, 'w')
+        output_fp = open(fn, "w")
         print(json_content, file=output_fp)
         output_fp.close()
 
     def _subtract_background(self):
-        '''
+        """
         Subtracts galactic and time-dependent background from the measured photon flux
-        '''
-        self.lcrv['glow_flux'] = self.lcrv['photon_flux'] - (self.lcrv['bkg_galaxy'] + self.lcrv['bkg_time'])
+        """
+        self.lcrv["glow_flux"] = self.lcrv["photon_flux"] - (
+            self.lcrv["bkg_galaxy"] + self.lcrv["bkg_time"]
+        )
 
     def _total_ion_from_lcrv_params(self):
-        '''
+        """
         Calculates total ionization rate profile
         for a set of light curve decomposition parameters.
-        '''
-        hion_base = np.array(self.model['params_hion']['hion_base'])
+        """
+        hion_base = np.array(self.model["params_hion"]["hion_base"])
         total_ion_params = self._total_ion_params_from_lcrv_params()
 
         # Reconstruction of the total ionization rate profile using base functions and total_ion_params
         # Base functions are polynomials and hion_base are coefficients c0, c1, c2,...
 
         # creating base functions
-        total_ion_base_functions = np.array([np.polynomial.Polynomial(matrix)
-                                             for matrix in hion_base])
+        total_ion_base_functions = np.array(
+            [np.polynomial.Polynomial(matrix) for matrix in hion_base]
+        )
 
         # creating a function that describes total ion profile
         total_ion_profile_fun = np.dot(total_ion_params, total_ion_base_functions)
         return total_ion_profile_fun
 
     def _total_ion_params_from_lcrv_params(self):
-        '''
+        """
         Calculates ionization rate coeffiecents that allows to reconstruct ionization rate profile in Legandre base
-        '''
+        """
         # Read model data from json file
 
-        lcrv_Trmatrix = np.array(self.model['vg_points']['parameters']['lcrv_Trmatrix'])
-        model_const = np.array(self.model['vg_points']['parameters']['model_const'])
-        model_matrix = np.array(self.model['vg_points']['parameters']['model_matrix'])
-        hion_const = np.array(self.model['params_hion']['hion_const'])
-        lcrv_filtration = np.array(self.model['vg_points']['parameters']['lcrv_filtration'])
+        lcrv_Trmatrix = np.array(self.model["vg_points"]["parameters"]["lcrv_Trmatrix"])
+        model_const = np.array(self.model["vg_points"]["parameters"]["model_const"])
+        model_matrix = np.array(self.model["vg_points"]["parameters"]["model_matrix"])
+        hion_const = np.array(self.model["params_hion"]["hion_const"])
+        lcrv_filtration = np.array(
+            self.model["vg_points"]["parameters"]["lcrv_filtration"]
+        )
 
         # Transformation PCA (we are taking only the first few components of the light curve decomposition)
-        lcrv_params_PCA = np.dot(self.lcrv['decomposition_coeff'], lcrv_Trmatrix)[:lcrv_filtration]
+        lcrv_params_PCA = np.dot(self.lcrv["decomposition_coeff"], lcrv_Trmatrix)[
+            :lcrv_filtration
+        ]
         # Transformation from light curve parameters into total ion parameters
-        total_ion_params = np.concatenate((model_const + np.dot(model_matrix, lcrv_params_PCA)
-                                           , hion_const))
-        self.ion['decomposition_coeff'] = total_ion_params
+        total_ion_params = np.concatenate(
+            (model_const + np.dot(model_matrix, lcrv_params_PCA), hion_const)
+        )
+        self.ion["decomposition_coeff"] = total_ion_params
         return total_ion_params
diff --git a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_CarringtonSolarWind.py b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_CarringtonSolarWind.py
index d4e9290ec..8a4031b91 100644
--- a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_CarringtonSolarWind.py
+++ b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_CarringtonSolarWind.py
@@ -1,23 +1,26 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska (ikowalska@cbk.waw.pl)
 Carrington solar wind class
-'''
+"""
 
 import json
+import logging
 from dataclasses import dataclass
+
+import astropy.constants as const
+import astropy.units as u
 import numpy as np
 from astropy.time import Time
-import astropy.units as u
-import astropy.constants as const
+
 from . import funcs as fun
-from .constants import VERSION, PHISICAL_CONSTANTS
+from .constants import PHISICAL_CONSTANTS, VERSION
 
-import logging
 logging.basicConfig(level=logging.ERROR)
 
+
 @dataclass
-class CarringtonSolarWind():
-    '''
+class CarringtonSolarWind:
+    """
     Class for a GLOWS Carrington averaged solar wind speed and density
 
     Attributes
@@ -69,118 +72,137 @@ class CarringtonSolarWind():
         reads L3b data product file
     save_to_file(self,fn)
         saves L3c data product to the file
-    '''
+    """
 
     def __init__(self, anc_input_from_instr_team):
         """
         Class constructor
         """
 
-        self.header={
-            'software_version': VERSION,
-            'filename' : None,
-            'ancillary_data_files': anc_input_from_instr_team,
-            'external_dependeciens': None,
-            'l3b_input_filename': None
+        self.header = {
+            "software_version": VERSION,
+            "filename": None,
+            "ancillary_data_files": anc_input_from_instr_team,
+            "external_dependeciens": None,
+            "l3b_input_filename": None,
         }
 
         # read pipeline settings to be used for L3b
-        self.settings = fun.read_json(anc_input_from_instr_team['pipeline_settings'])
+        self.settings = fun.read_json(anc_input_from_instr_team["pipeline_settings"])
 
         # All parameters will be read from L3b file or computed later on
-        self.cx_profile={}
-        self.cx_profile['rate']=None
-        self.cx_profile['uncert']=None
-
-        self.sw_ecliptic={}
-        self.sw_ecliptic['mean_speed']=None
-        self.sw_ecliptic['mean_proton_density']=None
-        self.sw_ecliptic['mean_alpha_abundance']=None
-        self.sw_ecliptic['invariant']=None
-
-        self.sw_profile={}
-        self.sw_profile['date']=None
-        self.sw_profile['CR']=None
-        self.sw_profile['grid']=self.settings['solar_wind_grid']
-        self.sw_profile['plasma_speed']=None
-        self.sw_profile['proton_density']=None
-
+        self.cx_profile = {}
+        self.cx_profile["rate"] = None
+        self.cx_profile["uncert"] = None
+
+        self.sw_ecliptic = {}
+        self.sw_ecliptic["mean_speed"] = None
+        self.sw_ecliptic["mean_proton_density"] = None
+        self.sw_ecliptic["mean_alpha_abundance"] = None
+        self.sw_ecliptic["invariant"] = None
+
+        self.sw_profile = {}
+        self.sw_profile["date"] = None
+        self.sw_profile["CR"] = None
+        self.sw_profile["grid"] = self.settings["solar_wind_grid"]
+        self.sw_profile["plasma_speed"] = None
+        self.sw_profile["proton_density"] = None
 
     def calculate_sw_profile(self):
-        '''
+        """
         Main procedure to calculate solar wind parameters profiles.
         Sets of 2 equations is numerically solved.
         For detailed disscussion see Documentation Section 13.7
 
 
-        '''
+        """
 
         # initialization of the vectors that will be calculated later on
         # solar wind speed latitudinal profile
-        plasma_speed_profile=np.empty(len(self.settings['solar_wind_grid']))
+        plasma_speed_profile = np.empty(len(self.settings["solar_wind_grid"]))
         # solar wind density latitudinal profile
-        proton_dens_profile=np.empty(len(self.settings['solar_wind_grid']))
-
-
-        invariant=self.sw_ecliptic['invariant']
-        p_alpha=self.sw_ecliptic['mean_alpha_abundance']
-
-        v_grid=np.array(self.settings['plasma_speed_numerical_grid'])*u.km/u.s   # numerical grid to solve equations [km/s]
-        cs_grid=fun.cross_section_cx(fun.v2E(v_grid))             # cross section for charge exchange for speed grid [cm^2]
-        rho_grid=invariant/(const.m_p+p_alpha*PHISICAL_CONSTANTS['m_alpha'])/(0.5*v_grid**2+const.GM_sun/const.R_sun)
+        proton_dens_profile = np.empty(len(self.settings["solar_wind_grid"]))
+
+        invariant = self.sw_ecliptic["invariant"]
+        p_alpha = self.sw_ecliptic["mean_alpha_abundance"]
+
+        v_grid = (
+            np.array(self.settings["plasma_speed_numerical_grid"]) * u.km / u.s
+        )  # numerical grid to solve equations [km/s]
+        cs_grid = fun.cross_section_cx(
+            fun.v2E(v_grid)
+        )  # cross section for charge exchange for speed grid [cm^2]
+        rho_grid = (
+            invariant
+            / (const.m_p + p_alpha * PHISICAL_CONSTANTS["m_alpha"])
+            / (0.5 * v_grid**2 + const.GM_sun / const.R_sun)
+        )
 
         # calculation of the charge exchange rate for each speed in speed grid
-        cx_grid=(rho_grid*cs_grid).to('1/s')
+        cx_grid = (rho_grid * cs_grid).to("1/s")
 
         for i in np.arange(len(plasma_speed_profile)):
             # charge exchane rate obtained from lcrv
-            cx_obs=self.cx_profile['rate'][i]
-            if cx_obs<cx_grid.value.min():
-                logging.info('lat= %lf deg: observed cx rate is smaller than min of the cx_grid', self.sw_profile['grid'][i])
-                raise Exception('L3c not generated: observed cx rate is smaller than min of the cx_grid')
+            cx_obs = self.cx_profile["rate"][i]
+            if cx_obs < cx_grid.value.min():
+                logging.info(
+                    "lat= %lf deg: observed cx rate is smaller than min of the cx_grid",
+                    self.sw_profile["grid"][i],
+                )
+                raise Exception(
+                    "L3c not generated: observed cx rate is smaller than min of the cx_grid"
+                )
             # Searching for a measured charge exchange in the cx_grid. In that way we will find sw speed
-            idx=np.abs(cx_grid.value-cx_obs).argmin()
-            plasma_speed_profile[i]=v_grid[idx].to('km.s-1').value
-            proton_dens_profile[i]=(rho_grid[idx]/v_grid[idx]).to('cm-3').value
-        self.sw_profile['plasma_speed']=plasma_speed_profile
-        self.sw_profile['proton_density']=proton_dens_profile
-
-
+            idx = np.abs(cx_grid.value - cx_obs).argmin()
+            plasma_speed_profile[i] = v_grid[idx].to("km.s-1").value
+            proton_dens_profile[i] = (rho_grid[idx] / v_grid[idx]).to("cm-3").value
+        self.sw_profile["plasma_speed"] = plasma_speed_profile
+        self.sw_profile["proton_density"] = proton_dens_profile
 
     def read_l3b(self, data_l3b):
-        '''
+        """
         Read charge exchange rate profile from the L3b data product file
 
         Parameters
         -----------
         data_l3b : dict
             L3b data product
-        '''
+        """
 
-        self.sw_profile['date']=Time(data_l3b['date'])    # do I really need it in astropy.time format?
-        self.sw_profile['CR']=data_l3b['CR']
-        self.cx_profile['rate']=data_l3b['ion_rate_profile']['cx_rate']
-        self.cx_profile['uncert']=data_l3b['ion_rate_profile']['cx_uncert']
+        self.sw_profile["date"] = Time(
+            data_l3b["date"]
+        )  # do I really need it in astropy.time format?
+        self.sw_profile["CR"] = data_l3b["CR"]
+        self.cx_profile["rate"] = data_l3b["ion_rate_profile"]["cx_rate"]
+        self.cx_profile["uncert"] = data_l3b["ion_rate_profile"]["cx_uncert"]
 
     def get_dict(self):
         # Dictionary with the solar wind speed and density profiles  that will be saved in json file
         output = {}
-        output['header'] = self.header
-        output['date'] = self.sw_profile['date'].iso
-        output['CR'] = self.sw_profile['CR']
-        output['solar_wind_ecliptic'] = {}
-        output['solar_wind_ecliptic']['plasma_speed'] = self.sw_ecliptic['mean_speed']
-        output['solar_wind_ecliptic']['proton_density'] = self.sw_ecliptic['mean_proton_density']
-        output['solar_wind_ecliptic']['alpha_abundance'] = self.sw_ecliptic['mean_alpha_abundance']
-        output['solar_wind_profile'] = {}
-        output['solar_wind_profile']['lat_grid'] = self.sw_profile['grid']
-        output['solar_wind_profile']['plasma_speed'] = self.sw_profile['plasma_speed'].tolist()
-        output['solar_wind_profile']['proton_density'] = self.sw_profile['proton_density'].tolist()
+        output["header"] = self.header
+        output["date"] = self.sw_profile["date"].iso
+        output["CR"] = self.sw_profile["CR"]
+        output["solar_wind_ecliptic"] = {}
+        output["solar_wind_ecliptic"]["plasma_speed"] = self.sw_ecliptic["mean_speed"]
+        output["solar_wind_ecliptic"]["proton_density"] = self.sw_ecliptic[
+            "mean_proton_density"
+        ]
+        output["solar_wind_ecliptic"]["alpha_abundance"] = self.sw_ecliptic[
+            "mean_alpha_abundance"
+        ]
+        output["solar_wind_profile"] = {}
+        output["solar_wind_profile"]["lat_grid"] = self.sw_profile["grid"]
+        output["solar_wind_profile"]["plasma_speed"] = self.sw_profile[
+            "plasma_speed"
+        ].tolist()
+        output["solar_wind_profile"]["proton_density"] = self.sw_profile[
+            "proton_density"
+        ].tolist()
 
         return output
 
     def save_to_file(self, fn):
-        '''
+        """
         Carrington solar wind plasma speed and proton densite profiles file is an official GLOWS L3c data product
         Temporarly it is in json format, but in production code it should be in CDF
 
@@ -188,28 +210,36 @@ def save_to_file(self, fn):
         ------------
         fn: str
             name of the output file
-        '''
+        """
 
         # Dictionary with the solar wind speed and density profiles  that will be saved in json file
-        output={}
-        output['header']=self.header
-        output['date']=self.sw_profile['date'].iso
-        output['CR']=self.sw_profile['CR']
-        output['solar_wind_ecliptic']={}
-        output['solar_wind_ecliptic']['plasma_speed']=self.sw_ecliptic['mean_speed']
-        output['solar_wind_ecliptic']['proton_density']=self.sw_ecliptic['mean_proton_density']
-        output['solar_wind_ecliptic']['alpha_abundance']=self.sw_ecliptic['mean_alpha_abundance']
-        output['solar_wind_profile']={}
-        output['solar_wind_profile']['lat_grid']=self.sw_profile['grid']
-        output['solar_wind_profile']['plasma_speed']=self.sw_profile['plasma_speed'].tolist()
-        output['solar_wind_profile']['proton_density']=self.sw_profile['proton_density'].tolist()
+        output = {}
+        output["header"] = self.header
+        output["date"] = self.sw_profile["date"].iso
+        output["CR"] = self.sw_profile["CR"]
+        output["solar_wind_ecliptic"] = {}
+        output["solar_wind_ecliptic"]["plasma_speed"] = self.sw_ecliptic["mean_speed"]
+        output["solar_wind_ecliptic"]["proton_density"] = self.sw_ecliptic[
+            "mean_proton_density"
+        ]
+        output["solar_wind_ecliptic"]["alpha_abundance"] = self.sw_ecliptic[
+            "mean_alpha_abundance"
+        ]
+        output["solar_wind_profile"] = {}
+        output["solar_wind_profile"]["lat_grid"] = self.sw_profile["grid"]
+        output["solar_wind_profile"]["plasma_speed"] = self.sw_profile[
+            "plasma_speed"
+        ].tolist()
+        output["solar_wind_profile"]["proton_density"] = self.sw_profile[
+            "proton_density"
+        ].tolist()
 
         # Check validity of the data
-        fun.check_nan(output['solar_wind_ecliptic'])
-        fun.check_nan(output['solar_wind_profile'])
+        fun.check_nan(output["solar_wind_ecliptic"])
+        fun.check_nan(output["solar_wind_profile"])
 
         json_content = json.dumps(output, indent=3)
 
-        output_fp = open(fn, 'w')
+        output_fp = open(fn, "w")
         print(json_content, file=output_fp)
         output_fp.close()
diff --git a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_EclipticSolarWind.py b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_EclipticSolarWind.py
index 7219b1cdc..2fcc7dd22 100644
--- a/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_EclipticSolarWind.py
+++ b/imap_l3_processing/glows/l3bc/l3bc_toolkit/l3c_EclipticSolarWind.py
@@ -1,7 +1,7 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska (ikowalska@cbk.waw.pl)
 Solar wind parameters data in the ecliptic plane
-'''
+"""
 
 import logging
 from dataclasses import dataclass
@@ -15,8 +15,8 @@
 
 
 @dataclass
-class EclipticSolarWind():
-    '''
+class EclipticSolarWind:
+    """
     Class for a GLOWS Carrington averaged solar wind speed and density
 
     Attributes
@@ -47,7 +47,7 @@ class EclipticSolarWind():
     read_sw_ecliptic(self,ext_dependencies)
         reads solar wind parameters in the ecliptic plane (source of the data is set in pipeline settings)
 
-    '''
+    """
 
     def __init__(self, anc_input_from_instr_team, CR):
         """
@@ -55,7 +55,7 @@ def __init__(self, anc_input_from_instr_team, CR):
         """
 
         # read pipeline settings to be used
-        self.settings = fun.read_json(anc_input_from_instr_team['pipeline_settings'])
+        self.settings = fun.read_json(anc_input_from_instr_team["pipeline_settings"])
 
         self.external_dependeciens = None
         # All parameters will be read from L3b file or computed later on
@@ -68,7 +68,7 @@ def __init__(self, anc_input_from_instr_team, CR):
         self.invariant = None
 
     def calculate_invariant(self, ext_dependencies):
-        '''
+        """
         Solar wind energy flux was found to be latitudinal invariant (by Ulysses mission).
         Using OMNI, then SWAPI data daily invariant will be calculated and then averaged over Carrington rotation period
         We need long averages (~13 Carrington rotation periods)
@@ -82,46 +82,74 @@ def calculate_invariant(self, ext_dependencies):
         ------------
         invariant : astropy.units
             averaged solar wind energy flux as a object with unists [erg/s/cm^2]
-        '''
+        """
 
         # TODO: SWAPI if we have full 13 Carrington rotation periods of data
 
         # Read solar wind parameters (Carrington averaged parameters on common carrington number grid)
-        grid_cr, plasma_speed, proton_dens, p_alpha = self.read_sw_ecliptic(ext_dependencies)
+        grid_cr, plasma_speed, proton_dens, p_alpha = self.read_sw_ecliptic(
+            ext_dependencies
+        )
 
         # Calculate solar wind energy flux for each CR
-        sw_energy_flux = fun.calculate_sw_energy_flux(proton_dens / (u.cm) ** 3, plasma_speed * u.km / u.s, p_alpha)
+        sw_energy_flux = fun.calculate_sw_energy_flux(
+            proton_dens / (u.cm) ** 3, plasma_speed * u.km / u.s, p_alpha
+        )
 
-        N_Carr = self.settings['invariant_average_Carr_number']  # number of Carringtons that we want to average over
+        N_Carr = self.settings[
+            "invariant_average_Carr_number"
+        ]  # number of Carringtons that we want to average over
         self.invariant = sw_energy_flux[-N_Carr:].mean()
 
     def _read_OMNI_data(self, ext_dependencies):
-        '''
+        """
         Reads 3 types of data from OMNI2 database: solar wind speed, density and alpha-particles abundance in the ecliptic plane
 
         Parameters:
         ------------
         ext_dependencies : dict
             list of file names with external dependencies
-        '''
+        """
         # Read OMNI2 data (hour resolution) from last few CR (exact number is specified in pipeline settings)
-        cr_n = self.settings['invariant_average_Carr_number']
+        cr_n = self.settings["invariant_average_Carr_number"]
         cr_ini = self.CR - cr_n
         cr_grid = np.arange(cr_ini, self.CR + 1)
         # t_window=[Time(fun.jd_fm_Carrington(cr_ini),format='jd'),Time(fun.jd_fm_Carrington(self.CR+1),format='jd')]
 
         omni_raw = fun.read_raw_OMNI_data(ext_dependencies)
 
-        param_settings = {'density': {'column_numbers': (0, 1, 2, 4, 7), 'gap_marker': 999.9, 'scale': True, 'const_if_empty': False},
-                          'speed': {'column_numbers': (0, 1, 2, 5, 8), 'gap_marker': 9999, 'scale': False, 'const_if_empty': False},
-                          'alpha': {'column_numbers': (0, 1, 2, 6, 9), 'gap_marker': 9.999, 'scale': False, 'const_if_empty': True}
-                          }
-
-        proton_dens_carr, _ = fun.process_omni_param(omni_raw, cr_grid, param_settings['density'])
-        plasma_speed_carr, _ = fun.process_omni_param(omni_raw, cr_grid, param_settings['speed'])
-        p_alpha_carr, used_nominal_alpha_per_cr = fun.process_omni_param(omni_raw, cr_grid, param_settings['alpha'])
-
-        self.external_dependeciens = [ext_dependencies['omni_raw_data']]
+        param_settings = {
+            "density": {
+                "column_numbers": (0, 1, 2, 4, 7),
+                "gap_marker": 999.9,
+                "scale": True,
+                "const_if_empty": False,
+            },
+            "speed": {
+                "column_numbers": (0, 1, 2, 5, 8),
+                "gap_marker": 9999,
+                "scale": False,
+                "const_if_empty": False,
+            },
+            "alpha": {
+                "column_numbers": (0, 1, 2, 6, 9),
+                "gap_marker": 9.999,
+                "scale": False,
+                "const_if_empty": True,
+            },
+        }
+
+        proton_dens_carr, _ = fun.process_omni_param(
+            omni_raw, cr_grid, param_settings["density"]
+        )
+        plasma_speed_carr, _ = fun.process_omni_param(
+            omni_raw, cr_grid, param_settings["speed"]
+        )
+        p_alpha_carr, used_nominal_alpha_per_cr = fun.process_omni_param(
+            omni_raw, cr_grid, param_settings["alpha"]
+        )
+
+        self.external_dependeciens = [ext_dependencies["omni_raw_data"]]
         self.CR_grid = cr_grid
         self.mean_speed = plasma_speed_carr
         self.mean_proton_density = proton_dens_carr
@@ -131,7 +159,7 @@ def _read_OMNI_data(self, ext_dependencies):
         return cr_grid, plasma_speed_carr, proton_dens_carr, p_alpha_carr
 
     def _read_SWAPI_data(self, ext_dependencies):
-        '''
+        """
         Placeholder
 
         Parameters:
@@ -149,10 +177,10 @@ def _read_SWAPI_data(self, ext_dependencies):
             Carrington-period averaged plasma speed
         p_alpha_carr
             Carrington-period averaged alpha-particles abundance
-        '''
+        """
 
     def read_sw_ecliptic(self, ext_dependencies):
-        '''
+        """
         Calls procedure to read solar wind data in the ecliptic plane.
         It can be either from OMNI2 database or IMAP/SWAPI
         The source will be given in the pipeline settings file provided by the instrument team
@@ -174,14 +202,18 @@ def read_sw_ecliptic(self, ext_dependencies):
         p_alpha : float 1D vector
             alpha particle abundance wrt protons data in the ecliptic plane averaged over Carrington rotation period
 
-        '''
-        sw_ecliptic_source = self.settings['sw_ecliptic_source']
-
-        if sw_ecliptic_source == 'OMNI':
-            grid_cr, plasma_speed, proton_dens, p_alpha = self._read_OMNI_data(ext_dependencies)
-        elif sw_ecliptic_source == 'SWAPI':
-            grid_cr, plasma_speed, proton_dens, p_alpha = self._read_SWAPI_data(ext_dependencies)
+        """
+        sw_ecliptic_source = self.settings["sw_ecliptic_source"]
+
+        if sw_ecliptic_source == "OMNI":
+            grid_cr, plasma_speed, proton_dens, p_alpha = self._read_OMNI_data(
+                ext_dependencies
+            )
+        elif sw_ecliptic_source == "SWAPI":
+            grid_cr, plasma_speed, proton_dens, p_alpha = self._read_SWAPI_data(
+                ext_dependencies
+            )
         else:
-            raise Exception('Unknown solar wind source. Select OMNI or SWAPI')
+            raise Exception("Unknown solar wind source. Select OMNI or SWAPI")
 
         return grid_cr, plasma_speed, proton_dens, p_alpha
diff --git a/imap_l3_processing/glows/l3bc/models.py b/imap_l3_processing/glows/l3bc/models.py
index d9eafa4c6..f13d9d1d2 100644
--- a/imap_l3_processing/glows/l3bc/models.py
+++ b/imap_l3_processing/glows/l3bc/models.py
@@ -7,20 +7,23 @@
 from datetime import datetime, timedelta
 from itertools import chain
 from pathlib import Path
-from typing import Optional
 
 import imap_data_access
 import numpy as np
 from spacepy import pycdf
 
-from imap_l3_processing.constants import TEMP_CDF_FOLDER_PATH, ONE_SECOND_IN_NANOSECONDS
+from imap_l3_processing.constants import ONE_SECOND_IN_NANOSECONDS, TEMP_CDF_FOLDER_PATH
 from imap_l3_processing.glows.l3bc.dependency_validator import validate_dependencies
 from imap_l3_processing.glows.l3bc.utils import get_date_range_of_cr
 from imap_l3_processing.models import DataProduct, DataProductVariable, InputMetadata
 from imap_l3_processing.utils import download_external_dependency
 
-F107_FLUX_TABLE_URL = "https://www.spaceweather.gc.ca/solar_flux_data/daily_flux_values/fluxtable.txt"
-LYMAN_ALPHA_COMPOSITE_INDEX_URL = "https://lasp.colorado.edu/data/timed_see/composite_lya/lyman_alpha_composite.nc"
+F107_FLUX_TABLE_URL = (
+    "https://www.spaceweather.gc.ca/solar_flux_data/daily_flux_values/fluxtable.txt"
+)
+LYMAN_ALPHA_COMPOSITE_INDEX_URL = (
+    "https://lasp.colorado.edu/data/timed_see/composite_lya/lyman_alpha_composite.nc"
+)
 OMNI2_URL = "https://spdf.gsfc.nasa.gov/pub/data/omni/low_res_omni/omni2_all_years.dat"
 MAX_USED_L3A_FILES = 50
 
@@ -35,11 +38,20 @@ class L3BCAncillaryQueryResults:
     @classmethod
     def fetch(cls):
         return cls(
-            uv_anisotropy=imap_data_access.query(table="ancillary", instrument="glows", descriptor="uv-anisotropy-1CR"),
-            waw_helio_ion_mp=imap_data_access.query(table="ancillary", instrument="glows", descriptor="WawHelioIonMP"),
-            bad_days_list=imap_data_access.query(table="ancillary", instrument="glows", descriptor="bad-days-list"),
-            pipeline_settings=imap_data_access.query(table="ancillary", instrument="glows",
-                                                     descriptor="pipeline-settings-l3bcde"),
+            uv_anisotropy=imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="uv-anisotropy-1CR"
+            ),
+            waw_helio_ion_mp=imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="WawHelioIonMP"
+            ),
+            bad_days_list=imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="bad-days-list"
+            ),
+            pipeline_settings=imap_data_access.query(
+                table="ancillary",
+                instrument="glows",
+                descriptor="pipeline-settings-l3bcde",
+            ),
         )
 
     def missing_ancillaries(self) -> bool:
@@ -57,11 +69,15 @@ class ExternalDependencies:
 
     @classmethod
     def fetch_dependencies(cls):
-        f107_index_file_path = download_external_dependency(F107_FLUX_TABLE_URL,
-                                                            TEMP_CDF_FOLDER_PATH / 'f107_fluxtable.txt')
-        lyman_alpha_path = download_external_dependency(LYMAN_ALPHA_COMPOSITE_INDEX_URL,
-                                                        TEMP_CDF_FOLDER_PATH / 'lyman_alpha.txt')
-        omni2_data_path = download_external_dependency(OMNI2_URL, TEMP_CDF_FOLDER_PATH / 'omni2.txt')
+        f107_index_file_path = download_external_dependency(
+            F107_FLUX_TABLE_URL, TEMP_CDF_FOLDER_PATH / "f107_fluxtable.txt"
+        )
+        lyman_alpha_path = download_external_dependency(
+            LYMAN_ALPHA_COMPOSITE_INDEX_URL, TEMP_CDF_FOLDER_PATH / "lyman_alpha.txt"
+        )
+        omni2_data_path = download_external_dependency(
+            OMNI2_URL, TEMP_CDF_FOLDER_PATH / "omni2.txt"
+        )
 
         if f107_index_file_path is not None:
             ExternalDependencies._comment_headers(f107_index_file_path)
@@ -91,33 +107,39 @@ class CRToProcess:
     cr_end_date: datetime
     cr_rotation_number: int
 
-    _buffer_time: Optional[timedelta] = None
+    _buffer_time: timedelta | None = None
 
     def pipeline_dependency_file_names(self) -> set[str]:
         ancillary_files = {
             self.uv_anisotropy_file_name,
             self.waw_helio_ion_mp_file_name,
             self.bad_days_list_file_name,
-            self.pipeline_settings_file_name
+            self.pipeline_settings_file_name,
         }
 
         return self.l3a_file_names | ancillary_files
 
     def get_buffer_time(self) -> timedelta:
         if self._buffer_time is None:
-            pipeline_settings_local_path = imap_data_access.download(self.pipeline_settings_file_name)
+            pipeline_settings_local_path = imap_data_access.download(
+                self.pipeline_settings_file_name
+            )
             pipeline_settings = read_pipeline_settings(pipeline_settings_local_path)
-            self._buffer_time = timedelta(days=pipeline_settings["initializer_time_delta_days"])
+            self._buffer_time = timedelta(
+                days=pipeline_settings["initializer_time_delta_days"]
+            )
         return self._buffer_time
 
-    def has_valid_external_dependencies(self, external_deps: ExternalDependencies) -> bool:
+    def has_valid_external_dependencies(
+        self, external_deps: ExternalDependencies
+    ) -> bool:
         return validate_dependencies(
             self.cr_start_date,
             self.cr_end_date,
             self.get_buffer_time(),
             external_deps.omni2_data_path,
             external_deps.f107_index_file_path,
-            external_deps.lyman_alpha_path
+            external_deps.lyman_alpha_path,
         )
 
     def buffer_time_has_elapsed_since_cr(self):
@@ -150,26 +172,29 @@ class GlowsL3BIonizationRate(DataProduct):
     glows_flags: np.ndarray[int]
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
-        return [DataProductVariable("epoch", self.epoch),
-                DataProductVariable("epoch_delta", self.epoch_delta),
-                DataProductVariable("mean_time", self.mean_time),
-                DataProductVariable("cr", self.cr),
-                DataProductVariable("uv_anisotropy_factor", self.uv_anisotropy_factor),
-                DataProductVariable("lat_grid", self.lat_grid),
-                DataProductVariable("sum_rate", self.sum_rate),
-                DataProductVariable("ph_rate", self.ph_rate),
-                DataProductVariable("cx_rate", self.cx_rate),
-                DataProductVariable("sum_uncert", self.sum_uncert),
-                DataProductVariable("ph_uncert", self.ph_uncert),
-                DataProductVariable("cx_uncert", self.cx_uncert),
-                DataProductVariable("lat_grid_label", self.lat_grid_label),
-                DataProductVariable("uv_anisotropy_flag", self.uv_anisotropy_flag),
-                DataProductVariable("used_l3a", self.used_l3a),
-                DataProductVariable("glows_flags", self.glows_flags),
-                ]
+        return [
+            DataProductVariable("epoch", self.epoch),
+            DataProductVariable("epoch_delta", self.epoch_delta),
+            DataProductVariable("mean_time", self.mean_time),
+            DataProductVariable("cr", self.cr),
+            DataProductVariable("uv_anisotropy_factor", self.uv_anisotropy_factor),
+            DataProductVariable("lat_grid", self.lat_grid),
+            DataProductVariable("sum_rate", self.sum_rate),
+            DataProductVariable("ph_rate", self.ph_rate),
+            DataProductVariable("cx_rate", self.cx_rate),
+            DataProductVariable("sum_uncert", self.sum_uncert),
+            DataProductVariable("ph_uncert", self.ph_uncert),
+            DataProductVariable("cx_uncert", self.cx_uncert),
+            DataProductVariable("lat_grid_label", self.lat_grid_label),
+            DataProductVariable("uv_anisotropy_flag", self.uv_anisotropy_flag),
+            DataProductVariable("used_l3a", self.used_l3a),
+            DataProductVariable("glows_flags", self.glows_flags),
+        ]
 
     @classmethod
-    def from_instrument_team_dictionary(cls, model: dict, input_metadata: InputMetadata) -> GlowsL3BIonizationRate:
+    def from_instrument_team_dictionary(
+        cls, model: dict, input_metadata: InputMetadata
+    ) -> GlowsL3BIonizationRate:
         latitude_grid = model["ion_rate_profile"]["lat_grid"]
         mean_time = datetime.fromisoformat(model["date"])
         start_of_cr, end_of_cr = get_date_range_of_cr(model["CR"])
@@ -189,8 +214,10 @@ def from_instrument_team_dictionary(cls, model: dict, input_metadata: InputMetad
         l3a_file_names += [""] * (MAX_USED_L3A_FILES - len(l3a_file_names))
 
         parent_file_names = []
-        parent_file_names += collect_file_names(model['header']['ancillary_data_files'])
-        parent_file_names += collect_file_names(model['header']['external_dependeciens'])
+        parent_file_names += collect_file_names(model["header"]["ancillary_data_files"])
+        parent_file_names += collect_file_names(
+            model["header"]["external_dependeciens"]
+        )
         return cls(
             input_metadata=input_metadata,
             parent_file_names=parent_file_names,
@@ -207,9 +234,9 @@ def from_instrument_team_dictionary(cls, model: dict, input_metadata: InputMetad
             ph_uncert=np.array([model["ion_rate_profile"]["ph_uncert"]]),
             cx_uncert=np.array([model["ion_rate_profile"]["cx_uncert"]]),
             lat_grid_label=[f"{x}°" for x in latitude_grid],
-            uv_anisotropy_flag=np.array([model['uv_anisotropy_flag']]),
+            uv_anisotropy_flag=np.array([model["uv_anisotropy_flag"]]),
             used_l3a=np.array([l3a_file_names]),
-            glows_flags=np.array([model['glows_flags']], dtype=np.uint16),
+            glows_flags=np.array([model["glows_flags"]], dtype=np.uint16),
         )
 
 
@@ -237,51 +264,96 @@ class GlowsL3CSolarWind(DataProduct):
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
-            DataProductVariable("epoch", self.epoch, cdf_data_type=pycdf.const.CDF_TIME_TT2000),
-            DataProductVariable("epoch_delta", self.epoch_delta, cdf_data_type=pycdf.const.CDF_INT8),
-            DataProductVariable("mean_time", self.mean_time, cdf_data_type=pycdf.const.CDF_TIME_TT2000),
+            DataProductVariable(
+                "epoch", self.epoch, cdf_data_type=pycdf.const.CDF_TIME_TT2000
+            ),
+            DataProductVariable(
+                "epoch_delta", self.epoch_delta, cdf_data_type=pycdf.const.CDF_INT8
+            ),
+            DataProductVariable(
+                "mean_time", self.mean_time, cdf_data_type=pycdf.const.CDF_TIME_TT2000
+            ),
             DataProductVariable("cr", self.cr, cdf_data_type=pycdf.const.CDF_INT2),
-            DataProductVariable("lat_grid", self.lat_grid, cdf_data_type=pycdf.const.CDF_FLOAT, record_varying=False),
-            DataProductVariable("lat_grid_label", self.lat_grid_label, cdf_data_type=pycdf.const.CDF_CHAR,
-                                record_varying=False),
-            DataProductVariable("plasma_speed_ecliptic", self.plasma_speed_ecliptic,
-                                cdf_data_type=pycdf.const.CDF_FLOAT),
-            DataProductVariable("proton_density_ecliptic", self.proton_density_ecliptic,
-                                cdf_data_type=pycdf.const.CDF_FLOAT),
-            DataProductVariable("alpha_abundance_ecliptic", self.alpha_abundance_ecliptic,
-                                cdf_data_type=pycdf.const.CDF_FLOAT),
-            DataProductVariable("plasma_speed_profile", self.plasma_speed_profile, cdf_data_type=pycdf.const.CDF_FLOAT),
-            DataProductVariable("proton_density_profile", self.proton_density_profile,
-                                cdf_data_type=pycdf.const.CDF_FLOAT),
+            DataProductVariable(
+                "lat_grid",
+                self.lat_grid,
+                cdf_data_type=pycdf.const.CDF_FLOAT,
+                record_varying=False,
+            ),
+            DataProductVariable(
+                "lat_grid_label",
+                self.lat_grid_label,
+                cdf_data_type=pycdf.const.CDF_CHAR,
+                record_varying=False,
+            ),
+            DataProductVariable(
+                "plasma_speed_ecliptic",
+                self.plasma_speed_ecliptic,
+                cdf_data_type=pycdf.const.CDF_FLOAT,
+            ),
+            DataProductVariable(
+                "proton_density_ecliptic",
+                self.proton_density_ecliptic,
+                cdf_data_type=pycdf.const.CDF_FLOAT,
+            ),
+            DataProductVariable(
+                "alpha_abundance_ecliptic",
+                self.alpha_abundance_ecliptic,
+                cdf_data_type=pycdf.const.CDF_FLOAT,
+            ),
+            DataProductVariable(
+                "plasma_speed_profile",
+                self.plasma_speed_profile,
+                cdf_data_type=pycdf.const.CDF_FLOAT,
+            ),
+            DataProductVariable(
+                "proton_density_profile",
+                self.proton_density_profile,
+                cdf_data_type=pycdf.const.CDF_FLOAT,
+            ),
             DataProductVariable("glows_flags", self.glows_flags),
         ]
 
     @classmethod
-    def from_instrument_team_dictionary(cls, model: dict, input_metadata: InputMetadata) -> GlowsL3CSolarWind:
+    def from_instrument_team_dictionary(
+        cls, model: dict, input_metadata: InputMetadata
+    ) -> GlowsL3CSolarWind:
         latitude_grid = model["solar_wind_profile"]["lat_grid"]
         mean_time = datetime.fromisoformat(model["date"])
         start_of_cr, end_of_cr = get_date_range_of_cr(model["CR"])
 
-        epoch = start_of_cr + (end_of_cr - start_of_cr)/2
-        epoch_delta = (epoch - start_of_cr).total_seconds()*ONE_SECOND_IN_NANOSECONDS
+        epoch = start_of_cr + (end_of_cr - start_of_cr) / 2
+        epoch_delta = (epoch - start_of_cr).total_seconds() * ONE_SECOND_IN_NANOSECONDS
 
         parent_file_names = []
-        parent_file_names += collect_file_names(model['header']['ancillary_data_files'])
-        parent_file_names += collect_file_names(model['header']['external_dependeciens'])
+        parent_file_names += collect_file_names(model["header"]["ancillary_data_files"])
+        parent_file_names += collect_file_names(
+            model["header"]["external_dependeciens"]
+        )
         return cls(
             input_metadata=input_metadata,
             epoch=np.array([epoch]),
             epoch_delta=np.array([epoch_delta]),
             mean_time=np.array([mean_time]),
-            cr=np.array([model['CR']]),
+            cr=np.array([model["CR"]]),
             lat_grid=np.array(latitude_grid),
             lat_grid_label=[f"{x}°" for x in latitude_grid],
-            plasma_speed_ecliptic=np.array([model["solar_wind_ecliptic"]['plasma_speed']]),
-            proton_density_ecliptic=np.array([model["solar_wind_ecliptic"]['proton_density']]),
-            alpha_abundance_ecliptic=np.array([model["solar_wind_ecliptic"]['alpha_abundance']]),
-            plasma_speed_profile=np.array([model["solar_wind_profile"]['plasma_speed']]),
-            proton_density_profile=np.array([model["solar_wind_profile"]['proton_density']]),
-            glows_flags=np.array([model['glows_flags']], dtype=np.uint16),
+            plasma_speed_ecliptic=np.array(
+                [model["solar_wind_ecliptic"]["plasma_speed"]]
+            ),
+            proton_density_ecliptic=np.array(
+                [model["solar_wind_ecliptic"]["proton_density"]]
+            ),
+            alpha_abundance_ecliptic=np.array(
+                [model["solar_wind_ecliptic"]["alpha_abundance"]]
+            ),
+            plasma_speed_profile=np.array(
+                [model["solar_wind_profile"]["plasma_speed"]]
+            ),
+            proton_density_profile=np.array(
+                [model["solar_wind_profile"]["proton_density"]]
+            ),
+            glows_flags=np.array([model["glows_flags"]], dtype=np.uint16),
             parent_file_names=parent_file_names,
         )
 
diff --git a/imap_l3_processing/glows/l3bc/science/filter_out_bad_days.py b/imap_l3_processing/glows/l3bc/science/filter_out_bad_days.py
index b2efe6f25..5ae4ecb15 100644
--- a/imap_l3_processing/glows/l3bc/science/filter_out_bad_days.py
+++ b/imap_l3_processing/glows/l3bc/science/filter_out_bad_days.py
@@ -6,29 +6,39 @@
 from imap_l3_processing.glows.l3bc.l3bc_toolkit.funcs import jd_fm_Carrington
 
 
-def filter_l3a_files(l3a_data: list[dict], bad_day_list_path: Path, cr: int) -> list[dict]:
+def filter_l3a_files(
+    l3a_data: list[dict], bad_day_list_path: Path, cr: int
+) -> list[dict]:
     carrington_start_date = jd_fm_Carrington(float(cr))
-    cr_start_time = Time(carrington_start_date, format='jd')
-    cr_start_time.format = 'iso'
+    cr_start_time = Time(carrington_start_date, format="jd")
+    cr_start_time.format = "iso"
     carrington_end_date_non_inclusive = jd_fm_Carrington(cr + 1)
-    cr_end_time = Time(carrington_end_date_non_inclusive, format='jd')
-    cr_end_time.format = 'iso'
+    cr_end_time = Time(carrington_end_date_non_inclusive, format="jd")
+    cr_end_time.format = "iso"
 
     def mean_time(l3a_dict):
-        time = Time(l3a_dict['start_time']) + 0.5 * (Time(l3a_dict['end_time']) - Time(l3a_dict['start_time']))
+        time = Time(l3a_dict["start_time"]) + 0.5 * (
+            Time(l3a_dict["end_time"]) - Time(l3a_dict["start_time"])
+        )
         return time
 
-    l3a_in_cr = [l3a_dict for l3a_dict in l3a_data if cr_start_time <= mean_time(l3a_dict) < cr_end_time]
+    l3a_in_cr = [
+        l3a_dict
+        for l3a_dict in l3a_data
+        if cr_start_time <= mean_time(l3a_dict) < cr_end_time
+    ]
 
-    with open(bad_day_list_path, 'r') as bad_day_file:
+    with open(bad_day_list_path, "r") as bad_day_file:
         bad_day_list = Time(np.genfromtxt(bad_day_file, dtype=str)[:, 1])
         sorted_bad_days = bad_day_list.sort()
         filtered_data = []
         l3a_data_index = 0
         bad_day_list_index = 0
-        while l3a_data_index < len(l3a_in_cr) and bad_day_list_index < len(sorted_bad_days):
-            start_time = Time(l3a_in_cr[l3a_data_index]['start_time'])
-            end_time = Time(l3a_in_cr[l3a_data_index]['end_time'])
+        while l3a_data_index < len(l3a_in_cr) and bad_day_list_index < len(
+            sorted_bad_days
+        ):
+            start_time = Time(l3a_in_cr[l3a_data_index]["start_time"])
+            end_time = Time(l3a_in_cr[l3a_data_index]["end_time"])
             if start_time <= sorted_bad_days[bad_day_list_index] < end_time:
                 l3a_data_index += 1
             elif sorted_bad_days[bad_day_list_index] > end_time:
diff --git a/imap_l3_processing/glows/l3bc/science/generate_l3bc.py b/imap_l3_processing/glows/l3bc/science/generate_l3bc.py
index 45c99cd6f..e32f60e5f 100644
--- a/imap_l3_processing/glows/l3bc/science/generate_l3bc.py
+++ b/imap_l3_processing/glows/l3bc/science/generate_l3bc.py
@@ -1,13 +1,15 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska, ikowalska@cbk.waw.pl
 Modified by Menlo
 Main procedure to generate GLOWS L3b and L3c data products
-'''
+"""
 
 import numpy as np
 from astropy.time import Time
 
-from imap_l3_processing.glows.l3bc.cannot_process_carrington_rotation_error import CannotProcessCarringtonRotationError
+from imap_l3_processing.glows.l3bc.cannot_process_carrington_rotation_error import (
+    CannotProcessCarringtonRotationError,
+)
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
 from imap_l3_processing.glows.l3bc.l3bc_toolkit import l3b_CarringtonIonRate as cir
 from imap_l3_processing.glows.l3bc.l3bc_toolkit import l3b_DailyIonRate as dir
@@ -21,7 +23,9 @@ def generate_l3bc(dependencies: GlowsL3BCDependencies):
 
     # if all days in CR are bad, then there will be no L3bc data products
     if len(dependencies.l3a_data) == 0:
-        raise CannotProcessCarringtonRotationError("All days for Carrington Rotation are in a bad season.")
+        raise CannotProcessCarringtonRotationError(
+            "All days for Carrington Rotation are in a bad season."
+        )
 
     # Daily ionization rate from the daily light curves
     daily_ion_rate = []
@@ -29,48 +33,65 @@ def generate_l3bc(dependencies: GlowsL3BCDependencies):
     l3a_used = []
     for l3a in dependencies.l3a_data:
         ion_rate_daily = dir.DailyIonizationRate(
-            dependencies.ancillary_files)  # daily ionization rate object initialization
+            dependencies.ancillary_files
+        )  # daily ionization rate object initialization
         ion_rate_daily.read_l3a(l3a)  # reading L3a data (masked lightcurve)
         flag = ion_rate_daily.read_model_params(
-            dependencies.ancillary_files)  # reading WawHelioIon-MP parameters for lightcurve to ionization rate conversion
-        if flag == ion_rate_daily.settings['no_vg_point_flag']: continue
-        l3a_used_date.append(ion_rate_daily.lcrv['date'])  # mean observation time
-        l3a_used.append(l3a['filename'])
+            dependencies.ancillary_files
+        )  # reading WawHelioIon-MP parameters for lightcurve to ionization rate conversion
+        if flag == ion_rate_daily.settings["no_vg_point_flag"]:
+            continue
+        l3a_used_date.append(ion_rate_daily.lcrv["date"])  # mean observation time
+        l3a_used.append(l3a["filename"])
         ion_rate_daily.calculate_daily_ionization_rate()  # conversion to the daily ionization rate profile
-        daily_ion_rate.append(ion_rate_daily.ion['ion_rate'])
+        daily_ion_rate.append(ion_rate_daily.ion["ion_rate"])
 
     if len(daily_ion_rate) == 0:
-        raise CannotProcessCarringtonRotationError("All days for Carrington Rotation are in a bad season.")
+        raise CannotProcessCarringtonRotationError(
+            "All days for Carrington Rotation are in a bad season."
+        )
 
     # Carrington averaged ionization rate (L3b) and Carrington averaged solar wind parameters  objects initialization
-    ion_rate_Carr = cir.CarringtonIonizationRate(dependencies.ancillary_files, dependencies.external_files)
+    ion_rate_Carr = cir.CarringtonIonizationRate(
+        dependencies.ancillary_files, dependencies.external_files
+    )
     solar_wind_Carr = csw.CarringtonSolarWind(dependencies.ancillary_files)
 
     # File name of the L3b and L3c passed to the object structure
 
     # Save daily ionization rates into CarringtonIonizationRate structure
-    ion_rate_Carr.daily_ion_rate['date'] = Time(l3a_used_date)
-    ion_rate_Carr.daily_ion_rate['ion_rate'] = daily_ion_rate
+    ion_rate_Carr.daily_ion_rate["date"] = Time(l3a_used_date)
+    ion_rate_Carr.daily_ion_rate["ion_rate"] = daily_ion_rate
 
     # ion_rate_Carr.carr_ion_rate['date']=Time(fun.jd_fm_Carrington(CR+0.5),format='jd') # Date fixed, but in principle could be mean from used L3a
-    ion_rate_Carr.carr_ion_rate['CR'] = CR  # Carrington rotation number
+    ion_rate_Carr.carr_ion_rate["CR"] = CR  # Carrington rotation number
 
     # Solar wind in the ecliptic plane processing
     sw_ecliptic = esw.EclipticSolarWind(dependencies.ancillary_files, CR)
     sw_ecliptic.calculate_invariant(dependencies.external_files)
-    solar_wind_Carr.sw_ecliptic['invariant'] = sw_ecliptic.invariant
-    solar_wind_Carr.header['external_dependeciens'] = sw_ecliptic.external_dependeciens
+    solar_wind_Carr.sw_ecliptic["invariant"] = sw_ecliptic.invariant
+    solar_wind_Carr.header["external_dependeciens"] = sw_ecliptic.external_dependeciens
 
     # Solar wind parameters passed to the CarringtonSolarWind structure for L3c processing
-    idx_current_CR = np.abs(sw_ecliptic.CR_grid - ion_rate_Carr.carr_ion_rate['CR']).argmin()
-    solar_wind_Carr.sw_ecliptic['mean_speed'] = sw_ecliptic.mean_speed[idx_current_CR]
-    solar_wind_Carr.sw_ecliptic['mean_proton_density'] = sw_ecliptic.mean_proton_density[idx_current_CR]
-    solar_wind_Carr.sw_ecliptic['mean_alpha_abundance'] = sw_ecliptic.mean_alpha_abundance[idx_current_CR]
+    idx_current_CR = np.abs(
+        sw_ecliptic.CR_grid - ion_rate_Carr.carr_ion_rate["CR"]
+    ).argmin()
+    solar_wind_Carr.sw_ecliptic["mean_speed"] = sw_ecliptic.mean_speed[idx_current_CR]
+    solar_wind_Carr.sw_ecliptic["mean_proton_density"] = (
+        sw_ecliptic.mean_proton_density[idx_current_CR]
+    )
+    solar_wind_Carr.sw_ecliptic["mean_alpha_abundance"] = (
+        sw_ecliptic.mean_alpha_abundance[idx_current_CR]
+    )
     # Solar wind parameters passed to the CarringtonIonizationRate structure for L3c processing
-    ion_rate_Carr.sw_ecliptic['mean_speed'] = sw_ecliptic.mean_speed[idx_current_CR]
-    ion_rate_Carr.sw_ecliptic['mean_proton_density'] = sw_ecliptic.mean_proton_density[idx_current_CR]
+    ion_rate_Carr.sw_ecliptic["mean_speed"] = sw_ecliptic.mean_speed[idx_current_CR]
+    ion_rate_Carr.sw_ecliptic["mean_proton_density"] = sw_ecliptic.mean_proton_density[
+        idx_current_CR
+    ]
     # Calculate averaged ionization rate profile and save it to the file
-    ion_rate_Carr.calculate_photoion(dependencies.ancillary_files, dependencies.external_files)
+    ion_rate_Carr.calculate_photoion(
+        dependencies.ancillary_files, dependencies.external_files
+    )
 
     ion_rate_Carr.calculate_averaged_profile()
     ion_rate_Carr.calculate_charge_exchange()
@@ -81,13 +102,13 @@ def generate_l3bc(dependencies: GlowsL3BCDependencies):
     solar_wind_Carr.read_l3b(l3b_dat)
     solar_wind_Carr.calculate_sw_profile()
     l3c_dat = solar_wind_Carr.get_dict()
-    l3b_dat['header']['l3a_input_files_name'] = l3a_used
+    l3b_dat["header"]["l3a_input_files_name"] = l3a_used
 
     l3b_quality_flags = int(GlowsL3Flags.NONE)
     l3c_quality_flags = int(GlowsL3Flags.NONE)
     if bool(sw_ecliptic.used_nominal_alpha_per_cr[idx_current_CR]):
         l3c_quality_flags |= int(GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO)
-    l3b_dat['glows_flags'] = l3b_quality_flags
-    l3c_dat['glows_flags'] = l3c_quality_flags
+    l3b_dat["glows_flags"] = l3b_quality_flags
+    l3c_dat["glows_flags"] = l3c_quality_flags
 
     return l3b_dat, l3c_dat
diff --git a/imap_l3_processing/glows/l3bc/utils.py b/imap_l3_processing/glows/l3bc/utils.py
index 230e1bb70..79dbf7d13 100644
--- a/imap_l3_processing/glows/l3bc/utils.py
+++ b/imap_l3_processing/glows/l3bc/utils.py
@@ -1,36 +1,26 @@
-from datetime import timedelta, datetime
-from datetime import timedelta, datetime
+from datetime import datetime, timedelta
 from pathlib import Path
-from typing import Optional
 
-import imap_data_access
 import numpy as np
 import pandas as pd
 from imap_processing.spice.repoint import get_repoint_data
-from spacepy.pycdf import CDF
-
-from imap_l3_processing.glows.l3a.models import GlowsL3LightCurve, PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME, \
-    PHOTON_FLUX_CDF_VAR_NAME, RAW_HISTOGRAM_CDF_VAR_NAME, EXPOSURE_TIMES_CDF_VAR_NAME, EPOCH_CDF_VAR_NAME, \
-    EPOCH_DELTA_CDF_VAR_NAME, SPIN_ANGLE_CDF_VAR_NAME, SPIN_ANGLE_DELTA_CDF_VAR_NAME, LATITUDE_CDF_VAR_NAME, \
-    LONGITUDE_CDF_VAR_NAME, EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME, TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME, \
-    SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME, FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME, \
-    FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME, HV_VOLTAGE_AVERAGE_CDF_VAR_NAME, HV_VOLTAGE_STD_DEV_CDF_VAR_NAME, \
-    SPIN_PERIOD_AVERAGE_CDF_VAR_NAME, SPIN_PERIOD_STD_DEV_CDF_VAR_NAME, SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME, \
-    SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME, PULSE_LENGTH_AVERAGE_CDF_VAR_NAME, PULSE_LENGTH_STD_DEV_CDF_VAR_NAME, \
-    POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME, POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME, \
-    SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME, SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME, \
-    SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME, SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME, \
-    SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME, NUM_OF_BINS_CDF_VAR_NAME
-
-
-def get_best_ancillary(start_date: datetime, end_date: datetime, ancillary_query_results: list[dict]) -> Optional[str]:
+
+
+def get_best_ancillary(
+    start_date: datetime, end_date: datetime, ancillary_query_results: list[dict]
+) -> str | None:
     valid_ancillaries = []
     for ancillary_file in ancillary_query_results:
         ancillary_start_date = datetime.strptime(ancillary_file["start_date"], "%Y%m%d")
-        ancillary_end_date = datetime.strptime(ancillary_file["end_date"], "%Y%m%d") if ancillary_file[
-            "end_date"] else None
+        ancillary_end_date = (
+            datetime.strptime(ancillary_file["end_date"], "%Y%m%d")
+            if ancillary_file["end_date"]
+            else None
+        )
 
-        if ancillary_start_date <= end_date and (ancillary_end_date is None or ancillary_end_date >= start_date):
+        if ancillary_start_date <= end_date and (
+            ancillary_end_date is None or ancillary_end_date >= start_date
+        ):
             valid_ancillaries.append(ancillary_file)
 
     if len(valid_ancillaries) == 0:
@@ -44,26 +34,37 @@ def get_best_ancillary(start_date: datetime, end_date: datetime, ancillary_query
 first_carrington_start_date = datetime(1853, 11, 9, 19, 53, 45, 600000)
 carrington_length = timedelta(days=27.2753)
 
+
 def get_date_range_of_cr(cr_number: int) -> tuple[datetime, datetime]:
-    start_date = first_carrington_start_date + (cr_number - carrington_first) * carrington_length
+    start_date = (
+        first_carrington_start_date + (cr_number - carrington_first) * carrington_length
+    )
     return start_date, start_date + carrington_length
 
+
 def get_midpoint_of_cr(cr_number: int) -> datetime:
     start, _ = get_date_range_of_cr(cr_number)
     return start + carrington_length / 2
 
+
 def get_cr_for_date_time(datetime_to_check: datetime) -> int:
-    return int(carrington_first + (datetime_to_check - first_carrington_start_date) / carrington_length)
+    return int(
+        carrington_first
+        + (datetime_to_check - first_carrington_start_date) / carrington_length
+    )
+
 
 def get_pointing_date_range(repointing: int) -> (datetime, datetime):
     repointing_df: pd.DataFrame = get_repoint_data()
-    matching_rows_start = repointing_df[repointing_df['repoint_id'] == repointing]
-    matching_rows_end = repointing_df[repointing_df['repoint_id'] == repointing + 1]
+    matching_rows_start = repointing_df[repointing_df["repoint_id"] == repointing]
+    matching_rows_end = repointing_df[repointing_df["repoint_id"] == repointing + 1]
     if len(matching_rows_start) == 0 or len(matching_rows_end) == 0:
         raise ValueError(f"No pointing found for pointing: {repointing}")
     repointing_data_start = matching_rows_start.iloc[0]
     repointing_data_end = matching_rows_end.iloc[0]
-    start_time = repointing_data_start['repoint_end_utc']
-    end_time = repointing_data_end['repoint_start_utc']
+    start_time = repointing_data_start["repoint_end_utc"]
+    end_time = repointing_data_end["repoint_start_utc"]
 
-    return np.datetime64(start_time).astype(datetime), np.datetime64(end_time).astype(datetime)
+    return np.datetime64(start_time).astype(datetime), np.datetime64(end_time).astype(
+        datetime
+    )
diff --git a/imap_l3_processing/glows/l3d/glows_l3d_dependencies.py b/imap_l3_processing/glows/l3d/glows_l3d_dependencies.py
index 873f068a9..0bb521ba4 100644
--- a/imap_l3_processing/glows/l3d/glows_l3d_dependencies.py
+++ b/imap_l3_processing/glows/l3d/glows_l3d_dependencies.py
@@ -5,9 +5,17 @@
 from imap_data_access import ScienceFilePath
 from imap_data_access.processing_input import ProcessingInputCollection
 
-from imap_l3_processing.glows.descriptors import PROTON_DENSITY_DESCRIPTOR, PLASMA_SPEED_DESCRIPTOR, \
-    UV_ANISOTROPY_DESCRIPTOR, PHOTOION_DESCRIPTOR, LYA_DESCRIPTOR, ELECTRON_DENSITY_DESCRIPTOR, \
-    PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR, GLOWS_L3B_DESCRIPTOR, GLOWS_L3C_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    ELECTRON_DENSITY_DESCRIPTOR,
+    GLOWS_L3B_DESCRIPTOR,
+    GLOWS_L3C_DESCRIPTOR,
+    LYA_DESCRIPTOR,
+    PHOTOION_DESCRIPTOR,
+    PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR,
+    PLASMA_SPEED_DESCRIPTOR,
+    PROTON_DENSITY_DESCRIPTOR,
+    UV_ANISOTROPY_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3bc.models import ExternalDependencies
 
 
@@ -20,46 +28,75 @@ class GlowsL3DDependencies:
     end_cr: float
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection, external_dependencies: ExternalDependencies):
-        plasma_speed_legendre_path = dependencies.get_file_paths(source='glows', descriptor=PLASMA_SPEED_DESCRIPTOR)
-        proton_density_legendre_path = dependencies.get_file_paths(source='glows', descriptor=PROTON_DENSITY_DESCRIPTOR)
-        uv_anisotropy_path = dependencies.get_file_paths(source='glows', descriptor=UV_ANISOTROPY_DESCRIPTOR)
-        photoion_path = dependencies.get_file_paths(source='glows', descriptor=PHOTOION_DESCRIPTOR)
-        lya_path = dependencies.get_file_paths(source='glows', descriptor=LYA_DESCRIPTOR)
-        electron_density_path = dependencies.get_file_paths(source='glows', descriptor=ELECTRON_DENSITY_DESCRIPTOR)
-        pipeline_settings_l3bc_path = dependencies.get_file_paths(source='glows', descriptor=PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR)
+    def fetch_dependencies(
+        cls,
+        dependencies: ProcessingInputCollection,
+        external_dependencies: ExternalDependencies,
+    ):
+        plasma_speed_legendre_path = dependencies.get_file_paths(
+            source="glows", descriptor=PLASMA_SPEED_DESCRIPTOR
+        )
+        proton_density_legendre_path = dependencies.get_file_paths(
+            source="glows", descriptor=PROTON_DENSITY_DESCRIPTOR
+        )
+        uv_anisotropy_path = dependencies.get_file_paths(
+            source="glows", descriptor=UV_ANISOTROPY_DESCRIPTOR
+        )
+        photoion_path = dependencies.get_file_paths(
+            source="glows", descriptor=PHOTOION_DESCRIPTOR
+        )
+        lya_path = dependencies.get_file_paths(
+            source="glows", descriptor=LYA_DESCRIPTOR
+        )
+        electron_density_path = dependencies.get_file_paths(
+            source="glows", descriptor=ELECTRON_DENSITY_DESCRIPTOR
+        )
+        pipeline_settings_l3bc_path = dependencies.get_file_paths(
+            source="glows", descriptor=PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR
+        )
 
-        plasma_speed_legendre = imap_data_access.download(str(plasma_speed_legendre_path[0]))
-        proton_density_legendre = imap_data_access.download(str(proton_density_legendre_path[0]))
+        plasma_speed_legendre = imap_data_access.download(
+            str(plasma_speed_legendre_path[0])
+        )
+        proton_density_legendre = imap_data_access.download(
+            str(proton_density_legendre_path[0])
+        )
         uv_anisotropy = imap_data_access.download(str(uv_anisotropy_path[0]))
         photoion = imap_data_access.download(str(photoion_path[0]))
         lya_2010a = imap_data_access.download(str(lya_path[0]))
         electron_density = imap_data_access.download(str(electron_density_path[0]))
-        pipeline_settings_l3bc = imap_data_access.download(str(pipeline_settings_l3bc_path[0]))
+        pipeline_settings_l3bc = imap_data_access.download(
+            str(pipeline_settings_l3bc_path[0])
+        )
 
-        l3b_file_names = dependencies.get_file_paths(source="glows", descriptor=GLOWS_L3B_DESCRIPTOR)
-        l3c_file_names = dependencies.get_file_paths(source="glows", descriptor=GLOWS_L3C_DESCRIPTOR)
+        l3b_file_names = dependencies.get_file_paths(
+            source="glows", descriptor=GLOWS_L3B_DESCRIPTOR
+        )
+        l3c_file_names = dependencies.get_file_paths(
+            source="glows", descriptor=GLOWS_L3C_DESCRIPTOR
+        )
 
         l3b_file_paths = [imap_data_access.download(l3b) for l3b in l3b_file_names]
         l3c_file_paths = [imap_data_access.download(l3c) for l3c in l3c_file_names]
 
         ancillary_dict = {
-            'pipeline_settings': pipeline_settings_l3bc,
-            'WawHelioIon': {
-                'speed': plasma_speed_legendre,
-                'p-dens': proton_density_legendre,
-                'uv-anis': uv_anisotropy,
-                'phion': photoion,
-                'lya': lya_2010a,
-                'e-dens': electron_density
-            }}
-
-        external_dict = {
-            'lya_raw_data': external_dependencies.lyman_alpha_path
+            "pipeline_settings": pipeline_settings_l3bc,
+            "WawHelioIon": {
+                "speed": plasma_speed_legendre,
+                "p-dens": proton_density_legendre,
+                "uv-anis": uv_anisotropy,
+                "phion": photoion,
+                "lya": lya_2010a,
+                "e-dens": electron_density,
+            },
         }
 
+        external_dict = {"lya_raw_data": external_dependencies.lyman_alpha_path}
+
         last_l3b_cr = max([ScienceFilePath(file).cr for file in l3b_file_paths])
         last_l3c_cr = max([ScienceFilePath(file).cr for file in l3c_file_paths])
         end_cr = min(last_l3b_cr, last_l3c_cr) + 1
 
-        return cls(external_dict, ancillary_dict, l3b_file_paths, l3c_file_paths, end_cr)
+        return cls(
+            external_dict, ancillary_dict, l3b_file_paths, l3c_file_paths, end_cr
+        )
diff --git a/imap_l3_processing/glows/l3d/glows_l3d_initializer.py b/imap_l3_processing/glows/l3d/glows_l3d_initializer.py
index 304f6611e..c75adb054 100644
--- a/imap_l3_processing/glows/l3d/glows_l3d_initializer.py
+++ b/imap_l3_processing/glows/l3d/glows_l3d_initializer.py
@@ -1,28 +1,46 @@
 import logging
 from pathlib import Path
-from typing import Optional
 
 import imap_data_access
-from imap_data_access import ProcessingInputCollection, ScienceInput, AncillaryInput, ScienceFilePath
+from imap_data_access import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceFilePath,
+    ScienceInput,
+)
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.glows.descriptors import PLASMA_SPEED_DESCRIPTOR, PROTON_DENSITY_DESCRIPTOR, \
-    UV_ANISOTROPY_DESCRIPTOR, PHOTOION_DESCRIPTOR, LYA_DESCRIPTOR, ELECTRON_DENSITY_DESCRIPTOR, \
-    PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR, GLOWS_L3D_DESCRIPTOR
-from imap_l3_processing.glows.l3bc.models import ExternalDependencies, read_pipeline_settings
+from imap_l3_processing.glows.descriptors import (
+    ELECTRON_DENSITY_DESCRIPTOR,
+    GLOWS_L3D_DESCRIPTOR,
+    LYA_DESCRIPTOR,
+    PHOTOION_DESCRIPTOR,
+    PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR,
+    PLASMA_SPEED_DESCRIPTOR,
+    PROTON_DENSITY_DESCRIPTOR,
+    UV_ANISOTROPY_DESCRIPTOR,
+)
+from imap_l3_processing.glows.l3bc.models import (
+    ExternalDependencies,
+    read_pipeline_settings,
+)
 from imap_l3_processing.glows.l3d.glows_l3d_dependencies import GlowsL3DDependencies
 from imap_l3_processing.glows.l3d.utils import query_for_most_recent_l3d
 from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo
-from imap_l3_processing.utils import read_cdf_parents, get_version_from_query_result
+from imap_l3_processing.utils import get_version_from_query_result, read_cdf_parents
 
 logger = logging.getLogger(__name__)
 
 
 class GlowsL3DInitializer:
-
     @staticmethod
-    def should_process_l3d(external_deps: ExternalDependencies, l3bs: list[str], l3cs: list[str], reprocess_info: ReprocessInfo, major_version: int|None) -> Optional[
-        tuple[Version, GlowsL3DDependencies, Optional[str]]]:
+    def should_process_l3d(
+        external_deps: ExternalDependencies,
+        l3bs: list[str],
+        l3cs: list[str],
+        reprocess_info: ReprocessInfo,
+        major_version: int | None,
+    ) -> tuple[Version, GlowsL3DDependencies, str | None] | None:
         if len(l3bs) == 0 and len(l3cs) == 0:
             logger.info("Found no L3b and L3c files!")
             return None
@@ -30,52 +48,63 @@ def should_process_l3d(external_deps: ExternalDependencies, l3bs: list[str], l3c
         most_recent_l3d = query_for_most_recent_l3d(GLOWS_L3D_DESCRIPTOR)
 
         [plasma_speed_2026a] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=PLASMA_SPEED_DESCRIPTOR,
-            version='latest'
+            version="latest",
         )
         [proton_density_2026a] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=PROTON_DENSITY_DESCRIPTOR,
-            version='latest'
+            version="latest",
         )
         [uv_anisotropy_2026a] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=UV_ANISOTROPY_DESCRIPTOR,
-            version='latest')
+            version="latest",
+        )
         [photoion_2026a] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=PHOTOION_DESCRIPTOR,
-            version='latest'
+            version="latest",
         )
         [lya_2026a] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=LYA_DESCRIPTOR,
-            version='latest'
+            version="latest",
         )
         [electron_density_2026a] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=ELECTRON_DENSITY_DESCRIPTOR,
-            version='latest'
+            version="latest",
         )
         [pipeline_settings_l3bcde] = imap_data_access.query(
-            table='ancillary',
-            instrument='glows',
+            table="ancillary",
+            instrument="glows",
             descriptor=PIPELINE_SETTINGS_L3BCDE_DESCRIPTOR,
-            version='latest'
+            version="latest",
         )
 
         pipeline_settings_filename = Path(pipeline_settings_l3bcde["file_path"]).name
-        pipeline_settings = read_pipeline_settings(imap_data_access.download(pipeline_settings_filename))
+        pipeline_settings = read_pipeline_settings(
+            imap_data_access.download(pipeline_settings_filename)
+        )
 
-        l3bs = [l3b for l3b in l3bs if ScienceFilePath(l3b).cr >= pipeline_settings["start_cr"]]
-        l3cs = [l3c for l3c in l3cs if ScienceFilePath(l3c).cr >= pipeline_settings["start_cr"]]
+        l3bs = [
+            l3b
+            for l3b in l3bs
+            if ScienceFilePath(l3b).cr >= pipeline_settings["start_cr"]
+        ]
+        l3cs = [
+            l3c
+            for l3c in l3cs
+            if ScienceFilePath(l3c).cr >= pipeline_settings["start_cr"]
+        ]
 
         if len(l3bs) == 0 and len(l3cs) == 0:
             logger.info("Found no L3b and L3c files after start CR!")
@@ -95,12 +124,12 @@ def should_process_l3d(external_deps: ExternalDependencies, l3bs: list[str], l3c
         updated_input_files = {
             *l3bs,
             *l3cs,
-            Path(plasma_speed_2026a['file_path']).name,
-            Path(proton_density_2026a['file_path']).name,
-            Path(uv_anisotropy_2026a['file_path']).name,
-            Path(photoion_2026a['file_path']).name,
-            Path(lya_2026a['file_path']).name,
-            Path(electron_density_2026a['file_path']).name,
+            Path(plasma_speed_2026a["file_path"]).name,
+            Path(proton_density_2026a["file_path"]).name,
+            Path(uv_anisotropy_2026a["file_path"]).name,
+            Path(photoion_2026a["file_path"]).name,
+            Path(lya_2026a["file_path"]).name,
+            Path(electron_density_2026a["file_path"]).name,
         }
 
         old_l3d = None
@@ -118,20 +147,29 @@ def should_process_l3d(external_deps: ExternalDependencies, l3bs: list[str], l3c
             minor_version_to_generate = most_recent_l3d_version.minor + 1
 
             same_major_version = most_recent_l3d_version.major == major_version
-            inputs_unchanged = same_major_version and updated_input_files.issubset(l3d_parents)
+            inputs_unchanged = same_major_version and updated_input_files.issubset(
+                l3d_parents
+            )
 
             if inputs_unchanged and not reprocess_info.should_reprocess_l3d():
-                 return None
+                return None
 
         version_to_generate = Version(major_version, minor_version_to_generate)
-        return GlowsL3DInitializer.generate_result(version_to_generate, processing_input_collection, external_deps,
-                                                   old_l3d)
+        return GlowsL3DInitializer.generate_result(
+            version_to_generate, processing_input_collection, external_deps, old_l3d
+        )
 
     @staticmethod
-    def generate_result(version_to_generate: Version,
-                        processing_input_collection: ProcessingInputCollection,
-                        external_deps: ExternalDependencies,
-                        old_l3d: str | None) -> tuple[Version, GlowsL3DDependencies, str | None]:
-        return (version_to_generate,
-                GlowsL3DDependencies.fetch_dependencies(processing_input_collection, external_deps),
-                old_l3d)
+    def generate_result(
+        version_to_generate: Version,
+        processing_input_collection: ProcessingInputCollection,
+        external_deps: ExternalDependencies,
+        old_l3d: str | None,
+    ) -> tuple[Version, GlowsL3DDependencies, str | None]:
+        return (
+            version_to_generate,
+            GlowsL3DDependencies.fetch_dependencies(
+                processing_input_collection, external_deps
+            ),
+            old_l3d,
+        )
diff --git a/imap_l3_processing/glows/l3d/models.py b/imap_l3_processing/glows/l3d/models.py
index b22a973ef..114e86183 100644
--- a/imap_l3_processing/glows/l3d/models.py
+++ b/imap_l3_processing/glows/l3d/models.py
@@ -6,8 +6,8 @@
 from imap_l3_processing.constants import CARRINGTON_ROTATION_IN_NANOSECONDS
 from imap_l3_processing.models import DataProduct, DataProductVariable
 
-EPOCH_CDF_VAR_NAME = 'epoch'
-EPOCH_DELTA_CDF_VAR_NAME = 'epoch_delta'
+EPOCH_CDF_VAR_NAME = "epoch"
+EPOCH_DELTA_CDF_VAR_NAME = "epoch_delta"
 LATITUDE_CDF_CDF_VAR_NAME = "lat_grid"
 LATITUDE_LABEL_CDF_VAR_NAME = "lat_grid_label"
 CR_CDF_VAR_NAME = "cr_grid"
@@ -48,7 +48,7 @@ class GlowsL3DSolarParamsHistory(DataProduct):
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         epoch_delta = np.full_like(self.epoch, CARRINGTON_ROTATION_IN_NANOSECONDS / 2)
-        latitude_label = [f'{lat:.1f} degrees' for lat in self.latitude]
+        latitude_label = [f"{lat:.1f} degrees" for lat in self.latitude]
 
         return [
             DataProductVariable(EPOCH_CDF_VAR_NAME, self.epoch),
@@ -58,12 +58,18 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(CR_CDF_VAR_NAME, self.cr),
             DataProductVariable(SPEED_CDF_VAR_NAME, self.plasma_speed),
             DataProductVariable(PROTON_DENSITY_CDF_VAR_NAME, self.proton_density),
-            DataProductVariable(UV_ANISOTROPY_CDF_VAR_NAME, self.ultraviolet_anisotropy),
+            DataProductVariable(
+                UV_ANISOTROPY_CDF_VAR_NAME, self.ultraviolet_anisotropy
+            ),
             DataProductVariable(PHION_CDF_VAR_NAME, self.phion),
             DataProductVariable(LYMAN_ALPHA_CDF_VAR_NAME, self.lyman_alpha),
             DataProductVariable(ELECTRON_DENSITY_CDF_VAR_NAME, self.electron_density),
             DataProductVariable(PLASMA_SPEED_FLAG_CDF_VAR_NAME, self.plasma_speed_flag),
-            DataProductVariable(UV_ANISOTROPY_FLAG_CDF_VAR_NAME, self.uv_anisotropy_flag),
-            DataProductVariable(PROTON_DENSITY_FLAG_CDF_VAR_NAME, self.proton_density_flag),
+            DataProductVariable(
+                UV_ANISOTROPY_FLAG_CDF_VAR_NAME, self.uv_anisotropy_flag
+            ),
+            DataProductVariable(
+                PROTON_DENSITY_FLAG_CDF_VAR_NAME, self.proton_density_flag
+            ),
             DataProductVariable(GLOWS_FLAGS_CDF_VAR_NAME, self.glows_flags),
         ]
diff --git a/imap_l3_processing/glows/l3d/science/generate_l3d.py b/imap_l3_processing/glows/l3d/science/generate_l3d.py
index 0aa07b8f2..92abd0519 100644
--- a/imap_l3_processing/glows/l3d/science/generate_l3d.py
+++ b/imap_l3_processing/glows/l3d/science/generate_l3d.py
@@ -1,7 +1,7 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska, ikowalska@cbk.waw.pl
 Main procedure to generate GLOWS L3d data product
-'''
+"""
 
 import glob
 import json
@@ -9,7 +9,6 @@
 import warnings
 
 import numpy as np
-
 import toolkit.funcs as fun
 import toolkit.l3d_SolarParamHistory as sph
 
@@ -20,38 +19,56 @@
 
 dependencies = json.loads(sys.argv[2])
 
-ANC_INPUT_FROM_INSTRUMENT_TEAM = dependencies['ancillary_files']
-EXT_DEPENDENCIES = dependencies['external_files']
+ANC_INPUT_FROM_INSTRUMENT_TEAM = dependencies["ancillary_files"]
+EXT_DEPENDENCIES = dependencies["external_files"]
 
 # Create list of L3b/c files and read them
-l3b_fn_list = np.array(sorted(glob.glob('data_l3b/imap_glows_l3b*.json')))
-l3c_fn_list = np.array(sorted(glob.glob('data_l3c/imap_glows_l3c*.json')))
+l3b_fn_list = np.array(sorted(glob.glob("data_l3b/imap_glows_l3b*.json")))
+l3c_fn_list = np.array(sorted(glob.glob("data_l3c/imap_glows_l3c*.json")))
 # l3d_fn_list = np.array(sorted(glob.glob('data_l3d/imap_glows_l3d*.json')))
 data_l3b = [fun.read_json(fn) for fn in l3b_fn_list]
 data_l3c = [fun.read_json(fn) for fn in l3c_fn_list]
 
-solar_param_hist = sph.SolarParamsHistory(ANC_INPUT_FROM_INSTRUMENT_TEAM, EXT_DEPENDENCIES)
-fn_initial = ANC_INPUT_FROM_INSTRUMENT_TEAM['WawHelioIon']
+solar_param_hist = sph.SolarParamsHistory(
+    ANC_INPUT_FROM_INSTRUMENT_TEAM, EXT_DEPENDENCIES
+)
+fn_initial = ANC_INPUT_FROM_INSTRUMENT_TEAM["WawHelioIon"]
 
 # Start generating from the initial files (not using the last entry from L3d)
 
-solar_param_hist.header['ancillary_data_files'] = list(fn_initial.values())
+solar_param_hist.header["ancillary_data_files"] = list(fn_initial.values())
 solar_param_hist.generate_initial_history(fn_initial)
 
-solar_param_hist.update_solar_params_hist(EXT_DEPENDENCIES, data_l3b, data_l3c, CR_current)
+solar_param_hist.update_solar_params_hist(
+    EXT_DEPENDENCIES, data_l3b, data_l3c, CR_current
+)
 
 # filename of the data products (main json file and the text version)
 start_date = solar_param_hist.time_grid[0]
-start_date_str = ''.join((start_date.iso[:10]).split('-'))
-l3d_fn = 'data_l3d/imap_glows_l3d_solar-params-history_' + start_date_str + '-cr0' + str(CR_current) + '_v00.json'
-solar_param_hist.header['filename'] = l3d_fn
+start_date_str = "".join((start_date.iso[:10]).split("-"))
+l3d_fn = (
+    "data_l3d/imap_glows_l3d_solar-params-history_"
+    + start_date_str
+    + "-cr0"
+    + str(CR_current)
+    + "_v00.json"
+)
+solar_param_hist.header["filename"] = l3d_fn
 l3d_txt_fn = {}
-for k in solar_param_hist.ini_data['label']: l3d_txt_fn[
-    k] = 'data_l3d_txt/imap_glows_l3d_' + k + '_' + start_date_str + '-cr0' + str(CR_current) + '_v00.dat'
+for k in solar_param_hist.ini_data["label"]:
+    l3d_txt_fn[k] = (
+        "data_l3d_txt/imap_glows_l3d_"
+        + k
+        + "_"
+        + start_date_str
+        + "-cr0"
+        + str(CR_current)
+        + "_v00.dat"
+    )
 
 hdr = solar_param_hist.generate_hdr_txt(l3d_txt_fn)
 
 solar_param_hist.save_to_file(l3d_fn)
 solar_param_hist.save_to_txt(l3d_txt_fn, hdr)
 
-print('Processing CR=', CR_current)
+print("Processing CR=", CR_current)
diff --git a/imap_l3_processing/glows/l3d/science/toolkit/constants.py b/imap_l3_processing/glows/l3d/science/toolkit/constants.py
index 827b4c06c..0082d96da 100644
--- a/imap_l3_processing/glows/l3d/science/toolkit/constants.py
+++ b/imap_l3_processing/glows/l3d/science/toolkit/constants.py
@@ -5,33 +5,34 @@
 
 import astropy.units as u
 
-VERSION = "3.3" # version of ground processing software
+VERSION = "3.3"  # version of ground processing software
 
 # dictionary with names of ancillary input files expected from IMAP/GLOWS Instrument Team
 ANC_INPUT_FROM_INSTRUMENT_TEAM = {
-    'WawHelioIonMP_parameters': 'data_ancillary/imap_glows_WawHelioIonMP_20251113_v007.json',
-    'bad_day_list': 'data_ancillary/imap_glows_bad-days-list_20251113_v001.dat',
-    'pipeline_settings': 'data_ancillary/imap_glows_pipeline-settings-l3bcde_20251113_v005.json',
-    'uv_anisotropy' : 'data_ancillary/imap_glows_uv-anisotropy-1CR_20251113_v002.json',
-    'WawHelioIon' : {
-        'speed': 'data_ancillary/imap_glows_plasma-speed-2026d_20251113_v003.dat',
-        'p-dens': 'data_ancillary/imap_glows_proton-density-2026d_20251113_v003.dat',
-        'uv-anis': 'data_ancillary/imap_glows_uv-anisotropy-2026d_20251113_v003.dat',
-        'phion': 'data_ancillary/imap_glows_photoion-2026d_20251113_v003.dat',
-        'lya': 'data_ancillary/imap_glows_lya-2026d_20251113_v003.dat',
-        'e-dens': 'data_ancillary/imap_glows_electron-density-2026d_20251113_v003.dat'
-    }}
+    "WawHelioIonMP_parameters": "data_ancillary/imap_glows_WawHelioIonMP_20251113_v007.json",
+    "bad_day_list": "data_ancillary/imap_glows_bad-days-list_20251113_v001.dat",
+    "pipeline_settings": "data_ancillary/imap_glows_pipeline-settings-l3bcde_20251113_v005.json",
+    "uv_anisotropy": "data_ancillary/imap_glows_uv-anisotropy-1CR_20251113_v002.json",
+    "WawHelioIon": {
+        "speed": "data_ancillary/imap_glows_plasma-speed-2026d_20251113_v003.dat",
+        "p-dens": "data_ancillary/imap_glows_proton-density-2026d_20251113_v003.dat",
+        "uv-anis": "data_ancillary/imap_glows_uv-anisotropy-2026d_20251113_v003.dat",
+        "phion": "data_ancillary/imap_glows_photoion-2026d_20251113_v003.dat",
+        "lya": "data_ancillary/imap_glows_lya-2026d_20251113_v003.dat",
+        "e-dens": "data_ancillary/imap_glows_electron-density-2026d_20251113_v003.dat",
+    },
+}
 
-EXT_DEPENDENCIES={
-    'f107_raw_data': 'external_dependencies/f107_fluxtable.txt',
-    'omni_raw_data': 'external_dependencies/omni2_all_years.dat',
-    'lya_raw_data': 'external_dependencies/lyman_alpha_composite.nc'
+EXT_DEPENDENCIES = {
+    "f107_raw_data": "external_dependencies/f107_fluxtable.txt",
+    "omni_raw_data": "external_dependencies/omni2_all_years.dat",
+    "lya_raw_data": "external_dependencies/lyman_alpha_composite.nc",
 }
 
-PHISICAL_CONSTANTS={
-    'm_alpha' : 6.6446573357e-27*u.kg,
-    'jd_carrington_first' : 2.398167329*10**6,
-    'carrington_time' : 27.2753
+PHISICAL_CONSTANTS = {
+    "m_alpha": 6.6446573357e-27 * u.kg,
+    "jd_carrington_first": 2.398167329 * 10**6,
+    "carrington_time": 27.2753,
 }
 
-CONST_ALPHA_TO_PROTON=0.04
+CONST_ALPHA_TO_PROTON = 0.04
diff --git a/imap_l3_processing/glows/l3d/science/toolkit/funcs.py b/imap_l3_processing/glows/l3d/science/toolkit/funcs.py
index e669c40f5..2caf64ce7 100644
--- a/imap_l3_processing/glows/l3d/science/toolkit/funcs.py
+++ b/imap_l3_processing/glows/l3d/science/toolkit/funcs.py
@@ -1,27 +1,30 @@
-'''
+"""
 Author: Izabela Kowalska-Leszczynska (ikowalska@cbk.waw.pl)
 Functions used in L3a->L3b processing that are not class methods
-'''
+"""
 
-import numpy as np
-import astropy.units as u
-import astropy.constants as const
 import json
+import logging
+
+import astropy.constants as const
+import astropy.units as u
 import netCDF4
-from astropy.time import Time
+import numpy as np
 from astropy.coordinates import get_sun
+from astropy.time import Time
 
-from imap_l3_processing.glows.quality_flags import NOMINAL_ALPHA_PROTON_RATIO_VALUE
-from .constants import PHISICAL_CONSTANTS, CONST_ALPHA_TO_PROTON
+from .constants import CONST_ALPHA_TO_PROTON, PHISICAL_CONSTANTS
 
-import logging
 logging.basicConfig(level=logging.ERROR)
 
+
 ##########################
 # Exceptions
 ##########################
 class ArrayShapeError(Exception):
     pass
+
+
 class OutOfLimitsError(Exception):
     pass
 
@@ -41,87 +44,109 @@ def base_funcs_def(i, sin_or_cos):
     """
 
     if i == 0:
+
         def fun(angle):
-            return 1.0/(2.0*np.sqrt(np.pi))*np.ones_like(angle)
+            return 1.0 / (2.0 * np.sqrt(np.pi)) * np.ones_like(angle)
+
         return fun
-    if sin_or_cos == 's':
+    if sin_or_cos == "s":
+
         def fun_s(angle):
-            return np.sin(i*angle)/np.sqrt(np.pi)
+            return np.sin(i * angle) / np.sqrt(np.pi)
+
         return fun_s
-    if sin_or_cos == 'c':
+    if sin_or_cos == "c":
+
         def fun_c(angle):
-            return np.cos(i*angle)/np.sqrt(np.pi)
+            return np.cos(i * angle) / np.sqrt(np.pi)
+
         return fun_c
     # TODO: maybe we need some error if sin_or_cos is anything beside 'c' or 's'
-    return 'None'
+    return "None"
+
 
-def calculate_lya_monthly(t,flux):
-   '''
-   Calculates data that are averaged over each Carrington rotation period. Time is mean value of decimal year in each period.
-   '''
-   t_carr,flux_carr=resize_by_carr(t,flux)
-   t_mean=Time(np.array([np.mean(t.decimalyear) for t in t_carr]),format='decimalyear')
-   flux_mean=np.array([f.mean() for f in flux_carr])
+def calculate_lya_monthly(t, flux):
+    """
+    Calculates data that are averaged over each Carrington rotation period. Time is mean value of decimal year in each period.
+    """
+    t_carr, flux_carr = resize_by_carr(t, flux)
+    t_mean = Time(
+        np.array([np.mean(t.decimalyear) for t in t_carr]), format="decimalyear"
+    )
+    flux_mean = np.array([f.mean() for f in flux_carr])
+
+    return t_mean, flux_mean
 
-   return t_mean,flux_mean
 
 def calculate_mean_date(fn_list):
-    daily_dates=[]
+    daily_dates = []
     for f in fn_list:
-        l3a=read_json(f)
-        daily_dates.append(l3a['start_time'])
-    date_mean=Time(daily_dates).mean()
+        l3a = read_json(f)
+        daily_dates.append(l3a["start_time"])
+    date_mean = Time(daily_dates).mean()
     return date_mean
 
-def calculate_sw_energy_flux(n,v,p):
-        '''
-        Solar wind energy flux that is latitudinal invariant
-        Le Chat et al. 2012a
-        Parameters must be given as a object with unit
 
-        Parameters:
-        --------------
-        n - protons number density
-        v - plasma speed
-        p - concentration of the alpha particles wrt to the protons
+def calculate_sw_energy_flux(n, v, p):
+    """
+    Solar wind energy flux that is latitudinal invariant
+    Le Chat et al. 2012a
+    Parameters must be given as a object with unit
+
+    Parameters:
+    --------------
+    n - protons number density
+    v - plasma speed
+    p - concentration of the alpha particles wrt to the protons
+
+    Output:
+    ------------
+    energy_flux: energy flux at 1au in the ecliptic plane (invariant)
+    """
+    energy_flux = (
+        n
+        * (const.m_p + p * PHISICAL_CONSTANTS["m_alpha"])
+        * v
+        * (0.5 * v**2 + const.GM_sun / const.R_sun)
+    )
 
-        Output:
-        ------------
-        energy_flux: energy flux at 1au in the ecliptic plane (invariant)
-        '''
-        energy_flux=n*(const.m_p+p*PHISICAL_CONSTANTS['m_alpha'])*v*(0.5*v**2+const.GM_sun/const.R_sun)
+    return energy_flux.to("erg.s-1.cm-2")
 
-        return energy_flux.to('erg.s-1.cm-2')
 
 def carrington(jd):
-    '''
+    """
     Converts time in Julian Day format into Carrington Rotation number
-    '''
-    ans=(jd-PHISICAL_CONSTANTS['jd_carrington_first'])/PHISICAL_CONSTANTS['carrington_time']+1
+    """
+    ans = (jd - PHISICAL_CONSTANTS["jd_carrington_first"]) / PHISICAL_CONSTANTS[
+        "carrington_time"
+    ] + 1
     return ans
 
+
 def check_nan(data):
-    '''
+    """
     Checks if there are any NaNs in the output data. If so, raises Exception and data product is not produced
 
     Parameters:
     --------------
     data - dictionary with the data product
-    '''
+    """
     for key in data:
-            if np.any((np.isnan(np.array(data[key])))):
-                raise Exception("There are NaN in the output data")
+        if np.any(np.isnan(np.array(data[key]))):
+            raise Exception("There are NaN in the output data")
+
 
 def cr_from_l3b_fn(fn):
-    '''
+    """
     Extracts CR from the L3b filename. This may change if the naming convention will change
-    '''
-    cr=fn.split('_')[5]
+    """
+    cr = fn.split("_")[5]
 
     return cr
 
+
 def cross_section_cx_LS(E):
-    '''
+    """
     Lindsay&Stebbings 2005
     Returns cross_section as a astro.unit object
 
@@ -133,14 +158,15 @@ def cross_section_cx_LS(E):
     ------------
     sigma: cross-section as a astropy.unit object
 
-    '''
-    EkeV=E.to('keV').value
-    out=1e-16*(1-np.exp(-67.3/EkeV))**4.5*(4.15-0.531*np.log(EkeV))**2
-    sigma=out*u.cm*u.cm
+    """
+    EkeV = E.to("keV").value
+    out = 1e-16 * (1 - np.exp(-67.3 / EkeV)) ** 4.5 * (4.15 - 0.531 * np.log(EkeV)) ** 2
+    sigma = out * u.cm * u.cm
     return sigma
 
+
 def cross_section_cx(E):
-    '''
+    """
     Swaczyna et al. 2025
     https://doi.org/10.3847/1538-4365/adaf17
     Returns cross_section as a astro.unit object
@@ -153,24 +179,29 @@ def cross_section_cx(E):
     ------------
     sigma: cross-section as a astropy.unit object
 
-    '''
+    """
 
     # Parameters
-    A1=4.8569
-    A2=21.906
-    A3=31.487
-    A4=0.12018
-    A5=4.1402E-6
-    A6=3.7524
-    A7=8.8476E-12
-    A8=6.1091
-
-    EkeV=E.to('keV').value
-    out=(1e-16)*(A1*np.log(A2/EkeV+A3))/(1+A4*EkeV+A5*EkeV**A6+A7*EkeV**A8)
-    sigma=out*u.cm*u.cm
+    A1 = 4.8569
+    A2 = 21.906
+    A3 = 31.487
+    A4 = 0.12018
+    A5 = 4.1402e-6
+    A6 = 3.7524
+    A7 = 8.8476e-12
+    A8 = 6.1091
+
+    EkeV = E.to("keV").value
+    out = (
+        (1e-16)
+        * (A1 * np.log(A2 / EkeV + A3))
+        / (1 + A4 * EkeV + A5 * EkeV**A6 + A7 * EkeV**A8)
+    )
+    sigma = out * u.cm * u.cm
 
     return sigma
 
+
 def derive_sin_cos(order):
     """
     equivalent of Mathematica's deriveSinCos function
@@ -184,135 +215,147 @@ def derive_sin_cos(order):
     """
 
     funcs = []
-    for i in range(order+1):
+    for i in range(order + 1):
         if i == 0:
-            funcs.append(base_funcs_def(i, 'None'))
+            funcs.append(base_funcs_def(i, "None"))
         else:
-            funcs.append(base_funcs_def(i, 's'))
-            funcs.append(base_funcs_def(i, 'c'))
+            funcs.append(base_funcs_def(i, "s"))
+            funcs.append(base_funcs_def(i, "c"))
     return funcs
 
-def f107_daily_data(t,flux):
-        '''
-        Returns adjusted F10.7 flux with daily cadence. In the case of multiple measurements during a day,
-        the closest to the noon is chosen
-
-        Parameters:
-        ------------
-        t:    time array of raw data (astropy.time)
-        flux: F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
-
-        Output:
-        ------------
-        t_out: daily time array (astropy.time)
-        flux_out: daily F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
-        '''
-        idx_noon,idx_dup=select_noon(t)        # select one measurment per day
-        idx_no0=np.where(flux[idx_noon]>0)[0]
-        t_out=t[idx_noon[idx_no0]]
-        flux_out=flux[idx_noon[idx_no0]]
-        return t_out,flux_out
+
+def f107_daily_data(t, flux):
+    """
+    Returns adjusted F10.7 flux with daily cadence. In the case of multiple measurements during a day,
+    the closest to the noon is chosen
+
+    Parameters:
+    ------------
+    t:    time array of raw data (astropy.time)
+    flux: F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
+
+    Output:
+    ------------
+    t_out: daily time array (astropy.time)
+    flux_out: daily F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
+    """
+    idx_noon, idx_dup = select_noon(t)  # select one measurment per day
+    idx_no0 = np.where(flux[idx_noon] > 0)[0]
+    t_out = t[idx_noon[idx_no0]]
+    flux_out = flux[idx_noon[idx_no0]]
+    return t_out, flux_out
+
 
 def find_CR_idx(CR, CR_list):
-    '''
+    """
     finds index of the value in the CR_list that is closest to the CR and the next idx
     it corresponds to the two L3bc files: close to current CR and the next one (for interpolation)
-    '''
-    idx_CR = np.abs(np.array(CR_list)-int(CR)).argmin()
-   # OLD idx_read=[idx_CR-1,idx_CR,idx_CR+1]
-    idx_read=[idx_CR,idx_CR+1]
+    """
+    idx_CR = np.abs(np.array(CR_list) - int(CR)).argmin()
+    # OLD idx_read=[idx_CR-1,idx_CR,idx_CR+1]
+    idx_read = [idx_CR, idx_CR + 1]
     return idx_read
 
+
 def generate_cr_lya(CR, data_lya):
-    '''
+    """
     calculates Lyman-alpha composite irradiance interpolated on the center of a given CR
-    '''
+    """
 
-    t_CR = Time(jd_fm_Carrington(CR),format='jd')
+    t_CR = Time(jd_fm_Carrington(CR), format="jd")
 
     CR_list = [int(carrington(t.jd)) for t in data_lya[0]]
     idx_read = find_CR_idx(CR, CR_list)
 
     t_lya = np.array([data_lya[0][i].jd for i in idx_read])
     lya = np.array([data_lya[1][i] for i in idx_read])
-    lya_CR = np.interp(t_CR.jd,t_lya,lya)
+    lya_CR = np.interp(t_CR.jd, t_lya, lya)
 
     return lya_CR
 
 
-
 def get_spher_coord_rad(vec):
-  """
-  Compute radius r, longitude l and latitude b for a vector vec
-
-  Parameters
-  ----------
-    vec: 1D or 2D numpy array
-      input vector(s)
-
-  Returns
-  -------
-    if vec is an 1D numpy array, return 1D numpy array rlb (see below)
-    if vec is a 2D numpy array, assume that its rows represent vectors and return 2D array
-      rlb (see below), where every row of rlb corresponds to every row of vec
-
-    rlb: 1D or 2D numpy array
-      if 1D then rlb=[r,l,b] where: r - magnitude of vector vec, l - longitude of the direction
-        pointed by vec in radians, b - latitude of the direction pointed by vec in radians
-      if 2D then columns of the array contain r,l,b
-
-  Examples
-  --------
-    >>> import geom_fun_libr_box as gflb
-    >>> import numpy as np
-    >>> rlb=gflb.get_spher_coord_rad(np.array([0,1,1]))
-    >>> print(rlb[0],np.degrees(rlb[1]),np.degrees(rlb[2]))
-    1.41421356237 90.0 45.0
-    >>> n=10
-    >>> phi=np.radians(np.linspace(0,360,n))
-    >>> tht=np.radians(np.linspace(-90,90,n))
-    >>> vecs=np.column_stack([np.cos(phi)*np.cos(tht),np.sin(phi)*np.cos(tht),np.sin(tht)])
-    >>> print(gflb.get_spher_coord_rad(vecs))
-    [[ 1.          0.         -1.57079633]
-     [ 1.          0.6981317  -1.22173048]
-     [ 1.          1.3962634  -0.87266463]
-     [ 1.          2.0943951  -0.52359878]
-     [ 1.          2.7925268  -0.17453293]
-     [ 1.          3.4906585   0.17453293]
-     [ 1.          4.1887902   0.52359878]
-     [ 1.          4.88692191  0.87266463]
-     [ 1.          5.58505361  1.22173048]
-     [ 1.          6.28318531  1.57079633]]
-  """
-  if vec.ndim==1:
-    if np.shape(vec)[0]!=3:
-      raise "get_spher_coord_rad() error: np.shape(vec)[0]!=3"
-    r=np.sqrt(np.dot(vec,vec))
-    if r==0.0: b=0.0;
-    else: b=np.arcsin(vec[2]/r) # compute latitude
-    # compute longitude
-    if vec[0]==0.0 and vec[1]==0.0: l=0.0
-    else: l=np.arctan2(vec[1],vec[0])%(2*np.pi)
-    return np.array([r,l,b]) # return one array similarly as in Mathematica
-  else:
-    if np.shape(vec)[1]!=3:
-      raise "get_spher_coord_rad() error: np.shape(vec)[1]!=3"
-    r=np.sqrt((vec * vec).sum(axis=1))
-    idxs=np.nonzero(r!=0.0)
-    b=np.zeros((len(r)),np.float64)
-    b[idxs[0]]=np.arcsin(vec[idxs[0],2]/r[idxs[0]]) # compute latitude
-    l=np.arctan2(vec[:,1],vec[:,0])%(2*np.pi) # compute longitude
-    idxs=np.nonzero(np.logical_and(vec[:,0]==0.0,vec[:,1]==0.0))
-    l[idxs[0]]=0.0 # set l=0 if vec[:,0]==0.0 and vec[:,1]==0.0
-    return np.column_stack([r,l,b]) # return one array similarly as in Mathematica
+    """
+    Compute radius r, longitude l and latitude b for a vector vec
+
+    Parameters
+    ----------
+      vec: 1D or 2D numpy array
+        input vector(s)
+
+    Returns
+    -------
+      if vec is an 1D numpy array, return 1D numpy array rlb (see below)
+      if vec is a 2D numpy array, assume that its rows represent vectors and return 2D array
+        rlb (see below), where every row of rlb corresponds to every row of vec
+
+      rlb: 1D or 2D numpy array
+        if 1D then rlb=[r,l,b] where: r - magnitude of vector vec, l - longitude of the direction
+          pointed by vec in radians, b - latitude of the direction pointed by vec in radians
+        if 2D then columns of the array contain r,l,b
+
+    Examples
+    --------
+      >>> import geom_fun_libr_box as gflb
+      >>> import numpy as np
+      >>> rlb=gflb.get_spher_coord_rad(np.array([0,1,1]))
+      >>> print(rlb[0],np.degrees(rlb[1]),np.degrees(rlb[2]))
+      1.41421356237 90.0 45.0
+      >>> n=10
+      >>> phi=np.radians(np.linspace(0,360,n))
+      >>> tht=np.radians(np.linspace(-90,90,n))
+      >>> vecs=np.column_stack([np.cos(phi)*np.cos(tht),np.sin(phi)*np.cos(tht),np.sin(tht)])
+      >>> print(gflb.get_spher_coord_rad(vecs))
+      [[ 1.          0.         -1.57079633]
+       [ 1.          0.6981317  -1.22173048]
+       [ 1.          1.3962634  -0.87266463]
+       [ 1.          2.0943951  -0.52359878]
+       [ 1.          2.7925268  -0.17453293]
+       [ 1.          3.4906585   0.17453293]
+       [ 1.          4.1887902   0.52359878]
+       [ 1.          4.88692191  0.87266463]
+       [ 1.          5.58505361  1.22173048]
+       [ 1.          6.28318531  1.57079633]]
+    """
+    if vec.ndim == 1:
+        if np.shape(vec)[0] != 3:
+            raise "get_spher_coord_rad() error: np.shape(vec)[0]!=3"
+        r = np.sqrt(np.dot(vec, vec))
+        if r == 0.0:
+            b = 0.0
+        else:
+            b = np.arcsin(vec[2] / r)  # compute latitude
+        # compute longitude
+        if vec[0] == 0.0 and vec[1] == 0.0:
+            l = 0.0
+        else:
+            l = np.arctan2(vec[1], vec[0]) % (2 * np.pi)
+        return np.array([r, l, b])  # return one array similarly as in Mathematica
+    else:
+        if np.shape(vec)[1] != 3:
+            raise "get_spher_coord_rad() error: np.shape(vec)[1]!=3"
+        r = np.sqrt((vec * vec).sum(axis=1))
+        idxs = np.nonzero(r != 0.0)
+        b = np.zeros((len(r)), np.float64)
+        b[idxs[0]] = np.arcsin(vec[idxs[0], 2] / r[idxs[0]])  # compute latitude
+        l = np.arctan2(vec[:, 1], vec[:, 0]) % (2 * np.pi)  # compute longitude
+        idxs = np.nonzero(np.logical_and(vec[:, 0] == 0.0, vec[:, 1] == 0.0))
+        l[idxs[0]] = 0.0  # set l=0 if vec[:,0]==0.0 and vec[:,1]==0.0
+        return np.column_stack(
+            [r, l, b]
+        )  # return one array similarly as in Mathematica
+
 
 def jd_fm_Carrington(carr):
-    '''
+    """
     calculates time in format of Julian Days for a given Carrington Rotation number
-    '''
-    ans=(carr-1)*PHISICAL_CONSTANTS['carrington_time']+PHISICAL_CONSTANTS['jd_carrington_first']
+    """
+    ans = (carr - 1) * PHISICAL_CONSTANTS["carrington_time"] + PHISICAL_CONSTANTS[
+        "jd_carrington_first"
+    ]
     return ans
 
+
 def make_lsq_matrix(psi, base_funcs):
     """
     make data matrix and vector for least-squares method in the matrix form
@@ -332,52 +375,56 @@ def make_lsq_matrix(psi, base_funcs):
         i = i + 1
     return lsq_mtrx
 
+
 def process_omni_param(omni_raw, cr_grid, param_settings):
-    '''
+    """
     Processes OMNI2 raw data to obtain averaged solar wind speed, density and alpha-particles abundance
-    '''
+    """
 
     # select from the raw data columns relevant for a given parameter
-    param_raw=omni_raw[:,param_settings['column_numbers']]
+    param_raw = omni_raw[:, param_settings["column_numbers"]]
 
     # remove gaps where there was no data available based on coded value
-    param=param_raw[param_raw[:,3]<param_settings['gap_marker']]
-    date=time_from_yday(param)
+    param = param_raw[param_raw[:, 3] < param_settings["gap_marker"]]
+    date = time_from_yday(param)
 
-    date_cr=carrington(date.jd)
+    date_cr = carrington(date.jd)
 
     # Scale parameter to 1au. OMNI Observations are done at different distance and we need to scale proton density
-    if param_settings['scale']: param[:,3]=scale_density(date,param[:,3])
+    if param_settings["scale"]:
+        param[:, 3] = scale_density(date, param[:, 3])
 
-    param_v=param[:,3]  # values of the parameter
+    param_v = param[:, 3]  # values of the parameter
 
     # fill alpha abudance from the end of the measurements to the last point in cr_grid by constant value 0.04 based on Ulysses data McComas et al. 2000
     original_last_date = date_cr[-1]
     used_nominal_per_cr = np.full(len(cr_grid), False)
-    if param_settings['const_if_empty'] and original_last_date < cr_grid[-1]:
-        date_cr=np.append(date_cr,cr_grid[-1])
+    if param_settings["const_if_empty"] and original_last_date < cr_grid[-1]:
+        date_cr = np.append(date_cr, cr_grid[-1])
         used_nominal_per_cr = cr_grid > original_last_date
-        param_v=np.append(param_v,CONST_ALPHA_TO_PROTON)
+        param_v = np.append(param_v, CONST_ALPHA_TO_PROTON)
 
     # split array into 1-Carrington chunks
-    param_cr, idx_param=np.unique(np.floor(date_cr), return_index=True,axis=0)
-    param_s=np.split(param_v,idx_param)[1:]
+    param_cr, idx_param = np.unique(np.floor(date_cr), return_index=True, axis=0)
+    param_s = np.split(param_v, idx_param)[1:]
 
     # calculate averaged over 1 Carrington time and parameter value
-    param_value=np.array([p.mean() for p in param_s])
+    param_value = np.array([p.mean() for p in param_s])
 
     # interpolate into cr_grid
-    if np.logical_and(param_cr[0]<=cr_grid[0], param_cr[-1]>=cr_grid[-1]):
-        param_value_interp=np.interp(cr_grid,param_cr,param_value)
+    if np.logical_and(param_cr[0] <= cr_grid[0], param_cr[-1] >= cr_grid[-1]):
+        param_value_interp = np.interp(cr_grid, param_cr, param_value)
     else:
-        logging.info('There are gaps in the OMNI2 data that cannot be filled by the interpolation')
-        raise Exception('OMNI Error: not enough data for interpolation')
+        logging.info(
+            "There are gaps in the OMNI2 data that cannot be filled by the interpolation"
+        )
+        raise Exception("OMNI Error: not enough data for interpolation")
 
     return param_value_interp, used_nominal_per_cr
 
 
 def read_f107_raw_data(fn):
-    '''
+    """
     Read raw F10.7 data from file
 
     Parameters:
@@ -388,61 +435,76 @@ def read_f107_raw_data(fn):
     ------------
     t0:  time array (astropy.time)
     flux_adjusted: F10.7 flux adjusted to 1au (10^(-22) W/m^2/Hz)
-    '''
+    """
 
     data = np.loadtxt(fn)
 
-    jd=data[:,2]                  # time of observation in JD
-    flux_adjusted=data[:,5]       # adjusted do 1AU flux in sfu 1sfu=10^(-22) W/m^2/Hz (taking into accont that Sun-Earth distance is changing)
-    t0=Time(jd,format='jd')       # time converted to astropy.time format
+    jd = data[:, 2]  # time of observation in JD
+    flux_adjusted = data[
+        :, 5
+    ]  # adjusted do 1AU flux in sfu 1sfu=10^(-22) W/m^2/Hz (taking into accont that Sun-Earth distance is changing)
+    t0 = Time(jd, format="jd")  # time converted to astropy.time format
+
+    return t0, flux_adjusted
 
-    return t0,flux_adjusted
 
-def read_hdr_txt(fn,N):
-    '''
+def read_hdr_txt(fn, N):
+    """
     Reads header from the text file and preapers it to the further analysis
-    '''
+    """
 
     # open file and read header
     with open(fn) as file:
-        hdr=[next(file) for _ in range(N)]
+        hdr = [next(file) for _ in range(N)]
 
     # remove hash
-    hdr_str=remove_hash(hdr)
+    hdr_str = remove_hash(hdr)
     return hdr_str
 
+
 def read_lya_data(fn):
-    '''
+    """
     reads daily Lyman-alpha composite irradiance and calculates monthly averages
-    '''
-    t_lya,flux_lya,_= read_lya_raw_data(fn)
-    t_lya_m,flux_lya_m = calculate_lya_monthly(t_lya,flux_lya)
-    return [t_lya_m,flux_lya_m]
+    """
+    t_lya, flux_lya, _ = read_lya_raw_data(fn)
+    t_lya_m, flux_lya_m = calculate_lya_monthly(t_lya, flux_lya)
+    return [t_lya_m, flux_lya_m]
+
 
 def read_lya_raw_data(filename):
-    '''
+    """
     Reads *.nc file with composite Lyman-alpha solar irradiance (from LAPS)
     Returns daily product adjusted to 1au. Flux is in ph/(cm^2 s^2)
-    '''
-
-    data=netCDF4.Dataset(filename)
-
-    convert_to_ph=data.variables['convert_to_photons'][0].data # Multiplicative factor to convert irr_121 from W/m^2 to ph/(cm^2 s^2) [ph / (cm^2 s^2)] / (W / m^2)
-    t_d1947=data.variables['time'][:].data                     # time of observation [days since 1947-01-01 12:00:00 UTC]
-    flux0=data.variables['irr_121'][:].data                    # Averaged daily irradiance in band from 121.0 to 122.0 nm at 1 AU [W/m^2]
-    flux=flux0*convert_to_ph                                   # Flux in ph/(cm^2 s^2)
-    flux_uncert=data.variables['irr_121_uncertainty'][:].data  # Uncertainty (1 standard deviation) of average daily irradiance [W/m^2]
+    """
 
-    t0=Time('1947-01-01 12:00')
+    data = netCDF4.Dataset(filename)
+
+    convert_to_ph = data.variables[
+        "convert_to_photons"
+    ][
+        0
+    ].data  # Multiplicative factor to convert irr_121 from W/m^2 to ph/(cm^2 s^2) [ph / (cm^2 s^2)] / (W / m^2)
+    t_d1947 = data.variables["time"][
+        :
+    ].data  # time of observation [days since 1947-01-01 12:00:00 UTC]
+    flux0 = data.variables["irr_121"][
+        :
+    ].data  # Averaged daily irradiance in band from 121.0 to 122.0 nm at 1 AU [W/m^2]
+    flux = flux0 * convert_to_ph  # Flux in ph/(cm^2 s^2)
+    flux_uncert = data.variables["irr_121_uncertainty"][
+        :
+    ].data  # Uncertainty (1 standard deviation) of average daily irradiance [W/m^2]
+
+    t0 = Time("1947-01-01 12:00")
 
     # convertion to jd and adding days since 1947-01-01 - it is more accurate than simple adding days to date
-    t=Time(t0.jd+t_d1947,format='jd')
+    t = Time(t0.jd + t_d1947, format="jd")
 
     return t, flux, flux_uncert
 
 
-def read_raw_OMNI_data(ext_dependencies,t_window=None):
-    '''
+def read_raw_OMNI_data(ext_dependencies, t_window=None):
+    """
     Reads 3 types of data from OMNI2 database: plasma speed, proton density, and alpha-particles abundance in the ecliptic plane
 
     Parameters:
@@ -451,48 +513,55 @@ def read_raw_OMNI_data(ext_dependencies,t_window=None):
         list of file names with external dependencies
     t_window: list [astropy.time, astropy.time]
         time window of the data that are needed
-    '''
+    """
     # Read OMNI2 data (hour resolution)
     # year, DOY, hour, S/C Id, proton_dens, plasma_speed, alpha_abundance, sigma proton_dens, sigma plasma_speed, sigma alpha_abundance
-    omni_raw=np.loadtxt(ext_dependencies['omni_raw_data'],usecols=(0,1,2,5,23,24,27,30,31,34))
-    omni_raw_date=time_from_yday(omni_raw[:,(0,1,2)])
-    if t_window==None: out=omni_raw
+    omni_raw = np.loadtxt(
+        ext_dependencies["omni_raw_data"], usecols=(0, 1, 2, 5, 23, 24, 27, 30, 31, 34)
+    )
+    omni_raw_date = time_from_yday(omni_raw[:, (0, 1, 2)])
+    if t_window == None:
+        out = omni_raw
     else:
-        idx_ini=np.abs(omni_raw_date-t_window[0]).argmin()
-        idx_fin=np.abs(omni_raw_date-t_window[1]).argmin()
+        idx_ini = np.abs(omni_raw_date - t_window[0]).argmin()
+        idx_fin = np.abs(omni_raw_date - t_window[1]).argmin()
 
-        out=omni_raw[idx_ini:idx_fin]
+        out = omni_raw[idx_ini:idx_fin]
     return out
 
+
 def read_json(fn):
-    '''
+    """
     Opens, reads and returns the content of the json file
-    '''
-    fp = open(fn, 'r')
+    """
+    fp = open(fn, "r")
     data = json.load(fp)
     fp.close()
     return data
 
+
 def remove_hash(hdr):
-    '''
+    """
     Removes # from the header that is read from the text files and returns string that will
     be used to generate another header
-    '''
-    hdr_str=[l[2:] for l in hdr]
+    """
+    hdr_str = [l[2:] for l in hdr]
     return hdr_str
 
-def resize_by_carr(t,flux):
-   '''
-   Reshaping flux array in such a way that each element is a list of fluxes from the same carrington rotation number
-   '''
-   t_carr=carrington(t.jd)
-   n_carr=np.array([int(t_carr[i]) for i in np.arange(len(t_carr))])
-   flux_carr_sort=np.split(flux,np.unique(n_carr,return_index=True)[1][1:])
-   t_carr_sort=np.split(t,np.unique(n_carr,return_index=True)[1][1:])
-   return t_carr_sort,flux_carr_sort
+
+def resize_by_carr(t, flux):
+    """
+    Reshaping flux array in such a way that each element is a list of fluxes from the same carrington rotation number
+    """
+    t_carr = carrington(t.jd)
+    n_carr = np.array([int(t_carr[i]) for i in np.arange(len(t_carr))])
+    flux_carr_sort = np.split(flux, np.unique(n_carr, return_index=True)[1][1:])
+    t_carr_sort = np.split(t, np.unique(n_carr, return_index=True)[1][1:])
+    return t_carr_sort, flux_carr_sort
+
 
 def scale_density(date, dens):
-    '''
+    """
     Scaling density measurements that are taken at different distance than 1au.
     We need solar wind parameters ajusted to 1au.
     Proton density decreases as 1/r^2
@@ -503,21 +572,22 @@ def scale_density(date, dens):
         time of the measurement
     dens : 1D float vector
         measured density [#/cm^3]
-    '''
+    """
 
     # Sun-Earth distance at given moment
-    d_sun=get_sun(date).distance
+    d_sun = get_sun(date).distance
 
     # Sun-L1 distance
-    r_obs=d_sun*(1-(const.M_earth/(3*const.M_sun))**(1./3))
+    r_obs = d_sun * (1 - (const.M_earth / (3 * const.M_sun)) ** (1.0 / 3))
 
     # density ajustment
-    dens_1au=dens*(r_obs/u.au)**2
+    dens_1au = dens * (r_obs / u.au) ** 2
 
     return dens_1au
 
-def select_data_window(data,t_window):
-    '''
+
+def select_data_window(data, t_window):
+    """
     Selects data whitin a given time window
 
     Parameters:
@@ -527,15 +597,16 @@ def select_data_window(data,t_window):
     t_ini: astropy.time
     t_end: astropy.time
 
-    '''
-    idx=np.where(np.logical_and(data[0]>=t_window[0],data[0]<=t_window[1]))[0]
-    t=data[0][idx]
-    data=data[1][idx]
+    """
+    idx = np.where(np.logical_and(data[0] >= t_window[0], data[0] <= t_window[1]))[0]
+    t = data[0][idx]
+    data = data[1][idx]
+
+    return [t, data]
 
-    return [t,data]
 
 def select_noon(t_pen):
-    '''
+    """
     Select the dates that are closest to the local noon
 
      Parameters:
@@ -547,119 +618,203 @@ def select_noon(t_pen):
         1. index list of all data taken at noon (20 UT) or the closest one if there is no data at noon
         2. a list of indexes of data taken on the same day (if there is only one measurement then this
             element is just 1 element list np. [[1],[2,3,4],[6]]
-    '''
-    #TODO it should be done in a dynamical way - it should determine how many measurements are in a given day
-    index=np.array([0])
-    index_duplicate=[]
-    i=0
-    while i<(len(t_pen)-3):
-
-        if (t_pen[i].yday[:8]==t_pen[i+1].yday[:8]):
-
-            if(t_pen[i].yday[:8]==t_pen[i+2].yday[:8]):
-                if(t_pen[i].yday[:8]==t_pen[i+3].yday[:8]):
-                    h=np.array([int(t_pen[i].yday[9:11]),int(t_pen[i+1].yday[9:11]),int(t_pen[i+2].yday[9:11]),int(t_pen[i+3].yday[9:11])])
-                    k=i+np.abs(h-20).argmin()   # index of the hour that is near 20UT (local noon)
-                    d=[i,i+1,i+2,i+3]
-                    i=i+4
+    """
+    # TODO it should be done in a dynamical way - it should determine how many measurements are in a given day
+    index = np.array([0])
+    index_duplicate = []
+    i = 0
+    while i < (len(t_pen) - 3):
+        if t_pen[i].yday[:8] == t_pen[i + 1].yday[:8]:
+            if t_pen[i].yday[:8] == t_pen[i + 2].yday[:8]:
+                if t_pen[i].yday[:8] == t_pen[i + 3].yday[:8]:
+                    h = np.array(
+                        [
+                            int(t_pen[i].yday[9:11]),
+                            int(t_pen[i + 1].yday[9:11]),
+                            int(t_pen[i + 2].yday[9:11]),
+                            int(t_pen[i + 3].yday[9:11]),
+                        ]
+                    )
+                    k = (
+                        i + np.abs(h - 20).argmin()
+                    )  # index of the hour that is near 20UT (local noon)
+                    d = [i, i + 1, i + 2, i + 3]
+                    i = i + 4
                 else:
-                    h=np.array([int(t_pen[i].yday[9:11]),int(t_pen[i+1].yday[9:11]),int(t_pen[i+2].yday[9:11])])
-                    k=i+np.abs(h-20).argmin()   # index of the hour that is near 20UT (local noon)
-                    d=[i,i+1,i+2]
-                    i=i+3
+                    h = np.array(
+                        [
+                            int(t_pen[i].yday[9:11]),
+                            int(t_pen[i + 1].yday[9:11]),
+                            int(t_pen[i + 2].yday[9:11]),
+                        ]
+                    )
+                    k = (
+                        i + np.abs(h - 20).argmin()
+                    )  # index of the hour that is near 20UT (local noon)
+                    d = [i, i + 1, i + 2]
+                    i = i + 3
             else:
-                h=np.array([int(t_pen[i].yday[9:11]),int(t_pen[i+1].yday[9:11])])
-                k=i+np.abs(h-20).argmin()   # index of the hour that is near 20UT (local noon)
-                d=[i,i+1]
-            i=i+2
+                h = np.array([int(t_pen[i].yday[9:11]), int(t_pen[i + 1].yday[9:11])])
+                k = (
+                    i + np.abs(h - 20).argmin()
+                )  # index of the hour that is near 20UT (local noon)
+                d = [i, i + 1]
+            i = i + 2
         else:
-            k=i
-            d=[i]
-            i=i+1
-        index=np.append(index,k)
+            k = i
+            d = [i]
+            i = i + 1
+        index = np.append(index, k)
         index_duplicate.append(d)
 
-    return index[1:],index_duplicate
+    return index[1:], index_duplicate
+
 
 def test_WawHelioInMP_file(model):
-    '''
+    """
     test if json file has what it should inside
-    '''
-
-    vg_points_number=model['header']['vg_points_number']
-    hion_filtration=model['params_hion']['hion_filtration']
-    hion_order=model['params_hion']['hion_order']
-
-    hion_const_0=np.array(model['params_hion']['hion_const']).shape[0]
-    hion_base=np.array(model['params_hion']['hion_base'])
-    hion_base_0=np.array(model['params_hion']['hion_base']).shape[0]
-    hion_base_1=np.array(model['params_hion']['hion_base']).shape[1]
-
-    if vg_points_number!=len(model['vg_points']):
-        logging.error('vg_points_number is incorrect.\nIt is '+str(vg_points_number)
-                      +'\nIt should be '+str(len(model['vg_points'])))
-        raise ArrayShapeError('Array shape error: Array shape is incorrect')
-
-    elif hion_const_0!=(hion_order-1-hion_filtration):
-            logging.error('hion_const shape is incorrect.\nIt is '+str(hion_const_0)
-                          +'\nIt should be '+str(hion_order-1-hion_filtration))
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
-
-    elif np.logical_or(hion_base_0!=hion_order-1,hion_base_1!=hion_order+1):
-            logging.error('hion_base shape is incorrect.\nIt is '+str(hion_base.shape)
-                          +'\nIt should be ('+str(hion_order-1)+','+str(hion_order+1)+')')
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
-
-
-    for k in np.arange(model['header']['vg_points_number']):
-
-        lcrv_filtration=model['vg_points'][k]['parameters']['lcrv_filtration']
-        lcrv_order=model['vg_points'][k]['parameters']['lcrv_order']
-
-        model_const_0=np.array(model['vg_points'][k]['parameters']['model_const']).shape[0]
-        model_matrix=np.array(model['vg_points'][k]['parameters']['model_matrix'])
-        model_matrix_0=np.array(model['vg_points'][k]['parameters']['model_matrix']).shape[0]
-        model_matrix_1=np.array(model['vg_points'][k]['parameters']['model_matrix']).shape[1]
-
-        lcrv_Trmatrix=np.array(model['vg_points'][k]['parameters']['lcrv_Trmatrix'])
-        lcrv_Trmatrix_0=np.array(model['vg_points'][k]['parameters']['lcrv_Trmatrix']).shape[0]
-        lcrv_Trmatrix_1=np.array(model['vg_points'][k]['parameters']['lcrv_Trmatrix']).shape[1]
-
-        if model_const_0!=hion_filtration:
-            logging.error('model_const shape is incorrect.\nIt is '+str(model_const_0)
-                        +'\nIt should be '+str(hion_filtration))
-            raise ArrayShapeError('Array shape error: Array shape is incorrect')
+    """
 
-        elif np.logical_or(model_matrix_0!=hion_filtration,model_matrix_1!=lcrv_filtration):
-            logging.error('model_matrix shape is incorrect.\nIt is '+str(model_matrix.shape)
-                          +'\nIt should be ('+str(hion_filtration)+','+str(lcrv_filtration)+')')
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
+    vg_points_number = model["header"]["vg_points_number"]
+    hion_filtration = model["params_hion"]["hion_filtration"]
+    hion_order = model["params_hion"]["hion_order"]
+
+    hion_const_0 = np.array(model["params_hion"]["hion_const"]).shape[0]
+    hion_base = np.array(model["params_hion"]["hion_base"])
+    hion_base_0 = np.array(model["params_hion"]["hion_base"]).shape[0]
+    hion_base_1 = np.array(model["params_hion"]["hion_base"]).shape[1]
+
+    if vg_points_number != len(model["vg_points"]):
+        logging.error(
+            "vg_points_number is incorrect.\nIt is "
+            + str(vg_points_number)
+            + "\nIt should be "
+            + str(len(model["vg_points"]))
+        )
+        raise ArrayShapeError("Array shape error: Array shape is incorrect")
+
+    elif hion_const_0 != (hion_order - 1 - hion_filtration):
+        logging.error(
+            "hion_const shape is incorrect.\nIt is "
+            + str(hion_const_0)
+            + "\nIt should be "
+            + str(hion_order - 1 - hion_filtration)
+        )
+        raise ArrayShapeError("Array shape error: Array shape is incorect")
+
+    elif np.logical_or(hion_base_0 != hion_order - 1, hion_base_1 != hion_order + 1):
+        logging.error(
+            "hion_base shape is incorrect.\nIt is "
+            + str(hion_base.shape)
+            + "\nIt should be ("
+            + str(hion_order - 1)
+            + ","
+            + str(hion_order + 1)
+            + ")"
+        )
+        raise ArrayShapeError("Array shape error: Array shape is incorect")
+
+    for k in np.arange(model["header"]["vg_points_number"]):
+        lcrv_filtration = model["vg_points"][k]["parameters"]["lcrv_filtration"]
+        lcrv_order = model["vg_points"][k]["parameters"]["lcrv_order"]
+
+        model_const_0 = np.array(
+            model["vg_points"][k]["parameters"]["model_const"]
+        ).shape[0]
+        model_matrix = np.array(model["vg_points"][k]["parameters"]["model_matrix"])
+        model_matrix_0 = np.array(
+            model["vg_points"][k]["parameters"]["model_matrix"]
+        ).shape[0]
+        model_matrix_1 = np.array(
+            model["vg_points"][k]["parameters"]["model_matrix"]
+        ).shape[1]
+
+        lcrv_Trmatrix = np.array(model["vg_points"][k]["parameters"]["lcrv_Trmatrix"])
+        lcrv_Trmatrix_0 = np.array(
+            model["vg_points"][k]["parameters"]["lcrv_Trmatrix"]
+        ).shape[0]
+        lcrv_Trmatrix_1 = np.array(
+            model["vg_points"][k]["parameters"]["lcrv_Trmatrix"]
+        ).shape[1]
+
+        if model_const_0 != hion_filtration:
+            logging.error(
+                "model_const shape is incorrect.\nIt is "
+                + str(model_const_0)
+                + "\nIt should be "
+                + str(hion_filtration)
+            )
+            raise ArrayShapeError("Array shape error: Array shape is incorrect")
+
+        elif np.logical_or(
+            model_matrix_0 != hion_filtration, model_matrix_1 != lcrv_filtration
+        ):
+            logging.error(
+                "model_matrix shape is incorrect.\nIt is "
+                + str(model_matrix.shape)
+                + "\nIt should be ("
+                + str(hion_filtration)
+                + ","
+                + str(lcrv_filtration)
+                + ")"
+            )
+            raise ArrayShapeError("Array shape error: Array shape is incorect")
+
+        elif np.logical_or(
+            lcrv_Trmatrix_0 != 2 * lcrv_order + 1, lcrv_Trmatrix_1 != 2 * lcrv_order + 1
+        ):
+            logging.error(
+                "lcrv_Trmatrix shape is incorrect.\nIt is "
+                + str(lcrv_Trmatrix.shape)
+                + "\nIt should be ("
+                + str(2 * lcrv_order + 1)
+                + ","
+                + str(2 * lcrv_order + 1)
+                + ")"
+            )
+            raise ArrayShapeError("Array shape error: Array shape is incorect")
 
-        elif np.logical_or(lcrv_Trmatrix_0!=2*lcrv_order+1,lcrv_Trmatrix_1!=2*lcrv_order+1):
-            logging.error('lcrv_Trmatrix shape is incorrect.\nIt is '+str(lcrv_Trmatrix.shape)
-                          +'\nIt should be ('+str(2*lcrv_order+1)+','+str(2*lcrv_order+1)+')')
-            raise ArrayShapeError('Array shape error: Array shape is incorect')
 
 def time_string_from_l3a_fn(fn):
-    '''
+    """
     Extracts date from the L3a filename. This may change if the naming convention will change
-    '''
-    fn_string=fn.split('_')[4]
-    time_string=fn_string[0:4]+'-'+fn_string[4:6]+'-'+fn_string[6:8]+' '+fn_string[8:10]+':'+fn_string[10:12]+':'+fn_string[12:14]
+    """
+    fn_string = fn.split("_")[4]
+    time_string = (
+        fn_string[0:4]
+        + "-"
+        + fn_string[4:6]
+        + "-"
+        + fn_string[6:8]
+        + " "
+        + fn_string[8:10]
+        + ":"
+        + fn_string[10:12]
+        + ":"
+        + fn_string[12:14]
+    )
     return time_string
 
+
 def time_from_l3a(fn):
-    data_l3a=read_json(fn)
-    mean_time=Time(data_l3a['start_time'])+0.5*(Time(data_l3a['end_time'])-Time(data_l3a['start_time']))
-    return [mean_time, Time(data_l3a['start_time']), Time(data_l3a['end_time'])]
+    data_l3a = read_json(fn)
+    mean_time = Time(data_l3a["start_time"]) + 0.5 * (
+        Time(data_l3a["end_time"]) - Time(data_l3a["start_time"])
+    )
+    return [mean_time, Time(data_l3a["start_time"]), Time(data_l3a["end_time"])]
+
 
 def time_from_yday(yday_list):
-        string = [f'{int(yday_list[i, 0])}:{int(yday_list[i, 1]):03d}:{int(yday_list[i, 2]):02d}:00' for i in range(len(yday_list))]
-        date=Time(string,format='yday')
-        return date
+    string = [
+        f"{int(yday_list[i, 0])}:{int(yday_list[i, 1]):03d}:{int(yday_list[i, 2]):02d}:00"
+        for i in range(len(yday_list))
+    ]
+    date = Time(string, format="yday")
+    return date
+
 
 def v2E(v):
-    '''
+    """
     Speed to kinetic energy transformation for hydrogen
 
     Parameters:
@@ -669,8 +824,8 @@ def v2E(v):
     Output:
     ------------
     E: energy as an astropy.unit object
-    '''
+    """
 
-    m_H=1.6733e-24*u.g
-    E=(0.5*m_H*v**2).to('eV')
+    m_H = 1.6733e-24 * u.g
+    E = (0.5 * m_H * v**2).to("eV")
     return E
diff --git a/imap_l3_processing/glows/l3d/science/toolkit/l3d_SolarParamHistory.py b/imap_l3_processing/glows/l3d/science/toolkit/l3d_SolarParamHistory.py
index 21d3a8758..90ac14a87 100644
--- a/imap_l3_processing/glows/l3d/science/toolkit/l3d_SolarParamHistory.py
+++ b/imap_l3_processing/glows/l3d/science/toolkit/l3d_SolarParamHistory.py
@@ -1,17 +1,19 @@
-import numpy as np
-from astropy.time import Time
-import json
 import glob
+import json
+from dataclasses import dataclass
+
+import numpy as np
 import toolkit.funcs as fun
+from astropy.time import Time
 
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
+
 from .constants import VERSION
-from dataclasses import dataclass
 
 
 @dataclass
-class SolarParamsHistory():
-    '''
+class SolarParamsHistory:
+    """
     Class for an Ionization files generator
     Text files (solar wind speed, density, uv-anisotropy, Lyman-alpha irradiance, photoionization, and electron density)
     are requaried as a input files for L3e processing
@@ -41,9 +43,9 @@ class SolarParamsHistory():
     _update_lya_data(self,ext_dependencies,CR)
     update_solar_params_hist(self,ext_dependencies,data_l3b,data_l3c)
 
-    '''
+    """
 
-    def __init__(self,anc_input_from_instr_team,ext_dependencies):
+    def __init__(self, anc_input_from_instr_team, ext_dependencies):
         """
         Class constructor
 
@@ -101,276 +103,392 @@ def __init__(self,anc_input_from_instr_team,ext_dependencies):
 
         """
         # read pipeline settings to be used for L3d
-        self.settings = fun.read_json(anc_input_from_instr_team['pipeline_settings'])
-
-        self.header={
-            'software_version': VERSION,
-            'filename': None,
-            'ancillary_data_files': None,
-            'external_dependeciens': ext_dependencies['lya_raw_data'],
-            'l3b_input_filename': None,
-            'l3c_input_filename': None
+        self.settings = fun.read_json(anc_input_from_instr_team["pipeline_settings"])
+
+        self.header = {
+            "software_version": VERSION,
+            "filename": None,
+            "ancillary_data_files": None,
+            "external_dependeciens": ext_dependencies["lya_raw_data"],
+            "l3b_input_filename": None,
+            "l3c_input_filename": None,
         }
 
-        self.ini_data={}
-        self.ini_data['CR_last']=None
-        self.ini_data['label']=list(anc_input_from_instr_team['WawHelioIon'].keys())
-        N=len(self.ini_data['label'])
-        self.ini_data['time']={}
-        for k in anc_input_from_instr_team['WawHelioIon']: self.ini_data['time'][k]=None
-        self.ini_data['data']={}
-        for k in anc_input_from_instr_team['WawHelioIon']: self.ini_data['data'][k]=None
-        self.ini_data['hdr_txt']={}
-        for k in anc_input_from_instr_team['WawHelioIon']: self.ini_data['hdr_txt'][k]=None
-        self.ini_data['flags']={}
-        for k in anc_input_from_instr_team['WawHelioIon']: self.ini_data['flags'][k]=None
-
-        self.lat_grid=self.settings['solar_wind_grid']      # latitudinal grid [deg]
-        self.CR_grid=None                                   # regular grid with Carrington rotation period resolution. Bin centered on Carr halves
-        self.time_grid=None                                 # decimal year corresponding to the regular grid with bins on Carrington halves
-        self.CR_last=None
-
-        self.solar_params={}                                                # ionization parameters interplolated to the Carrington rotation halves
-        for k in anc_input_from_instr_team['WawHelioIon']: self.solar_params[k]=None
-
-        self.flags={}                                                # ionization parameters interplolated to the Carrington rotation halves
-        for k in anc_input_from_instr_team['WawHelioIon']: self.flags[k]=None
-
-        self.glows_flags=None                                        # GLOWS quality-flag bitmask per CR (initialized in generate_initial_history)
+        self.ini_data = {}
+        self.ini_data["CR_last"] = None
+        self.ini_data["label"] = list(anc_input_from_instr_team["WawHelioIon"].keys())
+        N = len(self.ini_data["label"])
+        self.ini_data["time"] = {}
+        for k in anc_input_from_instr_team["WawHelioIon"]:
+            self.ini_data["time"][k] = None
+        self.ini_data["data"] = {}
+        for k in anc_input_from_instr_team["WawHelioIon"]:
+            self.ini_data["data"][k] = None
+        self.ini_data["hdr_txt"] = {}
+        for k in anc_input_from_instr_team["WawHelioIon"]:
+            self.ini_data["hdr_txt"][k] = None
+        self.ini_data["flags"] = {}
+        for k in anc_input_from_instr_team["WawHelioIon"]:
+            self.ini_data["flags"][k] = None
+
+        self.lat_grid = self.settings["solar_wind_grid"]  # latitudinal grid [deg]
+        self.CR_grid = None  # regular grid with Carrington rotation period resolution. Bin centered on Carr halves
+        self.time_grid = None  # decimal year corresponding to the regular grid with bins on Carrington halves
+        self.CR_last = None
+
+        self.solar_params = {}  # ionization parameters interplolated to the Carrington rotation halves
+        for k in anc_input_from_instr_team["WawHelioIon"]:
+            self.solar_params[k] = None
+
+        self.flags = {}  # ionization parameters interplolated to the Carrington rotation halves
+        for k in anc_input_from_instr_team["WawHelioIon"]:
+            self.flags[k] = None
+
+        self.glows_flags = None  # GLOWS quality-flag bitmask per CR (initialized in generate_initial_history)
 
     def _check_ini_data(self):
-        '''
+        """
         Check if initial data ends at the same CR
-        '''
+        """
         # Resolution of the file should be Carrington rotation
 
         # Last CR in every file should be the same
-        eps=0.001
-        diff=np.array([self.ini_data['time'][k][-1]-self.ini_data['time']['lya'][-1] for k in self.ini_data['time']])
-        if (diff>eps).any():
-            raise Exception('Incorrect format of the input files. Last CR is not the same in all files')
-        else: return True
+        eps = 0.001
+        diff = np.array(
+            [
+                self.ini_data["time"][k][-1] - self.ini_data["time"]["lya"][-1]
+                for k in self.ini_data["time"]
+            ]
+        )
+        if (diff > eps).any():
+            raise Exception(
+                "Incorrect format of the input files. Last CR is not the same in all files"
+            )
+        else:
+            return True
 
     def find_fn_initial(self):
-        '''
+        """
         Finds text files from the previous CR that now are initial files for the current CR.
         For each type of the text file (speed, p-dens, etc.) a file with the highest CR is selected
         In the final version there should be additional option for version checking
-        '''
-        l3d_txt_fn_list={}
-        for k in self.ini_data['label']: l3d_txt_fn_list[k]=np.array(sorted(glob.glob('data_l3d_txt/imap_glows_l3d*'+k+'*.dat')))
+        """
+        l3d_txt_fn_list = {}
+        for k in self.ini_data["label"]:
+            l3d_txt_fn_list[k] = np.array(
+                sorted(glob.glob("data_l3d_txt/imap_glows_l3d*" + k + "*.dat"))
+            )
 
         # list of the L3d text files from the latest CR
-        fn_initial={}
+        fn_initial = {}
         for k in l3d_txt_fn_list:
-            idx_latest=np.array([f.split('cr_')[1].split('_')[0] for f in l3d_txt_fn_list[k]]).argmax()
-            fn_initial[k]=l3d_txt_fn_list[k][idx_latest]
+            idx_latest = np.array(
+                [f.split("cr_")[1].split("_")[0] for f in l3d_txt_fn_list[k]]
+            ).argmax()
+            fn_initial[k] = l3d_txt_fn_list[k][idx_latest]
         return fn_initial
 
-
-    def _generate_cr_solar_params(self,CR, data_l3b, data_l3c):
-        '''
+    def _generate_cr_solar_params(self, CR, data_l3b, data_l3c):
+        """
         Calculates solar parameters (plasma speed, proton density, photoionization, electron density, and uv anisotropy)
         interpolated on the center of a given CR
-        '''
+        """
 
-        cr_params={}
+        cr_params = {}
         # find indexes of the closest to the current CR and the next CR where L3bc are available
 
-        t_CR = Time(fun.jd_fm_Carrington(CR),format='jd')
+        t_CR = Time(fun.jd_fm_Carrington(CR), format="jd")
 
-        CR_list_b=[data['CR'] for data in data_l3b]
-        CR_list_c=[data['CR'] for data in data_l3b]
+        CR_list_b = [data["CR"] for data in data_l3b]
+        CR_list_c = [data["CR"] for data in data_l3b]
 
-        idx_read_b = fun.find_CR_idx(CR, CR_list_b)  # two value vector with idx of the current CR and the next
+        idx_read_b = fun.find_CR_idx(
+            CR, CR_list_b
+        )  # two value vector with idx of the current CR and the next
         idx_read_c = fun.find_CR_idx(CR, CR_list_c)
 
         # if there is only current CR, but not the next one available, then we can't interpolate
         # but we can make a temporary entry with values copied from L3bc as they are (not exactly at CR.5)
-        Nmid=int(len(data_l3b[0]['ion_rate_profile']['lat_grid'])/2)  # idx of the middle bin in phion
-        idx_current=idx_read_b[0]
+        Nmid = int(
+            len(data_l3b[0]["ion_rate_profile"]["lat_grid"]) / 2
+        )  # idx of the middle bin in phion
+        idx_current = idx_read_b[0]
         # Patched temporary record of L3d
-        if int(CR)==CR_list_b[-1]:
-            idx_read_b=[idx_read_b[0]]
-            idx_read_c=[idx_read_c[0]]
-            cr_params['uv-anis']=data_l3b[idx_current]['uv_anisotropy_factor']
-            cr_params['phion']=data_l3b[idx_current]['ion_rate_profile']['ph_rate'][Nmid]
-            cr_params['p-dens']=data_l3c[idx_current]['solar_wind_profile']['proton_density']
-            cr_params['speed']=data_l3c[idx_current]['solar_wind_profile']['plasma_speed']
-            p_dens_ecl=data_l3c[idx_current]['solar_wind_ecliptic']['proton_density']
-            a_abundance_ecl=data_l3c[idx_current]['solar_wind_ecliptic']['alpha_abundance']
-            cr_params['e-dens']=p_dens_ecl*(1+2*a_abundance_ecl)
+        if int(CR) == CR_list_b[-1]:
+            idx_read_b = [idx_read_b[0]]
+            idx_read_c = [idx_read_c[0]]
+            cr_params["uv-anis"] = data_l3b[idx_current]["uv_anisotropy_factor"]
+            cr_params["phion"] = data_l3b[idx_current]["ion_rate_profile"]["ph_rate"][
+                Nmid
+            ]
+            cr_params["p-dens"] = data_l3c[idx_current]["solar_wind_profile"][
+                "proton_density"
+            ]
+            cr_params["speed"] = data_l3c[idx_current]["solar_wind_profile"][
+                "plasma_speed"
+            ]
+            p_dens_ecl = data_l3c[idx_current]["solar_wind_ecliptic"]["proton_density"]
+            a_abundance_ecl = data_l3c[idx_current]["solar_wind_ecliptic"][
+                "alpha_abundance"
+            ]
+            cr_params["e-dens"] = p_dens_ecl * (1 + 2 * a_abundance_ecl)
         else:
-        #if idx_read_b[-1]>=len(CR_list_b): raise Exception('L3d not generated: there is not enough L3b data to interpolate')
-        #if idx_read_c[-1]>=len(CR_list_c): raise Exception('L3d not generated: there is not enough L3c data to interpolate')
-
+            # if idx_read_b[-1]>=len(CR_list_b): raise Exception('L3d not generated: there is not enough L3b data to interpolate')
+            # if idx_read_c[-1]>=len(CR_list_c): raise Exception('L3d not generated: there is not enough L3c data to interpolate')
 
             # mean time based on the light curves that were actually used during L3b processing (after removing bad-days and bad-seasons)
-            t_b=Time(np.array([data_l3b[i]['date'] for i in idx_read_b]))
-            t_c=Time(np.array([data_l3b[i]['date'] for i in idx_read_c]))
+            t_b = Time(np.array([data_l3b[i]["date"] for i in idx_read_b]))
+            t_c = Time(np.array([data_l3b[i]["date"] for i in idx_read_c]))
 
             # time nodes that will be used for interpolation. The first one is the last time stamp from the previous CR, the second and the third are next available L3bc dates
             # Usually t_b=t_c, but there could be a cases when for a current CR L3b was generated and L3c is not. Then we can still generate L3d if we have both L3b and L3c for later CR
-            t_nods_b=np.concatenate([[fun.jd_fm_Carrington(self.ini_data['CR_last'])],t_b.jd])
-            t_nods_c=np.concatenate([[fun.jd_fm_Carrington(self.ini_data['CR_last'])],t_c.jd])
+            t_nods_b = np.concatenate(
+                [[fun.jd_fm_Carrington(self.ini_data["CR_last"])], t_b.jd]
+            )
+            t_nods_c = np.concatenate(
+                [[fun.jd_fm_Carrington(self.ini_data["CR_last"])], t_c.jd]
+            )
 
             # UV-anisotropy
-            anisotropy=np.concatenate([[self.ini_data['data']['uv-anis'][-1]],np.array([data_l3b[i]['uv_anisotropy_factor'] for i in idx_read_b])])
-            cr_params['uv-anis']=np.array([np.interp(t_CR.jd,t_nods_b,anisotropy[:,i]) for i in np.arange(len(anisotropy[0]))])
+            anisotropy = np.concatenate(
+                [
+                    [self.ini_data["data"]["uv-anis"][-1]],
+                    np.array([data_l3b[i]["uv_anisotropy_factor"] for i in idx_read_b]),
+                ]
+            )
+            cr_params["uv-anis"] = np.array(
+                [
+                    np.interp(t_CR.jd, t_nods_b, anisotropy[:, i])
+                    for i in np.arange(len(anisotropy[0]))
+                ]
+            )
 
             # photoion in the ecliptic plane
-            Nmid=int(len(data_l3b[0]['ion_rate_profile']['lat_grid'])/2)
-            ph_b=np.concatenate([[self.ini_data['data']['phion'][-1]],np.array([data_l3b[i]['ion_rate_profile']['ph_rate'][Nmid] for i in idx_read_b])])
-            cr_params['phion']=np.interp(t_CR.jd,t_nods_b,ph_b)
+            Nmid = int(len(data_l3b[0]["ion_rate_profile"]["lat_grid"]) / 2)
+            ph_b = np.concatenate(
+                [
+                    [self.ini_data["data"]["phion"][-1]],
+                    np.array(
+                        [
+                            data_l3b[i]["ion_rate_profile"]["ph_rate"][Nmid]
+                            for i in idx_read_b
+                        ]
+                    ),
+                ]
+            )
+            cr_params["phion"] = np.interp(t_CR.jd, t_nods_b, ph_b)
 
             # solar wind parameters (plasma speed, proton density)
-            p_dens=np.concatenate([[self.ini_data['data']['p-dens'][-1]],np.array([data_l3c[i]['solar_wind_profile']['proton_density'] for i in idx_read_c])])
-            sw_speed=np.concatenate([[self.ini_data['data']['speed'][-1]],np.array([data_l3c[i]['solar_wind_profile']['plasma_speed'] for i in idx_read_c])])
-            cr_params['p-dens']=np.array([np.interp(t_CR.jd,t_nods_c,p_dens[:,i]) for i in np.arange(len(p_dens[0]))])
-            cr_params['speed']=np.array([np.interp(t_CR.jd,t_nods_c,sw_speed[:,i]) for i in np.arange(len(sw_speed[0]))])
+            p_dens = np.concatenate(
+                [
+                    [self.ini_data["data"]["p-dens"][-1]],
+                    np.array(
+                        [
+                            data_l3c[i]["solar_wind_profile"]["proton_density"]
+                            for i in idx_read_c
+                        ]
+                    ),
+                ]
+            )
+            sw_speed = np.concatenate(
+                [
+                    [self.ini_data["data"]["speed"][-1]],
+                    np.array(
+                        [
+                            data_l3c[i]["solar_wind_profile"]["plasma_speed"]
+                            for i in idx_read_c
+                        ]
+                    ),
+                ]
+            )
+            cr_params["p-dens"] = np.array(
+                [
+                    np.interp(t_CR.jd, t_nods_c, p_dens[:, i])
+                    for i in np.arange(len(p_dens[0]))
+                ]
+            )
+            cr_params["speed"] = np.array(
+                [
+                    np.interp(t_CR.jd, t_nods_c, sw_speed[:, i])
+                    for i in np.arange(len(sw_speed[0]))
+                ]
+            )
 
             # electron density in the ecliptic plane calculated from p-dens and alpha-abundance
-            p_dens_ecl=np.array([data_l3c[i]['solar_wind_ecliptic']['proton_density'] for i in idx_read_c])
-            a_abundance_ecl=np.array([data_l3c[i]['solar_wind_ecliptic']['alpha_abundance'] for i in idx_read_c])
-            e_dens_ecl=np.concatenate([[self.ini_data['data']['e-dens'][-1]],p_dens_ecl*(1+2*a_abundance_ecl)])
-            cr_params['e-dens']=np.interp(t_CR.jd,t_nods_c,e_dens_ecl)
+            p_dens_ecl = np.array(
+                [
+                    data_l3c[i]["solar_wind_ecliptic"]["proton_density"]
+                    for i in idx_read_c
+                ]
+            )
+            a_abundance_ecl = np.array(
+                [
+                    data_l3c[i]["solar_wind_ecliptic"]["alpha_abundance"]
+                    for i in idx_read_c
+                ]
+            )
+            e_dens_ecl = np.concatenate(
+                [
+                    [self.ini_data["data"]["e-dens"][-1]],
+                    p_dens_ecl * (1 + 2 * a_abundance_ecl),
+                ]
+            )
+            cr_params["e-dens"] = np.interp(t_CR.jd, t_nods_c, e_dens_ecl)
 
         return cr_params, idx_read_b, idx_read_c
 
-    def generate_initial_history(self,fn_list):
-        '''
+    def generate_initial_history(self, fn_list):
+        """
         Reads text files with a time history. Usually it is the previous CR file
         Function fills ini_data structure that will be passed to the next text file
-        '''
-        fill_value=self.settings['WawHelioIonGlows_fill_value']
+        """
+        fill_value = self.settings["WawHelioIonGlows_fill_value"]
 
         self._read_files(fn_list)
         self._check_ini_data()
 
         # Find file that has the longest history
-        k_max=max(self.ini_data['time'],key=lambda k: len(self.ini_data['time'][k]))
+        k_max = max(self.ini_data["time"], key=lambda k: len(self.ini_data["time"][k]))
 
         # Time grid is taken from the file with the logest history
-        self.time_grid=self.ini_data['time'][k_max]
-        self.CR_grid=[np.round(fun.carrington(t.jd),2) for t in self.time_grid]
-        self.ini_data['CR_last']=self.CR_grid[-1]
+        self.time_grid = self.ini_data["time"][k_max]
+        self.CR_grid = [np.round(fun.carrington(t.jd), 2) for t in self.time_grid]
+        self.ini_data["CR_last"] = self.CR_grid[-1]
 
         # Dimensions of the ionization parameters on common grid
-        Nt=len(self.time_grid)
-        Nl=len(self.lat_grid)
+        Nt = len(self.time_grid)
+        Nl = len(self.lat_grid)
 
         # Initialization of the ionization parameters on common grid.
         # At first all values are set at fill value defined in pipeline settings
 
         for f in fn_list:
-            if np.logical_or(np.logical_or(f=='speed',f=='p-dens'),f=='uv-anis'):
-                self.solar_params[f]=np.ones((Nt,Nl))*fill_value
-                self.flags[f]=np.ones(Nt)*fill_value
-            elif np.logical_or(np.logical_or(f=='lya',f=='phion'),f=='e-dens'):
-                self.solar_params[f]=np.ones(Nt)*fill_value
+            if np.logical_or(
+                np.logical_or(f == "speed", f == "p-dens"), f == "uv-anis"
+            ):
+                self.solar_params[f] = np.ones((Nt, Nl)) * fill_value
+                self.flags[f] = np.ones(Nt) * fill_value
+            elif np.logical_or(np.logical_or(f == "lya", f == "phion"), f == "e-dens"):
+                self.solar_params[f] = np.ones(Nt) * fill_value
 
         # Indexes where actual data starts
-        idx={}
-        for k,v in self.ini_data['time'].items(): idx[k]=np.abs(self.time_grid-v[0]).argmin()
+        idx = {}
+        for k, v in self.ini_data["time"].items():
+            idx[k] = np.abs(self.time_grid - v[0]).argmin()
 
         # solar_params structure is filled by values from the ini_data on the uniform time grid
 
-
         for f in fn_list:
-            self.solar_params[f][idx[f]:]=self.ini_data['data'][f]
-
+            self.solar_params[f][idx[f] :] = self.ini_data["data"][f]
 
         # Flags are in the 3 ionization files
-        for k in ['speed', 'uv-anis', 'p-dens']:
-            self.flags[k][idx[k]:]=self.ini_data['flags'][k]
+        for k in ["speed", "uv-anis", "p-dens"]:
+            self.flags[k][idx[k] :] = self.ini_data["flags"][k]
 
-        self.glows_flags=np.zeros(Nt, dtype=np.uint16)
+        self.glows_flags = np.zeros(Nt, dtype=np.uint16)
 
-    def generate_hdr_txt(self,fn_dict):
-        '''
+    def generate_hdr_txt(self, fn_dict):
+        """
         Generates text header for a text L3d files from prievious L3d text files
-        '''
-        hdr_dict={}
+        """
+        hdr_dict = {}
 
         for f in fn_dict:
-            hdr=self._generate_hdr_template()
-            hdr_dict[f]=self._generate_hdr_txt_glows(hdr, self.ini_data['hdr_txt'][f],fn_dict[f])
+            hdr = self._generate_hdr_template()
+            hdr_dict[f] = self._generate_hdr_txt_glows(
+                hdr, self.ini_data["hdr_txt"][f], fn_dict[f]
+            )
         return hdr_dict
 
-    def _generate_hdr_txt_glows(self,hdr_temp,hdr_ini_str,fn_out):
-        '''
+    def _generate_hdr_txt_glows(self, hdr_temp, hdr_ini_str, fn_out):
+        """
         Copy some lines from the input header to the output header structure.
         As an input there is unfied header structure
-        '''
+        """
 
-        hdr_temp[1]='filename: ' + fn_out+'\n'
-        hdr_temp[2:4]=hdr_ini_str[2:4]
-        hdr_temp[16:29]=hdr_ini_str[16:29]
+        hdr_temp[1] = "filename: " + fn_out + "\n"
+        hdr_temp[2:4] = hdr_ini_str[2:4]
+        hdr_temp[16:29] = hdr_ini_str[16:29]
 
         return hdr_temp
 
     def _generate_hdr_template(self):
-        '''
+        """
         Generates unified header for the L3d text files
-        '''
+        """
 
         # Elements in the header that are common for all files
-        N=self.settings['hdr_txt_lines_number']
+        N = self.settings["hdr_txt_lines_number"]
         # initialize uniform header structure
-        hdr_temp=N*['\n']
-        hdr_temp[0]='lines in header: '+str(N)+'\n'
-        hdr_temp[4]='ground software version: '+VERSION+'\n'
-        hdr_temp[5]='creation date: '+ Time.now().iso +'\n'
-        hdr_temp[6]='input files: ' + ', '.join(self.header['l3b_input_filename']) + ', ' + ', '.join(self.header['l3c_input_filename']) +'\n'
-        hdr_temp[7]='external dependencies: ' + self.header['external_dependeciens'] + '\n'
-        hdr_temp[8]='last CR: '+ str(self.CR_last) + '\n'
-        hdr_temp[9]='fill value: '+ str(self.settings['WawHelioIonGlows_fill_value']) + '\n'
-        hdr_temp[15]='Remarks:\n'
-        hdr_temp[29]='###############################################'
+        hdr_temp = N * ["\n"]
+        hdr_temp[0] = "lines in header: " + str(N) + "\n"
+        hdr_temp[4] = "ground software version: " + VERSION + "\n"
+        hdr_temp[5] = "creation date: " + Time.now().iso + "\n"
+        hdr_temp[6] = (
+            "input files: "
+            + ", ".join(self.header["l3b_input_filename"])
+            + ", "
+            + ", ".join(self.header["l3c_input_filename"])
+            + "\n"
+        )
+        hdr_temp[7] = (
+            "external dependencies: " + self.header["external_dependeciens"] + "\n"
+        )
+        hdr_temp[8] = "last CR: " + str(self.CR_last) + "\n"
+        hdr_temp[9] = (
+            "fill value: " + str(self.settings["WawHelioIonGlows_fill_value"]) + "\n"
+        )
+        hdr_temp[15] = "Remarks:\n"
+        hdr_temp[29] = "###############################################"
         return hdr_temp
 
-
-    def _read_ecliptic(self,fn):
-        '''
+    def _read_ecliptic(self, fn):
+        """
         Reads L3d text files that are 1D value in the ecliptic plane
-        '''
-        data=np.loadtxt(fn)
+        """
+        data = np.loadtxt(fn)
         # time, value
 
-        t=Time(data[:,0],format='decimalyear')
-
-        return t, data[:,1]
+        t = Time(data[:, 0], format="decimalyear")
 
+        return t, data[:, 1]
 
-    def _read_files(self,fn_list):
-        '''
+    def _read_files(self, fn_list):
+        """
         Reads L3d text files
-        '''
+        """
         # Read ionization files
-        N=self.settings['hdr_txt_lines_number']
+        N = self.settings["hdr_txt_lines_number"]
         for f in fn_list:
-            self.ini_data['hdr_txt'][f]=fun.read_hdr_txt(fn_list[f],N)
-            if np.logical_or(np.logical_or(f=='speed',f=='p-dens'),f=='uv-anis'):
-                self.ini_data['time'][f], self.ini_data['data'][f], self.ini_data['flags'][f]=self._read_profile(fn_list[f])
-            elif np.logical_or(np.logical_or(f=='lya',f=='phion'),f=='e-dens'):
-                self.ini_data['time'][f], self.ini_data['data'][f]=self._read_ecliptic(fn_list[f])
-
-    def _read_profile(self,fn):
-        '''
+            self.ini_data["hdr_txt"][f] = fun.read_hdr_txt(fn_list[f], N)
+            if np.logical_or(
+                np.logical_or(f == "speed", f == "p-dens"), f == "uv-anis"
+            ):
+                (
+                    self.ini_data["time"][f],
+                    self.ini_data["data"][f],
+                    self.ini_data["flags"][f],
+                ) = self._read_profile(fn_list[f])
+            elif np.logical_or(np.logical_or(f == "lya", f == "phion"), f == "e-dens"):
+                self.ini_data["time"][f], self.ini_data["data"][f] = (
+                    self._read_ecliptic(fn_list[f])
+                )
+
+    def _read_profile(self, fn):
+        """
         Reads L3d text files that are 2D profiles (on the time, latitude grid)
-        '''
-        data=np.loadtxt(fn)
+        """
+        data = np.loadtxt(fn)
         # time, value(-90), value(-80), ... value(0), ..., value(90), CR, flag
 
-        t=Time(data[:,0],format='decimalyear')
-        flag=data[:,-1]
-        param=data[:,1:-2]
+        t = Time(data[:, 0], format="decimalyear")
+        flag = data[:, -1]
+        param = data[:, 1:-2]
 
-        return t, param,flag
+        return t, param, flag
 
-
-    def save_to_file(self,fn):
-        '''
+    def save_to_file(self, fn):
+        """
         Solar params history file is an official GLOWS L3d data product
         Temporarly it is in json format, but in production code it should be in CDF
 
@@ -378,182 +496,231 @@ def save_to_file(self,fn):
         ------------
         fn: str
             name of the output file
-        '''
-
+        """
 
-        output={}
-        output['header']=self.header
-        output['lat_grid']=self.lat_grid
-        output['cr_grid']=self.CR_grid.tolist()
-        output['time_grid']=[t.iso for t in self.time_grid]
-        output['solar_params']={}
-        output['flags']={}
-        for k in self.ini_data['label']: output['solar_params'][k]=self.solar_params[k].tolist()
-        for k in ['speed','uv-anis','p-dens']: output['flags'][k]=self.flags[k].tolist()
-        output['glows_flags']=[int(v) for v in self.glows_flags]
+        output = {}
+        output["header"] = self.header
+        output["lat_grid"] = self.lat_grid
+        output["cr_grid"] = self.CR_grid.tolist()
+        output["time_grid"] = [t.iso for t in self.time_grid]
+        output["solar_params"] = {}
+        output["flags"] = {}
+        for k in self.ini_data["label"]:
+            output["solar_params"][k] = self.solar_params[k].tolist()
+        for k in ["speed", "uv-anis", "p-dens"]:
+            output["flags"][k] = self.flags[k].tolist()
+        output["glows_flags"] = [int(v) for v in self.glows_flags]
 
         json_content = json.dumps(output, indent=3)
-        output_fp = open(fn, 'w')
+        output_fp = open(fn, "w")
         print(json_content, file=output_fp)
         output_fp.close()
 
-    def save_to_txt(self,fn_out,hdr):
-        '''
+    def save_to_txt(self, fn_out, hdr):
+        """
         Write to the text file
-        '''
+        """
 
         for f in fn_out:
-            if np.logical_or(np.logical_or(f=='speed',f=='p-dens'),f=='uv-anis'):
-                self._save_to_txt_profile(fn_out[f],f,hdr[f])
-            elif np.logical_or(np.logical_or(f=='lya',f=='phion'),f=='e-dens'):
-                self._save_to_txt_ecliptic(fn_out[f],f,hdr[f])
-
-    def _save_to_txt_ecliptic(self,fn,k,hdr):
-        '''
+            if np.logical_or(
+                np.logical_or(f == "speed", f == "p-dens"), f == "uv-anis"
+            ):
+                self._save_to_txt_profile(fn_out[f], f, hdr[f])
+            elif np.logical_or(np.logical_or(f == "lya", f == "phion"), f == "e-dens"):
+                self._save_to_txt_ecliptic(fn_out[f], f, hdr[f])
+
+    def _save_to_txt_ecliptic(self, fn, k, hdr):
+        """
         Write to the text file parameters in the ecliptic plane (1D)
-        '''
-        Ncol= 3   # number of columns in eliptic-type files (time, value, CR)
-        Nrow=len(self.solar_params[k])
-        output = np.zeros((Nrow,Ncol))
+        """
+        Ncol = 3  # number of columns in eliptic-type files (time, value, CR)
+        Nrow = len(self.solar_params[k])
+        output = np.zeros((Nrow, Ncol))
 
-        output[:,0]=np.array([t.decimalyear for t in self.time_grid])
-        output[:,1]=self.solar_params[k]
-        output[:,-1]=np.array(self.CR_grid)
+        output[:, 0] = np.array([t.decimalyear for t in self.time_grid])
+        output[:, 1] = self.solar_params[k]
+        output[:, -1] = np.array(self.CR_grid)
 
-        np.savetxt(fn,output,fmt='%4.12f %.15e %4.2f',header=('').join(hdr))
+        np.savetxt(fn, output, fmt="%4.12f %.15e %4.2f", header=("").join(hdr))
         return 0
 
-    def _save_to_txt_profile(self,fn,k,hdr):
-        '''
+    def _save_to_txt_profile(self, fn, k, hdr):
+        """
         Write to the text file parameters that are profiles on the latitudinal grid
-        '''
-        Ncol=len(self.settings['solar_wind_grid'])+3   # number of columns in profile-type files (time, grid bins, CR)
-        Nrow=len(self.solar_params[k])
-        output = np.zeros((Nrow,Ncol))
-
-        output[:,0]=np.array([t.decimalyear for t in self.time_grid])
-        output[:,1:Ncol-2]=self.solar_params[k]
-        output[:,-2]=np.array(self.CR_grid)
-        output[:,-1]=np.array(self.flags[k])
-
-        np.savetxt(fn,output,fmt='%4.12f '+ (Ncol-3)*'%.15e ' + '%4.2f' + '%7d',header=('').join(hdr))
+        """
+        Ncol = (
+            len(self.settings["solar_wind_grid"]) + 3
+        )  # number of columns in profile-type files (time, grid bins, CR)
+        Nrow = len(self.solar_params[k])
+        output = np.zeros((Nrow, Ncol))
+
+        output[:, 0] = np.array([t.decimalyear for t in self.time_grid])
+        output[:, 1 : Ncol - 2] = self.solar_params[k]
+        output[:, -2] = np.array(self.CR_grid)
+        output[:, -1] = np.array(self.flags[k])
+
+        np.savetxt(
+            fn,
+            output,
+            fmt="%4.12f " + (Ncol - 3) * "%.15e " + "%4.2f" + "%7d",
+            header=("").join(hdr),
+        )
         return 0
 
-
-    def _update_l3bc_data(self,data_l3b,data_l3c,CR):
-        '''
+    def _update_l3bc_data(self, data_l3b, data_l3c, CR):
+        """
         Generates new value for given CR of the solar parameters.
-        '''
-        cr_params, idx_read_b, idx_read_c = self._generate_cr_solar_params(CR, data_l3b, data_l3c)
+        """
+        cr_params, idx_read_b, idx_read_c = self._generate_cr_solar_params(
+            CR, data_l3b, data_l3c
+        )
 
-        self.header['l3b_input_filename']=[data_l3b[i]['header']['filename'] for i in idx_read_b]
-        self.header['l3c_input_filename']=[data_l3c[i]['header']['filename'] for i in idx_read_c]
+        self.header["l3b_input_filename"] = [
+            data_l3b[i]["header"]["filename"] for i in idx_read_b
+        ]
+        self.header["l3c_input_filename"] = [
+            data_l3c[i]["header"]["filename"] for i in idx_read_c
+        ]
 
         # add a row with values for current CR
-        for k in self.ini_data['label']:
-
-            if np.logical_or(np.logical_or(k=='speed',k=='p-dens'),k=='uv-anis'):
-                self.solar_params[k] = np.r_[self.solar_params[k],[cr_params[k]]]
-            elif np.logical_or(k=='phion',k=='e-dens'):
+        for k in self.ini_data["label"]:
+            if np.logical_or(
+                np.logical_or(k == "speed", k == "p-dens"), k == "uv-anis"
+            ):
+                self.solar_params[k] = np.r_[self.solar_params[k], [cr_params[k]]]
+            elif np.logical_or(k == "phion", k == "e-dens"):
                 self.solar_params[k] = np.append(self.solar_params[k], cr_params[k])
 
         # define source flags for p-dens and speed that are result of the GLOWS analysis
         # we have want to flag those values that are based on generated L3b and L3c, as well as those that are interpolated
 
-        if int(data_l3b[idx_read_b[0]]['CR'])==int(CR):
+        if int(data_l3b[idx_read_b[0]]["CR"]) == int(CR):
             # there is L3b for a current CR
 
-            self.flags['uv-anis']=np.append(self.flags['uv-anis'],data_l3b[idx_read_b[0]]['uv_anisotropy_flag'])
-            self.glows_flags=np.append(self.glows_flags,np.uint16(data_l3b[idx_read_b[0]]['glows_flags']))
+            self.flags["uv-anis"] = np.append(
+                self.flags["uv-anis"], data_l3b[idx_read_b[0]]["uv_anisotropy_flag"]
+            )
+            self.glows_flags = np.append(
+                self.glows_flags, np.uint16(data_l3b[idx_read_b[0]]["glows_flags"])
+            )
         else:
             # there is no L3b for a current CR
-            self.flags['uv-anis']=np.append(self.flags['uv-anis'],self.settings['l3d_source_flags']['uv_anis_interpolated'])
-            self.glows_flags=np.append(self.glows_flags,np.uint16(0))
+            self.flags["uv-anis"] = np.append(
+                self.flags["uv-anis"],
+                self.settings["l3d_source_flags"]["uv_anis_interpolated"],
+            )
+            self.glows_flags = np.append(self.glows_flags, np.uint16(0))
 
-        if int(data_l3c[idx_read_c[0]]['CR'])==int(CR):
+        if int(data_l3c[idx_read_c[0]]["CR"]) == int(CR):
             # there is L3c for a current CR
-            self.flags['speed']=np.append(self.flags['speed'],self.settings['l3d_source_flags']['speed_GLOWS'])
-            self.flags['p-dens']=np.append(self.flags['p-dens'],self.settings['l3d_source_flags']['dens_from_speed'])
+            self.flags["speed"] = np.append(
+                self.flags["speed"], self.settings["l3d_source_flags"]["speed_GLOWS"]
+            )
+            self.flags["p-dens"] = np.append(
+                self.flags["p-dens"],
+                self.settings["l3d_source_flags"]["dens_from_speed"],
+            )
         else:
             # there is no L3c for a current CR
-            self.flags['speed']=np.append(self.flags['speed'],self.settings['l3d_source_flags']['speed_GLOWS_interpolated'])
-            self.flags['p-dens']=np.append(self.flags['p-dens'],self.settings['l3d_source_flags']['dens_from_speed_interpolated'])
+            self.flags["speed"] = np.append(
+                self.flags["speed"],
+                self.settings["l3d_source_flags"]["speed_GLOWS_interpolated"],
+            )
+            self.flags["p-dens"] = np.append(
+                self.flags["p-dens"],
+                self.settings["l3d_source_flags"]["dens_from_speed_interpolated"],
+            )
 
-
-    def _freeze_l3bc_data(self,data_l3b,data_l3c):
+    def _freeze_l3bc_data(self, data_l3b, data_l3c):
 
         # add a row with values copied from the last CR generated from the GLOWS data
-        for k in self.ini_data['label']:
-
-            if np.logical_or(np.logical_or(k=='speed',k=='p-dens'),k=='uv-anis'):
-                self.solar_params[k] = np.r_[self.solar_params[k],[self.solar_params[k][-1]]]
-
-            elif np.logical_or(k=='phion',k=='e-dens'):
-                self.solar_params[k] = np.append(self.solar_params[k], self.solar_params[k][-1])
+        for k in self.ini_data["label"]:
+            if np.logical_or(
+                np.logical_or(k == "speed", k == "p-dens"), k == "uv-anis"
+            ):
+                self.solar_params[k] = np.r_[
+                    self.solar_params[k], [self.solar_params[k][-1]]
+                ]
+
+            elif np.logical_or(k == "phion", k == "e-dens"):
+                self.solar_params[k] = np.append(
+                    self.solar_params[k], self.solar_params[k][-1]
+                )
         # add flags
-        self.flags['uv-anis']=np.append(self.flags['uv-anis'],self.settings['l3d_source_flags']['freezed'])
-        self.flags['speed']=np.append(self.flags['speed'],self.settings['l3d_source_flags']['freezed'])
-        self.flags['p-dens']=np.append(self.flags['p-dens'],self.settings['l3d_source_flags']['freezed'])
-        self.glows_flags=np.append(self.glows_flags, self.glows_flags[-1] | GlowsL3Flags.PERSISTED_LAST_POINT)
-
+        self.flags["uv-anis"] = np.append(
+            self.flags["uv-anis"], self.settings["l3d_source_flags"]["freezed"]
+        )
+        self.flags["speed"] = np.append(
+            self.flags["speed"], self.settings["l3d_source_flags"]["freezed"]
+        )
+        self.flags["p-dens"] = np.append(
+            self.flags["p-dens"], self.settings["l3d_source_flags"]["freezed"]
+        )
+        self.glows_flags = np.append(
+            self.glows_flags, self.glows_flags[-1] | GlowsL3Flags.PERSISTED_LAST_POINT
+        )
 
     def _freeze_lya_data(self):
 
-        self.solar_params['lya'] = np.append(self.solar_params['lya'], self.solar_params['lya'][-1])
+        self.solar_params["lya"] = np.append(
+            self.solar_params["lya"], self.solar_params["lya"][-1]
+        )
 
-    def _update_lya_data(self,ext_dependencies,CR):
-        '''
+    def _update_lya_data(self, ext_dependencies, CR):
+        """
         Generates next value of the Lyman-alpha irradiance based on external daily measurements.
-        '''
+        """
 
-        data_lya = fun.read_lya_data(ext_dependencies['lya_raw_data'])
+        data_lya = fun.read_lya_data(ext_dependencies["lya_raw_data"])
         lya_CR = fun.generate_cr_lya(CR, data_lya)
-        self.solar_params['lya'] = np.append(self.solar_params['lya'], lya_CR)
+        self.solar_params["lya"] = np.append(self.solar_params["lya"], lya_CR)
 
-    def update_solar_params_hist(self,ext_dependencies,data_l3b,data_l3c,CR_current):
-        '''
+    def update_solar_params_hist(
+        self, ext_dependencies, data_l3b, data_l3c, CR_current
+    ):
+        """
         Updates structure by calculating parameters values from last CR in the initial data until currently processed CR
-        '''
-        CR_l3b_last=np.round(fun.carrington(Time(data_l3b[-1]['date']).jd),2)
+        """
+        CR_l3b_last = np.round(fun.carrington(Time(data_l3b[-1]["date"]).jd), 2)
 
-        #for CR in np.arange(self.ini_data['CR_last']+1,CR_current+1.5):
-        CR=self.ini_data['CR_last'] + 1
+        # for CR in np.arange(self.ini_data['CR_last']+1,CR_current+1.5):
+        CR = self.ini_data["CR_last"] + 1
 
-        while(int(CR)<CR_l3b_last):
+        while int(CR) < CR_l3b_last:
             # update CR grid
-            if int(CR)==int(CR_l3b_last): self.CR_grid = np.append(self.CR_grid, CR_l3b_last)
-            else: self.CR_grid = np.append(self.CR_grid, CR)
+            if int(CR) == int(CR_l3b_last):
+                self.CR_grid = np.append(self.CR_grid, CR_l3b_last)
+            else:
+                self.CR_grid = np.append(self.CR_grid, CR)
 
             # update time grid
-            t_CR = Time(fun.jd_fm_Carrington(CR),format='jd')
+            t_CR = Time(fun.jd_fm_Carrington(CR), format="jd")
             self.time_grid = np.append(self.time_grid, t_CR)
 
             # update Lyman-alpha data
-            self._update_lya_data(ext_dependencies,CR)
+            self._update_lya_data(ext_dependencies, CR)
 
             # upade rest of the parameters and flags
-            self._update_l3bc_data(data_l3b,data_l3c,CR)
+            self._update_l3bc_data(data_l3b, data_l3c, CR)
 
             # update last CR
-            self.CR_last=CR
-            CR=CR+1
+            self.CR_last = CR
+            CR = CR + 1
 
-        while(int(CR)<=CR_current):
+        while int(CR) <= CR_current:
             # update CR grid
             self.CR_grid = np.append(self.CR_grid, CR)
 
             # update time grid
-            t_CR = Time(fun.jd_fm_Carrington(CR),format='jd')
+            t_CR = Time(fun.jd_fm_Carrington(CR), format="jd")
             self.time_grid = np.append(self.time_grid, t_CR)
 
             # copy l3d values from the last GLOWS CR and flag them
-            self._freeze_l3bc_data(data_l3b,data_l3c)
+            self._freeze_l3bc_data(data_l3b, data_l3c)
 
             # copy Lyman-alpha data from the last CR
             self._freeze_lya_data()
 
             # update last CR
-            self.CR_last=CR
-            CR=CR+1
-
-
+            self.CR_last = CR
+            CR = CR + 1
diff --git a/imap_l3_processing/glows/l3d/utils.py b/imap_l3_processing/glows/l3d/utils.py
index 80a13812d..d0d72fd2f 100644
--- a/imap_l3_processing/glows/l3d/utils.py
+++ b/imap_l3_processing/glows/l3d/utils.py
@@ -2,7 +2,6 @@
 import os
 from datetime import datetime
 from pathlib import Path
-from typing import Optional
 
 import imap_data_access
 import numpy as np
@@ -10,91 +9,104 @@
 from spacepy.pycdf import CDF
 
 import imap_l3_processing
+from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
 from imap_l3_processing.glows.l3bc.utils import get_midpoint_of_cr
 from imap_l3_processing.glows.l3d.models import GlowsL3DSolarParamsHistory
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
 
-PATH_TO_L3D_TOOLKIT = Path(imap_l3_processing.__file__).parent / 'glows' / 'l3d' / 'science'
+PATH_TO_L3D_TOOLKIT = (
+    Path(imap_l3_processing.__file__).parent / "glows" / "l3d" / "science"
+)
 
 
 def create_glows_l3c_json_file_from_cdf(cdf_file_path: Path):
     with CDF(str(cdf_file_path)) as cdf:
-        cr_number = cdf['cr'][...][0]
+        cr_number = cdf["cr"][...][0]
 
         json_dict = {
-            'header': {
-                'filename': cdf_file_path.name
+            "header": {"filename": cdf_file_path.name},
+            "solar_wind_profile": {
+                "proton_density": read_numeric_variable(cdf["proton_density_profile"])[
+                    0
+                ].tolist(),
+                "plasma_speed": read_numeric_variable(cdf["plasma_speed_profile"])[
+                    0
+                ].tolist(),
             },
-            'solar_wind_profile': {
-                'proton_density': read_numeric_variable(cdf["proton_density_profile"])[0].tolist(),
-                'plasma_speed': read_numeric_variable(cdf["plasma_speed_profile"])[0].tolist(),
+            "solar_wind_ecliptic": {
+                "proton_density": float(
+                    read_numeric_variable(cdf["proton_density_ecliptic"])[0]
+                ),
+                "alpha_abundance": float(
+                    read_numeric_variable(cdf["alpha_abundance_ecliptic"])[0]
+                ),
             },
-            'solar_wind_ecliptic': {
-                'proton_density': float(read_numeric_variable(cdf["proton_density_ecliptic"])[0]),
-                'alpha_abundance': float(read_numeric_variable(cdf["alpha_abundance_ecliptic"])[0]),
-            },
-            'CR': float(cr_number)
+            "CR": float(cr_number),
         }
-        version = cdf_file_path.name.split('_')[-1].split('.')[0]
-        json_file_name = f'imap_glows_l3c_cr_{cr_number}_{version}.json'
+        version = cdf_file_path.name.split("_")[-1].split(".")[0]
+        json_file_name = f"imap_glows_l3c_cr_{cr_number}_{version}.json"
 
-        os.makedirs(PATH_TO_L3D_TOOLKIT / 'data_l3c', exist_ok=True)
+        os.makedirs(PATH_TO_L3D_TOOLKIT / "data_l3c", exist_ok=True)
 
-        with open(PATH_TO_L3D_TOOLKIT / 'data_l3c' / json_file_name, 'w') as fp:
+        with open(PATH_TO_L3D_TOOLKIT / "data_l3c" / json_file_name, "w") as fp:
             json.dump(json_dict, fp)
 
 
 def create_glows_l3b_json_file_from_cdf(cdf_file_path: Path):
     with CDF(str(cdf_file_path)) as cdf:
-        cr_number = int(cdf['cr'][...][0])
+        cr_number = int(cdf["cr"][...][0])
         json_dict = {
-            'header': {
-                'filename': cdf_file_path.name,
+            "header": {
+                "filename": cdf_file_path.name,
             },
-            'date': cdf['mean_time'][0].isoformat(),
-            'CR': cr_number,
-            'uv_anisotropy_factor': read_numeric_variable(cdf['uv_anisotropy_factor'])[0].tolist(),
-            'ion_rate_profile': {
-                'lat_grid': read_numeric_variable(cdf['lat_grid']).tolist(),
-                'ph_rate': read_numeric_variable(cdf['ph_rate'])[0].tolist()
+            "date": cdf["mean_time"][0].isoformat(),
+            "CR": cr_number,
+            "uv_anisotropy_factor": read_numeric_variable(cdf["uv_anisotropy_factor"])[
+                0
+            ].tolist(),
+            "ion_rate_profile": {
+                "lat_grid": read_numeric_variable(cdf["lat_grid"]).tolist(),
+                "ph_rate": read_numeric_variable(cdf["ph_rate"])[0].tolist(),
             },
-            'uv_anisotropy_flag': cdf['uv_anisotropy_flag'][0],
-            'glows_flags': int(cdf['glows_flags'][0]),
+            "uv_anisotropy_flag": cdf["uv_anisotropy_flag"][0],
+            "glows_flags": int(cdf["glows_flags"][0]),
         }
 
-        version = cdf_file_path.name.split('_')[-1].split('.')[0]
-        json_file_name = f'imap_glows_l3b_cr_{cr_number}_{version}.json'
+        version = cdf_file_path.name.split("_")[-1].split(".")[0]
+        json_file_name = f"imap_glows_l3b_cr_{cr_number}_{version}.json"
 
-        os.makedirs(PATH_TO_L3D_TOOLKIT / 'data_l3b', exist_ok=True)
-        with open(PATH_TO_L3D_TOOLKIT / 'data_l3b' / json_file_name, 'w') as fp:
+        os.makedirs(PATH_TO_L3D_TOOLKIT / "data_l3b", exist_ok=True)
+        with open(PATH_TO_L3D_TOOLKIT / "data_l3b" / json_file_name, "w") as fp:
             json.dump(json_dict, fp)
 
 
-def convert_json_to_l3d_data_product(json_file_path: Path, input_metadata: InputMetadata,
-                                     parent_file_names: list[str]) -> GlowsL3DSolarParamsHistory:
-    with open(json_file_path, 'r') as json_file:
+def convert_json_to_l3d_data_product(
+    json_file_path: Path, input_metadata: InputMetadata, parent_file_names: list[str]
+) -> GlowsL3DSolarParamsHistory:
+    with open(json_file_path, "r") as json_file:
         l3d_json_dict = json.load(json_file)
 
-    new_start = datetime.fromisoformat(l3d_json_dict['time_grid'][0])
+    new_start = datetime.fromisoformat(l3d_json_dict["time_grid"][0])
     input_metadata.start_date = new_start.date()
 
     return GlowsL3DSolarParamsHistory(
         input_metadata=input_metadata,
         parent_file_names=parent_file_names,
-        latitude=l3d_json_dict['lat_grid'],
-        cr=l3d_json_dict['cr_grid'],
-        epoch=np.array([datetime.fromisoformat(time) for time in l3d_json_dict['time_grid']]),
-        plasma_speed=l3d_json_dict['solar_params']['speed'],
-        proton_density=l3d_json_dict['solar_params']['p-dens'],
-        ultraviolet_anisotropy=l3d_json_dict['solar_params']['uv-anis'],
-        phion=l3d_json_dict['solar_params']['phion'],
-        lyman_alpha=l3d_json_dict['solar_params']['lya'],
-        electron_density=l3d_json_dict['solar_params']['e-dens'],
-        plasma_speed_flag=l3d_json_dict['flags']['speed'],
-        uv_anisotropy_flag=l3d_json_dict['flags']['uv-anis'],
-        proton_density_flag=l3d_json_dict['flags']['p-dens'],
-        glows_flags=np.array(l3d_json_dict['glows_flags'], dtype=np.uint16),
+        latitude=l3d_json_dict["lat_grid"],
+        cr=l3d_json_dict["cr_grid"],
+        epoch=np.array(
+            [datetime.fromisoformat(time) for time in l3d_json_dict["time_grid"]]
+        ),
+        plasma_speed=l3d_json_dict["solar_params"]["speed"],
+        proton_density=l3d_json_dict["solar_params"]["p-dens"],
+        ultraviolet_anisotropy=l3d_json_dict["solar_params"]["uv-anis"],
+        phion=l3d_json_dict["solar_params"]["phion"],
+        lyman_alpha=l3d_json_dict["solar_params"]["lya"],
+        electron_density=l3d_json_dict["solar_params"]["e-dens"],
+        plasma_speed_flag=l3d_json_dict["flags"]["speed"],
+        uv_anisotropy_flag=l3d_json_dict["flags"]["uv-anis"],
+        proton_density_flag=l3d_json_dict["flags"]["p-dens"],
+        glows_flags=np.array(l3d_json_dict["glows_flags"], dtype=np.uint16),
     )
 
 
@@ -107,47 +119,65 @@ def get_parent_file_names_from_l3d_json(l3d_folder: Path) -> list[str]:
 
     with open(l3d_folder / l3d_file_paths[0]) as l3d:
         l3d_data = json.load(l3d)
-        ancillary_files = [Path(file).name for file in l3d_data['header']['ancillary_data_files']]
-        external_dependencies = Path(l3d_data['header']['external_dependeciens']).name
+        ancillary_files = [
+            Path(file).name for file in l3d_data["header"]["ancillary_data_files"]
+        ]
+        external_dependencies = Path(l3d_data["header"]["external_dependeciens"]).name
         parent_file_names.update(ancillary_files)
         parent_file_names.add(external_dependencies)
 
     for file_path in l3d_file_paths:
-        with open(l3d_folder / file_path, 'r') as l3d:
+        with open(l3d_folder / file_path, "r") as l3d:
             l3d_data = json.load(l3d)
-            l3b_parents = [Path(file).name for file in l3d_data['header']['l3b_input_filename']]
-            l3c_parents = [Path(file).name for file in l3d_data['header']['l3c_input_filename']]
+            l3b_parents = [
+                Path(file).name for file in l3d_data["header"]["l3b_input_filename"]
+            ]
+            l3c_parents = [
+                Path(file).name for file in l3d_data["header"]["l3c_input_filename"]
+            ]
             parent_file_names.update(l3b_parents)
             parent_file_names.update(l3c_parents)
 
     return list(parent_file_names)
 
+
 def get_most_recently_uploaded_ancillary(query_result: list[dict]) -> dict:
-    query_result = max(query_result, key=lambda x: x['ingestion_date'], default=None)
+    query_result = max(query_result, key=lambda x: x["ingestion_date"], default=None)
     return query_result
 
+
 def rename_l3d_text_outputs(paths: list[Path], version: str) -> list[Path]:
     out_paths = []
     for path in paths:
-        filename_without_extension, extension = path.name.split('.')
-        original_name_components = filename_without_extension.split('_')
+        filename_without_extension, extension = path.name.split(".")
+        original_name_components = filename_without_extension.split("_")
         descriptor = original_name_components[3]
         start_date_with_cr = original_name_components[4]
-        start_date, cr = start_date_with_cr.split('-cr')
+        start_date, cr = start_date_with_cr.split("-cr")
         midpoint_of_cr = get_midpoint_of_cr(int(cr))
-        new_path = path.parent / f"imap_glows_{descriptor}_{start_date}_{midpoint_of_cr.strftime('%Y%m%d')}_{version}.{extension}"
+        new_path = (
+            path.parent
+            / f"imap_glows_{descriptor}_{start_date}_{midpoint_of_cr.strftime('%Y%m%d')}_{version}.{extension}"
+        )
         os.rename(path, new_path)
         out_paths.append(new_path)
     return out_paths
 
 
-def query_for_most_recent_l3d(descriptor: str) -> Optional[dict]:
-    query_result = imap_data_access.query(instrument="glows", data_level="l3d", descriptor=descriptor)
+def query_for_most_recent_l3d(descriptor: str) -> dict | None:
+    query_result = imap_data_access.query(
+        instrument="glows", data_level="l3d", descriptor=descriptor
+    )
     sorted_query_result = sorted(
         query_result,
         key=lambda qr: (
             qr["cr"],
-            (qr["minor_version"] if "minor_version" in qr else Version.from_version(qr["version"]).minor)
+            (
+                qr["minor_version"]
+                if "minor_version" in qr
+                else Version.from_version(qr["version"]).minor
+            ),
         ),
-        reverse=True)
+        reverse=True,
+    )
     return next(iter(sorted_query_result), None)
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_call_arguments.py b/imap_l3_processing/glows/l3e/glows_l3e_call_arguments.py
index 69254d2f8..3d108a828 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_call_arguments.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_call_arguments.py
@@ -24,6 +24,7 @@ def to_argument_list(self):
             f"{self.spin_axis_latitude:.4f}",
         ]
 
+
 @dataclass
 class GlowsL3eCallArguments:
     formatted_date: str
@@ -36,5 +37,5 @@ def to_argument_list(self):
             self.formatted_date,
             self.decimal_date,
             *self.spacecraft_info.to_argument_list(),
-            f"{self.elongation:.3f}"
-        ]
\ No newline at end of file
+            f"{self.elongation:.3f}",
+        ]
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_dependencies.py b/imap_l3_processing/glows/l3e/glows_l3e_dependencies.py
index 809e3cb4a..730244a3b 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_dependencies.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_dependencies.py
@@ -1,14 +1,14 @@
 import json
 import logging
 import shutil
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from datetime import datetime
 from pathlib import Path
 
 import imap_data_access
 from imap_data_access.processing_input import ProcessingInputCollection, RepointInput
 
-from imap_l3_processing.utils import furnish_spice_metakernel, SpiceKernelTypes
+from imap_l3_processing.utils import SpiceKernelTypes, furnish_spice_metakernel
 
 logger = logging.getLogger(__name__)
 
@@ -23,6 +23,7 @@
     SpiceKernelTypes.SpacecraftClock,
 ]
 
+
 @dataclass
 class GlowsL3EDependencies:
     energy_grid_lo: Path
@@ -42,42 +43,76 @@ class GlowsL3EDependencies:
 
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
-        lya_series_dependency = dependencies.get_file_paths(source='glows', descriptor='lya')
-        solar_uv_anisotropy_dependency = dependencies.get_file_paths(source='glows', descriptor='uv-anis')
-        speed_3d_dependency = dependencies.get_file_paths(source='glows', descriptor='speed')
-        density_3d_dependency = dependencies.get_file_paths(source='glows', descriptor='p-dens')
-        phion_hydrogen_dependency = dependencies.get_file_paths(source='glows', descriptor='phion')
-        sw_eqtr_electrons_dependency = dependencies.get_file_paths(source='glows', descriptor='e-dens')
+        lya_series_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="lya"
+        )
+        solar_uv_anisotropy_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="uv-anis"
+        )
+        speed_3d_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="speed"
+        )
+        density_3d_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="p-dens"
+        )
+        phion_hydrogen_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="phion"
+        )
+        sw_eqtr_electrons_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="e-dens"
+        )
 
-        pipeline_settings_dependency = dependencies.get_file_paths(source='glows', descriptor='pipeline-settings-l3bcde')
+        pipeline_settings_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="pipeline-settings-l3bcde"
+        )
 
-        tess_xyz_dependency = dependencies.get_file_paths(source='glows', descriptor='tess-xyz-8')
-        tess_ang_dependency = dependencies.get_file_paths(source='glows', descriptor='tess-ang-16')
+        tess_xyz_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="tess-xyz-8"
+        )
+        tess_ang_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="tess-ang-16"
+        )
 
-        energy_grid_lo_dependency = dependencies.get_file_paths(source='glows', descriptor='energy-grid-lo')
-        energy_grid_hi_dependency = dependencies.get_file_paths(source='glows', descriptor='energy-grid-hi')
-        energy_grid_ultra_dependency = dependencies.get_file_paths(source='glows', descriptor='energy-grid-ultra')
+        energy_grid_lo_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="energy-grid-lo"
+        )
+        energy_grid_hi_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="energy-grid-hi"
+        )
+        energy_grid_ultra_dependency = dependencies.get_file_paths(
+            source="glows", descriptor="energy-grid-ultra"
+        )
 
         lya_series_path = imap_data_access.download(lya_series_dependency[0])
-        solar_uv_anisotropy_path = imap_data_access.download(solar_uv_anisotropy_dependency[0])
+        solar_uv_anisotropy_path = imap_data_access.download(
+            solar_uv_anisotropy_dependency[0]
+        )
         speed_3d_path = imap_data_access.download(speed_3d_dependency[0])
         density_3d_path = imap_data_access.download(density_3d_dependency[0])
         phion_hydrogen_path = imap_data_access.download(phion_hydrogen_dependency[0])
-        sw_eqtr_electrons_path = imap_data_access.download(sw_eqtr_electrons_dependency[0])
+        sw_eqtr_electrons_path = imap_data_access.download(
+            sw_eqtr_electrons_dependency[0]
+        )
 
-        pipeline_settings_path = imap_data_access.download(pipeline_settings_dependency[0])
+        pipeline_settings_path = imap_data_access.download(
+            pipeline_settings_dependency[0]
+        )
 
         tess_xyz_path = imap_data_access.download(tess_xyz_dependency[0])
         tess_ang_path = imap_data_access.download(tess_ang_dependency[0])
 
         energy_grid_lo_path = imap_data_access.download(energy_grid_lo_dependency[0])
         energy_grid_hi_path = imap_data_access.download(energy_grid_hi_dependency[0])
-        energy_grid_ultra_path = imap_data_access.download(energy_grid_ultra_dependency[0])
+        energy_grid_ultra_path = imap_data_access.download(
+            energy_grid_ultra_dependency[0]
+        )
 
         with open(pipeline_settings_path) as f:
             pipeline_settings = json.load(f)
 
-        repoint_file_dependency = dependencies.get_file_paths(data_type=RepointInput.data_type)
+        repoint_file_dependency = dependencies.get_file_paths(
+            data_type=RepointInput.data_type
+        )
         repoint_file_path = imap_data_access.download(repoint_file_dependency[0])
 
         return cls(
@@ -99,26 +134,60 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
 
     @staticmethod
     def collect_spice_dependencies(start_date: datetime, end_date: datetime):
-        logger.info(f"Querying for SPICE data over the range: {start_date} to {end_date}")
+        logger.info(
+            f"Querying for SPICE data over the range: {start_date} to {end_date}"
+        )
 
         kernel_types_with_predicted = GLOWS_L3E_REQUIRED_SPICE_KERNELS
-        kernel_types_without_predicted = [kernel for kernel in GLOWS_L3E_REQUIRED_SPICE_KERNELS if kernel != SpiceKernelTypes.EphemerisPredicted]
+        kernel_types_without_predicted = [
+            kernel
+            for kernel in GLOWS_L3E_REQUIRED_SPICE_KERNELS
+            if kernel != SpiceKernelTypes.EphemerisPredicted
+        ]
 
-        return (furnish_spice_metakernel(start_date=start_date, end_date=end_date, kernel_types=kernel_types_with_predicted, metakernel_file_name="metakernel_with_predict_ephem.txt"),
-            furnish_spice_metakernel(start_date=start_date, end_date=end_date, kernel_types=kernel_types_without_predicted, metakernel_file_name="metakernel_without_predict_ephem.txt"))
+        return (
+            furnish_spice_metakernel(
+                start_date=start_date,
+                end_date=end_date,
+                kernel_types=kernel_types_with_predicted,
+                metakernel_file_name="metakernel_with_predict_ephem.txt",
+            ),
+            furnish_spice_metakernel(
+                start_date=start_date,
+                end_date=end_date,
+                kernel_types=kernel_types_without_predicted,
+                metakernel_file_name="metakernel_without_predict_ephem.txt",
+            ),
+        )
 
     def copy_dependencies(self):
         if self.energy_grid_lo is not None:
-            shutil.copy(self.energy_grid_lo, self.pipeline_settings['executable_dependency_paths']['energy-grid-lo'])
+            shutil.copy(
+                self.energy_grid_lo,
+                self.pipeline_settings["executable_dependency_paths"]["energy-grid-lo"],
+            )
         if self.energy_grid_hi is not None:
-            shutil.copy(self.energy_grid_hi, self.pipeline_settings['executable_dependency_paths']['energy-grid-hi'])
+            shutil.copy(
+                self.energy_grid_hi,
+                self.pipeline_settings["executable_dependency_paths"]["energy-grid-hi"],
+            )
         if self.energy_grid_ultra is not None:
-            shutil.copy(self.energy_grid_ultra,
-                        self.pipeline_settings['executable_dependency_paths']['energy-grid-ultra'])
+            shutil.copy(
+                self.energy_grid_ultra,
+                self.pipeline_settings["executable_dependency_paths"][
+                    "energy-grid-ultra"
+                ],
+            )
         if self.tess_xyz_8 is not None:
-            shutil.copy(self.tess_xyz_8, self.pipeline_settings['executable_dependency_paths']['tess-xyz-8'])
+            shutil.copy(
+                self.tess_xyz_8,
+                self.pipeline_settings["executable_dependency_paths"]["tess-xyz-8"],
+            )
         if self.tess_ang16 is not None:
-            shutil.copy(self.tess_ang16, self.pipeline_settings['executable_dependency_paths']['tess-ang-16'])
+            shutil.copy(
+                self.tess_ang16,
+                self.pipeline_settings["executable_dependency_paths"]["tess-ang-16"],
+            )
         shutil.copy(self.lya_series, self.lya_series.name)
         shutil.copy(self.solar_uv_anisotropy, self.solar_uv_anisotropy.name)
         shutil.copy(self.speed_3d_sw, self.speed_3d_sw.name)
@@ -126,15 +195,20 @@ def copy_dependencies(self):
         shutil.copy(self.phion_hydrogen, self.phion_hydrogen.name)
         shutil.copy(self.sw_eqtr_electrons, self.sw_eqtr_electrons.name)
 
-        with open("ionization.files.dat", 'w') as ionization_file:
-            ionization_file.write("\n".join([
-                self.lya_series.name,
-                self.solar_uv_anisotropy.name,
-                self.speed_3d_sw.name,
-                self.density_3d_sw.name,
-                self.phion_hydrogen.name,
-                self.sw_eqtr_electrons.name,
-            ]) + "\n")
+        with open("ionization.files.dat", "w") as ionization_file:
+            ionization_file.write(
+                "\n".join(
+                    [
+                        self.lya_series.name,
+                        self.solar_uv_anisotropy.name,
+                        self.speed_3d_sw.name,
+                        self.density_3d_sw.name,
+                        self.phion_hydrogen.name,
+                        self.sw_eqtr_electrons.name,
+                    ]
+                )
+                + "\n"
+            )
 
     def get_hi_parents(self):
         return [
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_hi_model.py b/imap_l3_processing/glows/l3e/glows_l3e_hi_model.py
index cc4c31062..fd0fca959 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_hi_model.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_hi_model.py
@@ -54,45 +54,66 @@ class GlowsL3EHiData(DataProduct):
     glows_flags: np.ndarray
 
     @classmethod
-    def convert_dat_to_glows_l3e_hi_product(cls, input_metadata: InputMetadata, file_path: Path,
-                                            epoch: datetime,
-                                            epoch_delta: timedelta,
-                                            args: GlowsL3eCallArguments):
+    def convert_dat_to_glows_l3e_hi_product(
+        cls,
+        input_metadata: InputMetadata,
+        file_path: Path,
+        epoch: datetime,
+        epoch_delta: timedelta,
+        args: GlowsL3eCallArguments,
+    ):
         with open(file_path) as input_data:
             lines = input_data.readlines()
 
             energy_line = [line for line in lines if line.startswith("#energy_grid")]
-            energies = np.array([float(i) for i in re.findall(r"\d+.\d+", energy_line[0])])
+            energies = np.array(
+                [float(i) for i in re.findall(r"\d+.\d+", energy_line[0])]
+            )
 
-            code_version_line = [line for line in lines if line.startswith("# code version")]
-            code_version = code_version_line[0].split(',')[0][14:].strip()
+            code_version_line = [
+                line for line in lines if line.startswith("# code version")
+            ]
+            code_version = code_version_line[0].split(",")[0][14:].strip()
 
-        spin_angle_and_survival_probabilities = np.loadtxt(file_path, skiprows=200, dtype=np.float64)
+        spin_angle_and_survival_probabilities = np.loadtxt(
+            file_path, skiprows=200, dtype=np.float64
+        )
         spin_angles = spin_angle_and_survival_probabilities[:, 0]
-        survival_probabilities = np.array([spin_angle_and_survival_probabilities[:, 1:].T])
+        survival_probabilities = np.array(
+            [spin_angle_and_survival_probabilities[:, 1:].T]
+        )
 
         energy_delta_plus, energy_delta_minus = calculate_energy_deltas(energies)
 
-        return cls(input_metadata,
-                   epoch=np.array([epoch]),
-                   epoch_delta=np.array([epoch_delta.total_seconds() * ONE_SECOND_IN_NANOSECONDS]),
-                   energy=energies,
-                   energy_delta_plus=energy_delta_plus,
-                   energy_delta_minus=energy_delta_minus,
-                   spin_angle=spin_angles,
-                   probability_of_survival=survival_probabilities,
-                   spin_axis_lat=np.array([args.spacecraft_info.spin_axis_latitude]),
-                   spin_axis_lon=np.array([args.spacecraft_info.spin_axis_longitude]),
-                   program_version=np.array([code_version]),
-                   spacecraft_radius=np.array([args.spacecraft_info.spacecraft_radius]),
-                   spacecraft_longitude=np.array([args.spacecraft_info.spacecraft_longitude]),
-                   spacecraft_latitude=np.array([args.spacecraft_info.spacecraft_latitude]),
-                   spacecraft_velocity_x=np.array([args.spacecraft_info.spacecraft_velocity_x]),
-                   spacecraft_velocity_y=np.array([args.spacecraft_info.spacecraft_velocity_y]),
-                   spacecraft_velocity_z=np.array([args.spacecraft_info.spacecraft_velocity_z]),
-                   elongation=np.array([args.elongation]),
-                   glows_flags=np.array([0], dtype=np.uint16),
-                   )
+        return cls(
+            input_metadata,
+            epoch=np.array([epoch]),
+            epoch_delta=np.array(
+                [epoch_delta.total_seconds() * ONE_SECOND_IN_NANOSECONDS]
+            ),
+            energy=energies,
+            energy_delta_plus=energy_delta_plus,
+            energy_delta_minus=energy_delta_minus,
+            spin_angle=spin_angles,
+            probability_of_survival=survival_probabilities,
+            spin_axis_lat=np.array([args.spacecraft_info.spin_axis_latitude]),
+            spin_axis_lon=np.array([args.spacecraft_info.spin_axis_longitude]),
+            program_version=np.array([code_version]),
+            spacecraft_radius=np.array([args.spacecraft_info.spacecraft_radius]),
+            spacecraft_longitude=np.array([args.spacecraft_info.spacecraft_longitude]),
+            spacecraft_latitude=np.array([args.spacecraft_info.spacecraft_latitude]),
+            spacecraft_velocity_x=np.array(
+                [args.spacecraft_info.spacecraft_velocity_x]
+            ),
+            spacecraft_velocity_y=np.array(
+                [args.spacecraft_info.spacecraft_velocity_y]
+            ),
+            spacecraft_velocity_z=np.array(
+                [args.spacecraft_info.spacecraft_velocity_z]
+            ),
+            elongation=np.array([args.elongation]),
+            glows_flags=np.array([0], dtype=np.uint16),
+        )
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         spin_angle_labels = [f"{i:.0f}" for i in self.spin_angle]
@@ -104,7 +125,9 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(ENERGY_DELTA_PLUS_VAR_NAME, self.energy_delta_plus),
             DataProductVariable(ENERGY_DELTA_MINUS_VAR_NAME, self.energy_delta_minus),
             DataProductVariable(SPIN_ANGLE_VAR_NAME, self.spin_angle),
-            DataProductVariable(PROBABILITY_OF_SURVIVAL_VAR_NAME, self.probability_of_survival),
+            DataProductVariable(
+                PROBABILITY_OF_SURVIVAL_VAR_NAME, self.probability_of_survival
+            ),
             DataProductVariable(ENERGY_LABEL_VAR_NAME, energy_labels),
             DataProductVariable(SPIN_ANGLE_LABEL_VAR_NAME, spin_angle_labels),
             DataProductVariable(SPIN_AXIS_LATITUDE_VAR_NAME, self.spin_axis_lat),
@@ -112,10 +135,18 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(PROGRAM_VERSION_VAR_NAME, self.program_version),
             DataProductVariable(SPACECRAFT_RADIUS_VAR_NAME, self.spacecraft_radius),
             DataProductVariable(SPACECRAFT_LATITUDE_VAR_NAME, self.spacecraft_latitude),
-            DataProductVariable(SPACECRAFT_LONGITUDE_VAR_NAME, self.spacecraft_longitude),
-            DataProductVariable(SPACECRAFT_VELOCITY_X_VAR_NAME, self.spacecraft_velocity_x),
-            DataProductVariable(SPACECRAFT_VELOCITY_Y_VAR_NAME, self.spacecraft_velocity_y),
-            DataProductVariable(SPACECRAFT_VELOCITY_Z_VAR_NAME, self.spacecraft_velocity_z),
+            DataProductVariable(
+                SPACECRAFT_LONGITUDE_VAR_NAME, self.spacecraft_longitude
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_X_VAR_NAME, self.spacecraft_velocity_x
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_Y_VAR_NAME, self.spacecraft_velocity_y
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_Z_VAR_NAME, self.spacecraft_velocity_z
+            ),
             DataProductVariable(ELONGATION, self.elongation),
             DataProductVariable(GLOWS_FLAGS_VAR_NAME, self.glows_flags),
         ]
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_initializer.py b/imap_l3_processing/glows/l3e/glows_l3e_initializer.py
index bf8b0436e..f440d5f10 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_initializer.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_initializer.py
@@ -1,14 +1,13 @@
 import logging
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional
 
 import imap_data_access
 from imap_data_access import (
-    ProcessingInputCollection,
     AncillaryInput,
-    ScienceInput,
+    ProcessingInputCollection,
     RepointInput,
+    ScienceInput,
 )
 
 from imap_l3_processing.glows.l3bc.utils import get_pointing_date_range
@@ -16,9 +15,9 @@
 from imap_l3_processing.glows.l3d.utils import get_most_recently_uploaded_ancillary
 from imap_l3_processing.glows.l3e.glows_l3e_dependencies import GlowsL3EDependencies
 from imap_l3_processing.glows.l3e.glows_l3e_utils import (
+    GlowsL3eVersionsForRepointings,
     find_first_updated_cr,
     identify_versions_for_l3e_output_files,
-    GlowsL3eVersionsForRepointings,
 )
 from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo
 from imap_l3_processing.models import VersionMap
@@ -26,6 +25,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 @dataclass
 class GlowsL3EInitializerOutput:
     dependencies: GlowsL3EDependencies
@@ -38,18 +38,44 @@ class GlowsL3EInitializerOutput:
 class GlowsL3EInitializer:
     @staticmethod
     def get_repointings_to_process(
-            l3d_output: GlowsL3DProcessorOutput,
-            previous_l3d: Optional[str],
-            repointing_file_path: Path,
-            version_map: VersionMap,
-            reprocess_info: ReprocessInfo,
-    ) -> Optional[GlowsL3EInitializerOutput]:
-        pipeline_settings_l3bcde = get_most_recently_uploaded_ancillary(imap_data_access.query(table='ancillary', instrument='glows', descriptor='pipeline-settings-l3bcde'))
-        energy_grid_lo = get_most_recently_uploaded_ancillary(imap_data_access.query(table='ancillary', instrument='glows', descriptor='energy-grid-lo'))
-        tess_xyz_8 = get_most_recently_uploaded_ancillary(imap_data_access.query(table='ancillary', instrument='glows', descriptor='tess-xyz-8'))
-        energy_grid_hi = get_most_recently_uploaded_ancillary(imap_data_access.query(table='ancillary', instrument='glows', descriptor='energy-grid-hi'))
-        energy_grid_ultra = get_most_recently_uploaded_ancillary(imap_data_access.query(table='ancillary', instrument='glows', descriptor='energy-grid-ultra'))
-        tess_ang_16 = get_most_recently_uploaded_ancillary(imap_data_access.query(table='ancillary', instrument='glows', descriptor='tess-ang-16'))
+        l3d_output: GlowsL3DProcessorOutput,
+        previous_l3d: str | None,
+        repointing_file_path: Path,
+        version_map: VersionMap,
+        reprocess_info: ReprocessInfo,
+    ) -> GlowsL3EInitializerOutput | None:
+        pipeline_settings_l3bcde = get_most_recently_uploaded_ancillary(
+            imap_data_access.query(
+                table="ancillary",
+                instrument="glows",
+                descriptor="pipeline-settings-l3bcde",
+            )
+        )
+        energy_grid_lo = get_most_recently_uploaded_ancillary(
+            imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="energy-grid-lo"
+            )
+        )
+        tess_xyz_8 = get_most_recently_uploaded_ancillary(
+            imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="tess-xyz-8"
+            )
+        )
+        energy_grid_hi = get_most_recently_uploaded_ancillary(
+            imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="energy-grid-hi"
+            )
+        )
+        energy_grid_ultra = get_most_recently_uploaded_ancillary(
+            imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="energy-grid-ultra"
+            )
+        )
+        tess_ang_16 = get_most_recently_uploaded_ancillary(
+            imap_data_access.query(
+                table="ancillary", instrument="glows", descriptor="tess-ang-16"
+            )
+        )
 
         processing_input_collection = ProcessingInputCollection(
             ScienceInput(l3d_output.l3d_cdf_file_path.name),
@@ -60,7 +86,7 @@ def get_repointings_to_process(
             AncillaryInput(str(energy_grid_hi["file_path"])),
             AncillaryInput(str(energy_grid_ultra["file_path"])),
             AncillaryInput(str(tess_ang_16["file_path"])),
-            RepointInput(str(repointing_file_path))
+            RepointInput(str(repointing_file_path)),
         )
 
         l3e_deps = GlowsL3EDependencies.fetch_dependencies(processing_input_collection)
@@ -70,7 +96,9 @@ def get_repointings_to_process(
 
         first_updated_cr = first_cr
         if previous_l3d is not None:
-            first_updated_cr = find_first_updated_cr(l3d_output.l3d_cdf_file_path, previous_l3d)
+            first_updated_cr = find_first_updated_cr(
+                l3d_output.l3d_cdf_file_path, previous_l3d
+            )
             if first_updated_cr is not None:
                 first_updated_cr -= 1
 
@@ -105,4 +133,3 @@ def get_repointings_to_process(
             metakernel_with_predict_ephem=furnished_metakernels[0],
             metakernel_without_predict_ephem=furnished_metakernels[1],
         )
-
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_lo_model.py b/imap_l3_processing/glows/l3e/glows_l3e_lo_model.py
index ed48c175c..9cf9515a3 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_lo_model.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_lo_model.py
@@ -54,28 +54,44 @@ class GlowsL3ELoData(DataProduct):
     glows_flags: np.ndarray
 
     @classmethod
-    def convert_dat_to_glows_l3e_lo_product(cls, input_metadata: InputMetadata, file_path: Path,
-                                            epoch: datetime, epoch_delta: timedelta, elongation: int,
-                                            args: GlowsL3eCallArguments):
+    def convert_dat_to_glows_l3e_lo_product(
+        cls,
+        input_metadata: InputMetadata,
+        file_path: Path,
+        epoch: datetime,
+        epoch_delta: timedelta,
+        elongation: int,
+        args: GlowsL3eCallArguments,
+    ):
         with open(file_path) as input_data:
             lines = input_data.readlines()
 
             energy_line = [line for line in lines if line.startswith("#energy_grid")]
-            energies = np.array([float(i) for i in re.findall(r"\d+.\d+", energy_line[0])])
+            energies = np.array(
+                [float(i) for i in re.findall(r"\d+.\d+", energy_line[0])]
+            )
 
-            code_version_line = [line for line in lines if line.startswith("# code version")]
-            code_version = code_version_line[0].split(',')[0][14:].strip()
+            code_version_line = [
+                line for line in lines if line.startswith("# code version")
+            ]
+            code_version = code_version_line[0].split(",")[0][14:].strip()
 
-        spin_angle_and_survival_probabilities = np.loadtxt(file_path, skiprows=200, dtype=np.float64)
+        spin_angle_and_survival_probabilities = np.loadtxt(
+            file_path, skiprows=200, dtype=np.float64
+        )
         spin_angles = spin_angle_and_survival_probabilities[:, 0]
-        survival_probabilities = np.array([spin_angle_and_survival_probabilities[:, 1:].T])
+        survival_probabilities = np.array(
+            [spin_angle_and_survival_probabilities[:, 1:].T]
+        )
 
         energy_delta_plus, energy_delta_minus = calculate_energy_deltas(energies)
 
         return cls(
             input_metadata=input_metadata,
             epoch=np.array([epoch]),
-            epoch_delta=np.array([epoch_delta.total_seconds() * ONE_SECOND_IN_NANOSECONDS]),
+            epoch_delta=np.array(
+                [epoch_delta.total_seconds() * ONE_SECOND_IN_NANOSECONDS]
+            ),
             energy=energies,
             energy_delta_plus=energy_delta_plus,
             energy_delta_minus=energy_delta_minus,
@@ -88,11 +104,17 @@ def convert_dat_to_glows_l3e_lo_product(cls, input_metadata: InputMetadata, file
             spacecraft_radius=np.array([args.spacecraft_info.spacecraft_radius]),
             spacecraft_longitude=np.array([args.spacecraft_info.spacecraft_longitude]),
             spacecraft_latitude=np.array([args.spacecraft_info.spacecraft_latitude]),
-            spacecraft_velocity_x=np.array([args.spacecraft_info.spacecraft_velocity_x]),
-            spacecraft_velocity_y=np.array([args.spacecraft_info.spacecraft_velocity_y]),
-            spacecraft_velocity_z=np.array([args.spacecraft_info.spacecraft_velocity_z]),
+            spacecraft_velocity_x=np.array(
+                [args.spacecraft_info.spacecraft_velocity_x]
+            ),
+            spacecraft_velocity_y=np.array(
+                [args.spacecraft_info.spacecraft_velocity_y]
+            ),
+            spacecraft_velocity_z=np.array(
+                [args.spacecraft_info.spacecraft_velocity_z]
+            ),
             glows_flags=np.array([0], dtype=np.uint16),
-       )
+        )
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         spin_angle_labels = [f"{i:.0f}" for i in self.spin_angle]
@@ -105,7 +127,9 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(ENERGY_DELTA_PLUS_VAR_NAME, self.energy_delta_plus),
             DataProductVariable(ENERGY_DELTA_MINUS_VAR_NAME, self.energy_delta_minus),
             DataProductVariable(SPIN_ANGLE_VAR_NAME, self.spin_angle),
-            DataProductVariable(PROBABILITY_OF_SURVIVAL_VAR_NAME, self.probability_of_survival),
+            DataProductVariable(
+                PROBABILITY_OF_SURVIVAL_VAR_NAME, self.probability_of_survival
+            ),
             DataProductVariable(ENERGY_LABEL_VAR_NAME, energy_labels),
             DataProductVariable(SPIN_ANGLE_LABEL_VAR_NAME, spin_angle_labels),
             DataProductVariable(ELONGATION_VAR_NAME, self.elongation),
@@ -114,9 +138,17 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(PROGRAM_VERSION_VAR_NAME, self.program_version),
             DataProductVariable(SPACECRAFT_RADIUS_VAR_NAME, self.spacecraft_radius),
             DataProductVariable(SPACECRAFT_LATITUDE_VAR_NAME, self.spacecraft_latitude),
-            DataProductVariable(SPACECRAFT_LONGITUDE_VAR_NAME, self.spacecraft_longitude),
-            DataProductVariable(SPACECRAFT_VELOCITY_X_VAR_NAME, self.spacecraft_velocity_x),
-            DataProductVariable(SPACECRAFT_VELOCITY_Y_VAR_NAME, self.spacecraft_velocity_y),
-            DataProductVariable(SPACECRAFT_VELOCITY_Z_VAR_NAME, self.spacecraft_velocity_z),
+            DataProductVariable(
+                SPACECRAFT_LONGITUDE_VAR_NAME, self.spacecraft_longitude
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_X_VAR_NAME, self.spacecraft_velocity_x
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_Y_VAR_NAME, self.spacecraft_velocity_y
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_Z_VAR_NAME, self.spacecraft_velocity_z
+            ),
             DataProductVariable(GLOWS_FLAGS_VAR_NAME, self.glows_flags),
         ]
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_ultra_model.py b/imap_l3_processing/glows/l3e/glows_l3e_ultra_model.py
index 64f0b6e1d..7eef9be5e 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_ultra_model.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_ultra_model.py
@@ -58,18 +58,26 @@ class GlowsL3EUltraData(DataProduct):
     glows_flags: np.ndarray
 
     @classmethod
-    def convert_dat_to_glows_l3e_ul_product(cls, input_metadata: InputMetadata, file_path: Path,
-                                            epoch: datetime,
-                                            epoch_delta: timedelta,
-                                            args: GlowsL3eCallArguments):
+    def convert_dat_to_glows_l3e_ul_product(
+        cls,
+        input_metadata: InputMetadata,
+        file_path: Path,
+        epoch: datetime,
+        epoch_delta: timedelta,
+        args: GlowsL3eCallArguments,
+    ):
         with open(file_path) as input_data:
             lines = input_data.readlines()
 
             energy_line = [line for line in lines if line.startswith("#energy_grid")]
-            energies = np.array([float(i) for i in re.findall(r"\d+.\d+", energy_line[0])])
+            energies = np.array(
+                [float(i) for i in re.findall(r"\d+.\d+", energy_line[0])]
+            )
 
-            code_version_line = [line for line in lines if line.startswith("# code version")]
-            code_version = code_version_line[0].split(',')[0][14:].strip()
+            code_version_line = [
+                line for line in lines if line.startswith("# code version")
+            ]
+            code_version = code_version_line[0].split(",")[0][14:].strip()
 
         data_table = np.loadtxt(file_path, skiprows=200, dtype=np.float64)
 
@@ -80,12 +88,15 @@ def convert_dat_to_glows_l3e_ul_product(cls, input_metadata: InputMetadata, file
         pixel_latitude = data_table[:, 1]
         pixel_longitude = data_table[:, 2]
 
-
-        probability_of_survival_to_return = np.full((len(energies), len(healpix_indexes)), np.nan, dtype=float)
+        probability_of_survival_to_return = np.full(
+            (len(energies), len(healpix_indexes)), np.nan, dtype=float
+        )
         pixel_latitude_to_return = np.full(len(healpix_indexes), np.nan, dtype=float)
         pixel_longitude_to_return = np.full(len(healpix_indexes), np.nan, dtype=float)
 
-        for healpix, prob_sur, pixel_lat, pixel_lon in zip(existing_healpix, probability_of_survival, pixel_latitude, pixel_longitude):
+        for healpix, prob_sur, pixel_lat, pixel_lon in zip(
+            existing_healpix, probability_of_survival, pixel_latitude, pixel_longitude
+        ):
             probability_of_survival_to_return[:, int(healpix)] = prob_sur
             pixel_latitude_to_return[int(healpix)] = pixel_lat
             pixel_longitude_to_return[int(healpix)] = pixel_lon
@@ -97,7 +108,9 @@ def convert_dat_to_glows_l3e_ul_product(cls, input_metadata: InputMetadata, file
         return cls(
             input_metadata,
             epoch=np.array([epoch]),
-            epoch_delta=np.array([epoch_delta.total_seconds() * ONE_SECOND_IN_NANOSECONDS]),
+            epoch_delta=np.array(
+                [epoch_delta.total_seconds() * ONE_SECOND_IN_NANOSECONDS]
+            ),
             energy=energies,
             energy_delta_plus=energy_delta_plus,
             energy_delta_minus=energy_delta_minus,
@@ -109,9 +122,15 @@ def convert_dat_to_glows_l3e_ul_product(cls, input_metadata: InputMetadata, file
             spacecraft_radius=np.array([args.spacecraft_info.spacecraft_radius]),
             spacecraft_longitude=np.array([args.spacecraft_info.spacecraft_longitude]),
             spacecraft_latitude=np.array([args.spacecraft_info.spacecraft_latitude]),
-            spacecraft_velocity_x=np.array([args.spacecraft_info.spacecraft_velocity_x]),
-            spacecraft_velocity_y=np.array([args.spacecraft_info.spacecraft_velocity_y]),
-            spacecraft_velocity_z=np.array([args.spacecraft_info.spacecraft_velocity_z]),
+            spacecraft_velocity_x=np.array(
+                [args.spacecraft_info.spacecraft_velocity_x]
+            ),
+            spacecraft_velocity_y=np.array(
+                [args.spacecraft_info.spacecraft_velocity_y]
+            ),
+            spacecraft_velocity_z=np.array(
+                [args.spacecraft_info.spacecraft_velocity_z]
+            ),
             elongation_excluded=np.array([args.elongation]),
             pixel_latitude=np.array([pixel_latitude_to_return]),
             pixel_longitude=np.array([pixel_longitude_to_return]),
@@ -128,7 +147,9 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(ENERGY_DELTA_PLUS_VAR_NAME, self.energy_delta_plus),
             DataProductVariable(ENERGY_DELTA_MINUS_VAR_NAME, self.energy_delta_minus),
             DataProductVariable(HEALPIX_INDEX_VAR_NAME, self.healpix_index),
-            DataProductVariable(PROBABILITY_OF_SURVIVAL_VAR_NAME, self.probability_of_survival),
+            DataProductVariable(
+                PROBABILITY_OF_SURVIVAL_VAR_NAME, self.probability_of_survival
+            ),
             DataProductVariable(ENERGY_LABEL_VAR_NAME, energy_labels),
             DataProductVariable(PIXEL_INDEX_LABEL_VAR_NAME, pixel_labels),
             DataProductVariable(SPIN_AXIS_LATITUDE_VAR_NAME, self.spin_axis_lat),
@@ -136,10 +157,18 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(PROGRAM_VERSION_VAR_NAME, self.program_version),
             DataProductVariable(SPACECRAFT_RADIUS_VAR_NAME, self.spacecraft_radius),
             DataProductVariable(SPACECRAFT_LATITUDE_VAR_NAME, self.spacecraft_latitude),
-            DataProductVariable(SPACECRAFT_LONGITUDE_VAR_NAME, self.spacecraft_longitude),
-            DataProductVariable(SPACECRAFT_VELOCITY_X_VAR_NAME, self.spacecraft_velocity_x),
-            DataProductVariable(SPACECRAFT_VELOCITY_Y_VAR_NAME, self.spacecraft_velocity_y),
-            DataProductVariable(SPACECRAFT_VELOCITY_Z_VAR_NAME, self.spacecraft_velocity_z),
+            DataProductVariable(
+                SPACECRAFT_LONGITUDE_VAR_NAME, self.spacecraft_longitude
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_X_VAR_NAME, self.spacecraft_velocity_x
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_Y_VAR_NAME, self.spacecraft_velocity_y
+            ),
+            DataProductVariable(
+                SPACECRAFT_VELOCITY_Z_VAR_NAME, self.spacecraft_velocity_z
+            ),
             DataProductVariable(ELONGATION_EXCLUDED_VAR_NAME, self.elongation_excluded),
             DataProductVariable(PIXEL_LATITUDE_VAR_NAME, self.pixel_latitude),
             DataProductVariable(PIXEL_LONGITUDE_VAR_NAME, self.pixel_longitude),
diff --git a/imap_l3_processing/glows/l3e/glows_l3e_utils.py b/imap_l3_processing/glows/l3e/glows_l3e_utils.py
index 480849a25..91e573c5e 100644
--- a/imap_l3_processing/glows/l3e/glows_l3e_utils.py
+++ b/imap_l3_processing/glows/l3e/glows_l3e_utils.py
@@ -9,28 +9,46 @@
 import numpy as np
 import spiceypy
 from astropy.time import Time
-from spiceypy import SpiceyError
-
-from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_data_access.file_validation import Version
-from imap_processing.spice.repoint import set_global_repoint_table_paths, get_repoint_data
+from imap_processing.spice.repoint import (
+    get_repoint_data,
+    set_global_repoint_table_paths,
+)
 from spacepy.pycdf import CDF
+from spiceypy import SpiceyError
 
-from typing import Optional
-
-from imap_l3_processing.constants import ONE_AU_IN_KM, TT2000_EPOCH, ONE_SECOND_IN_NANOSECONDS
-from imap_l3_processing.glows.descriptors import GLOWS_L3E_ULTRA_HF_DESCRIPTOR, GLOWS_L3E_ULTRA_SF_DESCRIPTOR, \
-    GLOWS_L3E_LO_DESCRIPTOR, GLOWS_L3E_HI_45_DESCRIPTOR, GLOWS_L3E_HI_90_DESCRIPTOR, GLOWS_L3E_DESCRIPTORS
+from imap_l3_processing.constants import (
+    ONE_AU_IN_KM,
+    ONE_SECOND_IN_NANOSECONDS,
+    TT2000_EPOCH,
+)
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3E_DESCRIPTORS,
+    GLOWS_L3E_HI_45_DESCRIPTOR,
+    GLOWS_L3E_HI_90_DESCRIPTOR,
+    GLOWS_L3E_LO_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3bc.l3bc_toolkit.funcs import jd_fm_Carrington
-from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eCallArguments, GlowsL3eSpacecraftInfo
-from imap_l3_processing.utils import FurnishMetakernelOutput
+from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
+    GlowsL3eCallArguments,
+    GlowsL3eSpacecraftInfo,
+)
+from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.models import VersionMap
+from imap_l3_processing.utils import FurnishMetakernelOutput
 
 if typing.TYPE_CHECKING:
     from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo
 
-def determine_call_args_for_l3e_executable(start_date: datetime, repointing_midpoint: datetime, elongation: float,
-                                           spacecraft_info: GlowsL3eSpacecraftInfo) -> GlowsL3eCallArguments:
+
+def determine_call_args_for_l3e_executable(
+    start_date: datetime,
+    repointing_midpoint: datetime,
+    elongation: float,
+    spacecraft_info: GlowsL3eSpacecraftInfo,
+) -> GlowsL3eCallArguments:
     formatted_date = start_date.strftime("%Y%m%d_%H%M%S")
     decimal_date = _decimal_time(repointing_midpoint)
 
@@ -41,10 +59,15 @@ def determine_call_args_for_l3e_executable(start_date: datetime, repointing_midp
         spacecraft_info=spacecraft_info,
     )
 
-def determine_spacecraft_info_for_l3e_executable(repointing_midpoint: datetime) -> GlowsL3eSpacecraftInfo:
+
+def determine_spacecraft_info_for_l3e_executable(
+    repointing_midpoint: datetime,
+) -> GlowsL3eSpacecraftInfo:
     ephemeris_time = spiceypy.datetime2et(repointing_midpoint)
 
-    [x, y, z, vx, vy, vz], _ = spiceypy.spkezr("IMAP", ephemeris_time, "ECLIPJ2000", "NONE", "SUN")
+    [x, y, z, vx, vy, vz], _ = spiceypy.spkezr(
+        "IMAP", ephemeris_time, "ECLIPJ2000", "NONE", "SUN"
+    )
 
     radius, longitude, latitude = spiceypy.reclat([x, y, z])
 
@@ -65,18 +88,24 @@ def determine_spacecraft_info_for_l3e_executable(repointing_midpoint: datetime)
     )
 
 
-def determine_spacecraft_info_using_predict_if_needed(repointing_midpoint: datetime, spice_with_predict: FurnishMetakernelOutput, spice_without_predict: FurnishMetakernelOutput) -> \
-    tuple[GlowsL3eSpacecraftInfo, GlowsL3Flags, list[str]]:
+def determine_spacecraft_info_using_predict_if_needed(
+    repointing_midpoint: datetime,
+    spice_with_predict: FurnishMetakernelOutput,
+    spice_without_predict: FurnishMetakernelOutput,
+) -> tuple[GlowsL3eSpacecraftInfo, GlowsL3Flags, list[str]]:
     try:
         with spiceypy.KernelPool([str(spice_without_predict.metakernel_path)]):
-            spacecraft_info: GlowsL3eSpacecraftInfo = determine_spacecraft_info_for_l3e_executable(repointing_midpoint)
+            spacecraft_info: GlowsL3eSpacecraftInfo = (
+                determine_spacecraft_info_for_l3e_executable(repointing_midpoint)
+            )
 
             glows_l3_flags: GlowsL3Flags = GlowsL3Flags.NONE
             kernel_names = [n.name for n in spice_without_predict.spice_kernel_paths]
     except SpiceyError:
         with spiceypy.KernelPool([str(spice_with_predict.metakernel_path)]):
-            spacecraft_info: GlowsL3eSpacecraftInfo = determine_spacecraft_info_for_l3e_executable(
-                repointing_midpoint)
+            spacecraft_info: GlowsL3eSpacecraftInfo = (
+                determine_spacecraft_info_for_l3e_executable(repointing_midpoint)
+            )
 
             glows_l3_flags: GlowsL3Flags = GlowsL3Flags.PREDICTIVE_EPHEMERIS
             kernel_names = [n.name for n in spice_with_predict.spice_kernel_paths]
@@ -87,7 +116,7 @@ def determine_spacecraft_info_using_predict_if_needed(repointing_midpoint: datet
 def _decimal_time(t: datetime) -> str:
     year_start = datetime(t.year, 1, 1)
     year_end = datetime(t.year + 1, 1, 1)
-    return "{:10.5f}".format(t.year + (t - year_start) / (year_end - year_start))
+    return f"{t.year + (t - year_start) / (year_end - year_start):10.5f}"
 
 
 @dataclass
@@ -99,14 +128,25 @@ class GlowsL3eVersionsForRepointings:
     ultra_sf_repointings: dict[int, Version]
     ultra_hf_repointings: dict[int, Version]
 
-def identify_versions_for_l3e_output_files(start_cr_of_mission: int, end_cr_of_mission: int, first_updated_cr_from_l3d: Optional[int],
-                                           repointing_path: Path, version_map: VersionMap, reprocess_info: ReprocessInfo) -> GlowsL3eVersionsForRepointings:
+
+def identify_versions_for_l3e_output_files(
+    start_cr_of_mission: int,
+    end_cr_of_mission: int,
+    first_updated_cr_from_l3d: int | None,
+    repointing_path: Path,
+    version_map: VersionMap,
+    reprocess_info: ReprocessInfo,
+) -> GlowsL3eVersionsForRepointings:
 
     set_global_repoint_table_paths([repointing_path])
     repointing_data = get_repoint_data()
 
-    all_pointing_numbers = get_repoint_numbers_within_cr_window(start_cr_of_mission, end_cr_of_mission, repointing_data)
-    pointing_numbers_updated_by_l3d = get_repoint_numbers_within_cr_window(first_updated_cr_from_l3d, end_cr_of_mission, repointing_data)
+    all_pointing_numbers = get_repoint_numbers_within_cr_window(
+        start_cr_of_mission, end_cr_of_mission, repointing_data
+    )
+    pointing_numbers_updated_by_l3d = get_repoint_numbers_within_cr_window(
+        first_updated_cr_from_l3d, end_cr_of_mission, repointing_data
+    )
 
     updated_pointings_per_instruments = {}
     updated_pointing_numbers = {}
@@ -114,14 +154,25 @@ def identify_versions_for_l3e_output_files(start_cr_of_mission: int, end_cr_of_m
         repointings_to_force_processing = reprocess_info.get_repoints_for_descriptor(
             descriptor, repointing_data
         )
-        repointings_to_process = set(pointing_numbers_updated_by_l3d + repointings_to_force_processing)
-        l3e_files = imap_data_access.query(instrument='glows', data_level='l3e', version="latest", descriptor=descriptor)
+        repointings_to_process = set(
+            pointing_numbers_updated_by_l3d + repointings_to_force_processing
+        )
+        l3e_files = imap_data_access.query(
+            instrument="glows",
+            data_level="l3e",
+            version="latest",
+            descriptor=descriptor,
+        )
         existing_file_versions = {}
         for l3e in l3e_files:
-            if 'major_version' in l3e.keys() and 'minor_version' in l3e.keys():
-                existing_file_versions[int(l3e['repointing'])] = Version(l3e["major_version"], l3e["minor_version"])
-            elif 'version' in l3e.keys():
-                existing_file_versions[int(l3e['repointing'])] = Version.from_version(l3e['version'])
+            if "major_version" in l3e.keys() and "minor_version" in l3e.keys():
+                existing_file_versions[int(l3e["repointing"])] = Version(
+                    l3e["major_version"], l3e["minor_version"]
+                )
+            elif "version" in l3e.keys():
+                existing_file_versions[int(l3e["repointing"])] = Version.from_version(
+                    l3e["version"]
+                )
             else:
                 continue
         new_file_versions = {}
@@ -135,7 +186,9 @@ def identify_versions_for_l3e_output_files(start_cr_of_mission: int, end_cr_of_m
                     higher_major_version_given
                     or pointing_number in repointings_to_process
                 ):
-                    new_file_versions[pointing_number] = Version(new_major_version, previous_version.minor + 1)
+                    new_file_versions[pointing_number] = Version(
+                        new_major_version, previous_version.minor + 1
+                    )
 
             else:
                 new_file_versions[pointing_number] = Version(new_major_version, 1)
@@ -143,20 +196,22 @@ def identify_versions_for_l3e_output_files(start_cr_of_mission: int, end_cr_of_m
         updated_pointings_per_instruments[descriptor] = new_file_versions
         updated_pointing_numbers = updated_pointing_numbers | new_file_versions.keys()
 
-
-    return GlowsL3eVersionsForRepointings(list(updated_pointing_numbers),
-                                          updated_pointings_per_instruments[GLOWS_L3E_HI_90_DESCRIPTOR],
-                                          updated_pointings_per_instruments[GLOWS_L3E_HI_45_DESCRIPTOR],
-                                          updated_pointings_per_instruments[GLOWS_L3E_LO_DESCRIPTOR],
-                                          updated_pointings_per_instruments[GLOWS_L3E_ULTRA_SF_DESCRIPTOR],
-                                          updated_pointings_per_instruments[GLOWS_L3E_ULTRA_HF_DESCRIPTOR],
-                                          )
+    return GlowsL3eVersionsForRepointings(
+        list(updated_pointing_numbers),
+        updated_pointings_per_instruments[GLOWS_L3E_HI_90_DESCRIPTOR],
+        updated_pointings_per_instruments[GLOWS_L3E_HI_45_DESCRIPTOR],
+        updated_pointings_per_instruments[GLOWS_L3E_LO_DESCRIPTOR],
+        updated_pointings_per_instruments[GLOWS_L3E_ULTRA_SF_DESCRIPTOR],
+        updated_pointings_per_instruments[GLOWS_L3E_ULTRA_HF_DESCRIPTOR],
+    )
 
 
-def compute_glows_flags_for_repoint(l3d_cdf_path: Path, repoint_midpoint: datetime) -> int:
+def compute_glows_flags_for_repoint(
+    l3d_cdf_path: Path, repoint_midpoint: datetime
+) -> int:
     with CDF(str(l3d_cdf_path)) as cdf:
-        cr_epochs = cdf['epoch'][...]
-        flags = cdf['glows_flags'][...]
+        cr_epochs = cdf["epoch"][...]
+        flags = cdf["glows_flags"][...]
 
     cr_after_repoint = np.searchsorted(cr_epochs, repoint_midpoint)
     cr_before_repoint = cr_after_repoint - 1
@@ -173,36 +228,69 @@ def compute_glows_flags_for_repoint(l3d_cdf_path: Path, repoint_midpoint: dateti
     return int(np.bitwise_or.reduce(selected.astype(np.uint16), initial=0))
 
 
-def find_first_updated_cr(new_l3d: Path, old_l3d: str) -> Optional[int]:
+def find_first_updated_cr(new_l3d: Path, old_l3d: str) -> int | None:
     downloaded_old_l3d = imap_data_access.download(old_l3d)
 
     old_l3d_cdf = CDF(str(downloaded_old_l3d))
     new_l3d_cdf = CDF(str(new_l3d))
 
-    for i, cr in enumerate(old_l3d_cdf['cr_grid'][...]):
-        lya_matches = np.isclose(old_l3d_cdf['lyman_alpha'][i], new_l3d_cdf['lyman_alpha'][i])
-        phion_matches = np.isclose(old_l3d_cdf['phion'][i], new_l3d_cdf['phion'][i])
-        plasma_speed_flag_matches = np.isclose(old_l3d_cdf['plasma_speed_flag'][i], new_l3d_cdf['plasma_speed_flag'][i])
-        proton_density_flag_matches = np.isclose(old_l3d_cdf['proton_density_flag'][i], new_l3d_cdf['proton_density_flag'][i])
-        uv_anisotropy_flag_matches = np.isclose(old_l3d_cdf['uv_anisotropy_flag'][i], new_l3d_cdf['uv_anisotropy_flag'][i])
-        glows_flags_matches = np.isclose(old_l3d_cdf['glows_flags'][i], new_l3d_cdf['glows_flags'][i])
+    for i, cr in enumerate(old_l3d_cdf["cr_grid"][...]):
+        lya_matches = np.isclose(
+            old_l3d_cdf["lyman_alpha"][i], new_l3d_cdf["lyman_alpha"][i]
+        )
+        phion_matches = np.isclose(old_l3d_cdf["phion"][i], new_l3d_cdf["phion"][i])
+        plasma_speed_flag_matches = np.isclose(
+            old_l3d_cdf["plasma_speed_flag"][i], new_l3d_cdf["plasma_speed_flag"][i]
+        )
+        proton_density_flag_matches = np.isclose(
+            old_l3d_cdf["proton_density_flag"][i], new_l3d_cdf["proton_density_flag"][i]
+        )
+        uv_anisotropy_flag_matches = np.isclose(
+            old_l3d_cdf["uv_anisotropy_flag"][i], new_l3d_cdf["uv_anisotropy_flag"][i]
+        )
+        glows_flags_matches = np.isclose(
+            old_l3d_cdf["glows_flags"][i], new_l3d_cdf["glows_flags"][i]
+        )
 
-        plasma_speed_matches = np.all(np.isclose(old_l3d_cdf['plasma_speed'][i], new_l3d_cdf['plasma_speed'][i]))
-        proton_density_matches = np.all(np.isclose(old_l3d_cdf['proton_density'][i], new_l3d_cdf['proton_density'][i]))
-        uv_anisotropy_matches = np.all(np.isclose(old_l3d_cdf['uv_anisotropy'][i], new_l3d_cdf['uv_anisotropy'][i]))
+        plasma_speed_matches = np.all(
+            np.isclose(old_l3d_cdf["plasma_speed"][i], new_l3d_cdf["plasma_speed"][i])
+        )
+        proton_density_matches = np.all(
+            np.isclose(
+                old_l3d_cdf["proton_density"][i], new_l3d_cdf["proton_density"][i]
+            )
+        )
+        uv_anisotropy_matches = np.all(
+            np.isclose(old_l3d_cdf["uv_anisotropy"][i], new_l3d_cdf["uv_anisotropy"][i])
+        )
 
-        if np.any(np.logical_not([lya_matches, phion_matches, plasma_speed_matches, plasma_speed_flag_matches, proton_density_matches, proton_density_flag_matches, uv_anisotropy_matches, uv_anisotropy_flag_matches, glows_flags_matches])):
+        if np.any(
+            np.logical_not(
+                [
+                    lya_matches,
+                    phion_matches,
+                    plasma_speed_matches,
+                    plasma_speed_flag_matches,
+                    proton_density_matches,
+                    proton_density_flag_matches,
+                    uv_anisotropy_matches,
+                    uv_anisotropy_flag_matches,
+                    glows_flags_matches,
+                ]
+            )
+        ):
             return int(cr)
 
-    if old_l3d_cdf['cr_grid'].shape != new_l3d_cdf['cr_grid'].shape:
-        return int(old_l3d_cdf['cr_grid'][-1]) + 1
+    if old_l3d_cdf["cr_grid"].shape != new_l3d_cdf["cr_grid"].shape:
+        return int(old_l3d_cdf["cr_grid"][-1]) + 1
 
     return None
 
+
 def get_lo_pivot_angle_from_l1b_file(path: Path) -> float:
     with CDF(str(path)) as cdf:
-        epoch = cdf['epoch'][...]
-        angles = cdf['pcc_coarse_pot_pri'][...]
+        epoch = cdf["epoch"][...]
+        angles = cdf["pcc_coarse_pot_pri"][...]
     if len(epoch) == 0:
         return 90.0
     t0 = epoch[0]
@@ -214,11 +302,13 @@ def get_lo_pivot_angle_from_l1b_file(path: Path) -> float:
         return 90.0
     return np.round(np.median(angles_to_consider))
 
+
 @dataclass
 class LoPivotAngle:
-    parent_filename: Optional[str]
+    parent_filename: str | None
     pivot_angle: float
 
+
 def get_lo_pivot_angles(repointings: list[int]) -> dict[int, LoPivotAngle]:
     l1b_results = imap_data_access.query(
         instrument="lo",
@@ -226,38 +316,61 @@ def get_lo_pivot_angles(repointings: list[int]) -> dict[int, LoPivotAngle]:
         descriptor="nhk",
         version="latest",
     )
-    paths_by_repointing = {f["repointing"]:f["file_path"] for f in l1b_results}
+    paths_by_repointing = {f["repointing"]: f["file_path"] for f in l1b_results}
     result = {}
     for repointing in repointings:
         if path := paths_by_repointing.get(repointing):
             downloaded_path = imap_data_access.download(path)
-            result[repointing] = LoPivotAngle(parent_filename=Path(path).name, pivot_angle=get_lo_pivot_angle_from_l1b_file(downloaded_path))
+            result[repointing] = LoPivotAngle(
+                parent_filename=Path(path).name,
+                pivot_angle=get_lo_pivot_angle_from_l1b_file(downloaded_path),
+            )
         else:
             result[repointing] = LoPivotAngle(parent_filename=None, pivot_angle=90.0)
     return result
 
-def get_repoint_numbers_within_cr_window(start_cr_number: int | None, end_cr_number: int, repointing_data) -> list[int]:
+
+def get_repoint_numbers_within_cr_window(
+    start_cr_number: int | None, end_cr_number: int, repointing_data
+) -> list[int]:
     if start_cr_number is None:
         return []
-    first_carrington_start_date = Time(jd_fm_Carrington(float(start_cr_number)), format='jd')
-    last_cr_end_date = Time(jd_fm_Carrington(float(end_cr_number + 0.5)), format='jd')
-
-    start_ns = (first_carrington_start_date.to_datetime() - TT2000_EPOCH).total_seconds() * ONE_SECOND_IN_NANOSECONDS
-    end_ns = (last_cr_end_date.to_datetime() - TT2000_EPOCH).total_seconds() * ONE_SECOND_IN_NANOSECONDS
-
-    vectorized_date_conv = np.vectorize(lambda d: (Time(d, format="iso").to_datetime(
-        leap_second_strict='silent') - TT2000_EPOCH).total_seconds() * ONE_SECOND_IN_NANOSECONDS)
+    first_carrington_start_date = Time(
+        jd_fm_Carrington(float(start_cr_number)), format="jd"
+    )
+    last_cr_end_date = Time(jd_fm_Carrington(float(end_cr_number + 0.5)), format="jd")
+
+    start_ns = (
+        first_carrington_start_date.to_datetime() - TT2000_EPOCH
+    ).total_seconds() * ONE_SECOND_IN_NANOSECONDS
+    end_ns = (
+        last_cr_end_date.to_datetime() - TT2000_EPOCH
+    ).total_seconds() * ONE_SECOND_IN_NANOSECONDS
+
+    vectorized_date_conv = np.vectorize(
+        lambda d: (
+            (
+                Time(d, format="iso").to_datetime(leap_second_strict="silent")
+                - TT2000_EPOCH
+            ).total_seconds()
+            * ONE_SECOND_IN_NANOSECONDS
+        )
+    )
     repoint_starts = vectorized_date_conv(repointing_data["repoint_start_utc"])
     repoint_ends = vectorized_date_conv(repointing_data["repoint_end_utc"])
     repoint_ids = repointing_data["repoint_id"]
 
     repoint_numbers = []
     for i in range(len(repoint_ids)):
-        if i + 1 < len(repoint_ids) and start_ns < (repoint_starts[i + 1] + repoint_ends[i])/2 < end_ns:
+        if (
+            i + 1 < len(repoint_ids)
+            and start_ns < (repoint_starts[i + 1] + repoint_ends[i]) / 2 < end_ns
+        ):
             repoint_numbers.append(int(repoint_ids[i]))
 
     return repoint_numbers
 
+
 def calculate_energy_deltas(centers: np.ndarray):
     edges = np.empty_like(centers, shape=(len(centers) + 1,))
     edges[1:-1] = np.sqrt(centers[1:] * centers[:-1])
@@ -268,4 +381,3 @@ def calculate_energy_deltas(centers: np.ndarray):
     delta_minus = centers - edges[:-1]
 
     return delta_plus, delta_minus
-
diff --git a/imap_l3_processing/glows/l3e/reprocess_info.py b/imap_l3_processing/glows/l3e/reprocess_info.py
index efea81565..555c37699 100644
--- a/imap_l3_processing/glows/l3e/reprocess_info.py
+++ b/imap_l3_processing/glows/l3e/reprocess_info.py
@@ -57,16 +57,22 @@ def _parse_header(line: str) -> datetime:
             ignore_after = ignore_after.replace(tzinfo=timezone.utc)
         return ignore_after
 
-
     @staticmethod
     def _parse_line(line: str) -> tuple[str, str, ReprocessTargets]:
         product_name, _, remainder = line.strip().partition(" ")
         data_level, _, descriptor = product_name.partition("_")
 
         targets = remainder.replace(",", " ").split()
-        repoints = [int(target.removeprefix(REPOINT_PREFIX)) for target in targets if target.startswith(REPOINT_PREFIX)]
-        carrington_rotations = [int(target.removeprefix(CARRINGTON_ROTATION_PREFIX)) for target in targets if
-                                target.startswith(CARRINGTON_ROTATION_PREFIX)]
+        repoints = [
+            int(target.removeprefix(REPOINT_PREFIX))
+            for target in targets
+            if target.startswith(REPOINT_PREFIX)
+        ]
+        carrington_rotations = [
+            int(target.removeprefix(CARRINGTON_ROTATION_PREFIX))
+            for target in targets
+            if target.startswith(CARRINGTON_ROTATION_PREFIX)
+        ]
 
         return data_level, descriptor, ReprocessTargets(repoints, carrington_rotations)
 
@@ -87,11 +93,18 @@ def get_repoints_for_descriptor(
         repoints = set(reprocess_targets.repoints)
 
         for cr in reprocess_targets.carrington_rotations:
-            repoints.update(get_repoint_numbers_within_cr_window(cr, cr, repointing_data))
+            repoints.update(
+                get_repoint_numbers_within_cr_window(cr, cr, repointing_data)
+            )
 
         return sorted(repoints)
 
+
 def fetch_reprocess_info(input_collection: ProcessingInputCollection) -> ReprocessInfo:
-    [reprocessing_file_path] = input_collection.get_file_paths(source="glows", descriptor=GLOWS_REPROCESSING_DESCRIPTOR)
-    path_to_downloaded_ancillary = imap_data_access.download(reprocessing_file_path.name)
-    return ReprocessInfo.parse_from_ancillary(path_to_downloaded_ancillary)
\ No newline at end of file
+    [reprocessing_file_path] = input_collection.get_file_paths(
+        source="glows", descriptor=GLOWS_REPROCESSING_DESCRIPTOR
+    )
+    path_to_downloaded_ancillary = imap_data_access.download(
+        reprocessing_file_path.name
+    )
+    return ReprocessInfo.parse_from_ancillary(path_to_downloaded_ancillary)
diff --git a/imap_l3_processing/glows/quality_flags.py b/imap_l3_processing/glows/quality_flags.py
index af1878fe8..c8f4e6395 100644
--- a/imap_l3_processing/glows/quality_flags.py
+++ b/imap_l3_processing/glows/quality_flags.py
@@ -1,11 +1,10 @@
 from imap_processing.quality_flags import CommonFlags, FlagNameMixin
 
-
 NOMINAL_ALPHA_PROTON_RATIO_VALUE: float = 0.04
 
 
 class GlowsL3Flags(FlagNameMixin):
     NONE = CommonFlags.NONE
-    NOMINAL_ALPHA_PROTON_RATIO = 2 ** 2
-    PERSISTED_LAST_POINT = 2 ** 3
-    PREDICTIVE_EPHEMERIS = 2 ** 15
+    NOMINAL_ALPHA_PROTON_RATIO = 2**2
+    PERSISTED_LAST_POINT = 2**3
+    PREDICTIVE_EPHEMERIS = 2**15
diff --git a/imap_l3_processing/hi/hi_combined_initializer.py b/imap_l3_processing/hi/hi_combined_initializer.py
index 4c88bbd0c..c834736ed 100644
--- a/imap_l3_processing/hi/hi_combined_initializer.py
+++ b/imap_l3_processing/hi/hi_combined_initializer.py
@@ -5,9 +5,14 @@
 from dateutil.relativedelta import relativedelta
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.maps.map_descriptors import MapDescriptorParts, parse_map_descriptor, Sensor, \
-    map_descriptor_parts_to_string, SpinPhase
-from imap_l3_processing.maps.map_initializer import PossibleMapToProduce, MapInitializer
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    Sensor,
+    SpinPhase,
+    map_descriptor_parts_to_string,
+    parse_map_descriptor,
+)
+from imap_l3_processing.maps.map_initializer import MapInitializer, PossibleMapToProduce
 from imap_l3_processing.models import InputMetadata, VersionMap
 
 HI_COMBINED_DESCRIPTORS = [
@@ -25,37 +30,58 @@ class HiCombinedInitializer(MapInitializer):
     input_map_filenames: dict[str, list[str]]
 
     def __init__(self, canonical_map_start_date: datetime = datetime(2026, 1, 1)):
-        super().__init__('hi')
+        super().__init__("hi")
 
         l2_query_results = imap_data_access.query(instrument="hi", data_level="l2")
-        l2_by_descriptor_and_start_date = self.get_latest_version_by_descriptor_and_start_date(l2_query_results)
+        l2_by_descriptor_and_start_date = (
+            self.get_latest_version_by_descriptor_and_start_date(l2_query_results)
+        )
         self.input_maps = dict(self.existing_l3_maps, **l2_by_descriptor_and_start_date)
         self.canonical_map_start_date = canonical_map_start_date
 
     def furnish_spice_dependencies(self, map_to_produce: PossibleMapToProduce):
         pass
 
-    def get_maps_that_can_be_produced(self, input_descriptor: str) -> list[PossibleMapToProduce]:
+    def get_maps_that_can_be_produced(
+        self, input_descriptor: str
+    ) -> list[PossibleMapToProduce]:
         possible_maps_to_produce = []
         map_descriptor: MapDescriptorParts = parse_map_descriptor(input_descriptor)
 
         hi90_ram_descriptor = map_descriptor_parts_to_string(
-            dataclasses.replace(map_descriptor, sensor=Sensor.Hi90, spin_phase=SpinPhase.RamOnly))
+            dataclasses.replace(
+                map_descriptor, sensor=Sensor.Hi90, spin_phase=SpinPhase.RamOnly
+            )
+        )
         hi45_ram_descriptor = map_descriptor_parts_to_string(
-            dataclasses.replace(map_descriptor, sensor=Sensor.Hi45, spin_phase=SpinPhase.RamOnly))
+            dataclasses.replace(
+                map_descriptor, sensor=Sensor.Hi45, spin_phase=SpinPhase.RamOnly
+            )
+        )
 
         hi90_anti_descriptor = map_descriptor_parts_to_string(
-            dataclasses.replace(map_descriptor, sensor=Sensor.Hi90, spin_phase=SpinPhase.AntiRamOnly))
+            dataclasses.replace(
+                map_descriptor, sensor=Sensor.Hi90, spin_phase=SpinPhase.AntiRamOnly
+            )
+        )
         hi45_anti_descriptor = map_descriptor_parts_to_string(
-            dataclasses.replace(map_descriptor, sensor=Sensor.Hi45, spin_phase=SpinPhase.AntiRamOnly))
-
-        if hi90_ram_descriptor not in self.input_maps or hi90_anti_descriptor not in self.input_maps or hi45_ram_descriptor not in self.input_maps or hi45_anti_descriptor not in self.input_maps:
+            dataclasses.replace(
+                map_descriptor, sensor=Sensor.Hi45, spin_phase=SpinPhase.AntiRamOnly
+            )
+        )
+
+        if (
+            hi90_ram_descriptor not in self.input_maps
+            or hi90_anti_descriptor not in self.input_maps
+            or hi45_ram_descriptor not in self.input_maps
+            or hi45_anti_descriptor not in self.input_maps
+        ):
             return []
 
         hi90_start_dates = sorted(self.input_maps[hi90_ram_descriptor].keys())
 
         for start_date_str in hi90_start_dates:
-            start_date = datetime.strptime(start_date_str, '%Y%m%d')
+            start_date = datetime.strptime(start_date_str, "%Y%m%d")
 
             input_files = [
                 self.input_maps[hi90_ram_descriptor].get(start_date_str),
@@ -67,17 +93,19 @@ def get_maps_that_can_be_produced(self, input_descriptor: str) -> list[PossibleM
             if not all(input_files):
                 continue
 
-            possible_maps_to_produce.append(PossibleMapToProduce(
-                input_files=set(input_files),
-                input_metadata=InputMetadata(
-                    instrument='hi',
-                    data_level='l3',
-                    start_date=start_date,
-                    end_date=start_date + relativedelta(months=+12),
-                    version=VersionMap({},Version(None,1)),
-                    descriptor=input_descriptor,
-                    repointing=None
+            possible_maps_to_produce.append(
+                PossibleMapToProduce(
+                    input_files=set(input_files),
+                    input_metadata=InputMetadata(
+                        instrument="hi",
+                        data_level="l3",
+                        start_date=start_date,
+                        end_date=start_date + relativedelta(months=+12),
+                        version=VersionMap({}, Version(None, 1)),
+                        descriptor=input_descriptor,
+                        repointing=None,
+                    ),
                 )
-            ))
+            )
 
         return possible_maps_to_produce
diff --git a/imap_l3_processing/hi/hi_combined_sensor_dependencies.py b/imap_l3_processing/hi/hi_combined_sensor_dependencies.py
index 8f71ca572..2992d05cf 100644
--- a/imap_l3_processing/hi/hi_combined_sensor_dependencies.py
+++ b/imap_l3_processing/hi/hi_combined_sensor_dependencies.py
@@ -15,15 +15,30 @@ class HiL3CombinedMapDependencies:
     h90_maps: list[RectangularIntensityMapData]
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> HiL3CombinedMapDependencies:
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection
+    ) -> HiL3CombinedMapDependencies:
         input_map_filenames = dependencies.get_file_paths(source="hi")
 
-        map_files = [imap_data_access.download(input_map.name) for input_map in input_map_filenames]
+        map_files = [
+            imap_data_access.download(input_map.name)
+            for input_map in input_map_filenames
+        ]
         return cls.from_file_paths(map_files)
 
     @classmethod
-    def from_file_paths(cls, hi_l3_map_paths: list[Path]) -> HiL3CombinedMapDependencies:
-        return cls([RectangularIntensityMapData.read_from_path(path) for path in hi_l3_map_paths if
-                    'h45' == path.name.split('_')[3][:3]],
-                   [RectangularIntensityMapData.read_from_path(path) for path in hi_l3_map_paths if
-                    'h90' == path.name.split('_')[3][:3]])
+    def from_file_paths(
+        cls, hi_l3_map_paths: list[Path]
+    ) -> HiL3CombinedMapDependencies:
+        return cls(
+            [
+                RectangularIntensityMapData.read_from_path(path)
+                for path in hi_l3_map_paths
+                if "h45" == path.name.split("_")[3][:3]
+            ],
+            [
+                RectangularIntensityMapData.read_from_path(path)
+                for path in hi_l3_map_paths
+                if "h90" == path.name.split("_")[3][:3]
+            ],
+        )
diff --git a/imap_l3_processing/hi/hi_processor.py b/imap_l3_processing/hi/hi_processor.py
index 5d2a7c052..de7c0e7d9 100644
--- a/imap_l3_processing/hi/hi_processor.py
+++ b/imap_l3_processing/hi/hi_processor.py
@@ -1,100 +1,162 @@
 from pathlib import Path
-from typing import Optional
 
 from imap_processing.spice.geometry import SpiceFrame
 
-from imap_l3_processing.hi.hi_combined_sensor_dependencies import HiL3CombinedMapDependencies
-from imap_l3_processing.hi.hi_spectral_fit_dependencies import HiSpectralIndexDependencies
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies, \
-    HiL3SingleSensorFullSpinDependencies
-from imap_l3_processing.maps.map_combination import UncertaintyWeightedCombination, UnweightedCombination, \
-    CombinationStrategy, ExposureWeightedCombination
-from imap_l3_processing.maps.map_descriptors import parse_map_descriptor, MapQuantity, MapDescriptorParts, \
-    SurvivalCorrection, \
-    SpinPhase, Sensor, ReferenceFrame
-from imap_l3_processing.maps.map_models import RectangularSpectralIndexDataProduct, RectangularSpectralIndexMapData, \
-    RectangularIntensityMapData, RectangularIntensityDataProduct
+from imap_l3_processing.hi.hi_combined_sensor_dependencies import (
+    HiL3CombinedMapDependencies,
+)
+from imap_l3_processing.hi.hi_spectral_fit_dependencies import (
+    HiSpectralIndexDependencies,
+)
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiL3SingleSensorFullSpinDependencies,
+    HiLoL3SurvivalDependencies,
+)
+from imap_l3_processing.maps.map_combination import (
+    CombinationStrategy,
+    ExposureWeightedCombination,
+    UncertaintyWeightedCombination,
+    UnweightedCombination,
+)
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    MapQuantity,
+    ReferenceFrame,
+    Sensor,
+    SpinPhase,
+    SurvivalCorrection,
+    parse_map_descriptor,
+)
+from imap_l3_processing.maps.map_models import (
+    RectangularIntensityDataProduct,
+    RectangularIntensityMapData,
+    RectangularSpectralIndexDataProduct,
+    RectangularSpectralIndexMapData,
+)
 from imap_l3_processing.maps.map_processor import MapProcessor
-from imap_l3_processing.maps.spectral_fit import fit_spectral_index_map, slice_energy_range_by_bin
-from imap_l3_processing.maps.survival_probability_processing import process_survival_probabilities
+from imap_l3_processing.maps.spectral_fit import (
+    fit_spectral_index_map,
+    slice_energy_range_by_bin,
+)
+from imap_l3_processing.maps.survival_probability_processing import (
+    process_survival_probabilities,
+)
 from imap_l3_processing.models import Instrument
 from imap_l3_processing.utils import save_data
 
 
 class HiProcessor(MapProcessor):
-    def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000) -> list[Path]:
+    def process(
+        self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000
+    ) -> list[Path]:
         set_of_parent_file_names = set(self.get_parent_file_names())
 
         parsed_descriptor = parse_map_descriptor(self.input_metadata.descriptor)
         match parsed_descriptor:
             case MapDescriptorParts(quantity=MapQuantity.SpectralIndex):
-                hi_l3_spectral_fit_dependencies = HiSpectralIndexDependencies.fetch_dependencies(self.dependencies)
-                map_data = self.process_spectral_fit_index(hi_l3_spectral_fit_dependencies, parsed_descriptor.spectral_index_energy_range)
+                hi_l3_spectral_fit_dependencies = (
+                    HiSpectralIndexDependencies.fetch_dependencies(self.dependencies)
+                )
+                map_data = self.process_spectral_fit_index(
+                    hi_l3_spectral_fit_dependencies,
+                    parsed_descriptor.spectral_index_energy_range,
+                )
                 data_product = RectangularSpectralIndexDataProduct(
                     data=map_data,
                     input_metadata=self.input_metadata,
-                    spice_frame_name=spice_frame_name
+                    spice_frame_name=spice_frame_name,
+                )
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                sensor=Sensor.Hi90 | Sensor.Hi45,
+                spin_phase=SpinPhase.RamOnly | SpinPhase.AntiRamOnly,
+            ):
+                hi_l3_survival_probabilities_dependencies = (
+                    HiLoL3SurvivalDependencies.fetch_dependencies(
+                        self.dependencies, Instrument.IMAP_HI
+                    )
                 )
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    sensor=Sensor.Hi90 | Sensor.Hi45,
-                                    spin_phase=SpinPhase.RamOnly | SpinPhase.AntiRamOnly):
-                hi_l3_survival_probabilities_dependencies = HiLoL3SurvivalDependencies.fetch_dependencies(
-                    self.dependencies, Instrument.IMAP_HI)
                 data_product = RectangularIntensityDataProduct(
-                    data=process_survival_probabilities(hi_l3_survival_probabilities_dependencies, spice_frame_name),
+                    data=process_survival_probabilities(
+                        hi_l3_survival_probabilities_dependencies, spice_frame_name
+                    ),
                     input_metadata=self.input_metadata,
-                    spice_frame_name=spice_frame_name
+                    spice_frame_name=spice_frame_name,
                 )
                 set_of_parent_file_names.update(
-                    p.name for p in hi_l3_survival_probabilities_dependencies.dependency_file_paths)
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    sensor=Sensor.Hi90 | Sensor.Hi45,
-                                    spin_phase=SpinPhase.FullSpin,
-                                    reference_frame=ReferenceFrame.Spacecraft):
-                hi_l3_full_spin_dependencies = HiL3SingleSensorFullSpinDependencies.fetch_dependencies(
-                    self.dependencies)
+                    p.name
+                    for p in hi_l3_survival_probabilities_dependencies.dependency_file_paths
+                )
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                sensor=Sensor.Hi90 | Sensor.Hi45,
+                spin_phase=SpinPhase.FullSpin,
+                reference_frame=ReferenceFrame.Spacecraft,
+            ):
+                hi_l3_full_spin_dependencies = (
+                    HiL3SingleSensorFullSpinDependencies.fetch_dependencies(
+                        self.dependencies
+                    )
+                )
                 combination_strategy = UnweightedCombination()
-                combined_map = self.process_full_spin_single_sensor(hi_l3_full_spin_dependencies, spice_frame_name,
-                                                                    combination_strategy)
+                combined_map = self.process_full_spin_single_sensor(
+                    hi_l3_full_spin_dependencies, spice_frame_name, combination_strategy
+                )
                 data_product = RectangularIntensityDataProduct(
                     data=combined_map,
                     input_metadata=self.input_metadata,
-                    spice_frame_name=spice_frame_name
+                    spice_frame_name=spice_frame_name,
+                )
+                set_of_parent_file_names.update(
+                    p.name for p in hi_l3_full_spin_dependencies.dependency_file_paths
+                )
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                sensor=Sensor.Hi90 | Sensor.Hi45,
+                spin_phase=SpinPhase.FullSpin,
+                reference_frame=ReferenceFrame.Heliospheric,
+            ):
+                hi_l3_full_spin_cg_corrected_dependencies = (
+                    HiL3SingleSensorFullSpinDependencies.fetch_dependencies(
+                        self.dependencies
+                    )
                 )
-                set_of_parent_file_names.update(p.name for p in hi_l3_full_spin_dependencies.dependency_file_paths)
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    sensor=Sensor.Hi90 | Sensor.Hi45,
-                                    spin_phase=SpinPhase.FullSpin,
-                                    reference_frame=ReferenceFrame.Heliospheric):
-
-                hi_l3_full_spin_cg_corrected_dependencies = HiL3SingleSensorFullSpinDependencies.fetch_dependencies(
-                    self.dependencies)
 
                 combination_strategy = UncertaintyWeightedCombination()
-                combined_map = self.process_full_spin_single_sensor(hi_l3_full_spin_cg_corrected_dependencies,
-                                                                    spice_frame_name, combination_strategy)
+                combined_map = self.process_full_spin_single_sensor(
+                    hi_l3_full_spin_cg_corrected_dependencies,
+                    spice_frame_name,
+                    combination_strategy,
+                )
                 data_product = RectangularIntensityDataProduct(
                     data=combined_map,
                     input_metadata=self.input_metadata,
-                    spice_frame_name=spice_frame_name
+                    spice_frame_name=spice_frame_name,
                 )
             case MapDescriptorParts(sensor=Sensor.HiCombined):
-                dependencies = HiL3CombinedMapDependencies.fetch_dependencies(self.dependencies)
+                dependencies = HiL3CombinedMapDependencies.fetch_dependencies(
+                    self.dependencies
+                )
                 h45_full_spin_12mo = UncertaintyWeightedCombination().combine_rectangular_intensity_map_data(
-                    dependencies.h45_maps)
+                    dependencies.h45_maps
+                )
                 h90_full_spin_12mo = UncertaintyWeightedCombination().combine_rectangular_intensity_map_data(
-                    dependencies.h90_maps)
+                    dependencies.h90_maps
+                )
 
                 combined_map_data = ExposureWeightedCombination().combine_rectangular_intensity_map_data(
-                    [h45_full_spin_12mo, h90_full_spin_12mo])
+                    [h45_full_spin_12mo, h90_full_spin_12mo]
+                )
 
                 data_product = RectangularIntensityDataProduct(
                     data=combined_map_data,
                     input_metadata=self.input_metadata,
-                    spice_frame_name=spice_frame_name
+                    spice_frame_name=spice_frame_name,
                 )
             case None:
-                raise ValueError(f"Could not parse descriptor {self.input_metadata.descriptor}")
+                raise ValueError(
+                    f"Could not parse descriptor {self.input_metadata.descriptor}"
+                )
             case _:
                 raise NotImplementedError(self.input_metadata.descriptor)
 
@@ -102,23 +164,35 @@ def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000) -> list[
 
         return [save_data(data_product)]
 
-    def process_full_spin_single_sensor(self, hi_l3_full_spin_dependencies: HiL3SingleSensorFullSpinDependencies,
-                                        spice_frame_name: SpiceFrame, combination_strategy: CombinationStrategy) \
-            -> RectangularIntensityMapData:
-        ram_data_product = process_survival_probabilities(hi_l3_full_spin_dependencies.ram_dependencies,
-                                                          spice_frame_name)
-        antiram_data_product = process_survival_probabilities(hi_l3_full_spin_dependencies.antiram_dependencies,
-                                                              spice_frame_name)
+    def process_full_spin_single_sensor(
+        self,
+        hi_l3_full_spin_dependencies: HiL3SingleSensorFullSpinDependencies,
+        spice_frame_name: SpiceFrame,
+        combination_strategy: CombinationStrategy,
+    ) -> RectangularIntensityMapData:
+        ram_data_product = process_survival_probabilities(
+            hi_l3_full_spin_dependencies.ram_dependencies, spice_frame_name
+        )
+        antiram_data_product = process_survival_probabilities(
+            hi_l3_full_spin_dependencies.antiram_dependencies, spice_frame_name
+        )
 
-        return combination_strategy.combine_rectangular_intensity_map_data([ram_data_product, antiram_data_product])
+        return combination_strategy.combine_rectangular_intensity_map_data(
+            [ram_data_product, antiram_data_product]
+        )
 
-    def process_spectral_fit_index(self, hi_l3_spectral_fit_dependencies: HiSpectralIndexDependencies, spectral_index_energy_range: Optional[tuple[int,int]] = None) \
-            -> RectangularSpectralIndexMapData:
+    def process_spectral_fit_index(
+        self,
+        hi_l3_spectral_fit_dependencies: HiSpectralIndexDependencies,
+        spectral_index_energy_range: tuple[int, int] | None = None,
+    ) -> RectangularSpectralIndexMapData:
         intensity_map_data = hi_l3_spectral_fit_dependencies.map_data.intensity_map_data
         if spectral_index_energy_range is not None:
             start_bin, end_bin = spectral_index_energy_range
-            intensity_map_data = slice_energy_range_by_bin(intensity_map_data, start_bin, end_bin)
+            intensity_map_data = slice_energy_range_by_bin(
+                intensity_map_data, start_bin, end_bin
+            )
         return RectangularSpectralIndexMapData(
             spectral_index_map_data=fit_spectral_index_map(intensity_map_data),
-            coords=hi_l3_spectral_fit_dependencies.map_data.coords
+            coords=hi_l3_spectral_fit_dependencies.map_data.coords,
         )
diff --git a/imap_l3_processing/hi/hi_sp_initializer.py b/imap_l3_processing/hi/hi_sp_initializer.py
index bfaad67ad..664107ddb 100644
--- a/imap_l3_processing/hi/hi_sp_initializer.py
+++ b/imap_l3_processing/hi/hi_sp_initializer.py
@@ -4,7 +4,12 @@
 
 import imap_data_access
 
-from imap_l3_processing.maps.map_descriptors import MapDescriptorParts, Sensor, SurvivalCorrection, SpinPhase
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    Sensor,
+    SpinPhase,
+    SurvivalCorrection,
+)
 from imap_l3_processing.maps.map_initializer import PossibleMapToProduce
 from imap_l3_processing.maps.sp_map_initializer import SPMapInitializer
 from imap_l3_processing.utils import SpiceKernelTypes, furnish_spice_metakernel
@@ -54,7 +59,6 @@
     "h90-ena-h-sf-sp-ram-hae-6deg-1yr",
     "h90-ena-h-sf-sp-anti-hae-6deg-1yr",
     "h90-ena-h-sf-sp-full-hae-6deg-6mo",
-
     "h45-ena-h-hf-sp-ram-hae-4deg-1yr",
     "h45-ena-h-hf-sp-anti-hae-4deg-1yr",
     "h90-ena-h-hf-sp-ram-hae-4deg-1yr",
@@ -66,54 +70,80 @@
     "h90-ena-h-hf-sp-ram-hae-6deg-1yr",
     "h90-ena-h-hf-sp-anti-hae-6deg-1yr",
     "h45-ena-h-hf-sp-full-hae-6deg-6mo",
-    "h90-ena-h-hf-sp-full-hae-6deg-6mo"
+    "h90-ena-h-hf-sp-full-hae-6deg-6mo",
 ]
 
 
 class HiSPInitializer(SPMapInitializer):
     def __init__(self):
         sp_hi45_query_result = imap_data_access.query(
-            instrument='glows',
-            data_level='l3e',
-            descriptor=f'survival-probability-hi-45',
-            version='latest'
+            instrument="glows",
+            data_level="l3e",
+            descriptor="survival-probability-hi-45",
+            version="latest",
         )
-        self.glows_hi45_file_by_repoint = {int(r["repointing"]): Path(r["file_path"]).name for r in
-                                           sp_hi45_query_result}
+        self.glows_hi45_file_by_repoint = {
+            int(r["repointing"]): Path(r["file_path"]).name
+            for r in sp_hi45_query_result
+        }
 
         sp_hi90_query_result = imap_data_access.query(
-            instrument='glows',
-            data_level='l3e',
-            descriptor=f'survival-probability-hi-90',
-            version='latest'
+            instrument="glows",
+            data_level="l3e",
+            descriptor="survival-probability-hi-90",
+            version="latest",
         )
-        self.glows_hi90_file_by_repoint = {int(r["repointing"]): Path(r["file_path"]).name for r in
-                                           sp_hi90_query_result}
+        self.glows_hi90_file_by_repoint = {
+            int(r["repointing"]): Path(r["file_path"]).name
+            for r in sp_hi90_query_result
+        }
 
-        hi_l2_query_result = imap_data_access.query(instrument='hi', data_level='l2')
+        hi_l2_query_result = imap_data_access.query(instrument="hi", data_level="l2")
         super().__init__("hi", hi_l2_query_result)
 
     def furnish_spice_dependencies(self, map_to_produce: PossibleMapToProduce):
-        furnish_spice_metakernel(start_date=map_to_produce.input_metadata.start_date,
-                                 end_date=map_to_produce.input_metadata.end_date, kernel_types=HI_SP_SPICE_KERNELS)
+        furnish_spice_metakernel(
+            start_date=map_to_produce.input_metadata.start_date,
+            end_date=map_to_produce.input_metadata.end_date,
+            kernel_types=HI_SP_SPICE_KERNELS,
+        )
 
-    def _get_l2_dependencies(self, descriptor: MapDescriptorParts) -> list[MapDescriptorParts]:
+    def _get_l2_dependencies(
+        self, descriptor: MapDescriptorParts
+    ) -> list[MapDescriptorParts]:
         match descriptor:
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    spin_phase=SpinPhase.RamOnly | SpinPhase.AntiRamOnly):
-                return [dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected)]
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    spin_phase=SpinPhase.FullSpin):
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                spin_phase=SpinPhase.RamOnly | SpinPhase.AntiRamOnly,
+            ):
+                return [
+                    dataclasses.replace(
+                        descriptor,
+                        survival_correction=SurvivalCorrection.NotSurvivalCorrected,
+                    )
+                ]
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                spin_phase=SpinPhase.FullSpin,
+            ):
                 return [
-                    dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected,
-                                        spin_phase=SpinPhase.AntiRamOnly),
-                    dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected,
-                                        spin_phase=SpinPhase.RamOnly),
+                    dataclasses.replace(
+                        descriptor,
+                        survival_correction=SurvivalCorrection.NotSurvivalCorrected,
+                        spin_phase=SpinPhase.AntiRamOnly,
+                    ),
+                    dataclasses.replace(
+                        descriptor,
+                        survival_correction=SurvivalCorrection.NotSurvivalCorrected,
+                        spin_phase=SpinPhase.RamOnly,
+                    ),
                 ]
             case _:
                 raise ValueError("Expected map to be produced to use a single sensor!")
 
-    def _collect_glows_psets_by_repoint(self, descriptor: MapDescriptorParts) -> dict[int, str]:
+    def _collect_glows_psets_by_repoint(
+        self, descriptor: MapDescriptorParts
+    ) -> dict[int, str]:
         match descriptor:
             case MapDescriptorParts(sensor=Sensor.Hi45):
                 return self.glows_hi45_file_by_repoint
diff --git a/imap_l3_processing/hi/hi_spectral_fit_dependencies.py b/imap_l3_processing/hi/hi_spectral_fit_dependencies.py
index 86374c3fb..4462a4aa5 100644
--- a/imap_l3_processing/hi/hi_spectral_fit_dependencies.py
+++ b/imap_l3_processing/hi/hi_spectral_fit_dependencies.py
@@ -7,16 +7,21 @@
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection
 
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData, SpectralIndexDependencies
+from imap_l3_processing.maps.map_models import (
+    RectangularIntensityMapData,
+    SpectralIndexDependencies,
+)
 
 
 @dataclass
 class HiSpectralIndexDependencies(SpectralIndexDependencies):
-
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> HiSpectralIndexDependencies:
-        input_map_filenames = dependencies.get_file_paths(source="hi", data_type="l2") + \
-                              dependencies.get_file_paths(source="hi", data_type="l3")
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection
+    ) -> HiSpectralIndexDependencies:
+        input_map_filenames = dependencies.get_file_paths(
+            source="hi", data_type="l2"
+        ) + dependencies.get_file_paths(source="hi", data_type="l3")
 
         if len(input_map_filenames) != 1:
             raise ValueError("Missing Hi dependency.")
@@ -30,8 +35,12 @@ def from_file_paths(cls, hi_l3_path: Path) -> HiSpectralIndexDependencies:
 
     def get_fit_energy_ranges(self) -> np.ndarray:
         energy_min = np.min(
-            self.map_data.intensity_map_data.energy - self.map_data.intensity_map_data.energy_delta_minus)
+            self.map_data.intensity_map_data.energy
+            - self.map_data.intensity_map_data.energy_delta_minus
+        )
         energy_max = np.max(
-            self.map_data.intensity_map_data.energy + self.map_data.intensity_map_data.energy_delta_plus)
+            self.map_data.intensity_map_data.energy
+            + self.map_data.intensity_map_data.energy_delta_plus
+        )
 
         return np.array([[energy_min, energy_max]])
diff --git a/imap_l3_processing/hi/utils.py b/imap_l3_processing/hi/utils.py
index b597bda2a..8df199dff 100644
--- a/imap_l3_processing/hi/utils.py
+++ b/imap_l3_processing/hi/utils.py
@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Union
 
 from imap_data_access import ScienceFilePath
 from imap_processing.spice.time import met_to_ttj2000ns
@@ -7,40 +6,60 @@
 
 from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
 from imap_l3_processing.glows.l3e.glows_l3e_hi_model import (
-    PROBABILITY_OF_SURVIVAL_VAR_NAME,
-    EPOCH_CDF_VAR_NAME,
     ENERGY_VAR_NAME,
-    SPIN_ANGLE_VAR_NAME,
+    EPOCH_CDF_VAR_NAME,
     GLOWS_FLAGS_VAR_NAME,
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+    SPIN_ANGLE_VAR_NAME,
+)
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
 )
-from imap_l3_processing.maps.map_models import GlowsL3eRectangularMapInputData, InputRectangularPointingSet
 
 
-def read_l1c_rectangular_pointing_set_data(path: Union[Path, str]) -> InputRectangularPointingSet:
+def read_l1c_rectangular_pointing_set_data(
+    path: Path | str,
+) -> InputRectangularPointingSet:
     repointing = ScienceFilePath(path).repointing
     with CDF(str(path)) as cdf:
-        exposure_time_variable = cdf['exposure_time'] if 'exposure_time' in cdf else cdf['exposure_times']
+        exposure_time_variable = (
+            cdf["exposure_time"] if "exposure_time" in cdf else cdf["exposure_times"]
+        )
 
         assert ("epoch_delta" in cdf) or (
-                "pointing_start_met" in cdf and "pointing_end_met" in cdf), "Expected the pset to have either epoch_delta or pointing_start_met and pointing_end_met defined"
-
-        pointing_start_met = cdf["pointing_start_met"][...] if "pointing_start_met" in cdf else None
-        pointing_end_met = cdf["pointing_end_met"][...] if "pointing_end_met" in cdf else None
-        epoch_delta = cdf["epoch_delta"][...] if "epoch_delta" in cdf else met_to_ttj2000ns(
-            pointing_end_met) - met_to_ttj2000ns(pointing_start_met)
-        return InputRectangularPointingSet(epoch=cdf["epoch"][0],
-                                           epoch_delta=epoch_delta,
-                                           epoch_j2000=cdf.raw_var("epoch")[...],
-                                           repointing=repointing,
-                                           exposure_times=exposure_time_variable[...],
-                                           esa_energy_step=cdf["esa_energy_step"][...],
-                                           pointing_start_met=pointing_start_met,
-                                           pointing_end_met=pointing_end_met,
-                                           hae_longitude=cdf['hae_longitude'][...],
-                                           hae_latitude=cdf['hae_latitude'][...])
-
-
-def read_glows_l3e_data(cdf_path: Union[Path, str]) -> GlowsL3eRectangularMapInputData:
+            "pointing_start_met" in cdf and "pointing_end_met" in cdf
+        ), (
+            "Expected the pset to have either epoch_delta or pointing_start_met and pointing_end_met defined"
+        )
+
+        pointing_start_met = (
+            cdf["pointing_start_met"][...] if "pointing_start_met" in cdf else None
+        )
+        pointing_end_met = (
+            cdf["pointing_end_met"][...] if "pointing_end_met" in cdf else None
+        )
+        epoch_delta = (
+            cdf["epoch_delta"][...]
+            if "epoch_delta" in cdf
+            else met_to_ttj2000ns(pointing_end_met)
+            - met_to_ttj2000ns(pointing_start_met)
+        )
+        return InputRectangularPointingSet(
+            epoch=cdf["epoch"][0],
+            epoch_delta=epoch_delta,
+            epoch_j2000=cdf.raw_var("epoch")[...],
+            repointing=repointing,
+            exposure_times=exposure_time_variable[...],
+            esa_energy_step=cdf["esa_energy_step"][...],
+            pointing_start_met=pointing_start_met,
+            pointing_end_met=pointing_end_met,
+            hae_longitude=cdf["hae_longitude"][...],
+            hae_latitude=cdf["hae_latitude"][...],
+        )
+
+
+def read_glows_l3e_data(cdf_path: Path | str) -> GlowsL3eRectangularMapInputData:
     repointing = ScienceFilePath(cdf_path).repointing
     with CDF(str(cdf_path)) as cdf:
         return GlowsL3eRectangularMapInputData(
diff --git a/imap_l3_processing/hit/l3/hit_l3_sectored_dependencies.py b/imap_l3_processing/hit/l3/hit_l3_sectored_dependencies.py
index e07fdc78e..002e38cf8 100644
--- a/imap_l3_processing/hit/l3/hit_l3_sectored_dependencies.py
+++ b/imap_l3_processing/hit/l3/hit_l3_sectored_dependencies.py
@@ -21,12 +21,18 @@ class HITL3SectoredDependencies:
     mag_is_preliminary: bool = False
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> HITL3SectoredDependencies:
-        hit_data_dependency = dependencies.get_file_paths(source="hit", descriptor=HIT_L2_DESCRIPTOR)
-        mag_l2_dependencies = dependencies.get_file_paths(source="mag", data_type="l2",
-                                                          descriptor=MAG_L1D_DESCRIPTOR)
-        mag_l1d_dependencies = dependencies.get_file_paths(source="mag", data_type="l1d",
-                                                           descriptor=MAG_L1D_DESCRIPTOR)
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection
+    ) -> HITL3SectoredDependencies:
+        hit_data_dependency = dependencies.get_file_paths(
+            source="hit", descriptor=HIT_L2_DESCRIPTOR
+        )
+        mag_l2_dependencies = dependencies.get_file_paths(
+            source="mag", data_type="l2", descriptor=MAG_L1D_DESCRIPTOR
+        )
+        mag_l1d_dependencies = dependencies.get_file_paths(
+            source="mag", data_type="l1d", descriptor=MAG_L1D_DESCRIPTOR
+        )
         mag_dependencies = [*mag_l2_dependencies, *mag_l1d_dependencies]
 
         mag_is_preliminary = len(mag_l2_dependencies) == 0
@@ -37,5 +43,6 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> HITL3Sec
         mag_data = read_mag_data(mag_data_path)
         hit_data = read_l2_hit_data(hit_data_path)
 
-        return HITL3SectoredDependencies(data=hit_data, mag_data=mag_data,
-                                         mag_is_preliminary=mag_is_preliminary)
+        return HITL3SectoredDependencies(
+            data=hit_data, mag_data=mag_data, mag_is_preliminary=mag_is_preliminary
+        )
diff --git a/imap_l3_processing/hit/l3/hit_processor.py b/imap_l3_processing/hit/l3/hit_processor.py
index f2a9b2593..6c5adbe6a 100644
--- a/imap_l3_processing/hit/l3/hit_processor.py
+++ b/imap_l3_processing/hit/l3/hit_processor.py
@@ -3,19 +3,31 @@
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection
 
-from imap_l3_processing.constants import UNSIGNED_INT1_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE
-from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import HITL3SectoredDependencies
+from imap_l3_processing.constants import (
+    UNSIGNED_INT1_FILL_VALUE,
+    UNSIGNED_INT2_FILL_VALUE,
+)
+from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import (
+    HITL3SectoredDependencies,
+)
 from imap_l3_processing.hit.l3.models import HitDirectEventDataProduct
 from imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies import HitL3PhaDependencies
 from imap_l3_processing.hit.l3.pha.pha_event_reader import PHAEventReader, RawPHAEvent
 from imap_l3_processing.hit.l3.pha.science.calculate_pha import process_pha_event
 from imap_l3_processing.hit.l3.sectored_products.models import HitPitchAngleDataProduct
-from imap_l3_processing.hit.l3.sectored_products.science.sectored_products_algorithms import get_sector_unit_vectors, \
-    get_hit_bin_polar_coordinates
+from imap_l3_processing.hit.l3.sectored_products.science.sectored_products_algorithms import (
+    get_hit_bin_polar_coordinates,
+    get_sector_unit_vectors,
+)
 from imap_l3_processing.hit.quality_flags import HitL3Flags
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.pitch_angles import calculate_unit_vector, calculate_pitch_angle, calculate_gyrophase, \
-    rotate_particle_vectors_from_hit_despun_to_imap_despun, rebin_by_pitch_angle_and_gyrophase
+from imap_l3_processing.pitch_angles import (
+    calculate_gyrophase,
+    calculate_pitch_angle,
+    calculate_unit_vector,
+    rebin_by_pitch_angle_and_gyrophase,
+    rotate_particle_vectors_from_hit_despun_to_imap_despun,
+)
 from imap_l3_processing.processor import Processor
 from imap_l3_processing.utils import save_data
 
@@ -23,37 +35,51 @@
 
 
 class HitProcessor(Processor):
-    def __init__(self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         super().__init__(dependencies, input_metadata)
 
     def process(self):
         if self.input_metadata.descriptor == "macropixel":
-            dependencies = HITL3SectoredDependencies.fetch_dependencies(self.dependencies)
+            dependencies = HITL3SectoredDependencies.fetch_dependencies(
+                self.dependencies
+            )
             pitch_angle_data_product = self.process_pitch_angle_product(dependencies)
             pitch_angle_data_product.parent_file_names = self.get_parent_file_names()
             cdf_file_path = save_data(pitch_angle_data_product)
             return [cdf_file_path]
         elif self.input_metadata.descriptor == "direct-events":
-            direct_event_dependencies = HitL3PhaDependencies.fetch_dependencies(self.dependencies)
-            direct_event_data_product = self.process_direct_event_product(direct_event_dependencies)
+            direct_event_dependencies = HitL3PhaDependencies.fetch_dependencies(
+                self.dependencies
+            )
+            direct_event_data_product = self.process_direct_event_product(
+                direct_event_dependencies
+            )
             direct_event_data_product.parent_file_names = self.get_parent_file_names()
             cdf_file_path = save_data(direct_event_data_product)
             return [cdf_file_path]
         else:
             raise ValueError(
-                f"Don't know how to generate '{self.input_metadata.descriptor}' /n Known HIT l3 data products: 'macropixel', 'direct-events'.")
+                f"Don't know how to generate '{self.input_metadata.descriptor}' /n Known HIT l3 data products: 'macropixel', 'direct-events'."
+            )
 
-    def process_direct_event_product(self,
-                                     direct_event_dependencies: HitL3PhaDependencies) -> HitDirectEventDataProduct:
+    def process_direct_event_product(
+        self, direct_event_dependencies: HitL3PhaDependencies
+    ) -> HitDirectEventDataProduct:
 
         epochs = []
         raw_pha_events: list[RawPHAEvent] = []
-        for epoch, event_binary in zip(direct_event_dependencies.hit_l1_data.epoch,
-                                       direct_event_dependencies.hit_l1_data.event_binary):
+        for epoch, event_binary in zip(
+            direct_event_dependencies.hit_l1_data.epoch,
+            direct_event_dependencies.hit_l1_data.event_binary,
+        ):
             try:
                 event_raw_pha_events = PHAEventReader.read_all_pha_events(event_binary)
-            except Exception as e:
-                logger.warning(f"Could not read all pha events for epoch {epoch}", exc_info=True)
+            except Exception:
+                logger.warning(
+                    f"Could not read all pha events for epoch {epoch}", exc_info=True
+                )
                 continue
 
             epochs += [epoch] * len(event_raw_pha_events)
@@ -64,9 +90,15 @@ def process_direct_event_product(self,
         e_delta = np.full(shape=(len(raw_pha_events)), fill_value=np.nan)
         e_prime = np.full(shape=(len(raw_pha_events)), fill_value=np.nan)
         detected_range = np.full(shape=(len(raw_pha_events)), fill_value=np.nan)
-        particle_id = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE)
-        priority_buffer_number = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT1_FILL_VALUE)
-        latency = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT1_FILL_VALUE)
+        particle_id = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE
+        )
+        priority_buffer_number = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT1_FILL_VALUE
+        )
+        latency = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT1_FILL_VALUE
+        )
         stim_tag = np.full(shape=(len(raw_pha_events)), fill_value=False)
         long_event_flag = np.full(shape=(len(raw_pha_events)), fill_value=False)
         haz_tag = np.full(shape=(len(raw_pha_events)), fill_value=False)
@@ -74,17 +106,29 @@ def process_direct_event_product(self,
         has_unread_adcs = np.full(shape=(len(raw_pha_events)), fill_value=False)
         culling_flag = np.full(shape=(len(raw_pha_events)), fill_value=False)
 
-        pha_value = np.full(shape=(len(raw_pha_events), 64), fill_value=UNSIGNED_INT2_FILL_VALUE)
+        pha_value = np.full(
+            shape=(len(raw_pha_events), 64), fill_value=UNSIGNED_INT2_FILL_VALUE
+        )
         energy_at_detector = np.full(shape=(len(raw_pha_events), 64), fill_value=np.nan)
         is_low_gain = np.full(shape=(len(raw_pha_events), 64), fill_value=False)
 
-        detector_flags = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE)
-        deindex = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE)
-        epindex = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE)
+        detector_flags = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE
+        )
+        deindex = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE
+        )
+        epindex = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE
+        )
         stim_gain = np.full(shape=(len(raw_pha_events)), fill_value=False)
         a_l_stim = np.full(shape=(len(raw_pha_events)), fill_value=False)
-        stim_step = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT1_FILL_VALUE)
-        dac_value = np.full(shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE)
+        stim_step = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT1_FILL_VALUE
+        )
+        dac_value = np.full(
+            shape=(len(raw_pha_events)), fill_value=UNSIGNED_INT2_FILL_VALUE
+        )
 
         for i, raw_event in enumerate(raw_pha_events):
             event_output = process_pha_event(
@@ -92,7 +136,7 @@ def process_direct_event_product(self,
                 direct_event_dependencies.cosine_correction_lookup,
                 direct_event_dependencies.gain_lookup,
                 direct_event_dependencies.range_fit_lookup,
-                direct_event_dependencies.event_type_lookup
+                direct_event_dependencies.event_type_lookup,
             )
 
             charge[i] = event_output.charge
@@ -115,7 +159,9 @@ def process_direct_event_product(self,
             has_unread_adcs[i] = raw_event.has_unread_adcs
             culling_flag[i] = raw_event.culling_flag
 
-            for event_energy_at_detector, word in zip(event_output.energies, raw_event.pha_words):
+            for event_energy_at_detector, word in zip(
+                event_output.energies, raw_event.pha_words
+            ):
                 pha_value[i, word.detector.address] = word.adc_value
                 energy_at_detector[i, word.detector.address] = event_energy_at_detector
                 is_low_gain[i, word.detector.address] = word.is_low_gain
@@ -161,7 +207,9 @@ def process_direct_event_product(self,
             input_metadata=self.input_metadata,
         )
 
-    def process_pitch_angle_product(self, dependencies: HITL3SectoredDependencies) -> HitPitchAngleDataProduct:
+    def process_pitch_angle_product(
+        self, dependencies: HITL3SectoredDependencies
+    ) -> HitPitchAngleDataProduct:
         number_of_pitch_angle_bins = 8
         number_of_gyrophase_bins = 15
 
@@ -170,45 +218,102 @@ def process_pitch_angle_product(self, dependencies: HITL3SectoredDependencies) -
 
         input_intensity_data_by_species = {
             "hydrogen": (hit_data.h, hit_data.delta_plus_h, hit_data.delta_minus_h),
-            "helium4": (hit_data.he4, hit_data.delta_plus_he4, hit_data.delta_minus_he4),
+            "helium4": (
+                hit_data.he4,
+                hit_data.delta_plus_he4,
+                hit_data.delta_minus_he4,
+            ),
             "cno": (hit_data.cno, hit_data.delta_plus_cno, hit_data.delta_minus_cno),
-            "NeMgSi": (hit_data.nemgsi, hit_data.delta_plus_nemgsi, hit_data.delta_minus_nemgsi),
-            "iron": (hit_data.fe, hit_data.delta_plus_fe, hit_data.delta_minus_fe)}
+            "NeMgSi": (
+                hit_data.nemgsi,
+                hit_data.delta_plus_nemgsi,
+                hit_data.delta_minus_nemgsi,
+            ),
+            "iron": (hit_data.fe, hit_data.delta_plus_fe, hit_data.delta_minus_fe),
+        }
         epoch_count = len(hit_data.epoch)
         cno_energy_count = hit_data.cno.shape[1]
         he4_energy_count = hit_data.he4.shape[1]
         h_energy_count = hit_data.h.shape[1]
         fe_energy_count = hit_data.fe.shape[1]
         nemgsi_energy_count = hit_data.nemgsi.shape[1]
-        rebinned_pa_gyro_intensity_by_species = {"cno": self._create_nan_array(
-            (epoch_count, cno_energy_count, number_of_pitch_angle_bins, number_of_gyrophase_bins)),
-            "helium4": self._create_nan_array((epoch_count, he4_energy_count,
-                                               number_of_pitch_angle_bins,
-                                               number_of_gyrophase_bins)),
-            "hydrogen": self._create_nan_array((epoch_count, h_energy_count,
-                                                number_of_pitch_angle_bins,
-                                                number_of_gyrophase_bins)),
-            "iron": self._create_nan_array((epoch_count, fe_energy_count,
-                                            number_of_pitch_angle_bins,
-                                            number_of_gyrophase_bins)),
-            "NeMgSi": self._create_nan_array((epoch_count, nemgsi_energy_count,
-                                              number_of_pitch_angle_bins,
-                                              number_of_gyrophase_bins))}
+        rebinned_pa_gyro_intensity_by_species = {
+            "cno": self._create_nan_array(
+                (
+                    epoch_count,
+                    cno_energy_count,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
+            ),
+            "helium4": self._create_nan_array(
+                (
+                    epoch_count,
+                    he4_energy_count,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
+            ),
+            "hydrogen": self._create_nan_array(
+                (
+                    epoch_count,
+                    h_energy_count,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
+            ),
+            "iron": self._create_nan_array(
+                (
+                    epoch_count,
+                    fe_energy_count,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
+            ),
+            "NeMgSi": self._create_nan_array(
+                (
+                    epoch_count,
+                    nemgsi_energy_count,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
+            ),
+        }
 
         rebinned_pa_only_intensity_by_species = {
-            "cno": self._create_nan_array((epoch_count, cno_energy_count, number_of_pitch_angle_bins)),
-            "helium4": self._create_nan_array((epoch_count, he4_energy_count, number_of_pitch_angle_bins)),
-            "hydrogen": self._create_nan_array((epoch_count, h_energy_count, number_of_pitch_angle_bins)),
-            "iron": self._create_nan_array((epoch_count, fe_energy_count, number_of_pitch_angle_bins)),
-            "NeMgSi": self._create_nan_array((epoch_count, nemgsi_energy_count, number_of_pitch_angle_bins))}
-
-        sector_unit_vectors = np.transpose(get_sector_unit_vectors(hit_data.zenith, hit_data.azimuth), (1, 0, 2))
+            "cno": self._create_nan_array(
+                (epoch_count, cno_energy_count, number_of_pitch_angle_bins)
+            ),
+            "helium4": self._create_nan_array(
+                (epoch_count, he4_energy_count, number_of_pitch_angle_bins)
+            ),
+            "hydrogen": self._create_nan_array(
+                (epoch_count, h_energy_count, number_of_pitch_angle_bins)
+            ),
+            "iron": self._create_nan_array(
+                (epoch_count, fe_energy_count, number_of_pitch_angle_bins)
+            ),
+            "NeMgSi": self._create_nan_array(
+                (epoch_count, nemgsi_energy_count, number_of_pitch_angle_bins)
+            ),
+        }
+
+        sector_unit_vectors = np.transpose(
+            get_sector_unit_vectors(hit_data.zenith, hit_data.azimuth), (1, 0, 2)
+        )
 
         particle_unit_vectors = -sector_unit_vectors
-        rotated_particle_unit_vectors = rotate_particle_vectors_from_hit_despun_to_imap_despun(particle_unit_vectors)
+        rotated_particle_unit_vectors = (
+            rotate_particle_vectors_from_hit_despun_to_imap_despun(
+                particle_unit_vectors
+            )
+        )
 
-        pitch_angles, gyrophases, pitch_angle_deltas, gyrophase_deltas = get_hit_bin_polar_coordinates(
-            number_of_pitch_angle_bins, number_of_gyrophase_bins)
+        pitch_angles, gyrophases, pitch_angle_deltas, gyrophase_deltas = (
+            get_hit_bin_polar_coordinates(
+                number_of_pitch_angle_bins, number_of_gyrophase_bins
+            )
+        )
 
         averaged_mag_data = mag_data.rebin_to(hit_data.epoch, hit_data.epoch_delta)
         hit_flags = np.full(len(hit_data.epoch), HitL3Flags.NONE)
@@ -219,82 +324,115 @@ def process_pitch_angle_product(self, dependencies: HITL3SectoredDependencies) -
         for time_index, average_mag_vector in enumerate(averaged_mag_data):
             mag_unit_vector = calculate_unit_vector(average_mag_vector)
 
-            input_bin_pitch_angles = calculate_pitch_angle(rotated_particle_unit_vectors, mag_unit_vector)
-            input_bin_gyrophases = calculate_gyrophase(rotated_particle_unit_vectors, mag_unit_vector)
+            input_bin_pitch_angles = calculate_pitch_angle(
+                rotated_particle_unit_vectors, mag_unit_vector
+            )
+            input_bin_gyrophases = calculate_gyrophase(
+                rotated_particle_unit_vectors, mag_unit_vector
+            )
 
             measurement_pitch_angle.append(input_bin_pitch_angles)
             measurement_gyrophase.append(input_bin_gyrophases)
             for species, intensity in input_intensity_data_by_species.items():
-                rebinned_result = rebin_by_pitch_angle_and_gyrophase(intensity[0][time_index], intensity[1][time_index],
-                                                                     intensity[2][time_index], input_bin_pitch_angles,
-                                                                     input_bin_gyrophases, number_of_pitch_angle_bins,
-                                                                     number_of_gyrophase_bins)
-
-                intensity_by_pa_gyro, intensity_delta_plus_by_pa_gyro, intensity_delta_minus_by_pa_gyro = rebinned_result[
-                                                                                                          0:3]
-                intensity_by_pa, intensity_delta_plus_by_pa, intensity_delta_minus_by_pa = rebinned_result[3:6]
-
-                rebinned_pa_gyro_intensity_by_species[species][0][time_index, ...] = intensity_by_pa_gyro
-                rebinned_pa_gyro_intensity_by_species[species][1][time_index, ...] = intensity_delta_plus_by_pa_gyro
-                rebinned_pa_gyro_intensity_by_species[species][2][time_index, ...] = intensity_delta_minus_by_pa_gyro
-                rebinned_pa_only_intensity_by_species[species][0][time_index, ...] = intensity_by_pa
-                rebinned_pa_only_intensity_by_species[species][1][time_index, ...] = intensity_delta_plus_by_pa
-                rebinned_pa_only_intensity_by_species[species][2][time_index, ...] = intensity_delta_minus_by_pa
-
-        return HitPitchAngleDataProduct(self.input_metadata, hit_data.epoch,
-                                        hit_data.epoch_delta, pitch_angles, pitch_angle_deltas,
-                                        gyrophases,
-                                        gyrophase_deltas,
-                                        rebinned_pa_gyro_intensity_by_species["hydrogen"][0],
-                                        rebinned_pa_gyro_intensity_by_species["hydrogen"][1],
-                                        rebinned_pa_gyro_intensity_by_species["hydrogen"][2],
-                                        rebinned_pa_only_intensity_by_species["hydrogen"][0],
-                                        rebinned_pa_only_intensity_by_species["hydrogen"][1],
-                                        rebinned_pa_only_intensity_by_species["hydrogen"][2],
-                                        hit_data.h_energy,
-                                        hit_data.h_energy_delta_plus,
-                                        hit_data.h_energy_delta_minus,
-                                        rebinned_pa_gyro_intensity_by_species["helium4"][0],
-                                        rebinned_pa_gyro_intensity_by_species["helium4"][1],
-                                        rebinned_pa_gyro_intensity_by_species["helium4"][2],
-                                        rebinned_pa_only_intensity_by_species["helium4"][0],
-                                        rebinned_pa_only_intensity_by_species["helium4"][1],
-                                        rebinned_pa_only_intensity_by_species["helium4"][2],
-                                        hit_data.he4_energy,
-                                        hit_data.he4_energy_delta_plus,
-                                        hit_data.he4_energy_delta_minus,
-                                        rebinned_pa_gyro_intensity_by_species["cno"][0],
-                                        rebinned_pa_gyro_intensity_by_species["cno"][1],
-                                        rebinned_pa_gyro_intensity_by_species["cno"][2],
-                                        rebinned_pa_only_intensity_by_species["cno"][0],
-                                        rebinned_pa_only_intensity_by_species["cno"][1],
-                                        rebinned_pa_only_intensity_by_species["cno"][2],
-                                        hit_data.cno_energy,
-                                        hit_data.cno_energy_delta_plus,
-                                        hit_data.cno_energy_delta_minus,
-                                        rebinned_pa_gyro_intensity_by_species["NeMgSi"][0],
-                                        rebinned_pa_gyro_intensity_by_species["NeMgSi"][1],
-                                        rebinned_pa_gyro_intensity_by_species["NeMgSi"][2],
-                                        rebinned_pa_only_intensity_by_species["NeMgSi"][0],
-                                        rebinned_pa_only_intensity_by_species["NeMgSi"][1],
-                                        rebinned_pa_only_intensity_by_species["NeMgSi"][2],
-                                        hit_data.nemgsi_energy,
-                                        hit_data.nemgsi_energy_delta_plus,
-                                        hit_data.nemgsi_energy_delta_minus,
-                                        rebinned_pa_gyro_intensity_by_species["iron"][0],
-                                        rebinned_pa_gyro_intensity_by_species["iron"][1],
-                                        rebinned_pa_gyro_intensity_by_species["iron"][2],
-                                        rebinned_pa_only_intensity_by_species["iron"][0],
-                                        rebinned_pa_only_intensity_by_species["iron"][1],
-                                        rebinned_pa_only_intensity_by_species["iron"][2],
-                                        hit_data.fe_energy,
-                                        hit_data.fe_energy_delta_plus,
-                                        hit_data.fe_energy_delta_minus,
-                                        np.array(measurement_pitch_angle),
-                                        np.array(measurement_gyrophase),
-                                        azimuth=hit_data.azimuth,
-                                        zenith=hit_data.zenith,
-                                        hit_flags=hit_flags)
+                rebinned_result = rebin_by_pitch_angle_and_gyrophase(
+                    intensity[0][time_index],
+                    intensity[1][time_index],
+                    intensity[2][time_index],
+                    input_bin_pitch_angles,
+                    input_bin_gyrophases,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
+
+                (
+                    intensity_by_pa_gyro,
+                    intensity_delta_plus_by_pa_gyro,
+                    intensity_delta_minus_by_pa_gyro,
+                ) = rebinned_result[0:3]
+                (
+                    intensity_by_pa,
+                    intensity_delta_plus_by_pa,
+                    intensity_delta_minus_by_pa,
+                ) = rebinned_result[3:6]
+
+                rebinned_pa_gyro_intensity_by_species[species][0][time_index, ...] = (
+                    intensity_by_pa_gyro
+                )
+                rebinned_pa_gyro_intensity_by_species[species][1][time_index, ...] = (
+                    intensity_delta_plus_by_pa_gyro
+                )
+                rebinned_pa_gyro_intensity_by_species[species][2][time_index, ...] = (
+                    intensity_delta_minus_by_pa_gyro
+                )
+                rebinned_pa_only_intensity_by_species[species][0][time_index, ...] = (
+                    intensity_by_pa
+                )
+                rebinned_pa_only_intensity_by_species[species][1][time_index, ...] = (
+                    intensity_delta_plus_by_pa
+                )
+                rebinned_pa_only_intensity_by_species[species][2][time_index, ...] = (
+                    intensity_delta_minus_by_pa
+                )
+
+        return HitPitchAngleDataProduct(
+            self.input_metadata,
+            hit_data.epoch,
+            hit_data.epoch_delta,
+            pitch_angles,
+            pitch_angle_deltas,
+            gyrophases,
+            gyrophase_deltas,
+            rebinned_pa_gyro_intensity_by_species["hydrogen"][0],
+            rebinned_pa_gyro_intensity_by_species["hydrogen"][1],
+            rebinned_pa_gyro_intensity_by_species["hydrogen"][2],
+            rebinned_pa_only_intensity_by_species["hydrogen"][0],
+            rebinned_pa_only_intensity_by_species["hydrogen"][1],
+            rebinned_pa_only_intensity_by_species["hydrogen"][2],
+            hit_data.h_energy,
+            hit_data.h_energy_delta_plus,
+            hit_data.h_energy_delta_minus,
+            rebinned_pa_gyro_intensity_by_species["helium4"][0],
+            rebinned_pa_gyro_intensity_by_species["helium4"][1],
+            rebinned_pa_gyro_intensity_by_species["helium4"][2],
+            rebinned_pa_only_intensity_by_species["helium4"][0],
+            rebinned_pa_only_intensity_by_species["helium4"][1],
+            rebinned_pa_only_intensity_by_species["helium4"][2],
+            hit_data.he4_energy,
+            hit_data.he4_energy_delta_plus,
+            hit_data.he4_energy_delta_minus,
+            rebinned_pa_gyro_intensity_by_species["cno"][0],
+            rebinned_pa_gyro_intensity_by_species["cno"][1],
+            rebinned_pa_gyro_intensity_by_species["cno"][2],
+            rebinned_pa_only_intensity_by_species["cno"][0],
+            rebinned_pa_only_intensity_by_species["cno"][1],
+            rebinned_pa_only_intensity_by_species["cno"][2],
+            hit_data.cno_energy,
+            hit_data.cno_energy_delta_plus,
+            hit_data.cno_energy_delta_minus,
+            rebinned_pa_gyro_intensity_by_species["NeMgSi"][0],
+            rebinned_pa_gyro_intensity_by_species["NeMgSi"][1],
+            rebinned_pa_gyro_intensity_by_species["NeMgSi"][2],
+            rebinned_pa_only_intensity_by_species["NeMgSi"][0],
+            rebinned_pa_only_intensity_by_species["NeMgSi"][1],
+            rebinned_pa_only_intensity_by_species["NeMgSi"][2],
+            hit_data.nemgsi_energy,
+            hit_data.nemgsi_energy_delta_plus,
+            hit_data.nemgsi_energy_delta_minus,
+            rebinned_pa_gyro_intensity_by_species["iron"][0],
+            rebinned_pa_gyro_intensity_by_species["iron"][1],
+            rebinned_pa_gyro_intensity_by_species["iron"][2],
+            rebinned_pa_only_intensity_by_species["iron"][0],
+            rebinned_pa_only_intensity_by_species["iron"][1],
+            rebinned_pa_only_intensity_by_species["iron"][2],
+            hit_data.fe_energy,
+            hit_data.fe_energy_delta_plus,
+            hit_data.fe_energy_delta_minus,
+            np.array(measurement_pitch_angle),
+            np.array(measurement_gyrophase),
+            azimuth=hit_data.azimuth,
+            zenith=hit_data.zenith,
+            hit_flags=hit_flags,
+        )
 
     @staticmethod
     def _create_nan_array(shape) -> tuple[np.array, np.array, np.array]:
diff --git a/imap_l3_processing/hit/l3/models.py b/imap_l3_processing/hit/l3/models.py
index f65b7048f..6be2fc22d 100644
--- a/imap_l3_processing/hit/l3/models.py
+++ b/imap_l3_processing/hit/l3/models.py
@@ -1,7 +1,6 @@
 from dataclasses import dataclass
 from datetime import datetime, timedelta
 from pathlib import Path
-from typing import Union
 
 import numpy as np
 from spacepy.pycdf import CDF
@@ -118,7 +117,9 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(E_PRIME_VAR_NAME, self.e_prime),
             DataProductVariable(DETECTED_RANGE_VAR_NAME, self.detected_range),
             DataProductVariable(PARTICLE_ID_VAR_NAME, self.particle_id),
-            DataProductVariable(PRIORITY_BUFFER_NUMBER_VAR_NAME, self.priority_buffer_number),
+            DataProductVariable(
+                PRIORITY_BUFFER_NUMBER_VAR_NAME, self.priority_buffer_number
+            ),
             DataProductVariable(LATENCY_VAR_NAME, self.latency),
             DataProductVariable(STIM_TAG_VAR_NAME, self.stim_tag),
             DataProductVariable(LONG_EVENT_FLAG_VAR_NAME, self.long_event_flag),
@@ -137,7 +138,7 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(STIM_STEP_VAR_NAME, self.stim_step),
             DataProductVariable(DAC_VALUE_VAR_NAME, self.dac_value),
             DataProductVariable(HIT_FLAGS_CDF_VAR_NAME, self.hit_flags),
-            DataProductVariable(DETECTOR_ID_VAR_NAME, np.arange(0, 64))
+            DataProductVariable(DETECTOR_ID_VAR_NAME, np.arange(0, 64)),
         ]
 
 
@@ -147,6 +148,6 @@ class HitL1Data:
     event_binary: np.ndarray[str]
 
     @classmethod
-    def read_from_cdf(cls, cdf_file_path: Union[Path, str]):
+    def read_from_cdf(cls, cdf_file_path: Path | str):
         with CDF(str(cdf_file_path)) as cdf:
             return cls(cdf["epoch"][...], cdf["pha_raw"][...])
diff --git a/imap_l3_processing/hit/l3/pha/hit_l3_pha_dependencies.py b/imap_l3_processing/hit/l3/pha/hit_l3_pha_dependencies.py
index fdc322e69..a6489685e 100644
--- a/imap_l3_processing/hit/l3/pha/hit_l3_pha_dependencies.py
+++ b/imap_l3_processing/hit/l3/pha/hit_l3_pha_dependencies.py
@@ -4,9 +4,13 @@
 from imap_data_access.processing_input import ProcessingInputCollection
 
 from imap_l3_processing.hit.l3.models import HitL1Data
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import CosineCorrectionLookupTable
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    CosineCorrectionLookupTable,
+)
 from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import GainLookupTable
-from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import HitEventTypeLookup
+from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import (
+    HitEventTypeLookup,
+)
 from imap_l3_processing.hit.l3.pha.science.range_fit_lookup import RangeFitLookup
 
 HIT_L1A_EVENT_DESCRIPTOR = "direct-events"
@@ -38,8 +42,12 @@ class HitL3PhaDependencies:
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         try:
-            l1_events_dependency = next(dependency for dependency in
-                                        dependencies.get_file_paths(source="hit", descriptor=HIT_L1A_EVENT_DESCRIPTOR))
+            l1_events_dependency = next(
+                dependency
+                for dependency in dependencies.get_file_paths(
+                    source="hit", descriptor=HIT_L1A_EVENT_DESCRIPTOR
+                )
+            )
         except StopIteration:
             raise ValueError(f"Missing {HIT_L1A_EVENT_DESCRIPTOR} dependency.")
 
@@ -47,53 +55,106 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         hit_l1_data = HitL1Data.read_from_cdf(l1_events_cdf_path)
 
         range_2A_cosine_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_2A_COSINE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_2A_COSINE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_3A_cosine_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_3A_COSINE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_3A_COSINE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_4A_cosine_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_4A_COSINE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_4A_COSINE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_2B_cosine_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_2B_COSINE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_2B_COSINE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_3B_cosine_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_3B_COSINE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_3B_COSINE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_4B_cosine_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_4B_COSINE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_4B_COSINE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
 
         lo_gain_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_LO_GAIN_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_LO_GAIN_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         hi_gain_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_HI_GAIN_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_HI_GAIN_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_2A_charge_fit_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_2A_CHARGE_FIT_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_2A_CHARGE_FIT_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_3A_charge_fit_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_3A_CHARGE_FIT_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_3A_CHARGE_FIT_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_4A_charge_fit_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_4A_CHARGE_FIT_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_4A_CHARGE_FIT_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_2B_charge_fit_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_2B_CHARGE_FIT_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_2B_CHARGE_FIT_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_3B_charge_fit_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_3B_CHARGE_FIT_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_3B_CHARGE_FIT_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         range_4B_charge_fit_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_RANGE_4B_CHARGE_FIT_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_RANGE_4B_CHARGE_FIT_LOOKUP_DESCRIPTOR
+            )[0]
+        )
         event_type_lookup_path = download(
-            dependencies.get_file_paths(source="hit", descriptor=HIT_L3_EVENT_TYPE_LOOKUP_DESCRIPTOR)[0])
+            dependencies.get_file_paths(
+                source="hit", descriptor=HIT_L3_EVENT_TYPE_LOOKUP_DESCRIPTOR
+            )[0]
+        )
 
-        cosine_lookup_table = CosineCorrectionLookupTable(range_2A_cosine_lookup_path, range_3A_cosine_lookup_path,
-                                                          range_4A_cosine_lookup_path,
-                                                          range_2B_cosine_lookup_path,
-                                                          range_3B_cosine_lookup_path,
-                                                          range_4B_cosine_lookup_path, )
-        gain_lookup_table = GainLookupTable.from_file(hi_gain_lookup_path, lo_gain_lookup_path)
-        range_fit_lookup_table = RangeFitLookup.from_files(range_2A_charge_fit_lookup_path,
-                                                           range_3A_charge_fit_lookup_path,
-                                                           range_4A_charge_fit_lookup_path,
-                                                           range_2B_charge_fit_lookup_path,
-                                                           range_3B_charge_fit_lookup_path,
-                                                           range_4B_charge_fit_lookup_path
-                                                           )
+        cosine_lookup_table = CosineCorrectionLookupTable(
+            range_2A_cosine_lookup_path,
+            range_3A_cosine_lookup_path,
+            range_4A_cosine_lookup_path,
+            range_2B_cosine_lookup_path,
+            range_3B_cosine_lookup_path,
+            range_4B_cosine_lookup_path,
+        )
+        gain_lookup_table = GainLookupTable.from_file(
+            hi_gain_lookup_path, lo_gain_lookup_path
+        )
+        range_fit_lookup_table = RangeFitLookup.from_files(
+            range_2A_charge_fit_lookup_path,
+            range_3A_charge_fit_lookup_path,
+            range_4A_charge_fit_lookup_path,
+            range_2B_charge_fit_lookup_path,
+            range_3B_charge_fit_lookup_path,
+            range_4B_charge_fit_lookup_path,
+        )
         event_type_lookup_table = HitEventTypeLookup.from_csv(event_type_lookup_path)
-        return cls(hit_l1_data=hit_l1_data,
-                   cosine_correction_lookup=cosine_lookup_table,
-                   gain_lookup=gain_lookup_table,
-                   range_fit_lookup=range_fit_lookup_table,
-                   event_type_lookup=event_type_lookup_table)
+        return cls(
+            hit_l1_data=hit_l1_data,
+            cosine_correction_lookup=cosine_lookup_table,
+            gain_lookup=gain_lookup_table,
+            range_fit_lookup=range_fit_lookup_table,
+            event_type_lookup=event_type_lookup_table,
+        )
diff --git a/imap_l3_processing/hit/l3/pha/pha_event_reader.py b/imap_l3_processing/hit/l3/pha/pha_event_reader.py
index 5a2f55173..98c98efe0 100644
--- a/imap_l3_processing/hit/l3/pha/pha_event_reader.py
+++ b/imap_l3_processing/hit/l3/pha/pha_event_reader.py
@@ -1,7 +1,7 @@
 import csv
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Literal
+from typing import Literal
 
 from bitstring import BitStream
 
@@ -22,7 +22,9 @@ def __str__(self):
     @classmethod
     def from_address(cls, address: int):
         if cls.detector_mapping == {}:
-            with open(Path(__file__).parent / "address_to_detector.csv") as detector_mapping_file:
+            with open(
+                Path(__file__).parent / "address_to_detector.csv"
+            ) as detector_mapping_file:
                 reader = csv.reader(detector_mapping_file)
                 next(reader)
                 for name, address_str, group in reader:
@@ -33,14 +35,22 @@ def from_address(cls, address: int):
         if detector_name_and_group is not None:
             detector_name, group = detector_name_and_group
             layer = int(detector_name[1])
-            return cls(layer=layer,
-                       side=detector_name[2],
-                       segment=detector_name[3:],
-                       address=address,
-                       group=group)
+            return cls(
+                layer=layer,
+                side=detector_name[2],
+                segment=detector_name[3:],
+                address=address,
+                group=group,
+            )
 
         else:
-            return cls(layer=4, side="A", segment=f"UNKNOWN_{address}", address=address, group="Unknown")
+            return cls(
+                layer=4,
+                side="A",
+                segment=f"UNKNOWN_{address}",
+                address=address,
+                group="Unknown",
+            )
 
 
 @dataclass
@@ -86,9 +96,9 @@ class RawPHAEvent:
     culling_flag: bool
     spare: bool
     pha_words: list[PHAWord]
-    extended_header: Optional[PHAExtendedHeader] = None
-    stim_block: Optional[StimBlock] = None
-    extended_stim_header: Optional[ExtendedStimHeader] = None
+    extended_header: PHAExtendedHeader | None = None
+    stim_block: StimBlock | None = None
+    extended_stim_header: ExtendedStimHeader | None = None
 
 
 class PHAEventReader:
@@ -116,7 +126,9 @@ def read_pha_event(cls, event_bitstream: BitStream) -> RawPHAEvent:
             delta_e_index = event_bitstream.read("uint:9")
             e_prime_index = event_bitstream.read("uint:7")
             detector_flags = event_bitstream.read("uint:8")
-            extended_header = PHAExtendedHeader(detector_flags, delta_e_index, e_prime_index)
+            extended_header = PHAExtendedHeader(
+                detector_flags, delta_e_index, e_prime_index
+            )
 
         elif stim_tag:
             if long_event_flag:
@@ -128,7 +140,12 @@ def read_pha_event(cls, event_bitstream: BitStream) -> RawPHAEvent:
             unused = event_bitstream.read("uint:2")
             stim_gain = event_bitstream.read("uint:1")
             stim_step = event_bitstream.read("uint:4")
-            stim_block = StimBlock(stim_step=stim_step, stim_gain=stim_gain, unused=unused, a_l_stim=a_l_stim)
+            stim_block = StimBlock(
+                stim_step=stim_step,
+                stim_gain=stim_gain,
+                unused=unused,
+                a_l_stim=a_l_stim,
+            )
 
         reading_pha = True
         pha_words = []
@@ -140,30 +157,34 @@ def read_pha_event(cls, event_bitstream: BitStream) -> RawPHAEvent:
             adc_value = event_bitstream.read("uint:11")
             reading_pha = not is_last_event
 
-            pha_word = PHAWord(adc_overflow=adc_overflow, adc_value=adc_value,
-                               detector=Detector.from_address(adc_detector_address),
-                               is_last_pha=is_last_event,
-                               is_low_gain=is_low_gain)
+            pha_word = PHAWord(
+                adc_overflow=adc_overflow,
+                adc_value=adc_value,
+                detector=Detector.from_address(adc_detector_address),
+                is_last_pha=is_last_event,
+                is_low_gain=is_low_gain,
+            )
             pha_words.append(pha_word)
 
         if len(pha_words) % 2 == 1:
             event_bitstream.read("uint:4")
 
-        return RawPHAEvent(particle_id=particle_id,
-                           priority_buffer_num=priority_buffer_num,
-                           stim_tag=stim_tag,
-                           haz_tag=haz_tag,
-                           time_tag=time_tag,
-                           a_b_side_flag=a_b_side_flag,
-                           has_unread_adcs=has_unread_adcs,
-                           long_event_flag=long_event_flag,
-                           culling_flag=culling_flag,
-                           spare=spare,
-                           pha_words=pha_words,
-                           extended_header=extended_header,
-                           extended_stim_header=extended_stim_header,
-                           stim_block=stim_block
-                           )
+        return RawPHAEvent(
+            particle_id=particle_id,
+            priority_buffer_num=priority_buffer_num,
+            stim_tag=stim_tag,
+            haz_tag=haz_tag,
+            time_tag=time_tag,
+            a_b_side_flag=a_b_side_flag,
+            has_unread_adcs=has_unread_adcs,
+            long_event_flag=long_event_flag,
+            culling_flag=culling_flag,
+            spare=spare,
+            pha_words=pha_words,
+            extended_header=extended_header,
+            extended_stim_header=extended_stim_header,
+            stim_block=stim_block,
+        )
 
     @classmethod
     def read_all_pha_events(cls, binary_pha_events: str) -> list[RawPHAEvent]:
diff --git a/imap_l3_processing/hit/l3/pha/science/calculate_pha.py b/imap_l3_processing/hit/l3/pha/science/calculate_pha.py
index 2c5356613..3d34c2512 100644
--- a/imap_l3_processing/hit/l3/pha/science/calculate_pha.py
+++ b/imap_l3_processing/hit/l3/pha/science/calculate_pha.py
@@ -1,14 +1,22 @@
 from dataclasses import dataclass
-from typing import Optional
 
 import numpy as np
 from numpy import clip
 
 from imap_l3_processing.hit.l3.pha.pha_event_reader import PHAWord, RawPHAEvent
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, Detector, \
-    CosineCorrectionLookupTable, DetectorRange
-from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import GainLookupTable, DetectorGain
-from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import HitEventTypeLookup
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    CosineCorrectionLookupTable,
+    DetectedRange,
+    Detector,
+    DetectorRange,
+)
+from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import (
+    DetectorGain,
+    GainLookupTable,
+)
+from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import (
+    HitEventTypeLookup,
+)
 from imap_l3_processing.hit.l3.pha.science.range_fit_lookup import RangeFitLookup
 
 
@@ -25,12 +33,12 @@ class EventAnalysis:
 @dataclass
 class EventOutput:
     original_event: RawPHAEvent
-    total_energy: Optional[float]
-    charge: Optional[float]
+    total_energy: float | None
+    charge: float | None
     energies: list[float]
-    detected_range: Optional[DetectedRange]
-    e_delta: Optional[float]
-    e_prime: Optional[float]
+    detected_range: DetectedRange | None
+    e_delta: float | None
+    e_prime: float | None
 
 
 def calculate_mev(word: PHAWord, gain_lookup_table: GainLookupTable) -> float:
@@ -39,7 +47,9 @@ def calculate_mev(word: PHAWord, gain_lookup_table: GainLookupTable) -> float:
     return word.adc_value * gain_coeffs.a + gain_coeffs.b
 
 
-def analyze_event(event: RawPHAEvent, rule_lookup: HitEventTypeLookup) -> Optional[EventAnalysis]:
+def analyze_event(
+    event: RawPHAEvent, rule_lookup: HitEventTypeLookup
+) -> EventAnalysis | None:
 
     def get_adc_value(pha_word: PHAWord):
         return 20 * pha_word.adc_value if pha_word.is_low_gain else pha_word.adc_value
@@ -47,7 +57,7 @@ def get_adc_value(pha_word: PHAWord):
     words = [word for word in event.pha_words]
     groups_to_words = {}
     for word in words:
-        if word.detector.group in groups_to_words.keys():
+        if word.detector.group in groups_to_words:
             groups_to_words[word.detector.group].append(word)
         else:
             groups_to_words[word.detector.group] = [word]
@@ -56,33 +66,57 @@ def get_adc_value(pha_word: PHAWord):
     if rule is not None:
         highest_value_words_per_group = {}
         for include_group in rule.included_detector_groups:
-            unsaturated_words = [word for word in groups_to_words[include_group] if word.adc_value < 2047]
+            unsaturated_words = [
+                word for word in groups_to_words[include_group] if word.adc_value < 2047
+            ]
             if len(unsaturated_words) == 0:
                 return None
-            highest_value_words_per_group[include_group] = max(unsaturated_words, key=get_adc_value)
-
-        l1_detector = [group for group in rule.included_detector_groups if group[0:3] == f"L1{rule.range.side.name}"][0]
-        l2_detector = [group for group in rule.included_detector_groups if group[0:3] == f"L2{rule.range.side.name}"][0]
+            highest_value_words_per_group[include_group] = max(
+                unsaturated_words, key=get_adc_value
+            )
+
+        l1_detector = [
+            group
+            for group in rule.included_detector_groups
+            if group[0:3] == f"L1{rule.range.side.name}"
+        ][0]
+        l2_detector = [
+            group
+            for group in rule.included_detector_groups
+            if group[0:3] == f"L2{rule.range.side.name}"
+        ][0]
 
         if rule.range.range == DetectorRange.R2:
             e_prime_group = l2_detector
             e_delta_group = l1_detector
         elif rule.range.range == DetectorRange.R3:
-            e_prime_group = \
-                [group for group in rule.included_detector_groups if group[0:3] == f"L3{rule.range.side.name}"][0]
+            e_prime_group = [
+                group
+                for group in rule.included_detector_groups
+                if group[0:3] == f"L3{rule.range.side.name}"
+            ][0]
             e_delta_group = l2_detector
         elif rule.range.range == DetectorRange.R4:
-            opposite_side = 'A' if rule.range.side.name == 'B' else 'B'
-            e_prime_group = [group for group in rule.included_detector_groups if group[0:3] == f"L3{opposite_side}"][0]
-            e_delta_group = \
-                [group for group in rule.included_detector_groups if group[0:3] == f"L3{rule.range.side.name}"][0]
-
-        return EventAnalysis(range=rule.range,
-                             l1_detector=highest_value_words_per_group[l1_detector].detector,
-                             l2_detector=highest_value_words_per_group[l2_detector].detector,
-                             e_delta_word=highest_value_words_per_group[e_delta_group],
-                             e_prime_word=highest_value_words_per_group[e_prime_group],
-                             words_with_highest_energy=list(highest_value_words_per_group.values()))
+            opposite_side = "A" if rule.range.side.name == "B" else "B"
+            e_prime_group = [
+                group
+                for group in rule.included_detector_groups
+                if group[0:3] == f"L3{opposite_side}"
+            ][0]
+            e_delta_group = [
+                group
+                for group in rule.included_detector_groups
+                if group[0:3] == f"L3{rule.range.side.name}"
+            ][0]
+
+        return EventAnalysis(
+            range=rule.range,
+            l1_detector=highest_value_words_per_group[l1_detector].detector,
+            l2_detector=highest_value_words_per_group[l2_detector].detector,
+            e_delta_word=highest_value_words_per_group[e_delta_group],
+            e_prime_word=highest_value_words_per_group[e_prime_group],
+            words_with_highest_energy=list(highest_value_words_per_group.values()),
+        )
 
 
 @dataclass
@@ -93,52 +127,92 @@ class ValidDetectorRange:
     delta_e_max: float
 
     def is_in_range(self, e_prime: float, delta_e: float) -> bool:
-        return self.e_prime_min <= e_prime <= self.e_prime_max and self.delta_e_min <= delta_e <= self.delta_e_max
+        return (
+            self.e_prime_min <= e_prime <= self.e_prime_max
+            and self.delta_e_min <= delta_e <= self.delta_e_max
+        )
 
 
 valid_ranges = {
-    DetectorRange.R2: ValidDetectorRange(e_prime_min=0.2, e_prime_max=860, delta_e_min=0.1, delta_e_max=430),
-    DetectorRange.R3: ValidDetectorRange(e_prime_min=1.0, e_prime_max=4300, delta_e_min=0.2, delta_e_max=860),
-    DetectorRange.R4: ValidDetectorRange(e_prime_min=1.0, e_prime_max=4300, delta_e_min=1.0, delta_e_max=4300),
+    DetectorRange.R2: ValidDetectorRange(
+        e_prime_min=0.2, e_prime_max=860, delta_e_min=0.1, delta_e_max=430
+    ),
+    DetectorRange.R3: ValidDetectorRange(
+        e_prime_min=1.0, e_prime_max=4300, delta_e_min=0.2, delta_e_max=860
+    ),
+    DetectorRange.R4: ValidDetectorRange(
+        e_prime_min=1.0, e_prime_max=4300, delta_e_min=1.0, delta_e_max=4300
+    ),
 }
 
 
-def compute_charge(detected_range: DetectedRange, delta_e: float, e_prime: float,
-                   double_power_law_lookup: RangeFitLookup) -> float:
+def compute_charge(
+    detected_range: DetectedRange,
+    delta_e: float,
+    e_prime: float,
+    double_power_law_lookup: RangeFitLookup,
+) -> float:
     if valid_ranges[detected_range.range].is_in_range(e_prime, delta_e):
-        charges, deltas = double_power_law_lookup.evaluate_e_prime(detected_range, e_prime)
+        charges, deltas = double_power_law_lookup.evaluate_e_prime(
+            detected_range, e_prime
+        )
         assert np.array_equal(deltas, np.sort(deltas)), "values are not increasing"
         index_2 = clip(np.searchsorted(deltas, delta_e), 1, len(charges) - 1)
         index_1 = index_2 - 1
 
-        B = np.log(charges[index_2] / charges[index_1]) / np.log(deltas[index_2] / deltas[index_1])
+        B = np.log(charges[index_2] / charges[index_1]) / np.log(
+            deltas[index_2] / deltas[index_1]
+        )
         A = charges[index_1] / (deltas[index_1] ** B)
 
-        return A * delta_e ** B
+        return A * delta_e**B
     return np.nan
 
 
-def process_pha_event(event: RawPHAEvent, cosine_table: CosineCorrectionLookupTable, gain_table: GainLookupTable,
-                      range_fit_lookup: RangeFitLookup, rule_lookup: HitEventTypeLookup) -> \
-        EventOutput:
+def process_pha_event(
+    event: RawPHAEvent,
+    cosine_table: CosineCorrectionLookupTable,
+    gain_table: GainLookupTable,
+    range_fit_lookup: RangeFitLookup,
+    rule_lookup: HitEventTypeLookup,
+) -> EventOutput:
     event_analysis = analyze_event(event, rule_lookup)
     if event_analysis:
-        correction = cosine_table.get_cosine_correction(event_analysis.range, event_analysis.l1_detector,
-                                                        event_analysis.l2_detector)
+        correction = cosine_table.get_cosine_correction(
+            event_analysis.range, event_analysis.l1_detector, event_analysis.l2_detector
+        )
 
         def calculate_corrected_energy(word):
             return correction * calculate_mev(word, gain_table)
 
         energies = [calculate_corrected_energy(word) for word in event.pha_words]
         total_energy = sum(
-            calculate_corrected_energy(word) for word in event_analysis.words_with_highest_energy)
+            calculate_corrected_energy(word)
+            for word in event_analysis.words_with_highest_energy
+        )
         e_delta = calculate_corrected_energy(event_analysis.e_delta_word)
         e_prime = calculate_corrected_energy(event_analysis.e_prime_word)
-        charge = compute_charge(event_analysis.range, e_delta, e_prime, range_fit_lookup)
-
-        return EventOutput(original_event=event, charge=charge, total_energy=total_energy, energies=energies,
-                           detected_range=event_analysis.range, e_delta=e_delta, e_prime=e_prime)
+        charge = compute_charge(
+            event_analysis.range, e_delta, e_prime, range_fit_lookup
+        )
+
+        return EventOutput(
+            original_event=event,
+            charge=charge,
+            total_energy=total_energy,
+            energies=energies,
+            detected_range=event_analysis.range,
+            e_delta=e_delta,
+            e_prime=e_prime,
+        )
     else:
         energies = [calculate_mev(word, gain_table) for word in event.pha_words]
-        return EventOutput(original_event=event, charge=None, total_energy=None, energies=energies, detected_range=None,
-                           e_delta=None, e_prime=None)
+        return EventOutput(
+            original_event=event,
+            charge=None,
+            total_energy=None,
+            energies=energies,
+            detected_range=None,
+            e_delta=None,
+            e_prime=None,
+        )
diff --git a/imap_l3_processing/hit/l3/pha/science/cosine_correction_lookup_table.py b/imap_l3_processing/hit/l3/pha/science/cosine_correction_lookup_table.py
index 85869e15a..63407dbe7 100644
--- a/imap_l3_processing/hit/l3/pha/science/cosine_correction_lookup_table.py
+++ b/imap_l3_processing/hit/l3/pha/science/cosine_correction_lookup_table.py
@@ -24,12 +24,45 @@ class DetectedRange:
 
 
 class CosineCorrectionLookupTable:
-    _l1_detector_order = ["L1A0a", "L1A0b", "L1A0c", "L1A1a", "L1A1b", "L1A1c", "L1A2a", "L1A2b", "L1A2c", "L1A3a",
-                          "L1A3b", "L1A3c", "L1A4a", "L1A4b", "L1A4c"]
-    _l2_detector_order = ["L2A0", "L2A1", "L2A2", "L2A3", "L2A4", "L2A5", "L2A6", "L2A7", "L2A8", "L2A9"]
+    _l1_detector_order = [
+        "L1A0a",
+        "L1A0b",
+        "L1A0c",
+        "L1A1a",
+        "L1A1b",
+        "L1A1c",
+        "L1A2a",
+        "L1A2b",
+        "L1A2c",
+        "L1A3a",
+        "L1A3b",
+        "L1A3c",
+        "L1A4a",
+        "L1A4b",
+        "L1A4c",
+    ]
+    _l2_detector_order = [
+        "L2A0",
+        "L2A1",
+        "L2A2",
+        "L2A3",
+        "L2A4",
+        "L2A5",
+        "L2A6",
+        "L2A7",
+        "L2A8",
+        "L2A9",
+    ]
 
-    def __init__(self, range_2A_file_path: Path, range_3A_file_path: Path, range_4A_file_path: Path,
-                 range_2B_file_path: Path, range_3B_file_path: Path, range_4B_file_path: Path):
+    def __init__(
+        self,
+        range_2A_file_path: Path,
+        range_3A_file_path: Path,
+        range_4A_file_path: Path,
+        range_2B_file_path: Path,
+        range_3B_file_path: Path,
+        range_4B_file_path: Path,
+    ):
         self._range2A_corrections = {}
         self._range3A_corrections = {}
         self._range4A_corrections = {}
@@ -37,23 +70,31 @@ def __init__(self, range_2A_file_path: Path, range_3A_file_path: Path, range_4A_
         self._range3B_corrections = {}
         self._range4B_corrections = {}
 
-        for path, lookup_table in [(range_2A_file_path, self._range2A_corrections),
-                                   (range_3A_file_path, self._range3A_corrections),
-                                   (range_4A_file_path, self._range4A_corrections),
-                                   (range_2B_file_path, self._range2B_corrections),
-                                   (range_3B_file_path, self._range3B_corrections),
-                                   (range_4B_file_path, self._range4B_corrections)]:
-
+        for path, lookup_table in [
+            (range_2A_file_path, self._range2A_corrections),
+            (range_3A_file_path, self._range3A_corrections),
+            (range_4A_file_path, self._range4A_corrections),
+            (range_2B_file_path, self._range2B_corrections),
+            (range_3B_file_path, self._range3B_corrections),
+            (range_4B_file_path, self._range4B_corrections),
+        ]:
             with open(str(path)) as file:
                 detected_range_reader = csv.reader(file, delimiter=",")
 
                 for row_num, row in enumerate(detected_range_reader):
                     for col_num, value in enumerate(row):
-                        lookup_key = (self._l1_detector_order[col_num][3:], self._l2_detector_order[row_num][3:])
+                        lookup_key = (
+                            self._l1_detector_order[col_num][3:],
+                            self._l2_detector_order[row_num][3:],
+                        )
                         lookup_table[lookup_key] = float(value)
 
-    def get_cosine_correction(self, detected_range: DetectedRange, l1_detector: Detector,
-                              l2_detector: Detector) -> float:
+    def get_cosine_correction(
+        self,
+        detected_range: DetectedRange,
+        l1_detector: Detector,
+        l2_detector: Detector,
+    ) -> float:
         corrections_by_range = {
             DetectedRange(DetectorRange.R2, DetectorSide.A): self._range2A_corrections,
             DetectedRange(DetectorRange.R3, DetectorSide.A): self._range3A_corrections,
@@ -62,4 +103,6 @@ def get_cosine_correction(self, detected_range: DetectedRange, l1_detector: Dete
             DetectedRange(DetectorRange.R3, DetectorSide.B): self._range3A_corrections,
             DetectedRange(DetectorRange.R4, DetectorSide.B): self._range4A_corrections,
         }
-        return corrections_by_range[detected_range][(l1_detector.segment, l2_detector.segment)]
+        return corrections_by_range[detected_range][
+            (l1_detector.segment, l2_detector.segment)
+        ]
diff --git a/imap_l3_processing/hit/l3/pha/science/hit_event_type_lookup.py b/imap_l3_processing/hit/l3/pha/science/hit_event_type_lookup.py
index 111417acd..bf06b4b94 100644
--- a/imap_l3_processing/hit/l3/pha/science/hit_event_type_lookup.py
+++ b/imap_l3_processing/hit/l3/pha/science/hit_event_type_lookup.py
@@ -2,8 +2,11 @@
 from dataclasses import dataclass
 from pathlib import Path
 
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, DetectorRange, \
-    DetectorSide
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    DetectedRange,
+    DetectorRange,
+    DetectorSide,
+)
 
 
 @dataclass
@@ -20,9 +23,13 @@ class HitEventTypeLookup:
     def lookup_range(self, detectors_groups_to_check: set[str]):
         for rule in self._rules:
             has_required_groups = all(
-                included_group in detectors_groups_to_check for included_group in rule.included_detector_groups)
+                included_group in detectors_groups_to_check
+                for included_group in rule.included_detector_groups
+            )
             does_not_have_excluding_groups = all(
-                excluded_group not in detectors_groups_to_check for excluded_group in rule.excluded_detector_groups)
+                excluded_group not in detectors_groups_to_check
+                for excluded_group in rule.excluded_detector_groups
+            )
 
             if has_required_groups and does_not_have_excluding_groups:
                 return rule
@@ -32,7 +39,7 @@ def lookup_range(self, detectors_groups_to_check: set[str]):
     @classmethod
     def from_csv(cls, file_path: Path):
         with open(file_path) as csv_file:
-            csv_reader = csv.reader(csv_file, delimiter=',')
+            csv_reader = csv.reader(csv_file, delimiter=",")
 
             detector_groups = next(csv_reader)
             data = list(csv_reader)
@@ -43,16 +50,22 @@ def from_csv(cls, file_path: Path):
                     included_detector_groups = []
                     excluded_detector_groups = []
 
-                    for detector_group, is_included in zip(detector_groups[:-1], row[:-1]):
+                    for detector_group, is_included in zip(
+                        detector_groups[:-1], row[:-1]
+                    ):
                         if is_included == "1":
                             included_detector_groups.append(detector_group)
                         elif is_included == "0":
                             excluded_detector_groups.append(detector_group)
                     rules.append(
                         Rule(
-                            range=DetectedRange(range=DetectorRange(int(row[-1][0])),
-                                                side=DetectorSide[(row[-1][-1])]),
+                            range=DetectedRange(
+                                range=DetectorRange(int(row[-1][0])),
+                                side=DetectorSide[(row[-1][-1])],
+                            ),
                             included_detector_groups=included_detector_groups,
-                            excluded_detector_groups=excluded_detector_groups))
+                            excluded_detector_groups=excluded_detector_groups,
+                        )
+                    )
 
         return cls(_rules=rules)
diff --git a/imap_l3_processing/hit/l3/pha/science/range_fit_lookup.py b/imap_l3_processing/hit/l3/pha/science/range_fit_lookup.py
index 37d6c806c..69e115312 100644
--- a/imap_l3_processing/hit/l3/pha/science/range_fit_lookup.py
+++ b/imap_l3_processing/hit/l3/pha/science/range_fit_lookup.py
@@ -6,11 +6,15 @@
 import numpy as np
 from numpy import ndarray
 
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, DetectorRange, DetectorSide
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    DetectedRange,
+    DetectorRange,
+    DetectorSide,
+)
 
 
 def double_power_law(e_prime, a1, b1, a2, b2, gamma):
-    return ((a1 * e_prime ** b1) ** gamma + (a2 * e_prime ** b2) ** gamma) ** (1 / gamma)
+    return ((a1 * e_prime**b1) ** gamma + (a2 * e_prime**b2) ** gamma) ** (1 / gamma)
 
 
 @dataclass
@@ -23,15 +27,23 @@ class RangeFitLookup:
     range_4B_table: ndarray
 
     @classmethod
-    def from_files(cls, range2A_file: str | Path, range3A_file: str | Path, range4A_file: str | Path,
-                   range2B_file: str | Path, range3B_file: str | Path, range4B_file: str | Path):
-        return cls(np.loadtxt(range2A_file, delimiter=',', usecols=range(6)),
-                   np.loadtxt(range3A_file, delimiter=',', usecols=range(6)),
-                   np.loadtxt(range4A_file, delimiter=',', usecols=range(6)),
-                   np.loadtxt(range2B_file, delimiter=',', usecols=range(6)),
-                   np.loadtxt(range3B_file, delimiter=',', usecols=range(6)),
-                   np.loadtxt(range4B_file, delimiter=',', usecols=range(6))
-                   )
+    def from_files(
+        cls,
+        range2A_file: str | Path,
+        range3A_file: str | Path,
+        range4A_file: str | Path,
+        range2B_file: str | Path,
+        range3B_file: str | Path,
+        range4B_file: str | Path,
+    ):
+        return cls(
+            np.loadtxt(range2A_file, delimiter=",", usecols=range(6)),
+            np.loadtxt(range3A_file, delimiter=",", usecols=range(6)),
+            np.loadtxt(range4A_file, delimiter=",", usecols=range(6)),
+            np.loadtxt(range2B_file, delimiter=",", usecols=range(6)),
+            np.loadtxt(range3B_file, delimiter=",", usecols=range(6)),
+            np.loadtxt(range4B_file, delimiter=",", usecols=range(6)),
+        )
 
     def evaluate_e_prime(self, detected_range: DetectedRange, energy):
         tables = {
@@ -40,7 +52,7 @@ def evaluate_e_prime(self, detected_range: DetectedRange, energy):
             DetectedRange(DetectorRange.R4, DetectorSide.A): self.range_4A_table,
             DetectedRange(DetectorRange.R2, DetectorSide.B): self.range_2B_table,
             DetectedRange(DetectorRange.R3, DetectorSide.B): self.range_3B_table,
-            DetectedRange(DetectorRange.R4, DetectorSide.B): self.range_4B_table
+            DetectedRange(DetectorRange.R4, DetectorSide.B): self.range_4B_table,
         }
 
         table = tables[detected_range]
diff --git a/imap_l3_processing/hit/l3/sectored_products/models.py b/imap_l3_processing/hit/l3/sectored_products/models.py
index 9383cb330..683a6b4c1 100644
--- a/imap_l3_processing/hit/l3/sectored_products/models.py
+++ b/imap_l3_processing/hit/l3/sectored_products/models.py
@@ -14,8 +14,12 @@
 H_MACROPIXEL_INTENSITY_DELTA_PLUS_CDF_VAR_NAME = "h_macropixel_intensity_delta_plus"
 H_MACROPIXEL_INTENSITY_DELTA_MINUS_CDF_VAR_NAME = "h_macropixel_intensity_delta_minus"
 H_MACROPIXEL_INTENSITY_PA_CDF_VAR_NAME = "h_macropixel_intensity_pa"
-H_MACROPIXEL_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME = "h_macropixel_intensity_pa_delta_plus"
-H_MACROPIXEL_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME = "h_macropixel_intensity_pa_delta_minus"
+H_MACROPIXEL_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME = (
+    "h_macropixel_intensity_pa_delta_plus"
+)
+H_MACROPIXEL_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME = (
+    "h_macropixel_intensity_pa_delta_minus"
+)
 H_ENERGY_CDF_VAR_NAME = "h_energy"
 H_ENERGY_DELTA_PLUS_CDF_VAR_NAME = "h_energy_delta_plus"
 H_ENERGY_DELTA_MINUS_CDF_VAR_NAME = "h_energy_delta_minus"
@@ -41,8 +45,12 @@
 NE_MG_SI_INTENSITY_DELTA_PLUS_CDF_VAR_NAME = "nemgsi_macropixel_intensity_delta_plus"
 NE_MG_SI_INTENSITY_DELTA_MINUS_CDF_VAR_NAME = "nemgsi_macropixel_intensity_delta_minus"
 NE_MG_SI_INTENSITY_PA_CDF_VAR_NAME = "nemgsi_macropixel_intensity_pa"
-NE_MG_SI_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME = "nemgsi_macropixel_intensity_pa_delta_plus"
-NE_MG_SI_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME = "nemgsi_macropixel_intensity_pa_delta_minus"
+NE_MG_SI_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME = (
+    "nemgsi_macropixel_intensity_pa_delta_plus"
+)
+NE_MG_SI_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME = (
+    "nemgsi_macropixel_intensity_pa_delta_minus"
+)
 NE_MG_SI_ENERGY_CDF_VAR_NAME = "nemgsi_energy"
 NE_MG_SI_ENERGY_DELTA_PLUS_CDF_VAR_NAME = "nemgsi_energy_delta_plus"
 NE_MG_SI_ENERGY_DELTA_MINUS_CDF_VAR_NAME = "nemgsi_energy_delta_minus"
@@ -133,87 +141,208 @@ class HitPitchAngleDataProduct(DataProduct):
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_CDF_VAR_NAME, self.epochs),
-            DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME,
-                                np.array([t.total_seconds() for t in self.epoch_deltas]) * 1e9),
+            DataProductVariable(
+                EPOCH_DELTA_CDF_VAR_NAME,
+                np.array([t.total_seconds() for t in self.epoch_deltas]) * 1e9,
+            ),
             DataProductVariable(PITCH_ANGLE_CDF_VAR_NAME, self.pitch_angles),
-            DataProductVariable(PITCH_ANGLE_DELTA_CDF_VAR_NAME, self.pitch_angle_deltas),
+            DataProductVariable(
+                PITCH_ANGLE_DELTA_CDF_VAR_NAME, self.pitch_angle_deltas
+            ),
             DataProductVariable(GYROPHASE_CDF_VAR_NAME, self.gyrophases),
             DataProductVariable(GYROPHASE_DELTA_CDF_VAR_NAME, self.gyrophase_deltas),
-            DataProductVariable(H_MACROPIXEL_INTENSITY_CDF_VAR_NAME, self.h_macropixel_intensity),
-            DataProductVariable(H_MACROPIXEL_INTENSITY_DELTA_PLUS_CDF_VAR_NAME, self.h_macropixel_intensity_delta_plus),
-            DataProductVariable(H_MACROPIXEL_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
-                                self.h_macropixel_intensity_delta_minus),
-            DataProductVariable(H_MACROPIXEL_INTENSITY_PA_CDF_VAR_NAME, self.h_macropixel_intensity_pa),
-            DataProductVariable(H_MACROPIXEL_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
-                                self.h_macropixel_intensity_pa_delta_plus),
-            DataProductVariable(H_MACROPIXEL_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
-                                self.h_macropixel_intensity_pa_delta_minus),
+            DataProductVariable(
+                H_MACROPIXEL_INTENSITY_CDF_VAR_NAME, self.h_macropixel_intensity
+            ),
+            DataProductVariable(
+                H_MACROPIXEL_INTENSITY_DELTA_PLUS_CDF_VAR_NAME,
+                self.h_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                H_MACROPIXEL_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
+                self.h_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                H_MACROPIXEL_INTENSITY_PA_CDF_VAR_NAME, self.h_macropixel_intensity_pa
+            ),
+            DataProductVariable(
+                H_MACROPIXEL_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
+                self.h_macropixel_intensity_pa_delta_plus,
+            ),
+            DataProductVariable(
+                H_MACROPIXEL_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
+                self.h_macropixel_intensity_pa_delta_minus,
+            ),
             DataProductVariable(H_ENERGY_CDF_VAR_NAME, self.h_energies),
-            DataProductVariable(H_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.h_energy_delta_plus),
-            DataProductVariable(H_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.h_energy_delta_minus),
-            DataProductVariable(HE4_INTENSITY_CDF_VAR_NAME, self.he4_macropixel_intensity),
-            DataProductVariable(HE4_INTENSITY_DELTA_PLUS_CDF_VAR_NAME, self.he4_macropixel_intensity_delta_plus),
-            DataProductVariable(HE4_INTENSITY_DELTA_MINUS_CDF_VAR_NAME, self.he4_macropixel_intensity_delta_minus),
-            DataProductVariable(HE4_INTENSITY_PA_CDF_VAR_NAME, self.he4_macropixel_intensity_pa),
-            DataProductVariable(HE4_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME, self.he4_macropixel_intensity_pa_delta_plus),
-            DataProductVariable(HE4_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
-                                self.he4_macropixel_intensity_pa_delta_minus),
+            DataProductVariable(
+                H_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.h_energy_delta_plus
+            ),
+            DataProductVariable(
+                H_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.h_energy_delta_minus
+            ),
+            DataProductVariable(
+                HE4_INTENSITY_CDF_VAR_NAME, self.he4_macropixel_intensity
+            ),
+            DataProductVariable(
+                HE4_INTENSITY_DELTA_PLUS_CDF_VAR_NAME,
+                self.he4_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                HE4_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
+                self.he4_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                HE4_INTENSITY_PA_CDF_VAR_NAME, self.he4_macropixel_intensity_pa
+            ),
+            DataProductVariable(
+                HE4_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
+                self.he4_macropixel_intensity_pa_delta_plus,
+            ),
+            DataProductVariable(
+                HE4_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
+                self.he4_macropixel_intensity_pa_delta_minus,
+            ),
             DataProductVariable(HE4_ENERGY_CDF_VAR_NAME, self.he4_energies),
-            DataProductVariable(HE4_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.he4_energy_delta_plus),
-            DataProductVariable(HE4_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.he4_energy_delta_minus),
-            DataProductVariable(CNO_INTENSITY_CDF_VAR_NAME, self.cno_macropixel_intensity),
-            DataProductVariable(CNO_INTENSITY_DELTA_PLUS_CDF_VAR_NAME, self.cno_macropixel_intensity_delta_plus),
-            DataProductVariable(CNO_INTENSITY_DELTA_MINUS_CDF_VAR_NAME, self.cno_macropixel_intensity_delta_minus),
-            DataProductVariable(CNO_INTENSITY_PA_CDF_VAR_NAME, self.cno_macropixel_intensity_pa),
-            DataProductVariable(CNO_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME, self.cno_macropixel_intensity_pa_delta_plus),
-            DataProductVariable(CNO_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
-                                self.cno_macropixel_intensity_pa_delta_minus),
+            DataProductVariable(
+                HE4_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.he4_energy_delta_plus
+            ),
+            DataProductVariable(
+                HE4_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.he4_energy_delta_minus
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_CDF_VAR_NAME, self.cno_macropixel_intensity
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_DELTA_PLUS_CDF_VAR_NAME,
+                self.cno_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
+                self.cno_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_PA_CDF_VAR_NAME, self.cno_macropixel_intensity_pa
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
+                self.cno_macropixel_intensity_pa_delta_plus,
+            ),
+            DataProductVariable(
+                CNO_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
+                self.cno_macropixel_intensity_pa_delta_minus,
+            ),
             DataProductVariable(CNO_ENERGY_CDF_VAR_NAME, self.cno_energies),
-            DataProductVariable(CNO_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.cno_energy_delta_plus),
-            DataProductVariable(CNO_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.cno_energy_delta_minus),
-            DataProductVariable(NE_MG_SI_INTENSITY_CDF_VAR_NAME, self.ne_mg_si_macropixel_intensity),
-            DataProductVariable(NE_MG_SI_INTENSITY_DELTA_PLUS_CDF_VAR_NAME,
-                                self.ne_mg_si_macropixel_intensity_delta_plus),
-            DataProductVariable(NE_MG_SI_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
-                                self.ne_mg_si_macropixel_intensity_delta_minus),
-            DataProductVariable(NE_MG_SI_INTENSITY_PA_CDF_VAR_NAME, self.ne_mg_si_macropixel_intensity_pa),
-            DataProductVariable(NE_MG_SI_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
-                                self.ne_mg_si_macropixel_intensity_pa_delta_plus),
-            DataProductVariable(NE_MG_SI_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
-                                self.ne_mg_si_macropixel_intensity_pa_delta_minus),
+            DataProductVariable(
+                CNO_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.cno_energy_delta_plus
+            ),
+            DataProductVariable(
+                CNO_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.cno_energy_delta_minus
+            ),
+            DataProductVariable(
+                NE_MG_SI_INTENSITY_CDF_VAR_NAME, self.ne_mg_si_macropixel_intensity
+            ),
+            DataProductVariable(
+                NE_MG_SI_INTENSITY_DELTA_PLUS_CDF_VAR_NAME,
+                self.ne_mg_si_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                NE_MG_SI_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
+                self.ne_mg_si_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                NE_MG_SI_INTENSITY_PA_CDF_VAR_NAME,
+                self.ne_mg_si_macropixel_intensity_pa,
+            ),
+            DataProductVariable(
+                NE_MG_SI_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
+                self.ne_mg_si_macropixel_intensity_pa_delta_plus,
+            ),
+            DataProductVariable(
+                NE_MG_SI_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
+                self.ne_mg_si_macropixel_intensity_pa_delta_minus,
+            ),
             DataProductVariable(NE_MG_SI_ENERGY_CDF_VAR_NAME, self.ne_mg_si_energies),
-            DataProductVariable(NE_MG_SI_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.ne_mg_si_energy_delta_plus),
-            DataProductVariable(NE_MG_SI_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.ne_mg_si_energy_delta_minus),
-            DataProductVariable(IRON_INTENSITY_CDF_VAR_NAME, self.iron_macropixel_intensity),
-            DataProductVariable(IRON_INTENSITY_DELTA_PLUS_CDF_VAR_NAME, self.iron_macropixel_intensity_delta_plus),
-            DataProductVariable(IRON_INTENSITY_DELTA_MINUS_CDF_VAR_NAME, self.iron_macropixel_intensity_delta_minus),
-            DataProductVariable(IRON_INTENSITY_PA_CDF_VAR_NAME, self.iron_macropixel_intensity_pa),
-            DataProductVariable(IRON_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
-                                self.iron_macropixel_intensity_pa_delta_plus),
-            DataProductVariable(IRON_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
-                                self.iron_macropixel_intensity_pa_delta_minus),
+            DataProductVariable(
+                NE_MG_SI_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.ne_mg_si_energy_delta_plus
+            ),
+            DataProductVariable(
+                NE_MG_SI_ENERGY_DELTA_MINUS_CDF_VAR_NAME,
+                self.ne_mg_si_energy_delta_minus,
+            ),
+            DataProductVariable(
+                IRON_INTENSITY_CDF_VAR_NAME, self.iron_macropixel_intensity
+            ),
+            DataProductVariable(
+                IRON_INTENSITY_DELTA_PLUS_CDF_VAR_NAME,
+                self.iron_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                IRON_INTENSITY_DELTA_MINUS_CDF_VAR_NAME,
+                self.iron_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                IRON_INTENSITY_PA_CDF_VAR_NAME, self.iron_macropixel_intensity_pa
+            ),
+            DataProductVariable(
+                IRON_INTENSITY_PA_DELTA_PLUS_CDF_VAR_NAME,
+                self.iron_macropixel_intensity_pa_delta_plus,
+            ),
+            DataProductVariable(
+                IRON_INTENSITY_PA_DELTA_MINUS_CDF_VAR_NAME,
+                self.iron_macropixel_intensity_pa_delta_minus,
+            ),
             DataProductVariable(IRON_ENERGY_CDF_VAR_NAME, self.iron_energies),
-            DataProductVariable(IRON_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.iron_energy_delta_plus),
-            DataProductVariable(IRON_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.iron_energy_delta_minus),
-            DataProductVariable(MEASUREMENT_PITCH_ANGLE_VAR_NAME, self.measurement_pitch_angle),
-            DataProductVariable(MEASUREMENT_GYROPHASE_VAR_NAME, self.measurement_gyrophase),
-            DataProductVariable(PITCH_ANGLE_LABEL_VAR_NAME,
-                                [f"Pitch Angle Label {str(i + 1)}" for i in range(len(self.pitch_angles))]),
-            DataProductVariable(GYROPHASE_LABEL_VAR_NAME,
-                                [f"Gyrophase Label {str(i + 1)}" for i in range(len(self.gyrophases))]),
-            DataProductVariable(H_ENERGY_LABEL_VAR_NAME,
-                                [f"H Energy Label {str(i + 1)}" for i in range(len(self.h_energies))]),
-            DataProductVariable(HE4_ENERGY_LABEL_VAR_NAME,
-                                [f"He4 Energy Label {str(i + 1)}" for i in range(len(self.he4_energies))]),
-            DataProductVariable(CNO_ENERGY_LABEL_VAR_NAME,
-                                [f"CNO Energy Label {str(i + 1)}" for i in range(len(self.cno_energies))]),
-            DataProductVariable(NE_MG_SI_ENERGY_LABEL_VAR_NAME,
-                                [f"NeMgSi Energy Label {str(i + 1)}" for i in range(len(self.ne_mg_si_energies))]),
-            DataProductVariable(IRON_ENERGY_LABEL_VAR_NAME,
-                                [f"Fe Energy Label {str(i + 1)}" for i in range(len(self.iron_energies))]),
+            DataProductVariable(
+                IRON_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.iron_energy_delta_plus
+            ),
+            DataProductVariable(
+                IRON_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.iron_energy_delta_minus
+            ),
+            DataProductVariable(
+                MEASUREMENT_PITCH_ANGLE_VAR_NAME, self.measurement_pitch_angle
+            ),
+            DataProductVariable(
+                MEASUREMENT_GYROPHASE_VAR_NAME, self.measurement_gyrophase
+            ),
+            DataProductVariable(
+                PITCH_ANGLE_LABEL_VAR_NAME,
+                [f"Pitch Angle Label {i + 1!s}" for i in range(len(self.pitch_angles))],
+            ),
+            DataProductVariable(
+                GYROPHASE_LABEL_VAR_NAME,
+                [f"Gyrophase Label {i + 1!s}" for i in range(len(self.gyrophases))],
+            ),
+            DataProductVariable(
+                H_ENERGY_LABEL_VAR_NAME,
+                [f"H Energy Label {i + 1!s}" for i in range(len(self.h_energies))],
+            ),
+            DataProductVariable(
+                HE4_ENERGY_LABEL_VAR_NAME,
+                [f"He4 Energy Label {i + 1!s}" for i in range(len(self.he4_energies))],
+            ),
+            DataProductVariable(
+                CNO_ENERGY_LABEL_VAR_NAME,
+                [f"CNO Energy Label {i + 1!s}" for i in range(len(self.cno_energies))],
+            ),
+            DataProductVariable(
+                NE_MG_SI_ENERGY_LABEL_VAR_NAME,
+                [
+                    f"NeMgSi Energy Label {i + 1!s}"
+                    for i in range(len(self.ne_mg_si_energies))
+                ],
+            ),
+            DataProductVariable(
+                IRON_ENERGY_LABEL_VAR_NAME,
+                [f"Fe Energy Label {i + 1!s}" for i in range(len(self.iron_energies))],
+            ),
             DataProductVariable(AZIMUTH_VAR_NAME, self.azimuth),
             DataProductVariable(ZENITH_VAR_NAME, self.zenith),
-            DataProductVariable(AZIMUTH_LABEL_VAR_NAME, [str(float(azimuth)) for azimuth in self.azimuth]),
-            DataProductVariable(ZENITH_LABEL_VAR_NAME, [str(float(zenith)) for zenith in self.zenith]),
+            DataProductVariable(
+                AZIMUTH_LABEL_VAR_NAME,
+                [str(float(azimuth)) for azimuth in self.azimuth],
+            ),
+            DataProductVariable(
+                ZENITH_LABEL_VAR_NAME, [str(float(zenith)) for zenith in self.zenith]
+            ),
             DataProductVariable(HIT_FLAGS_CDF_VAR_NAME, self.hit_flags),
         ]
diff --git a/imap_l3_processing/hit/l3/sectored_products/science/sectored_products_algorithms.py b/imap_l3_processing/hit/l3/sectored_products/science/sectored_products_algorithms.py
index 35cc374f7..56808e398 100644
--- a/imap_l3_processing/hit/l3/sectored_products/science/sectored_products_algorithms.py
+++ b/imap_l3_processing/hit/l3/sectored_products/science/sectored_products_algorithms.py
@@ -1,12 +1,17 @@
 import numpy as np
 
 
-def get_hit_bin_polar_coordinates(declination_bins=8, inclination_bins=15) -> tuple[
-    np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    declination_starts, declination_step = np.linspace(0, 180, declination_bins, endpoint=False, retstep=True)
+def get_hit_bin_polar_coordinates(
+    declination_bins=8, inclination_bins=15
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    declination_starts, declination_step = np.linspace(
+        0, 180, declination_bins, endpoint=False, retstep=True
+    )
     declination_delta = declination_step / 2
     declinations = declination_starts + declination_delta
-    inclination_starts, inclination_step = np.linspace(0, 360, inclination_bins, endpoint=False, retstep=True)
+    inclination_starts, inclination_step = np.linspace(
+        0, 360, inclination_bins, endpoint=False, retstep=True
+    )
     inclination_delta = inclination_step / 2
     inclinations = inclination_starts + inclination_delta
     declination_deltas = np.array([declination_delta] * declination_bins)
@@ -15,7 +20,9 @@ def get_hit_bin_polar_coordinates(declination_bins=8, inclination_bins=15) -> tu
     return declinations, inclinations, declination_deltas, inclination_deltas
 
 
-def get_sector_unit_vectors(declinations_degrees: np.ndarray, inclinations_degrees: np.ndarray) -> np.ndarray:
+def get_sector_unit_vectors(
+    declinations_degrees: np.ndarray, inclinations_degrees: np.ndarray
+) -> np.ndarray:
     declinations = np.deg2rad(declinations_degrees)
     inclinations = np.deg2rad(inclinations_degrees)
     declinations = declinations[:, np.newaxis]
diff --git a/imap_l3_processing/hit/l3/utils.py b/imap_l3_processing/hit/l3/utils.py
index 051b3cf9d..61b54a559 100644
--- a/imap_l3_processing/hit/l3/utils.py
+++ b/imap_l3_processing/hit/l3/utils.py
@@ -1,6 +1,5 @@
 from datetime import timedelta
 from pathlib import Path
-from typing import Union
 
 from spacepy.pycdf import CDF
 
@@ -8,7 +7,7 @@
 from imap_l3_processing.hit.l3.models import HitL2Data
 
 
-def read_l2_hit_data(cdf_file_path: Union[str, Path]) -> HitL2Data:
+def read_l2_hit_data(cdf_file_path: str | Path) -> HitL2Data:
     with CDF(str(cdf_file_path)) as cdf:
         return HitL2Data(
             epoch=cdf["epoch"][...],
diff --git a/imap_l3_processing/hit/quality_flags.py b/imap_l3_processing/hit/quality_flags.py
index adca15030..ae81bf16d 100644
--- a/imap_l3_processing/hit/quality_flags.py
+++ b/imap_l3_processing/hit/quality_flags.py
@@ -1,6 +1,6 @@
-from imap_processing.quality_flags import FlagNameMixin, CommonFlags
+from imap_processing.quality_flags import CommonFlags, FlagNameMixin
 
 
 class HitL3Flags(FlagNameMixin):
     NONE = CommonFlags.NONE
-    PRELIMINARY_MAG = 2 ** 2
+    PRELIMINARY_MAG = 2**2
diff --git a/imap_l3_processing/lo/l3/lo_combined_dependencies.py b/imap_l3_processing/lo/l3/lo_combined_dependencies.py
index 6bd4fe732..d392f144f 100644
--- a/imap_l3_processing/lo/l3/lo_combined_dependencies.py
+++ b/imap_l3_processing/lo/l3/lo_combined_dependencies.py
@@ -7,6 +7,7 @@
 
 from imap_l3_processing.maps.map_models import RectangularIntensityMapData
 
+
 @dataclass
 class LoCombinedDependencies:
     map_data: list[RectangularIntensityMapData]
@@ -15,7 +16,9 @@ class LoCombinedDependencies:
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> Self:
         lo_inputs = dependencies.get_file_paths(source="lo")
 
-        assert len(lo_inputs) == 3, f"Expected 3 input lo maps to produce combined map! got {len(lo_inputs)}"
+        assert len(lo_inputs) == 3, (
+            f"Expected 3 input lo maps to produce combined map! got {len(lo_inputs)}"
+        )
 
         downloaded_paths = [imap_data_access.download(Path(i).name) for i in lo_inputs]
 
@@ -23,5 +26,6 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> Self:
 
     @classmethod
     def from_file_paths(cls, file_paths: list[Path]) -> Self:
-        return cls(map_data=[RectangularIntensityMapData.read_from_path(p) for p in file_paths])
-
+        return cls(
+            map_data=[RectangularIntensityMapData.read_from_path(p) for p in file_paths]
+        )
diff --git a/imap_l3_processing/lo/l3/lo_l3_spectral_fit_dependencies.py b/imap_l3_processing/lo/l3/lo_l3_spectral_fit_dependencies.py
index 6d2af6448..43bd6e13a 100644
--- a/imap_l3_processing/lo/l3/lo_l3_spectral_fit_dependencies.py
+++ b/imap_l3_processing/lo/l3/lo_l3_spectral_fit_dependencies.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 from dataclasses import dataclass
 
 from imap_data_access import download
@@ -12,8 +13,12 @@ class LoL3SpectralFitDependencies:
     map_data: RectangularIntensityMapData
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> LoL3SpectralFitDependencies:
-        file_names = dependencies.get_file_paths(source="lo", data_type="l3") + dependencies.get_file_paths(source="lo", data_type="l2")
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection
+    ) -> LoL3SpectralFitDependencies:
+        file_names = dependencies.get_file_paths(
+            source="lo", data_type="l3"
+        ) + dependencies.get_file_paths(source="lo", data_type="l2")
 
         if len(file_names) != 1:
             raise ValueError("Incorrect number of dependencies")
diff --git a/imap_l3_processing/lo/l3/lo_sp_initializer.py b/imap_l3_processing/lo/l3/lo_sp_initializer.py
index ee5937b4d..7b4ddc5e0 100644
--- a/imap_l3_processing/lo/l3/lo_sp_initializer.py
+++ b/imap_l3_processing/lo/l3/lo_sp_initializer.py
@@ -3,7 +3,10 @@
 
 import imap_data_access
 
-from imap_l3_processing.maps.map_descriptors import MapDescriptorParts, SurvivalCorrection
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    SurvivalCorrection,
+)
 from imap_l3_processing.maps.map_initializer import PossibleMapToProduce
 from imap_l3_processing.maps.sp_map_initializer import SPMapInitializer
 from imap_l3_processing.utils import SpiceKernelTypes, furnish_spice_metakernel
@@ -25,7 +28,7 @@
 
 LO_ISN_MAP_DESCRIPTORS = [
     "l090-isn-h-sf-nsp-ram-hae-6deg-1yr",
-    "l090-isn-o-sf-nsp-ram-hae-6deg-1yr"
+    "l090-isn-o-sf-nsp-ram-hae-6deg-1yr",
 ]
 
 LO_SPECTRAL_INDEX_MAP_DESCRIPTORS = [
@@ -45,12 +48,14 @@
 class LoSPInitializer(SPMapInitializer):
     def __init__(self):
         glows_query_result = imap_data_access.query(
-            instrument='glows',
-            data_level='l3e',
+            instrument="glows",
+            data_level="l3e",
             descriptor="survival-probability-lo",
-            version="latest"
+            version="latest",
         )
-        self.glows_files_by_repointing = {int(r["repointing"]): Path(r["file_path"]).name for r in glows_query_result}
+        self.glows_files_by_repointing = {
+            int(r["repointing"]): Path(r["file_path"]).name for r in glows_query_result
+        }
         lo_l2_query_result = imap_data_access.query(instrument="lo", data_level="l2")
 
         super().__init__("lo", lo_l2_query_result)
@@ -58,12 +63,21 @@ def __init__(self):
     def _collect_glows_psets_by_repoint(self, descriptor: str) -> dict[int, str]:
         return self.glows_files_by_repointing
 
-    def _get_l2_dependencies(self, descriptor: MapDescriptorParts) -> list[MapDescriptorParts]:
-        return [dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected)]
+    def _get_l2_dependencies(
+        self, descriptor: MapDescriptorParts
+    ) -> list[MapDescriptorParts]:
+        return [
+            dataclasses.replace(
+                descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected
+            )
+        ]
 
     def furnish_spice_dependencies(self, map_to_produce: PossibleMapToProduce):
-        furnish_spice_metakernel(start_date=map_to_produce.input_metadata.start_date,
-                                 end_date=map_to_produce.input_metadata.end_date, kernel_types=LO_SP_MAP_KERNELS)
+        furnish_spice_metakernel(
+            start_date=map_to_produce.input_metadata.start_date,
+            end_date=map_to_produce.input_metadata.end_date,
+            kernel_types=LO_SP_MAP_KERNELS,
+        )
 
     def _get_ancillary_files(self) -> list[str]:
         return []
diff --git a/imap_l3_processing/lo/lo_processor.py b/imap_l3_processing/lo/lo_processor.py
index 88f88e4a7..523fa9696 100644
--- a/imap_l3_processing/lo/lo_processor.py
+++ b/imap_l3_processing/lo/lo_processor.py
@@ -5,64 +5,117 @@
 from imap_processing.spice.geometry import SpiceFrame
 
 from imap_l3_processing.lo.l3.lo_combined_dependencies import LoCombinedDependencies
-from imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies import \
-    LoL3ISNBackgroundSubtractedDependencies
-from imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies import LoL3SpectralFitDependencies
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies
+from imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies import (
+    LoL3ISNBackgroundSubtractedDependencies,
+)
+from imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies import (
+    LoL3SpectralFitDependencies,
+)
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiLoL3SurvivalDependencies,
+)
 from imap_l3_processing.maps.map_combination import ExposureWeightedCombination
-from imap_l3_processing.maps.map_descriptors import parse_map_descriptor, MapDescriptorParts, MapQuantity, \
-    SurvivalCorrection, ReferenceFrame, Sensor
-from imap_l3_processing.maps.map_models import ISNRateData, ISNBackgroundSubtractedData, ISNBackgroundSubtractedMapData
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData, RectangularSpectralIndexDataProduct, \
-    RectangularSpectralIndexMapData, RectangularIntensityDataProduct, InputRectangularPointingSet, \
-    ISNBackgroundSubtractedDataProduct
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    MapQuantity,
+    ReferenceFrame,
+    Sensor,
+    SurvivalCorrection,
+    parse_map_descriptor,
+)
+from imap_l3_processing.maps.map_models import (
+    InputRectangularPointingSet,
+    ISNBackgroundSubtractedData,
+    ISNBackgroundSubtractedDataProduct,
+    ISNBackgroundSubtractedMapData,
+    ISNRateData,
+    RectangularIntensityDataProduct,
+    RectangularIntensityMapData,
+    RectangularSpectralIndexDataProduct,
+    RectangularSpectralIndexMapData,
+)
 from imap_l3_processing.maps.map_processor import MapProcessor
-from imap_l3_processing.maps.spectral_fit import fit_spectral_index_map, slice_energy_range_by_bin
-from imap_l3_processing.maps.survival_probability_processing import process_survival_probabilities
+from imap_l3_processing.maps.spectral_fit import (
+    fit_spectral_index_map,
+    slice_energy_range_by_bin,
+)
+from imap_l3_processing.maps.survival_probability_processing import (
+    process_survival_probabilities,
+)
 from imap_l3_processing.models import Instrument
 from imap_l3_processing.utils import save_data
 
 
 class LoProcessor(MapProcessor):
-    def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000) -> list[Path]:
+    def process(
+        self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000
+    ) -> list[Path]:
         set_of_parent_file_names = set(self.get_parent_file_names())
         descriptor = parse_map_descriptor(self.input_metadata.descriptor)
         match descriptor:
             case MapDescriptorParts(quantity=MapQuantity.SpectralIndex):
                 deps = LoL3SpectralFitDependencies.fetch_dependencies(self.dependencies)
-                spectral_fit_data = perform_spectral_fit(deps.map_data,
-                                                         descriptor.spectral_index_energy_range or (4, 7))
-                data_product = RectangularSpectralIndexDataProduct(self.input_metadata, spectral_fit_data,
-                                                                   spice_frame_name=spice_frame_name)
+                spectral_fit_data = perform_spectral_fit(
+                    deps.map_data, descriptor.spectral_index_energy_range or (4, 7)
+                )
+                data_product = RectangularSpectralIndexDataProduct(
+                    self.input_metadata,
+                    spectral_fit_data,
+                    spice_frame_name=spice_frame_name,
+                )
             case MapDescriptorParts(quantity=MapQuantity.SpectralIndexNBS):
                 deps = LoL3SpectralFitDependencies.fetch_dependencies(self.dependencies)
-                spectral_fit_data = perform_spectral_fit(deps.map_data,
-                                                         descriptor.spectral_index_energy_range or (1, 7))
-                data_product = RectangularSpectralIndexDataProduct(self.input_metadata, spectral_fit_data,
-                                                                   spice_frame_name=spice_frame_name)
+                spectral_fit_data = perform_spectral_fit(
+                    deps.map_data, descriptor.spectral_index_energy_range or (1, 7)
+                )
+                data_product = RectangularSpectralIndexDataProduct(
+                    self.input_metadata,
+                    spectral_fit_data,
+                    spice_frame_name=spice_frame_name,
+                )
             case MapDescriptorParts(quantity=MapQuantity.ISNBackgroundSubtracted):
-                deps = LoL3ISNBackgroundSubtractedDependencies.fetch_dependencies(self.dependencies)
+                deps = LoL3ISNBackgroundSubtractedDependencies.fetch_dependencies(
+                    self.dependencies
+                )
                 background_subtracted = isn_background_subtraction(deps.map_data)
-                data_product = ISNBackgroundSubtractedDataProduct(self.input_metadata, background_subtracted,
-                                                                  spice_frame_name=spice_frame_name)
+                data_product = ISNBackgroundSubtractedDataProduct(
+                    self.input_metadata,
+                    background_subtracted,
+                    spice_frame_name=spice_frame_name,
+                )
             case MapDescriptorParts(sensor=Sensor.LoCombined):
                 deps = LoCombinedDependencies.fetch_dependencies(self.dependencies)
                 combination = ExposureWeightedCombination()
 
-                combined_map = combination.combine_rectangular_intensity_map_data(deps.map_data)
-                data_product = RectangularIntensityDataProduct(self.input_metadata, combined_map,spice_frame_name=spice_frame_name)
+                combined_map = combination.combine_rectangular_intensity_map_data(
+                    deps.map_data
+                )
+                data_product = RectangularIntensityDataProduct(
+                    self.input_metadata, combined_map, spice_frame_name=spice_frame_name
+                )
 
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    reference_frame=ReferenceFrame.Spacecraft | ReferenceFrame.Heliospheric):
-                deps = HiLoL3SurvivalDependencies.fetch_dependencies(self.dependencies, Instrument.IMAP_LO)
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                reference_frame=ReferenceFrame.Spacecraft | ReferenceFrame.Heliospheric,
+            ):
+                deps = HiLoL3SurvivalDependencies.fetch_dependencies(
+                    self.dependencies, Instrument.IMAP_LO
+                )
                 deps.l1c_data = list(map(self._collapse_pset_dimension, deps.l1c_data))
-                data = process_survival_probabilities(deps, spice_frame_name, cg_corrected=False)
+                data = process_survival_probabilities(
+                    deps, spice_frame_name, cg_corrected=False
+                )
 
-                data_product = RectangularIntensityDataProduct(self.input_metadata, data,
-                                                               spice_frame_name=spice_frame_name)
-                set_of_parent_file_names.update(path.name for path in deps.dependency_file_paths)
+                data_product = RectangularIntensityDataProduct(
+                    self.input_metadata, data, spice_frame_name=spice_frame_name
+                )
+                set_of_parent_file_names.update(
+                    path.name for path in deps.dependency_file_paths
+                )
             case None:
-                raise ValueError(f"Could not parse descriptor {self.input_metadata.descriptor}")
+                raise ValueError(
+                    f"Could not parse descriptor {self.input_metadata.descriptor}"
+                )
             case _:
                 raise NotImplementedError(self.input_metadata.descriptor)
 
@@ -70,26 +123,38 @@ def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000) -> list[
         return [save_data(data_product)]
 
     @staticmethod
-    def _collapse_pset_dimension(pset: InputRectangularPointingSet) -> InputRectangularPointingSet:
-        return dataclasses.replace(pset, exposure_times=np.sum(pset.exposure_times, axis=3),
-                                   hae_longitude=np.mean(pset.hae_longitude, axis=-1),
-                                   hae_latitude=np.mean(pset.hae_latitude, axis=-1))
+    def _collapse_pset_dimension(
+        pset: InputRectangularPointingSet,
+    ) -> InputRectangularPointingSet:
+        return dataclasses.replace(
+            pset,
+            exposure_times=np.sum(pset.exposure_times, axis=3),
+            hae_longitude=np.mean(pset.hae_longitude, axis=-1),
+            hae_latitude=np.mean(pset.hae_latitude, axis=-1),
+        )
 
 
-def perform_spectral_fit(data: RectangularIntensityMapData,
-                         spectral_index_range: tuple[int, int]) -> RectangularSpectralIndexMapData:
-    intensity_data = slice_energy_range_by_bin(data.intensity_map_data, spectral_index_range[0],
-                                               spectral_index_range[1])
+def perform_spectral_fit(
+    data: RectangularIntensityMapData, spectral_index_range: tuple[int, int]
+) -> RectangularSpectralIndexMapData:
+    intensity_data = slice_energy_range_by_bin(
+        data.intensity_map_data, spectral_index_range[0], spectral_index_range[1]
+    )
     return RectangularSpectralIndexMapData(
         spectral_index_map_data=fit_spectral_index_map(intensity_data),
-        coords=data.coords
+        coords=data.coords,
     )
 
 
-def isn_background_subtraction(isn_rate_data: ISNRateData) -> ISNBackgroundSubtractedMapData:
-    isn_rate_background_subtracted = isn_rate_data.ena_count_rate - isn_rate_data.bg_rate
+def isn_background_subtraction(
+    isn_rate_data: ISNRateData,
+) -> ISNBackgroundSubtractedMapData:
+    isn_rate_background_subtracted = (
+        isn_rate_data.ena_count_rate - isn_rate_data.bg_rate
+    )
     isn_bg_subtracted_stat_err = np.sqrt(
-        np.square(isn_rate_data.ena_count_rate_stat_uncert) + np.square(isn_rate_data.bg_rate_stat_uncert)
+        np.square(isn_rate_data.ena_count_rate_stat_uncert)
+        + np.square(isn_rate_data.bg_rate_stat_uncert)
     )
 
     map_data = ISNBackgroundSubtractedData(
@@ -114,7 +179,7 @@ def isn_background_subtraction(isn_rate_data: ISNRateData) -> ISNBackgroundSubtr
         longitude_label=isn_rate_data.longitude_label,
         isn_bg_rate_subtracted=isn_rate_background_subtracted,
         isn_bg_rate_subtracted_stat_uncert=isn_bg_subtracted_stat_err,
-        isn_bg_rate_subtracted_sys_err=isn_rate_data.bg_rate_sys_err
+        isn_bg_rate_subtracted_sys_err=isn_rate_data.bg_rate_sys_err,
     )
 
     return ISNBackgroundSubtractedMapData(isn_rate_map_data=map_data)
diff --git a/imap_l3_processing/lo/lo_txt_to_cdf_converter_spike.py b/imap_l3_processing/lo/lo_txt_to_cdf_converter_spike.py
index 42809e833..94987e0fd 100644
--- a/imap_l3_processing/lo/lo_txt_to_cdf_converter_spike.py
+++ b/imap_l3_processing/lo/lo_txt_to_cdf_converter_spike.py
@@ -6,7 +6,7 @@
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
 from pathlib import Path
-from typing import Self, Optional, override, Literal
+from typing import Literal, Self, override
 
 import imap_data_access
 import numpy as np
@@ -28,22 +28,26 @@
 from imap_l3_processing.utils import furnished_metakernel
 from tests.test_helpers import get_run_local_data_path
 
-LO_ENERGIES_IN_KEV = np.array([16.33, 30.47, 55.76, 106.3, 200.0, 405.0, 787.3]) / 1000.0
+LO_ENERGIES_IN_KEV = (
+    np.array([16.33, 30.47, 55.76, 106.3, 200.0, 405.0, 787.3]) / 1000.0
+)
 LO_ENERGY_BIN_LOWERS = np.array([10.9, 20.4, 36.8, 71.6, 135.0, 269.0, 504.7]) / 1000.0
-LO_ENERGY_BIN_UPPERS = np.array([21.7, 40.5, 74.7, 140.9, 265.0, 541.0, 1069.9]) / 1000.0
+LO_ENERGY_BIN_UPPERS = (
+    np.array([21.7, 40.5, 74.7, 140.9, 265.0, 541.0, 1069.9]) / 1000.0
+)
 
 
 class CsvNameToProduct:
     @abc.abstractmethod
-    def get_energy_and_quantity(self, filename) -> Optional[tuple[int, str]]:
+    def get_energy_and_quantity(self, filename) -> tuple[int, str] | None:
         raise NotImplementedError
 
 
 @dataclasses.dataclass
 class NBSNameMapping(CsvNameToProduct):
     @override
-    def get_energy_and_quantity(self, filename: str) -> Optional[tuple[int, str]]:
-        if fn_match := re.match("map_([a-zA-Z]+)_esa(\d{1}).csv", filename):
+    def get_energy_and_quantity(self, filename: str) -> tuple[int, str] | None:
+        if fn_match := re.match(r"map_([a-zA-Z]+)_esa(\d{1}).csv", filename):
             [quantity, energy] = fn_match.groups()
 
             csv_to_l2_cdf_mapping = {
@@ -55,7 +59,7 @@ def get_energy_and_quantity(self, filename: str) -> Optional[tuple[int, str]]:
                 "bflux": "bg_intensity",
                 "bfvar": "bg_intensity_stat_var",
                 "bfunc": "bg_intensity_sys_err",
-                "expo": "exposure_factor"
+                "expo": "exposure_factor",
             }
 
             if quantity in csv_to_l2_cdf_mapping:
@@ -64,8 +68,8 @@ def get_energy_and_quantity(self, filename: str) -> Optional[tuple[int, str]]:
 
 class CGNameMapping(CsvNameToProduct):
     @override
-    def get_energy_and_quantity(self, filename: str) -> Optional[tuple[int, str]]:
-        if fn_match := re.match("([a-zA-Z]+_[a-zA-Z]+)_esa(\d{1}).csv", filename):
+    def get_energy_and_quantity(self, filename: str) -> tuple[int, str] | None:
+        if fn_match := re.match(r"([a-zA-Z]+_[a-zA-Z]+)_esa(\d{1}).csv", filename):
             [quantity, energy] = fn_match.groups()
 
             csv_to_l2_cdf_mapping = {
@@ -88,8 +92,10 @@ def __init__(self, correction: Literal["sput", "boot"]):
         self.correction = correction
 
     @override
-    def get_energy_and_quantity(self, filename: str) -> Optional[tuple[int, str]]:
-        if fn_match := re.match("map_flux_(\d{1})_Hy_([a-zA-Z]+)_([a-zA-Z]+).csv", filename):
+    def get_energy_and_quantity(self, filename: str) -> tuple[int, str] | None:
+        if fn_match := re.match(
+            r"map_flux_(\d{1})_Hy_([a-zA-Z]+)_([a-zA-Z]+).csv", filename
+        ):
             [energy, correction, quantity] = fn_match.groups()
 
             csv_to_l2_cdf_mapping = {
@@ -140,34 +146,47 @@ class LoProcessingInput:
     def load_data_dir(data_dir: Path, name_mapping: CsvNameToProduct, **kwargs):
         temp_data = {}
         for data_file_path in data_dir.iterdir():
-            if energy_and_quantity := name_mapping.get_energy_and_quantity(data_file_path.name):
+            if energy_and_quantity := name_mapping.get_energy_and_quantity(
+                data_file_path.name
+            ):
                 esa_step, data_type = energy_and_quantity
                 if data_type not in temp_data:
                     temp_data[data_type] = np.full((1, 7, 60, 30), np.nan)
-                temp_data[data_type][0, esa_step - 1] = np.loadtxt(data_file_path, delimiter=",", **kwargs).T
+                temp_data[data_type][0, esa_step - 1] = np.loadtxt(
+                    data_file_path, delimiter=",", **kwargs
+                ).T
         return temp_data
 
     @classmethod
-    def load(cls,
-             manifest: Manifest,
-             l2_descriptor: str,
-             version: int,
-             input_path: Path,
-             name_mapping: CsvNameToProduct,
-             use_masked_data: bool,
-             **additional_map_data
-             ) -> Self:
+    def load(
+        cls,
+        manifest: Manifest,
+        l2_descriptor: str,
+        version: int,
+        input_path: Path,
+        name_mapping: CsvNameToProduct,
+        use_masked_data: bool,
+        **additional_map_data,
+    ) -> Self:
         if use_masked_data:
             loaded_data = {
-                **LoProcessingInput.load_data_dir(input_path / "maps", name_mapping, skiprows=1),
-                **LoProcessingInput.load_data_dir(input_path / "masked_maps", name_mapping)
+                **LoProcessingInput.load_data_dir(
+                    input_path / "maps", name_mapping, skiprows=1
+                ),
+                **LoProcessingInput.load_data_dir(
+                    input_path / "masked_maps", name_mapping
+                ),
             }
         else:
             loaded_data = {
-                **LoProcessingInput.load_data_dir(input_path / "maps", name_mapping, skiprows=1),
+                **LoProcessingInput.load_data_dir(
+                    input_path / "maps", name_mapping, skiprows=1
+                ),
             }
 
-        start_date_nanoseconds = (manifest.start_date - TT2000_EPOCH).total_seconds() * 1e9
+        start_date_nanoseconds = (
+            manifest.start_date - TT2000_EPOCH
+        ).total_seconds() * 1e9
         end_date_nanoseconds = (manifest.end_date - TT2000_EPOCH).total_seconds() * 1e9
 
         skymap = RectangularSkyMap(6, SpiceFrame.ECLIPJ2000)
@@ -176,16 +195,26 @@ def load(cls,
 
         map_data_coords = ["epoch", "energy", "longitude", "latitude"]
         l2_dataset = xr.Dataset(
-            data_vars=
-            {
+            data_vars={
                 "obs_date": (map_data_coords, np.full((1, 7, 60, 30), FILLVAL_INT64)),
                 "obs_date_range": (map_data_coords, np.full((1, 7, 60, 30), np.nan)),
-                "solid_angle": (["epoch", "longitude", "latitude"], np.full((1, 60, 30), np.nan)),
-                "energy_delta_minus": (["energy"], LO_ENERGIES_IN_KEV - LO_ENERGY_BIN_LOWERS),
-                "energy_delta_plus": (["energy"], LO_ENERGY_BIN_UPPERS - LO_ENERGIES_IN_KEV),
-
-                **{quantity: (map_data_coords, data) for quantity, data in loaded_data.items()},
-                **additional_map_data
+                "solid_angle": (
+                    ["epoch", "longitude", "latitude"],
+                    np.full((1, 60, 30), np.nan),
+                ),
+                "energy_delta_minus": (
+                    ["energy"],
+                    LO_ENERGIES_IN_KEV - LO_ENERGY_BIN_LOWERS,
+                ),
+                "energy_delta_plus": (
+                    ["energy"],
+                    LO_ENERGY_BIN_UPPERS - LO_ENERGIES_IN_KEV,
+                ),
+                **{
+                    quantity: (map_data_coords, data)
+                    for quantity, data in loaded_data.items()
+                },
+                **additional_map_data,
             },
             coords={
                 "epoch": np.array([start_date_nanoseconds]),
@@ -196,11 +225,15 @@ def load(cls,
         )
 
         if "ena_intensity_stat_var" in l2_dataset.data_vars:
-            l2_dataset["ena_intensity_stat_uncert"] = np.sqrt(l2_dataset["ena_intensity_stat_var"])
+            l2_dataset["ena_intensity_stat_uncert"] = np.sqrt(
+                l2_dataset["ena_intensity_stat_var"]
+            )
             l2_dataset.drop_vars(["ena_intensity_stat_var"])
 
         if "bg_intensity_stat_var" in l2_dataset.data_vars:
-            l2_dataset["bg_intensity_stat_uncert"] = np.sqrt(l2_dataset["bg_intensity_stat_var"])
+            l2_dataset["bg_intensity_stat_uncert"] = np.sqrt(
+                l2_dataset["bg_intensity_stat_var"]
+            )
             l2_dataset.drop_vars(["bg_intensity_stat_var"])
 
         variables_to_mask = [
@@ -244,7 +277,9 @@ def get_spx_dependencies(self):
     def get_survival_corrected_dependencies(self, l1c_files: list[Path]):
         # l1c_results = imap_data_access.query(instrument="lo", data_level="l1c", version="latest")
         # l1c_inputs = [Path(l1c["file_path"]) for l1c in l1c_results if l1c["repointing"] in self.repoints]
-        l1c_inputs = [p for p in l1c_files if ScienceFilePath(p.name).repointing in self.repoints]
+        l1c_inputs = [
+            p for p in l1c_files if ScienceFilePath(p.name).repointing in self.repoints
+        ]
 
         glows_query_results = imap_data_access.query(
             instrument="glows",
@@ -253,10 +288,17 @@ def get_survival_corrected_dependencies(self, l1c_files: list[Path]):
             version="latest",
         )
         glows_paths = [
-            Path(glows["file_path"]) for glows in glows_query_results if int(glows["repointing"]) in self.repoints
+            Path(glows["file_path"])
+            for glows in glows_query_results
+            if int(glows["repointing"]) in self.repoints
         ]
 
-        return ProcessingInputCollection(*(ScienceInput(p.name) for p in [self.l2_cdf_path, *glows_paths, *l1c_inputs]))
+        return ProcessingInputCollection(
+            *(
+                ScienceInput(p.name)
+                for p in [self.l2_cdf_path, *glows_paths, *l1c_inputs]
+            )
+        )
 
     def make_l3_input_metadata(self, l3_descriptor: str) -> InputMetadata:
         return InputMetadata(
@@ -270,30 +312,32 @@ def make_l3_input_metadata(self, l3_descriptor: str) -> InputMetadata:
 
 
 def copy_to_output_directory_and_rename_for_initial_release(
-        release_directory: Path, output_maps: list[Path]
+    release_directory: Path, output_maps: list[Path]
 ):
     for generated_path in output_maps:
         science_file_path = ScienceFilePath(generated_path.name)
 
         new_name = (
-                "_".join(
-                    [
-                        "imap",
-                        science_file_path.instrument,
-                        science_file_path.data_level,
-                        science_file_path.descriptor + "-INITIAL",
-                        science_file_path.start_date,
-                        science_file_path.version,
-                    ]
-                )
-                + ".cdf"
+            "_".join(
+                [
+                    "imap",
+                    science_file_path.instrument,
+                    science_file_path.data_level,
+                    science_file_path.descriptor + "-INITIAL",
+                    science_file_path.start_date,
+                    science_file_path.version,
+                ]
+            )
+            + ".cdf"
         )
 
         output_dir = release_directory / science_file_path.data_level
         output_dir.mkdir(parents=True, exist_ok=True)
         output_path = output_dir / new_name
 
-        with CDF(str(output_path), masterpath=str(generated_path), readonly=False) as cdf:
+        with CDF(
+            str(output_path), masterpath=str(generated_path), readonly=False
+        ) as cdf:
             cdf.attrs["Logical_file_id"] = output_path.stem
 
 
@@ -301,7 +345,9 @@ def download_all_l1c(path: Path):
     original_data_dir = imap_data_access.config["DATA_DIR"]
     try:
         imap_data_access.config["DATA_DIR"] = path
-        all_l1c = imap_data_access.query(instrument="lo", data_level="l1c", version="latest")
+        all_l1c = imap_data_access.query(
+            instrument="lo", data_level="l1c", version="latest"
+        )
 
         with ThreadPoolExecutor() as pool:
             for l1c in all_l1c:
@@ -312,7 +358,11 @@ def download_all_l1c(path: Path):
 
 
 if __name__ == "__main__":
-    logging.basicConfig(force=True, level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    logging.basicConfig(
+        force=True,
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
 
     output_data_path = get_run_local_data_path("lo_txt_pipeline")
     shutil.rmtree(output_data_path / "imap" / "lo" / "l2", ignore_errors=True)
@@ -361,7 +411,8 @@ def download_all_l1c(path: Path):
 
             [spx_nsnbs_map] = LoProcessor(
                 input_metadata=nbs_processing_input.make_l3_input_metadata(
-                    f"l{pivot:03d}-spxnsnbs{descriptor_suffix}-h-sf-nsp-ram-hae-6deg-6mo"),
+                    f"l{pivot:03d}-spxnsnbs{descriptor_suffix}-h-sf-nsp-ram-hae-6deg-6mo"
+                ),
                 dependencies=nbs_processing_input.get_spx_dependencies(),
             ).process()
 
@@ -372,25 +423,34 @@ def download_all_l1c(path: Path):
                 cg_corrected_input_path / "outdir" / f"pivot_{pivot}",
                 CGNameMapping(),
                 masked,
-                exposure_factor=nbs_processing_input.dataset["exposure_factor"]
+                exposure_factor=nbs_processing_input.dataset["exposure_factor"],
             )
 
             [spx_cg_nsp_map] = LoProcessor(
                 input_metadata=cg_processing_input.make_l3_input_metadata(
-                    f"l{pivot:03d}-spxsbs{descriptor_suffix}-h-hf-nsp-ram-hae-6deg-6mo"),
-                dependencies=cg_processing_input.get_spx_dependencies()
+                    f"l{pivot:03d}-spxsbs{descriptor_suffix}-h-hf-nsp-ram-hae-6deg-6mo"
+                ),
+                dependencies=cg_processing_input.get_spx_dependencies(),
             ).process()
 
-            with furnished_metakernel(cg_processing_input.start_date, cg_processing_input.end_date, LO_SP_MAP_KERNELS):
+            with furnished_metakernel(
+                cg_processing_input.start_date,
+                cg_processing_input.end_date,
+                LO_SP_MAP_KERNELS,
+            ):
                 [sp_map] = LoProcessor(
                     input_metadata=cg_processing_input.make_l3_input_metadata(
-                        f"l{pivot:03d}-enasbs{descriptor_suffix}-h-hf-sp-ram-hae-6deg-6mo"),
-                    dependencies=cg_processing_input.get_survival_corrected_dependencies(l1c_paths),
+                        f"l{pivot:03d}-enasbs{descriptor_suffix}-h-hf-sp-ram-hae-6deg-6mo"
+                    ),
+                    dependencies=cg_processing_input.get_survival_corrected_dependencies(
+                        l1c_paths
+                    ),
                 ).process()
 
             [spx_cg_sp_map] = LoProcessor(
                 input_metadata=cg_processing_input.make_l3_input_metadata(
-                    f"l{pivot:03d}-spxsbs{descriptor_suffix}-h-hf-sp-ram-hae-6deg-6mo"),
+                    f"l{pivot:03d}-spxsbs{descriptor_suffix}-h-hf-sp-ram-hae-6deg-6mo"
+                ),
                 dependencies=ProcessingInputCollection(ScienceInput(sp_map.name)),
             ).process()
 
@@ -398,32 +458,38 @@ def download_all_l1c(path: Path):
                 manifest=manifest,
                 l2_descriptor=f"l{pivot:03d}-enasnbs{descriptor_suffix}-h-sf-nsp-ram-hae-6deg-6mo",
                 version=1,
-                input_path=sputter_or_bootstrap_input_path / "outdir" / f"pivot_{pivot}",
+                input_path=sputter_or_bootstrap_input_path
+                / "outdir"
+                / f"pivot_{pivot}",
                 name_mapping=SputterOrBootStrapNameMapping("sput"),
                 use_masked_data=masked,
-                exposure_factor=nbs_processing_input.dataset["exposure_factor"]
+                exposure_factor=nbs_processing_input.dataset["exposure_factor"],
             )
 
             sputter_and_bootstrap_map = LoProcessingInput.load(
                 manifest=manifest,
                 l2_descriptor=f"l{pivot:03d}-enasbs{descriptor_suffix}-h-sf-nsp-ram-hae-6deg-6mo",
                 version=1,
-                input_path=sputter_or_bootstrap_input_path / "outdir" / f"pivot_{pivot}",
+                input_path=sputter_or_bootstrap_input_path
+                / "outdir"
+                / f"pivot_{pivot}",
                 name_mapping=SputterOrBootStrapNameMapping("boot"),
                 use_masked_data=masked,
-                exposure_factor=nbs_processing_input.dataset["exposure_factor"]
+                exposure_factor=nbs_processing_input.dataset["exposure_factor"],
             )
 
-            output_maps.extend([
-                nbs_processing_input.l2_cdf_path,
-                spx_nsnbs_map,
-                cg_processing_input.l2_cdf_path,
-                spx_cg_nsp_map,
-                sp_map,
-                spx_cg_sp_map,
-                sputter_no_bootstrap_map.l2_cdf_path,
-                sputter_and_bootstrap_map.l2_cdf_path
-            ])
+            output_maps.extend(
+                [
+                    nbs_processing_input.l2_cdf_path,
+                    spx_nsnbs_map,
+                    cg_processing_input.l2_cdf_path,
+                    spx_cg_nsp_map,
+                    sp_map,
+                    spx_cg_sp_map,
+                    sputter_no_bootstrap_map.l2_cdf_path,
+                    sputter_and_bootstrap_map.l2_cdf_path,
+                ]
+            )
 
     maps_needed_for_combination = [
         (
@@ -473,27 +539,32 @@ def download_all_l1c(path: Path):
     for combined_descriptor, combined_dependencies in maps_needed_for_combination:
         combined_inputs = []
         for output_map in output_maps:
-            if any([f"_{combined_dep}_" in output_map.name for combined_dep in combined_dependencies]):
+            if any(
+                [
+                    f"_{combined_dep}_" in output_map.name
+                    for combined_dep in combined_dependencies
+                ]
+            ):
                 combined_inputs.append(output_map)
 
         [combined_sp_map] = LoProcessor(
-            dependencies=ProcessingInputCollection(*[ScienceInput(map_path.name) for map_path in combined_inputs]),
+            dependencies=ProcessingInputCollection(
+                *[ScienceInput(map_path.name) for map_path in combined_inputs]
+            ),
             input_metadata=InputMetadata(
                 instrument="lo",
                 data_level="l3",
                 start_date=combined_start_date,
                 end_date=combined_end_date,
                 version=VersionMap({}, Version(None, 2)),
-                descriptor=combined_descriptor
-            )
+                descriptor=combined_descriptor,
+            ),
         ).process()
 
-        combined_spx_descriptor = combined_descriptor.replace(
-            "-enasbsMsk-", "-spxsbsMsk-"
-        ).replace(
-            "-enansnbsMsk-", "-spxnsnbsMsk-"
-        ).replace(
-            "-enasnbsMsk-", "-spxsnbsMsk-"
+        combined_spx_descriptor = (
+            combined_descriptor.replace("-enasbsMsk-", "-spxsbsMsk-")
+            .replace("-enansnbsMsk-", "-spxnsnbsMsk-")
+            .replace("-enasnbsMsk-", "-spxsnbsMsk-")
         )
 
         [combined_spx_map] = LoProcessor(
@@ -508,10 +579,9 @@ def download_all_l1c(path: Path):
             ),
         ).process()
 
-        output_maps.extend([
-            combined_sp_map,
-            combined_spx_map
-        ])
+        output_maps.extend([combined_sp_map, combined_spx_map])
 
     release_directory = get_run_local_data_path("IMAP-Lo June 2nd 2026 Maps")
-    copy_to_output_directory_and_rename_for_initial_release(release_directory, output_maps)
+    copy_to_output_directory_and_rename_for_initial_release(
+        release_directory, output_maps
+    )
diff --git a/imap_l3_processing/maps/hilo_l3_survival_dependencies.py b/imap_l3_processing/maps/hilo_l3_survival_dependencies.py
index 69ddcb71d..871d4979e 100644
--- a/imap_l3_processing/maps/hilo_l3_survival_dependencies.py
+++ b/imap_l3_processing/maps/hilo_l3_survival_dependencies.py
@@ -7,10 +7,20 @@
 from imap_data_access import ScienceFilePath
 from imap_data_access.processing_input import ProcessingInputCollection
 
-from imap_l3_processing.hi.utils import read_l1c_rectangular_pointing_set_data, read_glows_l3e_data
-from imap_l3_processing.maps.map_descriptors import MapDescriptorParts, parse_map_descriptor, SpinPhase
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData, GlowsL3eRectangularMapInputData, \
-    InputRectangularPointingSet
+from imap_l3_processing.hi.utils import (
+    read_glows_l3e_data,
+    read_l1c_rectangular_pointing_set_data,
+)
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    SpinPhase,
+    parse_map_descriptor,
+)
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
+    RectangularIntensityMapData,
+)
 from imap_l3_processing.models import Instrument
 
 
@@ -24,23 +34,39 @@ class HiLoL3SurvivalDependencies:
     dependency_file_paths: list[Path] = field(default_factory=list)
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection,
-                           instrument: Instrument) -> HiLoL3SurvivalDependencies:
-        [l2_map_path] = dependencies.get_file_paths(source=instrument.value, data_type="l2")
-        l1c_paths = dependencies.get_file_paths(source=instrument.value, data_type="l1c")
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection, instrument: Instrument
+    ) -> HiLoL3SurvivalDependencies:
+        [l2_map_path] = dependencies.get_file_paths(
+            source=instrument.value, data_type="l2"
+        )
+        l1c_paths = dependencies.get_file_paths(
+            source=instrument.value, data_type="l1c"
+        )
         glows_paths = dependencies.get_file_paths(source="glows")
 
         map_file_path = imap_data_access.download(l2_map_path.name)
-        l1c_downloaded_paths = [imap_data_access.download(l1c.name) for l1c in l1c_paths]
-        glows_downloaded_paths = [imap_data_access.download(path.name) for path in glows_paths]
+        l1c_downloaded_paths = [
+            imap_data_access.download(l1c.name) for l1c in l1c_paths
+        ]
+        glows_downloaded_paths = [
+            imap_data_access.download(path.name) for path in glows_paths
+        ]
 
         l2_descriptor = ScienceFilePath(l2_map_path).descriptor
 
-        return cls.from_file_paths(map_file_path, l1c_downloaded_paths, glows_downloaded_paths, l2_descriptor)
+        return cls.from_file_paths(
+            map_file_path, l1c_downloaded_paths, glows_downloaded_paths, l2_descriptor
+        )
 
     @classmethod
-    def from_file_paths(cls, map_file_path: Path, l1c_paths: list[Path],
-                        glows_l3e_paths: list[Path], l2_descriptor: str) -> HiLoL3SurvivalDependencies:
+    def from_file_paths(
+        cls,
+        map_file_path: Path,
+        l1c_paths: list[Path],
+        glows_l3e_paths: list[Path],
+        l2_descriptor: str,
+    ) -> HiLoL3SurvivalDependencies:
         glows_l3e_data = list(map(read_glows_l3e_data, glows_l3e_paths))
         l1c_data = list(map(read_l1c_rectangular_pointing_set_data, l1c_paths))
 
@@ -48,10 +74,13 @@ def from_file_paths(cls, map_file_path: Path, l1c_paths: list[Path],
 
         paths = [map_file_path] + l1c_paths + glows_l3e_paths
 
-        return cls(l2_data, l1c_data=l1c_data,
-                   glows_l3e_data=glows_l3e_data,
-                   l2_map_descriptor_parts=parse_map_descriptor(l2_descriptor),
-                   dependency_file_paths=paths)
+        return cls(
+            l2_data,
+            l1c_data=l1c_data,
+            glows_l3e_data=glows_l3e_data,
+            l2_map_descriptor_parts=parse_map_descriptor(l2_descriptor),
+            dependency_file_paths=paths,
+        )
 
 
 @dataclass
@@ -60,21 +89,37 @@ class HiL3SingleSensorFullSpinDependencies:
     antiram_dependencies: HiLoL3SurvivalDependencies
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> HiL3SingleSensorFullSpinDependencies:
-        parsed_descriptors = [parse_map_descriptor(pi.descriptor) for pi in dependencies.processing_input]
-
-        ram_dependencies = [pi for pi, descriptor in zip(dependencies.processing_input, parsed_descriptors) if
-                            descriptor is None or descriptor.spin_phase == SpinPhase.RamOnly]
-
-        antiram_dependencies = [pi for pi, descriptor in zip(dependencies.processing_input, parsed_descriptors) if
-                                descriptor is None or descriptor.spin_phase == SpinPhase.AntiRamOnly]
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection
+    ) -> HiL3SingleSensorFullSpinDependencies:
+        parsed_descriptors = [
+            parse_map_descriptor(pi.descriptor) for pi in dependencies.processing_input
+        ]
+
+        ram_dependencies = [
+            pi
+            for pi, descriptor in zip(dependencies.processing_input, parsed_descriptors)
+            if descriptor is None or descriptor.spin_phase == SpinPhase.RamOnly
+        ]
+
+        antiram_dependencies = [
+            pi
+            for pi, descriptor in zip(dependencies.processing_input, parsed_descriptors)
+            if descriptor is None or descriptor.spin_phase == SpinPhase.AntiRamOnly
+        ]
 
         return cls(
             ram_dependencies=HiLoL3SurvivalDependencies.fetch_dependencies(
-                ProcessingInputCollection(*ram_dependencies), Instrument.IMAP_HI),
+                ProcessingInputCollection(*ram_dependencies), Instrument.IMAP_HI
+            ),
             antiram_dependencies=HiLoL3SurvivalDependencies.fetch_dependencies(
-                ProcessingInputCollection(*antiram_dependencies), Instrument.IMAP_HI))
+                ProcessingInputCollection(*antiram_dependencies), Instrument.IMAP_HI
+            ),
+        )
 
     @property
     def dependency_file_paths(self) -> list[Path]:
-        return self.ram_dependencies.dependency_file_paths + self.antiram_dependencies.dependency_file_paths
+        return (
+            self.ram_dependencies.dependency_file_paths
+            + self.antiram_dependencies.dependency_file_paths
+        )
diff --git a/imap_l3_processing/maps/map_combination.py b/imap_l3_processing/maps/map_combination.py
index b7852d051..304388580 100644
--- a/imap_l3_processing/maps/map_combination.py
+++ b/imap_l3_processing/maps/map_combination.py
@@ -4,31 +4,52 @@
 import numpy as np
 
 from imap_l3_processing.data_utils import safe_divide
-from imap_l3_processing.maps.map_models import IntensityMapData, HealPixIntensityMapData, RectangularIntensityMapData, \
-    calculate_datetime_weighted_average
+from imap_l3_processing.maps.map_models import (
+    HealPixIntensityMapData,
+    IntensityMapData,
+    RectangularIntensityMapData,
+    calculate_datetime_weighted_average,
+)
 
 
 class CombinationStrategy(ABC):
-    def combine_rectangular_intensity_map_data(self,
-                                               maps: list[RectangularIntensityMapData]) -> RectangularIntensityMapData:
+    def combine_rectangular_intensity_map_data(
+        self, maps: list[RectangularIntensityMapData]
+    ) -> RectangularIntensityMapData:
         for m in maps[1:]:
-            assert np.array_equal(maps[0].coords.latitude_delta, m.coords.latitude_delta)
-            assert np.array_equal(maps[0].coords.longitude_delta, m.coords.longitude_delta)
-            assert np.array_equal(maps[0].coords.latitude_label, m.coords.latitude_label)
-            assert np.array_equal(maps[0].coords.longitude_label, m.coords.longitude_label)
+            assert np.array_equal(
+                maps[0].coords.latitude_delta, m.coords.latitude_delta
+            )
+            assert np.array_equal(
+                maps[0].coords.longitude_delta, m.coords.longitude_delta
+            )
+            assert np.array_equal(
+                maps[0].coords.latitude_label, m.coords.latitude_label
+            )
+            assert np.array_equal(
+                maps[0].coords.longitude_label, m.coords.longitude_label
+            )
 
         self._check_maps_match([m.intensity_map_data for m in maps])
         intensity_map_data = self._combine([m.intensity_map_data for m in maps])
-        return RectangularIntensityMapData(intensity_map_data=intensity_map_data, coords=maps[0].coords)
+        return RectangularIntensityMapData(
+            intensity_map_data=intensity_map_data, coords=maps[0].coords
+        )
 
-    def combine_healpix_intensity_map_data(self, maps: list[HealPixIntensityMapData]) -> HealPixIntensityMapData:
+    def combine_healpix_intensity_map_data(
+        self, maps: list[HealPixIntensityMapData]
+    ) -> HealPixIntensityMapData:
         for m in maps[1:]:
             assert np.array_equal(maps[0].coords.pixel_index, m.coords.pixel_index)
-            assert np.array_equal(maps[0].coords.pixel_index_label, m.coords.pixel_index_label)
+            assert np.array_equal(
+                maps[0].coords.pixel_index_label, m.coords.pixel_index_label
+            )
 
         self._check_maps_match([m.intensity_map_data for m in maps])
         intensity_map_data = self._combine([m.intensity_map_data for m in maps])
-        return HealPixIntensityMapData(intensity_map_data=intensity_map_data, coords=maps[0].coords)
+        return HealPixIntensityMapData(
+            intensity_map_data=intensity_map_data, coords=maps[0].coords
+        )
 
     @abstractmethod
     def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
@@ -62,20 +83,32 @@ def _check_maps_match(maps: list[IntensityMapData]):
         for field in dataclasses.fields(first_map):
             if field.name not in fields_which_may_differ:
                 differing_fields.append(field.name)
-                supports_nan = np.issubdtype(first_map_dict[field.name].dtype, np.floating)
+                supports_nan = np.issubdtype(
+                    first_map_dict[field.name].dtype, np.floating
+                )
                 assert np.all(
-                    [np.array_equal(dataclasses.asdict(m)[field.name], first_map_dict[field.name],
-                                    equal_nan=supports_nan)
-                     for m in maps]), f"{field.name}"
+                    [
+                        np.array_equal(
+                            dataclasses.asdict(m)[field.name],
+                            first_map_dict[field.name],
+                            equal_nan=supports_nan,
+                        )
+                        for m in maps
+                    ]
+                ), f"{field.name}"
 
     @staticmethod
-    def calculated_weighted_uncertainty(values: np.ndarray, weights: np.ndarray) -> np.ndarray:
+    def calculated_weighted_uncertainty(
+        values: np.ndarray, weights: np.ndarray
+    ) -> np.ndarray:
         masked_values = np.where(weights == 0, 0, values)
         numerator = np.sum(np.square(masked_values * weights), axis=0)
         return np.sqrt(safe_divide(numerator, np.square(np.sum(weights, axis=0))))
 
     @staticmethod
-    def calculate_weighted_sys_err(values: np.ndarray, weights: np.ndarray) -> np.ndarray:
+    def calculate_weighted_sys_err(
+        values: np.ndarray, weights: np.ndarray
+    ) -> np.ndarray:
         numerator = np.sum(values * weights, axis=0)
         denominator = np.sum(weights, axis=0)
 
@@ -89,7 +122,9 @@ def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
         intensity_stat_unc = np.array([m.ena_intensity_stat_uncert for m in maps])
         exposures = np.array([m.exposure_factor for m in maps])
         if any(m.ena_intensity_sys_err_plus is not None for m in maps):
-            raise NotImplementedError("Unweighted combination does not yet combine asymmetric systemic errors")
+            raise NotImplementedError(
+                "Unweighted combination does not yet combine asymmetric systemic errors"
+            )
 
         mask = np.isnan(intensities) | (exposures == 0) | np.isnan(exposures)
 
@@ -121,7 +156,9 @@ def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
         survival_probabilities = np.array([m.survival_probability for m in maps])
         if all(m.survival_probability is not None for m in maps):
             sp_mask = (
-                    np.isnan(survival_probabilities) | (exposures == 0) | np.isnan(exposures)
+                np.isnan(survival_probabilities)
+                | (exposures == 0)
+                | np.isnan(exposures)
             )
             masked_survival_probabilities = np.where(sp_mask, 0, survival_probabilities)
             sp_masked_weights = np.where(sp_mask, 0, 1)
@@ -178,32 +215,44 @@ def _combine(self, maps: list[IntensityMapData]):
 
         survival_probabilities = np.array([m.survival_probability for m in maps])
         if all(m.survival_probability is not None for m in maps):
-            sp_mask = np.isnan(survival_probabilities) | (exposures == 0) | np.isnan(exposures)
+            sp_mask = (
+                np.isnan(survival_probabilities)
+                | (exposures == 0)
+                | np.isnan(exposures)
+            )
             masked_survival_probabilities = np.where(sp_mask, 0, survival_probabilities)
             sp_masked_exposures = np.where(sp_mask, 0, exposures)
-            summed_survival_probability = np.sum(masked_survival_probabilities * sp_masked_exposures, axis=0)
+            summed_survival_probability = np.sum(
+                masked_survival_probabilities * sp_masked_exposures, axis=0
+            )
             exposure_weighted_summed_survival_probability = safe_divide(
                 summed_survival_probability, np.sum(sp_masked_exposures, axis=0)
             )
         else:
             exposure_weighted_summed_survival_probability = None
 
-        avg_obs_date = calculate_datetime_weighted_average(np.ma.array([m.obs_date for m in maps]), masked_exposures, 0)
+        avg_obs_date = calculate_datetime_weighted_average(
+            np.ma.array([m.obs_date for m in maps]), masked_exposures, 0
+        )
 
         bg_intensities = np.array([m.bg_intensity for m in maps])
         bg_intensity_sys_err = np.array([m.bg_intensity_sys_err for m in maps])
         bg_intensity_stat_uncert = np.array([m.bg_intensity_stat_uncert for m in maps])
 
-        ena_intensity_sys_err_plus = np.array([m.ena_intensity_sys_err_plus for m in maps])
-        ena_intensity_sys_err_minus = np.array([m.ena_intensity_sys_err_minus for m in maps])
+        ena_intensity_sys_err_plus = np.array(
+            [m.ena_intensity_sys_err_plus for m in maps]
+        )
+        ena_intensity_sys_err_minus = np.array(
+            [m.ena_intensity_sys_err_minus for m in maps]
+        )
 
         def all_elem_defined(array):
             return all(v is not None for v in array)
 
         if (
-                all_elem_defined(bg_intensities)
-                and all_elem_defined(bg_intensity_sys_err)
-                and all_elem_defined(bg_intensity_stat_uncert)
+            all_elem_defined(bg_intensities)
+            and all_elem_defined(bg_intensity_sys_err)
+            and all_elem_defined(bg_intensity_stat_uncert)
         ):
             bg_mask = np.isnan(bg_intensities) | (exposures == 0) | np.isnan(exposures)
 
@@ -212,27 +261,35 @@ def all_elem_defined(array):
             bg_intensity_stat_uncert = np.where(bg_mask, 0, bg_intensity_stat_uncert)
             bg_masked_exposures = np.where(bg_mask, 0, exposures)
 
-            combined_bg_intensity_stat_uncert = self.calculated_weighted_uncertainty(bg_intensity_stat_uncert,
-                                                                                     bg_masked_exposures)
-            combined_bg_intensity_sys_err = self.calculate_weighted_sys_err(bg_intensity_sys_err, bg_masked_exposures)
+            combined_bg_intensity_stat_uncert = self.calculated_weighted_uncertainty(
+                bg_intensity_stat_uncert, bg_masked_exposures
+            )
+            combined_bg_intensity_sys_err = self.calculate_weighted_sys_err(
+                bg_intensity_sys_err, bg_masked_exposures
+            )
             summed_bg_intensity = np.sum(bg_intensities * bg_masked_exposures, axis=0)
             summed_exposures = np.sum(bg_masked_exposures, axis=0)
-            exposure_weighted_summed_bg_intensity = safe_divide(summed_bg_intensity, summed_exposures)
+            exposure_weighted_summed_bg_intensity = safe_divide(
+                summed_bg_intensity, summed_exposures
+            )
         else:
             exposure_weighted_summed_bg_intensity = None
             combined_bg_intensity_sys_err = None
             combined_bg_intensity_stat_uncert = None
-        if (
-                all_elem_defined(ena_intensity_sys_err_plus)
-                and all_elem_defined(ena_intensity_sys_err_minus)
+        if all_elem_defined(ena_intensity_sys_err_plus) and all_elem_defined(
+            ena_intensity_sys_err_minus
         ):
             assert np.all(np.isfinite(ena_intensity_sys_err_plus) | mask)
             assert np.all(np.isfinite(ena_intensity_sys_err_minus) | mask)
             ena_intensity_sys_err_plus = np.where(mask, 0, ena_intensity_sys_err_plus)
             ena_intensity_sys_err_minus = np.where(mask, 0, ena_intensity_sys_err_minus)
 
-            combined_sys_err_plus = self.calculate_weighted_sys_err(ena_intensity_sys_err_plus, masked_exposures)
-            combined_sys_err_minus = self.calculate_weighted_sys_err(ena_intensity_sys_err_minus, masked_exposures)
+            combined_sys_err_plus = self.calculate_weighted_sys_err(
+                ena_intensity_sys_err_plus, masked_exposures
+            )
+            combined_sys_err_minus = self.calculate_weighted_sys_err(
+                ena_intensity_sys_err_minus, masked_exposures
+            )
 
         else:
             combined_sys_err_plus = None
@@ -241,20 +298,21 @@ def all_elem_defined(array):
         quality_flags = np.array([m.quality_flags for m in maps])
         combined_quality_flags = np.bitwise_or.reduce(quality_flags)
 
-        return dataclasses.replace(maps[0],
-                                   ena_intensity=exposure_weighted_summed_intensity,
-                                   ena_intensity_sys_err=combined_intensity_sys_err,
-                                   ena_intensity_stat_uncert=combined_intensity_stat_unc,
-                                   bg_intensity=exposure_weighted_summed_bg_intensity,
-                                   bg_intensity_sys_err=combined_bg_intensity_sys_err,
-                                   bg_intensity_stat_uncert=combined_bg_intensity_stat_uncert,
-                                   exposure_factor=summed_exposures,
-                                   obs_date=avg_obs_date,
-                                   survival_probability=exposure_weighted_summed_survival_probability,
-                                   ena_intensity_sys_err_plus=combined_sys_err_plus,
-                                   ena_intensity_sys_err_minus=combined_sys_err_minus,
-                                   quality_flags=combined_quality_flags
-                                   )
+        return dataclasses.replace(
+            maps[0],
+            ena_intensity=exposure_weighted_summed_intensity,
+            ena_intensity_sys_err=combined_intensity_sys_err,
+            ena_intensity_stat_uncert=combined_intensity_stat_unc,
+            bg_intensity=exposure_weighted_summed_bg_intensity,
+            bg_intensity_sys_err=combined_bg_intensity_sys_err,
+            bg_intensity_stat_uncert=combined_bg_intensity_stat_uncert,
+            exposure_factor=summed_exposures,
+            obs_date=avg_obs_date,
+            survival_probability=exposure_weighted_summed_survival_probability,
+            ena_intensity_sys_err_plus=combined_sys_err_plus,
+            ena_intensity_sys_err_minus=combined_sys_err_minus,
+            quality_flags=combined_quality_flags,
+        )
 
 
 class UncertaintyWeightedCombination(CombinationStrategy):
@@ -266,10 +324,15 @@ def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
 
         if any(m.ena_intensity_sys_err_plus is not None for m in maps):
             raise NotImplementedError(
-                "Uncertainty-weighted combination does not yet combine asymmetric systemic errors")
+                "Uncertainty-weighted combination does not yet combine asymmetric systemic errors"
+            )
 
-        missing_weights_or_exposures = (exposures == 0) | np.isnan(exposures) | np.isnan(intensity_stat_unc) | np.isinf(
-            intensity_stat_unc)
+        missing_weights_or_exposures = (
+            (exposures == 0)
+            | np.isnan(exposures)
+            | np.isnan(intensity_stat_unc)
+            | np.isinf(intensity_stat_unc)
+        )
         inten_mask = np.isnan(intensities) | missing_weights_or_exposures
 
         intensities = np.where(inten_mask, 0, intensities)
@@ -279,15 +342,24 @@ def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
         summed_exposures = np.sum(masked_exposures, axis=0)
 
         squared_stat_unc = np.square(intensity_stat_unc)
-        inverse_squared_stat_unc = safe_divide(np.ones_like(squared_stat_unc), squared_stat_unc)
+        inverse_squared_stat_unc = safe_divide(
+            np.ones_like(squared_stat_unc), squared_stat_unc
+        )
         uncertainty_weighted_combined_intensity = safe_divide(
             np.nansum(safe_divide(intensities, squared_stat_unc), axis=0),
-            np.nansum(inverse_squared_stat_unc, axis=0))
-        combined_intensity_stat_unc = safe_divide(np.ones_like(inverse_squared_stat_unc[0]),
-                                                  np.nansum(inverse_squared_stat_unc, axis=0))
-        combined_intensity_sys_err = self.calculate_weighted_sys_err(intensity_sys_err, masked_exposures)
+            np.nansum(inverse_squared_stat_unc, axis=0),
+        )
+        combined_intensity_stat_unc = safe_divide(
+            np.ones_like(inverse_squared_stat_unc[0]),
+            np.nansum(inverse_squared_stat_unc, axis=0),
+        )
+        combined_intensity_sys_err = self.calculate_weighted_sys_err(
+            intensity_sys_err, masked_exposures
+        )
 
-        avg_obs_date = calculate_datetime_weighted_average(np.ma.array([m.obs_date for m in maps]), masked_exposures, 0)
+        avg_obs_date = calculate_datetime_weighted_average(
+            np.ma.array([m.obs_date for m in maps]), masked_exposures, 0
+        )
 
         survival_probability = np.array([m.survival_probability for m in maps])
         if all(m.survival_probability is not None for m in maps):
@@ -295,11 +367,13 @@ def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
             masked_sp = np.where(sp_mask, 0, survival_probability)
             sp_masked_stat_unc = np.where(sp_mask, 0, intensity_stat_unc)
             squared_sp_masked_stat_unc = np.square(sp_masked_stat_unc)
-            inverse_squared_sp_masked_stat_unc = safe_divide(np.ones_like(squared_stat_unc), squared_sp_masked_stat_unc)
+            inverse_squared_sp_masked_stat_unc = safe_divide(
+                np.ones_like(squared_stat_unc), squared_sp_masked_stat_unc
+            )
 
             uncertainty_weighted_combined_sp = safe_divide(
                 np.nansum(safe_divide(masked_sp, squared_sp_masked_stat_unc), axis=0),
-                np.nansum(inverse_squared_sp_masked_stat_unc, axis=0)
+                np.nansum(inverse_squared_sp_masked_stat_unc, axis=0),
             )
         else:
             uncertainty_weighted_combined_sp = None
@@ -307,12 +381,13 @@ def _combine(self, maps: list[IntensityMapData]) -> IntensityMapData:
         quality_flags = np.array([m.quality_flags for m in maps])
         combined_quality_flags = np.bitwise_or.reduce(quality_flags)
 
-        return dataclasses.replace(maps[0],
-                                   ena_intensity=uncertainty_weighted_combined_intensity,
-                                   exposure_factor=summed_exposures,
-                                   ena_intensity_sys_err=combined_intensity_sys_err,
-                                   ena_intensity_stat_uncert=np.sqrt(combined_intensity_stat_unc),
-                                   obs_date=avg_obs_date,
-                                   survival_probability=uncertainty_weighted_combined_sp,
-                                   quality_flags=combined_quality_flags
-                                   )
+        return dataclasses.replace(
+            maps[0],
+            ena_intensity=uncertainty_weighted_combined_intensity,
+            exposure_factor=summed_exposures,
+            ena_intensity_sys_err=combined_intensity_sys_err,
+            ena_intensity_stat_uncert=np.sqrt(combined_intensity_stat_unc),
+            obs_date=avg_obs_date,
+            survival_probability=uncertainty_weighted_combined_sp,
+            quality_flags=combined_quality_flags,
+        )
diff --git a/imap_l3_processing/maps/map_descriptors.py b/imap_l3_processing/maps/map_descriptors.py
index 141bad19d..9482fbede 100644
--- a/imap_l3_processing/maps/map_descriptors.py
+++ b/imap_l3_processing/maps/map_descriptors.py
@@ -2,7 +2,6 @@
 import re
 from dataclasses import dataclass
 from datetime import timedelta
-from typing import Optional
 
 
 class Sensor(enum.Enum):
@@ -19,15 +18,19 @@ class Sensor(enum.Enum):
 
     @staticmethod
     def get_sensor_angle(sensor_name):
-        sensor_angles = {Sensor.Hi45: -45,
-                         Sensor.Hi90: 0,
-                         Sensor.Lo75: 15,
-                         Sensor.Lo90: 0,
-                         Sensor.Lo105: -15,
-                         Sensor.Ultra45: -45,
-                         Sensor.Ultra90: 0}
-
-        assert sensor_name in sensor_angles, "Invalid to attempt to get a sensor angle for " + sensor_name.name
+        sensor_angles = {
+            Sensor.Hi45: -45,
+            Sensor.Hi90: 0,
+            Sensor.Lo75: 15,
+            Sensor.Lo90: 0,
+            Sensor.Lo105: -15,
+            Sensor.Ultra45: -45,
+            Sensor.Ultra90: 0,
+        }
+
+        assert sensor_name in sensor_angles, (
+            "Invalid to attempt to get a sensor angle for " + sensor_name.name
+        )
 
         return sensor_angles[sensor_name]
 
@@ -75,7 +78,7 @@ class MapDescriptorParts:
     duration: str
     quantity: MapQuantity
     quantity_suffix: str
-    spectral_index_energy_range: Optional[tuple[int,int]] = None
+    spectral_index_energy_range: tuple[int, int] | None = None
 
 
 sensor_mapping = [
@@ -88,31 +91,31 @@ class MapDescriptorParts:
     ("ilo", Sensor.LoCombined),
     ("ulc", Sensor.UltraCombined),
     ("u45", Sensor.Ultra45),
-    ("u90", Sensor.Ultra90)
+    ("u90", Sensor.Ultra90),
 ]
 
 quantity_mapping = [
     ("spx", MapQuantity.SpectralIndex),
     ("spxnbs", MapQuantity.SpectralIndexNBS),
     ("ena", MapQuantity.Intensity),
-    ("isn", MapQuantity.ISNBackgroundSubtracted)
+    ("isn", MapQuantity.ISNBackgroundSubtracted),
 ]
 
 cg_correction_mapping = [
     ("sf", ReferenceFrame.Spacecraft),
     ("hf", ReferenceFrame.Heliospheric),
-    ("hk", ReferenceFrame.HeliosphericKinematic)
+    ("hk", ReferenceFrame.HeliosphericKinematic),
 ]
 
 sp_correction_mapping = [
     ("sp", SurvivalCorrection.SurvivalCorrected),
-    ("nsp", SurvivalCorrection.NotSurvivalCorrected)
+    ("nsp", SurvivalCorrection.NotSurvivalCorrected),
 ]
 
 spin_phase_mapping = [
     ("ram", SpinPhase.RamOnly),
     ("anti", SpinPhase.AntiRamOnly),
-    ("full", SpinPhase.FullSpin)
+    ("full", SpinPhase.FullSpin),
 ]
 
 grid_size_mapping = [
@@ -120,11 +123,11 @@ class MapDescriptorParts:
     ("4deg", PixelSize.FourDegrees),
     ("6deg", PixelSize.SixDegrees),
     ("nside8", PixelSize.Nside8),
-    ("nside16", PixelSize.Nside16)
+    ("nside16", PixelSize.Nside16),
 ]
 
 
-def parse_map_descriptor(descriptor: str) -> Optional[MapDescriptorParts]:
+def parse_map_descriptor(descriptor: str) -> MapDescriptorParts | None:
     descriptor_regex = """
         (?P<sensor>hic|h45|h90|l090|l105|l075|ulc|u45|u90|ilo)-
         (?P<quantity>ena|spxnbs|spx|isn)(?P<spectral_index_range>[0-9]{4})?(?P<quantity_suffix>[a-zA-Z]*)-
@@ -142,51 +145,80 @@ def parse_map_descriptor(descriptor: str) -> Optional[MapDescriptorParts]:
         return None
 
     sensor_part_from_str = {desc: sensor_part for desc, sensor_part in sensor_mapping}
-    quantity_part_from_str = {desc: quantity_part for desc, quantity_part in quantity_mapping}
-    cg_correction_part_from_str = {desc: cg_correction_part for desc, cg_correction_part in cg_correction_mapping}
-    sp_correction_part_from_str = {desc: sp_correction_part for desc, sp_correction_part in sp_correction_mapping}
-    spin_phase_part_from_str = {desc: spin_phase_part for desc, spin_phase_part in spin_phase_mapping}
-    grid_size_part_from_str = {desc: grid_size_part for desc, grid_size_part in grid_size_mapping}
+    quantity_part_from_str = {
+        desc: quantity_part for desc, quantity_part in quantity_mapping
+    }
+    cg_correction_part_from_str = {
+        desc: cg_correction_part for desc, cg_correction_part in cg_correction_mapping
+    }
+    sp_correction_part_from_str = {
+        desc: sp_correction_part for desc, sp_correction_part in sp_correction_mapping
+    }
+    spin_phase_part_from_str = {
+        desc: spin_phase_part for desc, spin_phase_part in spin_phase_mapping
+    }
+    grid_size_part_from_str = {
+        desc: grid_size_part for desc, grid_size_part in grid_size_mapping
+    }
     spectral_index_energy_range = None
     if descriptor_part_match["spectral_index_range"] is not None:
-        spectral_index_energy_range = (int(descriptor_part_match["spectral_index_range"][:2]), int(descriptor_part_match["spectral_index_range"][2:]))
+        spectral_index_energy_range = (
+            int(descriptor_part_match["spectral_index_range"][:2]),
+            int(descriptor_part_match["spectral_index_range"][2:]),
+        )
 
     return MapDescriptorParts(
         sensor=sensor_part_from_str[descriptor_part_match["sensor"]],
         quantity=quantity_part_from_str[descriptor_part_match["quantity"]],
         quantity_suffix=descriptor_part_match["quantity_suffix"],
         reference_frame=cg_correction_part_from_str[descriptor_part_match["frame"]],
-        survival_correction=sp_correction_part_from_str[descriptor_part_match["survival_corrected"]],
+        survival_correction=sp_correction_part_from_str[
+            descriptor_part_match["survival_corrected"]
+        ],
         spin_phase=spin_phase_part_from_str[descriptor_part_match["spin_phase"]],
         coord=descriptor_part_match["coord"],
         grid=grid_size_part_from_str[descriptor_part_match["grid"]],
         duration=descriptor_part_match["duration"],
-        spectral_index_energy_range=spectral_index_energy_range
+        spectral_index_energy_range=spectral_index_energy_range,
     )
 
 
 def map_descriptor_parts_to_string(descriptor_parts: MapDescriptorParts) -> str:
     sensor_part_to_str = {sensor_part: desc for desc, sensor_part in sensor_mapping}
-    quantity_part_to_str = {quantity_part: desc for desc, quantity_part in quantity_mapping}
-    cg_correction_part_to_str = {cg_correction_part: desc for desc, cg_correction_part in cg_correction_mapping}
-    sp_correction_part_to_str = {sp_correction_part: desc for desc, sp_correction_part in sp_correction_mapping}
-    spin_phase_part_to_str = {spin_phase_part: desc for desc, spin_phase_part in spin_phase_mapping}
-    grid_size_part_to_str = {grid_size_part: desc for desc, grid_size_part in grid_size_mapping}
+    quantity_part_to_str = {
+        quantity_part: desc for desc, quantity_part in quantity_mapping
+    }
+    cg_correction_part_to_str = {
+        cg_correction_part: desc for desc, cg_correction_part in cg_correction_mapping
+    }
+    sp_correction_part_to_str = {
+        sp_correction_part: desc for desc, sp_correction_part in sp_correction_mapping
+    }
+    spin_phase_part_to_str = {
+        spin_phase_part: desc for desc, spin_phase_part in spin_phase_mapping
+    }
+    grid_size_part_to_str = {
+        grid_size_part: desc for desc, grid_size_part in grid_size_mapping
+    }
     spectral_index_energy_range = ""
     if descriptor_parts.spectral_index_energy_range is not None:
         spectral_index_energy_range = f"{descriptor_parts.spectral_index_energy_range[0]:02}{descriptor_parts.spectral_index_energy_range[1]:02}"
 
-    return "-".join([
-        sensor_part_to_str[descriptor_parts.sensor],
-        quantity_part_to_str[descriptor_parts.quantity] + spectral_index_energy_range + descriptor_parts.quantity_suffix,
-        "h",
-        cg_correction_part_to_str[descriptor_parts.reference_frame],
-        sp_correction_part_to_str[descriptor_parts.survival_correction],
-        spin_phase_part_to_str[descriptor_parts.spin_phase],
-        descriptor_parts.coord,
-        grid_size_part_to_str[descriptor_parts.grid],
-        descriptor_parts.duration,
-    ])
+    return "-".join(
+        [
+            sensor_part_to_str[descriptor_parts.sensor],
+            quantity_part_to_str[descriptor_parts.quantity]
+            + spectral_index_energy_range
+            + descriptor_parts.quantity_suffix,
+            "h",
+            cg_correction_part_to_str[descriptor_parts.reference_frame],
+            sp_correction_part_to_str[descriptor_parts.survival_correction],
+            spin_phase_part_to_str[descriptor_parts.spin_phase],
+            descriptor_parts.coord,
+            grid_size_part_to_str[descriptor_parts.grid],
+            descriptor_parts.duration,
+        ]
+    )
 
 
 def get_duration_from_map_descriptor(descriptor: MapDescriptorParts) -> timedelta:
@@ -200,4 +232,6 @@ def get_duration_from_map_descriptor(descriptor: MapDescriptorParts) -> timedelt
         case MapDescriptorParts(duration="1yr" | "12mo"):
             return timedelta(days=365.25)
         case _:
-            raise ValueError(f"Expected a duration in the map descriptor, got: {descriptor} (e.g., '1mo', '3mo')")
+            raise ValueError(
+                f"Expected a duration in the map descriptor, got: {descriptor} (e.g., '1mo', '3mo')"
+            )
diff --git a/imap_l3_processing/maps/map_initializer.py b/imap_l3_processing/maps/map_initializer.py
index 98ed0daea..59596b9c4 100644
--- a/imap_l3_processing/maps/map_initializer.py
+++ b/imap_l3_processing/maps/map_initializer.py
@@ -5,7 +5,7 @@
 from pathlib import Path
 
 import imap_data_access
-from imap_data_access import ScienceFilePath, ImapFilePath, ProcessingInputCollection
+from imap_data_access import ImapFilePath, ProcessingInputCollection, ScienceFilePath
 from imap_data_access.file_validation import Version
 from imap_data_access.processing_input import generate_imap_input
 
@@ -22,13 +22,19 @@ class PossibleMapToProduce:
 
     @property
     def processing_input_collection(self) -> ProcessingInputCollection:
-        return ProcessingInputCollection(*[generate_imap_input(file_path) for file_path in sorted(self.input_files)])
+        return ProcessingInputCollection(
+            *[generate_imap_input(file_path) for file_path in sorted(self.input_files)]
+        )
 
 
 class MapInitializer(abc.ABC):
     def __init__(self, instrument: str):
-        l3_query_results = imap_data_access.query(instrument=instrument, data_level='l3')
-        self.existing_l3_maps = self.get_latest_version_by_descriptor_and_start_date(l3_query_results)
+        l3_query_results = imap_data_access.query(
+            instrument=instrument, data_level="l3"
+        )
+        self.existing_l3_maps = self.get_latest_version_by_descriptor_and_start_date(
+            l3_query_results
+        )
         self.instrument = instrument
 
     @abc.abstractmethod
@@ -39,16 +45,24 @@ def furnish_spice_dependencies(self, map_to_produce: PossibleMapToProduce):
     def get_maps_that_can_be_produced(self, descriptor: str):
         raise NotImplementedError()
 
-    def get_maps_that_should_be_produced(self, descriptor: str, major_version: int|None) -> list[PossibleMapToProduce]:
+    def get_maps_that_should_be_produced(
+        self, descriptor: str, major_version: int | None
+    ) -> list[PossibleMapToProduce]:
         possible_maps = self.get_maps_that_can_be_produced(descriptor)
 
         maps_to_make = []
         for possible_map in possible_maps:
             start_time = possible_map.input_metadata.start_date.strftime("%Y%m%d")
-            possible_map.input_metadata.version = VersionMap({descriptor: Version(major_version, 1)})
-
-            if highest_version_path_by_start_date := self.existing_l3_maps.get(descriptor):
-                if path_with_highest_version := highest_version_path_by_start_date.get(start_time):
+            possible_map.input_metadata.version = VersionMap(
+                {descriptor: Version(major_version, 1)}
+            )
+
+            if highest_version_path_by_start_date := self.existing_l3_maps.get(
+                descriptor
+            ):
+                if path_with_highest_version := highest_version_path_by_start_date.get(
+                    start_time
+                ):
                     existing_parents = read_cdf_parents(path_with_highest_version)
                     existing_path = ScienceFilePath(path_with_highest_version)
                     if (
@@ -59,7 +73,9 @@ def get_maps_that_should_be_produced(self, descriptor: str, major_version: int|N
                     new_version = Version(
                         major_version, existing_path.minor_version + 1
                     )
-                    possible_map.input_metadata.version = VersionMap({descriptor: new_version})
+                    possible_map.input_metadata.version = VersionMap(
+                        {descriptor: new_version}
+                    )
             maps_to_make.append(possible_map)
         return maps_to_make
 
@@ -69,21 +85,37 @@ def get_l1c_parents_from_map(map_file_path: str) -> list[str]:
         for l1 in read_cdf_parents(map_file_path):
             try:
                 l1c_science_file_path = ScienceFilePath(l1)
-                if l1c_science_file_path.data_level == 'l1c':
+                if l1c_science_file_path.data_level == "l1c":
                     l1c_names.append(l1c_science_file_path.filename.name)
             except ImapFilePath.InvalidImapFileError:
                 continue
         return l1c_names
 
     @staticmethod
-    def get_latest_version_by_descriptor_and_start_date(query_results: list[dict]) -> defaultdict:
+    def get_latest_version_by_descriptor_and_start_date(
+        query_results: list[dict],
+    ) -> defaultdict:
         qr_by_descriptor_and_start_date = defaultdict(list)
         for qr in query_results:
-            qr_by_descriptor_and_start_date[(qr['descriptor'], qr["start_date"])].append(qr)
+            qr_by_descriptor_and_start_date[
+                (qr["descriptor"], qr["start_date"])
+            ].append(qr)
 
         file_paths_by_descriptor = defaultdict(dict)
-        for (descriptor, start_date), query_results in qr_by_descriptor_and_start_date.items():
-            highest_version_qr = max(query_results, key=lambda x: x['minor_version'] if 'minor_version' in x else Version.from_version(x['version']).minor)
-            file_paths_by_descriptor[descriptor][start_date] = Path(highest_version_qr['file_path']).name
+        for (
+            descriptor,
+            start_date,
+        ), query_results in qr_by_descriptor_and_start_date.items():
+            highest_version_qr = max(
+                query_results,
+                key=lambda x: (
+                    x["minor_version"]
+                    if "minor_version" in x
+                    else Version.from_version(x["version"]).minor
+                ),
+            )
+            file_paths_by_descriptor[descriptor][start_date] = Path(
+                highest_version_qr["file_path"]
+            ).name
 
         return file_paths_by_descriptor
diff --git a/imap_l3_processing/maps/map_models.py b/imap_l3_processing/maps/map_models.py
index 94b72ff06..5430fff22 100644
--- a/imap_l3_processing/maps/map_models.py
+++ b/imap_l3_processing/maps/map_models.py
@@ -2,9 +2,9 @@
 
 import abc
 from dataclasses import dataclass
-from datetime import timedelta, datetime
+from datetime import datetime, timedelta
 from pathlib import Path
-from typing import Generic, Optional
+from typing import Generic
 
 import numpy as np
 import xarray
@@ -13,10 +13,13 @@
 from imap_processing.spice.geometry import SpiceFrame
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.cdf.cdf_utils import read_variable_and_mask_fill_values, read_numeric_variable
+from imap_l3_processing.cdf.cdf_utils import (
+    read_numeric_variable,
+    read_variable_and_mask_fill_values,
+)
 from imap_l3_processing.constants import TT2000_EPOCH
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.models import DataProduct, DataProductVariable, D
+from imap_l3_processing.models import D, DataProduct, DataProductVariable
 
 EPOCH_VAR_NAME = "epoch"
 EPOCH_DELTA_VAR_NAME = "epoch_delta"
@@ -39,7 +42,9 @@
 ENA_SPECTRAL_INDEX_VAR_NAME = "ena_spectral_index"
 ENA_SPECTRAL_INDEX_STAT_UNC_VAR_NAME = "ena_spectral_index_stat_uncert"
 ENA_SPECTRAL_INDEX_SCALAR_COEFFICIENT_VAR_NAME = "ena_spectral_scalar"
-ENA_SPECTRAL_INDEX_SCALAR_COEFFICIENT_STAT_UNCERT_VAR_NAME = "ena_spectral_scalar_stat_uncert"
+ENA_SPECTRAL_INDEX_SCALAR_COEFFICIENT_STAT_UNCERT_VAR_NAME = (
+    "ena_spectral_scalar_stat_uncert"
+)
 ENA_SPECTRAL_INDEX_CHISQ_VAR_NAME = "ena_spectral_index_chisq"
 
 ENA_INTENSITY_VAR_NAME = "ena_intensity"
@@ -110,12 +115,12 @@ class IntensityMapData(MapData):
     ena_intensity_stat_uncert: np.ndarray
     ena_intensity_sys_err: np.ndarray
     quality_flags: np.ndarray
-    ena_intensity_sys_err_minus: Optional[np.ndarray] = None
-    ena_intensity_sys_err_plus: Optional[np.ndarray] = None
-    bg_intensity: Optional[np.ndarray] = None
-    bg_intensity_stat_uncert: Optional[np.ndarray] = None
-    bg_intensity_sys_err: Optional[np.ndarray] = None
-    survival_probability: Optional[np.ndarray] = None
+    ena_intensity_sys_err_minus: np.ndarray | None = None
+    ena_intensity_sys_err_plus: np.ndarray | None = None
+    bg_intensity: np.ndarray | None = None
+    bg_intensity_stat_uncert: np.ndarray | None = None
+    bg_intensity_sys_err: np.ndarray | None = None
+    survival_probability: np.ndarray | None = None
 
 
 @dataclass
@@ -195,42 +200,56 @@ def read_from_path(cls, cdf_path: Path | str) -> ISNRateData:
             if "obs_date" in cdf and "obs_date_range" in cdf:
                 masked_obs_date = read_variable_and_mask_fill_values(cdf["obs_date"])
                 if np.issubdtype(cdf["obs_date"].dtype, np.number):
-                    obs_date = convert_tt2000_time_to_datetime(masked_obs_date.filled(0))
-                    masked_obs_date = np.ma.masked_array(data=obs_date, mask=masked_obs_date.mask)
-                obs_date_range = read_variable_and_mask_fill_values(cdf["obs_date_range"])
+                    obs_date = convert_tt2000_time_to_datetime(
+                        masked_obs_date.filled(0)
+                    )
+                    masked_obs_date = np.ma.masked_array(
+                        data=obs_date, mask=masked_obs_date.mask
+                    )
+                obs_date_range = read_variable_and_mask_fill_values(
+                    cdf["obs_date_range"]
+                )
             else:
                 obs_date_shape = cdf["bg_rate"].shape
                 all_mask_array = np.full(obs_date_shape, True)
-                masked_obs_date = np.ma.masked_array(np.full(obs_date_shape, TT2000_EPOCH), mask=all_mask_array)
-                obs_date_range = np.ma.masked_array(np.full(obs_date_shape, 0),
-                                                    mask=all_mask_array,
-                                                    dtype=np.int64)
+                masked_obs_date = np.ma.masked_array(
+                    np.full(obs_date_shape, TT2000_EPOCH), mask=all_mask_array
+                )
+                obs_date_range = np.ma.masked_array(
+                    np.full(obs_date_shape, 0), mask=all_mask_array, dtype=np.int64
+                )
 
             return ISNRateData(
-                epoch=cdf['epoch'][...],
-                epoch_delta=read_variable_and_mask_fill_values(cdf['epoch_delta']),
+                epoch=cdf["epoch"][...],
+                epoch_delta=read_variable_and_mask_fill_values(cdf["epoch_delta"]),
                 obs_date=masked_obs_date,
                 obs_date_range=obs_date_range,
-                bg_rate=read_numeric_variable(cdf['bg_rate']),
-                bg_rate_stat_uncert=read_numeric_variable(cdf['bg_rate_stat_uncert']),
-                bg_rate_sys_err=read_numeric_variable(cdf['bg_rate_sys_err']),
-                ena_count_rate=read_numeric_variable(cdf['ena_count_rate']),
-                ena_count_rate_stat_uncert=read_numeric_variable(cdf['ena_count_rate_stat_uncert']),
-                latitude=(cdf['latitude'][...]),
-                latitude_delta=(cdf['latitude_delta'][...]),
-                latitude_label=(cdf['latitude_label'][...]),
-                longitude=(cdf['longitude'][...]),
-                longitude_delta=(cdf['longitude_delta'][...]),
-                longitude_label=(cdf['longitude_label'][...]),
-                ena_intensity=read_numeric_variable(cdf['ena_intensity']),
-                ena_intensity_stat_uncert=read_numeric_variable(cdf['ena_intensity_stat_uncert']),
-                ena_intensity_sys_err=read_numeric_variable(cdf['ena_intensity_sys_err']),
-                energy=(cdf['energy'][...]),
-                energy_delta_plus=(cdf['energy_delta_plus'][...]),
-                energy_delta_minus=(cdf['energy_delta_minus'][...]),
-                energy_label=(cdf['energy_label'][...]),
-                exposure_factor=read_numeric_variable(cdf['exposure_factor']),
-                solid_angle=read_numeric_variable(cdf['solid_angle'])
+                bg_rate=read_numeric_variable(cdf["bg_rate"]),
+                bg_rate_stat_uncert=read_numeric_variable(cdf["bg_rate_stat_uncert"]),
+                bg_rate_sys_err=read_numeric_variable(cdf["bg_rate_sys_err"]),
+                ena_count_rate=read_numeric_variable(cdf["ena_count_rate"]),
+                ena_count_rate_stat_uncert=read_numeric_variable(
+                    cdf["ena_count_rate_stat_uncert"]
+                ),
+                latitude=(cdf["latitude"][...]),
+                latitude_delta=(cdf["latitude_delta"][...]),
+                latitude_label=(cdf["latitude_label"][...]),
+                longitude=(cdf["longitude"][...]),
+                longitude_delta=(cdf["longitude_delta"][...]),
+                longitude_label=(cdf["longitude_label"][...]),
+                ena_intensity=read_numeric_variable(cdf["ena_intensity"]),
+                ena_intensity_stat_uncert=read_numeric_variable(
+                    cdf["ena_intensity_stat_uncert"]
+                ),
+                ena_intensity_sys_err=read_numeric_variable(
+                    cdf["ena_intensity_sys_err"]
+                ),
+                energy=(cdf["energy"][...]),
+                energy_delta_plus=(cdf["energy_delta_plus"][...]),
+                energy_delta_minus=(cdf["energy_delta_minus"][...]),
+                energy_label=(cdf["energy_label"][...]),
+                exposure_factor=read_numeric_variable(cdf["exposure_factor"]),
+                solid_angle=read_numeric_variable(cdf["solid_angle"]),
             )
 
 
@@ -292,17 +311,19 @@ def to_healpix_skymap(self) -> HealpixSkyMap:
                 ),
                 "predicted_ephemeris_flag": (
                     full_shape,
-                    self.intensity_map_data.quality_flags & MapL3Flags.PREDICTIVE_EPHEMERIS,
+                    self.intensity_map_data.quality_flags
+                    & MapL3Flags.PREDICTIVE_EPHEMERIS,
                 ),
                 "nominal_alpha_proton_ratio_flag": (
                     full_shape,
-                    self.intensity_map_data.quality_flags & MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                    self.intensity_map_data.quality_flags
+                    & MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
                 ),
                 "persisted_last_point_flag": (
                     full_shape,
-                    self.intensity_map_data.quality_flags & MapL3Flags.PERSISTED_LAST_POINT,
-                )
-
+                    self.intensity_map_data.quality_flags
+                    & MapL3Flags.PERSISTED_LAST_POINT,
+                ),
             },
             coords={
                 CoordNames.TIME.value: self.intensity_map_data.epoch,
@@ -313,12 +334,21 @@ def to_healpix_skymap(self) -> HealpixSkyMap:
 
         if self.intensity_map_data.survival_probability is not None:
             data_1d_with_sp = healpix_map.data_1d.assign(
-                survival_probability=(full_shape, self.intensity_map_data.survival_probability))
+                survival_probability=(
+                    full_shape,
+                    self.intensity_map_data.survival_probability,
+                )
+            )
             healpix_map.data_1d = data_1d_with_sp
 
-        healpix_map.data_1d = healpix_map.data_1d \
-            .assign({"obs_date": (full_shape, healpix_map.data_1d["obs_date"].values.astype(np.float64))}) \
-            .rename({CoordNames.HEALPIX_INDEX.value: CoordNames.GENERIC_PIXEL.value})
+        healpix_map.data_1d = healpix_map.data_1d.assign(
+            {
+                "obs_date": (
+                    full_shape,
+                    healpix_map.data_1d["obs_date"].values.astype(np.float64),
+                )
+            }
+        ).rename({CoordNames.HEALPIX_INDEX.value: CoordNames.GENERIC_PIXEL.value})
 
         return healpix_map
 
@@ -386,16 +416,26 @@ def _read_intensity_map_data_from_open_cdf(cdf: CDF) -> IntensityMapData:
     )
 
     if "survival_probability" in cdf:
-        map_intensity_data.survival_probability = read_numeric_variable(cdf["survival_probability"])
+        map_intensity_data.survival_probability = read_numeric_variable(
+            cdf["survival_probability"]
+        )
 
     if "bg_intensity" in cdf:
         map_intensity_data.bg_intensity = read_numeric_variable(cdf["bg_intensity"])
-        map_intensity_data.bg_intensity_sys_err = read_numeric_variable(cdf["bg_intensity_sys_err"])
-        map_intensity_data.bg_intensity_stat_uncert = read_numeric_variable(cdf["bg_intensity_stat_uncert"])
+        map_intensity_data.bg_intensity_sys_err = read_numeric_variable(
+            cdf["bg_intensity_sys_err"]
+        )
+        map_intensity_data.bg_intensity_stat_uncert = read_numeric_variable(
+            cdf["bg_intensity_stat_uncert"]
+        )
 
     if "ena_intensity_sys_err_minus" in cdf:
-        map_intensity_data.ena_intensity_sys_err_minus = read_numeric_variable(cdf["ena_intensity_sys_err_minus"])
-        map_intensity_data.ena_intensity_sys_err_plus = read_numeric_variable(cdf["ena_intensity_sys_err_plus"])
+        map_intensity_data.ena_intensity_sys_err_minus = read_numeric_variable(
+            cdf["ena_intensity_sys_err_minus"]
+        )
+        map_intensity_data.ena_intensity_sys_err_plus = read_numeric_variable(
+            cdf["ena_intensity_sys_err_plus"]
+        )
 
     return map_intensity_data
 
@@ -403,7 +443,7 @@ def _read_intensity_map_data_from_open_cdf(cdf: CDF) -> IntensityMapData:
 def _read_healpix_coords_from_open_cdf(cdf: CDF) -> HealPixCoords:
     return HealPixCoords(
         pixel_index=cdf["pixel_index"][...],
-        pixel_index_label=cdf["pixel_index_label"][...]
+        pixel_index_label=cdf["pixel_index_label"][...],
     )
 
 
@@ -414,7 +454,9 @@ def _read_intensity_map_data_from_xarray(dataset: xarray.Dataset) -> IntensityMa
         epoch_delta=_replace_fill_values_in_xarray(dataset, "epoch_delta"),
         energy=dataset.coords[CoordNames.ENERGY_L2.value].values,
         energy_delta_plus=_replace_fill_values_in_xarray(dataset, "energy_delta_plus"),
-        energy_delta_minus=_replace_fill_values_in_xarray(dataset, "energy_delta_minus"),
+        energy_delta_minus=_replace_fill_values_in_xarray(
+            dataset, "energy_delta_minus"
+        ),
         energy_label=_replace_fill_values_in_xarray(dataset, "energy_label"),
         latitude=_replace_fill_values_in_xarray(dataset, "latitude"),
         longitude=_replace_fill_values_in_xarray(dataset, "longitude"),
@@ -423,23 +465,30 @@ def _read_intensity_map_data_from_xarray(dataset: xarray.Dataset) -> IntensityMa
         obs_date_range=_replace_fill_values_in_xarray(dataset, "obs_date_range"),
         solid_angle=np.squeeze(_replace_fill_values_in_xarray(dataset, "solid_angle")),
         ena_intensity=intensity,
-        ena_intensity_stat_uncert=_replace_fill_values_in_xarray(dataset, "ena_intensity_stat_uncert"),
-        ena_intensity_sys_err=_replace_fill_values_in_xarray(dataset, "ena_intensity_sys_err"),
+        ena_intensity_stat_uncert=_replace_fill_values_in_xarray(
+            dataset, "ena_intensity_stat_uncert"
+        ),
+        ena_intensity_sys_err=_replace_fill_values_in_xarray(
+            dataset, "ena_intensity_sys_err"
+        ),
         quality_flags=np.full(intensity.shape, MapL3Flags.NONE),
     )
 
 
 def _replace_fill_values_in_xarray(dataset, variable):
-    if 'FILLVAL' in dataset[variable].attrs:
-        return np.where(dataset[variable].values == dataset[variable].attrs['FILLVAL'], np.nan,
-                        dataset[variable].values)
+    if "FILLVAL" in dataset[variable].attrs:
+        return np.where(
+            dataset[variable].values == dataset[variable].attrs["FILLVAL"],
+            np.nan,
+            dataset[variable].values,
+        )
     return dataset[variable].values
 
 
 def _read_healpix_coords_from_xarray(dataset: xarray.Dataset) -> HealPixCoords:
     return HealPixCoords(
         pixel_index=dataset[CoordNames.HEALPIX_INDEX.value].values,
-        pixel_index_label=dataset["pixel_index_label"].values
+        pixel_index_label=dataset["pixel_index_label"].values,
     )
 
 
@@ -462,50 +511,93 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         raise NotImplementedError
 
 
-class RectangularSpectralIndexDataProduct(MapDataProduct[RectangularSpectralIndexMapData]):
+class RectangularSpectralIndexDataProduct(
+    MapDataProduct[RectangularSpectralIndexMapData]
+):
     data: RectangularSpectralIndexMapData
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
-        return _spectral_index_data_variables(self.data.spectral_index_map_data) \
-            + _rectangular_coords_to_variables(self.data.coords)
+        return _spectral_index_data_variables(
+            self.data.spectral_index_map_data
+        ) + _rectangular_coords_to_variables(self.data.coords)
 
 
 class RectangularIntensityDataProduct(MapDataProduct[RectangularIntensityMapData]):
     data: RectangularIntensityMapData
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
-        return _intensity_data_variables(self.data.intensity_map_data) \
-            + _rectangular_coords_to_variables(self.data.coords)
+        return _intensity_data_variables(
+            self.data.intensity_map_data
+        ) + _rectangular_coords_to_variables(self.data.coords)
 
 
-class ISNBackgroundSubtractedDataProduct(MapDataProduct[ISNBackgroundSubtractedMapData]):
+class ISNBackgroundSubtractedDataProduct(
+    MapDataProduct[ISNBackgroundSubtractedMapData]
+):
     data: ISNBackgroundSubtractedMapData
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_VAR_NAME, self.data.isn_rate_map_data.epoch),
-            DataProductVariable(EPOCH_DELTA_VAR_NAME, self.data.isn_rate_map_data.epoch_delta),
+            DataProductVariable(
+                EPOCH_DELTA_VAR_NAME, self.data.isn_rate_map_data.epoch_delta
+            ),
             DataProductVariable(ENERGY_VAR_NAME, self.data.isn_rate_map_data.energy),
-            DataProductVariable(ENERGY_DELTA_PLUS_VAR_NAME, self.data.isn_rate_map_data.energy_delta_plus),
-            DataProductVariable(ENERGY_DELTA_MINUS_VAR_NAME, self.data.isn_rate_map_data.energy_delta_minus),
-            DataProductVariable(ENERGY_LABEL_VAR_NAME, self.data.isn_rate_map_data.energy_label),
-            DataProductVariable(EXPOSURE_FACTOR_VAR_NAME, self.data.isn_rate_map_data.exposure_factor),
-            DataProductVariable(SOLID_ANGLE_VAR_NAME, self.data.isn_rate_map_data.solid_angle),
+            DataProductVariable(
+                ENERGY_DELTA_PLUS_VAR_NAME,
+                self.data.isn_rate_map_data.energy_delta_plus,
+            ),
+            DataProductVariable(
+                ENERGY_DELTA_MINUS_VAR_NAME,
+                self.data.isn_rate_map_data.energy_delta_minus,
+            ),
+            DataProductVariable(
+                ENERGY_LABEL_VAR_NAME, self.data.isn_rate_map_data.energy_label
+            ),
+            DataProductVariable(
+                EXPOSURE_FACTOR_VAR_NAME, self.data.isn_rate_map_data.exposure_factor
+            ),
+            DataProductVariable(
+                SOLID_ANGLE_VAR_NAME, self.data.isn_rate_map_data.solid_angle
+            ),
             DataProductVariable(BG_RATE_VAR_NAME, self.data.isn_rate_map_data.bg_rate),
-            DataProductVariable(BG_RATE_STAT_UNCERT_VAR_NAME, self.data.isn_rate_map_data.bg_rate_stat_uncert),
-            DataProductVariable(BG_RATE_SYS_ERR_VAR_NAME, self.data.isn_rate_map_data.bg_rate_sys_err),
-            DataProductVariable(LATITUDE_VAR_NAME, self.data.isn_rate_map_data.latitude),
-            DataProductVariable(LATITUDE_DELTA_VAR_NAME, self.data.isn_rate_map_data.latitude_delta),
-            DataProductVariable(LATITUDE_LABEL_VAR_NAME, self.data.isn_rate_map_data.latitude_label),
-            DataProductVariable(LONGITUDE_VAR_NAME, self.data.isn_rate_map_data.longitude),
-            DataProductVariable(LONGITUDE_DELTA_VAR_NAME, self.data.isn_rate_map_data.longitude_delta),
-            DataProductVariable(LONGITUDE_LABEL_VAR_NAME, self.data.isn_rate_map_data.longitude_label),
-            DataProductVariable(ISN_BG_RATE_SUBTRACTED_VAR_NAME,
-                                self.data.isn_rate_map_data.isn_bg_rate_subtracted),
-            DataProductVariable(ISN_BG_RATE_SUBTRACTED_VAR_SYS_ERR_NAME,
-                                self.data.isn_rate_map_data.isn_bg_rate_subtracted_sys_err),
-            DataProductVariable(ISN_BG_RATE_SUBTRACTED_STAT_UNCERT_VAR_NAME,
-                                self.data.isn_rate_map_data.isn_bg_rate_subtracted_stat_uncert)
+            DataProductVariable(
+                BG_RATE_STAT_UNCERT_VAR_NAME,
+                self.data.isn_rate_map_data.bg_rate_stat_uncert,
+            ),
+            DataProductVariable(
+                BG_RATE_SYS_ERR_VAR_NAME, self.data.isn_rate_map_data.bg_rate_sys_err
+            ),
+            DataProductVariable(
+                LATITUDE_VAR_NAME, self.data.isn_rate_map_data.latitude
+            ),
+            DataProductVariable(
+                LATITUDE_DELTA_VAR_NAME, self.data.isn_rate_map_data.latitude_delta
+            ),
+            DataProductVariable(
+                LATITUDE_LABEL_VAR_NAME, self.data.isn_rate_map_data.latitude_label
+            ),
+            DataProductVariable(
+                LONGITUDE_VAR_NAME, self.data.isn_rate_map_data.longitude
+            ),
+            DataProductVariable(
+                LONGITUDE_DELTA_VAR_NAME, self.data.isn_rate_map_data.longitude_delta
+            ),
+            DataProductVariable(
+                LONGITUDE_LABEL_VAR_NAME, self.data.isn_rate_map_data.longitude_label
+            ),
+            DataProductVariable(
+                ISN_BG_RATE_SUBTRACTED_VAR_NAME,
+                self.data.isn_rate_map_data.isn_bg_rate_subtracted,
+            ),
+            DataProductVariable(
+                ISN_BG_RATE_SUBTRACTED_VAR_SYS_ERR_NAME,
+                self.data.isn_rate_map_data.isn_bg_rate_subtracted_sys_err,
+            ),
+            DataProductVariable(
+                ISN_BG_RATE_SUBTRACTED_STAT_UNCERT_VAR_NAME,
+                self.data.isn_rate_map_data.isn_bg_rate_subtracted_stat_uncert,
+            ),
         ]
 
 
@@ -526,7 +618,9 @@ def _map_data_to_variables(data: MapData) -> list[DataProductVariable]:
     ]
 
 
-def _spectral_index_data_variables(data: SpectralIndexMapData) -> list[DataProductVariable]:
+def _spectral_index_data_variables(
+    data: SpectralIndexMapData,
+) -> list[DataProductVariable]:
     variables = _map_data_to_variables(data) + [
         DataProductVariable(ENA_SPECTRAL_INDEX_VAR_NAME, data.ena_spectral_index),
         DataProductVariable(
@@ -551,7 +645,9 @@ def _spectral_index_data_variables(data: SpectralIndexMapData) -> list[DataProdu
 def _intensity_data_variables(data: IntensityMapData) -> list[DataProductVariable]:
     intensity_variables = [
         DataProductVariable(ENA_INTENSITY_VAR_NAME, data.ena_intensity),
-        DataProductVariable(ENA_INTENSITY_STAT_UNCERT_VAR_NAME, data.ena_intensity_stat_uncert),
+        DataProductVariable(
+            ENA_INTENSITY_STAT_UNCERT_VAR_NAME, data.ena_intensity_stat_uncert
+        ),
         DataProductVariable(ENA_INTENSITY_SYS_ERR_VAR_NAME, data.ena_intensity_sys_err),
         DataProductVariable(QUALITY_FLAGS_VAR_NAME, data.quality_flags),
     ]
@@ -568,17 +664,31 @@ def _intensity_data_variables(data: IntensityMapData) -> list[DataProductVariabl
             ]
         )
     if data.survival_probability is not None:
-        intensity_variables.extend([
-            DataProductVariable(SURVIVAL_PROBABILITY_VAR_NAME, data.survival_probability),
-        ])
+        intensity_variables.extend(
+            [
+                DataProductVariable(
+                    SURVIVAL_PROBABILITY_VAR_NAME, data.survival_probability
+                ),
+            ]
+        )
     if data.ena_intensity_sys_err_minus is not None:
         intensity_variables.extend(
-            [DataProductVariable(ENA_INTENSITY_SYS_ERR_MINUS_VAR_NAME, data.ena_intensity_sys_err_minus),
-             DataProductVariable(ENA_INTENSITY_SYS_ERR_PLUS_VAR_NAME, data.ena_intensity_sys_err_plus)])
+            [
+                DataProductVariable(
+                    ENA_INTENSITY_SYS_ERR_MINUS_VAR_NAME,
+                    data.ena_intensity_sys_err_minus,
+                ),
+                DataProductVariable(
+                    ENA_INTENSITY_SYS_ERR_PLUS_VAR_NAME, data.ena_intensity_sys_err_plus
+                ),
+            ]
+        )
     return _map_data_to_variables(data) + intensity_variables
 
 
-def _rectangular_coords_to_variables(coords: RectangularCoords) -> list[DataProductVariable]:
+def _rectangular_coords_to_variables(
+    coords: RectangularCoords,
+) -> list[DataProductVariable]:
     return [
         DataProductVariable(LATITUDE_DELTA_VAR_NAME, coords.latitude_delta),
         DataProductVariable(LATITUDE_LABEL_VAR_NAME, coords.latitude_label),
@@ -587,16 +697,17 @@ def _rectangular_coords_to_variables(coords: RectangularCoords) -> list[DataProd
     ]
 
 
-
-def calculate_datetime_weighted_average(data: np.ndarray, weights: np.ndarray, axis: int,
-                                        **kwargs) -> np.ma.masked_array:
+def calculate_datetime_weighted_average(
+    data: np.ndarray, weights: np.ndarray, axis: int, **kwargs
+) -> np.ma.masked_array:
     if isinstance(np.ravel(np.ma.getdata(data))[0], datetime):
         masked_indices = np.ma.getmask(data)
         masked_weights = weights.copy()
         masked_weights[masked_indices] = 0
 
-        epoch_based_dates = np.array((np.ma.getdata(data) - TT2000_EPOCH) / timedelta(seconds=1),
-                                     dtype=float)
+        epoch_based_dates = np.array(
+            (np.ma.getdata(data) - TT2000_EPOCH) / timedelta(seconds=1), dtype=float
+        )
 
         averaged_dates_as_seconds = np.ma.average(
             epoch_based_dates, weights=masked_weights, axis=axis, **kwargs
@@ -624,12 +735,12 @@ class GlowsL3eRectangularMapInputData:
 @dataclass
 class InputRectangularPointingSet:
     epoch: datetime
-    epoch_delta: Optional[np.ndarray]
+    epoch_delta: np.ndarray | None
     epoch_j2000: np.ndarray
     repointing: int
     exposure_times: np.ndarray
     esa_energy_step: np.ndarray
-    pointing_start_met: Optional[float]
-    pointing_end_met: Optional[float]
+    pointing_start_met: float | None
+    pointing_end_met: float | None
     hae_longitude: np.ndarray
     hae_latitude: np.ndarray
diff --git a/imap_l3_processing/maps/map_processor.py b/imap_l3_processing/maps/map_processor.py
index 99403f697..1ee3c6acb 100644
--- a/imap_l3_processing/maps/map_processor.py
+++ b/imap_l3_processing/maps/map_processor.py
@@ -7,9 +7,8 @@
 
 
 class MapProcessor(Processor):
-
     @abstractmethod
-    def process(self, spice_reference_frame: SpiceFrame = SpiceFrame.ECLIPJ2000) -> list[Path]:
+    def process(
+        self, spice_reference_frame: SpiceFrame = SpiceFrame.ECLIPJ2000
+    ) -> list[Path]:
         pass
-
-
diff --git a/imap_l3_processing/maps/mpfit.py b/imap_l3_processing/maps/mpfit.py
index 767c24f89..27ddc8b1d 100644
--- a/imap_l3_processing/maps/mpfit.py
+++ b/imap_l3_processing/maps/mpfit.py
@@ -1,31 +1,31 @@
 """
 Perform Levenberg-Marquardt least-squares minimization, based on MINPACK-1.
 
-								   AUTHORS
+                                                                   AUTHORS
   The original version of this software, called LMFIT, was written in FORTRAN
   as part of the MINPACK-1 package by XXX.
 
   Craig Markwardt converted the FORTRAN code to IDL.  The information for the
   IDL version is:
-	 Craig B. Markwardt, NASA/GSFC Code 662, Greenbelt, MD 20770
-	 craigm@lheamail.gsfc.nasa.gov
-	 UPDATED VERSIONs can be found on my WEB PAGE:
-		http://cow.physics.wisc.edu/~craigm/idl/idl.html
+         Craig B. Markwardt, NASA/GSFC Code 662, Greenbelt, MD 20770
+         craigm@lheamail.gsfc.nasa.gov
+         UPDATED VERSIONs can be found on my WEB PAGE:
+                http://cow.physics.wisc.edu/~craigm/idl/idl.html
 
   Mark Rivers created this Python version from Craig's IDL version.
-	Mark Rivers, University of Chicago
-	Building 434A, Argonne National Laboratory
-	9700 South Cass Avenue, Argonne, IL 60439
-	rivers@cars.uchicago.edu
-	Updated versions can be found at http://cars.uchicago.edu/software
+        Mark Rivers, University of Chicago
+        Building 434A, Argonne National Laboratory
+        9700 South Cass Avenue, Argonne, IL 60439
+        rivers@cars.uchicago.edu
+        Updated versions can be found at http://cars.uchicago.edu/software
 
  Sergey Koposov converted the Mark's Python version from Numeric to numpy
-	Sergey Koposov, University of Cambridge, Institute of Astronomy,
-	Madingley road, CB3 0HA, Cambridge, UK
-	koposov@ast.cam.ac.uk
-	Updated versions can be found at http://code.google.com/p/astrolibpy/source/browse/trunk/
+        Sergey Koposov, University of Cambridge, Institute of Astronomy,
+        Madingley road, CB3 0HA, Cambridge, UK
+        koposov@ast.cam.ac.uk
+        Updated versions can be found at http://code.google.com/p/astrolibpy/source/browse/trunk/
 
-								 DESCRIPTION
+                                                                 DESCRIPTION
 
  MPFIT uses the Levenberg-Marquardt technique to solve the
  least-squares problem.  In its typical use, MPFIT will be used to
@@ -79,7 +79,7 @@
  least-squares minimization problem.
 
 
-							   USER FUNCTION
+                                                           USER FUNCTION
 
  The user must define a function which returns the appropriate
  values as specified above.  The function should return the weighted
@@ -89,15 +89,15 @@
  function should be declared in the following way:
 
    def myfunct(p, fjac=None, x=None, y=None, err=None)
-	# Parameter values are passed in "p"
-	# If fjac==None then partial derivatives should not be
-	# computed.  It will always be None if MPFIT is called with default
-	# flag.
-	model = F(x, p)
-	# Non-negative status value means MPFIT should continue, negative means
-	# stop the calculation.
-	status = 0
-	return([status, (y-model)/err]
+        # Parameter values are passed in "p"
+        # If fjac==None then partial derivatives should not be
+        # computed.  It will always be None if MPFIT is called with default
+        # flag.
+        model = F(x, p)
+        # Non-negative status value means MPFIT should continue, negative means
+        # stop the calculation.
+        status = 0
+        return([status, (y-model)/err]
 
  See below for applications with analytical derivatives.
 
@@ -116,7 +116,7 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
  -15 and -1 then MPFIT will stop the calculation and return to the caller.
 
 
-							ANALYTIC DERIVATIVES
+                                                        ANALYTIC DERIVATIVES
 
  In the search for the best-fit solution, MPFIT by default
  calculates derivatives numerically via a finite difference
@@ -131,21 +131,21 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
  FJAC, and if FJAC!=None then return the partial derivative array in the
  return list.
    def myfunct(p, fjac=None, x=None, y=None, err=None)
-	# Parameter values are passed in "p"
-	# If FJAC!=None then partial derivatives must be comptuer.
-	# FJAC contains an array of len(p), where each entry
-	# is 1 if that parameter is free and 0 if it is fixed.
-	model = F(x, p)
-	Non-negative status value means MPFIT should continue, negative means
-	# stop the calculation.
-	status = 0
-	if (dojac):
-	   pderiv = zeros([len(x), len(p)], Float)
-	   for j in range(len(p)):
-		 pderiv[:,j] = FGRAD(x, p, j)
-	else:
-	   pderiv = None
-	return([status, (y-model)/err, pderiv]
+        # Parameter values are passed in "p"
+        # If FJAC!=None then partial derivatives must be comptuer.
+        # FJAC contains an array of len(p), where each entry
+        # is 1 if that parameter is free and 0 if it is fixed.
+        model = F(x, p)
+        Non-negative status value means MPFIT should continue, negative means
+        # stop the calculation.
+        status = 0
+        if (dojac):
+           pderiv = zeros([len(x), len(p)], Float)
+           for j in range(len(p)):
+                 pderiv[:,j] = FGRAD(x, p, j)
+        else:
+           pderiv = None
+        return([status, (y-model)/err, pderiv]
 
  where FGRAD(x, p, i) is a user function which must compute the
  derivative of the model with respect to parameter P[i] at X.  When
@@ -170,7 +170,7 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
  50x50 image, "dp" should be 50x50xNPAR.
 
 
-		   CONSTRAINING PARAMETER VALUES WITH THE PARINFO KEYWORD
+                   CONSTRAINING PARAMETER VALUES WITH THE PARINFO KEYWORD
 
  The behavior of MPFIT can be modified with respect to each
  parameter to be fitted.  A parameter value can be fixed; simple
@@ -186,72 +186,72 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
  numerical order.  The dictionary can have the following keys
  (none are required, keys are case insensitive):
 
-	'value' - the starting parameter value (but see the START_PARAMS
-			 parameter for more information).
-
-	'fixed' - a boolean value, whether the parameter is to be held
-			 fixed or not.  Fixed parameters are not varied by
-			 MPFIT, but are passed on to MYFUNCT for evaluation.
-
-	'limited' - a two-element boolean array.  If the first/second
-			   element is set, then the parameter is bounded on the
-			   lower/upper side.  A parameter can be bounded on both
-			   sides.  Both LIMITED and LIMITS must be given
-			   together.
-
-	'limits' - a two-element float array.  Gives the
-			  parameter limits on the lower and upper sides,
-			  respectively.  Zero, one or two of these values can be
-			  set, depending on the values of LIMITED.  Both LIMITED
-			  and LIMITS must be given together.
-
-	'parname' - a string, giving the name of the parameter.  The
-			   fitting code of MPFIT does not use this tag in any
-			   way.  However, the default iterfunct will print the
-			   parameter name if available.
-
-	'step' - the step size to be used in calculating the numerical
-			derivatives.  If set to zero, then the step size is
-			computed automatically.  Ignored when AUTODERIVATIVE=0.
-
-	'mpside' - the sidedness of the finite difference when computing
-			  numerical derivatives.  This field can take four
-			  values:
-
-				 0 - one-sided derivative computed automatically
-				 1 - one-sided derivative (f(x+h) - f(x)  )/h
-				-1 - one-sided derivative (f(x)   - f(x-h))/h
-				 2 - two-sided derivative (f(x+h) - f(x-h))/(2*h)
-
-			 Where H is the STEP parameter described above.  The
-			 "automatic" one-sided derivative method will chose a
-			 direction for the finite difference which does not
-			 violate any constraints.  The other methods do not
-			 perform this check.  The two-sided method is in
-			 principle more precise, but requires twice as many
-			 function evaluations.  Default: 0.
-
-	'mpmaxstep' - the maximum change to be made in the parameter
-				 value.  During the fitting process, the parameter
-				 will never be changed by more than this value in
-				 one iteration.
-
-				 A value of 0 indicates no maximum.  Default: 0.
-
-	'tied' - a string expression which "ties" the parameter to other
-			free or fixed parameters.  Any expression involving
-			constants and the parameter array P are permitted.
-			Example: if parameter 2 is always to be twice parameter
-			1 then use the following: parinfo(2).tied = '2 * p(1)'.
-			Since they are totally constrained, tied parameters are
-			considered to be fixed; no errors are computed for them.
-			[ NOTE: the PARNAME can't be used in expressions. ]
-
-	'mpprint' - if set to 1, then the default iterfunct will print the
-			   parameter value.  If set to 0, the parameter value
-			   will not be printed.  This tag can be used to
-			   selectively print only a few parameter values out of
-			   many.  Default: 1 (all parameters printed)
+        'value' - the starting parameter value (but see the START_PARAMS
+                         parameter for more information).
+
+        'fixed' - a boolean value, whether the parameter is to be held
+                         fixed or not.  Fixed parameters are not varied by
+                         MPFIT, but are passed on to MYFUNCT for evaluation.
+
+        'limited' - a two-element boolean array.  If the first/second
+                           element is set, then the parameter is bounded on the
+                           lower/upper side.  A parameter can be bounded on both
+                           sides.  Both LIMITED and LIMITS must be given
+                           together.
+
+        'limits' - a two-element float array.  Gives the
+                          parameter limits on the lower and upper sides,
+                          respectively.  Zero, one or two of these values can be
+                          set, depending on the values of LIMITED.  Both LIMITED
+                          and LIMITS must be given together.
+
+        'parname' - a string, giving the name of the parameter.  The
+                           fitting code of MPFIT does not use this tag in any
+                           way.  However, the default iterfunct will print the
+                           parameter name if available.
+
+        'step' - the step size to be used in calculating the numerical
+                        derivatives.  If set to zero, then the step size is
+                        computed automatically.  Ignored when AUTODERIVATIVE=0.
+
+        'mpside' - the sidedness of the finite difference when computing
+                          numerical derivatives.  This field can take four
+                          values:
+
+                                 0 - one-sided derivative computed automatically
+                                 1 - one-sided derivative (f(x+h) - f(x)  )/h
+                                -1 - one-sided derivative (f(x)   - f(x-h))/h
+                                 2 - two-sided derivative (f(x+h) - f(x-h))/(2*h)
+
+                         Where H is the STEP parameter described above.  The
+                         "automatic" one-sided derivative method will chose a
+                         direction for the finite difference which does not
+                         violate any constraints.  The other methods do not
+                         perform this check.  The two-sided method is in
+                         principle more precise, but requires twice as many
+                         function evaluations.  Default: 0.
+
+        'mpmaxstep' - the maximum change to be made in the parameter
+                                 value.  During the fitting process, the parameter
+                                 will never be changed by more than this value in
+                                 one iteration.
+
+                                 A value of 0 indicates no maximum.  Default: 0.
+
+        'tied' - a string expression which "ties" the parameter to other
+                        free or fixed parameters.  Any expression involving
+                        constants and the parameter array P are permitted.
+                        Example: if parameter 2 is always to be twice parameter
+                        1 then use the following: parinfo(2).tied = '2 * p(1)'.
+                        Since they are totally constrained, tied parameters are
+                        considered to be fixed; no errors are computed for them.
+                        [ NOTE: the PARNAME can't be used in expressions. ]
+
+        'mpprint' - if set to 1, then the default iterfunct will print the
+                           parameter value.  If set to 0, the parameter value
+                           will not be printed.  This tag can be used to
+                           selectively print only a few parameter values out of
+                           many.  Default: 1 (all parameters printed)
 
 
  Future modifications to the PARINFO structure, if any, will involve
@@ -262,7 +262,7 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
 
  PARINFO Example:
  parinfo = [{'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
- 												for i in range(5)]
+                                                                                                for i in range(5)]
  parinfo[0]['fixed'] = 1
  parinfo[4]['limited'][0] = 1
  parinfo[4]['limits'][0]  = 50.
@@ -275,14 +275,14 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
  constrained to be above 50.
 
 
-								   EXAMPLE
+                                                                   EXAMPLE
 
    import mpfit
    import numpy.oldnumeric as Numeric
    x = arange(100, float)
    p0 = [5.7, 2.2, 500., 1.5, 2000.]
    y = ( p[0] + p[1]*[x] + p[2]*[x**2] + p[3]*sqrt(x) +
-		 p[4]*log(x))
+                 p[4]*log(x))
    fa = {'x':x, 'y':y, 'err':err}
    m = mpfit('myfunct', p0, functkw=fa)
    print 'status = ', m.status
@@ -294,16 +294,16 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
    results can be obtained from the returned object m.
 
 
-							THEORY OF OPERATION
+                                                        THEORY OF OPERATION
 
    There are many specific strategies for function minimization.  One
    very popular technique is to use function gradient information to
    realize the local structure of the function.  Near a local minimum
    the function value can be taylor expanded about x0 as follows:
 
-	  f(x) = f(x0) + f'(x0) . (x-x0) + (1/2) (x-x0) . f''(x0) . (x-x0)
-			 -----   ---------------   -------------------------------  (1)
-	 Order	0th		  1st					  2nd
+          f(x) = f(x0) + f'(x0) . (x-x0) + (1/2) (x-x0) . f''(x0) . (x-x0)
+                         -----   ---------------   -------------------------------  (1)
+         Order	0th		  1st					  2nd
 
    Here f'(x) is the gradient vector of f at x, and f''(x) is the
    Hessian matrix of second derivatives of f at x.  The vector x is
@@ -311,7 +311,7 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
    can find the minimum of f, f(xm) using Newton's method, and
    arrives at the following linear equation:
 
-	  f''(x0) . (xm-x0) = - f'(x0)							(2)
+          f''(x0) . (xm-x0) = - f'(x0)							(2)
 
    If an inverse can be found for f''(x0) then one can solve for
    (xm-x0), the step vector from the current position x0 to the new
@@ -323,7 +323,7 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
    It adds an additional diagonal term to the equation which may aid the
    convergence properties:
 
-	  (f''(x0) + nu I) . (xm-x0) = -f'(x0)				  (2a)
+          (f''(x0) + nu I) . (xm-x0) = -f'(x0)				  (2a)
 
    where I is the identity matrix.  When nu is large, the overall
    matrix is diagonally dominant, and the iterations follow steepest
@@ -341,14 +341,14 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
    which assist in solving eqn (2).  The function to be minimized is
    a sum of squares:
 
-	   f = Sum(hi^2)										 (3)
+           f = Sum(hi^2)										 (3)
 
    where hi is the ith residual out of m residuals as described
    above.  This can be substituted back into eqn (2) after computing
    the derivatives:
 
-	   f'  = 2 Sum(hi  hi')
-	   f'' = 2 Sum(hi' hj') + 2 Sum(hi hi'')				(4)
+           f'  = 2 Sum(hi  hi')
+           f'' = 2 Sum(hi' hj') + 2 Sum(hi hi'')				(4)
 
    If one assumes that the parameters are already close enough to a
    minimum, then one typically finds that the second term in f'' is
@@ -358,7 +358,7 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
 
    In matrix notation, the combination of eqns (2) and (4) becomes:
 
-		hT' . h' . dx = - hT' . h						  (5)
+                hT' . h' . dx = - hT' . h						  (5)
 
    Where h is the residual vector (length m), hT is its transpose, h'
    is the Jacobian matrix (dimensions n x m), and dx is (xm-x0).  The
@@ -374,9 +374,9 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
    Q = I, and R is upper right triangular.  Using h' = Q . R and the
    ortogonality of Q, eqn (5) becomes
 
-		(RT . QT) . (Q . R) . dx = - (RT . QT) . h
-					 RT . R . dx = - RT . QT . h		 (6)
-						  R . dx = - QT . h
+                (RT . QT) . (Q . R) . dx = - (RT . QT) . h
+                                         RT . R . dx = - RT . QT . h		 (6)
+                                                  R . dx = - QT . h
 
    where the last statement follows because R is upper triangular.
    Here, R, QT and h are known so this is a matter of solving for dx.
@@ -384,17 +384,17 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
    pivoting, and MPFIT_QRSOLV provides the solution for dx.
 
 
-								 REFERENCES
+                                                                 REFERENCES
 
    MINPACK-1, Jorge More', available from netlib (www.netlib.org).
    "Optimization Software Guide," Jorge More' and Stephen Wright,
-	 SIAM, *Frontiers in Applied Mathematics*, Number 14.
+         SIAM, *Frontiers in Applied Mathematics*, Number 14.
    More', Jorge J., "The Levenberg-Marquardt Algorithm:
-	 Implementation and Theory," in *Numerical Analysis*, ed. Watson,
-	 G. A., Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
+         Implementation and Theory," in *Numerical Analysis*, ed. Watson,
+         G. A., Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
 
 
-						   MODIFICATION HISTORY
+                                                   MODIFICATION HISTORY
 
    Translated from MINPACK-1 in FORTRAN, Apr-Jul 1998, CM
  Copyright (C) 1997-2002, Craig Markwardt
@@ -411,432 +411,453 @@ def myfunct(p, fjac=None, x=None, y=None, err=None)
 import numpy
 import scipy.linalg.blas
 
-
-#	 Original FORTRAN documentation
-#	 **********
+# 	 Original FORTRAN documentation
+# 	 **********
 #
-#	 subroutine lmdif
+# 	 subroutine lmdif
 #
-#	 the purpose of lmdif is to minimize the sum of the squares of
-#	 m nonlinear functions in n variables by a modification of
-#	 the levenberg-marquardt algorithm. the user must provide a
-#	 subroutine which calculates the functions. the jacobian is
-#	 then calculated by a forward-difference approximation.
+# 	 the purpose of lmdif is to minimize the sum of the squares of
+# 	 m nonlinear functions in n variables by a modification of
+# 	 the levenberg-marquardt algorithm. the user must provide a
+# 	 subroutine which calculates the functions. the jacobian is
+# 	 then calculated by a forward-difference approximation.
 #
-#	 the subroutine statement is
+# 	 the subroutine statement is
 #
-#	   subroutine lmdif(fcn,m,n,x,fvec,ftol,xtol,gtol,maxfev,epsfcn,
-#						diag,mode,factor,nprint,info,nfev,fjac,
-#						ldfjac,ipvt,qtf,wa1,wa2,wa3,wa4)
+# 	   subroutine lmdif(fcn,m,n,x,fvec,ftol,xtol,gtol,maxfev,epsfcn,
+# 						diag,mode,factor,nprint,info,nfev,fjac,
+# 						ldfjac,ipvt,qtf,wa1,wa2,wa3,wa4)
 #
-#	 where
+# 	 where
 #
-#	   fcn is the name of the user-supplied subroutine which
-#		 calculates the functions. fcn must be declared
-#		 in an external statement in the user calling
-#		 program, and should be written as follows.
+# 	   fcn is the name of the user-supplied subroutine which
+# 		 calculates the functions. fcn must be declared
+# 		 in an external statement in the user calling
+# 		 program, and should be written as follows.
 #
-#		 subroutine fcn(m,n,x,fvec,iflag)
-#		 integer m,n,iflag
-#		 double precision x(n),fvec(m)
-#		 ----------
-#		 calculate the functions at x and
-#		 return this vector in fvec.
-#		 ----------
-#		 return
-#		 end
+# 		 subroutine fcn(m,n,x,fvec,iflag)
+# 		 integer m,n,iflag
+# 		 double precision x(n),fvec(m)
+# 		 ----------
+# 		 calculate the functions at x and
+# 		 return this vector in fvec.
+# 		 ----------
+# 		 return
+# 		 end
 #
-#		 the value of iflag should not be changed by fcn unless
-#		 the user wants to terminate execution of lmdif.
-#		 in this case set iflag to a negative integer.
+# 		 the value of iflag should not be changed by fcn unless
+# 		 the user wants to terminate execution of lmdif.
+# 		 in this case set iflag to a negative integer.
 #
-#	   m is a positive integer input variable set to the number
-#		 of functions.
+# 	   m is a positive integer input variable set to the number
+# 		 of functions.
 #
-#	   n is a positive integer input variable set to the number
-#		 of variables. n must not exceed m.
+# 	   n is a positive integer input variable set to the number
+# 		 of variables. n must not exceed m.
 #
-#	   x is an array of length n. on input x must contain
-#		 an initial estimate of the solution vector. on output x
-#		 contains the final estimate of the solution vector.
+# 	   x is an array of length n. on input x must contain
+# 		 an initial estimate of the solution vector. on output x
+# 		 contains the final estimate of the solution vector.
 #
-#	   fvec is an output array of length m which contains
-#		 the functions evaluated at the output x.
+# 	   fvec is an output array of length m which contains
+# 		 the functions evaluated at the output x.
 #
-#	   ftol is a nonnegative input variable. termination
-#		 occurs when both the actual and predicted relative
-#		 reductions in the sum of squares are at most ftol.
-#		 therefore, ftol measures the relative error desired
-#		 in the sum of squares.
+# 	   ftol is a nonnegative input variable. termination
+# 		 occurs when both the actual and predicted relative
+# 		 reductions in the sum of squares are at most ftol.
+# 		 therefore, ftol measures the relative error desired
+# 		 in the sum of squares.
 #
-#	   xtol is a nonnegative input variable. termination
-#		 occurs when the relative error between two consecutive
-#		 iterates is at most xtol. therefore, xtol measures the
-#		 relative error desired in the approximate solution.
+# 	   xtol is a nonnegative input variable. termination
+# 		 occurs when the relative error between two consecutive
+# 		 iterates is at most xtol. therefore, xtol measures the
+# 		 relative error desired in the approximate solution.
 #
-#	   gtol is a nonnegative input variable. termination
-#		 occurs when the cosine of the angle between fvec and
-#		 any column of the jacobian is at most gtol in absolute
-#		 value. therefore, gtol measures the orthogonality
-#		 desired between the function vector and the columns
-#		 of the jacobian.
+# 	   gtol is a nonnegative input variable. termination
+# 		 occurs when the cosine of the angle between fvec and
+# 		 any column of the jacobian is at most gtol in absolute
+# 		 value. therefore, gtol measures the orthogonality
+# 		 desired between the function vector and the columns
+# 		 of the jacobian.
 #
-#	   maxfev is a positive integer input variable. termination
-#		 occurs when the number of calls to fcn is at least
-#		 maxfev by the end of an iteration.
+# 	   maxfev is a positive integer input variable. termination
+# 		 occurs when the number of calls to fcn is at least
+# 		 maxfev by the end of an iteration.
 #
-#	   epsfcn is an input variable used in determining a suitable
-#		 step length for the forward-difference approximation. this
-#		 approximation assumes that the relative errors in the
-#		 functions are of the order of epsfcn. if epsfcn is less
-#		 than the machine precision, it is assumed that the relative
-#		 errors in the functions are of the order of the machine
-#		 precision.
+# 	   epsfcn is an input variable used in determining a suitable
+# 		 step length for the forward-difference approximation. this
+# 		 approximation assumes that the relative errors in the
+# 		 functions are of the order of epsfcn. if epsfcn is less
+# 		 than the machine precision, it is assumed that the relative
+# 		 errors in the functions are of the order of the machine
+# 		 precision.
 #
-#	   diag is an array of length n. if mode = 1 (see
-#		 below), diag is internally set. if mode = 2, diag
-#		 must contain positive entries that serve as
-#		 multiplicative scale factors for the variables.
+# 	   diag is an array of length n. if mode = 1 (see
+# 		 below), diag is internally set. if mode = 2, diag
+# 		 must contain positive entries that serve as
+# 		 multiplicative scale factors for the variables.
 #
-#	   mode is an integer input variable. if mode = 1, the
-#		 variables will be scaled internally. if mode = 2,
-#		 the scaling is specified by the input diag. other
-#		 values of mode are equivalent to mode = 1.
+# 	   mode is an integer input variable. if mode = 1, the
+# 		 variables will be scaled internally. if mode = 2,
+# 		 the scaling is specified by the input diag. other
+# 		 values of mode are equivalent to mode = 1.
 #
-#	   factor is a positive input variable used in determining the
-#		 initial step bound. this bound is set to the product of
-#		 factor and the euclidean norm of diag*x if nonzero, or else
-#		 to factor itself. in most cases factor should lie in the
-#		 interval (.1,100.). 100. is a generally recommended value.
+# 	   factor is a positive input variable used in determining the
+# 		 initial step bound. this bound is set to the product of
+# 		 factor and the euclidean norm of diag*x if nonzero, or else
+# 		 to factor itself. in most cases factor should lie in the
+# 		 interval (.1,100.). 100. is a generally recommended value.
 #
-#	   nprint is an integer input variable that enables controlled
-#		 printing of iterates if it is positive. in this case,
-#		 fcn is called with iflag = 0 at the beginning of the first
-#		 iteration and every nprint iterations thereafter and
-#		 immediately prior to return, with x and fvec available
-#		 for printing. if nprint is not positive, no special calls
-#		 of fcn with iflag = 0 are made.
+# 	   nprint is an integer input variable that enables controlled
+# 		 printing of iterates if it is positive. in this case,
+# 		 fcn is called with iflag = 0 at the beginning of the first
+# 		 iteration and every nprint iterations thereafter and
+# 		 immediately prior to return, with x and fvec available
+# 		 for printing. if nprint is not positive, no special calls
+# 		 of fcn with iflag = 0 are made.
 #
-#	   info is an integer output variable. if the user has
-#		 terminated execution, info is set to the (negative)
-#		 value of iflag. see description of fcn. otherwise,
-#		 info is set as follows.
+# 	   info is an integer output variable. if the user has
+# 		 terminated execution, info is set to the (negative)
+# 		 value of iflag. see description of fcn. otherwise,
+# 		 info is set as follows.
 #
-#		 info = 0  improper input parameters.
+# 		 info = 0  improper input parameters.
 #
-#		 info = 1  both actual and predicted relative reductions
-#				   in the sum of squares are at most ftol.
+# 		 info = 1  both actual and predicted relative reductions
+# 				   in the sum of squares are at most ftol.
 #
-#		 info = 2  relative error between two consecutive iterates
-#				   is at most xtol.
+# 		 info = 2  relative error between two consecutive iterates
+# 				   is at most xtol.
 #
-#		 info = 3  conditions for info = 1 and info = 2 both hold.
+# 		 info = 3  conditions for info = 1 and info = 2 both hold.
 #
-#		 info = 4  the cosine of the angle between fvec and any
-#				   column of the jacobian is at most gtol in
-#				   absolute value.
+# 		 info = 4  the cosine of the angle between fvec and any
+# 				   column of the jacobian is at most gtol in
+# 				   absolute value.
 #
-#		 info = 5  number of calls to fcn has reached or
-#				   exceeded maxfev.
+# 		 info = 5  number of calls to fcn has reached or
+# 				   exceeded maxfev.
 #
-#		 info = 6  ftol is too small. no further reduction in
-#				   the sum of squares is possible.
+# 		 info = 6  ftol is too small. no further reduction in
+# 				   the sum of squares is possible.
 #
-#		 info = 7  xtol is too small. no further improvement in
-#				   the approximate solution x is possible.
+# 		 info = 7  xtol is too small. no further improvement in
+# 				   the approximate solution x is possible.
 #
-#		 info = 8  gtol is too small. fvec is orthogonal to the
-#				   columns of the jacobian to machine precision.
+# 		 info = 8  gtol is too small. fvec is orthogonal to the
+# 				   columns of the jacobian to machine precision.
 #
-#	   nfev is an integer output variable set to the number of
-#		 calls to fcn.
+# 	   nfev is an integer output variable set to the number of
+# 		 calls to fcn.
 #
-#	   fjac is an output m by n array. the upper n by n submatrix
-#		 of fjac contains an upper triangular matrix r with
-#		 diagonal elements of nonincreasing magnitude such that
+# 	   fjac is an output m by n array. the upper n by n submatrix
+# 		 of fjac contains an upper triangular matrix r with
+# 		 diagonal elements of nonincreasing magnitude such that
 #
-#				t	 t		   t
-#			   p *(jac *jac)*p = r *r,
+# 				t	 t		   t
+# 			   p *(jac *jac)*p = r *r,
 #
-#		 where p is a permutation matrix and jac is the final
-#		 calculated jacobian. column j of p is column ipvt(j)
-#		 (see below) of the identity matrix. the lower trapezoidal
-#		 part of fjac contains information generated during
-#		 the computation of r.
+# 		 where p is a permutation matrix and jac is the final
+# 		 calculated jacobian. column j of p is column ipvt(j)
+# 		 (see below) of the identity matrix. the lower trapezoidal
+# 		 part of fjac contains information generated during
+# 		 the computation of r.
 #
-#	   ldfjac is a positive integer input variable not less than m
-#		 which specifies the leading dimension of the array fjac.
+# 	   ldfjac is a positive integer input variable not less than m
+# 		 which specifies the leading dimension of the array fjac.
 #
-#	   ipvt is an integer output array of length n. ipvt
-#		 defines a permutation matrix p such that jac*p = q*r,
-#		 where jac is the final calculated jacobian, q is
-#		 orthogonal (not stored), and r is upper triangular
-#		 with diagonal elements of nonincreasing magnitude.
-#		 column j of p is column ipvt(j) of the identity matrix.
+# 	   ipvt is an integer output array of length n. ipvt
+# 		 defines a permutation matrix p such that jac*p = q*r,
+# 		 where jac is the final calculated jacobian, q is
+# 		 orthogonal (not stored), and r is upper triangular
+# 		 with diagonal elements of nonincreasing magnitude.
+# 		 column j of p is column ipvt(j) of the identity matrix.
 #
-#	   qtf is an output array of length n which contains
-#		 the first n elements of the vector (q transpose)*fvec.
+# 	   qtf is an output array of length n which contains
+# 		 the first n elements of the vector (q transpose)*fvec.
 #
-#	   wa1, wa2, and wa3 are work arrays of length n.
+# 	   wa1, wa2, and wa3 are work arrays of length n.
 #
-#	   wa4 is a work array of length m.
+# 	   wa4 is a work array of length m.
 #
-#	 subprograms called
+# 	 subprograms called
 #
-#	   user-supplied ...... fcn
+# 	   user-supplied ...... fcn
 #
-#	   minpack-supplied ... dpmpar,enorm,fdjac2,,qrfac
+# 	   minpack-supplied ... dpmpar,enorm,fdjac2,,qrfac
 #
-#	   fortran-supplied ... dabs,dmax1,dmin1,dsqrt,mod
+# 	   fortran-supplied ... dabs,dmax1,dmin1,dsqrt,mod
 #
-#	 argonne national laboratory. minpack project. march 1980.
-#	 burton s. garbow, kenneth e. hillstrom, jorge j. more
+# 	 argonne national laboratory. minpack project. march 1980.
+# 	 burton s. garbow, kenneth e. hillstrom, jorge j. more
 #
-#	 **********
+# 	 **********
+
 
 class mpfit:
-    blas_enorm32, = scipy.linalg.blas.get_blas_funcs(['nrm2'], numpy.array([0], dtype=numpy.float32))
-    blas_enorm64, = scipy.linalg.blas.get_blas_funcs(['nrm2'], numpy.array([0], dtype=numpy.float64))
-
-    def __init__(self, fcn, xall=None, functkw={}, parinfo=None,
-                 ftol=1.e-10, xtol=1.e-10, gtol=1.e-10,
-                 damp=0., maxiter=200, factor=100., nprint=1,
-                 iterfunct='default', iterkw={}, nocovar=0,
-                 rescale=0, autoderivative=1, quiet=0,
-                 diag=None, epsfcn=None, debug=0):
+    (blas_enorm32,) = scipy.linalg.blas.get_blas_funcs(
+        ["nrm2"], numpy.array([0], dtype=numpy.float32)
+    )
+    (blas_enorm64,) = scipy.linalg.blas.get_blas_funcs(
+        ["nrm2"], numpy.array([0], dtype=numpy.float64)
+    )
+
+    def __init__(
+        self,
+        fcn,
+        xall=None,
+        functkw={},
+        parinfo=None,
+        ftol=1.0e-10,
+        xtol=1.0e-10,
+        gtol=1.0e-10,
+        damp=0.0,
+        maxiter=200,
+        factor=100.0,
+        nprint=1,
+        iterfunct="default",
+        iterkw={},
+        nocovar=0,
+        rescale=0,
+        autoderivative=1,
+        quiet=0,
+        diag=None,
+        epsfcn=None,
+        debug=0,
+    ):
         """
-  Inputs:
-    fcn:
-       The function to be minimized.  The function should return the weighted
-       deviations between the model and the data, as described above.
-
-    xall:
-       An array of starting values for each of the parameters of the model.
-       The number of parameters should be fewer than the number of measurements.
-
-       This parameter is optional if the parinfo keyword is used (but see
-       parinfo).  The parinfo keyword provides a mechanism to fix or constrain
-       individual parameters.
-
-  Keywords:
-
-     autoderivative:
-        If this is set, derivatives of the function will be computed
-        automatically via a finite differencing procedure.  If not set, then
-        fcn must provide the (analytical) derivatives.
-           Default: set (=1)
-           NOTE: to supply your own analytical derivatives,
-                 explicitly pass autoderivative=0
-
-     ftol:
-        A nonnegative input variable. Termination occurs when both the actual
-        and predicted relative reductions in the sum of squares are at most
-        ftol (and status is accordingly set to 1 or 3).  Therefore, ftol
-        measures the relative error desired in the sum of squares.
-           Default: 1E-10
-
-     functkw:
-        A dictionary which contains the parameters to be passed to the
-        user-supplied function specified by fcn via the standard Python
-        keyword dictionary mechanism.  This is the way you can pass additional
-        data to your user-supplied function without using global variables.
-
-        Consider the following example:
-           if functkw = {'xval':[1.,2.,3.], 'yval':[1.,4.,9.],
-                         'errval':[1.,1.,1.] }
-        then the user supplied function should be declared like this:
-           def myfunct(p, fjac=None, xval=None, yval=None, errval=None):
-
-        Default: {}   No extra parameters are passed to the user-supplied
-                      function.
-
-     gtol:
-        A nonnegative input variable. Termination occurs when the cosine of
-        the angle between fvec and any column of the jacobian is at most gtol
-        in absolute value (and status is accordingly set to 4). Therefore,
-        gtol measures the orthogonality desired between the function vector
-        and the columns of the jacobian.
-           Default: 1e-10
-
-     iterkw:
-        The keyword arguments to be passed to iterfunct via the dictionary
-        keyword mechanism.  This should be a dictionary and is similar in
-        operation to FUNCTKW.
-           Default: {}  No arguments are passed.
-
-     iterfunct:
-        The name of a function to be called upon each NPRINT iteration of the
-        MPFIT routine.  It should be declared in the following way:
-           def iterfunct(myfunct, p, iter, fnorm, functkw=None,
-                         parinfo=None, quiet=0, dof=None, [iterkw keywords here])
-           # perform custom iteration update
-
-        iterfunct must accept all three keyword parameters (FUNCTKW, PARINFO
-        and QUIET).
-
-        myfunct:  The user-supplied function to be minimized,
-        p:		The current set of model parameters
-        iter:	 The iteration number
-        functkw:  The arguments to be passed to myfunct.
-        fnorm:	The chi-squared value.
-        quiet:	Set when no textual output should be printed.
-        dof:	  The number of degrees of freedom, normally the number of points
-                  less the number of free parameters.
-        See below for documentation of parinfo.
-
-        In implementation, iterfunct can perform updates to the terminal or
-        graphical user interface, to provide feedback while the fit proceeds.
-        If the fit is to be stopped for any reason, then iterfunct should return a
-        a status value between -15 and -1.  Otherwise it should return None
-        (e.g. no return statement) or 0.
-        In principle, iterfunct should probably not modify the parameter values,
-        because it may interfere with the algorithm's stability.  In practice it
-        is allowed.
-
-        Default: an internal routine is used to print the parameter values.
-
-        Set iterfunct=None if there is no user-defined routine and you don't
-        want the internal default routine be called.
-
-     maxiter:
-        The maximum number of iterations to perform.  If the number is exceeded,
-        then the status value is set to 5 and MPFIT returns.
-        Default: 200 iterations
-
-     nocovar:
-        Set this keyword to prevent the calculation of the covariance matrix
-        before returning (see COVAR)
-        Default: clear (=0)  The covariance matrix is returned
-
-     nprint:
-        The frequency with which iterfunct is called.  A value of 1 indicates
-        that iterfunct is called with every iteration, while 2 indicates every
-        other iteration, etc.  Note that several Levenberg-Marquardt attempts
-        can be made in a single iteration.
-        Default value: 1
-
-     parinfo
-        Provides a mechanism for more sophisticated constraints to be placed on
-        parameter values.  When parinfo is not passed, then it is assumed that
-        all parameters are free and unconstrained.  Values in parinfo are never
-        modified during a call to MPFIT.
-
-        See description above for the structure of PARINFO.
-
-        Default value: None  All parameters are free and unconstrained.
-
-     quiet:
-        Set this keyword when no textual output should be printed by MPFIT
-
-     damp:
-        A scalar number, indicating the cut-off value of residuals where
-        "damping" will occur.  Residuals with magnitudes greater than this
-        number will be replaced by their hyperbolic tangent.  This partially
-        mitigates the so-called large residual problem inherent in
-        least-squares solvers (as for the test problem CURVI,
-        http://www.maxthis.com/curviex.htm).
-        A value of 0 indicates no damping.
-           Default: 0
-
-        Note: DAMP doesn't work with autoderivative=0
-
-     xtol:
-        A nonnegative input variable. Termination occurs when the relative error
-        between two consecutive iterates is at most xtol (and status is
-        accordingly set to 2 or 3).  Therefore, xtol measures the relative error
-        desired in the approximate solution.
-        Default: 1E-10
-
-   Outputs:
-
-     Returns an object of type mpfit.  The results are attributes of this class,
-     e.g. mpfit.status, mpfit.errmsg, mpfit.params, npfit.niter, mpfit.covar.
-
-     .status
-        An integer status code is returned.  All values greater than zero can
-        represent success (however .status == 5 may indicate failure to
-        converge). It can have one of the following values:
-
-        -16
-           A parameter or function value has become infinite or an undefined
-           number.  This is usually a consequence of numerical overflow in the
-           user's model function, which must be avoided.
-
-        -15 to -1
-           These are error codes that either MYFUNCT or iterfunct may return to
-           terminate the fitting process.  Values from -15 to -1 are reserved
-           for the user functions and will not clash with MPFIT.
-
-        0  Improper input parameters.
-
-        1  Both actual and predicted relative reductions in the sum of squares
-           are at most ftol.
-
-        2  Relative error between two consecutive iterates is at most xtol
-
-        3  Conditions for status = 1 and status = 2 both hold.
-
-        4  The cosine of the angle between fvec and any column of the jacobian
-           is at most gtol in absolute value.
-
-        5  The maximum number of iterations has been reached.
+        Inputs:
+          fcn:
+             The function to be minimized.  The function should return the weighted
+             deviations between the model and the data, as described above.
+
+          xall:
+             An array of starting values for each of the parameters of the model.
+             The number of parameters should be fewer than the number of measurements.
+
+             This parameter is optional if the parinfo keyword is used (but see
+             parinfo).  The parinfo keyword provides a mechanism to fix or constrain
+             individual parameters.
+
+        Keywords:
+
+           autoderivative:
+              If this is set, derivatives of the function will be computed
+              automatically via a finite differencing procedure.  If not set, then
+              fcn must provide the (analytical) derivatives.
+                 Default: set (=1)
+                 NOTE: to supply your own analytical derivatives,
+                       explicitly pass autoderivative=0
+
+           ftol:
+              A nonnegative input variable. Termination occurs when both the actual
+              and predicted relative reductions in the sum of squares are at most
+              ftol (and status is accordingly set to 1 or 3).  Therefore, ftol
+              measures the relative error desired in the sum of squares.
+                 Default: 1E-10
+
+           functkw:
+              A dictionary which contains the parameters to be passed to the
+              user-supplied function specified by fcn via the standard Python
+              keyword dictionary mechanism.  This is the way you can pass additional
+              data to your user-supplied function without using global variables.
+
+              Consider the following example:
+                 if functkw = {'xval':[1.,2.,3.], 'yval':[1.,4.,9.],
+                               'errval':[1.,1.,1.] }
+              then the user supplied function should be declared like this:
+                 def myfunct(p, fjac=None, xval=None, yval=None, errval=None):
+
+              Default: {}   No extra parameters are passed to the user-supplied
+                            function.
+
+           gtol:
+              A nonnegative input variable. Termination occurs when the cosine of
+              the angle between fvec and any column of the jacobian is at most gtol
+              in absolute value (and status is accordingly set to 4). Therefore,
+              gtol measures the orthogonality desired between the function vector
+              and the columns of the jacobian.
+                 Default: 1e-10
+
+           iterkw:
+              The keyword arguments to be passed to iterfunct via the dictionary
+              keyword mechanism.  This should be a dictionary and is similar in
+              operation to FUNCTKW.
+                 Default: {}  No arguments are passed.
+
+           iterfunct:
+              The name of a function to be called upon each NPRINT iteration of the
+              MPFIT routine.  It should be declared in the following way:
+                 def iterfunct(myfunct, p, iter, fnorm, functkw=None,
+                               parinfo=None, quiet=0, dof=None, [iterkw keywords here])
+                 # perform custom iteration update
+
+              iterfunct must accept all three keyword parameters (FUNCTKW, PARINFO
+              and QUIET).
+
+              myfunct:  The user-supplied function to be minimized,
+              p:		The current set of model parameters
+              iter:	 The iteration number
+              functkw:  The arguments to be passed to myfunct.
+              fnorm:	The chi-squared value.
+              quiet:	Set when no textual output should be printed.
+              dof:	  The number of degrees of freedom, normally the number of points
+                        less the number of free parameters.
+              See below for documentation of parinfo.
+
+              In implementation, iterfunct can perform updates to the terminal or
+              graphical user interface, to provide feedback while the fit proceeds.
+              If the fit is to be stopped for any reason, then iterfunct should return a
+              a status value between -15 and -1.  Otherwise it should return None
+              (e.g. no return statement) or 0.
+              In principle, iterfunct should probably not modify the parameter values,
+              because it may interfere with the algorithm's stability.  In practice it
+              is allowed.
+
+              Default: an internal routine is used to print the parameter values.
+
+              Set iterfunct=None if there is no user-defined routine and you don't
+              want the internal default routine be called.
+
+           maxiter:
+              The maximum number of iterations to perform.  If the number is exceeded,
+              then the status value is set to 5 and MPFIT returns.
+              Default: 200 iterations
+
+           nocovar:
+              Set this keyword to prevent the calculation of the covariance matrix
+              before returning (see COVAR)
+              Default: clear (=0)  The covariance matrix is returned
+
+           nprint:
+              The frequency with which iterfunct is called.  A value of 1 indicates
+              that iterfunct is called with every iteration, while 2 indicates every
+              other iteration, etc.  Note that several Levenberg-Marquardt attempts
+              can be made in a single iteration.
+              Default value: 1
+
+           parinfo
+              Provides a mechanism for more sophisticated constraints to be placed on
+              parameter values.  When parinfo is not passed, then it is assumed that
+              all parameters are free and unconstrained.  Values in parinfo are never
+              modified during a call to MPFIT.
+
+              See description above for the structure of PARINFO.
+
+              Default value: None  All parameters are free and unconstrained.
+
+           quiet:
+              Set this keyword when no textual output should be printed by MPFIT
+
+           damp:
+              A scalar number, indicating the cut-off value of residuals where
+              "damping" will occur.  Residuals with magnitudes greater than this
+              number will be replaced by their hyperbolic tangent.  This partially
+              mitigates the so-called large residual problem inherent in
+              least-squares solvers (as for the test problem CURVI,
+              http://www.maxthis.com/curviex.htm).
+              A value of 0 indicates no damping.
+                 Default: 0
+
+              Note: DAMP doesn't work with autoderivative=0
+
+           xtol:
+              A nonnegative input variable. Termination occurs when the relative error
+              between two consecutive iterates is at most xtol (and status is
+              accordingly set to 2 or 3).  Therefore, xtol measures the relative error
+              desired in the approximate solution.
+              Default: 1E-10
+
+         Outputs:
+
+           Returns an object of type mpfit.  The results are attributes of this class,
+           e.g. mpfit.status, mpfit.errmsg, mpfit.params, npfit.niter, mpfit.covar.
+
+           .status
+              An integer status code is returned.  All values greater than zero can
+              represent success (however .status == 5 may indicate failure to
+              converge). It can have one of the following values:
+
+              -16
+                 A parameter or function value has become infinite or an undefined
+                 number.  This is usually a consequence of numerical overflow in the
+                 user's model function, which must be avoided.
+
+              -15 to -1
+                 These are error codes that either MYFUNCT or iterfunct may return to
+                 terminate the fitting process.  Values from -15 to -1 are reserved
+                 for the user functions and will not clash with MPFIT.
+
+              0  Improper input parameters.
+
+              1  Both actual and predicted relative reductions in the sum of squares
+                 are at most ftol.
+
+              2  Relative error between two consecutive iterates is at most xtol
+
+              3  Conditions for status = 1 and status = 2 both hold.
+
+              4  The cosine of the angle between fvec and any column of the jacobian
+                 is at most gtol in absolute value.
+
+              5  The maximum number of iterations has been reached.
 
-        6  ftol is too small. No further reduction in the sum of squares is
-           possible.
+              6  ftol is too small. No further reduction in the sum of squares is
+                 possible.
 
-        7  xtol is too small. No further improvement in the approximate solution
-           x is possible.
+              7  xtol is too small. No further improvement in the approximate solution
+                 x is possible.
 
-        8  gtol is too small. fvec is orthogonal to the columns of the jacobian
-           to machine precision.
+              8  gtol is too small. fvec is orthogonal to the columns of the jacobian
+                 to machine precision.
 
-     .fnorm
-        The value of the summed squared residuals for the returned parameter
-        values.
+           .fnorm
+              The value of the summed squared residuals for the returned parameter
+              values.
 
-     .covar
-        The covariance matrix for the set of parameters returned by MPFIT.
-        The matrix is NxN where N is the number of  parameters.  The square root
-        of the diagonal elements gives the formal 1-sigma statistical errors on
-        the parameters if errors were treated "properly" in fcn.
-        Parameter errors are also returned in .perror.
+           .covar
+              The covariance matrix for the set of parameters returned by MPFIT.
+              The matrix is NxN where N is the number of  parameters.  The square root
+              of the diagonal elements gives the formal 1-sigma statistical errors on
+              the parameters if errors were treated "properly" in fcn.
+              Parameter errors are also returned in .perror.
 
-        To compute the correlation matrix, pcor, use this example:
-           cov = mpfit.covar
-           pcor = cov * 0.
-           for i in range(n):
-              for j in range(n):
-                 pcor[i,j] = cov[i,j]/sqrt(cov[i,i]*cov[j,j])
+              To compute the correlation matrix, pcor, use this example:
+                 cov = mpfit.covar
+                 pcor = cov * 0.
+                 for i in range(n):
+                    for j in range(n):
+                       pcor[i,j] = cov[i,j]/sqrt(cov[i,i]*cov[j,j])
 
-        If nocovar is set or MPFIT terminated abnormally, then .covar is set to
-        a scalar with value None.
+              If nocovar is set or MPFIT terminated abnormally, then .covar is set to
+              a scalar with value None.
 
-     .errmsg
-        A string error or warning message is returned.
+           .errmsg
+              A string error or warning message is returned.
 
-     .nfev
-        The number of calls to MYFUNCT performed.
+           .nfev
+              The number of calls to MYFUNCT performed.
 
-     .niter
-        The number of iterations completed.
+           .niter
+              The number of iterations completed.
 
-     .perror
-        The formal 1-sigma errors in each parameter, computed from the
-        covariance matrix.  If a parameter is held fixed, or if it touches a
-        boundary, then the error is reported as zero.
+           .perror
+              The formal 1-sigma errors in each parameter, computed from the
+              covariance matrix.  If a parameter is held fixed, or if it touches a
+              boundary, then the error is reported as zero.
 
-        If the fit is unweighted (i.e. no errors were given, or the weights
-        were uniformly set to unity), then .perror will probably not represent
-        the true parameter uncertainties.
+              If the fit is unweighted (i.e. no errors were given, or the weights
+              were uniformly set to unity), then .perror will probably not represent
+              the true parameter uncertainties.
 
-        *If* you can assume that the true reduced chi-squared value is unity --
-        meaning that the fit is implicitly assumed to be of good quality --
-        then the estimated parameter uncertainties can be computed by scaling
-        .perror by the measured chi-squared value.
+              *If* you can assume that the true reduced chi-squared value is unity --
+              meaning that the fit is implicitly assumed to be of good quality --
+              then the estimated parameter uncertainties can be computed by scaling
+              .perror by the measured chi-squared value.
 
-           dof = len(x) - len(mpfit.params) # deg of freedom
-           # scaled uncertainties
-           pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof)
+                 dof = len(x) - len(mpfit.params) # deg of freedom
+                 # scaled uncertainties
+                 pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof)
 
         """
         self.niter = 0
@@ -845,7 +866,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         self.perror = None
         self.status = 0  # Invalid input flag set while we check inputs
         self.debug = debug
-        self.errmsg = ''
+        self.errmsg = ""
         self.nfev = 0
         self.damp = damp
         self.dof = 0
@@ -854,37 +875,39 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
             self.errmsg = "Usage: parms = mpfit('myfunt', ... )"
             return
 
-        if iterfunct == 'default':
+        if iterfunct == "default":
             iterfunct = self.defiter
 
         # Parameter damping doesn't work when user is providing their own
         # gradients.
         if (self.damp != 0) and (autoderivative == 0):
-            self.errmsg = 'ERROR: keywords DAMP and AUTODERIVATIVE are mutually exclusive'
+            self.errmsg = (
+                "ERROR: keywords DAMP and AUTODERIVATIVE are mutually exclusive"
+            )
             return
 
         # Parameters can either be stored in parinfo, or x. x takes precedence if it exists
         if (xall is None) and (parinfo is None):
-            self.errmsg = 'ERROR: must pass parameters in P or PARINFO'
+            self.errmsg = "ERROR: must pass parameters in P or PARINFO"
             return
 
         # Be sure that PARINFO is of the right type
         if parinfo is not None:
             if type(parinfo) is not list:
-                self.errmsg = 'ERROR: PARINFO must be a list of dictionaries.'
+                self.errmsg = "ERROR: PARINFO must be a list of dictionaries."
                 return
             else:
                 if type(parinfo[0]) is not dict:
-                    self.errmsg = 'ERROR: PARINFO must be a list of dictionaries.'
+                    self.errmsg = "ERROR: PARINFO must be a list of dictionaries."
                     return
-            if ((xall is not None) and (len(xall) != len(parinfo))):
-                self.errmsg = 'ERROR: number of elements in PARINFO and P must agree'
+            if (xall is not None) and (len(xall) != len(parinfo)):
+                self.errmsg = "ERROR: number of elements in PARINFO and P must agree"
                 return
 
         # If the parameters were not specified at the command line, then
         # extract them from PARINFO
         if xall is None:
-            xall = self.parinfo(parinfo, 'value')
+            xall = self.parinfo(parinfo, "value")
             if xall is None:
                 self.errmsg = 'ERROR: either P or PARINFO(*)["value"] must be supplied.'
                 return
@@ -893,50 +916,52 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         xall = numpy.asarray(xall)
         # In the case if the xall is not float or if is float but has less
         # than 64 bits we do convert it into double
-        if xall.dtype.kind != 'f' or xall.dtype.itemsize <= 4:
+        if xall.dtype.kind != "f" or xall.dtype.itemsize <= 4:
             xall = xall.astype(numpy.float64)
 
         npar = len(xall)
-        self.fnorm = -1.
-        fnorm1 = -1.
+        self.fnorm = -1.0
+        fnorm1 = -1.0
 
         # TIED parameters?
-        ptied = self.parinfo(parinfo, 'tied', default='', n=npar)
+        ptied = self.parinfo(parinfo, "tied", default="", n=npar)
         self.qanytied = 0
         for i in range(npar):
             ptied[i] = ptied[i].strip()
-            if ptied[i] != '':
+            if ptied[i] != "":
                 self.qanytied = 1
         self.ptied = ptied
 
         # FIXED parameters ?
-        pfixed = self.parinfo(parinfo, 'fixed', default=0, n=npar)
-        pfixed = (pfixed == 1)
+        pfixed = self.parinfo(parinfo, "fixed", default=0, n=npar)
+        pfixed = pfixed == 1
         for i in range(npar):
-            pfixed[i] = pfixed[i] or (ptied[i] != '')  # Tied parameters are also effectively fixed
+            pfixed[i] = pfixed[i] or (
+                ptied[i] != ""
+            )  # Tied parameters are also effectively fixed
 
         # Finite differencing step, absolute and relative, and sidedness of deriv.
-        step = self.parinfo(parinfo, 'step', default=0., n=npar)
-        dstep = self.parinfo(parinfo, 'relstep', default=0., n=npar)
-        dside = self.parinfo(parinfo, 'mpside', default=0, n=npar)
+        step = self.parinfo(parinfo, "step", default=0.0, n=npar)
+        dstep = self.parinfo(parinfo, "relstep", default=0.0, n=npar)
+        dside = self.parinfo(parinfo, "mpside", default=0, n=npar)
 
         # Maximum and minimum steps allowed to be taken in one iteration
-        maxstep = self.parinfo(parinfo, 'mpmaxstep', default=0., n=npar)
-        minstep = self.parinfo(parinfo, 'mpminstep', default=0., n=npar)
+        maxstep = self.parinfo(parinfo, "mpmaxstep", default=0.0, n=npar)
+        minstep = self.parinfo(parinfo, "mpminstep", default=0.0, n=npar)
         qmin = minstep != 0
         qmin[:] = False  # Remove minstep for now!!
         qmax = maxstep != 0
         if numpy.any(qmin & qmax & (maxstep < minstep)):
-            self.errmsg = 'ERROR: MPMINSTEP is greater than MPMAXSTEP'
+            self.errmsg = "ERROR: MPMINSTEP is greater than MPMAXSTEP"
             return
-        wh = (numpy.nonzero((qmin != 0.) | (qmax != 0.)))[0]
+        wh = (numpy.nonzero((qmin != 0.0) | (qmax != 0.0)))[0]
         qminmax = len(wh > 0)
 
         # Finish up the free parameters
         ifree = (numpy.nonzero(pfixed != 1))[0]
         nfree = len(ifree)
         if nfree == 0:
-            self.errmsg = 'ERROR: no free parameters'
+            self.errmsg = "ERROR: no free parameters"
             return
 
         # Compose only VARYING parameters
@@ -944,18 +969,22 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         x = self.params[ifree]  # x is the set of free parameters
 
         # LIMITED parameters ?
-        limited = self.parinfo(parinfo, 'limited', default=[0, 0], n=npar)
-        limits = self.parinfo(parinfo, 'limits', default=[0., 0.], n=npar)
+        limited = self.parinfo(parinfo, "limited", default=[0, 0], n=npar)
+        limits = self.parinfo(parinfo, "limits", default=[0.0, 0.0], n=npar)
         if (limited is not None) and (limits is not None):
             # Error checking on limits in parinfo
-            if numpy.any((limited[:, 0] & (xall < limits[:, 0])) |
-                         (limited[:, 1] & (xall > limits[:, 1]))):
-                self.errmsg = 'ERROR: parameters are not within PARINFO limits'
+            if numpy.any(
+                (limited[:, 0] & (xall < limits[:, 0]))
+                | (limited[:, 1] & (xall > limits[:, 1]))
+            ):
+                self.errmsg = "ERROR: parameters are not within PARINFO limits"
                 return
-            if numpy.any((limited[:, 0] & limited[:, 1]) &
-                         (limits[:, 0] >= limits[:, 1]) &
-                         (pfixed == 0)):
-                self.errmsg = 'ERROR: PARINFO parameter limits are not consistent'
+            if numpy.any(
+                (limited[:, 0] & limited[:, 1])
+                & (limits[:, 0] >= limits[:, 1])
+                & (pfixed == 0)
+            ):
+                self.errmsg = "ERROR: PARINFO parameter limits are not consistent"
                 return
 
             # Transfer structure values to local variables
@@ -964,32 +993,38 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
             qllim = (limited[:, 0])[ifree]
             llim = (limits[:, 0])[ifree]
 
-            if numpy.any((qulim != 0.) | (qllim != 0.)):
+            if numpy.any((qulim != 0.0) | (qllim != 0.0)):
                 qanylim = 1
             else:
                 qanylim = 0
         else:
             # Fill in local variables with dummy values
             qulim = numpy.zeros(nfree)
-            ulim = x * 0.
+            ulim = x * 0.0
             qllim = qulim
-            llim = x * 0.
+            llim = x * 0.0
             qanylim = 0
 
         n = len(x)
         # Check input parameters for errors
-        if (n < 0) or (ftol <= 0) or (xtol <= 0) or (gtol <= 0) \
-                or (maxiter < 0) or (factor <= 0):
-            self.errmsg = 'ERROR: input keywords are inconsistent'
+        if (
+            (n < 0)
+            or (ftol <= 0)
+            or (xtol <= 0)
+            or (gtol <= 0)
+            or (maxiter < 0)
+            or (factor <= 0)
+        ):
+            self.errmsg = "ERROR: input keywords are inconsistent"
             return
 
         if rescale != 0:
-            self.errmsg = 'ERROR: DIAG parameter scales are inconsistent'
+            self.errmsg = "ERROR: DIAG parameter scales are inconsistent"
             if len(diag) < n:
                 return
             if numpy.any(diag <= 0):
                 return
-            self.errmsg = ''
+            self.errmsg = ""
 
         [self.status, fvec] = self.call(fcn, self.params, functkw)
 
@@ -1010,22 +1045,21 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
         m = len(fvec)
         if m < n:
-            self.errmsg = 'ERROR: number of parameters must not exceed data'
+            self.errmsg = "ERROR: number of parameters must not exceed data"
             return
         self.dof = m - nfree
         self.fnorm = self.enorm(fvec)
 
         # Initialize Levelberg-Marquardt parameter and iteration counter
 
-        par = 0.
+        par = 0.0
         self.niter = 1
-        qtf = x * 0.
+        qtf = x * 0.0
         self.status = 0
 
         # Beginning of the outer loop
 
-        while (1):
-
+        while 1:
             # If requested, call fcn to enable printing of iterates
             self.params[ifree] = x
             if self.qanytied:
@@ -1037,15 +1071,25 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     xnew0 = self.params.copy()
 
                     dof = numpy.max([len(fvec) - len(x), 0])
-                    status = iterfunct(fcn, self.params, self.niter, self.fnorm ** 2,
-                                       functkw=functkw, parinfo=parinfo, quiet=quiet,
-                                       dof=dof, **iterkw)
+                    status = iterfunct(
+                        fcn,
+                        self.params,
+                        self.niter,
+                        self.fnorm**2,
+                        functkw=functkw,
+                        parinfo=parinfo,
+                        quiet=quiet,
+                        dof=dof,
+                        **iterkw,
+                    )
                     if status is not None:
                         self.status = status
 
                     # Check for user termination
                     if self.status < 0:
-                        self.errmsg = 'WARNING: premature termination by ' + str(iterfunct)
+                        self.errmsg = "WARNING: premature termination by " + str(
+                            iterfunct
+                        )
                         return
 
                     # If parameters were changed (grrr..) then re-tie
@@ -1056,18 +1100,29 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
             # Calculate the jacobian matrix
             self.status = 2
-            catch_msg = 'calling MPFIT_FDJAC2'
-            fjac = self.fdjac2(fcn, x, fvec, step, qulim, ulim, dside,
-                               epsfcn=epsfcn,
-                               autoderivative=autoderivative, dstep=dstep,
-                               functkw=functkw, ifree=ifree, xall=self.params)
+            catch_msg = "calling MPFIT_FDJAC2"
+            fjac = self.fdjac2(
+                fcn,
+                x,
+                fvec,
+                step,
+                qulim,
+                ulim,
+                dside,
+                epsfcn=epsfcn,
+                autoderivative=autoderivative,
+                dstep=dstep,
+                functkw=functkw,
+                ifree=ifree,
+                xall=self.params,
+            )
             if fjac is None:
-                self.errmsg = 'WARNING: premature termination by FDJAC2'
+                self.errmsg = "WARNING: premature termination by FDJAC2"
                 return
 
             # Determine if any of the parameters are pegged at the limits
             if qanylim:
-                catch_msg = 'zeroing derivatives of pegged parameters'
+                catch_msg = "zeroing derivatives of pegged parameters"
                 whlpeg = (numpy.nonzero(qllim & (x == llim)))[0]
                 nlpeg = len(whlpeg)
                 whupeg = (numpy.nonzero(qulim & (x == ulim)))[0]
@@ -1091,22 +1146,22 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
             # On the first iteration if "diag" is unspecified, scale
             # according to the norms of the columns of the initial jacobian
-            catch_msg = 'rescaling diagonal elements'
+            catch_msg = "rescaling diagonal elements"
             if self.niter == 1:
                 if (rescale == 0) or (len(diag) < n):
                     diag = wa2.copy()
-                    diag[diag == 0] = 1.
+                    diag[diag == 0] = 1.0
 
                 # On the first iteration, calculate the norm of the scaled x
                 # and initialize the step bound delta
                 wa3 = diag * x
                 xnorm = self.enorm(wa3)
                 delta = factor * xnorm
-                if delta == 0.:
+                if delta == 0.0:
                     delta = factor
 
             # Form (q transpose)*fvec and store the first n components in qtf
-            catch_msg = 'forming (q transpose)*fvec'
+            catch_msg = "forming (q transpose)*fvec"
             wa4 = fvec.copy()
             for j in range(n):
                 lj = ipvt[j]
@@ -1134,13 +1189,13 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
             # if ct GT 0 then goto, FAIL_OVERFLOW
 
             # Compute the norm of the scaled gradient
-            catch_msg = 'computing the scaled gradient'
-            gnorm = 0.
+            catch_msg = "computing the scaled gradient"
+            gnorm = 0.0
             if self.fnorm != 0:
                 for j in range(n):
                     l = ipvt[j]
                     if wa2[l] != 0:
-                        sum0 = sum(fjac[0:j + 1, j] * qtf[0:j + 1]) / self.fnorm
+                        sum0 = sum(fjac[0 : j + 1, j] * qtf[0 : j + 1]) / self.fnorm
                         gnorm = numpy.max([gnorm, numpy.abs(sum0 / wa2[l])])
 
             # Test for convergence of the gradient norm
@@ -1156,54 +1211,59 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 diag = numpy.choose(diag > wa2, (wa2, diag))
 
             # Beginning of the inner loop
-            while (1):
-
+            while 1:
                 # Determine the levenberg-marquardt parameter
-                catch_msg = 'calculating LM parameter (MPFIT_)'
-                [fjac, par, wa1, wa2] = self.lmpar(fjac, ipvt, diag, qtf,
-                                                   delta, wa1, wa2, par=par)
+                catch_msg = "calculating LM parameter (MPFIT_)"
+                [fjac, par, wa1, wa2] = self.lmpar(
+                    fjac, ipvt, diag, qtf, delta, wa1, wa2, par=par
+                )
                 # Store the direction p and x+p. Calculate the norm of p
                 wa1 = -wa1
 
                 if (qanylim == 0) and (qminmax == 0):
                     # No parameter limits, so just move to new position WA2
-                    alpha = 1.
+                    alpha = 1.0
                     wa2 = x + wa1
 
                 else:
-
                     # Respect the limits.  If a step were to go out of bounds, then
                     # we should take a step in the same direction but shorter distance.
                     # The step should take us right to the limit in that case.
-                    alpha = 1.
+                    alpha = 1.0
 
                     if qanylim:
                         # Do not allow any steps out of bounds
-                        catch_msg = 'checking for a step out of bounds'
+                        catch_msg = "checking for a step out of bounds"
                         if nlpeg > 0:
-                            wa1[whlpeg] = numpy.clip(wa1[whlpeg], 0., numpy.max(wa1))
+                            wa1[whlpeg] = numpy.clip(wa1[whlpeg], 0.0, numpy.max(wa1))
                         if nupeg > 0:
-                            wa1[whupeg] = numpy.clip(wa1[whupeg], numpy.min(wa1), 0.)
+                            wa1[whupeg] = numpy.clip(wa1[whupeg], numpy.min(wa1), 0.0)
 
                         dwa1 = numpy.abs(wa1) > machep
-                        whl = (numpy.nonzero(((dwa1 != 0.) & qllim) & ((x + wa1) < llim)))[0]
+                        whl = (
+                            numpy.nonzero(((dwa1 != 0.0) & qllim) & ((x + wa1) < llim))
+                        )[0]
                         if len(whl) > 0:
-                            t = ((llim[whl] - x[whl]) /
-                                 wa1[whl])
+                            t = (llim[whl] - x[whl]) / wa1[whl]
                             alpha = numpy.min([alpha, numpy.min(t)])
-                        whu = (numpy.nonzero(((dwa1 != 0.) & qulim) & ((x + wa1) > ulim)))[0]
+                        whu = (
+                            numpy.nonzero(((dwa1 != 0.0) & qulim) & ((x + wa1) > ulim))
+                        )[0]
                         if len(whu) > 0:
-                            t = ((ulim[whu] - x[whu]) /
-                                 wa1[whu])
+                            t = (ulim[whu] - x[whu]) / wa1[whu]
                             alpha = numpy.min([alpha, numpy.min(t)])
 
                     # Obey any max step values.
                     if qminmax:
                         nwa1 = wa1 * alpha
-                        whmax = (numpy.nonzero((qmax[ifree] != 0.) & (maxstep[ifree] > 0)))[0]
+                        whmax = (
+                            numpy.nonzero((qmax[ifree] != 0.0) & (maxstep[ifree] > 0))
+                        )[0]
                         if len(whmax) > 0:
-                            mrat = numpy.max(numpy.abs(nwa1[whmax]) /
-                                             numpy.abs(maxstep[ifree[whmax]]))
+                            mrat = numpy.max(
+                                numpy.abs(nwa1[whmax])
+                                / numpy.abs(maxstep[ifree[whmax]])
+                            )
                             if mrat > 1:
                                 alpha = alpha / mrat
 
@@ -1213,8 +1273,8 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
                     # Adjust the final output values.  If the step put us exactly
                     # on a boundary, make sure it is exact.
-                    sgnu = (ulim >= 0) * 2. - 1.
-                    sgnl = (llim >= 0) * 2. - 1.
+                    sgnu = (ulim >= 0) * 2.0 - 1.0
+                    sgnl = (llim >= 0) * 2.0 - 1.0
                     # Handles case of
                     #        ... nonzero *LIM ... ...zero * LIM
                     ulim1 = ulim * (1 - sgnu * machep) - (ulim == 0) * machep
@@ -1222,7 +1282,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     wh = (numpy.nonzero((qulim != 0) & (wa2 >= ulim1)))[0]
                     if len(wh) > 0:
                         wa2[wh] = ulim[wh]
-                    wh = (numpy.nonzero((qllim != 0.) & (wa2 <= llim1)))[0]
+                    wh = (numpy.nonzero((qllim != 0.0) & (wa2 <= llim1)))[0]
                     if len(wh) > 0:
                         wa2[wh] = llim[wh]
                 # endelse
@@ -1237,7 +1297,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
 
                 # Evaluate the function at x+p and calculate its norm
                 mperr = 0
-                catch_msg = 'calling ' + str(fcn)
+                catch_msg = "calling " + str(fcn)
                 [self.status, wa4] = self.call(fcn, self.params, functkw)
                 if self.status < 0:
                     self.errmsg = 'WARNING: premature termination by "' + fcn + '"'
@@ -1245,16 +1305,16 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 fnorm1 = self.enorm(wa4)
 
                 # Compute the scaled actual reduction
-                catch_msg = 'computing convergence criteria'
-                actred = -1.
+                catch_msg = "computing convergence criteria"
+                actred = -1.0
                 if (0.1 * fnorm1) < self.fnorm:
-                    actred = - (fnorm1 / self.fnorm) ** 2 + 1.
+                    actred = -((fnorm1 / self.fnorm) ** 2) + 1.0
 
                 # Compute the scaled predicted reduction and the scaled directional
                 # derivative
                 for j in range(n):
                     wa3[j] = 0
-                    wa3[0:j + 1] = wa3[0:j + 1] + fjac[0:j + 1, j] * wa1[ipvt[j]]
+                    wa3[0 : j + 1] = wa3[0 : j + 1] + fjac[0 : j + 1, j] * wa1[ipvt[j]]
 
                 # Remember, alpha is the fraction of the full LM step actually
                 # taken
@@ -1264,24 +1324,24 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 dirder = -(temp1 * temp1 + temp2 * temp2)
 
                 # Compute the ratio of the actual to the predicted reduction.
-                ratio = 0.
+                ratio = 0.0
                 if prered != 0:
                     ratio = actred / prered
 
                 # Update the step bound
                 if ratio <= 0.25:
                     if actred >= 0:
-                        temp = .5
+                        temp = 0.5
                     else:
-                        temp = .5 * dirder / (dirder + .5 * actred)
+                        temp = 0.5 * dirder / (dirder + 0.5 * actred)
                     if ((0.1 * fnorm1) >= self.fnorm) or (temp < 0.1):
                         temp = 0.1
                     delta = temp * numpy.min([delta, pnorm / 0.1])
                     par = par / temp
                 else:
                     if (par == 0) or (ratio >= 0.75):
-                        delta = pnorm / .5
-                        par = .5 * par
+                        delta = pnorm / 0.5
+                        par = 0.5 * par
 
                 # Test for successful iteration
                 if ratio >= 0.0001:
@@ -1294,13 +1354,20 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     self.niter = self.niter + 1
 
                 # Tests for convergence
-                if (numpy.abs(actred) <= ftol) and (prered <= ftol) \
-                        and (0.5 * ratio <= 1):
+                if (
+                    (numpy.abs(actred) <= ftol)
+                    and (prered <= ftol)
+                    and (0.5 * ratio <= 1)
+                ):
                     self.status = 1
                 if delta <= xtol * xnorm:
                     self.status = 2
-                if (numpy.abs(actred) <= ftol) and (prered <= ftol) \
-                        and (0.5 * ratio <= 1) and (self.status == 2):
+                if (
+                    (numpy.abs(actred) <= ftol)
+                    and (prered <= ftol)
+                    and (0.5 * ratio <= 1)
+                    and (self.status == 2)
+                ):
                     self.status = 3
                 if self.status != 0:
                     break
@@ -1308,8 +1375,11 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                 # Tests for termination and stringent tolerances
                 if self.niter >= maxiter:
                     self.status = 5
-                if (numpy.abs(actred) <= machep) and (prered <= machep) \
-                        and (0.5 * ratio <= 1):
+                if (
+                    (numpy.abs(actred) <= machep)
+                    and (prered <= machep)
+                    and (0.5 * ratio <= 1)
+                ):
                     self.status = 6
                 if delta <= machep * xnorm:
                     self.status = 7
@@ -1323,20 +1393,21 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     break
 
                 # Check for over/underflow
-                if ~numpy.all(numpy.isfinite(wa1) & numpy.isfinite(wa2) & \
-                              numpy.isfinite(x)) or ~numpy.isfinite(ratio):
-                    errmsg = ('''ERROR: parameter or function value(s) have become
-						'infinite; check model function for over- 'and underflow''')
+                if ~numpy.all(
+                    numpy.isfinite(wa1) & numpy.isfinite(wa2) & numpy.isfinite(x)
+                ) or ~numpy.isfinite(ratio):
+                    errmsg = """ERROR: parameter or function value(s) have become
+						'infinite; check model function for over- 'and underflow"""
                     self.status = -16
                     break
             # wh = where(finite(wa1) EQ 0 OR finite(wa2) EQ 0 OR finite(x) EQ 0, ct)
             # if ct GT 0 OR finite(ratio) EQ 0 then begin
 
             if self.status != 0:
-                break;
+                break
         # End of outer loop.
 
-        catch_msg = 'in the termination phase'
+        catch_msg = "in the termination phase"
         # Termination, either normal or user imposed.
         if len(self.params) == 0:
             return
@@ -1345,25 +1416,28 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         else:
             self.params[ifree] = x
         if (nprint > 0) and (self.status > 0):
-            catch_msg = 'calling ' + str(fcn)
+            catch_msg = "calling " + str(fcn)
             [status, fvec] = self.call(fcn, self.params, functkw)
-            catch_msg = 'in the termination phase'
+            catch_msg = "in the termination phase"
             self.fnorm = self.enorm(fvec)
 
         if (self.fnorm is not None) and (fnorm1 is not None):
             self.fnorm = numpy.max([self.fnorm, fnorm1])
-            self.fnorm = self.fnorm ** 2.
+            self.fnorm = self.fnorm**2.0
 
         self.covar = None
         self.perror = None
         # (very carefully) set the covariance matrix COVAR
-        if (self.status > 0) and (nocovar == 0) and (n is not None) \
-                and (fjac is not None) and (ipvt is not None):
+        if (
+            (self.status > 0)
+            and (nocovar == 0)
+            and (n is not None)
+            and (fjac is not None)
+            and (ipvt is not None)
+        ):
             sz = fjac.shape
-            if (n > 0) and (sz[0] >= n) and (sz[1] >= n) \
-                    and (len(ipvt) >= n):
-
-                catch_msg = 'computing the covariance matrix'
+            if (n > 0) and (sz[0] >= n) and (sz[1] >= n) and (len(ipvt) >= n):
+                catch_msg = "computing the covariance matrix"
                 cv = self.calc_covar(fjac[0:n, 0:n], ipvt[0:n])
                 cv.shape = [n, n]
                 nn = len(xall)
@@ -1375,7 +1449,7 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
                     self.covar[ifree, ifree[i]] = cv[:, i]
 
                 # Compute errors in parameters
-                catch_msg = 'computing parameter errors'
+                catch_msg = "computing parameter errors"
                 self.perror = numpy.zeros(nn, dtype=float)
                 d = numpy.diagonal(self.covar)
                 wh = (numpy.nonzero(d >= 0))[0]
@@ -1384,27 +1458,39 @@ def iterfunct(myfunct, p, iter, fnorm, functkw=None,
         return
 
     def __str__(self):
-        return {'params': self.params,
-                'niter': self.niter,
-                'params': self.params,
-                'covar': self.covar,
-                'perror': self.perror,
-                'status': self.status,
-                'debug': self.debug,
-                'errmsg': self.errmsg,
-                'nfev': self.nfev,
-                'damp': self.damp
-                # ,'machar':self.machar
-                }.__str__()
+        return {
+            "params": self.params,
+            "niter": self.niter,
+            "params": self.params,
+            "covar": self.covar,
+            "perror": self.perror,
+            "status": self.status,
+            "debug": self.debug,
+            "errmsg": self.errmsg,
+            "nfev": self.nfev,
+            "damp": self.damp,
+            # ,'machar':self.machar
+        }.__str__()
 
     # Default procedure to be called every iteration.  It simply prints
     # the parameter values.
-    def defiter(self, fcn, x, iter, fnorm=None, functkw=None,
-                quiet=0, iterstop=None, parinfo=None,
-                format=None, pformat='%.10g', dof=1):
+    def defiter(
+        self,
+        fcn,
+        x,
+        iter,
+        fnorm=None,
+        functkw=None,
+        quiet=0,
+        iterstop=None,
+        parinfo=None,
+        format=None,
+        pformat="%.10g",
+        dof=1,
+    ):
 
         if self.debug:
-            print('Entering defiter...')
+            print("Entering defiter...")
         if quiet:
             return
         if fnorm is None:
@@ -1413,40 +1499,47 @@ def defiter(self, fcn, x, iter, fnorm=None, functkw=None,
 
         # Determine which parameters to print
         nprint = len(x)
-        print("Iter ", ('%6i' % iter), "   CHI-SQUARE = ", ('%.10g' % fnorm), " DOF = ", ('%i' % dof))
+        print(
+            "Iter ",
+            ("%6i" % iter),
+            "   CHI-SQUARE = ",
+            ("%.10g" % fnorm),
+            " DOF = ",
+            ("%i" % dof),
+        )
         for i in range(nprint):
-            if (parinfo is not None) and ('parname' in parinfo[i]):
-                p = '   ' + parinfo[i]['parname'] + ' = '
+            if (parinfo is not None) and ("parname" in parinfo[i]):
+                p = "   " + parinfo[i]["parname"] + " = "
             else:
-                p = '   P' + str(i) + ' = '
-            if (parinfo is not None) and ('mpprint' in parinfo[i]):
-                iprint = parinfo[i]['mpprint']
+                p = "   P" + str(i) + " = "
+            if (parinfo is not None) and ("mpprint" in parinfo[i]):
+                iprint = parinfo[i]["mpprint"]
             else:
                 iprint = 1
             if iprint:
-                print(p + (pformat % x[i]) + '  ')
+                print(p + (pformat % x[i]) + "  ")
         return 0
 
     #  DO_ITERSTOP:
     #  if keyword_set(iterstop) then begin
-    #	  k = get_kbrd(0)
-    #	  if k EQ string(byte(7)) then begin
-    #		  message, 'WARNING: minimization not complete', /info
-    #		  print, 'Do you want to terminate this procedure? (y/n)', $
-    #			format='(A,$)'
-    #		  k = ''
-    #		  read, k
-    #		  if strupcase(strmid(k,0,1)) EQ 'Y' then begin
-    #			  message, 'WARNING: Procedure is terminating.', /info
-    #			  mperr = -1
-    #		  endif
-    #	  endif
+    # 	  k = get_kbrd(0)
+    # 	  if k EQ string(byte(7)) then begin
+    # 		  message, 'WARNING: minimization not complete', /info
+    # 		  print, 'Do you want to terminate this procedure? (y/n)', $
+    # 			format='(A,$)'
+    # 		  k = ''
+    # 		  read, k
+    # 		  if strupcase(strmid(k,0,1)) EQ 'Y' then begin
+    # 			  message, 'WARNING: Procedure is terminating.', /info
+    # 			  mperr = -1
+    # 		  endif
+    # 	  endif
     #  endif
 
     # Procedure to parse the parameter values in PARINFO, which is a list of dictionaries
-    def parinfo(self, parinfo=None, key='a', default=None, n=0):
+    def parinfo(self, parinfo=None, key="a", default=None, n=0):
         if self.debug:
-            print('Entering parinfo...')
+            print("Entering parinfo...")
         if (n == 0) and (parinfo is not None):
             n = len(parinfo)
         if n == 0:
@@ -1471,7 +1564,7 @@ def parinfo(self, parinfo=None, key='a', default=None, n=0):
     # derivatives or not.
     def call(self, fcn, x, functkw, fjac=None):
         if self.debug:
-            print('Entering call...')
+            print("Entering call...")
         if self.qanytied:
             x = self.tie(x, self.ptied)
         self.nfev = self.nfev + 1
@@ -1490,12 +1583,25 @@ def enorm(self, vec):
         ans = self.blas_enorm(vec)
         return ans
 
-    def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None,
-               epsfcn=None, autoderivative=1,
-               functkw=None, xall=None, ifree=None, dstep=None):
+    def fdjac2(
+        self,
+        fcn,
+        x,
+        fvec,
+        step=None,
+        ulimited=None,
+        ulimit=None,
+        dside=None,
+        epsfcn=None,
+        autoderivative=1,
+        functkw=None,
+        xall=None,
+        ifree=None,
+        dstep=None,
+    ):
 
         if self.debug:
-            print('Entering fdjac2...')
+            print("Entering fdjac2...")
         machep = self.machar.machep
         if epsfcn is None:
             epsfcn = machep
@@ -1504,7 +1610,7 @@ def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None
         if ifree is None:
             ifree = numpy.arange(len(xall))
         if step is None:
-            step = x * 0.
+            step = x * 0.0
         nall = len(xall)
 
         eps = numpy.sqrt(numpy.max([epsfcn, machep]))
@@ -1519,7 +1625,7 @@ def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None
             [status, fp] = self.call(fcn, xall, functkw, fjac=fjac)
 
             if len(fjac) != m * nall:
-                print('ERROR: Derivative matrix was not computed properly.')
+                print("ERROR: Derivative matrix was not computed properly.")
                 return None
 
             # This definition is consistent with CURVEFIT
@@ -1562,10 +1668,10 @@ def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None
         # varying ones)
         mask = dside[ifree] == -1
         if len(ulimited) > 0 and len(ulimit) > 0:
-            mask = (mask | ((ulimited != 0) & (x > ulimit - h)))
+            mask = mask | ((ulimited != 0) & (x > ulimit - h))
             wh = (numpy.nonzero(mask))[0]
             if len(wh) > 0:
-                h[wh] = - h[wh]
+                h[wh] = -h[wh]
         # Loop through parameters, computing the derivative for each
         for j in range(n):
             xp = xall.copy()
@@ -1592,82 +1698,82 @@ def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None
                 fjac[0:, j] = (fp - fm) / (2 * h[j])
         return fjac
 
-    #	 Original FORTRAN documentation
-    #	 **********
+    # 	 Original FORTRAN documentation
+    # 	 **********
     #
-    #	 subroutine qrfac
+    # 	 subroutine qrfac
     #
-    #	 this subroutine uses householder transformations with column
-    #	 pivoting (optional) to compute a qr factorization of the
-    #	 m by n matrix a. that is, qrfac determines an orthogonal
-    #	 matrix q, a permutation matrix p, and an upper trapezoidal
-    #	 matrix r with diagonal elements of nonincreasing magnitude,
-    #	 such that a*p = q*r. the householder transformation for
-    #	 column k, k = 1,2,...,min(m,n), is of the form
+    # 	 this subroutine uses householder transformations with column
+    # 	 pivoting (optional) to compute a qr factorization of the
+    # 	 m by n matrix a. that is, qrfac determines an orthogonal
+    # 	 matrix q, a permutation matrix p, and an upper trapezoidal
+    # 	 matrix r with diagonal elements of nonincreasing magnitude,
+    # 	 such that a*p = q*r. the householder transformation for
+    # 	 column k, k = 1,2,...,min(m,n), is of the form
     #
-    #						t
-    #		i - (1/u(k))*u*u
+    # 						t
+    # 		i - (1/u(k))*u*u
     #
-    #	 where u has zeros in the first k-1 positions. the form of
-    #	 this transformation and the method of pivoting first
-    #	 appeared in the corresponding linpack subroutine.
+    # 	 where u has zeros in the first k-1 positions. the form of
+    # 	 this transformation and the method of pivoting first
+    # 	 appeared in the corresponding linpack subroutine.
     #
-    #	 the subroutine statement is
+    # 	 the subroutine statement is
     #
-    #	subroutine qrfac(m,n,a,lda,pivot,ipvt,lipvt,rdiag,acnorm,wa)
+    # 	subroutine qrfac(m,n,a,lda,pivot,ipvt,lipvt,rdiag,acnorm,wa)
     #
-    #	 where
+    # 	 where
     #
-    #	m is a positive integer input variable set to the number
-    #	  of rows of a.
+    # 	m is a positive integer input variable set to the number
+    # 	  of rows of a.
     #
-    #	n is a positive integer input variable set to the number
-    #	  of columns of a.
+    # 	n is a positive integer input variable set to the number
+    # 	  of columns of a.
     #
-    #	a is an m by n array. on input a contains the matrix for
-    #	  which the qr factorization is to be computed. on output
-    #	  the strict upper trapezoidal part of a contains the strict
-    #	  upper trapezoidal part of r, and the lower trapezoidal
-    #	  part of a contains a factored form of q (the non-trivial
-    #	  elements of the u vectors described above).
+    # 	a is an m by n array. on input a contains the matrix for
+    # 	  which the qr factorization is to be computed. on output
+    # 	  the strict upper trapezoidal part of a contains the strict
+    # 	  upper trapezoidal part of r, and the lower trapezoidal
+    # 	  part of a contains a factored form of q (the non-trivial
+    # 	  elements of the u vectors described above).
     #
-    #	lda is a positive integer input variable not less than m
-    #	  which specifies the leading dimension of the array a.
+    # 	lda is a positive integer input variable not less than m
+    # 	  which specifies the leading dimension of the array a.
     #
-    #	pivot is a logical input variable. if pivot is set true,
-    #	  then column pivoting is enforced. if pivot is set false,
-    #	  then no column pivoting is done.
+    # 	pivot is a logical input variable. if pivot is set true,
+    # 	  then column pivoting is enforced. if pivot is set false,
+    # 	  then no column pivoting is done.
     #
-    #	ipvt is an integer output array of length lipvt. ipvt
-    #	  defines the permutation matrix p such that a*p = q*r.
-    #	  column j of p is column ipvt(j) of the identity matrix.
-    #	  if pivot is false, ipvt is not referenced.
+    # 	ipvt is an integer output array of length lipvt. ipvt
+    # 	  defines the permutation matrix p such that a*p = q*r.
+    # 	  column j of p is column ipvt(j) of the identity matrix.
+    # 	  if pivot is false, ipvt is not referenced.
     #
-    #	lipvt is a positive integer input variable. if pivot is false,
-    #	  then lipvt may be as small as 1. if pivot is true, then
-    #	  lipvt must be at least n.
+    # 	lipvt is a positive integer input variable. if pivot is false,
+    # 	  then lipvt may be as small as 1. if pivot is true, then
+    # 	  lipvt must be at least n.
     #
-    #	rdiag is an output array of length n which contains the
-    #	  diagonal elements of r.
+    # 	rdiag is an output array of length n which contains the
+    # 	  diagonal elements of r.
     #
-    #	acnorm is an output array of length n which contains the
-    #	  norms of the corresponding columns of the input matrix a.
-    #	  if this information is not needed, then acnorm can coincide
-    #	  with rdiag.
+    # 	acnorm is an output array of length n which contains the
+    # 	  norms of the corresponding columns of the input matrix a.
+    # 	  if this information is not needed, then acnorm can coincide
+    # 	  with rdiag.
     #
-    #	wa is a work array of length n. if pivot is false, then wa
-    #	  can coincide with rdiag.
+    # 	wa is a work array of length n. if pivot is false, then wa
+    # 	  can coincide with rdiag.
     #
-    #	 subprograms called
+    # 	 subprograms called
     #
-    #	minpack-supplied ... dpmpar,enorm
+    # 	minpack-supplied ... dpmpar,enorm
     #
-    #	fortran-supplied ... dmax1,dsqrt,min0
+    # 	fortran-supplied ... dmax1,dsqrt,min0
     #
-    #	 argonne national laboratory. minpack project. march 1980.
-    #	 burton s. garbow, kenneth e. hillstrom, jorge j. more
+    # 	 argonne national laboratory. minpack project. march 1980.
+    # 	 burton s. garbow, kenneth e. hillstrom, jorge j. more
     #
-    #	 **********
+    # 	 **********
     #
     # PIVOTING / PERMUTING:
     #
@@ -1688,21 +1794,21 @@ def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None
     # output.
     #
     #  EXAMPLE:  decompose the matrix [[9.,2.,6.],[4.,8.,7.]]
-    #	aa = [[9.,2.,6.],[4.,8.,7.]]
-    #	mpfit_qrfac, aa, aapvt, rdiag, aanorm
-    #	 IDL> print, aa
-    #		  1.81818*	 0.181818*	 0.545455*
-    #		 -8.54545+	  1.90160*	 0.432573*
-    #	 IDL> print, rdiag
-    #		 -11.0000+	 -7.48166+
+    # 	aa = [[9.,2.,6.],[4.,8.,7.]]
+    # 	mpfit_qrfac, aa, aapvt, rdiag, aanorm
+    # 	 IDL> print, aa
+    # 		  1.81818*	 0.181818*	 0.545455*
+    # 		 -8.54545+	  1.90160*	 0.432573*
+    # 	 IDL> print, rdiag
+    # 		 -11.0000+	 -7.48166+
     #
     # The components marked with a * are the components of the
     # reflectors, and those marked with a + are components of R.
     #
     # To reconstruct Q and R we proceed as follows.  First R.
-    #	r = fltarr(m, n)
-    #	for i = 0, n-1 do r(0:i,i) = aa(0:i,i)  # fill in lower diag
-    #	r(lindgen(n)*(m+1)) = rdiag
+    # 	r = fltarr(m, n)
+    # 	for i = 0, n-1 do r(0:i,i) = aa(0:i,i)  # fill in lower diag
+    # 	r(lindgen(n)*(m+1)) = rdiag
     #
     # Next, Q, which are composed from the reflectors.  Each reflector v
     # is taken from the upper trapezoid of aa, and converted to a matrix
@@ -1710,22 +1816,23 @@ def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None
     #
     #   hh = ident									# identity matrix
     #   for i = 0, n-1 do begin
-    #	v = aa(*,i) & if i GT 0 then v(0:i-1) = 0	# extract reflector
-    #	hh = hh # (ident - 2*(v # v)/total(v * v))  # generate matrix
+    # 	v = aa(*,i) & if i GT 0 then v(0:i-1) = 0	# extract reflector
+    # 	hh = hh # (ident - 2*(v # v)/total(v * v))  # generate matrix
     #   endfor
     #
     # Test the result:
-    #	IDL> print, hh # transpose(r)
-    #		  9.00000	  4.00000
-    #		  2.00000	  8.00000
-    #		  6.00000	  7.00000
+    # 	IDL> print, hh # transpose(r)
+    # 		  9.00000	  4.00000
+    # 		  2.00000	  8.00000
+    # 		  6.00000	  7.00000
     #
     # Note that it is usually never necessary to form the Q matrix
     # explicitly, and MPFIT does not.
 
     def qrfac(self, a, pivot=0):
 
-        if self.debug: print('Entering qrfac...')
+        if self.debug:
+            print("Entering qrfac...")
         machep = self.machar.machep
         sz = a.shape
         m = sz[0]
@@ -1756,8 +1863,8 @@ def qrfac(self, a, pivot=0):
                     # exchange occurs later, within the body of MPFIT, after the
                     # extraneous columns of the matrix have been shed.
                     if kmax != j:
-                        temp = ipvt[j];
-                        ipvt[j] = ipvt[kmax];
+                        temp = ipvt[j]
+                        ipvt[j] = ipvt[kmax]
                         ipvt[kmax] = temp
                         rdiag[kmax] = rdiag[j]
                         wa[kmax] = wa[j]
@@ -1793,95 +1900,97 @@ def qrfac(self, a, pivot=0):
                         a[j:, lk] = ajk - ajj * sum(ajk * ajj) / a[j, lj]
                         if (pivot != 0) and (rdiag[k] != 0):
                             temp = a[j, lk] / rdiag[k]
-                            rdiag[k] = rdiag[k] * numpy.sqrt(numpy.max([(1. - temp ** 2), 0.]))
+                            rdiag[k] = rdiag[k] * numpy.sqrt(
+                                numpy.max([(1.0 - temp**2), 0.0])
+                            )
                             temp = rdiag[k] / wa[k]
                             if (0.05 * temp * temp) <= machep:
-                                rdiag[k] = self.enorm(a[j + 1:, lk])
+                                rdiag[k] = self.enorm(a[j + 1 :, lk])
                                 wa[k] = rdiag[k]
             rdiag[j] = -ajnorm
         return [a, ipvt, rdiag, acnorm]
 
-    #	 Original FORTRAN documentation
-    #	 **********
+    # 	 Original FORTRAN documentation
+    # 	 **********
     #
-    #	 subroutine qrsolv
+    # 	 subroutine qrsolv
     #
-    #	 given an m by n matrix a, an n by n diagonal matrix d,
-    #	 and an m-vector b, the problem is to determine an x which
-    #	 solves the system
+    # 	 given an m by n matrix a, an n by n diagonal matrix d,
+    # 	 and an m-vector b, the problem is to determine an x which
+    # 	 solves the system
     #
-    #		   a*x = b ,	 d*x = 0 ,
+    # 		   a*x = b ,	 d*x = 0 ,
     #
-    #	 in the least squares sense.
+    # 	 in the least squares sense.
     #
-    #	 this subroutine completes the solution of the problem
-    #	 if it is provided with the necessary information from the
-    #	 factorization, with column pivoting, of a. that is, if
-    #	 a*p = q*r, where p is a permutation matrix, q has orthogonal
-    #	 columns, and r is an upper triangular matrix with diagonal
-    #	 elements of nonincreasing magnitude, then qrsolv expects
-    #	 the full upper triangle of r, the permutation matrix p,
-    #	 and the first n components of (q transpose)*b. the system
-    #	 a*x = b, d*x = 0, is then equivalent to
+    # 	 this subroutine completes the solution of the problem
+    # 	 if it is provided with the necessary information from the
+    # 	 factorization, with column pivoting, of a. that is, if
+    # 	 a*p = q*r, where p is a permutation matrix, q has orthogonal
+    # 	 columns, and r is an upper triangular matrix with diagonal
+    # 	 elements of nonincreasing magnitude, then qrsolv expects
+    # 	 the full upper triangle of r, the permutation matrix p,
+    # 	 and the first n components of (q transpose)*b. the system
+    # 	 a*x = b, d*x = 0, is then equivalent to
     #
-    #				  t	   t
-    #		   r*z = q *b ,  p *d*p*z = 0 ,
+    # 				  t	   t
+    # 		   r*z = q *b ,  p *d*p*z = 0 ,
     #
-    #	 where x = p*z. if this system does not have full rank,
-    #	 then a least squares solution is obtained. on output qrsolv
-    #	 also provides an upper triangular matrix s such that
+    # 	 where x = p*z. if this system does not have full rank,
+    # 	 then a least squares solution is obtained. on output qrsolv
+    # 	 also provides an upper triangular matrix s such that
     #
-    #			t   t			   t
-    #		   p *(a *a + d*d)*p = s *s .
+    # 			t   t			   t
+    # 		   p *(a *a + d*d)*p = s *s .
     #
-    #	 s is computed within qrsolv and may be of separate interest.
+    # 	 s is computed within qrsolv and may be of separate interest.
     #
-    #	 the subroutine statement is
+    # 	 the subroutine statement is
     #
-    #	   subroutine qrsolv(n,r,ldr,ipvt,diag,qtb,x,sdiag,wa)
+    # 	   subroutine qrsolv(n,r,ldr,ipvt,diag,qtb,x,sdiag,wa)
     #
-    #	 where
+    # 	 where
     #
-    #	   n is a positive integer input variable set to the order of r.
+    # 	   n is a positive integer input variable set to the order of r.
     #
-    #	   r is an n by n array. on input the full upper triangle
-    #		 must contain the full upper triangle of the matrix r.
-    #		 on output the full upper triangle is unaltered, and the
-    #		 strict lower triangle contains the strict upper triangle
-    #		 (transposed) of the upper triangular matrix s.
+    # 	   r is an n by n array. on input the full upper triangle
+    # 		 must contain the full upper triangle of the matrix r.
+    # 		 on output the full upper triangle is unaltered, and the
+    # 		 strict lower triangle contains the strict upper triangle
+    # 		 (transposed) of the upper triangular matrix s.
     #
-    #	   ldr is a positive integer input variable not less than n
-    #		 which specifies the leading dimension of the array r.
+    # 	   ldr is a positive integer input variable not less than n
+    # 		 which specifies the leading dimension of the array r.
     #
-    #	   ipvt is an integer input array of length n which defines the
-    #		 permutation matrix p such that a*p = q*r. column j of p
-    #		 is column ipvt(j) of the identity matrix.
+    # 	   ipvt is an integer input array of length n which defines the
+    # 		 permutation matrix p such that a*p = q*r. column j of p
+    # 		 is column ipvt(j) of the identity matrix.
     #
-    #	   diag is an input array of length n which must contain the
-    #		 diagonal elements of the matrix d.
+    # 	   diag is an input array of length n which must contain the
+    # 		 diagonal elements of the matrix d.
     #
-    #	   qtb is an input array of length n which must contain the first
-    #		 n elements of the vector (q transpose)*b.
+    # 	   qtb is an input array of length n which must contain the first
+    # 		 n elements of the vector (q transpose)*b.
     #
-    #	   x is an output array of length n which contains the least
-    #		 squares solution of the system a*x = b, d*x = 0.
+    # 	   x is an output array of length n which contains the least
+    # 		 squares solution of the system a*x = b, d*x = 0.
     #
-    #	   sdiag is an output array of length n which contains the
-    #		 diagonal elements of the upper triangular matrix s.
+    # 	   sdiag is an output array of length n which contains the
+    # 		 diagonal elements of the upper triangular matrix s.
     #
-    #	   wa is a work array of length n.
+    # 	   wa is a work array of length n.
     #
-    #	 subprograms called
+    # 	 subprograms called
     #
-    #	   fortran-supplied ... dabs,dsqrt
+    # 	   fortran-supplied ... dabs,dsqrt
     #
-    #	 argonne national laboratory. minpack project. march 1980.
-    #	 burton s. garbow, kenneth e. hillstrom, jorge j. more
+    # 	 argonne national laboratory. minpack project. march 1980.
+    # 	 burton s. garbow, kenneth e. hillstrom, jorge j. more
     #
 
     def qrsolv(self, r, ipvt, diag, qtb, sdiag):
         if self.debug:
-            print('Entering qrsolv...')
+            print("Entering qrsolv...")
         sz = r.shape
         m = sz[0]
         n = sz[1]
@@ -1906,17 +2015,17 @@ def qrsolv(self, r, ipvt, diag, qtb, sdiag):
             # single element of (q transpose)*b beyond the first n, which
             # is initially zero.
 
-            qtbpj = 0.
+            qtbpj = 0.0
             for k in range(j, n):
                 if sdiag[k] == 0:
                     break
                 if numpy.abs(r[k, k]) < numpy.abs(sdiag[k]):
                     cotan = r[k, k] / sdiag[k]
-                    sine = 0.5 / numpy.sqrt(.25 + .25 * cotan * cotan)
+                    sine = 0.5 / numpy.sqrt(0.25 + 0.25 * cotan * cotan)
                     cosine = sine * cotan
                 else:
                     tang = sdiag[k] / r[k, k]
-                    cosine = 0.5 / numpy.sqrt(.25 + .25 * tang * tang)
+                    cosine = 0.5 / numpy.sqrt(0.25 + 0.25 * tang * tang)
                     sine = cosine * tang
 
                 # Compute the modified diagonal element of r and the
@@ -1928,9 +2037,11 @@ def qrsolv(self, r, ipvt, diag, qtb, sdiag):
 
                 # Accumulate the transformation in the row of s
                 if n > k + 1:
-                    temp = cosine * r[k + 1:n, k] + sine * sdiag[k + 1:n]
-                    sdiag[k + 1:n] = -sine * r[k + 1:n, k] + cosine * sdiag[k + 1:n]
-                    r[k + 1:n, k] = temp
+                    temp = cosine * r[k + 1 : n, k] + sine * sdiag[k + 1 : n]
+                    sdiag[k + 1 : n] = (
+                        -sine * r[k + 1 : n, k] + cosine * sdiag[k + 1 : n]
+                    )
+                    r[k + 1 : n, k] = temp
             sdiag[j] = r[j, j]
             r[j, j] = x[j]
 
@@ -1946,111 +2057,111 @@ def qrsolv(self, r, ipvt, diag, qtb, sdiag):
             wa[nsing - 1] = wa[nsing - 1] / sdiag[nsing - 1]  # Degenerate case
             # *** Reverse loop ***
             for j in range(nsing - 2, -1, -1):
-                sum0 = sum(r[j + 1:nsing, j] * wa[j + 1:nsing])
+                sum0 = sum(r[j + 1 : nsing, j] * wa[j + 1 : nsing])
                 wa[j] = (wa[j] - sum0) / sdiag[j]
 
         # Permute the components of z back to components of x
         x[ipvt] = wa
         return (r, x, sdiag)
 
-    #	 Original FORTRAN documentation
+    # 	 Original FORTRAN documentation
     #
-    #	 subroutine lmpar
+    # 	 subroutine lmpar
     #
-    #	 given an m by n matrix a, an n by n nonsingular diagonal
-    #	 matrix d, an m-vector b, and a positive number delta,
-    #	 the problem is to determine a value for the parameter
-    #	 par such that if x solves the system
+    # 	 given an m by n matrix a, an n by n nonsingular diagonal
+    # 	 matrix d, an m-vector b, and a positive number delta,
+    # 	 the problem is to determine a value for the parameter
+    # 	 par such that if x solves the system
     #
-    #		a*x = b ,	 sqrt(par)*d*x = 0 ,
+    # 		a*x = b ,	 sqrt(par)*d*x = 0 ,
     #
-    #	 in the least squares sense, and dxnorm is the euclidean
-    #	 norm of d*x, then either par is zero and
+    # 	 in the least squares sense, and dxnorm is the euclidean
+    # 	 norm of d*x, then either par is zero and
     #
-    #		(dxnorm-delta) .le. 0.1*delta ,
+    # 		(dxnorm-delta) .le. 0.1*delta ,
     #
-    #	 or par is positive and
+    # 	 or par is positive and
     #
-    #		abs(dxnorm-delta) .le. 0.1*delta .
+    # 		abs(dxnorm-delta) .le. 0.1*delta .
     #
-    #	 this subroutine completes the solution of the problem
-    #	 if it is provided with the necessary information from the
-    #	 qr factorization, with column pivoting, of a. that is, if
-    #	 a*p = q*r, where p is a permutation matrix, q has orthogonal
-    #	 columns, and r is an upper triangular matrix with diagonal
-    #	 elements of nonincreasing magnitude, then lmpar expects
-    #	 the full upper triangle of r, the permutation matrix p,
-    #	 and the first n components of (q transpose)*b. on output
-    #	 lmpar also provides an upper triangular matrix s such that
+    # 	 this subroutine completes the solution of the problem
+    # 	 if it is provided with the necessary information from the
+    # 	 qr factorization, with column pivoting, of a. that is, if
+    # 	 a*p = q*r, where p is a permutation matrix, q has orthogonal
+    # 	 columns, and r is an upper triangular matrix with diagonal
+    # 	 elements of nonincreasing magnitude, then lmpar expects
+    # 	 the full upper triangle of r, the permutation matrix p,
+    # 	 and the first n components of (q transpose)*b. on output
+    # 	 lmpar also provides an upper triangular matrix s such that
     #
-    #		 t   t				   t
-    #		p *(a *a + par*d*d)*p = s *s .
+    # 		 t   t				   t
+    # 		p *(a *a + par*d*d)*p = s *s .
     #
-    #	 s is employed within lmpar and may be of separate interest.
+    # 	 s is employed within lmpar and may be of separate interest.
     #
-    #	 only a few iterations are generally needed for convergence
-    #	 of the algorithm. if, however, the limit of 10 iterations
-    #	 is reached, then the output par will contain the best
-    #	 value obtained so far.
+    # 	 only a few iterations are generally needed for convergence
+    # 	 of the algorithm. if, however, the limit of 10 iterations
+    # 	 is reached, then the output par will contain the best
+    # 	 value obtained so far.
     #
-    #	 the subroutine statement is
+    # 	 the subroutine statement is
     #
-    #	subroutine lmpar(n,r,ldr,ipvt,diag,qtb,delta,par,x,sdiag,
-    #					 wa1,wa2)
+    # 	subroutine lmpar(n,r,ldr,ipvt,diag,qtb,delta,par,x,sdiag,
+    # 					 wa1,wa2)
     #
-    #	 where
+    # 	 where
     #
-    #	n is a positive integer input variable set to the order of r.
+    # 	n is a positive integer input variable set to the order of r.
     #
-    #	r is an n by n array. on input the full upper triangle
-    #	  must contain the full upper triangle of the matrix r.
-    #	  on output the full upper triangle is unaltered, and the
-    #	  strict lower triangle contains the strict upper triangle
-    #	  (transposed) of the upper triangular matrix s.
+    # 	r is an n by n array. on input the full upper triangle
+    # 	  must contain the full upper triangle of the matrix r.
+    # 	  on output the full upper triangle is unaltered, and the
+    # 	  strict lower triangle contains the strict upper triangle
+    # 	  (transposed) of the upper triangular matrix s.
     #
-    #	ldr is a positive integer input variable not less than n
-    #	  which specifies the leading dimension of the array r.
+    # 	ldr is a positive integer input variable not less than n
+    # 	  which specifies the leading dimension of the array r.
     #
-    #	ipvt is an integer input array of length n which defines the
-    #	  permutation matrix p such that a*p = q*r. column j of p
-    #	  is column ipvt(j) of the identity matrix.
+    # 	ipvt is an integer input array of length n which defines the
+    # 	  permutation matrix p such that a*p = q*r. column j of p
+    # 	  is column ipvt(j) of the identity matrix.
     #
-    #	diag is an input array of length n which must contain the
-    #	  diagonal elements of the matrix d.
+    # 	diag is an input array of length n which must contain the
+    # 	  diagonal elements of the matrix d.
     #
-    #	qtb is an input array of length n which must contain the first
-    #	  n elements of the vector (q transpose)*b.
+    # 	qtb is an input array of length n which must contain the first
+    # 	  n elements of the vector (q transpose)*b.
     #
-    #	delta is a positive input variable which specifies an upper
-    #	  bound on the euclidean norm of d*x.
+    # 	delta is a positive input variable which specifies an upper
+    # 	  bound on the euclidean norm of d*x.
     #
-    #	par is a nonnegative variable. on input par contains an
-    #	  initial estimate of the levenberg-marquardt parameter.
-    #	  on output par contains the final estimate.
+    # 	par is a nonnegative variable. on input par contains an
+    # 	  initial estimate of the levenberg-marquardt parameter.
+    # 	  on output par contains the final estimate.
     #
-    #	x is an output array of length n which contains the least
-    #	  squares solution of the system a*x = b, sqrt(par)*d*x = 0,
-    #	  for the output par.
+    # 	x is an output array of length n which contains the least
+    # 	  squares solution of the system a*x = b, sqrt(par)*d*x = 0,
+    # 	  for the output par.
     #
-    #	sdiag is an output array of length n which contains the
-    #	  diagonal elements of the upper triangular matrix s.
+    # 	sdiag is an output array of length n which contains the
+    # 	  diagonal elements of the upper triangular matrix s.
     #
-    #	wa1 and wa2 are work arrays of length n.
+    # 	wa1 and wa2 are work arrays of length n.
     #
-    #	 subprograms called
+    # 	 subprograms called
     #
-    #	minpack-supplied ... dpmpar,enorm,qrsolv
+    # 	minpack-supplied ... dpmpar,enorm,qrsolv
     #
-    #	fortran-supplied ... dabs,dmax1,dmin1,dsqrt
+    # 	fortran-supplied ... dabs,dmax1,dmin1,dsqrt
     #
-    #	 argonne national laboratory. minpack project. march 1980.
-    #	 burton s. garbow, kenneth e. hillstrom, jorge j. more
+    # 	 argonne national laboratory. minpack project. march 1980.
+    # 	 burton s. garbow, kenneth e. hillstrom, jorge j. more
     #
 
     def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
 
         if self.debug:
-            print('Entering lmpar...')
+            print("Entering lmpar...")
         dwarf = self.machar.minnum
         machep = self.machar.machep
         sz = r.shape
@@ -2065,7 +2176,7 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
         wh = (numpy.nonzero(numpy.abs(numpy.diagonal(r)) < rthresh))[0]
         if len(wh) > 0:
             nsing = wh[0]
-            wa1[wh[0]:] = 0
+            wa1[wh[0] :] = 0
         if nsing >= 1:
             # *** Reverse loop ***
             for j in range(nsing - 1, -1, -1):
@@ -2083,13 +2194,13 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
         dxnorm = self.enorm(wa2)
         fp = dxnorm - delta
         if fp <= 0.1 * delta:
-            return [r, 0., x, sdiag]
+            return [r, 0.0, x, sdiag]
 
         # If the jacobian is not rank deficient, the newton step provides a
         # lower bound, parl, for the zero of the function.  Otherwise set
         # this bound to zero.
 
-        parl = 0.
+        parl = 0.0
         if nsing >= n:
             wa1 = diag[ipvt] * wa2[ipvt] / dxnorm
             wa1[0] = wa1[0] / r[0, 0]  # Degenerate case
@@ -2102,7 +2213,7 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
 
         # Calculate an upper bound, paru, for the zero of the function
         for j in range(n):
-            sum0 = sum(r[0:j + 1, j] * qtb[0:j + 1])
+            sum0 = sum(r[0 : j + 1, j] * qtb[0 : j + 1])
             wa1[j] = sum0 / diag[ipvt[j]]
         gnorm = self.enorm(wa1)
         paru = gnorm / delta
@@ -2118,7 +2229,7 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
             par = gnorm / dxnorm
 
         # Beginning of an interation
-        while (1):
+        while 1:
             iter = iter + 1
 
             # Evaluate the function at the current value of par
@@ -2132,17 +2243,19 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
             temp = fp
             fp = dxnorm - delta
 
-            if (numpy.abs(fp) <= 0.1 * delta) or \
-                    ((parl == 0) and (fp <= temp) and (temp < 0)) or \
-                    (iter == 10):
-                break;
+            if (
+                (numpy.abs(fp) <= 0.1 * delta)
+                or ((parl == 0) and (fp <= temp < 0))
+                or (iter == 10)
+            ):
+                break
 
             # Compute the newton correction
             wa1 = diag[ipvt] * wa2[ipvt] / dxnorm
 
             for j in range(n - 1):
                 wa1[j] = wa1[j] / sdiag[j]
-                wa1[j + 1:n] = wa1[j + 1:n] - r[j + 1:n, j] * wa1[j]
+                wa1[j + 1 : n] = wa1[j + 1 : n] - r[j + 1 : n, j] * wa1[j]
             wa1[n - 1] = wa1[n - 1] / sdiag[n - 1]  # Degenerate case
 
             temp = self.enorm(wa1)
@@ -2165,94 +2278,94 @@ def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None):
     # Procedure to tie one parameter to another.
     def tie(self, p, ptied=None):
         if self.debug:
-            print('Entering tie...')
+            print("Entering tie...")
         if ptied is None:
             return
         for i in range(len(ptied)):
-            if ptied[i] == '':
+            if ptied[i] == "":
                 continue
-            cmd = 'p[' + str(i) + '] = ' + ptied[i]
+            cmd = "p[" + str(i) + "] = " + ptied[i]
             exec(cmd)
         return p
 
-    #	 Original FORTRAN documentation
-    #	 **********
+    # 	 Original FORTRAN documentation
+    # 	 **********
     #
-    #	 subroutine covar
+    # 	 subroutine covar
     #
-    #	 given an m by n matrix a, the problem is to determine
-    #	 the covariance matrix corresponding to a, defined as
+    # 	 given an m by n matrix a, the problem is to determine
+    # 	 the covariance matrix corresponding to a, defined as
     #
-    #					t
-    #		   inverse(a *a) .
+    # 					t
+    # 		   inverse(a *a) .
     #
-    #	 this subroutine completes the solution of the problem
-    #	 if it is provided with the necessary information from the
-    #	 qr factorization, with column pivoting, of a. that is, if
-    #	 a*p = q*r, where p is a permutation matrix, q has orthogonal
-    #	 columns, and r is an upper triangular matrix with diagonal
-    #	 elements of nonincreasing magnitude, then covar expects
-    #	 the full upper triangle of r and the permutation matrix p.
-    #	 the covariance matrix is then computed as
+    # 	 this subroutine completes the solution of the problem
+    # 	 if it is provided with the necessary information from the
+    # 	 qr factorization, with column pivoting, of a. that is, if
+    # 	 a*p = q*r, where p is a permutation matrix, q has orthogonal
+    # 	 columns, and r is an upper triangular matrix with diagonal
+    # 	 elements of nonincreasing magnitude, then covar expects
+    # 	 the full upper triangle of r and the permutation matrix p.
+    # 	 the covariance matrix is then computed as
     #
-    #					  t	 t
-    #		   p*inverse(r *r)*p  .
+    # 					  t	 t
+    # 		   p*inverse(r *r)*p  .
     #
-    #	 if a is nearly rank deficient, it may be desirable to compute
-    #	 the covariance matrix corresponding to the linearly independent
-    #	 columns of a. to define the numerical rank of a, covar uses
-    #	 the tolerance tol. if l is the largest integer such that
+    # 	 if a is nearly rank deficient, it may be desirable to compute
+    # 	 the covariance matrix corresponding to the linearly independent
+    # 	 columns of a. to define the numerical rank of a, covar uses
+    # 	 the tolerance tol. if l is the largest integer such that
     #
-    #		   abs(r(l,l)) .gt. tol*abs(r(1,1)) ,
+    # 		   abs(r(l,l)) .gt. tol*abs(r(1,1)) ,
     #
-    #	 then covar computes the covariance matrix corresponding to
-    #	 the first l columns of r. for k greater than l, column
-    #	 and row ipvt(k) of the covariance matrix are set to zero.
+    # 	 then covar computes the covariance matrix corresponding to
+    # 	 the first l columns of r. for k greater than l, column
+    # 	 and row ipvt(k) of the covariance matrix are set to zero.
     #
-    #	 the subroutine statement is
+    # 	 the subroutine statement is
     #
-    #	   subroutine covar(n,r,ldr,ipvt,tol,wa)
+    # 	   subroutine covar(n,r,ldr,ipvt,tol,wa)
     #
-    #	 where
+    # 	 where
     #
-    #	   n is a positive integer input variable set to the order of r.
+    # 	   n is a positive integer input variable set to the order of r.
     #
-    #	   r is an n by n array. on input the full upper triangle must
-    #		 contain the full upper triangle of the matrix r. on output
-    #		 r contains the square symmetric covariance matrix.
+    # 	   r is an n by n array. on input the full upper triangle must
+    # 		 contain the full upper triangle of the matrix r. on output
+    # 		 r contains the square symmetric covariance matrix.
     #
-    #	   ldr is a positive integer input variable not less than n
-    #		 which specifies the leading dimension of the array r.
+    # 	   ldr is a positive integer input variable not less than n
+    # 		 which specifies the leading dimension of the array r.
     #
-    #	   ipvt is an integer input array of length n which defines the
-    #		 permutation matrix p such that a*p = q*r. column j of p
-    #		 is column ipvt(j) of the identity matrix.
+    # 	   ipvt is an integer input array of length n which defines the
+    # 		 permutation matrix p such that a*p = q*r. column j of p
+    # 		 is column ipvt(j) of the identity matrix.
     #
-    #	   tol is a nonnegative input variable used to define the
-    #		 numerical rank of a in the manner described above.
+    # 	   tol is a nonnegative input variable used to define the
+    # 		 numerical rank of a in the manner described above.
     #
-    #	   wa is a work array of length n.
+    # 	   wa is a work array of length n.
     #
-    #	 subprograms called
+    # 	 subprograms called
     #
-    #	   fortran-supplied ... dabs
+    # 	   fortran-supplied ... dabs
     #
-    #	 argonne national laboratory. minpack project. august 1980.
-    #	 burton s. garbow, kenneth e. hillstrom, jorge j. more
+    # 	 argonne national laboratory. minpack project. august 1980.
+    # 	 burton s. garbow, kenneth e. hillstrom, jorge j. more
     #
-    #	 **********
+    # 	 **********
 
-    def calc_covar(self, rr, ipvt=None, tol=1.e-14):
+    def calc_covar(self, rr, ipvt=None, tol=1.0e-14):
 
         if self.debug:
-            print('Entering calc_covar...')
+            print("Entering calc_covar...")
         if numpy.ndim(rr) != 2:
-            print('ERROR: r must be a two-dimensional matrix')
+            print("ERROR: r must be a two-dimensional matrix")
             return -1
         s = rr.shape
         n = s[0]
         if s[0] != s[1]:
-            print('ERROR: r must be a square matrix')
+            print("ERROR: r must be a square matrix")
             return -1
 
         if ipvt is None:
@@ -2266,11 +2379,11 @@ def calc_covar(self, rr, ipvt=None, tol=1.e-14):
         for k in range(n):
             if numpy.abs(r[k, k]) <= tolr:
                 break
-            r[k, k] = 1. / r[k, k]
+            r[k, k] = 1.0 / r[k, k]
             for j in range(k):
                 temp = r[k, k] * r[j, k]
-                r[j, k] = 0.
-                r[0:j + 1, k] = r[0:j + 1, k] - temp * r[0:j + 1, j]
+                r[j, k] = 0.0
+                r[0 : j + 1, k] = r[0 : j + 1, k] - temp * r[0 : j + 1, j]
             l = k
 
         # Form the full upper triangle of the inverse of (r transpose)*r
@@ -2279,9 +2392,9 @@ def calc_covar(self, rr, ipvt=None, tol=1.e-14):
             for k in range(l + 1):
                 for j in range(k):
                     temp = r[j, k]
-                    r[0:j + 1, j] = r[0:j + 1, j] + temp * r[0:j + 1, k]
+                    r[0 : j + 1, j] = r[0 : j + 1, j] + temp * r[0 : j + 1, k]
                 temp = r[k, k]
-                r[0:k + 1, k] = temp * r[0:k + 1, k]
+                r[0 : k + 1, k] = temp * r[0 : k + 1, k]
 
         # For the full lower triangle of the covariance matrix
         # in the strict lower triangle or and in wa
@@ -2291,7 +2404,7 @@ def calc_covar(self, rr, ipvt=None, tol=1.e-14):
             sing = j > l
             for i in range(j + 1):
                 if sing:
-                    r[i, j] = 0.
+                    r[i, j] = 0.0
                 ii = ipvt[i]
                 if ii > jj:
                     r[ii, jj] = r[i, j]
@@ -2301,7 +2414,7 @@ def calc_covar(self, rr, ipvt=None, tol=1.e-14):
 
         # Symmetrize the covariance matrix in r
         for j in range(n):
-            r[0:j + 1, j] = r[j, 0:j + 1]
+            r[0 : j + 1, j] = r[j, 0 : j + 1]
             r[j, j] = wa[j]
 
         return r
diff --git a/imap_l3_processing/maps/quality_flags.py b/imap_l3_processing/maps/quality_flags.py
index 2e097d5cf..3d2b295ba 100644
--- a/imap_l3_processing/maps/quality_flags.py
+++ b/imap_l3_processing/maps/quality_flags.py
@@ -1,4 +1,4 @@
-from imap_processing.quality_flags import FlagNameMixin, CommonFlags
+from imap_processing.quality_flags import CommonFlags, FlagNameMixin
 
 
 class MapL3Flags(FlagNameMixin):
diff --git a/imap_l3_processing/maps/rectangular_survival_probability.py b/imap_l3_processing/maps/rectangular_survival_probability.py
index c942284c8..39691e921 100644
--- a/imap_l3_processing/maps/rectangular_survival_probability.py
+++ b/imap_l3_processing/maps/rectangular_survival_probability.py
@@ -1,20 +1,29 @@
 import numpy as np
 import xarray as xr
-from imap_processing.ena_maps.ena_maps import RectangularSkyMap, PointingSet
+from imap_processing.ena_maps.ena_maps import PointingSet, RectangularSkyMap
 from imap_processing.ena_maps.utils.coordinates import CoordNames
-from imap_processing.ena_maps.utils.corrections import apply_compton_getting_correction, \
-    add_spacecraft_position_and_velocity_to_pset, calculate_ram_mask
+from imap_processing.ena_maps.utils.corrections import (
+    add_spacecraft_position_and_velocity_to_pset,
+    apply_compton_getting_correction,
+    calculate_ram_mask,
+)
 from imap_processing.spice.geometry import SpiceFrame
 
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.maps.map_descriptors import Sensor, SpinPhase
-from imap_l3_processing.maps.map_models import GlowsL3eRectangularMapInputData, InputRectangularPointingSet
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
+)
 from imap_l3_processing.maps.quality_flags import MapL3Flags
 
 
-def interpolate_angular_data_to_nearest_neighbor(input_azimuths: np.array, glows_azimuths: np.array,
-                                                 glows_data: np.array) -> np.array:
-    expanded_az = np.concatenate([glows_azimuths - 360, glows_azimuths, glows_azimuths + 360])
+def interpolate_angular_data_to_nearest_neighbor(
+    input_azimuths: np.array, glows_azimuths: np.array, glows_data: np.array
+) -> np.array:
+    expanded_az = np.concatenate(
+        [glows_azimuths - 360, glows_azimuths, glows_azimuths + 360]
+    )
     expanded_glows_data = np.concatenate([glows_data, glows_data, glows_data], axis=1)
     sort = np.argsort(expanded_az)
     sorted_az = expanded_az[sort]
@@ -24,21 +33,31 @@ def interpolate_angular_data_to_nearest_neighbor(input_azimuths: np.array, glows
 
 
 class RectangularSurvivalProbabilityPointingSet(PointingSet):
-    def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spin_phase: SpinPhase,
-                 glows_dataset: GlowsL3eRectangularMapInputData, energies: np.ndarray, cg_corrected: bool = False):
+    def __init__(
+        self,
+        l1c_dataset: InputRectangularPointingSet,
+        sensor: Sensor,
+        spin_phase: SpinPhase,
+        glows_dataset: GlowsL3eRectangularMapInputData,
+        energies: np.ndarray,
+        cg_corrected: bool = False,
+    ):
         num_spin_angle_bins = l1c_dataset.exposure_times.shape[-1]
 
         deg_spacing = 360 / num_spin_angle_bins
         half_bin_width = deg_spacing / 2
 
-        self.azimuths = np.linspace(0, 360, num_spin_angle_bins,
-                                    endpoint=False) + half_bin_width
+        self.azimuths = (
+            np.linspace(0, 360, num_spin_angle_bins, endpoint=False) + half_bin_width
+        )
 
         sensor_angle = Sensor.get_sensor_angle(sensor)
         self.elevations = np.repeat(sensor_angle, num_spin_angle_bins)
 
         hae_az_el_points = xr.DataArray(
-            np.column_stack([l1c_dataset.hae_longitude[0], l1c_dataset.hae_latitude[0]]),
+            np.column_stack(
+                [l1c_dataset.hae_longitude[0], l1c_dataset.hae_latitude[0]]
+            ),
             dims=[CoordNames.GENERIC_PIXEL.value, CoordNames.AZ_EL_VECTOR.value],
         )
 
@@ -46,36 +65,48 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
 
         if l1c_dataset.epoch_delta is not None:
             repointing_midpoint = l1c_dataset.epoch_j2000 + l1c_dataset.epoch_delta / 2
-        elif l1c_dataset.pointing_start_met is not None and l1c_dataset.pointing_end_met is not None:
-            pointing_duration = l1c_dataset.pointing_end_met - l1c_dataset.pointing_start_met
-            repointing_midpoint = l1c_dataset.epoch_j2000 + (1e9 * pointing_duration / 2)
-
-        initial_dataset = xr.Dataset({},
-                                     coords={
-                                         CoordNames.TIME.value: l1c_dataset.epoch_j2000,
-                                         CoordNames.ENERGY_ULTRA_L1C.value: l1c_dataset.esa_energy_step,
-                                         CoordNames.AZIMUTH_L1C.value: self.azimuths,
-                                     })
-
-        initial_dataset['epoch'] = l1c_dataset.epoch_j2000
-        initial_dataset['epoch_delta'] = l1c_dataset.epoch_delta
-        initial_dataset['hae_longitude'] = xr.DataArray(
+        elif (
+            l1c_dataset.pointing_start_met is not None
+            and l1c_dataset.pointing_end_met is not None
+        ):
+            pointing_duration = (
+                l1c_dataset.pointing_end_met - l1c_dataset.pointing_start_met
+            )
+            repointing_midpoint = l1c_dataset.epoch_j2000 + (
+                1e9 * pointing_duration / 2
+            )
+
+        initial_dataset = xr.Dataset(
+            {},
+            coords={
+                CoordNames.TIME.value: l1c_dataset.epoch_j2000,
+                CoordNames.ENERGY_ULTRA_L1C.value: l1c_dataset.esa_energy_step,
+                CoordNames.AZIMUTH_L1C.value: self.azimuths,
+            },
+        )
+
+        initial_dataset["epoch"] = l1c_dataset.epoch_j2000
+        initial_dataset["epoch_delta"] = l1c_dataset.epoch_delta
+        initial_dataset["hae_longitude"] = xr.DataArray(
             l1c_dataset.hae_longitude,
             dims=[CoordNames.TIME.value, CoordNames.AZIMUTH_L1C.value],
         )
-        initial_dataset['hae_latitude'] = xr.DataArray(
+        initial_dataset["hae_latitude"] = xr.DataArray(
             l1c_dataset.hae_latitude,
             dims=[CoordNames.TIME.value, CoordNames.AZIMUTH_L1C.value],
         )
-        initial_dataset['pointing_start_met'] = l1c_dataset.pointing_start_met
-        initial_dataset['pointing_end_met'] = l1c_dataset.pointing_end_met
+        initial_dataset["pointing_start_met"] = l1c_dataset.pointing_start_met
+        initial_dataset["pointing_end_met"] = l1c_dataset.pointing_end_met
 
         if sensor == Sensor.Hi90 or sensor == Sensor.Hi45:
-            initial_dataset.attrs['Logical_source'] = 'imap_hi'
+            initial_dataset.attrs["Logical_source"] = "imap_hi"
         elif sensor in (Sensor.Lo75, Sensor.Lo90, Sensor.Lo105):
-            initial_dataset.attrs['Logical_source'] = 'imap_lo'
+            initial_dataset.attrs["Logical_source"] = "imap_lo"
         else:
-            raise ValueError("Unexpected sensor when performing survival probability correction!", sensor.name)
+            raise ValueError(
+                "Unexpected sensor when performing survival probability correction!",
+                sensor.name,
+            )
         dataset = add_spacecraft_position_and_velocity_to_pset(initial_dataset)
 
         if cg_corrected:
@@ -83,21 +114,35 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
 
             dataset = apply_compton_getting_correction(
                 dataset,
-                xr.DataArray(energy_in_ev, dims=[CoordNames.ENERGY_ULTRA_L1C.value])
+                xr.DataArray(energy_in_ev, dims=[CoordNames.ENERGY_ULTRA_L1C.value]),
             )
             self.az_el_points = xr.DataArray(
-                np.stack([dataset['hae_longitude'].values[0], dataset['hae_latitude'].values[0]], axis=2),
-                dims=[CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.GENERIC_PIXEL.value, CoordNames.AZ_EL_VECTOR.value],
+                np.stack(
+                    [
+                        dataset["hae_longitude"].values[0],
+                        dataset["hae_latitude"].values[0],
+                    ],
+                    axis=2,
+                ),
+                dims=[
+                    CoordNames.ENERGY_ULTRA_L1C.value,
+                    CoordNames.GENERIC_PIXEL.value,
+                    CoordNames.AZ_EL_VECTOR.value,
+                ],
             )
 
             spacecraft_frame_energies_in_kev = dataset["energy_sc"].values / 1000.0
 
             exposure = np.full_like(l1c_dataset.exposure_times, np.nan)
-            for (energy_i, spin_angle_i), cg_energy in np.ndenumerate(dataset['energy_sc'].values[0]):
+            for (energy_i, spin_angle_i), cg_energy in np.ndenumerate(
+                dataset["energy_sc"].values[0]
+            ):
                 distance_to_bins = np.abs(np.log10(energy_in_ev) - np.log10(cg_energy))
                 closest_energy_bin_index = np.argmin(distance_to_bins)
 
-                exposure[0, energy_i, spin_angle_i] = l1c_dataset.exposure_times[0, closest_energy_bin_index, spin_angle_i]
+                exposure[0, energy_i, spin_angle_i] = l1c_dataset.exposure_times[
+                    0, closest_energy_bin_index, spin_angle_i
+                ]
         else:
             self.az_el_points = hae_az_el_points
             exposure = l1c_dataset.exposure_times
@@ -110,8 +155,13 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
             dataset["directional_mask"] = ~dataset["ram_mask"]
 
         if cg_corrected:
-            sp_interpolated_to_pset_angles = interpolate_angular_data_to_nearest_neighbor(
-                self.azimuths, glows_dataset.spin_angle, glows_dataset.probability_of_survival[0])
+            sp_interpolated_to_pset_angles = (
+                interpolate_angular_data_to_nearest_neighbor(
+                    self.azimuths,
+                    glows_dataset.spin_angle,
+                    glows_dataset.probability_of_survival[0],
+                )
+            )
             log_sc_frame_energies = np.log10(spacecraft_frame_energies_in_kev[0])
 
             sp_final = np.empty((1, len(energies), num_spin_angle_bins))
@@ -119,11 +169,13 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
                 sp_final[0, :, spin_angle_index] = np.interp(
                     log_sc_frame_energies[:, spin_angle_index],
                     np.log10(glows_dataset.energy),
-                    sp_interpolated_to_pset_angles[:, spin_angle_index]
+                    sp_interpolated_to_pset_angles[:, spin_angle_index],
                 )
         else:
             glows_spin_bin_count = len(glows_dataset.spin_angle)
-            sp_interpolated_to_hi_energies = np.empty(shape=(len(energies), glows_spin_bin_count))
+            sp_interpolated_to_hi_energies = np.empty(
+                shape=(len(energies), glows_spin_bin_count)
+            )
             for spin_angle_index in range(glows_spin_bin_count):
                 sp_interpolated_to_hi_energies[:, spin_angle_index] = np.interp(
                     np.log10(energies),
@@ -150,7 +202,7 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
                 CoordNames.AZIMUTH_L1C.value,
-            ]
+            ],
         )
         dataset["exposure"] = xr.DataArray(
             exposure,
@@ -170,24 +222,28 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
                 CoordNames.AZIMUTH_L1C.value,
             ],
         )
-        nominal_alpha_proton_flag_set = flag_value & GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO != 0
+        nominal_alpha_proton_flag_set = (
+            flag_value & GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO != 0
+        )
         dataset["nominal_alpha_proton_ratio_flag"] = xr.DataArray(
             exposure * nominal_alpha_proton_flag_set,
             dims=[
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
                 CoordNames.AZIMUTH_L1C.value,
-            ]
+            ],
         )
 
-        persisted_last_point_flag_set = flag_value & GlowsL3Flags.PERSISTED_LAST_POINT != 0
+        persisted_last_point_flag_set = (
+            flag_value & GlowsL3Flags.PERSISTED_LAST_POINT != 0
+        )
         dataset["persisted_last_point_flag"] = xr.DataArray(
             exposure * persisted_last_point_flag_set,
             dims=[
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
                 CoordNames.AZIMUTH_L1C.value,
-            ]
+            ],
         )
 
         frame = SpiceFrame.IMAP_HAE
@@ -195,22 +251,44 @@ def __init__(self, l1c_dataset: InputRectangularPointingSet, sensor: Sensor, spi
 
 
 class RectangularSurvivalProbabilitySkyMap(RectangularSkyMap):
-    def __init__(self, survival_probability_pointing_sets: list[RectangularSurvivalProbabilityPointingSet],
-                 spacing_degree: float, spice_frame: SpiceFrame):
+    def __init__(
+        self,
+        survival_probability_pointing_sets: list[
+            RectangularSurvivalProbabilityPointingSet
+        ],
+        spacing_degree: float,
+        spice_frame: SpiceFrame,
+    ):
         super().__init__(spacing_degree, spice_frame)
-        for  sp_pset in survival_probability_pointing_sets:
-            value_keys = ["survival_probability_times_exposure", "exposure", "predicted_ephemeris_flag", "nominal_alpha_proton_ratio_flag", "persisted_last_point_flag"]
-            self.project_pset_values_to_map(sp_pset, value_keys, pset_valid_mask=sp_pset.data["directional_mask"])
+        for sp_pset in survival_probability_pointing_sets:
+            value_keys = [
+                "survival_probability_times_exposure",
+                "exposure",
+                "predicted_ephemeris_flag",
+                "nominal_alpha_proton_ratio_flag",
+                "persisted_last_point_flag",
+            ]
+            self.project_pset_values_to_map(
+                sp_pset, value_keys, pset_valid_mask=sp_pset.data["directional_mask"]
+            )
 
         predicted_ephemeris_set = self.data_1d["predicted_ephemeris_flag"] != 0
-        nominal_alpha_proton_ratio_set = self.data_1d["nominal_alpha_proton_ratio_flag"] != 0
+        nominal_alpha_proton_ratio_set = (
+            self.data_1d["nominal_alpha_proton_ratio_flag"] != 0
+        )
         persisted_last_point_set = self.data_1d["persisted_last_point_flag"] != 0
-        quality_flags_1d = (predicted_ephemeris_set * MapL3Flags.PREDICTIVE_EPHEMERIS) | \
-                           (nominal_alpha_proton_ratio_set * MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO) | \
-                           (persisted_last_point_set * MapL3Flags.PERSISTED_LAST_POINT)
-
-        self.data_1d = xr.Dataset({
-            "exposure_weighted_survival_probabilities": self.data_1d["survival_probability_times_exposure"] /
-                                                        self.data_1d["exposure"],
-            "quality_flags": quality_flags_1d,
-        })
+        quality_flags_1d = (
+            (predicted_ephemeris_set * MapL3Flags.PREDICTIVE_EPHEMERIS)
+            | (nominal_alpha_proton_ratio_set * MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO)
+            | (persisted_last_point_set * MapL3Flags.PERSISTED_LAST_POINT)
+        )
+
+        self.data_1d = xr.Dataset(
+            {
+                "exposure_weighted_survival_probabilities": self.data_1d[
+                    "survival_probability_times_exposure"
+                ]
+                / self.data_1d["exposure"],
+                "quality_flags": quality_flags_1d,
+            }
+        )
diff --git a/imap_l3_processing/maps/sp_map_initializer.py b/imap_l3_processing/maps/sp_map_initializer.py
index 3694bc1fa..be1ea8042 100644
--- a/imap_l3_processing/maps/sp_map_initializer.py
+++ b/imap_l3_processing/maps/sp_map_initializer.py
@@ -5,9 +5,14 @@
 from imap_data_access import ScienceFilePath
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.maps.map_descriptors import MapDescriptorParts, parse_map_descriptor, \
-    get_duration_from_map_descriptor, map_descriptor_parts_to_string, Sensor
-from imap_l3_processing.maps.map_initializer import PossibleMapToProduce, MapInitializer
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    Sensor,
+    get_duration_from_map_descriptor,
+    map_descriptor_parts_to_string,
+    parse_map_descriptor,
+)
+from imap_l3_processing.maps.map_initializer import MapInitializer, PossibleMapToProduce
 from imap_l3_processing.models import InputMetadata, VersionMap
 
 logger = logging.getLogger(__name__)
@@ -16,10 +21,14 @@
 class SPMapInitializer(MapInitializer):
     def __init__(self, instrument: str, l2_query_results: list[dict[str, str]]):
         super().__init__(instrument)
-        self.input_maps_by_descriptor = self.get_latest_version_by_descriptor_and_start_date(l2_query_results)
+        self.input_maps_by_descriptor = (
+            self.get_latest_version_by_descriptor_and_start_date(l2_query_results)
+        )
 
     @abstractmethod
-    def _collect_glows_psets_by_repoint(self, descriptor: MapDescriptorParts) -> dict[int, str]:
+    def _collect_glows_psets_by_repoint(
+        self, descriptor: MapDescriptorParts
+    ) -> dict[int, str]:
         raise NotImplementedError()
 
     @abstractmethod
@@ -27,25 +36,41 @@ def _get_ancillary_files(self) -> list[str]:
         raise NotImplementedError()
 
     @abstractmethod
-    def _get_l2_dependencies(self, descriptor: MapDescriptorParts) -> list[MapDescriptorParts]:
+    def _get_l2_dependencies(
+        self, descriptor: MapDescriptorParts
+    ) -> list[MapDescriptorParts]:
         raise NotImplementedError()
 
-    def get_maps_that_can_be_produced(self, descriptor: str) -> list[PossibleMapToProduce]:
+    def get_maps_that_can_be_produced(
+        self, descriptor: str
+    ) -> list[PossibleMapToProduce]:
         l3_descriptor_parts = parse_map_descriptor(descriptor)
         map_duration = get_duration_from_map_descriptor(l3_descriptor_parts)
 
-        glows_file_by_repointing = self._collect_glows_psets_by_repoint(l3_descriptor_parts)
+        glows_file_by_repointing = self._collect_glows_psets_by_repoint(
+            l3_descriptor_parts
+        )
 
         if len(glows_file_by_repointing) == 0:
-            logger.info(f"No GLOWS data available for descriptor {descriptor}, no maps will be produced!")
+            logger.info(
+                f"No GLOWS data available for descriptor {descriptor}, no maps will be produced!"
+            )
 
         l2_descriptors = self._get_l2_dependencies(l3_descriptor_parts)
-        l2_descriptor_strs = [map_descriptor_parts_to_string(parts) for parts in l2_descriptors]
-        assert l2_descriptor_strs, f"Expected at least one L2 dependency for l3 map: {descriptor}"
-
-        possible_start_dates = set(self.input_maps_by_descriptor[l2_descriptor_strs[0]].keys())
+        l2_descriptor_strs = [
+            map_descriptor_parts_to_string(parts) for parts in l2_descriptors
+        ]
+        assert l2_descriptor_strs, (
+            f"Expected at least one L2 dependency for l3 map: {descriptor}"
+        )
+
+        possible_start_dates = set(
+            self.input_maps_by_descriptor[l2_descriptor_strs[0]].keys()
+        )
         for l2_descriptor in l2_descriptor_strs[1:]:
-            possible_start_dates.intersection_update(self.input_maps_by_descriptor[l2_descriptor].keys())
+            possible_start_dates.intersection_update(
+                self.input_maps_by_descriptor[l2_descriptor].keys()
+            )
 
         possible_maps = []
         for str_start_date in sorted(list(possible_start_dates)):
@@ -53,10 +78,16 @@ def get_maps_that_can_be_produced(self, descriptor: str) -> list[PossibleMapToPr
 
             l2_file_paths = []
             for l2_descriptor in l2_descriptor_strs:
-                l2_file_paths.append(self.input_maps_by_descriptor[l2_descriptor][str_start_date])
+                l2_file_paths.append(
+                    self.input_maps_by_descriptor[l2_descriptor][str_start_date]
+                )
 
             if l3_descriptor_parts.sensor in [Sensor.UltraCombined, Sensor.HiCombined]:
-                l1c_names = [l1c for l2 in l2_file_paths for l1c in self.get_l1c_parents_from_map(l2)]
+                l1c_names = [
+                    l1c
+                    for l2 in l2_file_paths
+                    for l1c in self.get_l1c_parents_from_map(l2)
+                ]
 
             else:
                 l1c_names = self.get_l1c_parents_from_map(l2_file_paths[0])
@@ -76,19 +107,34 @@ def get_maps_that_can_be_produced(self, descriptor: str) -> list[PossibleMapToPr
 
             l1c_repointings = [ScienceFilePath(l1c).repointing for l1c in l1c_names]
 
-            glows_files = [glows_file_by_repointing[repoint] for repoint in l1c_repointings if
-                           repoint in glows_file_by_repointing]
+            glows_files = [
+                glows_file_by_repointing[repoint]
+                for repoint in l1c_repointings
+                if repoint in glows_file_by_repointing
+            ]
 
             if len(glows_files) == 0:
-                logger.info(f"No GLOWS data available for l3 map: {descriptor} with start date: {str_start_date}. Skipping")
+                logger.info(
+                    f"No GLOWS data available for l3 map: {descriptor} with start date: {str_start_date}. Skipping"
+                )
                 continue
 
-            input_metadata = InputMetadata(instrument=self.instrument, data_level='l3', start_date=start_date,
-                                           end_date=start_date + map_duration, version=VersionMap({}, Version(None, 1)),
-                                           descriptor=descriptor)
+            input_metadata = InputMetadata(
+                instrument=self.instrument,
+                data_level="l3",
+                start_date=start_date,
+                end_date=start_date + map_duration,
+                version=VersionMap({}, Version(None, 1)),
+                descriptor=descriptor,
+            )
             possible_map_to_produce = PossibleMapToProduce(
-                input_files=set(l2_file_paths + glows_files + l1c_names + self._get_ancillary_files()),
-                input_metadata=input_metadata
+                input_files=set(
+                    l2_file_paths
+                    + glows_files
+                    + l1c_names
+                    + self._get_ancillary_files()
+                ),
+                input_metadata=input_metadata,
             )
             possible_maps.append(possible_map_to_produce)
         return possible_maps
diff --git a/imap_l3_processing/maps/spectral_fit.py b/imap_l3_processing/maps/spectral_fit.py
index 039321083..d41384781 100644
--- a/imap_l3_processing/maps/spectral_fit.py
+++ b/imap_l3_processing/maps/spectral_fit.py
@@ -3,18 +3,21 @@
 import numpy as np
 import scipy
 
-from imap_l3_processing.maps.map_models import SpectralIndexMapData, IntensityMapData, \
-    calculate_datetime_weighted_average
+from imap_l3_processing.maps.map_models import (
+    IntensityMapData,
+    SpectralIndexMapData,
+    calculate_datetime_weighted_average,
+)
 from imap_l3_processing.maps.mpfit import mpfit
 
 
-def calculate_spectral_index_for_multiple_ranges(intensity_data: IntensityMapData, map_ranges) -> SpectralIndexMapData:
+def calculate_spectral_index_for_multiple_ranges(
+    intensity_data: IntensityMapData, map_ranges
+) -> SpectralIndexMapData:
     spectral_maps = []
     for start, end in map_ranges:
         spectral_maps.append(
-            fit_spectral_index_map(
-                slice_energy_range(intensity_data, start, end)
-            )
+            fit_spectral_index_map(slice_energy_range(intensity_data, start, end))
         )
     return SpectralIndexMapData(
         epoch=intensity_data.epoch,
@@ -48,31 +51,37 @@ def calculate_spectral_index_for_multiple_ranges(intensity_data: IntensityMapDat
         ena_spectral_index_chisq=np.concat(
             [m.ena_spectral_index_chisq for m in spectral_maps], axis=1
         ),
-        quality_flags=np.concat(
-            [m.quality_flags for m in spectral_maps], axis=1
-        ),
+        quality_flags=np.concat([m.quality_flags for m in spectral_maps], axis=1),
     )
 
 
-def slice_energy_range(data: IntensityMapData, start: float, end: float) -> IntensityMapData:
+def slice_energy_range(
+    data: IntensityMapData, start: float, end: float
+) -> IntensityMapData:
     energy_mask = np.logical_and(data.energy >= start, data.energy < end)
-    return dataclasses.replace(data,
-                               energy=data.energy[energy_mask],
-                               energy_delta_plus=data.energy_delta_plus[energy_mask],
-                               energy_delta_minus=data.energy_delta_minus[energy_mask],
-                               energy_label=data.energy_label[energy_mask],
-                               exposure_factor=data.exposure_factor[:, energy_mask],
-                               obs_date=data.obs_date[:, energy_mask],
-                               obs_date_range=data.obs_date_range[:, energy_mask],
-                               ena_intensity=data.ena_intensity[:, energy_mask],
-                               ena_intensity_sys_err=data.ena_intensity_sys_err[:, energy_mask],
-                               ena_intensity_stat_uncert=data.ena_intensity_stat_uncert[:, energy_mask],
-                               quality_flags=data.quality_flags[:, energy_mask],
-                               )
-
-def slice_energy_range_by_bin(data: IntensityMapData, start_bin_id: int, end_bin_id: int) -> IntensityMapData:
+    return dataclasses.replace(
+        data,
+        energy=data.energy[energy_mask],
+        energy_delta_plus=data.energy_delta_plus[energy_mask],
+        energy_delta_minus=data.energy_delta_minus[energy_mask],
+        energy_label=data.energy_label[energy_mask],
+        exposure_factor=data.exposure_factor[:, energy_mask],
+        obs_date=data.obs_date[:, energy_mask],
+        obs_date_range=data.obs_date_range[:, energy_mask],
+        ena_intensity=data.ena_intensity[:, energy_mask],
+        ena_intensity_sys_err=data.ena_intensity_sys_err[:, energy_mask],
+        ena_intensity_stat_uncert=data.ena_intensity_stat_uncert[:, energy_mask],
+        quality_flags=data.quality_flags[:, energy_mask],
+    )
+
+
+def slice_energy_range_by_bin(
+    data: IntensityMapData, start_bin_id: int, end_bin_id: int
+) -> IntensityMapData:
     max_bin_id = 1 + len(data.energy)
-    bin_ids_in_range =  (1 <= start_bin_id <= max_bin_id) and (1 <= end_bin_id <= max_bin_id)
+    bin_ids_in_range = (1 <= start_bin_id <= max_bin_id) and (
+        1 <= end_bin_id <= max_bin_id
+    )
     bin_ids_valid = bin_ids_in_range and start_bin_id < end_bin_id
     if not bin_ids_valid:
         raise ValueError(f"Error slicing energy bins {start_bin_id},{end_bin_id}")
@@ -93,6 +102,7 @@ def slice_energy_range_by_bin(data: IntensityMapData, start_bin_id: int, end_bin
         quality_flags=data.quality_flags[:, energy_slice],
     )
 
+
 def fit_spectral_index_map(intensity_data: IntensityMapData) -> SpectralIndexMapData:
     fluxes = intensity_data.ena_intensity
     uncertainty = intensity_data.ena_intensity_stat_uncert
@@ -103,20 +113,36 @@ def fit_spectral_index_map(intensity_data: IntensityMapData) -> SpectralIndexMap
     mean_energy = np.sqrt(min_energy * max_energy)
     new_energy_label = f"{min_energy} - {max_energy}"
 
-    output_scalar_coefficients, output_scalar_errors, output_gammas, output_gamma_errors, chisq = fit_arrays_to_power_law(fluxes, uncertainty, energy)
-    mean_obs_date = calculate_datetime_weighted_average(intensity_data.obs_date,
-                                                        weights=intensity_data.exposure_factor,
-                                                        axis=1, keepdims=True)
-    mean_obs_date_range = np.ma.average(intensity_data.obs_date_range, weights=intensity_data.exposure_factor,
-                                        axis=1,
-                                        keepdims=True)
-    total_exposure_factor = np.sum(intensity_data.exposure_factor, axis=1, keepdims=True)
+    (
+        output_scalar_coefficients,
+        output_scalar_errors,
+        output_gammas,
+        output_gamma_errors,
+        chisq,
+    ) = fit_arrays_to_power_law(fluxes, uncertainty, energy)
+    mean_obs_date = calculate_datetime_weighted_average(
+        intensity_data.obs_date,
+        weights=intensity_data.exposure_factor,
+        axis=1,
+        keepdims=True,
+    )
+    mean_obs_date_range = np.ma.average(
+        intensity_data.obs_date_range,
+        weights=intensity_data.exposure_factor,
+        axis=1,
+        keepdims=True,
+    )
+    total_exposure_factor = np.sum(
+        intensity_data.exposure_factor, axis=1, keepdims=True
+    )
     positive_gammas = output_gammas < 0
     output_gammas[positive_gammas] = np.nan
     output_scalar_coefficients[positive_gammas] = np.nan
     output_gamma_errors[positive_gammas] = np.nan
     chisq[positive_gammas] = np.nan
-    quality_flags = np.bitwise_or.reduce(intensity_data.quality_flags, axis=1, keepdims=True)
+    quality_flags = np.bitwise_or.reduce(
+        intensity_data.quality_flags, axis=1, keepdims=True
+    )
 
     return SpectralIndexMapData(
         epoch=intensity_data.epoch,
@@ -136,15 +162,16 @@ def fit_spectral_index_map(intensity_data: IntensityMapData) -> SpectralIndexMap
         ena_spectral_index_scalar_coefficient=output_scalar_coefficients,
         ena_spectral_index_scalar_coefficient_stat_uncert=output_scalar_errors,
         ena_spectral_index_chisq=chisq,
-        quality_flags=quality_flags
+        quality_flags=quality_flags,
     )
 
 
-def fit_arrays_to_power_law(fluxes: np.ndarray, uncertainties: np.ndarray, energy: np.ndarray) -> tuple[
-    np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+def fit_arrays_to_power_law(
+    fluxes: np.ndarray, uncertainties: np.ndarray, energy: np.ndarray
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     par_info = [
-        {'limits': [0.0, 1000.0]},
-        {'limits': [0.0, 1000.0]},
+        {"limits": [0.0, 1000.0]},
+        {"limits": [0.0, 1000.0]},
     ]
 
     output_shape = (fluxes.shape[0], 1, *fluxes.shape[2:])
@@ -168,15 +195,24 @@ def fit_arrays_to_power_law(fluxes: np.ndarray, uncertainties: np.ndarray, energ
             flux = intensity[:, i]
             uncertainty = unc[:, i]
             flux_and_variance_are_zero = np.equal(flux, 0) & np.equal(uncertainty, 0)
-            flux_or_error_is_invalid = np.isnan(flux) | np.isnan(uncertainty) | flux_and_variance_are_zero
+            flux_or_error_is_invalid = (
+                np.isnan(flux) | np.isnan(uncertainty) | flux_and_variance_are_zero
+            )
             flux = flux[~flux_or_error_is_invalid]
             uncertainty = uncertainty[~flux_or_error_is_invalid]
             filtered_energy = energy[~flux_or_error_is_invalid]
 
             positive_flux = flux > 0
             if np.count_nonzero(positive_flux) > 1:
-                keywords = {'xval': filtered_energy, 'yval': flux, 'errval': uncertainty}
-                result = scipy.stats.linregress(np.log10(filtered_energy[positive_flux]), np.log10(flux[positive_flux]))
+                keywords = {
+                    "xval": filtered_energy,
+                    "yval": flux,
+                    "errval": uncertainty,
+                }
+                result = scipy.stats.linregress(
+                    np.log10(filtered_energy[positive_flux]),
+                    np.log10(flux[positive_flux]),
+                )
 
                 initial_parameters = (10**result.intercept, -result.slope)
 
@@ -192,17 +228,27 @@ def fit_arrays_to_power_law(fluxes: np.ndarray, uncertainties: np.ndarray, energ
                     chisqs[i] = fit.fnorm / fit.dof
         output_gammas[epoch, 0] = gammas.reshape(fluxes.shape[2:])
         output_gamma_errors[epoch, 0] = gamma_errors.reshape(fluxes.shape[2:])
-        output_scalar_coefficients[epoch, 0] = scalar_coefficients.reshape(fluxes.shape[2:])
-        output_scalar_coefficients_errors[epoch, 0] = scalar_coefficient_errors.reshape(fluxes.shape[2:])
+        output_scalar_coefficients[epoch, 0] = scalar_coefficients.reshape(
+            fluxes.shape[2:]
+        )
+        output_scalar_coefficients_errors[epoch, 0] = scalar_coefficient_errors.reshape(
+            fluxes.shape[2:]
+        )
         output_chisqs[epoch, 0] = chisqs.reshape(fluxes.shape[2:])
-    return output_scalar_coefficients, output_scalar_coefficients_errors, output_gammas, output_gamma_errors, output_chisqs
+    return (
+        output_scalar_coefficients,
+        output_scalar_coefficients_errors,
+        output_gammas,
+        output_gamma_errors,
+        output_chisqs,
+    )
 
 
 def power_law(params, **kwargs):
     A, B = params
-    x = kwargs['xval']
-    y = kwargs['yval']
-    err = kwargs['errval']
+    x = kwargs["xval"]
+    y = kwargs["yval"]
+    err = kwargs["errval"]
 
     model = A * np.power(x, -B)
 
diff --git a/imap_l3_processing/maps/survival_probability_processing.py b/imap_l3_processing/maps/survival_probability_processing.py
index ebcc2808f..bbf7021bd 100644
--- a/imap_l3_processing/maps/survival_probability_processing.py
+++ b/imap_l3_processing/maps/survival_probability_processing.py
@@ -1,59 +1,95 @@
-from typing import Union, TypeVar
+from typing import TypeVar
 
 import numpy as np
 from imap_processing.spice.geometry import SpiceFrame
 
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiLoL3SurvivalDependencies,
+)
 from imap_l3_processing.maps.map_descriptors import ReferenceFrame
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData, IntensityMapData, \
-    InputRectangularPointingSet, GlowsL3eRectangularMapInputData
-from imap_l3_processing.maps.rectangular_survival_probability import RectangularSurvivalProbabilityPointingSet, \
-    RectangularSurvivalProbabilitySkyMap
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
+    IntensityMapData,
+    RectangularIntensityMapData,
+)
+from imap_l3_processing.maps.rectangular_survival_probability import (
+    RectangularSurvivalProbabilityPointingSet,
+    RectangularSurvivalProbabilitySkyMap,
+)
 from imap_l3_processing.ultra.models import UltraGlowsL3eData, UltraL1CPSet
 
-L1CPointingSet = TypeVar("L1CPointingSet", bound=Union[InputRectangularPointingSet, UltraL1CPSet])
-GlowsL3eData = TypeVar("GlowsL3eData", bound=Union[GlowsL3eRectangularMapInputData, UltraGlowsL3eData])
+L1CPointingSet = TypeVar(
+    "L1CPointingSet", bound=InputRectangularPointingSet | UltraL1CPSet
+)
+GlowsL3eData = TypeVar(
+    "GlowsL3eData", bound=GlowsL3eRectangularMapInputData | UltraGlowsL3eData
+)
 
 
-def combine_glows_l3e_with_l1c_pointing(glows_l3e_data: list[GlowsL3eData], l1c_data: list[L1CPointingSet]) -> list[
-    tuple[L1CPointingSet, GlowsL3eData]]:
+def combine_glows_l3e_with_l1c_pointing(
+    glows_l3e_data: list[GlowsL3eData], l1c_data: list[L1CPointingSet]
+) -> list[tuple[L1CPointingSet, GlowsL3eData]]:
     l1c_by_repoint = {l1c.repointing: l1c for l1c in l1c_data}
     glows_by_repoint = {l3e.repointing: l3e for l3e in glows_l3e_data}
 
-    return [(l1c_by_repoint[repoint], glows_by_repoint[repoint])
-            for repoint in l1c_by_repoint.keys() if repoint in glows_by_repoint]
+    return [
+        (l1c_by_repoint[repoint], glows_by_repoint[repoint])
+        for repoint in l1c_by_repoint
+        if repoint in glows_by_repoint
+    ]
+
 
 def filter_bad_days(input_psets: list[L1CPointingSet]) -> list[L1CPointingSet]:
     return [pset for pset in input_psets if not np.all(pset.exposure_times == 0.0)]
 
-def process_survival_probabilities(survival_probabilities_dependencies: HiLoL3SurvivalDependencies,
-                                   spice_frame_name: SpiceFrame, cg_corrected: bool = None) \
-        -> RectangularIntensityMapData:
+
+def process_survival_probabilities(
+    survival_probabilities_dependencies: HiLoL3SurvivalDependencies,
+    spice_frame_name: SpiceFrame,
+    cg_corrected: bool = None,
+) -> RectangularIntensityMapData:
     l2_descriptor_parts = survival_probabilities_dependencies.l2_map_descriptor_parts
 
     l1c_data = filter_bad_days(survival_probabilities_dependencies.l1c_data)
 
-    combined_glows = combine_glows_l3e_with_l1c_pointing(survival_probabilities_dependencies.glows_l3e_data, l1c_data)
+    combined_glows = combine_glows_l3e_with_l1c_pointing(
+        survival_probabilities_dependencies.glows_l3e_data, l1c_data
+    )
     pointing_sets = []
 
     if cg_corrected is None:
-        cg_corrected = l2_descriptor_parts.reference_frame == ReferenceFrame.Heliospheric
+        cg_corrected = (
+            l2_descriptor_parts.reference_frame == ReferenceFrame.Heliospheric
+        )
 
     for l1c, glows_l3e in combined_glows:
-        pointing_sets.append(RectangularSurvivalProbabilityPointingSet(
-            l1c, l2_descriptor_parts.sensor, l2_descriptor_parts.spin_phase, glows_l3e,
-            survival_probabilities_dependencies.l2_data.intensity_map_data.energy, cg_corrected=cg_corrected))
+        pointing_sets.append(
+            RectangularSurvivalProbabilityPointingSet(
+                l1c,
+                l2_descriptor_parts.sensor,
+                l2_descriptor_parts.spin_phase,
+                glows_l3e,
+                survival_probabilities_dependencies.l2_data.intensity_map_data.energy,
+                cg_corrected=cg_corrected,
+            )
+        )
     assert len(pointing_sets) > 0
 
-    survival_sky_map = RectangularSurvivalProbabilitySkyMap(pointing_sets, int(l2_descriptor_parts.grid),
-                                                            spice_frame_name)
+    survival_sky_map = RectangularSurvivalProbabilitySkyMap(
+        pointing_sets, int(l2_descriptor_parts.grid), spice_frame_name
+    )
 
     survival_dataset = survival_sky_map.to_dataset()
 
     input_data = survival_probabilities_dependencies.l2_data.intensity_map_data
-    survival_probabilities = survival_dataset["exposure_weighted_survival_probabilities"].values
+    survival_probabilities = survival_dataset[
+        "exposure_weighted_survival_probabilities"
+    ].values
     survival_corrected_intensity = input_data.ena_intensity / survival_probabilities
-    corrected_stat_uncert = input_data.ena_intensity_stat_uncert / survival_probabilities
+    corrected_stat_uncert = (
+        input_data.ena_intensity_stat_uncert / survival_probabilities
+    )
     corrected_sys_err = input_data.ena_intensity_sys_err / survival_probabilities
 
     map_data = RectangularIntensityMapData(
@@ -80,12 +116,22 @@ def process_survival_probabilities(survival_probabilities_dependencies: HiLoL3Su
     )
 
     if input_data.bg_intensity is not None:
-        map_data.intensity_map_data.bg_intensity = input_data.bg_intensity / survival_probabilities
-        map_data.intensity_map_data.bg_intensity_sys_err = input_data.bg_intensity_sys_err / survival_probabilities
-        map_data.intensity_map_data.bg_intensity_stat_uncert = input_data.bg_intensity_stat_uncert / survival_probabilities
+        map_data.intensity_map_data.bg_intensity = (
+            input_data.bg_intensity / survival_probabilities
+        )
+        map_data.intensity_map_data.bg_intensity_sys_err = (
+            input_data.bg_intensity_sys_err / survival_probabilities
+        )
+        map_data.intensity_map_data.bg_intensity_stat_uncert = (
+            input_data.bg_intensity_stat_uncert / survival_probabilities
+        )
 
     if input_data.ena_intensity_sys_err_minus is not None:
-        map_data.intensity_map_data.ena_intensity_sys_err_minus = input_data.ena_intensity_sys_err_minus / survival_probabilities
-        map_data.intensity_map_data.ena_intensity_sys_err_plus = input_data.ena_intensity_sys_err_plus / survival_probabilities
+        map_data.intensity_map_data.ena_intensity_sys_err_minus = (
+            input_data.ena_intensity_sys_err_minus / survival_probabilities
+        )
+        map_data.intensity_map_data.ena_intensity_sys_err_plus = (
+            input_data.ena_intensity_sys_err_plus / survival_probabilities
+        )
 
     return map_data
diff --git a/imap_l3_processing/models.py b/imap_l3_processing/models.py
index 6b1b1b47d..c2408b7ca 100644
--- a/imap_l3_processing/models.py
+++ b/imap_l3_processing/models.py
@@ -4,7 +4,7 @@
 from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
-from typing import Union, Optional, TypeVar, Generic
+from typing import Generic, TypeVar
 
 import numpy as np
 from imap_data_access.file_validation import Version
@@ -29,10 +29,10 @@ class InputMetadata:
     instrument: str
     data_level: str
     start_date: datetime
-    end_date: Optional[datetime]
+    end_date: datetime | None
     version: VersionMap
     descriptor: str = ""
-    repointing: Optional[int] = None
+    repointing: int | None = None
 
     @property
     def logical_source(self):
@@ -42,13 +42,14 @@ def logical_source(self):
 @dataclass
 class DataProductVariable:
     name: str
-    value: Union[np.ndarray, int, float, list[str], list[int]]
+    value: np.ndarray | int | float | list[str] | list[int]
     cdf_data_type: ctypes.c_long = None
     record_varying: bool = None
 
 
 D = TypeVar("D")
 
+
 @dataclass
 class DataProduct(abc.ABC, Generic[D]):
     input_metadata: InputMetadata
@@ -57,13 +58,15 @@ class DataProduct(abc.ABC, Generic[D]):
 
     @abc.abstractmethod
     def to_data_product_variables(self) -> list[DataProductVariable]:
-        raise NotImplemented
+        raise NotImplementedError
 
     def add_paths_to_parents(self, paths: list[Path]):
         self.add_filenames_to_parents([path.name for path in paths])
 
     def add_filenames_to_parents(self, filenames):
-        self.parent_file_names.extend(filename for filename in filenames if filename not in self.parent_file_names)
+        self.parent_file_names.extend(
+            filename for filename in filenames if filename not in self.parent_file_names
+        )
 
 
 @dataclass
@@ -71,7 +74,9 @@ class MagData:
     epoch: np.ndarray
     mag_data: np.ndarray
 
-    def rebin_to(self, epoch: np.ndarray[float], epoch_delta: np.ndarray[float]) -> np.ndarray[float]:
+    def rebin_to(
+        self, epoch: np.ndarray[float], epoch_delta: np.ndarray[float]
+    ) -> np.ndarray[float]:
         return rebin(self.epoch, self.mag_data, epoch, epoch_delta)
 
 
diff --git a/imap_l3_processing/pitch_angles.py b/imap_l3_processing/pitch_angles.py
index d56252f32..27b462698 100644
--- a/imap_l3_processing/pitch_angles.py
+++ b/imap_l3_processing/pitch_angles.py
@@ -2,8 +2,9 @@
 from uncertainties.unumpy import nominal_values, std_devs, uarray
 
 
-def calculate_pitch_angle(particle_vectors: np.ndarray[float], magnetic_field_vector: np.ndarray[float]) -> np.ndarray[
-    float]:
+def calculate_pitch_angle(
+    particle_vectors: np.ndarray[float], magnetic_field_vector: np.ndarray[float]
+) -> np.ndarray[float]:
     norm_x = calculate_unit_vector(particle_vectors)
     norm_y = calculate_unit_vector(magnetic_field_vector)
     dot = np.sum(norm_x * norm_y, axis=-1)
@@ -15,17 +16,23 @@ def calculate_unit_vector(vector: np.ndarray[float]) -> np.ndarray[float]:
     return vector / norm
 
 
-def rotate_from_imap_despun_to_hit_despun(vector: np.ndarray[float]) -> np.ndarray[float]:
-    rotation_matrix_from_imap_despun_frame_to_hit_despun_instrument_frame = [[0.866025, 0.5, 0],
-                                                                             [-0.5, 0.866025, 0],
-                                                                             [0, 0, 1]]
-    return rotation_matrix_from_imap_despun_frame_to_hit_despun_instrument_frame @ vector
+def rotate_from_imap_despun_to_hit_despun(
+    vector: np.ndarray[float],
+) -> np.ndarray[float]:
+    rotation_matrix_from_imap_despun_frame_to_hit_despun_instrument_frame = [
+        [0.866025, 0.5, 0],
+        [-0.5, 0.866025, 0],
+        [0, 0, 1],
+    ]
+    return (
+        rotation_matrix_from_imap_despun_frame_to_hit_despun_instrument_frame @ vector
+    )
 
 
-def rotate_particle_vectors_from_hit_despun_to_imap_despun(vector: np.ndarray[float]) -> np.ndarray[float]:
-    rotation_matrix = [[0.866025, -0.5, 0],
-                       [0.5, 0.866025, 0],
-                       [0, 0, 1]]
+def rotate_particle_vectors_from_hit_despun_to_imap_despun(
+    vector: np.ndarray[float],
+) -> np.ndarray[float]:
+    rotation_matrix = [[0.866025, -0.5, 0], [0.5, 0.866025, 0], [0, 0, 1]]
 
     output = np.full_like(vector, np.nan, dtype=float)
 
@@ -36,99 +43,140 @@ def rotate_particle_vectors_from_hit_despun_to_imap_despun(vector: np.ndarray[fl
     return output
 
 
-def calculate_gyrophase(particle_vectors: np.ndarray, magnetic_field_vector: np.ndarray):
+def calculate_gyrophase(
+    particle_vectors: np.ndarray, magnetic_field_vector: np.ndarray
+):
     magnetic_field_plus_z = magnetic_field_vector
     imap_dps_plus_y = [0, 1, 0]
     if np.all(np.cross(magnetic_field_plus_z, imap_dps_plus_y) == 0):
         return np.full(shape=particle_vectors.shape[:-1], fill_value=np.nan)
-    magnetic_field_plus_x = calculate_unit_vector(np.cross(imap_dps_plus_y, magnetic_field_plus_z))
-    magnetic_field_plus_y = calculate_unit_vector(np.cross(magnetic_field_plus_z, magnetic_field_plus_x))
-    particle_magnetic_field_x_component = np.sum(particle_vectors * magnetic_field_plus_x, axis=-1)
-    particle_magnetic_field_y_component = np.sum(particle_vectors * magnetic_field_plus_y, axis=-1)
-    gyrophases = np.atan2(particle_magnetic_field_x_component, particle_magnetic_field_y_component)
+    magnetic_field_plus_x = calculate_unit_vector(
+        np.cross(imap_dps_plus_y, magnetic_field_plus_z)
+    )
+    magnetic_field_plus_y = calculate_unit_vector(
+        np.cross(magnetic_field_plus_z, magnetic_field_plus_x)
+    )
+    particle_magnetic_field_x_component = np.sum(
+        particle_vectors * magnetic_field_plus_x, axis=-1
+    )
+    particle_magnetic_field_y_component = np.sum(
+        particle_vectors * magnetic_field_plus_y, axis=-1
+    )
+    gyrophases = np.atan2(
+        particle_magnetic_field_x_component, particle_magnetic_field_y_component
+    )
 
     return np.mod(np.degrees(gyrophases), 360)
 
 
-def rebin_by_pitch_angle_and_gyrophase(intensity_data: np.array,
-                                       intensity_delta_plus: np.array,
-                                       intensity_delta_minus: np.array,
-                                       pitch_angles: np.array,
-                                       gyrophases: np.array,
-                                       number_of_pitch_angle_bins: int,
-                                       number_of_gyrophase_bins: int):
+def rebin_by_pitch_angle_and_gyrophase(
+    intensity_data: np.array,
+    intensity_delta_plus: np.array,
+    intensity_delta_minus: np.array,
+    pitch_angles: np.array,
+    gyrophases: np.array,
+    number_of_pitch_angle_bins: int,
+    number_of_gyrophase_bins: int,
+):
     intensity_with_delta_plus = uarray(intensity_data, intensity_delta_plus)
     intensity_with_delta_minus = uarray(intensity_data, intensity_delta_minus)
 
-    output_shape_pa_and_gyro = (intensity_data.shape[0], number_of_pitch_angle_bins, number_of_gyrophase_bins)
+    output_shape_pa_and_gyro = (
+        intensity_data.shape[0],
+        number_of_pitch_angle_bins,
+        number_of_gyrophase_bins,
+    )
     output_shape_pa_only = (intensity_data.shape[0], number_of_pitch_angle_bins)
 
-    rebinned_summed_by_pa_and_gyro_with_delta_plus = uarray(np.zeros(shape=output_shape_pa_and_gyro), 0)
-    rebinned_summed_by_pa_and_gyro_with_delta_minus = uarray(np.zeros(shape=output_shape_pa_and_gyro), 0)
+    rebinned_summed_by_pa_and_gyro_with_delta_plus = uarray(
+        np.zeros(shape=output_shape_pa_and_gyro), 0
+    )
+    rebinned_summed_by_pa_and_gyro_with_delta_minus = uarray(
+        np.zeros(shape=output_shape_pa_and_gyro), 0
+    )
 
-    rebinned_summed_pa_only_with_delta_plus = uarray(np.zeros(shape=output_shape_pa_only), 0)
-    rebinned_summed_pa_only_with_delta_minus = uarray(np.zeros(shape=output_shape_pa_only), 0)
+    rebinned_summed_pa_only_with_delta_plus = uarray(
+        np.zeros(shape=output_shape_pa_only), 0
+    )
+    rebinned_summed_pa_only_with_delta_minus = uarray(
+        np.zeros(shape=output_shape_pa_only), 0
+    )
 
     rebinned_count_by_pa_and_gyro = np.zeros(shape=output_shape_pa_and_gyro)
     rebinned_count_pa_only = np.zeros(shape=output_shape_pa_only)
 
     for i in range(intensity_data.shape[0]):
         for pitch_angle, gyrophase, intensity_with_plus, intensity_with_minus in zip(
-                np.ravel(pitch_angles),
-                np.ravel(gyrophases),
-                np.ravel(intensity_with_delta_plus[i]),
-                np.ravel(intensity_with_delta_minus[i])):
-            if not (np.isnan(intensity_with_plus.nominal_value) or np.isnan(pitch_angle)):
+            np.ravel(pitch_angles),
+            np.ravel(gyrophases),
+            np.ravel(intensity_with_delta_plus[i]),
+            np.ravel(intensity_with_delta_minus[i]),
+        ):
+            if not (
+                np.isnan(intensity_with_plus.nominal_value) or np.isnan(pitch_angle)
+            ):
                 if pitch_angle == 180:
                     pitch_angle_bin = number_of_pitch_angle_bins - 1
                 else:
-                    pitch_angle_bin = np.floor(pitch_angle / (180 / number_of_pitch_angle_bins)).astype(int)
+                    pitch_angle_bin = np.floor(
+                        pitch_angle / (180 / number_of_pitch_angle_bins)
+                    ).astype(int)
 
                 if not np.isnan(gyrophase):
-                    bin_size = (360 / number_of_gyrophase_bins)
+                    bin_size = 360 / number_of_gyrophase_bins
                     normalized_gyrophase = gyrophase % 360
 
                     gyrophase_bin = int(normalized_gyrophase // bin_size)
 
                     rebinned_summed_by_pa_and_gyro_with_delta_plus[
-                        i, pitch_angle_bin, gyrophase_bin] += intensity_with_plus
+                        i, pitch_angle_bin, gyrophase_bin
+                    ] += intensity_with_plus
                     rebinned_summed_by_pa_and_gyro_with_delta_minus[
-                        i, pitch_angle_bin, gyrophase_bin] += intensity_with_minus
-                    rebinned_count_by_pa_and_gyro[i, pitch_angle_bin, gyrophase_bin] += 1
-
-                rebinned_summed_pa_only_with_delta_plus[i, pitch_angle_bin] += intensity_with_plus
-                rebinned_summed_pa_only_with_delta_minus[i, pitch_angle_bin] += intensity_with_minus
+                        i, pitch_angle_bin, gyrophase_bin
+                    ] += intensity_with_minus
+                    rebinned_count_by_pa_and_gyro[
+                        i, pitch_angle_bin, gyrophase_bin
+                    ] += 1
+
+                rebinned_summed_pa_only_with_delta_plus[i, pitch_angle_bin] += (
+                    intensity_with_plus
+                )
+                rebinned_summed_pa_only_with_delta_minus[i, pitch_angle_bin] += (
+                    intensity_with_minus
+                )
                 rebinned_count_pa_only[i, pitch_angle_bin] += 1
 
     averaged_rebinned_intensity_by_pa_and_gyro_with_delta_plus = np.divide(
         rebinned_summed_by_pa_and_gyro_with_delta_plus,
         rebinned_count_by_pa_and_gyro,
-        out=np.full_like(
-            rebinned_summed_by_pa_and_gyro_with_delta_plus, np.nan),
-        where=rebinned_count_by_pa_and_gyro != 0)
+        out=np.full_like(rebinned_summed_by_pa_and_gyro_with_delta_plus, np.nan),
+        where=rebinned_count_by_pa_and_gyro != 0,
+    )
     averaged_rebinned_intensity_by_pa_and_gyro_with_delta_minus = np.divide(
         rebinned_summed_by_pa_and_gyro_with_delta_minus,
         rebinned_count_by_pa_and_gyro,
-        out=np.full_like(
-            rebinned_summed_by_pa_and_gyro_with_delta_minus, np.nan),
-        where=rebinned_count_by_pa_and_gyro != 0)
-
-    averaged_rebinned_intensity_by_pa_only_with_delta_plus = np.divide(rebinned_summed_pa_only_with_delta_plus,
-                                                                       rebinned_count_pa_only,
-                                                                       out=np.full_like(
-                                                                           rebinned_summed_pa_only_with_delta_plus,
-                                                                           np.nan),
-                                                                       where=rebinned_count_pa_only != 0)
-    averaged_rebinned_intensity_by_pa_only_with_delta_minus = np.divide(rebinned_summed_pa_only_with_delta_minus,
-                                                                        rebinned_count_pa_only,
-                                                                        out=np.full_like(
-                                                                            rebinned_summed_pa_only_with_delta_minus,
-                                                                            np.nan),
-                                                                        where=rebinned_count_pa_only != 0)
-
-    return (nominal_values(averaged_rebinned_intensity_by_pa_and_gyro_with_delta_plus),
-            std_devs(averaged_rebinned_intensity_by_pa_and_gyro_with_delta_plus),
-            std_devs(averaged_rebinned_intensity_by_pa_and_gyro_with_delta_minus),
-            nominal_values(averaged_rebinned_intensity_by_pa_only_with_delta_plus),
-            std_devs(averaged_rebinned_intensity_by_pa_only_with_delta_plus),
-            std_devs(averaged_rebinned_intensity_by_pa_only_with_delta_minus))
+        out=np.full_like(rebinned_summed_by_pa_and_gyro_with_delta_minus, np.nan),
+        where=rebinned_count_by_pa_and_gyro != 0,
+    )
+
+    averaged_rebinned_intensity_by_pa_only_with_delta_plus = np.divide(
+        rebinned_summed_pa_only_with_delta_plus,
+        rebinned_count_pa_only,
+        out=np.full_like(rebinned_summed_pa_only_with_delta_plus, np.nan),
+        where=rebinned_count_pa_only != 0,
+    )
+    averaged_rebinned_intensity_by_pa_only_with_delta_minus = np.divide(
+        rebinned_summed_pa_only_with_delta_minus,
+        rebinned_count_pa_only,
+        out=np.full_like(rebinned_summed_pa_only_with_delta_minus, np.nan),
+        where=rebinned_count_pa_only != 0,
+    )
+
+    return (
+        nominal_values(averaged_rebinned_intensity_by_pa_and_gyro_with_delta_plus),
+        std_devs(averaged_rebinned_intensity_by_pa_and_gyro_with_delta_plus),
+        std_devs(averaged_rebinned_intensity_by_pa_and_gyro_with_delta_minus),
+        nominal_values(averaged_rebinned_intensity_by_pa_only_with_delta_plus),
+        std_devs(averaged_rebinned_intensity_by_pa_only_with_delta_plus),
+        std_devs(averaged_rebinned_intensity_by_pa_only_with_delta_minus),
+    )
diff --git a/imap_l3_processing/predicted_ephemeris_tracker.py b/imap_l3_processing/predicted_ephemeris_tracker.py
index 36cad0053..c05d48643 100644
--- a/imap_l3_processing/predicted_ephemeris_tracker.py
+++ b/imap_l3_processing/predicted_ephemeris_tracker.py
@@ -1,7 +1,7 @@
-from typing import Callable
+from collections.abc import Callable
 
 from imap_data_access import SPICEFilePath
-from spiceypy import spiceypy, KernelPool, SpiceyError
+from spiceypy import KernelPool, SpiceyError, spiceypy
 
 
 class PredictedEphemerisTracker:
diff --git a/imap_l3_processing/processor.py b/imap_l3_processing/processor.py
index 15d2dd1a4..9ea73c8c4 100644
--- a/imap_l3_processing/processor.py
+++ b/imap_l3_processing/processor.py
@@ -1,7 +1,6 @@
 import abc
 from pathlib import Path
 
-import spiceypy
 from imap_data_access.processing_input import ProcessingInputCollection
 
 from imap_l3_processing.models import InputMetadata
@@ -9,8 +8,9 @@
 
 
 class Processor(abc.ABC):
-    def __init__(self, dependencies: ProcessingInputCollection,
-                 input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         self.input_metadata = input_metadata
         self.dependencies = dependencies
 
@@ -20,10 +20,13 @@ def process(self) -> list[Path]:
 
     def get_parent_file_names(self, file_paths: list[Path | str] = None) -> list[str]:
         if file_paths:
-            parent_file_names = [Path(parent_file_name).name for parent_file_name in file_paths]
+            parent_file_names = [
+                Path(parent_file_name).name for parent_file_name in file_paths
+            ]
         else:
-            parent_file_names = [parent_file_name.name for parent_file_name in self.dependencies.get_file_paths()]
+            parent_file_names = [
+                parent_file_name.name
+                for parent_file_name in self.dependencies.get_file_paths()
+            ]
 
         return parent_file_names + get_spice_parent_file_names()
-
-
diff --git a/imap_l3_processing/swapi/constants.py b/imap_l3_processing/swapi/constants.py
index e272eca7e..de72012a6 100644
--- a/imap_l3_processing/swapi/constants.py
+++ b/imap_l3_processing/swapi/constants.py
@@ -22,7 +22,9 @@
 SWAPI_BIN_PERIOD_S = 12 / 72  # duration of one ESA step in the 12 s, 72-bin sweep
 # Counts accumulate over the 0.145 s livetime that starts after the voltage ramp-up;
 # sample geometry at the center of that window rather than the bin's start time.
-SWAPI_LIVETIME_CENTER_OFFSET_S = (SWAPI_BIN_PERIOD_S - SWAPI_LIVETIME_S) + SWAPI_LIVETIME_S / 2
+SWAPI_LIVETIME_CENTER_OFFSET_S = (
+    SWAPI_BIN_PERIOD_S - SWAPI_LIVETIME_S
+) + SWAPI_LIVETIME_S / 2
 
 # baseline instrument background is approximately 0.1 Hz
 SWAPI_BACKGROUND_RATE = 0.1
diff --git a/imap_l3_processing/swapi/l3a/chunk_fits.py b/imap_l3_processing/swapi/l3a/chunk_fits.py
index e3d6bde33..595864908 100644
--- a/imap_l3_processing/swapi/l3a/chunk_fits.py
+++ b/imap_l3_processing/swapi/l3a/chunk_fits.py
@@ -15,6 +15,12 @@
     ONE_SECOND_IN_NANOSECONDS,
     THIRTY_SECONDS_IN_NANOSECONDS,
 )
+from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+    SWAPI_SCIENCE_BINS,
+)
 from imap_l3_processing.swapi.l3a.science.pickup_ion.calculate_pickup_ion_values import (
     PickupIonFitInputData,
     calculate_pickup_ion_values,
@@ -26,18 +32,12 @@
     AlphaSolarWindFitResult,
     fit_solar_wind_alpha_model,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
-    ProtonSolarWindFitResult,
-    fit_solar_wind_proton_model,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
-from imap_l3_processing.swapi.species import Species
-from imap_l3_processing.swapi.constants import (
-    SWAPI_COARSE_SWEEP_BINS,
-    SWAPI_L2_K_FACTOR,
-    SWAPI_SCIENCE_BINS,
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
+    ProtonSolarWindFitResult,
+    fit_solar_wind_proton_model,
 )
 from imap_l3_processing.swapi.l3a.utils import (
     compute_direction_of_mean_magnetic_field_over_chunk,
@@ -49,7 +49,7 @@
     solar_wind_chunk_epoch,
 )
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
-from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
+from imap_l3_processing.swapi.species import Species
 
 logger = logging.getLogger(__name__)
 
diff --git a/imap_l3_processing/swapi/l3a/models.py b/imap_l3_processing/swapi/l3a/models.py
index c04d82eba..f6d190bdf 100644
--- a/imap_l3_processing/swapi/l3a/models.py
+++ b/imap_l3_processing/swapi/l3a/models.py
@@ -9,8 +9,8 @@
 
 from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
 from imap_l3_processing.constants import (
-    THIRTY_SECONDS_IN_NANOSECONDS,
     FIVE_MINUTES_IN_NANOSECONDS,
+    THIRTY_SECONDS_IN_NANOSECONDS,
 )
 from imap_l3_processing.models import DataProduct, DataProductVariable
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
@@ -48,9 +48,7 @@
 ALPHA_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME = (
     "alpha_sw_velocity_rtn_covariance"
 )
-ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME = (
-    "alpha_sw_velocity_rtn_uncert"
-)
+ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME = "alpha_sw_velocity_rtn_uncert"
 
 PUI_IONIZATION_RATE_CDF_VAR_NAME = "pui_ionization_rate"
 PUI_CUTOFF_SPEED_CDF_VAR_NAME = "pui_cutoff_speed"
@@ -67,7 +65,9 @@
 ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME = "alpha_sw_velocity_rtn_label"
 ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME = "alpha_sw_velocity_rtn_sun_label"
 
-PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME = "proton_sw_velocity_rtn_sun_label"
+PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME = (
+    "proton_sw_velocity_rtn_sun_label"
+)
 PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME = "proton_sw_velocity_rtn_label"
 
 
@@ -145,8 +145,14 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             ),
             DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.quality_flags),
             DataProductVariable(VELOCITY_RTN_LABEL_CDF_VAR_NAME, value=["R", "T", "N"]),
-            DataProductVariable(PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME, value=["Vp SC R", "Vp SC T", "Vp SC N"]),
-            DataProductVariable(PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME, value=["Vp Sun R", "Vp Sun T", "Vp Sun N"]),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+                value=["Vp SC R", "Vp SC T", "Vp SC N"],
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME,
+                value=["Vp Sun R", "Vp Sun T", "Vp Sun N"],
+            ),
         ]
 
 
@@ -217,15 +223,19 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(
                 ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME,
                 np.sqrt(
-                    np.diagonal(
-                        self.alpha_sw_velocity_rtn_covariance, axis1=1, axis2=2
-                    )
+                    np.diagonal(self.alpha_sw_velocity_rtn_covariance, axis1=1, axis2=2)
                 ),
             ),
             DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.quality_flags),
             DataProductVariable(VELOCITY_RTN_LABEL_CDF_VAR_NAME, value=["R", "T", "N"]),
-            DataProductVariable(ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME, value=["Va SC R", "Va SC T", "Va SC N"]),
-            DataProductVariable(ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME, value=["Va Sun R", "Va Sun T", "Va Sun N"]),
+            DataProductVariable(
+                ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+                value=["Va SC R", "Va SC T", "Va SC N"],
+            ),
+            DataProductVariable(
+                ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME,
+                value=["Va Sun R", "Va Sun T", "Va Sun N"],
+            ),
         ]
 
 
@@ -290,13 +300,23 @@ class SwapiL3aProtonDataFromCDF:
     quality_flags: np.ndarray
 
     @classmethod
-    def from_file(cls, data_file:Path)-> Self:
+    def from_file(cls, data_file: Path) -> Self:
         with CDF(str(data_file)) as cdf:
-            l2_parent_file_name = next(parent for parent in cdf.attrs["Parents"] if parent.startswith("imap_swapi_l2_"))
-            velocity_rtn = read_numeric_variable(cdf[PROTON_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME])
-            velocity_rtn_covariance = read_numeric_variable(cdf[PROTON_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME])
+            l2_parent_file_name = next(
+                parent
+                for parent in cdf.attrs["Parents"]
+                if parent.startswith("imap_swapi_l2_")
+            )
+            velocity_rtn = read_numeric_variable(
+                cdf[PROTON_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME]
+            )
+            velocity_rtn_covariance = read_numeric_variable(
+                cdf[PROTON_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME]
+            )
             density = read_numeric_variable(cdf[PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME])
-            temperature = read_numeric_variable(cdf[PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME])
+            temperature = read_numeric_variable(
+                cdf[PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME]
+            )
             quality_flags = cdf[SWAPI_QUALITY_FLAGS_CDF_VAR_NAME][...]
         return cls(
             l2_parent_file_name,
@@ -304,5 +324,5 @@ def from_file(cls, data_file:Path)-> Self:
             velocity_rtn_covariance,
             density,
             temperature,
-            quality_flags
-        )
\ No newline at end of file
+            quality_flags,
+        )
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/calculate_pickup_ion_values.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/calculate_pickup_ion_values.py
index f2e926c17..fa24c54fc 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/calculate_pickup_ion_values.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/calculate_pickup_ion_values.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
-from functools import partial
 
 from dataclasses import dataclass
+from functools import partial
 
 import numpy as np
 import scipy.optimize
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/collapsed_response_grid.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/collapsed_response_grid.py
index 9fd9de129..49f8e25be 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/collapsed_response_grid.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/collapsed_response_grid.py
@@ -11,22 +11,30 @@
     CENTIMETERS_PER_METER,
     METERS_PER_KILOMETER,
 )
-from imap_l3_processing.swapi.l3a.science.pickup_ion.uniform_speed_grid import UniformSpeedGrid
-from imap_l3_processing.swapi.l3a.utils import velocity_components_to_angles_in_instrument_frame
+from imap_l3_processing.swapi.l3a.science.pickup_ion.uniform_speed_grid import (
+    UniformSpeedGrid,
+)
+from imap_l3_processing.swapi.l3a.utils import (
+    velocity_components_to_angles_in_instrument_frame,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    interpolate_azimuthal_transmission,
+)
 from imap_l3_processing.swapi.response.passband_grid import interpolate_passband
 from imap_l3_processing.swapi.response.swapi_response import ResponseGrid, SwapiResponse
 from imap_l3_processing.swapi.species import Species
-from imap_l3_processing.swapi.response.azimuthal_transmission import interpolate_azimuthal_transmission
 
 
 class ChunkCollapsedResponse(NamedTuple):
-    speed_grid: UniformSpeedGrid       # shared v' grid of N cells
-    bin_weights: NDArray[float]        # (n_sweeps, n_steps, N); count_rate = bin_weights @ f(v')
+    speed_grid: UniformSpeedGrid  # shared v' grid of N cells
+    bin_weights: NDArray[
+        float
+    ]  # (n_sweeps, n_steps, N); count_rate = bin_weights @ f(v')
 
 
 class CollapsedResponseGrid(NamedTuple):
     speed_in_sw_frame: NDArray[float]  # (N,) v' samples
-    values: NDArray[float]             # (N,) H(v', V) [km^3/s] at each v'
+    values: NDArray[float]  # (N,) H(v', V) [km^3/s] at each v'
 
 
 _ELEVATION_RESOLUTION = 32
@@ -65,8 +73,10 @@ def build_chunk_collapsed_response(
             voltage = float(voltages_v[step_index])
             bulk_vec = bulk_sw_per_bin_kms[sweep_index, step_index]
             bulk_speed = float(bulk_speeds[sweep_index, step_index])
-            bulk_azimuth_deg, bulk_elevation_deg = velocity_components_to_angles_in_instrument_frame(
-                bulk_vec[0], bulk_vec[1], bulk_vec[2]
+            bulk_azimuth_deg, bulk_elevation_deg = (
+                velocity_components_to_angles_in_instrument_frame(
+                    bulk_vec[0], bulk_vec[1], bulk_vec[2]
+                )
             )
             response_grid = swapi_response.get_response_grid(
                 time_as_tt2000, voltage, species
@@ -114,9 +124,7 @@ def build_collapsed_response_grid(
             bulk_elevation,
         )
 
-    return CollapsedResponseGrid(
-        speed_in_sw_frame=speed_in_sw_frame, values=values
-    )
+    return CollapsedResponseGrid(speed_in_sw_frame=speed_in_sw_frame, values=values)
 
 
 def solar_wind_frame_speed_range(
@@ -170,20 +178,21 @@ def _collapse_response(
                 continue
 
             cell_coeff = (
-                delta_speed * delta_elevation * speed**2 * central_effective_area_km2
+                delta_speed
+                * delta_elevation
+                * speed**2
+                * central_effective_area_km2
                 / (bulk_speed * cos_bulk_elevation)
             )
 
             for i in range(speed_in_sw_frame.shape[0]):
                 v_prime = speed_in_sw_frame[i]
-                cos_angle = (
-                    (speed**2 + bulk_speed**2 - v_prime**2)
-                    / (2.0 * speed * bulk_speed)
+                cos_angle = (speed**2 + bulk_speed**2 - v_prime**2) / (
+                    2.0 * speed * bulk_speed
                 )
 
-                cos_delta_azimuth = (
-                    (cos_angle - sin_elevation * sin_bulk_elevation)
-                    / (cos_elevation * cos_bulk_elevation)
+                cos_delta_azimuth = (cos_angle - sin_elevation * sin_bulk_elevation) / (
+                    cos_elevation * cos_bulk_elevation
                 )
 
                 if abs(cos_delta_azimuth) >= 1.0 - 1e-5:
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/density_of_neutral_helium_lookup_table.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/density_of_neutral_helium_lookup_table.py
index 275addb66..bb22d99e6 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/density_of_neutral_helium_lookup_table.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/density_of_neutral_helium_lookup_table.py
@@ -1,5 +1,3 @@
-from typing import Union
-
 import numpy as np
 from numpy import ndarray
 from scipy.interpolate import RegularGridInterpolator
@@ -21,10 +19,11 @@ def __init__(self, calibration_table: np.ndarray):
             self.grid = (angle_with_360, distance)
             self.densities = np.append(self.densities, self.densities[0:1], axis=0)
 
-        self._interp = RegularGridInterpolator(self.grid, self.densities, bounds_error=False, fill_value=0,
-                                               method='linear')
+        self._interp = RegularGridInterpolator(
+            self.grid, self.densities, bounds_error=False, fill_value=0, method="linear"
+        )
 
-    def density(self, angle: Union[ndarray, float], distance: Union[ndarray, float]):
+    def density(self, angle: ndarray | float, distance: ndarray | float):
         if isinstance(distance, float):
             coords = np.array((angle % 360, distance))
             result = self._interp(coords)[0]
@@ -39,9 +38,11 @@ def density(self, angle: Union[ndarray, float], distance: Union[ndarray, float])
                 distance > self.get_minimum_distance(),
                 result,
                 (
-                    self.density(angle, np.ones_like(distance) * self.get_minimum_distance())
+                    self.density(
+                        angle, np.ones_like(distance) * self.get_minimum_distance()
+                    )
                     * (distance / self.get_minimum_distance())
-                )
+                ),
             )
 
         return result
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/goodness_of_fit.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/goodness_of_fit.py
index 4a64cbb68..c4e4e0983 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/goodness_of_fit.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/goodness_of_fit.py
@@ -6,7 +6,9 @@
 MAX_CUTOFF_SPEED_KMS = 550.0  # v_cutoff <= 550 km/s
 MAX_MEAN_RELATIVE_ERROR = 0.12  # Delta_rel <= 0.12
 MAX_PAST_PEAK_RATIO = 0.4  # R_past_peak <= 0.4
-CUTOFF_DROP_RATIO = 0.25  # extend up to and including the last step with model rate >= 0.25 * peak
+CUTOFF_DROP_RATIO = (
+    0.25  # extend up to and including the last step with model rate >= 0.25 * peak
+)
 MIN_CUTOFF_SPEED_RATIO = 0.5  # v_cutoff >= 0.5 * v_sw
 MAX_CUTOFF_SPEED_RATIO = 1.5  # v_cutoff <= 1.5 * v_sw
 MIN_IONIZATION_RATE = 0.6e-9  # beta >= 0.6 * 10^-9 s^-1
@@ -67,13 +69,14 @@ def is_good_fit(
     in_range = ~past_range
 
     mean_absolute_percent_error = (
-        np.abs(chunk_mean_model_rates + SWAPI_BACKGROUND_RATE - chunk_mean_observed_rates)
+        np.abs(
+            chunk_mean_model_rates + SWAPI_BACKGROUND_RATE - chunk_mean_observed_rates
+        )
         / (chunk_mean_model_rates + SWAPI_BACKGROUND_RATE)
     )[in_range].mean()
 
     past_cutoff_ratio = (
-        chunk_mean_observed_rates[past_range].mean()
-        / chunk_mean_model_rates.max()
+        chunk_mean_observed_rates[past_range].mean() / chunk_mean_model_rates.max()
     )
 
     cutoff_speed_ratio = cutoff_speed_kms / sw_speed_kms
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/inflow_vector.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/inflow_vector.py
index 9b5a5d1b9..ab66bb33f 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/inflow_vector.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/inflow_vector.py
@@ -19,10 +19,12 @@ def from_file(cls, path: PathLike) -> InflowVector:
 
         loaded_vector = np.squeeze(np.loadtxt(path, dtype=float))
 
-        assert loaded_vector.shape == (3,), f"Failed to parse Inflow Vector from {path.name}"
+        assert loaded_vector.shape == (3,), (
+            f"Failed to parse Inflow Vector from {path.name}"
+        )
 
         return cls(
             speed_km_per_s=(loaded_vector[0]),
             longitude_deg_eclipj2000=loaded_vector[1],
-            latitude_deg_eclipj2000=loaded_vector[2]
+            latitude_deg_eclipj2000=loaded_vector[2],
         )
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/moments.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/moments.py
index ea20d1311..69f8114f8 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/moments.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/moments.py
@@ -36,6 +36,7 @@ def calculate_helium_pui_density(
 
     Uncertainty on the fit parameters is propagated through the integral.
     """
+
     @uncertainties.wrap
     def calculate(ionization_rate, cutoff_speed):
         f_pui = vasyliunas_siscoe_vdf(
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/utils.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/utils.py
index 74f0f7b55..f44af1c74 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/utils.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/utils.py
@@ -36,8 +36,7 @@ def rotate_rtn_velocity_to_swapi_per_bin(
     chunk: SwapiL2Data,
     sw_velocity_rtn_kms: ndarray,
 ) -> ndarray:
-    """Apply the IMAP_RTN to IMAP_SWAPI rotation at each point in time.
-    """
+    """Apply the IMAP_RTN to IMAP_SWAPI rotation at each point in time."""
     measurement_times_tt2000_ns = measurement_times(chunk.sci_start_time)
     n_sweeps, n_bins = measurement_times_tt2000_ns.shape
     ephemeris_times = ttj2000ns_to_et(measurement_times_tt2000_ns.ravel())
diff --git a/imap_l3_processing/swapi/l3a/science/pickup_ion/vasyliunas_siscoe_distribution.py b/imap_l3_processing/swapi/l3a/science/pickup_ion/vasyliunas_siscoe_distribution.py
index 9ba699523..31d49b106 100644
--- a/imap_l3_processing/swapi/l3a/science/pickup_ion/vasyliunas_siscoe_distribution.py
+++ b/imap_l3_processing/swapi/l3a/science/pickup_ion/vasyliunas_siscoe_distribution.py
@@ -3,11 +3,18 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from imap_l3_processing.constants import ONE_AU_IN_KM, CENTIMETERS_PER_METER, METERS_PER_KILOMETER
+from imap_l3_processing.constants import (
+    CENTIMETERS_PER_METER,
+    METERS_PER_KILOMETER,
+    ONE_AU_IN_KM,
+)
 from imap_l3_processing.swapi.constants import SWAPI_PUI_COOLING_INDEX
-from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import \
-    DensityOfNeutralHeliumLookupTable
-from imap_l3_processing.swapi.l3a.science.pickup_ion.uniform_speed_grid import UniformSpeedGrid
+from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import (
+    DensityOfNeutralHeliumLookupTable,
+)
+from imap_l3_processing.swapi.l3a.science.pickup_ion.uniform_speed_grid import (
+    UniformSpeedGrid,
+)
 
 
 def vasyliunas_siscoe_vdf(
@@ -70,10 +77,8 @@ def _filled_shell_vdf_without_cutoff(
 ) -> NDArray:
     w = speed_in_sw_frame / cutoff_speed
     radius_in_au = distance / ONE_AU_IN_KM
-    neutral_helium_density_per_cm3 = (
-        density_of_neutral_helium_lookup_table.density(
-            inflow_angle, radius_in_au * w**SWAPI_PUI_COOLING_INDEX
-        )
+    neutral_helium_density_per_cm3 = density_of_neutral_helium_lookup_table.density(
+        inflow_angle, radius_in_au * w**SWAPI_PUI_COOLING_INDEX
     )
     neutral_helium_density_per_km3 = (
         neutral_helium_density_per_cm3
@@ -81,9 +86,7 @@ def _filled_shell_vdf_without_cutoff(
     )
     term1 = SWAPI_PUI_COOLING_INDEX / (4 * np.pi)
     term2 = (ionization_rate * ONE_AU_IN_KM**2) / (
-        distance
-        * solar_wind_speed_inertial_frame
-        * cutoff_speed**3
+        distance * solar_wind_speed_inertial_frame * cutoff_speed**3
     )
     term3 = w ** (SWAPI_PUI_COOLING_INDEX - 3)
     term4 = neutral_helium_density_per_km3
@@ -110,4 +113,4 @@ def _apply_partial_heaviside_at_cutoff(
     bin_min = speed_grid.centers[cutoff_index] - delta_v_prime / 2
     fraction_below_cutoff = (cutoff_speed - bin_min) / delta_v_prime
     f_pui[cutoff_index] *= fraction_below_cutoff
-    f_pui[cutoff_index + 1:] = 0.0
+    f_pui[cutoff_index + 1 :] = 0.0
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_initial_guess.py b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_initial_guess.py
index 44adb4165..7fa605f7e 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_initial_guess.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_initial_guess.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 import numpy as np
 from numpy import ndarray
 
@@ -19,7 +17,7 @@ def calculate_initial_guess(
     proton_true_rate: ndarray,
     proton_temperature: float,
     proton_velocity_rtn: ndarray,
-) -> Optional[tuple[float, float, float, ndarray]]:
+) -> tuple[float, float, float, ndarray] | None:
     if alpha_ctx.count_rate.size == 0 or alpha_ctx.count_rate.ndim != 2:
         return None
 
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/fit_solar_wind_alpha_model.py b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/fit_solar_wind_alpha_model.py
index a28d39c06..07462e579 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/fit_solar_wind_alpha_model.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/fit_solar_wind_alpha_model.py
@@ -1,12 +1,10 @@
 from dataclasses import dataclass
-from typing import Optional
 
 import numpy as np
 import scipy.optimize
 from numpy import ndarray
 from uncertainties import UFloat, covariance_matrix, ufloat
 
-from imap_l3_processing.constants import ALPHA_MASS_PER_CHARGE_M_P_PER_E
 from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.calculate_initial_guess import (
     calculate_initial_guess,
 )
@@ -19,8 +17,8 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     LOG_DENSITY_IDX,
     LOG_TEMPERATURE_IDX,
-    SolarWindParams,
     VELOCITY_SLICE,
+    SolarWindParams,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
     ProtonSolarWindFitResult,
@@ -161,9 +159,8 @@ def _construct_alpha_fit_result(
     delta_v_fit = float(result.x[2])
     velocity_rtn = proton_bulk + delta_v_fit * magnetic_field_direction
 
-    speed_ratio = (
-        np.linalg.norm(velocity_rtn, axis=-1)
-        / np.linalg.norm(proton_bulk, axis=-1)
+    speed_ratio = np.linalg.norm(velocity_rtn, axis=-1) / np.linalg.norm(
+        proton_bulk, axis=-1
     )
     if speed_ratio < MIN_TOLERABLE_ALPHA_SPEED_RATIO:
         return _nan_alpha_fit_result(bad_fit_flag | SwapiL3Flags.BAD_FIT)
@@ -191,9 +188,7 @@ def _construct_alpha_fit_result(
     return AlphaSolarWindFitResult(
         density=ufloat(alpha_density_fit, density_sigma),
         temperature=ufloat(alpha_temperature_fit, temperature_sigma),
-        velocity_rtn=make_correlated_velocity(
-            velocity_rtn, velocity_covariance_rtn
-        ),
+        velocity_rtn=make_correlated_velocity(velocity_rtn, velocity_covariance_rtn),
         delta_v=ufloat(delta_v_fit, delta_v_sigma),
         quality_flag=int(bad_fit_flag),
     )
@@ -203,9 +198,7 @@ def _alpha_r_squared(
     residuals: ndarray, count_rate: ndarray, n_sweeps: int, n_peak_bins: int
 ) -> float:
     averaged_count_rate = np.nanmean(count_rate, axis=0)
-    averaged_residual = np.nanmean(
-        residuals.reshape(n_sweeps, n_peak_bins), axis=0
-    )
+    averaged_residual = np.nanmean(residuals.reshape(n_sweeps, n_peak_bins), axis=0)
     return r_squared(averaged_residual, averaged_count_rate)
 
 
@@ -221,9 +214,9 @@ def __init__(
         self.magnetic_field_direction = magnetic_field_direction
         self.proton_true_rate = proton_true_rate
         self.alpha_ctx = alpha_ctx
-        self._last_state: Optional[ndarray] = None
-        self._last_residuals: Optional[ndarray] = None
-        self._last_jacobian: Optional[ndarray] = None
+        self._last_state: ndarray | None = None
+        self._last_residuals: ndarray | None = None
+        self._last_jacobian: ndarray | None = None
 
     def _eval(self, x: ndarray) -> None:
         alpha_density = float(np.exp(x[0]))
@@ -232,7 +225,10 @@ def _eval(self, x: ndarray) -> None:
         alpha_velocity_rtn = self.proton_bulk + delta_v * self.magnetic_field_direction
         alpha_true, jacobian_alpha_5d = model_solar_wind_ideal_coincidence_rates(
             SolarWindParams(
-                alpha_density, alpha_velocity_rtn, alpha_temperature, self.alpha_ctx.mass_kg
+                alpha_density,
+                alpha_velocity_rtn,
+                alpha_temperature,
+                self.alpha_ctx.mass_kg,
             ),
             self.alpha_ctx,
         )
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py b/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py
index a2f7a03d0..44baffd6d 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py
@@ -13,27 +13,27 @@
     REGION_SUNGLASSES,
     AzimuthalRegion,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.integration_limits import (
     AngularQuadrature,
     get_angular_quadrature,
     get_speed_quadrature,
     speed_window_misses_passband,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.utils import (
-    count_rate_conversion_factor,
-)
-from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
-    SolarWindFitContext,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     LOG_DENSITY_IDX,
     LOG_TEMPERATURE_IDX,
     N_STATE,
-    SolarWindParams,
     VELOCITY_SLICE,
+    SolarWindParams,
     bulk_speed,
     thermal_speed,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import (
+    count_rate_conversion_factor,
+)
 from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
 
 
@@ -135,10 +135,14 @@ def _integrate_region(
             for i_speed in range(speed_quadrature.points.shape[0]):
                 speed = speed_quadrature.points[i_speed]
                 exponent = (
-                    speed ** 2
-                    + bulk_speed(sw_params) ** 2
-                    - 2 * speed * velocity_along_direction
-                ) * 1.0 / (2 * thermal_speed(sw_params) ** 2)
+                    (
+                        speed**2
+                        + bulk_speed(sw_params) ** 2
+                        - 2 * speed * velocity_along_direction
+                    )
+                    * 1.0
+                    / (2 * thermal_speed(sw_params) ** 2)
+                )
                 weighted_integrand = (
                     speed_quadrature.weights[i_speed]
                     * speed_quadrature.speed_cubed_times_passband[i_speed]
@@ -190,13 +194,13 @@ def _integrate_region(
         elevation_log_temperature_jacobian * count_rate_factor - 1.5 * rate
     )
     jacobian[VELOCITY_SLICE.start] = (
-            elevation_jacobian_r * count_rate_factor / thermal_speed(sw_params) ** 2
+        elevation_jacobian_r * count_rate_factor / thermal_speed(sw_params) ** 2
     )
     jacobian[VELOCITY_SLICE.start + 1] = (
-            elevation_jacobian_t * count_rate_factor / thermal_speed(sw_params) ** 2
+        elevation_jacobian_t * count_rate_factor / thermal_speed(sw_params) ** 2
     )
     jacobian[VELOCITY_SLICE.start + 2] = (
-            elevation_jacobian_n * count_rate_factor / thermal_speed(sw_params) ** 2
+        elevation_jacobian_n * count_rate_factor / thermal_speed(sw_params) ** 2
     )
 
     return rate, jacobian
@@ -222,4 +226,3 @@ def _direction_in_rtn(angular_quadrature, rotation_xyz_to_rtn, i_elevation, i_az
         + rotation_xyz_to_rtn[2, 1] * direction_y
         + rotation_xyz_to_rtn[2, 2] * direction_z,
     )
-
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py b/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py
index 74b9dec5d..3c435d48a 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py
@@ -6,14 +6,14 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.azimuthal_regions import (
     AzimuthalRegion,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.trim_open_aperture import (
-    trim_open_aperture,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     SolarWindParams,
     bulk_speed,
     thermal_speed,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.trim_open_aperture import (
+    trim_open_aperture,
+)
 from imap_l3_processing.swapi.l3a.utils import (
     velocity_to_angles_in_instrument_frame,
 )
@@ -113,9 +113,7 @@ def rescale(nodes, weights, lower, upper):
     )
     transmission_azimuth = np.array(
         [
-            interpolate_azimuthal_transmission(
-                response_grid.azimuthal_transmission, az
-            )
+            interpolate_azimuthal_transmission(response_grid.azimuthal_transmission, az)
             for az in azimuth_points
         ]
     )
@@ -179,7 +177,9 @@ def _angular_limits(
     half_width = _maxwellian_angular_extent(
         sw_params, response_grid.central_speed, EPSILON
     )
-    bulk_az, bulk_el = velocity_to_angles_in_instrument_frame(sw_params, rotation_matrix)
+    bulk_az, bulk_el = velocity_to_angles_in_instrument_frame(
+        sw_params, rotation_matrix
+    )
 
     if region.is_sunglasses:
         el_lo, el_hi = response_grid.sg_passband.elevation_range
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/calculate_initial_guess.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/calculate_initial_guess.py
index 10192db9a..f6afe9b45 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/calculate_initial_guess.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/calculate_initial_guess.py
@@ -6,20 +6,22 @@
     BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
     PROTON_CHARGE_COULOMBS,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
     model_solar_wind_ideal_coincidence_rates,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
-    SolarWindFitContext,
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+    temperature_to_thermal_speed,
+    thermal_speed_to_temperature,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import average_spin_axis_rtn
 from imap_l3_processing.swapi.l3a.utils import (
     esa_voltage_to_proton_speed,
     optimal_density_scale,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.utils import average_spin_axis_rtn
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams, temperature_to_thermal_speed, \
-    thermal_speed_to_temperature
-
 
 # 1 eV
 INITIAL_TEMPERATURE_FLOOR_K = (
@@ -99,5 +101,3 @@ def gaussian(v, amplitude, mean, sigma):
         INITIAL_TEMPERATURE_FLOOR_K,
     )
     return float(bulk_speed_fit), float(temperature_fit)
-
-
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/escape_local_minimum.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/escape_local_minimum.py
index 6669cbafd..655798efa 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/escape_local_minimum.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/escape_local_minimum.py
@@ -16,7 +16,6 @@
 from imap_l3_processing.swapi.l3a.utils import optimal_density_scale
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
 
-
 _MAX_BASIN_REFINE_ITERS = 6
 _ROTATED_RMSE_RATIO_THRESHOLD = 10
 
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_solar_wind_proton_model.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_solar_wind_proton_model.py
index ecb5e6193..a061f9413 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_solar_wind_proton_model.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_solar_wind_proton_model.py
@@ -13,12 +13,12 @@
     LOG_TEMPERATURE_IDX,
     VELOCITY_SLICE,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.escape_local_minimum import (
-    escape_local_minimum,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.calculate_initial_guess import (
     calculate_initial_guess,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.escape_local_minimum import (
+    escape_local_minimum,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.optimize_solar_wind_proton_params import (
     OptimizeSolarWindProtonParamsResult,
     optimize_solar_wind_proton_params,
@@ -30,7 +30,6 @@
 )
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 
-
 _R_SQUARED_PEAK_HALF_WIDTH = 4
 
 
@@ -62,10 +61,7 @@ def _construct_fit_result(final_result, ctx):
     fit_r_squared = _coarse_peak_averaged_r_squared(
         final_result.residuals, ctx.count_rate
     )
-    flag_bad = (
-        fit_r_squared < 0.9
-        or final_result.sw_params.temperature > 5.0e5
-    )
+    flag_bad = fit_r_squared < 0.9 or final_result.sw_params.temperature > 5.0e5
     if flag_bad:
         return _nan_proton_fit_result(int(SwapiL3Flags.BAD_FIT))
 
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/optimize_solar_wind_proton_params.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/optimize_solar_wind_proton_params.py
index 09d07ad4d..45c954b88 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/optimize_solar_wind_proton_params.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/optimize_solar_wind_proton_params.py
@@ -17,8 +17,8 @@
 @dataclass
 class OptimizeSolarWindProtonParamsResult:
     sw_params: SolarWindParams
-    residuals: ndarray      # count-rate residuals at the solution
-    jacobian: ndarray       # ∂residuals/∂param, columns ordered per the params vector
+    residuals: ndarray  # count-rate residuals at the solution
+    jacobian: ndarray  # ∂residuals/∂param, columns ordered per the params vector
     success: bool
 
     @property
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/trim_open_aperture.py b/imap_l3_processing/swapi/l3a/science/solar_wind/trim_open_aperture.py
index 5d551d1fb..0b56f659b 100644
--- a/imap_l3_processing/swapi/l3a/science/solar_wind/trim_open_aperture.py
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/trim_open_aperture.py
@@ -5,7 +5,8 @@
 
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     SolarWindParams,
-    bulk_speed, thermal_speed,
+    bulk_speed,
+    thermal_speed,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.utils import (
     count_rate_conversion_factor,
@@ -21,7 +22,6 @@
 )
 from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
 
-
 OA_SCAN_THRESHOLD = 1e-6
 OA_SCAN_RESOLUTION = 64
 OA_SKIP_FRACTION = 1e-3
@@ -62,9 +62,11 @@ def trim_open_aperture(
             break
 
     dphi_deg = scan_azimuths[1] - scan_azimuths[0]
-    transmission_az_integral = float(np.trapezoid(
-        transmission_x_maxwellian[lower_index : upper_index + 1], dx=dphi_deg
-    ))
+    transmission_az_integral = float(
+        np.trapezoid(
+            transmission_x_maxwellian[lower_index : upper_index + 1], dx=dphi_deg
+        )
+    )
     upper_bound = _oa_rate_upper_bound(
         response_grid,
         sw_params,
@@ -88,15 +90,16 @@ def _evaluate_oa_integrand_along_azimuth(
     sigma = thermal_speed(sw_params)
     speed = bulk_speed(sw_params)
     central_speed = response_grid.central_speed
-    bulk_az, bulk_el = velocity_to_angles_in_instrument_frame(sw_params, rotation_matrix)
+    bulk_az, bulk_el = velocity_to_angles_in_instrument_frame(
+        sw_params, rotation_matrix
+    )
 
     sin_bulk_el = math.sin(np.radians(bulk_el))
     cos_bulk_el = math.cos(np.radians(bulk_el))
     sin_scan_el = math.sin(np.radians(scan_elevation))
     cos_scan_el = math.cos(np.radians(scan_elevation))
-    cos_view_to_bulk = (
-        sin_bulk_el * sin_scan_el + cos_bulk_el * cos_scan_el
-        * np.cos(np.radians(scan_azimuths - bulk_az))
+    cos_view_to_bulk = sin_bulk_el * sin_scan_el + cos_bulk_el * cos_scan_el * np.cos(
+        np.radians(scan_azimuths - bulk_az)
     )
     delta_v_sq = (
         central_speed**2 + speed**2 - 2.0 * central_speed * speed * cos_view_to_bulk
@@ -125,9 +128,9 @@ def _oa_rate_upper_bound(
     ratio_lo, ratio_hi = speed_ratio_range_at_elevation(response_grid.oa_passband, 0.0)
     delta_v = central_speed * (ratio_hi - ratio_lo)
     return (
-            count_rate_conversion_factor(sw_params, response_grid)
-            * central_speed ** 3
-            * delta_theta_deg
-            * delta_v
-            * transmission_az_integral
+        count_rate_conversion_factor(sw_params, response_grid)
+        * central_speed**3
+        * delta_theta_deg
+        * delta_v
+        * transmission_az_integral
     )
diff --git a/imap_l3_processing/swapi/l3a/science/uncertainties.py b/imap_l3_processing/swapi/l3a/science/uncertainties.py
index d35c3d2a4..d10c8033a 100644
--- a/imap_l3_processing/swapi/l3a/science/uncertainties.py
+++ b/imap_l3_processing/swapi/l3a/science/uncertainties.py
@@ -3,9 +3,7 @@
 from uncertainties import UFloat, correlated_values, ufloat
 
 
-def compute_hc3_parameter_covariance(
-    jacobian: ndarray, residuals: ndarray
-) -> ndarray:
+def compute_hc3_parameter_covariance(jacobian: ndarray, residuals: ndarray) -> ndarray:
     n_params = jacobian.shape[1]
     try:
         JT_J_pseudoinverse = np.linalg.pinv(jacobian.T @ jacobian)
@@ -17,9 +15,7 @@ def compute_hc3_parameter_covariance(
         )
         h_ii_clipped = np.clip(h_ii, 0.0, 0.9999)
         hc3_weights = (residuals / (1.0 - h_ii_clipped)) ** 2
-        sandwich_middle = np.einsum(
-            "ki,k,kj->ij", jacobian, hc3_weights, jacobian
-        )
+        sandwich_middle = np.einsum("ki,k,kj->ij", jacobian, hc3_weights, jacobian)
         return JT_J_pseudoinverse @ sandwich_middle @ JT_J_pseudoinverse
     except np.linalg.LinAlgError:
         return np.full((n_params, n_params), np.nan)
diff --git a/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py b/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py
index da826015f..80ffee85d 100644
--- a/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py
+++ b/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py
@@ -1,25 +1,33 @@
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional
 
 from imap_data_access import download
 from imap_data_access.processing_input import ProcessingInputCollection
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.models import MagData
-from imap_l3_processing.swapi.descriptors import SWAPI_L2_DESCRIPTOR, \
-    DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, \
-    HELIUM_INFLOW_VECTOR_DESCRIPTOR, AZIMUTHAL_TRANSMISSION_DESCRIPTOR, \
-    CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR, \
-    MAG_RTN_DESCRIPTOR
+from imap_l3_processing.swapi.descriptors import (
+    AZIMUTHAL_TRANSMISSION_DESCRIPTOR,
+    CENTRAL_EFFECTIVE_AREA_DESCRIPTOR,
+    DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR,
+    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR,
+    HELIUM_INFLOW_VECTOR_DESCRIPTOR,
+    HYDROGEN_INFLOW_VECTOR_DESCRIPTOR,
+    MAG_RTN_DESCRIPTOR,
+    PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR,
+    SWAPI_L2_DESCRIPTOR,
+)
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
-from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import \
-    DensityOfNeutralHeliumLookupTable
+from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import (
+    DensityOfNeutralHeliumLookupTable,
+)
 from imap_l3_processing.swapi.l3a.science.pickup_ion.inflow_vector import InflowVector
 from imap_l3_processing.swapi.l3a.utils import read_l2_swapi_data, read_mag_rtn_data
-from imap_l3_processing.utils import select_mag_path
-from imap_l3_processing.swapi.response.efficiency_calibration_table import EfficiencyCalibrationTable
+from imap_l3_processing.swapi.response.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from imap_l3_processing.utils import select_mag_path
 
 
 @dataclass
@@ -30,20 +38,36 @@ class SwapiL3ADependencies:
     hydrogen_inflow_vector: InflowVector
     helium_inflow_vector: InflowVector
     swapi_response: SwapiResponse
-    mag_data: Optional[MagData] = None
+    mag_data: MagData | None = None
     mag_is_preliminary: bool = False
 
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         # @formatter:off
-        science_dependency_file = dependencies.get_file_paths(source='swapi', descriptor=SWAPI_L2_DESCRIPTOR)
-        efficiency_calibration_table = dependencies.get_file_paths(source='swapi', descriptor=EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR)
-        neutral_helium_table = dependencies.get_file_paths(source='swapi', descriptor=DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR)
-        hydrogen_vector_paths = dependencies.get_file_paths(source='swapi', descriptor=HYDROGEN_INFLOW_VECTOR_DESCRIPTOR)
-        helium_vector_paths = dependencies.get_file_paths(source='swapi', descriptor=HELIUM_INFLOW_VECTOR_DESCRIPTOR)
-        azimuthal_transmission_paths = dependencies.get_file_paths(source='swapi', descriptor=AZIMUTHAL_TRANSMISSION_DESCRIPTOR)
-        central_effective_area_paths = dependencies.get_file_paths(source='swapi', descriptor=CENTRAL_EFFECTIVE_AREA_DESCRIPTOR)
-        passband_fit_coefficients_paths = dependencies.get_file_paths(source='swapi', descriptor=PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR)
+        science_dependency_file = dependencies.get_file_paths(
+            source="swapi", descriptor=SWAPI_L2_DESCRIPTOR
+        )
+        efficiency_calibration_table = dependencies.get_file_paths(
+            source="swapi", descriptor=EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR
+        )
+        neutral_helium_table = dependencies.get_file_paths(
+            source="swapi", descriptor=DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR
+        )
+        hydrogen_vector_paths = dependencies.get_file_paths(
+            source="swapi", descriptor=HYDROGEN_INFLOW_VECTOR_DESCRIPTOR
+        )
+        helium_vector_paths = dependencies.get_file_paths(
+            source="swapi", descriptor=HELIUM_INFLOW_VECTOR_DESCRIPTOR
+        )
+        azimuthal_transmission_paths = dependencies.get_file_paths(
+            source="swapi", descriptor=AZIMUTHAL_TRANSMISSION_DESCRIPTOR
+        )
+        central_effective_area_paths = dependencies.get_file_paths(
+            source="swapi", descriptor=CENTRAL_EFFECTIVE_AREA_DESCRIPTOR
+        )
+        passband_fit_coefficients_paths = dependencies.get_file_paths(
+            source="swapi", descriptor=PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR
+        )
         # @formatter:on
 
         mag_path, mag_level = select_mag_path(dependencies, MAG_RTN_DESCRIPTOR)
@@ -63,25 +87,35 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         )
 
     @classmethod
-    def from_file_paths(cls, science_dependency_path: Path,
-                        efficiency_calibration_path: Path,
-                        neutral_helium_path: Path, hydrogen_inflow_vector_path: Path,
-                        helium_inflow_vector_path: Path, azimuthal_transmission_path: Path,
-                        central_effective_area_path: Path, passband_fit_coefficients_path: Path,
-                        mag_path: Optional[Path] = None,
-                        mag_is_preliminary: bool = False):
+    def from_file_paths(
+        cls,
+        science_dependency_path: Path,
+        efficiency_calibration_path: Path,
+        neutral_helium_path: Path,
+        hydrogen_inflow_vector_path: Path,
+        helium_inflow_vector_path: Path,
+        azimuthal_transmission_path: Path,
+        central_effective_area_path: Path,
+        passband_fit_coefficients_path: Path,
+        mag_path: Path | None = None,
+        mag_is_preliminary: bool = False,
+    ):
         return cls(
             data=read_l2_swapi_data(CDF(str(science_dependency_path))),
-            efficiency_calibration_table=EfficiencyCalibrationTable(efficiency_calibration_path),
+            efficiency_calibration_table=EfficiencyCalibrationTable(
+                efficiency_calibration_path
+            ),
             density_of_neutral_helium_calibration_table=DensityOfNeutralHeliumLookupTable.from_file(
-                neutral_helium_path),
+                neutral_helium_path
+            ),
             hydrogen_inflow_vector=InflowVector.from_file(hydrogen_inflow_vector_path),
             helium_inflow_vector=InflowVector.from_file(helium_inflow_vector_path),
             swapi_response=SwapiResponse.from_files(
                 azimuthal_transmission_path=azimuthal_transmission_path,
                 central_effective_area_path=central_effective_area_path,
                 passband_fit_coefficients_path=passband_fit_coefficients_path,
-                efficiency_table_path=efficiency_calibration_path),
+                efficiency_table_path=efficiency_calibration_path,
+            ),
             mag_data=read_mag_rtn_data(mag_path) if mag_path is not None else None,
             mag_is_preliminary=mag_is_preliminary,
         )
diff --git a/imap_l3_processing/swapi/l3a/utils.py b/imap_l3_processing/swapi/l3a/utils.py
index 39ec38e39..915410662 100644
--- a/imap_l3_processing/swapi/l3a/utils.py
+++ b/imap_l3_processing/swapi/l3a/utils.py
@@ -1,10 +1,16 @@
 import math
+from collections.abc import Iterable
 from datetime import datetime
-from typing import Iterable
 
 import numba
 import numpy as np
 import scipy.optimize
+from imap_processing.spice.geometry import (
+    SpiceFrame,
+    get_rotation_matrix,
+    imap_state,
+)
+from imap_processing.spice.time import ttj2000ns_to_et
 from numpy import ndarray
 from numpy.typing import ArrayLike
 from spacepy import pycdf
@@ -31,13 +37,6 @@
 )
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
-from imap_processing.spice.geometry import (
-    SpiceFrame,
-    get_rotation_matrix,
-    imap_state,
-)
-from imap_processing.spice.time import ttj2000ns_to_et
-
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
 
 
@@ -150,7 +149,9 @@ def velocity_to_angles_in_instrument_frame(
     flow-vs-look sign convention.
     """
     v_xyz = rotation_xyz_to_rtn.T @ sw_params.velocity_rtn
-    return velocity_components_to_angles_in_instrument_frame(v_xyz[0], v_xyz[1], v_xyz[2])
+    return velocity_components_to_angles_in_instrument_frame(
+        v_xyz[0], v_xyz[1], v_xyz[2]
+    )
 
 
 def compute_direction_of_mean_magnetic_field_over_chunk(
diff --git a/imap_l3_processing/swapi/l3b/models.py b/imap_l3_processing/swapi/l3b/models.py
index 87a54d45f..26e7c34f4 100644
--- a/imap_l3_processing/swapi/l3b/models.py
+++ b/imap_l3_processing/swapi/l3b/models.py
@@ -7,15 +7,22 @@
 
 from imap_l3_processing.constants import FIVE_MINUTES_IN_NANOSECONDS
 from imap_l3_processing.models import DataProduct, DataProductVariable
-from imap_l3_processing.swapi.l3a.models import EPOCH_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME
+from imap_l3_processing.swapi.l3a.models import (
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+)
 
 SOLAR_WIND_ENERGY_CDF_VAR_NAME = "combined_energy"
-SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME = "combined_energy_stat_uncert_minus"
+SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME = (
+    "combined_energy_stat_uncert_minus"
+)
 SOLAR_WIND_COMBINED_ENERGY_DELTA_PLUS_CDF_VAR_NAME = "combined_energy_stat_uncert_plus"
 SOLAR_WIND_ENERGY_LABEL_CDF_VAR_NAME = "combined_energy_label"
 
 COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_CDF_VAR_NAME = "combined_differential_flux"
-COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME = "combined_differential_flux_stat_uncert"
+COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME = (
+    "combined_differential_flux_stat_uncert"
+)
 
 
 @dataclass
@@ -44,16 +51,31 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             "ESA %02d", np.arange(start=1, stop=energy_count + 1)
         )
         return [
-            DataProductVariable(EPOCH_CDF_VAR_NAME, self.epoch, cdf_data_type=pycdf.const.CDF_TIME_TT2000),
-            DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, np.full_like(self.epoch, FIVE_MINUTES_IN_NANOSECONDS)),
-
+            DataProductVariable(
+                EPOCH_CDF_VAR_NAME,
+                self.epoch,
+                cdf_data_type=pycdf.const.CDF_TIME_TT2000,
+            ),
+            DataProductVariable(
+                EPOCH_DELTA_CDF_VAR_NAME,
+                np.full_like(self.epoch, FIVE_MINUTES_IN_NANOSECONDS),
+            ),
             DataProductVariable(SOLAR_WIND_ENERGY_CDF_VAR_NAME, self.combined_energy),
-            DataProductVariable(SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME, self.combined_energy_delta_minus),
-            DataProductVariable(SOLAR_WIND_COMBINED_ENERGY_DELTA_PLUS_CDF_VAR_NAME, self.combined_energy_delta_plus),
+            DataProductVariable(
+                SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME,
+                self.combined_energy_delta_minus,
+            ),
+            DataProductVariable(
+                SOLAR_WIND_COMBINED_ENERGY_DELTA_PLUS_CDF_VAR_NAME,
+                self.combined_energy_delta_plus,
+            ),
             DataProductVariable(SOLAR_WIND_ENERGY_LABEL_CDF_VAR_NAME, energy_labels),
-
-            DataProductVariable(COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_CDF_VAR_NAME,
-                                nominal_values(self.combined_differential_flux)),
-            DataProductVariable(COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME,
-                                std_devs(self.combined_differential_flux)),
+            DataProductVariable(
+                COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_CDF_VAR_NAME,
+                nominal_values(self.combined_differential_flux),
+            ),
+            DataProductVariable(
+                COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME,
+                std_devs(self.combined_differential_flux),
+            ),
         ]
diff --git a/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_differential_flux.py b/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_differential_flux.py
index ec6695a56..a014fed21 100644
--- a/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_differential_flux.py
+++ b/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_differential_flux.py
@@ -1,15 +1,22 @@
 from numpy import ndarray
 
-from imap_l3_processing.constants import METERS_PER_KILOMETER, CENTIMETERS_PER_METER
-from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
+from imap_l3_processing.constants import CENTIMETERS_PER_METER, METERS_PER_KILOMETER
+from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import (
+    GeometricFactorCalibrationTable,
+)
 
 
-def calculate_combined_solar_wind_differential_flux(energies: ndarray, average_count_rates: ndarray,
-                                                    efficiency: float,
-                                                    geometric_factor_table: GeometricFactorCalibrationTable):
+def calculate_combined_solar_wind_differential_flux(
+    energies: ndarray,
+    average_count_rates: ndarray,
+    efficiency: float,
+    geometric_factor_table: GeometricFactorCalibrationTable,
+):
     geometric_factor = geometric_factor_table.lookup_geometric_factor(energies)
 
     denominator = energies * geometric_factor * efficiency
     result_per_square_km = average_count_rates / denominator
-    result_per_square_cm = result_per_square_km / (METERS_PER_KILOMETER * CENTIMETERS_PER_METER) ** 2
+    result_per_square_cm = (
+        result_per_square_km / (METERS_PER_KILOMETER * CENTIMETERS_PER_METER) ** 2
+    )
     return result_per_square_cm
diff --git a/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py b/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py
index 96d8e6fcb..09b214dee 100644
--- a/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py
+++ b/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py
@@ -5,15 +5,30 @@
 import numpy as np
 from numpy import ndarray
 
-from imap_l3_processing.constants import PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, ALPHA_PARTICLE_CHARGE_COULOMBS, \
-    ALPHA_PARTICLE_MASS_KG, HE_PUI_PARTICLE_MASS_KG, PUI_PARTICLE_CHARGE_COULOMBS, METERS_PER_KILOMETER, \
-    CENTIMETERS_PER_METER
+from imap_l3_processing.constants import (
+    ALPHA_PARTICLE_CHARGE_COULOMBS,
+    ALPHA_PARTICLE_MASS_KG,
+    CENTIMETERS_PER_METER,
+    HE_PUI_PARTICLE_MASS_KG,
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+    PUI_PARTICLE_CHARGE_COULOMBS,
+)
 from imap_l3_processing.swapi.l3a.utils import calculate_sw_speed
-from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
-
-
-def calculate_vdf(particle_mass, particle_charge, energies: ndarray, average_count_rates: ndarray,
-                  efficiency: float, geometric_factor_table: GeometricFactorCalibrationTable):
+from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import (
+    GeometricFactorCalibrationTable,
+)
+
+
+def calculate_vdf(
+    particle_mass,
+    particle_charge,
+    energies: ndarray,
+    average_count_rates: ndarray,
+    efficiency: float,
+    geometric_factor_table: GeometricFactorCalibrationTable,
+):
     velocities = calculate_sw_speed(particle_mass, particle_charge, energies)
     geometric_factors = geometric_factor_table.lookup_geometric_factor(energies)
     geometric_factors *= (METERS_PER_KILOMETER * CENTIMETERS_PER_METER) ** 2
@@ -22,31 +37,59 @@ def calculate_vdf(particle_mass, particle_charge, energies: ndarray, average_cou
 
     numerator = 4 * np.pi * proton_mass_per_charge * average_count_rates
 
-    denominator = (energies * geometric_factors * efficiency)
+    denominator = energies * geometric_factors * efficiency
 
     probabilities = numerator / denominator
 
     return velocities, probabilities
 
 
-def calculate_proton_solar_wind_vdf(energies: ndarray, average_count_rates: ndarray,
-                                    efficiency: float, geometric_factor_table: GeometricFactorCalibrationTable):
-    return calculate_vdf(PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, energies, average_count_rates, efficiency,
-                         geometric_factor_table)
-
-
-def calculate_alpha_solar_wind_vdf(energies: ndarray, average_count_rates: ndarray,
-                                   efficiency: float, geometric_factor_table: GeometricFactorCalibrationTable):
-    return calculate_vdf(ALPHA_PARTICLE_MASS_KG, ALPHA_PARTICLE_CHARGE_COULOMBS, energies,
-                         average_count_rates, efficiency,
-                         geometric_factor_table)
-
-
-def calculate_pui_solar_wind_vdf(energies: ndarray, average_count_rates: ndarray, efficiency: float,
-                                 geometric_factor_table: GeometricFactorCalibrationTable):
-    return calculate_vdf(HE_PUI_PARTICLE_MASS_KG, PUI_PARTICLE_CHARGE_COULOMBS, energies,
-                         average_count_rates, efficiency,
-                         geometric_factor_table)
+def calculate_proton_solar_wind_vdf(
+    energies: ndarray,
+    average_count_rates: ndarray,
+    efficiency: float,
+    geometric_factor_table: GeometricFactorCalibrationTable,
+):
+    return calculate_vdf(
+        PROTON_MASS_KG,
+        PROTON_CHARGE_COULOMBS,
+        energies,
+        average_count_rates,
+        efficiency,
+        geometric_factor_table,
+    )
+
+
+def calculate_alpha_solar_wind_vdf(
+    energies: ndarray,
+    average_count_rates: ndarray,
+    efficiency: float,
+    geometric_factor_table: GeometricFactorCalibrationTable,
+):
+    return calculate_vdf(
+        ALPHA_PARTICLE_MASS_KG,
+        ALPHA_PARTICLE_CHARGE_COULOMBS,
+        energies,
+        average_count_rates,
+        efficiency,
+        geometric_factor_table,
+    )
+
+
+def calculate_pui_solar_wind_vdf(
+    energies: ndarray,
+    average_count_rates: ndarray,
+    efficiency: float,
+    geometric_factor_table: GeometricFactorCalibrationTable,
+):
+    return calculate_vdf(
+        HE_PUI_PARTICLE_MASS_KG,
+        PUI_PARTICLE_CHARGE_COULOMBS,
+        energies,
+        average_count_rates,
+        efficiency,
+        geometric_factor_table,
+    )
 
 
 @dataclass
@@ -62,4 +105,7 @@ def calculate_delta_minus_plus(nominal_values: ndarray) -> DeltaMinusPlus:
     right_edges = nominal_values * [*half_ratios, half_ratios[-1]]
     lower_bounds = np.minimum(left_edges, right_edges)
     upper_bounds = np.maximum(left_edges, right_edges)
-    return DeltaMinusPlus(delta_minus=nominal_values - lower_bounds, delta_plus=upper_bounds - nominal_values)
+    return DeltaMinusPlus(
+        delta_minus=nominal_values - lower_bounds,
+        delta_plus=upper_bounds - nominal_values,
+    )
diff --git a/imap_l3_processing/swapi/l3b/science/geometric_factor_calibration_table.py b/imap_l3_processing/swapi/l3b/science/geometric_factor_calibration_table.py
index 25e0a4f63..076188583 100644
--- a/imap_l3_processing/swapi/l3b/science/geometric_factor_calibration_table.py
+++ b/imap_l3_processing/swapi/l3b/science/geometric_factor_calibration_table.py
@@ -14,5 +14,5 @@ def lookup_geometric_factor(self, energy):
 
     @classmethod
     def from_file(cls, file_path) -> GeometricFactorCalibrationTable:
-        data = np.loadtxt(file_path, skiprows=1, delimiter=',')
+        data = np.loadtxt(file_path, skiprows=1, delimiter=",")
         return cls(data)
diff --git a/imap_l3_processing/swapi/l3b/swapi_l3b_dependencies.py b/imap_l3_processing/swapi/l3b/swapi_l3b_dependencies.py
index b4774f320..a04b47921 100644
--- a/imap_l3_processing/swapi/l3b/swapi_l3b_dependencies.py
+++ b/imap_l3_processing/swapi/l3b/swapi_l3b_dependencies.py
@@ -5,12 +5,19 @@
 from imap_data_access.processing_input import ProcessingInputCollection
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.swapi.descriptors import EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
-    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR, SWAPI_L2_DESCRIPTOR
+from imap_l3_processing.swapi.descriptors import (
+    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR,
+    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR,
+    SWAPI_L2_DESCRIPTOR,
+)
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
 from imap_l3_processing.swapi.l3a.utils import read_l2_swapi_data
-from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import GeometricFactorCalibrationTable
-from imap_l3_processing.swapi.response.efficiency_calibration_table import EfficiencyCalibrationTable
+from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import (
+    GeometricFactorCalibrationTable,
+)
+from imap_l3_processing.swapi.response.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
 
 
 @dataclass
@@ -21,28 +28,43 @@ class SwapiL3BDependencies:
 
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
-        science_dependency_file = dependencies.get_file_paths(source='swapi', descriptor=SWAPI_L2_DESCRIPTOR)
-        geometric_factor_lookup_table_file = dependencies.get_file_paths(source='swapi',
-                                                                         descriptor=GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR)
-        efficiency_table_lookup_file = dependencies.get_file_paths(source='swapi',
-                                                                   descriptor=EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR)
+        science_dependency_file = dependencies.get_file_paths(
+            source="swapi", descriptor=SWAPI_L2_DESCRIPTOR
+        )
+        geometric_factor_lookup_table_file = dependencies.get_file_paths(
+            source="swapi", descriptor=GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR
+        )
+        efficiency_table_lookup_file = dependencies.get_file_paths(
+            source="swapi", descriptor=EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR
+        )
 
         download(science_dependency_file[0])
         download(geometric_factor_lookup_table_file[0])
         download(efficiency_table_lookup_file[0])
 
-        return cls.from_file_paths(science_dependency_file[0],
-                                   geometric_factor_lookup_table_file[0],
-                                   efficiency_table_lookup_file[0])
+        return cls.from_file_paths(
+            science_dependency_file[0],
+            geometric_factor_lookup_table_file[0],
+            efficiency_table_lookup_file[0],
+        )
 
     @classmethod
-    def from_file_paths(cls, science_dependency_path: Path, geometric_factor_calibration_path: Path,
-                        efficiency_calibration_table_path: Path):
+    def from_file_paths(
+        cls,
+        science_dependency_path: Path,
+        geometric_factor_calibration_path: Path,
+        efficiency_calibration_table_path: Path,
+    ):
         swapi_l2_data = read_l2_swapi_data(CDF(str(science_dependency_path)))
         geometric_factor_calibration_lookup = GeometricFactorCalibrationTable.from_file(
-            geometric_factor_calibration_path)
-        efficiency_calibration_table = EfficiencyCalibrationTable(efficiency_calibration_table_path)
+            geometric_factor_calibration_path
+        )
+        efficiency_calibration_table = EfficiencyCalibrationTable(
+            efficiency_calibration_table_path
+        )
 
-        return cls(swapi_l2_data,
-                   geometric_factor_calibration_lookup,
-                   efficiency_calibration_table)
+        return cls(
+            swapi_l2_data,
+            geometric_factor_calibration_lookup,
+            efficiency_calibration_table,
+        )
diff --git a/imap_l3_processing/swapi/quality_flags.py b/imap_l3_processing/swapi/quality_flags.py
index 23dd06515..e299678f6 100644
--- a/imap_l3_processing/swapi/quality_flags.py
+++ b/imap_l3_processing/swapi/quality_flags.py
@@ -1,4 +1,4 @@
-from imap_processing.quality_flags import FlagNameMixin, CommonFlags
+from imap_processing.quality_flags import CommonFlags, FlagNameMixin
 
 
 class SwapiL3Flags(FlagNameMixin):
diff --git a/imap_l3_processing/swapi/response/azimuthal_transmission.py b/imap_l3_processing/swapi/response/azimuthal_transmission.py
index c7c84589d..becce5941 100644
--- a/imap_l3_processing/swapi/response/azimuthal_transmission.py
+++ b/imap_l3_processing/swapi/response/azimuthal_transmission.py
@@ -5,7 +5,6 @@
 import numpy as np
 from numpy import ndarray
 
-
 SG_PLATEAU_AZIMUTH_MAX_DEG = 9.0
 SG_PLATEAU_TRANSMISSION = 1 / 1000
 
@@ -44,10 +43,7 @@ def interpolate_azimuthal_transmission(
 
     weight_lower = float(i_upper) - i_float
     weight_upper = i_float - float(i_lower)
-    return (
-        grid.values[i_lower] * weight_lower
-        + grid.values[i_upper] * weight_upper
-    )
+    return grid.values[i_lower] * weight_lower + grid.values[i_upper] * weight_upper
 
 
 def validate_azimuthal_transmission_values(values: ndarray, spacing: float) -> None:
diff --git a/imap_l3_processing/swapi/response/efficiency_calibration_table.py b/imap_l3_processing/swapi/response/efficiency_calibration_table.py
index 447ff9e2c..7d74dd09f 100644
--- a/imap_l3_processing/swapi/response/efficiency_calibration_table.py
+++ b/imap_l3_processing/swapi/response/efficiency_calibration_table.py
@@ -15,14 +15,29 @@
 
 class EfficiencyCalibrationTable:
     def __init__(self, path):
-        self.data = np.loadtxt(path, dtype=[("time", "M8[ns]"), ("MET", "i8"), (_PROTON_EFFICIENCY_COLUMN, "f8"), (_HELIUM_EFFICIENCY_COLUMN, "f8")], ndmin=1)
+        self.data = np.loadtxt(
+            path,
+            dtype=[
+                ("time", "M8[ns]"),
+                ("MET", "i8"),
+                (_PROTON_EFFICIENCY_COLUMN, "f8"),
+                (_HELIUM_EFFICIENCY_COLUMN, "f8"),
+            ],
+            ndmin=1,
+        )
 
     def relative_efficiency(self, time_as_tt2000: int, species: Species) -> float:
-        return self._get_entry_for(time_as_tt2000, _EFFICIENCY_COLUMN_BY_SPECIES[species])
+        return self._get_entry_for(
+            time_as_tt2000, _EFFICIENCY_COLUMN_BY_SPECIES[species]
+        )
 
     def _get_entry_for(self, time_as_tt2000, column: str) -> float:
         for d in reversed(self.data):
-            if d["time"] < np.datetime64(pycdf.lib.tt2000_to_datetime(int(time_as_tt2000)), "ns"):
+            if d["time"] < np.datetime64(
+                pycdf.lib.tt2000_to_datetime(int(time_as_tt2000)), "ns"
+            ):
                 return float(d[column])
 
-        raise ValueError(f"No efficiency data for {pycdf.lib.tt2000_to_datetime(time_as_tt2000)}")
+        raise ValueError(
+            f"No efficiency data for {pycdf.lib.tt2000_to_datetime(time_as_tt2000)}"
+        )
diff --git a/imap_l3_processing/swapi/response/passband_grid.py b/imap_l3_processing/swapi/response/passband_grid.py
index 96a54cc5e..b72a4ad01 100644
--- a/imap_l3_processing/swapi/response/passband_grid.py
+++ b/imap_l3_processing/swapi/response/passband_grid.py
@@ -68,8 +68,8 @@ def speed_ratio_range_at_elevation(grid, elevation: float):
         return upper_min, upper_max
     if upper_is_nan:
         return lower_min, lower_max
-    lo = lower_min if lower_min < upper_min else upper_min
-    hi = lower_max if lower_max > upper_max else upper_max
+    lo = min(upper_min, lower_min)
+    hi = max(upper_max, lower_max)
     return lo, hi
 
 
diff --git a/imap_l3_processing/swapi/response/swapi_response.py b/imap_l3_processing/swapi/response/swapi_response.py
index 7f2a2a856..8a0632ceb 100644
--- a/imap_l3_processing/swapi/response/swapi_response.py
+++ b/imap_l3_processing/swapi/response/swapi_response.py
@@ -1,5 +1,6 @@
+from collections.abc import Callable
 from pathlib import Path
-from typing import Callable, NamedTuple
+from typing import NamedTuple
 
 import numpy as np
 import pandas as pd
@@ -10,16 +11,18 @@
     METERS_PER_KILOMETER,
     PROTON_CHARGE_OVER_MASS_C_PER_KG,
 )
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.response.azimuthal_transmission import (
     AzimuthalTransmissionGrid,
     validate_azimuthal_transmission_values,
 )
-from imap_l3_processing.swapi.response.efficiency_calibration_table import EfficiencyCalibrationTable
+from imap_l3_processing.swapi.response.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
 from imap_l3_processing.swapi.response.passband_grid import (
     PassbandGrid,
     build_passband_grid,
 )
-from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.species import Species
 
 
@@ -43,9 +46,9 @@ def __init__(
         efficiency_table: EfficiencyCalibrationTable,
     ):
         self._azimuthal_transmission_grid = AzimuthalTransmissionGrid(
-                values=np.asarray(azimuthal_transmission, dtype=float),
-                spacing=float(self.AZIMUTHAL_TRANSMISSION_SPACING_DEG),
-            )
+            values=np.asarray(azimuthal_transmission, dtype=float),
+            spacing=float(self.AZIMUTHAL_TRANSMISSION_SPACING_DEG),
+        )
         self._central_effective_area_at_voltage = central_effective_area_at_voltage
         self._passband_fit_coefficients = passband_fit_coefficients
         self._passband_esa_voltage_limits = passband_esa_voltage_limits
@@ -137,7 +140,9 @@ def get_response_grid(
         esa_voltage: float,
         species: Species,
     ) -> ResponseGrid:
-        relative_efficiency = self._efficiency_table.relative_efficiency(time_as_tt2000, species)
+        relative_efficiency = self._efficiency_table.relative_efficiency(
+            time_as_tt2000, species
+        )
 
         cache_key = (
             self._cache_key(float(esa_voltage)),
@@ -160,8 +165,11 @@ def get_response_grid(
         response_grid = ResponseGrid(
             sg_passband=passband_pair["SG"],
             oa_passband=passband_pair["OA"],
-            central_speed=self._central_speed(esa_voltage, species.mass_per_charge_m_p_per_e),
-            central_effective_area=self._get_central_effective_area(esa_voltage) * relative_efficiency,
+            central_speed=self._central_speed(
+                esa_voltage, species.mass_per_charge_m_p_per_e
+            ),
+            central_effective_area=self._get_central_effective_area(esa_voltage)
+            * relative_efficiency,
             azimuthal_transmission=self._azimuthal_transmission_grid,
         )
 
diff --git a/imap_l3_processing/swapi/swapi_processor.py b/imap_l3_processing/swapi/swapi_processor.py
index 352b8f227..215cd6d2f 100644
--- a/imap_l3_processing/swapi/swapi_processor.py
+++ b/imap_l3_processing/swapi/swapi_processor.py
@@ -8,6 +8,10 @@
 from imap_l3_processing.constants import FIVE_MINUTES_IN_NANOSECONDS
 from imap_l3_processing.models import InputMetadata
 from imap_l3_processing.processor import Processor
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+)
 from imap_l3_processing.swapi.l3a.chunk_fits import (
     AlphaChunkFitter,
     ParallelChunkRunner,
@@ -15,13 +19,9 @@
     PuiChunkFitter,
 )
 from imap_l3_processing.swapi.l3a.models import (
-    SwapiL3ProtonSolarWindData,
     SwapiL3AlphaSolarWindData,
     SwapiL3PickupIonData,
-)
-from imap_l3_processing.swapi.constants import (
-    SWAPI_COARSE_SWEEP_BINS,
-    SWAPI_L2_K_FACTOR,
+    SwapiL3ProtonSolarWindData,
 )
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
 from imap_l3_processing.swapi.l3a.utils import (
@@ -32,10 +32,10 @@
     calculate_combined_solar_wind_differential_flux,
 )
 from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import (
-    calculate_proton_solar_wind_vdf,
     calculate_alpha_solar_wind_vdf,
-    calculate_pui_solar_wind_vdf,
     calculate_delta_minus_plus,
+    calculate_proton_solar_wind_vdf,
+    calculate_pui_solar_wind_vdf,
 )
 from imap_l3_processing.swapi.l3b.swapi_l3b_dependencies import SwapiL3BDependencies
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
@@ -107,7 +107,9 @@ def process_l3a_alpha(self, data, dependencies) -> SwapiL3AlphaSolarWindData:
         result = runner.run(chunks, fitter)
 
         if dependencies.mag_is_preliminary:
-            result["quality_flags"] = result["quality_flags"] | int(SwapiL3Flags.PRELIMINARY_MAG)
+            result["quality_flags"] = result["quality_flags"] | int(
+                SwapiL3Flags.PRELIMINARY_MAG
+            )
 
         metadata = replace(self.input_metadata, descriptor="alpha-sw")
         return SwapiL3AlphaSolarWindData(metadata, **result)
@@ -161,8 +163,12 @@ def process_l3b(self, data, dependencies):
                 data_chunk.coincidence_count_rate,
                 data_chunk.coincidence_count_rate_uncertainty,
             )
-            coarse_rates = coincidence_count_rates_with_uncertainty[:, SWAPI_COARSE_SWEEP_BINS]
-            average_coincident_count_rates = np.sum(coarse_rates, axis=0) / len(coarse_rates)
+            coarse_rates = coincidence_count_rates_with_uncertainty[
+                :, SWAPI_COARSE_SWEEP_BINS
+            ]
+            average_coincident_count_rates = np.sum(coarse_rates, axis=0) / len(
+                coarse_rates
+            )
             energies = np.mean(data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS], axis=0)
             proton_velocities, proton_probabilities = calculate_proton_solar_wind_vdf(
                 energies,
diff --git a/imap_l3_processing/swe/l3/models.py b/imap_l3_processing/swe/l3/models.py
index 7a3a776aa..f25102c4f 100644
--- a/imap_l3_processing/swe/l3/models.py
+++ b/imap_l3_processing/swe/l3/models.py
@@ -17,14 +17,22 @@
 GYROPHASE_BINS_CDF_VAR_NAME = "gyrophase"
 GYROPHASE_DELTA_CDF_VAR_NAME = "gyrophase_delta"
 PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME = "phase_space_density_by_pitch_angle"
-PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME = "phase_space_density_by_pitch_angle_and_gyrophase"
+PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME = (
+    "phase_space_density_by_pitch_angle_and_gyrophase"
+)
 PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME = "phase_space_density_1d"
 PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME = "phase_space_density_inward"
 PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME = "phase_space_density_outward"
 INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME = "intensity_by_pitch_angle"
-INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME = "intensity_by_pitch_angle_and_gyrophase"
-INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME = "intensity_uncertainty_by_pitch_angle"
-INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME = "intensity_uncertainty_by_pitch_angle_and_gyrophase"
+INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME = (
+    "intensity_by_pitch_angle_and_gyrophase"
+)
+INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME = (
+    "intensity_uncertainty_by_pitch_angle"
+)
+INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME = (
+    "intensity_uncertainty_by_pitch_angle_and_gyrophase"
+)
 SPACECRAFT_POTENTIAL_CDF_VAR_NAME = "spacecraft_potential"
 CORE_HALO_BREAKPOINT_CDF_VAR_NAME = "core_halo_breakpoint"
 CORE_FIT_NUM_POINTS_CDF_VAR_NAME = "core_fit_num_points"
@@ -52,40 +60,70 @@
 TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME = "total_speed_integrated"
 CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME = "core_velocity_vector_rtn_integrated"
 HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME = "halo_velocity_vector_rtn_integrated"
-TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME = "total_velocity_vector_rtn_integrated"
+TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME = (
+    "total_velocity_vector_rtn_integrated"
+)
 CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME = "core_heat_flux_magnitude_integrated"
 CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME = "core_heat_flux_theta_integrated"
 CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME = "core_heat_flux_phi_integrated"
 HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME = "halo_heat_flux_magnitude_integrated"
 HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME = "halo_heat_flux_theta_integrated"
 HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME = "halo_heat_flux_phi_integrated"
-TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME = "total_heat_flux_magnitude_integrated"
+TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME = (
+    "total_heat_flux_magnitude_integrated"
+)
 TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME = "total_heat_flux_theta_integrated"
 TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME = "total_heat_flux_phi_integrated"
 CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME = "core_t_parallel_integrated"
 CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME = "core_t_perpendicular_integrated"
-CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME = "core_t_ratio_perpendicular_integrated"
+CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME = (
+    "core_t_ratio_perpendicular_integrated"
+)
 HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME = "halo_t_parallel_integrated"
 HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME = "halo_t_perpendicular_integrated"
-HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME = "halo_t_ratio_perpendicular_integrated"
+HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME = (
+    "halo_t_ratio_perpendicular_integrated"
+)
 TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME = "total_t_parallel_integrated"
 TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME = "total_t_perpendicular_integrated"
-TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME = "total_t_ratio_perpendicular_integrated"
-CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME = "core_temperature_theta_rtn_integrated"
+TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME = (
+    "total_t_ratio_perpendicular_integrated"
+)
+CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME = (
+    "core_temperature_theta_rtn_integrated"
+)
 CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME = "core_temperature_phi_rtn_integrated"
-HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME = "halo_temperature_theta_rtn_integrated"
+HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME = (
+    "halo_temperature_theta_rtn_integrated"
+)
 HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME = "halo_temperature_phi_rtn_integrated"
-TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME = "total_temperature_theta_rtn_integrated"
-TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME = "total_temperature_phi_rtn_integrated"
+TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME = (
+    "total_temperature_theta_rtn_integrated"
+)
+TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME = (
+    "total_temperature_phi_rtn_integrated"
+)
 CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME = "core_temperature_parallel_to_mag"
-CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = "core_temperature_perpendicular_to_mag"
-CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = "core_temperature_ratio_perpendicular_to_mag"
+CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = (
+    "core_temperature_perpendicular_to_mag"
+)
+CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = (
+    "core_temperature_ratio_perpendicular_to_mag"
+)
 HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME = "halo_temperature_parallel_to_mag"
-HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = "halo_temperature_perpendicular_to_mag"
-HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = "halo_temperature_ratio_perpendicular_to_mag"
+HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = (
+    "halo_temperature_perpendicular_to_mag"
+)
+HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = (
+    "halo_temperature_ratio_perpendicular_to_mag"
+)
 TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME = "total_temperature_parallel_to_mag"
-TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = "total_temperature_perpendicular_to_mag"
-TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = "total_temperature_ratio_perpendicular_to_mag"
+TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = (
+    "total_temperature_perpendicular_to_mag"
+)
+TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME = (
+    "total_temperature_ratio_perpendicular_to_mag"
+)
 CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME = "core_temperature_tensor_integrated"
 HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME = "halo_temperature_tensor_integrated"
 TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME = "total_temperature_tensor_integrated"
@@ -100,7 +138,9 @@
 HALO_VELOCITY_VECTOR_RTN_FIT_LABEL = "halo_velocity_vector_rtn_fit_label"
 CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL = "core_velocity_vector_rtn_integrated_label"
 HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL = "halo_velocity_vector_rtn_integrated_label"
-TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL = "total_velocity_vector_rtn_integrated_label"
+TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL = (
+    "total_velocity_vector_rtn_integrated_label"
+)
 CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL = "core_temperature_tensor_integrated_label"
 HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL = "halo_temperature_tensor_integrated_label"
 TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL = "total_temperature_tensor_integrated_label"
@@ -242,204 +282,385 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable(EPOCH_CDF_VAR_NAME, value=self.epoch),
             DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, value=self.epoch_delta),
             DataProductVariable(ENERGY_CDF_VAR_NAME, value=self.energy),
-            DataProductVariable(ENERGY_DELTA_PLUS_CDF_VAR_NAME, value=self.energy_delta_plus),
-            DataProductVariable(ENERGY_DELTA_MINUS_CDF_VAR_NAME, value=self.energy_delta_minus),
+            DataProductVariable(
+                ENERGY_DELTA_PLUS_CDF_VAR_NAME, value=self.energy_delta_plus
+            ),
+            DataProductVariable(
+                ENERGY_DELTA_MINUS_CDF_VAR_NAME, value=self.energy_delta_minus
+            ),
             DataProductVariable(PITCH_ANGLE_CDF_VAR_NAME, value=self.pitch_angle),
-            DataProductVariable(PITCH_ANGLE_DELTA_CDF_VAR_NAME, value=self.pitch_angle_delta),
-
+            DataProductVariable(
+                PITCH_ANGLE_DELTA_CDF_VAR_NAME, value=self.pitch_angle_delta
+            ),
             DataProductVariable(GYROPHASE_BINS_CDF_VAR_NAME, value=self.gyrophase_bins),
-            DataProductVariable(GYROPHASE_DELTA_CDF_VAR_NAME, value=self.gyrophase_delta),
-            DataProductVariable(PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
-                                value=self.phase_space_density_by_pitch_angle),
-            DataProductVariable(PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
-                                value=self.phase_space_density_by_pitch_angle_and_gyrophase),
-            DataProductVariable(PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME,
-                                value=self.phase_space_density_1d),
-            DataProductVariable(PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME,
-                                value=self.phase_space_density_inward),
-            DataProductVariable(PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME,
-                                value=self.phase_space_density_outward),
-            DataProductVariable(INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
-                                value=self.intensity_by_pitch_angle),
-            DataProductVariable(INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
-                                value=self.intensity_by_pitch_angle_and_gyrophase),
-            DataProductVariable(INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME,
-                                value=self.intensity_uncertainty_by_pitch_angle),
-            DataProductVariable(INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
-                                value=self.intensity_uncertainty_by_pitch_angle_and_gyrophase),
-            DataProductVariable(SPACECRAFT_POTENTIAL_CDF_VAR_NAME,
-                                value=self.spacecraft_potential),
-            DataProductVariable(CORE_HALO_BREAKPOINT_CDF_VAR_NAME,
-                                value=self.core_halo_breakpoint),
-            DataProductVariable(CORE_FIT_NUM_POINTS_CDF_VAR_NAME,
-                                value=self.moment_data.core_fit_num_points),
-            DataProductVariable(CHISQ_C_CDF_VAR_NAME,
-                                value=self.moment_data.core_chisq),
-            DataProductVariable(CHISQ_H_CDF_VAR_NAME,
-                                value=self.moment_data.halo_chisq),
-            DataProductVariable(CORE_DENSITY_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_density_fit),
-            DataProductVariable(HALO_DENSITY_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_density_fit),
-            DataProductVariable(CORE_T_PARALLEL_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_t_parallel_fit),
-            DataProductVariable(HALO_T_PARALLEL_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_t_parallel_fit),
-            DataProductVariable(CORE_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_t_perpendicular_fit),
-            DataProductVariable(HALO_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_t_perpendicular_fit),
-            DataProductVariable(CORE_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_phi_rtn_fit),
-            DataProductVariable(HALO_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_phi_rtn_fit),
-            DataProductVariable(CORE_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_theta_rtn_fit),
-            DataProductVariable(HALO_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_theta_rtn_fit),
-            DataProductVariable(CORE_SPEED_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_speed_fit),
-            DataProductVariable(HALO_SPEED_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_speed_fit),
-            DataProductVariable(CORE_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.core_velocity_vector_rtn_fit),
-            DataProductVariable(HALO_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
-                                value=self.moment_data.halo_velocity_vector_rtn_fit),
-            DataProductVariable(CORE_DENSITY_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_density_integrated),
-            DataProductVariable(HALO_DENSITY_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_density_integrated),
-            DataProductVariable(TOTAL_DENSITY_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_density_integrated),
-            DataProductVariable(CORE_SPEED_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_speed_integrated),
-            DataProductVariable(HALO_SPEED_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_speed_integrated),
-            DataProductVariable(TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_speed_integrated),
-            DataProductVariable(CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_velocity_vector_rtn_integrated),
-            DataProductVariable(HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_velocity_vector_rtn_integrated),
-            DataProductVariable(TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_velocity_vector_rtn_integrated),
-            DataProductVariable(CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_heat_flux_magnitude_integrated),
-            DataProductVariable(CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_heat_flux_theta_integrated),
-            DataProductVariable(CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_heat_flux_phi_integrated),
-            DataProductVariable(HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_heat_flux_magnitude_integrated),
-            DataProductVariable(HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_heat_flux_theta_integrated),
-            DataProductVariable(HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_heat_flux_phi_integrated),
-            DataProductVariable(TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_heat_flux_magnitude_integrated),
-            DataProductVariable(TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_heat_flux_theta_integrated),
-            DataProductVariable(TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_heat_flux_phi_integrated),
-            DataProductVariable(CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_t_parallel_integrated),
-            DataProductVariable(CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_t_perpendicular_integrated[:, 0]),
-            DataProductVariable(CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_t_perpendicular_integrated[:, 1]),
-            DataProductVariable(HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_t_parallel_integrated),
-            DataProductVariable(HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_t_perpendicular_integrated[:, 0]),
-            DataProductVariable(HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_t_perpendicular_integrated[:, 1]),
-            DataProductVariable(TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_t_parallel_integrated),
-            DataProductVariable(TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_t_perpendicular_integrated[:, 0]),
-            DataProductVariable(TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_t_perpendicular_integrated[:, 1]),
-            DataProductVariable(CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_theta_rtn_integrated),
-            DataProductVariable(CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_phi_rtn_integrated),
-            DataProductVariable(HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_theta_rtn_integrated),
-            DataProductVariable(HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_phi_rtn_integrated),
-            DataProductVariable(TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_temperature_theta_rtn_integrated),
-            DataProductVariable(TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_temperature_phi_rtn_integrated),
-            DataProductVariable(CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_parallel_to_mag),
-            DataProductVariable(CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_perpendicular_to_mag[:, 0]),
-            DataProductVariable(CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_perpendicular_to_mag[:, 1]),
-            DataProductVariable(HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_parallel_to_mag),
-            DataProductVariable(HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_perpendicular_to_mag[:, 0]),
-            DataProductVariable(HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_perpendicular_to_mag[:, 1]),
-            DataProductVariable(TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.total_temperature_parallel_to_mag),
-            DataProductVariable(TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.total_temperature_perpendicular_to_mag[:, 0]),
-            DataProductVariable(TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
-                                value=self.moment_data.total_temperature_perpendicular_to_mag[:, 1]),
-            DataProductVariable(CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.core_temperature_tensor_integrated),
-            DataProductVariable(HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.halo_temperature_tensor_integrated),
-            DataProductVariable(TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
-                                value=self.moment_data.total_temperature_tensor_integrated),
+            DataProductVariable(
+                GYROPHASE_DELTA_CDF_VAR_NAME, value=self.gyrophase_delta
+            ),
+            DataProductVariable(
+                PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+                value=self.phase_space_density_by_pitch_angle,
+            ),
+            DataProductVariable(
+                PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+                value=self.phase_space_density_by_pitch_angle_and_gyrophase,
+            ),
+            DataProductVariable(
+                PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME, value=self.phase_space_density_1d
+            ),
+            DataProductVariable(
+                PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME,
+                value=self.phase_space_density_inward,
+            ),
+            DataProductVariable(
+                PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME,
+                value=self.phase_space_density_outward,
+            ),
+            DataProductVariable(
+                INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+                value=self.intensity_by_pitch_angle,
+            ),
+            DataProductVariable(
+                INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+                value=self.intensity_by_pitch_angle_and_gyrophase,
+            ),
+            DataProductVariable(
+                INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+                value=self.intensity_uncertainty_by_pitch_angle,
+            ),
+            DataProductVariable(
+                INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+                value=self.intensity_uncertainty_by_pitch_angle_and_gyrophase,
+            ),
+            DataProductVariable(
+                SPACECRAFT_POTENTIAL_CDF_VAR_NAME, value=self.spacecraft_potential
+            ),
+            DataProductVariable(
+                CORE_HALO_BREAKPOINT_CDF_VAR_NAME, value=self.core_halo_breakpoint
+            ),
+            DataProductVariable(
+                CORE_FIT_NUM_POINTS_CDF_VAR_NAME,
+                value=self.moment_data.core_fit_num_points,
+            ),
+            DataProductVariable(
+                CHISQ_C_CDF_VAR_NAME, value=self.moment_data.core_chisq
+            ),
+            DataProductVariable(
+                CHISQ_H_CDF_VAR_NAME, value=self.moment_data.halo_chisq
+            ),
+            DataProductVariable(
+                CORE_DENSITY_FIT_CDF_VAR_NAME, value=self.moment_data.core_density_fit
+            ),
+            DataProductVariable(
+                HALO_DENSITY_FIT_CDF_VAR_NAME, value=self.moment_data.halo_density_fit
+            ),
+            DataProductVariable(
+                CORE_T_PARALLEL_FIT_CDF_VAR_NAME,
+                value=self.moment_data.core_t_parallel_fit,
+            ),
+            DataProductVariable(
+                HALO_T_PARALLEL_FIT_CDF_VAR_NAME,
+                value=self.moment_data.halo_t_parallel_fit,
+            ),
+            DataProductVariable(
+                CORE_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
+                value=self.moment_data.core_t_perpendicular_fit,
+            ),
+            DataProductVariable(
+                HALO_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
+                value=self.moment_data.halo_t_perpendicular_fit,
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_phi_rtn_fit,
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_phi_rtn_fit,
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_theta_rtn_fit,
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_theta_rtn_fit,
+            ),
+            DataProductVariable(
+                CORE_SPEED_FIT_CDF_VAR_NAME, value=self.moment_data.core_speed_fit
+            ),
+            DataProductVariable(
+                HALO_SPEED_FIT_CDF_VAR_NAME, value=self.moment_data.halo_speed_fit
+            ),
+            DataProductVariable(
+                CORE_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
+                value=self.moment_data.core_velocity_vector_rtn_fit,
+            ),
+            DataProductVariable(
+                HALO_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
+                value=self.moment_data.halo_velocity_vector_rtn_fit,
+            ),
+            DataProductVariable(
+                CORE_DENSITY_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_density_integrated,
+            ),
+            DataProductVariable(
+                HALO_DENSITY_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_density_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_DENSITY_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_density_integrated,
+            ),
+            DataProductVariable(
+                CORE_SPEED_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_speed_integrated,
+            ),
+            DataProductVariable(
+                HALO_SPEED_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_speed_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_speed_integrated,
+            ),
+            DataProductVariable(
+                CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_velocity_vector_rtn_integrated,
+            ),
+            DataProductVariable(
+                HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_velocity_vector_rtn_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_velocity_vector_rtn_integrated,
+            ),
+            DataProductVariable(
+                CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_heat_flux_magnitude_integrated,
+            ),
+            DataProductVariable(
+                CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_heat_flux_theta_integrated,
+            ),
+            DataProductVariable(
+                CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_heat_flux_phi_integrated,
+            ),
+            DataProductVariable(
+                HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_heat_flux_magnitude_integrated,
+            ),
+            DataProductVariable(
+                HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_heat_flux_theta_integrated,
+            ),
+            DataProductVariable(
+                HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_heat_flux_phi_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_heat_flux_magnitude_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_heat_flux_theta_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_heat_flux_phi_integrated,
+            ),
+            DataProductVariable(
+                CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_t_parallel_integrated,
+            ),
+            DataProductVariable(
+                CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_t_perpendicular_integrated[:, 0],
+            ),
+            DataProductVariable(
+                CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_t_perpendicular_integrated[:, 1],
+            ),
+            DataProductVariable(
+                HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_t_parallel_integrated,
+            ),
+            DataProductVariable(
+                HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_t_perpendicular_integrated[:, 0],
+            ),
+            DataProductVariable(
+                HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_t_perpendicular_integrated[:, 1],
+            ),
+            DataProductVariable(
+                TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_t_parallel_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_t_perpendicular_integrated[:, 0],
+            ),
+            DataProductVariable(
+                TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_t_perpendicular_integrated[:, 1],
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_theta_rtn_integrated,
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_phi_rtn_integrated,
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_theta_rtn_integrated,
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_phi_rtn_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_temperature_theta_rtn_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_temperature_phi_rtn_integrated,
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_parallel_to_mag,
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_perpendicular_to_mag[:, 0],
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_perpendicular_to_mag[:, 1],
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_parallel_to_mag,
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_perpendicular_to_mag[:, 0],
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_perpendicular_to_mag[:, 1],
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.total_temperature_parallel_to_mag,
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.total_temperature_perpendicular_to_mag[:, 0],
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+                value=self.moment_data.total_temperature_perpendicular_to_mag[:, 1],
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.core_temperature_tensor_integrated,
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.halo_temperature_tensor_integrated,
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+                value=self.moment_data.total_temperature_tensor_integrated,
+            ),
             DataProductVariable(SWE_FLAGS_VAR_NAME, value=self.swe_flags),
             DataProductVariable("raw_1d_psd_rebinned", value=self.raw_1d_psd_rebinned),
-            DataProductVariable("raw_psd_by_theta_rebinned", value=self.raw_psd_by_theta_rebinned),
-            DataProductVariable("raw_psd_by_phi_rebinned", value=self.raw_psd_by_phi_rebinned),
+            DataProductVariable(
+                "raw_psd_by_theta_rebinned", value=self.raw_psd_by_theta_rebinned
+            ),
+            DataProductVariable(
+                "raw_psd_by_phi_rebinned", value=self.raw_psd_by_phi_rebinned
+            ),
             DataProductVariable("inst_az", value=self.inst_az),
             DataProductVariable("inst_el", value=self.inst_el),
-            DataProductVariable(ENERGY_LABEL,
-                                value=np.char.mod("%.1f eV", self.energy),
-                                ),
-            DataProductVariable(PITCH_ANGLE_LABEL,
-                                value=np.char.mod("PA %.0f", self.pitch_angle),
-                                ),
-            DataProductVariable(GYROPHASE_LABEL,
-                                value=np.char.mod("Gyrophase %.0f", self.gyrophase_bins),
-                                ),
-            DataProductVariable(RTN_LABEL,
-                                value=["R", "T", "N"],
-                                ),
-            DataProductVariable(CORE_VELOCITY_VECTOR_RTN_FIT_LABEL,
-                                value=["Core v fit R", "Core v fit T", "Core v fit N"],
-                                ),
-            DataProductVariable(HALO_VELOCITY_VECTOR_RTN_FIT_LABEL,
-                                value=["Halo v fit R", "Halo v fit T", "Halo v fit N"],
-                                ),
-            DataProductVariable(CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
-                                value=["Core v int. R", "Core v int. T", "Core v int. N"],
-                                ),
-            DataProductVariable(HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
-                                value=["Halo v int. R", "Halo v int. T", "Halo v int. N"],
-                                ),
-            DataProductVariable(TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
-                                value=["Total v int. R", "Total v int. T", "Total v int. N"],
-                                ),
-            DataProductVariable(CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
-                                value=['Core T_XX', 'Core T_XY', 'Core T_YY', 'Core T_XZ', 'Core T_YZ', 'Core T_ZZ'],
-                                ),
-            DataProductVariable(HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
-                                value=['Halo T_XX', 'Halo T_XY', 'Halo T_YY', 'Halo T_XZ', 'Halo T_YZ', 'Halo T_ZZ'],
-                                ),
-            DataProductVariable(TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
-                                value=['Total T_XX', 'Total T_XY', 'Total T_YY', 'Total T_XZ', 'Total T_YZ', 'Total T_ZZ'],
-                                ),
+            DataProductVariable(
+                ENERGY_LABEL,
+                value=np.char.mod("%.1f eV", self.energy),
+            ),
+            DataProductVariable(
+                PITCH_ANGLE_LABEL,
+                value=np.char.mod("PA %.0f", self.pitch_angle),
+            ),
+            DataProductVariable(
+                GYROPHASE_LABEL,
+                value=np.char.mod("Gyrophase %.0f", self.gyrophase_bins),
+            ),
+            DataProductVariable(
+                RTN_LABEL,
+                value=["R", "T", "N"],
+            ),
+            DataProductVariable(
+                CORE_VELOCITY_VECTOR_RTN_FIT_LABEL,
+                value=["Core v fit R", "Core v fit T", "Core v fit N"],
+            ),
+            DataProductVariable(
+                HALO_VELOCITY_VECTOR_RTN_FIT_LABEL,
+                value=["Halo v fit R", "Halo v fit T", "Halo v fit N"],
+            ),
+            DataProductVariable(
+                CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+                value=["Core v int. R", "Core v int. T", "Core v int. N"],
+            ),
+            DataProductVariable(
+                HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+                value=["Halo v int. R", "Halo v int. T", "Halo v int. N"],
+            ),
+            DataProductVariable(
+                TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+                value=["Total v int. R", "Total v int. T", "Total v int. N"],
+            ),
+            DataProductVariable(
+                CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+                value=[
+                    "Core T_XX",
+                    "Core T_XY",
+                    "Core T_YY",
+                    "Core T_XZ",
+                    "Core T_YZ",
+                    "Core T_ZZ",
+                ],
+            ),
+            DataProductVariable(
+                HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+                value=[
+                    "Halo T_XX",
+                    "Halo T_XY",
+                    "Halo T_YY",
+                    "Halo T_XZ",
+                    "Halo T_YZ",
+                    "Halo T_ZZ",
+                ],
+            ),
+            DataProductVariable(
+                TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+                value=[
+                    "Total T_XX",
+                    "Total T_XY",
+                    "Total T_YY",
+                    "Total T_XZ",
+                    "Total T_YZ",
+                    "Total T_ZZ",
+                ],
+            ),
             DataProductVariable("inst_az_label", value=self.inst_az_label),
             DataProductVariable("inst_el_label", value=self.inst_el_label),
-            DataProductVariable(TENSOR_ID, value=np.array([1, 2, 3, 4, 5, 6]))
+            DataProductVariable(TENSOR_ID, value=np.array([1, 2, 3, 4, 5, 6])),
         ]
 
 
diff --git a/imap_l3_processing/swe/l3/science/moment_calculations.py b/imap_l3_processing/swe/l3/science/moment_calculations.py
index 665ff5956..36b2535aa 100644
--- a/imap_l3_processing/swe/l3/science/moment_calculations.py
+++ b/imap_l3_processing/swe/l3/science/moment_calculations.py
@@ -2,26 +2,35 @@
 import math
 from dataclasses import dataclass
 from datetime import datetime
-from typing import Optional, Union, Tuple
 
 import numpy as np
 import spiceypy
 
-from imap_l3_processing.constants import ELECTRON_MASS_KG, \
-    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN, METERS_PER_KILOMETER, \
-    CENTIMETERS_PER_METER, PROTON_CHARGE_COULOMBS, GRAMS_PER_KILOGRAM
+from imap_l3_processing.constants import (
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+    CENTIMETERS_PER_METER,
+    ELECTRON_MASS_KG,
+    GRAMS_PER_KILOGRAM,
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+)
 from imap_l3_processing.pitch_angles import calculate_unit_vector
 from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
 
-ELECTRON_MASS_OVER_BOLTZMANN_IN_CGS_UNITS = ELECTRON_MASS_KG / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN * 1e-4
+ELECTRON_MASS_OVER_BOLTZMANN_IN_CGS_UNITS = (
+    ELECTRON_MASS_KG / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN * 1e-4
+)
 NUMBER_OF_DETECTORS = 7
 NUMBER_OF_SPIN_SECTORS = 30
 ZMK = 3.2971e-12
 
-ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR = np.sqrt(2 * PROTON_CHARGE_COULOMBS / ELECTRON_MASS_KG) * 100
+ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR = (
+    np.sqrt(2 * PROTON_CHARGE_COULOMBS / ELECTRON_MASS_KG) * 100
+)
 
 logger = logging.getLogger(__name__)
 
+
 @dataclass
 class Moments:
     alpha: float
@@ -37,17 +46,18 @@ class Moments:
 
     @classmethod
     def construct_all_fill(cls):
-        return cls(alpha=np.nan,
-                   beta=np.nan,
-                   t_parallel=np.nan,
-                   t_perpendicular=np.nan,
-                   velocity_x=np.nan,
-                   velocity_y=np.nan,
-                   velocity_z=np.nan,
-                   density=np.nan,
-                   aoo=np.nan,
-                   ao=np.nan,
-                   )
+        return cls(
+            alpha=np.nan,
+            beta=np.nan,
+            t_parallel=np.nan,
+            t_perpendicular=np.nan,
+            velocity_x=np.nan,
+            velocity_y=np.nan,
+            velocity_z=np.nan,
+            density=np.nan,
+            aoo=np.nan,
+            ao=np.nan,
+        )
 
 
 @dataclass
@@ -82,13 +92,15 @@ class ScaleDensityOutput:
     cdelt: np.ndarray
 
 
-def core_fit_moments_retrying_on_failure(corrected_energy_bins: np.ndarray,
-                                         velocity_vectors: np.ndarray,
-                                         phase_space_density: np.ndarray,
-                                         weights: np.ndarray,
-                                         energy_start: int,
-                                         energy_end: int,
-                                         density_history: Union[np.ndarray, list[float]]) -> Optional[MomentFitResults]:
+def core_fit_moments_retrying_on_failure(
+    corrected_energy_bins: np.ndarray,
+    velocity_vectors: np.ndarray,
+    phase_space_density: np.ndarray,
+    weights: np.ndarray,
+    energy_start: int,
+    energy_end: int,
+    density_history: np.ndarray | list[float],
+) -> MomentFitResults | None:
     return _fit_moments_retrying_on_failure(
         corrected_energy_bins,
         velocity_vectors,
@@ -97,18 +109,21 @@ def core_fit_moments_retrying_on_failure(corrected_energy_bins: np.ndarray,
         energy_start,
         energy_end,
         density_history,
-        1.85
+        1.85,
     )
 
 
-def halo_fit_moments_retrying_on_failure(corrected_energy_bins: np.ndarray, velocity_vectors: np.ndarray,
-                                         phase_space_density: np.ndarray,
-                                         weights: np.ndarray,
-                                         energy_start: int,
-                                         energy_end: int,
-                                         density_history: Union[np.ndarray, list[float]],
-                                         spacecraft_potential: Union[np.float64, float],
-                                         core_halo_breakpoint: Union[np.float64, float]) -> Optional[MomentFitResults]:
+def halo_fit_moments_retrying_on_failure(
+    corrected_energy_bins: np.ndarray,
+    velocity_vectors: np.ndarray,
+    phase_space_density: np.ndarray,
+    weights: np.ndarray,
+    energy_start: int,
+    energy_end: int,
+    density_history: np.ndarray | list[float],
+    spacecraft_potential: np.float64 | float,
+    core_halo_breakpoint: np.float64 | float,
+) -> MomentFitResults | None:
     return _fit_moments_retrying_on_failure(
         corrected_energy_bins,
         velocity_vectors,
@@ -118,41 +133,53 @@ def halo_fit_moments_retrying_on_failure(corrected_energy_bins: np.ndarray, velo
         energy_end,
         density_history,
         1.35,
-        halo_correction_parameters=HaloCorrectionParameters(spacecraft_potential,
-                                                            core_halo_breakpoint)
+        halo_correction_parameters=HaloCorrectionParameters(
+            spacecraft_potential, core_halo_breakpoint
+        ),
     )
 
 
-def _fit_moments_retrying_on_failure(corrected_energy_bins: np.ndarray,
-                                     velocity_vectors: np.ndarray,
-                                     phase_space_density: np.ndarray,
-                                     weights: np.ndarray,
-                                     energy_start: int,
-                                     energy_end: int,
-                                     density_history: Union[np.ndarray, list[float]],
-                                     history_scalar: float,
-                                     halo_correction_parameters: Optional[HaloCorrectionParameters] = None) -> Optional[
-    MomentFitResults]:
-    filtered_velocity_vectors, filtered_weights, filtered_yreg = filter_and_flatten_regress_parameters(
-        corrected_energy_bins,
-        velocity_vectors,
-        phase_space_density,
-        weights,
-        energy_start,
-        energy_end)
+def _fit_moments_retrying_on_failure(
+    corrected_energy_bins: np.ndarray,
+    velocity_vectors: np.ndarray,
+    phase_space_density: np.ndarray,
+    weights: np.ndarray,
+    energy_start: int,
+    energy_end: int,
+    density_history: np.ndarray | list[float],
+    history_scalar: float,
+    halo_correction_parameters: HaloCorrectionParameters | None = None,
+) -> MomentFitResults | None:
+    filtered_velocity_vectors, filtered_weights, filtered_yreg = (
+        filter_and_flatten_regress_parameters(
+            corrected_energy_bins,
+            velocity_vectors,
+            phase_space_density,
+            weights,
+            energy_start,
+            energy_end,
+        )
+    )
 
-    fit_function, chi_squared = regress(filtered_velocity_vectors, filtered_weights, filtered_yreg)
+    fit_function, chi_squared = regress(
+        filtered_velocity_vectors, filtered_weights, filtered_yreg
+    )
     moment = calculate_fit_temperature_density_velocity(fit_function)
     average_density = np.average(density_history) * history_scalar
 
     if halo_correction_parameters is not None:
-        moment.density = halotrunc(moment, halo_correction_parameters.core_halo_breakpoint,
-                                   halo_correction_parameters.spacecraft_potential)
+        moment.density = halotrunc(
+            moment,
+            halo_correction_parameters.core_halo_breakpoint,
+            halo_correction_parameters.spacecraft_potential,
+        )
 
-    results = MomentFitResults(moments=moment, chisq=chi_squared,
-                               number_of_points=energy_end - energy_start,
-                               regress_result=fit_function
-                               )
+    results = MomentFitResults(
+        moments=moment,
+        chisq=chi_squared,
+        number_of_points=energy_end - energy_start,
+        regress_result=fit_function,
+    )
     if moment.density is not None and 0 < moment.density < average_density:
         return results
     elif energy_end - energy_start < 4:
@@ -170,21 +197,22 @@ def _fit_moments_retrying_on_failure(corrected_energy_bins: np.ndarray,
             energy_end - 1,
             density_history,
             history_scalar,
-            halo_correction_parameters
+            halo_correction_parameters,
         )
 
 
-def regress(velocity_vectors: np.ndarray, weight: np.ndarray, yreg: np.ndarray) -> \
-        np.ndarray:
+def regress(
+    velocity_vectors: np.ndarray, weight: np.ndarray, yreg: np.ndarray
+) -> np.ndarray:
     fit_function = np.zeros((len(velocity_vectors), 9))
 
     velocity_xs = velocity_vectors[:, 0]
     velocity_ys = velocity_vectors[:, 1]
     velocity_zs = velocity_vectors[:, 2]
 
-    fit_function[:, 0] = -1 * ZMK * (velocity_xs ** 2)
-    fit_function[:, 1] = -1 * ZMK * (velocity_ys ** 2)
-    fit_function[:, 2] = -1 * ZMK * (velocity_zs ** 2)
+    fit_function[:, 0] = -1 * ZMK * (velocity_xs**2)
+    fit_function[:, 1] = -1 * ZMK * (velocity_ys**2)
+    fit_function[:, 2] = -1 * ZMK * (velocity_zs**2)
     fit_function[:, 3] = -1 * ZMK * 2 * velocity_xs * velocity_ys
     fit_function[:, 4] = -1 * ZMK * 2 * velocity_xs * velocity_zs
     fit_function[:, 5] = -1 * ZMK * 2 * velocity_ys * velocity_zs
@@ -261,17 +289,18 @@ def regress(velocity_vectors: np.ndarray, weight: np.ndarray, yreg: np.ndarray)
 
 
 def calculate_fit_temperature_density_velocity(parameters: np.ndarray[float]):
-    moments = Moments(alpha=0,
-                      beta=0,
-                      t_parallel=0,
-                      t_perpendicular=0,
-                      velocity_x=0,
-                      velocity_y=0,
-                      velocity_z=0,
-                      density=0,
-                      ao=0,
-                      aoo=0
-                      )
+    moments = Moments(
+        alpha=0,
+        beta=0,
+        t_parallel=0,
+        t_perpendicular=0,
+        velocity_x=0,
+        velocity_y=0,
+        velocity_z=0,
+        density=0,
+        ao=0,
+        aoo=0,
+    )
 
     moments.ao = parameters[9]
     if moments.ao == 0:
@@ -283,8 +312,15 @@ def calculate_fit_temperature_density_velocity(parameters: np.ndarray[float]):
         moments.t_perpendicular = 1.0 / parameters[0]
         moments.t_parallel = 1.0 / parameters[2]
     else:
-        moments.t_perpendicular = 1.0 / (parameters[0] - parameters[3] * parameters[4] / parameters[5])
-        moments.t_parallel = 1.0 / (parameters[0] + parameters[1] + parameters[2] - 2.0 / moments.t_perpendicular)
+        moments.t_perpendicular = 1.0 / (
+            parameters[0] - parameters[3] * parameters[4] / parameters[5]
+        )
+        moments.t_parallel = 1.0 / (
+            parameters[0]
+            + parameters[1]
+            + parameters[2]
+            - 2.0 / moments.t_perpendicular
+        )
 
     c11 = parameters[1] * parameters[2] - parameters[5] * parameters[5]
     c12 = parameters[5] * parameters[4] - parameters[3] * parameters[2]
@@ -300,16 +336,28 @@ def calculate_fit_temperature_density_velocity(parameters: np.ndarray[float]):
         moments.velocity_y = 0
         moments.velocity_z = 0
     else:
-        moments.velocity_x = -1 * (parameters[6] * c11 + parameters[7] * c12 + parameters[8] * c13) / d
-        moments.velocity_y = -1 * (parameters[6] * c12 + parameters[7] * c22 + parameters[8] * c23) / d
-        moments.velocity_z = -1 * (parameters[6] * c13 + parameters[7] * c23 + parameters[8] * c33) / d
+        moments.velocity_x = (
+            -1 * (parameters[6] * c11 + parameters[7] * c12 + parameters[8] * c13) / d
+        )
+        moments.velocity_y = (
+            -1 * (parameters[6] * c12 + parameters[7] * c22 + parameters[8] * c23) / d
+        )
+        moments.velocity_z = (
+            -1 * (parameters[6] * c13 + parameters[7] * c23 + parameters[8] * c33) / d
+        )
 
-    fact = parameters[0] * moments.velocity_x ** 2 + parameters[1] * moments.velocity_y ** 2 + parameters[
-        2] * moments.velocity_z ** 2
+    fact = (
+        parameters[0] * moments.velocity_x**2
+        + parameters[1] * moments.velocity_y**2
+        + parameters[2] * moments.velocity_z**2
+    )
 
-    moments.aoo = fact + parameters[3] * moments.velocity_x * moments.velocity_y + parameters[4] \
-                  * moments.velocity_x * moments.velocity_z + \
-                  parameters[5] * moments.velocity_y * moments.velocity_z
+    moments.aoo = (
+        fact
+        + parameters[3] * moments.velocity_x * moments.velocity_y
+        + parameters[4] * moments.velocity_x * moments.velocity_z
+        + parameters[5] * moments.velocity_y * moments.velocity_z
+    )
 
     moments.velocity_x *= 1e-5
     moments.velocity_y *= 1e-5
@@ -318,20 +366,25 @@ def calculate_fit_temperature_density_velocity(parameters: np.ndarray[float]):
     if (moments.t_parallel < 0) or (moments.aoo < 0) or (moments.aoo > 1e13):
         moments.density = 0.0
     else:
-        moments.density = (moments.t_perpendicular * np.sqrt(moments.t_parallel) /
-                           ((ZMK / math.pi) * np.sqrt(ZMK / math.pi))) * np.exp(
-            moments.ao + ZMK * moments.aoo)
+        moments.density = (
+            moments.t_perpendicular
+            * np.sqrt(moments.t_parallel)
+            / ((ZMK / math.pi) * np.sqrt(ZMK / math.pi))
+        ) * np.exp(moments.ao + ZMK * moments.aoo)
     return moments
 
 
 LIMIT = np.array([32, 64, 128, 256, 1024, 2048, 3072, 5120, 9216, 17408, 33792])
-SIGMA2 = np.array([0, 0.25, 1.25, 5.25, 21.25, 85.25, 341.25, 1365.25, 5461.25, 21845.25, 87381.25])
+SIGMA2 = np.array(
+    [0, 0.25, 1.25, 5.25, 21.25, 85.25, 341.25, 1365.25, 5461.25, 21845.25, 87381.25]
+)
 MINIMUM_WEIGHT = 0.8165
 MAX_VARIANCE = 349525.25
 
 
-def compute_maxwellian_weight_factors(count_rates: np.ndarray, acquisition_durations: np.ndarray) -> \
-        np.ndarray[float]:
+def compute_maxwellian_weight_factors(
+    count_rates: np.ndarray, acquisition_durations: np.ndarray
+) -> np.ndarray[float]:
     correction = 1.0 - 1e-9 * LIMIT / acquisition_durations[:, :, np.newaxis]
     correction[correction < 0.1] = 0.1
     xlimits_per_measurement = LIMIT / correction
@@ -349,18 +402,21 @@ def compute_maxwellian_weight_factors(count_rates: np.ndarray, acquisition_durat
                 if corrected_count < xlimit:
                     variance = sigma
                     break
-            weights[energy_i, spin_i, declination_i] = np.sqrt(variance + corrected_count) / corrected_count
+            weights[energy_i, spin_i, declination_i] = (
+                np.sqrt(variance + corrected_count) / corrected_count
+            )
 
     return weights
 
 
-def filter_and_flatten_regress_parameters(corrected_energy_bins: np.ndarray,
-                                          velocity_vectors: np.ndarray,
-                                          phase_space_density: np.ndarray,
-                                          weights: np.ndarray,
-                                          start_index: int,
-                                          end_index: int) -> tuple[
-    np.ndarray, np.ndarray, np.ndarray]:
+def filter_and_flatten_regress_parameters(
+    corrected_energy_bins: np.ndarray,
+    velocity_vectors: np.ndarray,
+    phase_space_density: np.ndarray,
+    weights: np.ndarray,
+    start_index: int,
+    end_index: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     valid_mask = np.full_like(phase_space_density, fill_value=False, dtype=bool)
     valid_mask[start_index:end_index] = True
     valid_mask[corrected_energy_bins <= 0] = False
@@ -369,7 +425,8 @@ def filter_and_flatten_regress_parameters(corrected_energy_bins: np.ndarray,
     filtered_phase_space_density = phase_space_density[valid_mask]
     yreg = np.zeros_like(filtered_phase_space_density)
     yreg[filtered_phase_space_density > 1e-35] = np.log(
-        filtered_phase_space_density[filtered_phase_space_density > 1e-35])
+        filtered_phase_space_density[filtered_phase_space_density > 1e-35]
+    )
     yreg[filtered_phase_space_density <= 1e-35] = -80.6
 
     return velocity_vectors[valid_mask], weights[valid_mask], yreg
@@ -379,18 +436,24 @@ def get_dps_to_rtn_rotation_matrix(epoch: datetime) -> np.ndarray:
     return spiceypy.pxform("IMAP_DPS", "IMAP_RTN", spiceypy.datetime2et(epoch))
 
 
-def apply_rotation_matrix(rotation_matrix: np.ndarray, vector: np.ndarray) -> np.ndarray:
+def apply_rotation_matrix(
+    rotation_matrix: np.ndarray, vector: np.ndarray
+) -> np.ndarray:
     return rotation_matrix @ vector
 
 
-def rotate_rtn_vectors_to_dps(epochs: np.ndarray, vectors_rtn: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+def rotate_rtn_vectors_to_dps(
+    epochs: np.ndarray, vectors_rtn: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
     result = np.full_like(vectors_rtn, np.nan, dtype=float)
     predicted_ephemeris_flags = np.zeros_like(epochs, dtype=bool)
     for i, epoch in enumerate(epochs):
         tracker = PredictedEphemerisTracker()
         try:
             et_time = spiceypy.datetime2et(epoch)
-            rotation_matrix = tracker.run(spiceypy.pxform,"IMAP_RTN", "IMAP_DPS", et_time)
+            rotation_matrix = tracker.run(
+                spiceypy.pxform, "IMAP_RTN", "IMAP_DPS", et_time
+            )
         except spiceypy.SpiceyError as e:
             logger.info(f"Failed to rotate RTN→DPS at epoch {epoch}: {e}")
             continue
@@ -400,7 +463,9 @@ def rotate_rtn_vectors_to_dps(epochs: np.ndarray, vectors_rtn: np.ndarray) -> Tu
     return result, predicted_ephemeris_flags
 
 
-def rotate_temperature(rotation_matrix: np.ndarray, alpha: float, beta: float) -> tuple[float, float]:
+def rotate_temperature(
+    rotation_matrix: np.ndarray, alpha: float, beta: float
+) -> tuple[float, float]:
     sin_dec = np.sin(beta)
     x = sin_dec * np.cos(alpha)
     y = sin_dec * np.sin(alpha)
@@ -414,8 +479,9 @@ def rotate_temperature(rotation_matrix: np.ndarray, alpha: float, beta: float) -
     return theta, phi
 
 
-def rotate_vector_to_rtn_spherical_coordinates(rotation_matrix: np.ndarray, heat_flux: np.ndarray) -> tuple[
-    float, float, float]:
+def rotate_vector_to_rtn_spherical_coordinates(
+    rotation_matrix: np.ndarray, heat_flux: np.ndarray
+) -> tuple[float, float, float]:
     r, t, n = apply_rotation_matrix(rotation_matrix, heat_flux)
     magnitude = np.linalg.norm(heat_flux, axis=-1)
     rt = np.sqrt(r * r + t * t)
@@ -424,7 +490,9 @@ def rotate_vector_to_rtn_spherical_coordinates(rotation_matrix: np.ndarray, heat
     return magnitude, theta, phi
 
 
-def compute_density_scale(core_electron_energy_range: float, speed: float, temperature: float) -> float:
+def compute_density_scale(
+    core_electron_energy_range: float, speed: float, temperature: float
+) -> float:
     energy_multplier = 11600.0
     density_multiplier = 0.88623
     ev_speed = 593.097
@@ -446,17 +514,25 @@ def compute_density_scale(core_electron_energy_range: float, speed: float, tempe
     escalep = math.erfc(scalep) if scalep < 9 else 0
     escalem = math.erfc(scalem) if scalem < 9 else 0
 
-    dscalep = density_multiplier / (scalep * np.exp(-scalep2) + density_multiplier * escalep)
-    dscalem = density_multiplier / (scalem * np.exp(-scalem2) + density_multiplier * escalem)
+    dscalep = density_multiplier / (
+        scalep * np.exp(-scalep2) + density_multiplier * escalep
+    )
+    dscalem = density_multiplier / (
+        scalem * np.exp(-scalem2) + density_multiplier * escalem
+    )
 
     return 0.5 * (dscalep + dscalem)
 
 
-def halotrunc(moments: Moments, core_halo_breakpoint: float, spacecraft_potential: float) -> Optional[np.float64]:
+def halotrunc(
+    moments: Moments, core_halo_breakpoint: float, spacecraft_potential: float
+) -> np.float64 | None:
     htmax = moments.t_parallel
     htmin = moments.t_perpendicular
 
-    speed = math.sqrt(moments.velocity_x ** 2 + moments.velocity_y ** 2 + moments.velocity_z ** 2)
+    speed = math.sqrt(
+        moments.velocity_x**2 + moments.velocity_y**2 + moments.velocity_z**2
+    )
 
     halod = moments.density
 
@@ -468,7 +544,9 @@ def halotrunc(moments: Moments, core_halo_breakpoint: float, spacecraft_potentia
     if htmax <= 1e4 or htmin <= 1e4:
         dscale = 1
     elif core_halo_breakpoint - spacecraft_potential > 5 and temp < 1.0e7:
-        dscale = compute_density_scale(core_halo_breakpoint - spacecraft_potential, speed, temp)
+        dscale = compute_density_scale(
+            core_halo_breakpoint - spacecraft_potential, speed, temp
+        )
     else:
         dscale = 1
 
@@ -477,41 +555,68 @@ def halotrunc(moments: Moments, core_halo_breakpoint: float, spacecraft_potentia
     return halod
 
 
-def integrate(istart, iend, energy: np.ndarray, sintheta: np.ndarray,
-              costheta: np.ndarray, deltheta: np.ndarray, fv: np.ndarray, phi: np.ndarray,
-              spacecraft_potential: float, cdelnv: np.ndarray, cdelt: np.ndarray) -> Optional[
-    IntegrateOutputs]:
+def integrate(
+    istart,
+    iend,
+    energy: np.ndarray,
+    sintheta: np.ndarray,
+    costheta: np.ndarray,
+    deltheta: np.ndarray,
+    fv: np.ndarray,
+    phi: np.ndarray,
+    spacecraft_potential: float,
+    cdelnv: np.ndarray,
+    cdelt: np.ndarray,
+) -> IntegrateOutputs | None:
     sumn = 0
     sumvx = 0
     sumvy = 0
     sumvz = 0
     base = 1000
     delphi = np.full(7, 2 * np.pi / 30)
-    delv = np.empty((len(energy)))
+    delv = np.empty(len(energy))
     fv_with_axis_order_energy_cem_spin = np.moveaxis(fv, 1, 2)
     for i in range(istart, iend + 1):
         if energy[i] > 0:
-            v2mid = (ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR ** 2) * energy[i]
+            v2mid = (ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR**2) * energy[i]
             v3mid = v2mid * np.sqrt(v2mid)
         else:
             v2mid = 0
             v3mid = 0
         ehigh = 1.175 * energy[i]
         if i < len(energy) - 1:
-            ehigh = np.sqrt((energy[i + 1] + spacecraft_potential) * (energy[i] + spacecraft_potential))
-        elow = np.sqrt((energy[i] + spacecraft_potential) * (energy[i - 1] + spacecraft_potential))
+            ehigh = np.sqrt(
+                (energy[i + 1] + spacecraft_potential)
+                * (energy[i] + spacecraft_potential)
+            )
+        elow = np.sqrt(
+            (energy[i] + spacecraft_potential) * (energy[i - 1] + spacecraft_potential)
+        )
 
-        delv[i] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * (np.sqrt(ehigh) - np.sqrt(elow))
-        if elow < base:
-            base = elow
+        delv[i] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * (
+            np.sqrt(ehigh) - np.sqrt(elow)
+        )
+        base = min(base, elow)
         for j in range(7):
             for k in range(28):  # why 28 and not 30?
                 if energy[i] > 0:
                     delta = delv[i] * deltheta[j] * delphi[j]
                     fact = sintheta[j] * fv_with_axis_order_energy_cem_spin[i, j, k]
                     sumn += delta * v2mid * fact
-                    sumvx += delta * v3mid * fact * sintheta[j] * np.cos(np.deg2rad(phi[i, k]))
-                    sumvy += delta * v3mid * fact * sintheta[j] * np.sin(np.deg2rad(phi[i, k]))
+                    sumvx += (
+                        delta
+                        * v3mid
+                        * fact
+                        * sintheta[j]
+                        * np.cos(np.deg2rad(phi[i, k]))
+                    )
+                    sumvy += (
+                        delta
+                        * v3mid
+                        * fact
+                        * sintheta[j]
+                        * np.sin(np.deg2rad(phi[i, k]))
+                    )
                     sumvz += delta * v3mid * fact * costheta[j]
 
     totden = sumn + cdelnv[0]
@@ -521,11 +626,13 @@ def integrate(istart, iend, energy: np.ndarray, sintheta: np.ndarray,
     base -= spacecraft_potential
     CM_PER_KM = METERS_PER_KILOMETER * CENTIMETERS_PER_METER
     KM_PER_CM = 1 / CM_PER_KM
-    output_velocities = np.array([
-        (-KM_PER_CM * sumvx + cdelnv[1]) / totden,
-        (-KM_PER_CM * sumvy + cdelnv[2]) / totden,
-        (-KM_PER_CM * sumvz + cdelnv[3]) / totden,
-    ])
+    output_velocities = np.array(
+        [
+            (-KM_PER_CM * sumvx + cdelnv[1]) / totden,
+            (-KM_PER_CM * sumvy + cdelnv[2]) / totden,
+            (-KM_PER_CM * sumvz + cdelnv[3]) / totden,
+        ]
+    )
 
     sumtxx = 0
     sumtxy = 0
@@ -538,25 +645,41 @@ def integrate(istart, iend, energy: np.ndarray, sintheta: np.ndarray,
     sumqz = 0
     for i in range(istart, iend + 1):
         for j in range(7):
-            v2mid = (ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR ** 2) * energy[i]
+            v2mid = (ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR**2) * energy[i]
             for k in range(28):  # again why 28?
                 if energy[i] > 0:
                     angx = sintheta[j] * np.cos(np.deg2rad(phi[i, k]))
-                    vx = -ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(energy[i]) * angx - CM_PER_KM * \
-                         output_velocities[0]
+                    vx = (
+                        -ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR
+                        * np.sqrt(energy[i])
+                        * angx
+                        - CM_PER_KM * output_velocities[0]
+                    )
 
                     angy = sintheta[j] * np.sin(np.deg2rad(phi[i, k]))
-                    vy = -ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(energy[i]) * angy - CM_PER_KM * \
-                         output_velocities[1]
+                    vy = (
+                        -ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR
+                        * np.sqrt(energy[i])
+                        * angy
+                        - CM_PER_KM * output_velocities[1]
+                    )
 
                     angz = costheta[j]
-                    vz = -ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(energy[i]) * angz - CM_PER_KM * \
-                         output_velocities[2]
+                    vz = (
+                        -ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR
+                        * np.sqrt(energy[i])
+                        * angz
+                        - CM_PER_KM * output_velocities[2]
+                    )
 
                     vmag2 = vx * vx + vy * vy + vz * vz
 
                     delta = delv[i] * deltheta[j] * delphi[j]
-                    fact = v2mid * sintheta[j] * fv_with_axis_order_energy_cem_spin[i, j, k]
+                    fact = (
+                        v2mid
+                        * sintheta[j]
+                        * fv_with_axis_order_energy_cem_spin[i, j, k]
+                    )
                     sumtxx += delta * fact * vx * vx
                     sumtxy += delta * fact * vx * vy
                     sumtxz += delta * fact * vx * vz
@@ -568,22 +691,34 @@ def integrate(istart, iend, energy: np.ndarray, sintheta: np.ndarray,
                     sumqz += delta * fact * vmag2 * vz
 
     TEMPERATURE_SCALING_FACTOR_TO_UNDO_IN_EIGEN = 1e-4
-    temperature = (np.array([sumtxx, sumtxy, sumtyy, sumtxz, sumtyz, sumtzz]) *
-                   TEMPERATURE_SCALING_FACTOR_TO_UNDO_IN_EIGEN * ELECTRON_MASS_OVER_BOLTZMANN_IN_CGS_UNITS + cdelt) / totden
-
-    heat_flux = np.array([sumqx, sumqy, sumqz]) * 500 * ELECTRON_MASS_KG * GRAMS_PER_KILOGRAM
+    temperature = (
+        np.array([sumtxx, sumtxy, sumtyy, sumtxz, sumtyz, sumtzz])
+        * TEMPERATURE_SCALING_FACTOR_TO_UNDO_IN_EIGEN
+        * ELECTRON_MASS_OVER_BOLTZMANN_IN_CGS_UNITS
+        + cdelt
+    ) / totden
+
+    heat_flux = (
+        np.array([sumqx, sumqy, sumqz]) * 500 * ELECTRON_MASS_KG * GRAMS_PER_KILOGRAM
+    )
 
     return IntegrateOutputs(totden, output_velocities, temperature, heat_flux, base)
 
 
-def scale_core_density(core_density: float,
-                       core_velocity: np.ndarray, core_temp: np.ndarray,
-                       core_moment_fit: Moments, ifit: int, energy: np.ndarray,
-                       spacecraft_potential: float, cosin_p: np.ndarray,
-                       aperture_field_of_view: list,
-                       phi: np.ndarray,
-                       regress_outputs: np.ndarray,
-                       base_energy: float) -> ScaleDensityOutput:
+def scale_core_density(
+    core_density: float,
+    core_velocity: np.ndarray,
+    core_temp: np.ndarray,
+    core_moment_fit: Moments,
+    ifit: int,
+    energy: np.ndarray,
+    spacecraft_potential: float,
+    cosin_p: np.ndarray,
+    aperture_field_of_view: list,
+    phi: np.ndarray,
+    regress_outputs: np.ndarray,
+    base_energy: float,
+) -> ScaleDensityOutput:
     zmk = ELECTRON_MASS_KG / (2 * BOLTZMANN_CONSTANT_JOULES_PER_KELVIN * 1e4)
     MAX_SPIN_SECTOR_INDEX = 28  # why 28 and not 30?
 
@@ -592,21 +727,37 @@ def scale_core_density(core_density: float,
     delv = np.zeros((2, NUMBER_OF_DETECTORS))
     velocity_in_sc_frame = np.zeros((2, NUMBER_OF_DETECTORS))
     for j in range(NUMBER_OF_DETECTORS):
-        ehigh = np.sqrt((energy[ifit + 1] + spacecraft_potential) *
-                        (energy[ifit] + spacecraft_potential))
-        elow = np.sqrt((energy[ifit] + spacecraft_potential) *
-                       (energy[ifit - 1] + spacecraft_potential))
-        delv[0, j] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * (np.sqrt(ehigh) - np.sqrt(elow))
-        velocity_in_sc_frame[0, j] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(energy[ifit])
+        ehigh = np.sqrt(
+            (energy[ifit + 1] + spacecraft_potential)
+            * (energy[ifit] + spacecraft_potential)
+        )
+        elow = np.sqrt(
+            (energy[ifit] + spacecraft_potential)
+            * (energy[ifit - 1] + spacecraft_potential)
+        )
+        delv[0, j] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * (
+            np.sqrt(ehigh) - np.sqrt(elow)
+        )
+        velocity_in_sc_frame[0, j] = (
+            ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(energy[ifit])
+        )
         base_energy = min(base_energy, elow)
     base_energy -= spacecraft_potential
     if base_energy > 0:
         number_of_energies = 2
-        velocity_in_sc_frame[1, :] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(0.5 * base_energy)
-        delv[1, :] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(base_energy)
+        velocity_in_sc_frame[1, :] = (
+            ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(0.5 * base_energy)
+        )
+        delv[1, :] = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(
+            base_energy
+        )
 
-    factor = core_moment_fit.density * zmk / (np.pi * core_moment_fit.t_perpendicular) * np.sqrt(
-        zmk / (np.pi * core_moment_fit.t_parallel))
+    factor = (
+        core_moment_fit.density
+        * zmk
+        / (np.pi * core_moment_fit.t_perpendicular)
+        * np.sqrt(zmk / (np.pi * core_moment_fit.t_parallel))
+    )
 
     cos_theta = cosin_p[np.newaxis, :, np.newaxis]
     sin_theta = np.sin(np.arccos(cos_theta))
@@ -620,14 +771,22 @@ def scale_core_density(core_density: float,
     vy = -velocity_3_dim * sin_theta * sin_phi
     vz = -velocity_3_dim * cos_theta
 
-    fun = -zmk * np.stack(np.broadcast_arrays(vx * vx, vy * vy, vz * vz, vx * vy, vx * vz, vy * vz, vx, vy, vz),
-                          axis=-1)
+    fun = -zmk * np.stack(
+        np.broadcast_arrays(
+            vx * vx, vy * vy, vz * vz, vx * vy, vx * vz, vy * vz, vx, vy, vz
+        ),
+        axis=-1,
+    )
     exponent = -zmk * core_moment_fit.aoo + np.dot(fun, regress_outputs[:9])
     delt = delv[:, :, np.newaxis] * delta_theta * delta_phi
-    common_factor = np.square(velocity_3_dim) * delt * sin_theta * factor * np.exp(exponent)
+    common_factor = (
+        np.square(velocity_3_dim) * delt * sin_theta * factor * np.exp(exponent)
+    )
 
     def integrate(array):
-        return np.sum(array[:number_of_energies, :NUMBER_OF_DETECTORS, :MAX_SPIN_SECTOR_INDEX])
+        return np.sum(
+            array[:number_of_energies, :NUMBER_OF_DETECTORS, :MAX_SPIN_SECTOR_INDEX]
+        )
 
     sumint = integrate(common_factor)
     sumvx = integrate(vx * common_factor)
@@ -660,30 +819,44 @@ def integrate(array):
 
     cdelnv = np.append(sumint, delta_v)
 
-    return ScaleDensityOutput(density=corrected_density, velocity=corrected_core_velocity,
-                              temperature=corrected_core_temp, cdelnv=cdelnv, cdelt=cdelt)
+    return ScaleDensityOutput(
+        density=corrected_density,
+        velocity=corrected_core_velocity,
+        temperature=corrected_core_temp,
+        cdelnv=cdelnv,
+        cdelt=cdelt,
+    )
 
 
-def scale_halo_density(halo_density: float,
-                       halo_velocity: np.ndarray, halo_temp: np.ndarray,
-                       halo_moment_fit: Moments,
-                       spacecraft_potential: float,
-                       core_halo_break: float,
-                       cosin_p: np.ndarray,
-                       aperture_field_of_view: list,
-                       phi: np.ndarray,
-                       regress_outputs: np.ndarray,
-                       base_energy: float) -> ScaleDensityOutput:
+def scale_halo_density(
+    halo_density: float,
+    halo_velocity: np.ndarray,
+    halo_temp: np.ndarray,
+    halo_moment_fit: Moments,
+    spacecraft_potential: float,
+    core_halo_break: float,
+    cosin_p: np.ndarray,
+    aperture_field_of_view: list,
+    phi: np.ndarray,
+    regress_outputs: np.ndarray,
+    base_energy: float,
+) -> ScaleDensityOutput:
     zmk = ELECTRON_MASS_KG / (2 * BOLTZMANN_CONSTANT_JOULES_PER_KELVIN * 1e4)
     MAX_SPIN_SECTOR_INDEX = 28  # why 28 and not 30?
 
     hchbreak = core_halo_break - spacecraft_potential
     scval = abs(hchbreak - base_energy)
     min_energy = min(base_energy, hchbreak)
-    vsch = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(min_energy + 0.5 * scval)
+    vsch = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(
+        min_energy + 0.5 * scval
+    )
     deltav = ENERGY_EV_TO_SPEED_CM_PER_S_CONVERSION_FACTOR * np.sqrt(scval)
-    factor = halo_moment_fit.density * zmk / (np.pi * halo_moment_fit.t_parallel) * np.sqrt(
-        zmk / (np.pi * halo_moment_fit.t_perpendicular))
+    factor = (
+        halo_moment_fit.density
+        * zmk
+        / (np.pi * halo_moment_fit.t_parallel)
+        * np.sqrt(zmk / (np.pi * halo_moment_fit.t_perpendicular))
+    )
 
     cos_theta = cosin_p[:, np.newaxis]
     sin_theta = np.sin(np.arccos(cos_theta))
@@ -696,8 +869,12 @@ def scale_halo_density(halo_density: float,
     vy = -vsch * sin_theta * sin_phi
     vz = -vsch * cos_theta
 
-    fun = -zmk * np.stack(np.broadcast_arrays(vx * vx, vy * vy, vz * vz, vx * vy, vx * vz, vy * vz, vx, vy, vz),
-                          axis=-1)
+    fun = -zmk * np.stack(
+        np.broadcast_arrays(
+            vx * vx, vy * vy, vz * vz, vx * vy, vx * vz, vy * vz, vx, vy, vz
+        ),
+        axis=-1,
+    )
     exponent = np.dot(fun, regress_outputs[:9])
     delt = deltav * delta_theta * delta_phi
     common_factor = np.square(vsch) * delt * sin_theta * factor * np.exp(exponent)
@@ -737,20 +914,38 @@ def integrate(array):
 
     corrected_halo_temp = halo_temp.copy()
     corrected_halo_temp *= halo_density / corrected_density
-    corrected_halo_temp += delta_temperature * 1e-4 * ELECTRON_MASS_OVER_BOLTZMANN_IN_CGS_UNITS / corrected_density
+    corrected_halo_temp += (
+        delta_temperature
+        * 1e-4
+        * ELECTRON_MASS_OVER_BOLTZMANN_IN_CGS_UNITS
+        / corrected_density
+    )
 
-    return ScaleDensityOutput(density=corrected_density, velocity=corrected_halo_velocity,
-                              temperature=corrected_halo_temp, cdelnv=None, cdelt=None)
+    return ScaleDensityOutput(
+        density=corrected_density,
+        velocity=corrected_halo_velocity,
+        temperature=corrected_halo_temp,
+        cdelnv=None,
+        cdelt=None,
+    )
 
 
-def calculate_primary_eigenvector(temperature_tensor: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+def calculate_primary_eigenvector(
+    temperature_tensor: np.ndarray,
+) -> tuple[np.ndarray, np.ndarray]:
     symmetric_temperature_tensor = np.zeros(shape=(3, 3))
 
     symmetric_temperature_tensor[0][0] = temperature_tensor[0]
-    symmetric_temperature_tensor[0][1] = symmetric_temperature_tensor[1][0] = temperature_tensor[1]
+    symmetric_temperature_tensor[0][1] = symmetric_temperature_tensor[1][0] = (
+        temperature_tensor[1]
+    )
     symmetric_temperature_tensor[1][1] = temperature_tensor[2]
-    symmetric_temperature_tensor[0][2] = symmetric_temperature_tensor[2][0] = temperature_tensor[3]
-    symmetric_temperature_tensor[1][2] = symmetric_temperature_tensor[2][1] = temperature_tensor[4]
+    symmetric_temperature_tensor[0][2] = symmetric_temperature_tensor[2][0] = (
+        temperature_tensor[3]
+    )
+    symmetric_temperature_tensor[1][2] = symmetric_temperature_tensor[2][1] = (
+        temperature_tensor[4]
+    )
     symmetric_temperature_tensor[2][2] = temperature_tensor[5]
 
     nan_array = np.full(3, np.nan)
@@ -759,7 +954,11 @@ def calculate_primary_eigenvector(temperature_tensor: np.ndarray) -> tuple[np.nd
     except np.linalg.LinAlgError:
         return nan_array, nan_array
 
-    if np.any(eigen_values < 0) or np.all(eigen_values == 0) or np.any(np.isnan(eigen_values)):
+    if (
+        np.any(eigen_values < 0)
+        or np.all(eigen_values == 0)
+        or np.any(np.isnan(eigen_values))
+    ):
         return nan_array, nan_array
 
     eigen_values_mean = np.mean(eigen_values)
@@ -777,12 +976,16 @@ def calculate_primary_eigenvector(temperature_tensor: np.ndarray) -> tuple[np.nd
     other_evals = [v for i, v in enumerate(eigen_values) if i != ipar]
     max_eval = max(other_evals)
     min_eval = min(other_evals)
-    perpendicular_temperature = TEMPERATURE_SCALING_FACTOR * np.sqrt(min_eval * max_eval)
+    perpendicular_temperature = TEMPERATURE_SCALING_FACTOR * np.sqrt(
+        min_eval * max_eval
+    )
     if min_eval == 0:
         gyro = 1
     else:
         gyro = max(other_evals) / min_eval
-    return primary_evec, np.array([parallel_temperature, perpendicular_temperature, gyro])
+    return primary_evec, np.array(
+        [parallel_temperature, perpendicular_temperature, gyro]
+    )
 
 
 def rotation_matrix_builder(mag_vector: np.ndarray) -> np.ndarray(shape=(3, 3)):
@@ -795,15 +998,22 @@ def rotation_matrix_builder(mag_vector: np.ndarray) -> np.ndarray(shape=(3, 3)):
     return np.stack((normalized_mag_vector, row_1, row_2), axis=0)
 
 
-def rotate_temperature_tensor_to_mag(temperature_tensor: np.ndarray, mag_vector: np.ndarray) -> tuple[
-    float, float, float]:
+def rotate_temperature_tensor_to_mag(
+    temperature_tensor: np.ndarray, mag_vector: np.ndarray
+) -> tuple[float, float, float]:
     symmetric_temperature_tensor = np.zeros(shape=(3, 3))
 
     symmetric_temperature_tensor[0][0] = temperature_tensor[0]
-    symmetric_temperature_tensor[0][1] = symmetric_temperature_tensor[1][0] = temperature_tensor[1]
+    symmetric_temperature_tensor[0][1] = symmetric_temperature_tensor[1][0] = (
+        temperature_tensor[1]
+    )
     symmetric_temperature_tensor[1][1] = temperature_tensor[2]
-    symmetric_temperature_tensor[0][2] = symmetric_temperature_tensor[2][0] = temperature_tensor[3]
-    symmetric_temperature_tensor[1][2] = symmetric_temperature_tensor[2][1] = temperature_tensor[4]
+    symmetric_temperature_tensor[0][2] = symmetric_temperature_tensor[2][0] = (
+        temperature_tensor[3]
+    )
+    symmetric_temperature_tensor[1][2] = symmetric_temperature_tensor[2][1] = (
+        temperature_tensor[4]
+    )
     symmetric_temperature_tensor[2][2] = temperature_tensor[5]
 
     rotation_matrix = rotation_matrix_builder(mag_vector)
diff --git a/imap_l3_processing/swe/l3/science/pitch_calculations.py b/imap_l3_processing/swe/l3/science/pitch_calculations.py
index 5fd820ccd..ded6c1dc9 100644
--- a/imap_l3_processing/swe/l3/science/pitch_calculations.py
+++ b/imap_l3_processing/swe/l3/science/pitch_calculations.py
@@ -5,12 +5,23 @@
 import numpy as np
 import scipy
 
-from imap_l3_processing.constants import ELECTRON_MASS_KG, PROTON_CHARGE_COULOMBS, METERS_PER_KILOMETER
-from imap_l3_processing.pitch_angles import calculate_pitch_angle, calculate_unit_vector, calculate_gyrophase
+from imap_l3_processing.constants import (
+    ELECTRON_MASS_KG,
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+)
+from imap_l3_processing.pitch_angles import (
+    calculate_gyrophase,
+    calculate_pitch_angle,
+    calculate_unit_vector,
+)
 from imap_l3_processing.swe.l3.models import SweConfiguration
 from imap_l3_processing.swe.quality_flags import SweL3Flags
 
-def mec_breakpoint_finder(energies: np.ndarray, averaged_psd: np.ndarray) -> tuple[float, float, SweL3Flags]:
+
+def mec_breakpoint_finder(
+    energies: np.ndarray, averaged_psd: np.ndarray
+) -> tuple[float, float, SweL3Flags]:
     """
     Input:
         energies - energy bins
@@ -25,14 +36,16 @@ def mec_breakpoint_finder(energies: np.ndarray, averaged_psd: np.ndarray) -> tup
     # If True, then potential is likely less than 2.7 V (lower than first energy bin)
     def line_model(params, x):
         return params[0] + params[1] * x
+
     from scipy import odr
+
     odr_model = odr.Model(line_model)
     x = log_energy[:4]
     y = log_psd[:4]
-    mydata = odr.RealData(x=x,y=y)
-    myodr = odr.ODR(mydata, odr_model, beta0=[y.max(),-5])
+    mydata = odr.RealData(x=x, y=y)
+    myodr = odr.ODR(mydata, odr_model, beta0=[y.max(), -5])
     myodr.set_job(fit_type=2)
-    myoutput= myodr.run()
+    myoutput = myodr.run()
     if myoutput.res_var <= 0.01:
         FALLBACK_POTENTIAL_ESTIMATE = SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE
         return_value = 2.5
@@ -43,9 +56,15 @@ def line_model(params, x):
     # Mirror real point as fake point to left of first energy bin to improve spline concavity
     ewidth = np.nanmean(log_energy[1:] - log_energy[:-1])
     from scipy.interpolate import UnivariateSpline as uspline
-    spline = uspline(np.concatenate([[log_energy[0]-ewidth],log_energy]),
-                     np.concatenate([[log_psd[2]],log_psd]), s=.25)
-    spline_energies = np.geomspace(energies.min()*np.exp(-ewidth), energies.max(), 100)
+
+    spline = uspline(
+        np.concatenate([[log_energy[0] - ewidth], log_energy]),
+        np.concatenate([[log_psd[2]], log_psd]),
+        s=0.25,
+    )
+    spline_energies = np.geomspace(
+        energies.min() * np.exp(-ewidth), energies.max(), 100
+    )
     spline_derivative = spline.derivative(2)
     curvature = spline_derivative(np.log(spline_energies))
     try:
@@ -64,8 +83,11 @@ def piece_wise_model_mec(x, b0, b1, b2, b3):
         Modified Piecewise to fit Potential and Core-Halo Break separately
         The breakpoint is b2
         """
-        return np.piecewise(x, [x<=b2, x>b2],
-                               [lambda x: b0 - b1*x, lambda x: b0 + b2*(b3-b1) - b3*x])
+        return np.piecewise(
+            x,
+            [x <= b2, x > b2],
+            [lambda x: b0 - b1 * x, lambda x: b0 + b2 * (b3 - b1) - b3 * x],
+        )
 
     def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
         """
@@ -73,8 +95,12 @@ def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
         energy at which the lines intersect is the refined_breakpoint
         """
         # Find Nearest energy bin to breakpoint_value
-        nearest_energy_idx = np.argmin(np.abs(energy-breakpoint_value))
-        if np.abs(breakpoint_value - energy[nearest_energy_idx])/energy[nearest_energy_idx] <= .075:
+        nearest_energy_idx = np.argmin(np.abs(energy - breakpoint_value))
+        if (
+            np.abs(breakpoint_value - energy[nearest_energy_idx])
+            / energy[nearest_energy_idx]
+            <= 0.075
+        ):
             # Breakpoint_value was within FWHM of nearest energy bin
             # return that energy bin as refined_breakpoint
             return energy[nearest_energy_idx]
@@ -90,7 +116,7 @@ def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
         z_left = np.polyfit(e_left[-num_points:], psd_left[-num_points:], 1)
         z_right = np.polyfit(e_right[:num_points], psd_right[:num_points], 1)
         # Determine energy of their intersection
-        refined_breakpoint = (z_left[1]-z_right[1]) / (z_right[0]-z_left[0])
+        refined_breakpoint = (z_left[1] - z_right[1]) / (z_right[0] - z_left[0])
         if (refined_breakpoint < z_left[-1]) | (refined_breakpoint > z_right[0]):
             # Refined breakpoint lies outside of expected range
             return breakpoint_value
@@ -100,19 +126,27 @@ def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
     mask_sc = log_energy <= np.log(30)
     mask_ch = (log_energy > np.log(30)) & (log_energy < np.log(400))
 
-    log_energy_sc = log_energy[mask_sc]; log_psd_sc = log_psd[mask_sc]
-    log_energy_ch = log_energy[mask_ch]; log_psd_ch = log_psd[mask_ch]
+    log_energy_sc = log_energy[mask_sc]
+    log_psd_sc = log_psd[mask_sc]
+    log_energy_ch = log_energy[mask_ch]
+    log_psd_ch = log_psd[mask_ch]
 
     fitting_model = piece_wise_model_mec
     # Try Spacecraft Potential Fit
     POTENTIAL_FIT_UNCONVERGED = SweL3Flags.NONE
     if FALLBACK_POTENTIAL_ESTIMATE == SweL3Flags.NONE:
         try:
-            initial_guess = [log_psd[0],1,7,1]
-            z, cov = scipy.optimize.curve_fit(fitting_model, np.exp(log_energy_sc), log_psd_sc, p0=initial_guess)
+            initial_guess = [log_psd[0], 1, 7, 1]
+            z, cov = scipy.optimize.curve_fit(
+                fitting_model, np.exp(log_energy_sc), log_psd_sc, p0=initial_guess
+            )
             # Make sure the fit converged
-            if ((z[0] == initial_guess[0]) | (z[1] == initial_guess[1])
-                | (z[2] == initial_guess[2]) | (z[3] == initial_guess[3])):
+            if (
+                (z[0] == initial_guess[0])
+                | (z[1] == initial_guess[1])
+                | (z[2] == initial_guess[2])
+                | (z[3] == initial_guess[3])
+            ):
                 # Fall back on Spline method
                 # Fit did not converge
                 POTENTIAL_FIT_UNCONVERGED = SweL3Flags.POTENTIAL_FIT_UNCONVERGED
@@ -121,7 +155,9 @@ def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
                 # Fit worked
                 # Check whether breakpoint has two points to left and right
                 # If so, then find intersection of linear fits to each side
-                sc_pot_output = refine_breakpoint_value(np.exp(log_energy_sc), log_psd_sc, z[2], 2)
+                sc_pot_output = refine_breakpoint_value(
+                    np.exp(log_energy_sc), log_psd_sc, z[2], 2
+                )
                 # spline not used
                 BACKUP_SPLINE_UNRESOLVED = SweL3Flags.NONE
         except:
@@ -134,11 +170,17 @@ def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
     # Try Core-Halo Breakpoint Fit
     BREAKPOINT_FIT_UNCONVERGED = SweL3Flags.NONE
     try:
-        initial_guess = [log_psd_ch[0],1,65,1]
-        z, cov = scipy.optimize.curve_fit(fitting_model, np.exp(log_energy_ch), log_psd_ch, p0=initial_guess)
+        initial_guess = [log_psd_ch[0], 1, 65, 1]
+        z, cov = scipy.optimize.curve_fit(
+            fitting_model, np.exp(log_energy_ch), log_psd_ch, p0=initial_guess
+        )
         # Make sure the fit converged
-        if ((z[0] == initial_guess[0]) & (z[1] == initial_guess[1])
-            & (z[2] == initial_guess[2]) & (z[3] == initial_guess[3])):
+        if (
+            (z[0] == initial_guess[0])
+            & (z[1] == initial_guess[1])
+            & (z[2] == initial_guess[2])
+            & (z[3] == initial_guess[3])
+        ):
             # Fall back on Spline method
             # Fit did not converge
             BREAKPOINT_FIT_UNCONVERGED = SweL3Flags.BREAKPOINT_FIT_UNCONVERGED
@@ -147,19 +189,33 @@ def refine_breakpoint_value(energy, psd, breakpoint_value, num_points):
             # Fit worked
             # Check whether breakpoint has three points to left and right
             # If so, then find intersection of linear fits to each side
-            ch_break_output = refine_breakpoint_value(np.exp(log_energy_ch), log_psd_ch, z[2], 3)
+            ch_break_output = refine_breakpoint_value(
+                np.exp(log_energy_ch), log_psd_ch, z[2], 3
+            )
     except:
         # Fall back on Spline method
         # Fit did not converge
         BREAKPOINT_FIT_UNCONVERGED = SweL3Flags.BREAKPOINT_FIT_UNCONVERGED
         ch_break_output = ch_break
-    return sc_pot_output, ch_break_output, FALLBACK_POTENTIAL_ESTIMATE | BACKUP_SPLINE_UNRESOLVED | POTENTIAL_FIT_UNCONVERGED | BREAKPOINT_FIT_UNCONVERGED
-
-
-def average_over_look_directions(phase_space_density: np.ndarray, geometric_weights: np.ndarray,
-                                 minimum_psd_value: float) -> np.ndarray:
+    return (
+        sc_pot_output,
+        ch_break_output,
+        FALLBACK_POTENTIAL_ESTIMATE
+        | BACKUP_SPLINE_UNRESOLVED
+        | POTENTIAL_FIT_UNCONVERGED
+        | BREAKPOINT_FIT_UNCONVERGED,
+    )
+
+
+def average_over_look_directions(
+    phase_space_density: np.ndarray,
+    geometric_weights: np.ndarray,
+    minimum_psd_value: float,
+) -> np.ndarray:
     enforced_minimum_psd = np.maximum(phase_space_density, minimum_psd_value)
-    weighted_average = np.sum(enforced_minimum_psd * geometric_weights, axis=-1) / np.sum(geometric_weights)
+    weighted_average = np.sum(
+        enforced_minimum_psd * geometric_weights, axis=-1
+    ) / np.sum(geometric_weights)
     return np.mean(weighted_average, axis=1)
 
 
@@ -173,9 +229,13 @@ def calculate_look_directions(inst_el: np.ndarray, inst_az: np.ndarray) -> np.nd
     return np.stack(np.broadcast_arrays(x, y, z), axis=-1)
 
 
-def calculate_velocity_in_dsp_frame_km_s(energy: np.ndarray, inst_el: np.ndarray, inst_az: np.ndarray) -> np.ndarray:
-    particle_direction = - calculate_look_directions(inst_el, inst_az)
-    speed_meters_per_second = np.sqrt(energy * PROTON_CHARGE_COULOMBS * 2 / ELECTRON_MASS_KG)
+def calculate_velocity_in_dsp_frame_km_s(
+    energy: np.ndarray, inst_el: np.ndarray, inst_az: np.ndarray
+) -> np.ndarray:
+    particle_direction = -calculate_look_directions(inst_el, inst_az)
+    speed_meters_per_second = np.sqrt(
+        energy * PROTON_CHARGE_COULOMBS * 2 / ELECTRON_MASS_KG
+    )
     speed_km_per_second = speed_meters_per_second / METERS_PER_KILOMETER
 
     for i in range(len(particle_direction)):
@@ -184,13 +244,19 @@ def calculate_velocity_in_dsp_frame_km_s(energy: np.ndarray, inst_el: np.ndarray
     return particle_direction
 
 
-def rebin_by_pitch_angle(flux, pitch_angles, energies, config: SweConfiguration) -> np.ndarray:
-    all_the_bins = rebin_by_pitch_angle_and_gyrophase(flux, pitch_angles, None, energies, config)
+def rebin_by_pitch_angle(
+    flux, pitch_angles, energies, config: SweConfiguration
+) -> np.ndarray:
+    all_the_bins = rebin_by_pitch_angle_and_gyrophase(
+        flux, pitch_angles, None, energies, config
+    )
     remove_gyrophase_axis = all_the_bins[:, :, 0]
     return remove_gyrophase_axis
 
 
-def _rebin(flux, pitch_angles, energies, config: SweConfiguration, gyrophase: np.ndarray = None) -> np.ndarray:
+def _rebin(
+    flux, pitch_angles, energies, config: SweConfiguration, gyrophase: np.ndarray = None
+) -> np.ndarray:
     pitch_angle_bins = np.array(config["pitch_angle_bins"])
     pitch_angle_delta = np.array(config["pitch_angle_deltas"])
     energy_bins = config["energy_bins"]
@@ -215,20 +281,28 @@ def _rebin(flux, pitch_angles, energies, config: SweConfiguration, gyrophase: np
         gyrophase_bins_range = [0]
         num_gyrophase_bins = 1
 
-    rebinned = np.full((num_energy_bins, num_pitch_bins, num_gyrophase_bins), np.nan, dtype=float)
+    rebinned = np.full(
+        (num_energy_bins, num_pitch_bins, num_gyrophase_bins), np.nan, dtype=float
+    )
     for g in gyrophase_bins_range:
         for j in range(num_pitch_bins):
-            mask_pitch_angle = (pitch_angles_for_masked_psd >= pitch_angle_left_edges[j]) & (
-                    pitch_angles_for_masked_psd < pitch_angle_right_edges[j])
+            mask_pitch_angle = (
+                pitch_angles_for_masked_psd >= pitch_angle_left_edges[j]
+            ) & (pitch_angles_for_masked_psd < pitch_angle_right_edges[j])
 
             if gyrophase is not None:
-                mask_gyrophase = (gyrophase_for_masked_psd >= gyrophase_left_edges[g]) & (
-                        gyrophase_for_masked_psd < gyrophase_right_edges[g])
+                mask_gyrophase = (
+                    gyrophase_for_masked_psd >= gyrophase_left_edges[g]
+                ) & (gyrophase_for_masked_psd < gyrophase_right_edges[g])
             else:
                 mask_gyrophase = np.ones_like(mask_pitch_angle, dtype=bool)
 
-            psd_by_pitch_angle = psd_greater_than_zero[mask_pitch_angle & mask_gyrophase]
-            energy_by_pitch_angle = energies_for_masked_psd[mask_pitch_angle & mask_gyrophase]
+            psd_by_pitch_angle = psd_greater_than_zero[
+                mask_pitch_angle & mask_gyrophase
+            ]
+            energy_by_pitch_angle = energies_for_masked_psd[
+                mask_pitch_angle & mask_gyrophase
+            ]
 
             # maintain in new method
             if len(energy_by_pitch_angle) < 2:
@@ -242,7 +316,9 @@ def _rebin(flux, pitch_angles, energies, config: SweConfiguration, gyrophase: np
                 left = config["energy_bin_low_multiplier"] * center
                 right = config["energy_bin_high_multiplier"] * center
 
-                mask_energy = (energy_by_pitch_angle > left) & (energy_by_pitch_angle < right)
+                mask_energy = (energy_by_pitch_angle > left) & (
+                    energy_by_pitch_angle < right
+                )
 
                 energy_to_fit = energy_by_pitch_angle[mask_energy]
                 psd_to_fit = psd_by_pitch_angle[mask_energy]
@@ -253,23 +329,41 @@ def _rebin(flux, pitch_angles, energies, config: SweConfiguration, gyrophase: np
                 max_within_window = np.max(energy_to_fit)
                 min_within_window = np.min(energy_to_fit)
 
-                has_points_on_both_sides = max_within_window > center and min_within_window < center
-                is_overall_max_within_window = overall_max_energy_for_pitch_angle in energy_to_fit
-                max_within_case = is_overall_max_within_window and closest_energy_ratio < config[
-                    "high_energy_proximity_threshold"]
-
-                is_overall_second_min_within_window = overall_second_min_energy_for_pitch_angle in energy_to_fit
-
-                mins_within_case = is_overall_second_min_within_window and (closest_energy_ratio < config[
-                    "low_energy_proximity_threshold"] or has_points_on_both_sides)
-                max_and_mins_outside_window_case = (not is_overall_max_within_window
-                                                    and not is_overall_second_min_within_window
-                                                    and has_points_on_both_sides)
-
-                if max_within_case or mins_within_case or max_and_mins_outside_window_case:
+                has_points_on_both_sides = (
+                    max_within_window > center and min_within_window < center
+                )
+                is_overall_max_within_window = (
+                    overall_max_energy_for_pitch_angle in energy_to_fit
+                )
+                max_within_case = (
+                    is_overall_max_within_window
+                    and closest_energy_ratio < config["high_energy_proximity_threshold"]
+                )
+
+                is_overall_second_min_within_window = (
+                    overall_second_min_energy_for_pitch_angle in energy_to_fit
+                )
+
+                mins_within_case = is_overall_second_min_within_window and (
+                    closest_energy_ratio < config["low_energy_proximity_threshold"]
+                    or has_points_on_both_sides
+                )
+                max_and_mins_outside_window_case = (
+                    not is_overall_max_within_window
+                    and not is_overall_second_min_within_window
+                    and has_points_on_both_sides
+                )
+
+                if (
+                    max_within_case
+                    or mins_within_case
+                    or max_and_mins_outside_window_case
+                ):
                     log_energy_to_fit = np.log(energy_to_fit)
                     log_psd_to_fit = np.log(psd_to_fit)
-                    intercept, slope = np.polynomial.polynomial.polyfit(log_energy_to_fit, log_psd_to_fit, 1)
+                    intercept, slope = np.polynomial.polynomial.polyfit(
+                        log_energy_to_fit, log_psd_to_fit, 1
+                    )
                     log_psd_to_nom = slope * np.log(center) + intercept
                     value = np.exp(log_psd_to_nom)
 
@@ -278,21 +372,27 @@ def _rebin(flux, pitch_angles, energies, config: SweConfiguration, gyrophase: np
     return rebinned
 
 
-def rebin_by_pitch_angle_and_gyrophase(psd, pitch_angles, gyrophase, energies, config: SweConfiguration) -> np.ndarray:
+def rebin_by_pitch_angle_and_gyrophase(
+    psd, pitch_angles, gyrophase, energies, config: SweConfiguration
+) -> np.ndarray:
     return _rebin(psd, pitch_angles, energies, config, gyrophase=gyrophase)
 
 
-def calculate_velocity_in_sw_frame(velocity_in_despun_frame: np.ndarray[(..., 3)],
-                                   solar_wind_velocity: np.ndarray[(3,)]) -> np.ndarray[(..., 3)]:
+def calculate_velocity_in_sw_frame(
+    velocity_in_despun_frame: np.ndarray[(..., 3)],
+    solar_wind_velocity: np.ndarray[(3,)],
+) -> np.ndarray[(..., 3)]:
     return velocity_in_despun_frame - solar_wind_velocity
 
 
-def calculate_energy_in_ev_from_velocity_in_km_per_second(velocity: np.ndarray[(..., 3)]):
+def calculate_energy_in_ev_from_velocity_in_km_per_second(
+    velocity: np.ndarray[(..., 3)],
+):
     joule_to_ev = 1 / PROTON_CHARGE_COULOMBS
 
     velocity_m_s = velocity * 1e3
 
-    speed_squared = np.sum(velocity_m_s ** 2, axis=-1)
+    speed_squared = np.sum(velocity_m_s**2, axis=-1)
     energy_joules = 0.5 * ELECTRON_MASS_KG * speed_squared
 
     return energy_joules * joule_to_ev
@@ -305,42 +405,84 @@ def calculate_energy_in_ev_from_velocity_in_km_per_second(velocity: np.ndarray[(
 GYROPHASE_BINS = TypeVar("GYROPHASE_BINS")
 
 
-def correct_and_rebin(flux_or_psd: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS)],
-                      solar_wind_vector: np.ndarray[(3,)],
-                      dsp_velocities: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS, 3,)],
-                      mag_vector: np.ndarray[(E_BINS, SPIN_SECTORS, 3,)],
-                      config: SweConfiguration) -> tuple[np.ndarray, np.ndarray]:
-    velocity_in_sw_frame = calculate_velocity_in_sw_frame(dsp_velocities, solar_wind_vector)
-    energy_in_sw_frame = calculate_energy_in_ev_from_velocity_in_km_per_second(velocity_in_sw_frame)
-    pitch_angle = calculate_pitch_angle(velocity_in_sw_frame, mag_vector[..., np.newaxis, :])
-    gyrophase = calculate_gyrophase(velocity_in_sw_frame, mag_vector[..., np.newaxis, :])
-
-    rebinned_by_pa = rebin_by_pitch_angle(flux_or_psd, pitch_angle, energy_in_sw_frame, config)
-    rebinned_by_pa_and_gyro = rebin_by_pitch_angle_and_gyrophase(flux_or_psd, pitch_angle, gyrophase,
-                                                                 energy_in_sw_frame, config)
+def correct_and_rebin(
+    flux_or_psd: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS)],
+    solar_wind_vector: np.ndarray[(3,)],
+    dsp_velocities: np.ndarray[
+        (
+            E_BINS,
+            SPIN_SECTORS,
+            CEMS,
+            3,
+        )
+    ],
+    mag_vector: np.ndarray[
+        (
+            E_BINS,
+            SPIN_SECTORS,
+            3,
+        )
+    ],
+    config: SweConfiguration,
+) -> tuple[np.ndarray, np.ndarray]:
+    velocity_in_sw_frame = calculate_velocity_in_sw_frame(
+        dsp_velocities, solar_wind_vector
+    )
+    energy_in_sw_frame = calculate_energy_in_ev_from_velocity_in_km_per_second(
+        velocity_in_sw_frame
+    )
+    pitch_angle = calculate_pitch_angle(
+        velocity_in_sw_frame, mag_vector[..., np.newaxis, :]
+    )
+    gyrophase = calculate_gyrophase(
+        velocity_in_sw_frame, mag_vector[..., np.newaxis, :]
+    )
+
+    rebinned_by_pa = rebin_by_pitch_angle(
+        flux_or_psd, pitch_angle, energy_in_sw_frame, config
+    )
+    rebinned_by_pa_and_gyro = rebin_by_pitch_angle_and_gyrophase(
+        flux_or_psd, pitch_angle, gyrophase, energy_in_sw_frame, config
+    )
 
     return rebinned_by_pa, rebinned_by_pa_and_gyro
 
 
-def integrate_distribution_to_get_1d_spectrum(psd_by_energy_and_pitch_angle: np.ndarray[(E_BINS, PITCH_ANGLE_BINS)],
-                                              config: SweConfiguration) -> np.ndarray[E_BINS]:
-    pitch_angle_bin_factors = np.sin(np.deg2rad(config["pitch_angle_bins"])) * 2 * np.deg2rad(
-        config["pitch_angle_deltas"]) / 2
+def integrate_distribution_to_get_1d_spectrum(
+    psd_by_energy_and_pitch_angle: np.ndarray[(E_BINS, PITCH_ANGLE_BINS)],
+    config: SweConfiguration,
+) -> np.ndarray[E_BINS]:
+    pitch_angle_bin_factors = (
+        np.sin(np.deg2rad(config["pitch_angle_bins"]))
+        * 2
+        * np.deg2rad(config["pitch_angle_deltas"])
+        / 2
+    )
 
     return np.nansum(psd_by_energy_and_pitch_angle * pitch_angle_bin_factors, axis=1)
 
 
 def integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
-        psd_by_pitch_angle: np.ndarray[(E_BINS, PITCH_ANGLE_BINS)],
-        config: SweConfiguration) -> (np.ndarray[E_BINS], np.ndarray[E_BINS]):
-    pitch_angle_bin_factors = np.sin(np.deg2rad(config["pitch_angle_bins"])) * 2 * np.deg2rad(
-        config["pitch_angle_deltas"])
+    psd_by_pitch_angle: np.ndarray[(E_BINS, PITCH_ANGLE_BINS)], config: SweConfiguration
+) -> (np.ndarray[E_BINS], np.ndarray[E_BINS]):
+    pitch_angle_bin_factors = (
+        np.sin(np.deg2rad(config["pitch_angle_bins"]))
+        * 2
+        * np.deg2rad(config["pitch_angle_deltas"])
+    )
     pitch_less_than_90 = np.array(config["pitch_angle_bins"]) < 90
 
-    spectrum_a = np.nansum((psd_by_pitch_angle * pitch_angle_bin_factors)[:, pitch_less_than_90], axis=1)
-    spectrum_b = np.nansum((psd_by_pitch_angle * pitch_angle_bin_factors)[:, ~pitch_less_than_90], axis=1)
-
-    if spectrum_a[config["in_vs_out_energy_index"]] > spectrum_b[config["in_vs_out_energy_index"]]:
+    spectrum_a = np.nansum(
+        (psd_by_pitch_angle * pitch_angle_bin_factors)[:, pitch_less_than_90], axis=1
+    )
+    spectrum_b = np.nansum(
+        (psd_by_pitch_angle * pitch_angle_bin_factors)[:, ~pitch_less_than_90], axis=1
+    )
+
+    if (
+        spectrum_a[config["in_vs_out_energy_index"]]
+        > spectrum_b[config["in_vs_out_energy_index"]]
+    ):
         inbound = spectrum_b
         outbound = spectrum_a
     else:
@@ -350,20 +492,40 @@ def integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
     return inbound, outbound
 
 
-def swe_rebin_intensity_by_pitch_angle_and_gyrophase(intensity_data: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS)],
-                                                     counts: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS)],
-                                                     dsp_velocities: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS, 3)],
-                                                     mag_vectors: np.ndarray[([(E_BINS, SPIN_SECTORS, 3,)])],
-                                                     config: SweConfiguration) -> [np.ndarray]:
+def swe_rebin_intensity_by_pitch_angle_and_gyrophase(
+    intensity_data: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS)],
+    counts: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS)],
+    dsp_velocities: np.ndarray[(E_BINS, SPIN_SECTORS, CEMS, 3)],
+    mag_vectors: np.ndarray[
+        (
+            [
+                (
+                    E_BINS,
+                    SPIN_SECTORS,
+                    3,
+                )
+            ]
+        )
+    ],
+    config: SweConfiguration,
+) -> [np.ndarray]:
     normalized_velocities = calculate_unit_vector(dsp_velocities)
     normalized_mag_vectors = calculate_unit_vector(mag_vectors)
 
-    pitch_angles = calculate_pitch_angle(normalized_velocities, normalized_mag_vectors[..., np.newaxis, :])
-    gyrophases = calculate_gyrophase(normalized_velocities, normalized_mag_vectors[..., np.newaxis, :])
+    pitch_angles = calculate_pitch_angle(
+        normalized_velocities, normalized_mag_vectors[..., np.newaxis, :]
+    )
+    gyrophases = calculate_gyrophase(
+        normalized_velocities, normalized_mag_vectors[..., np.newaxis, :]
+    )
 
     num_pitch_angle_bins = len(config["pitch_angle_bins"])
     num_gyrophase_bins = len(config["gyrophase_bins"])
-    output_shape_pa_and_gyro = (intensity_data.shape[0], num_pitch_angle_bins, num_gyrophase_bins)
+    output_shape_pa_and_gyro = (
+        intensity_data.shape[0],
+        num_pitch_angle_bins,
+        num_gyrophase_bins,
+    )
     output_shape_pa_only = (intensity_data.shape[0], num_pitch_angle_bins)
 
     rebinned_summed_by_pa_and_gyro = np.zeros(shape=output_shape_pa_and_gyro)
@@ -377,52 +539,82 @@ def swe_rebin_intensity_by_pitch_angle_and_gyrophase(intensity_data: np.ndarray[
 
     for i in range(intensity_data.shape[0]):
         for pitch_angle, gyrophase, intensity, count in zip(
-                np.ravel(pitch_angles[i]),
-                np.ravel(gyrophases[i]),
-                np.ravel(intensity_data[i]),
-                np.ravel(counts[i])):
+            np.ravel(pitch_angles[i]),
+            np.ravel(gyrophases[i]),
+            np.ravel(intensity_data[i]),
+            np.ravel(counts[i]),
+        ):
             if not (np.isnan(intensity) or np.isnan(pitch_angle)):
-                pitch_angle_bin = next((i for i, (center, delta) in
-                                        enumerate(zip(config["pitch_angle_bins"], config["pitch_angle_deltas"])) if
-                                        center - delta <= pitch_angle < center + delta),
-                                       num_pitch_angle_bins - 1)
+                pitch_angle_bin = next(
+                    (
+                        i
+                        for i, (center, delta) in enumerate(
+                            zip(
+                                config["pitch_angle_bins"], config["pitch_angle_deltas"]
+                            )
+                        )
+                        if center - delta <= pitch_angle < center + delta
+                    ),
+                    num_pitch_angle_bins - 1,
+                )
                 if not np.isnan(gyrophase):
-                    gyrophase_bin = next((i for i, (center, delta) in
-                                          enumerate(zip(config["gyrophase_bins"], config["gyrophase_deltas"])) if
-                                          center - delta <= gyrophase < center + delta),
-                                         num_gyrophase_bins - 1)
-
-                    rebinned_summed_by_pa_and_gyro[i, pitch_angle_bin, gyrophase_bin] += intensity
-                    rebinned_count_by_pa_and_gyro[i, pitch_angle_bin, gyrophase_bin] += 1
-
-                    rebinned_summed_counts_by_pa_and_gyro[i, pitch_angle_bin, gyrophase_bin] += count
+                    gyrophase_bin = next(
+                        (
+                            i
+                            for i, (center, delta) in enumerate(
+                                zip(
+                                    config["gyrophase_bins"], config["gyrophase_deltas"]
+                                )
+                            )
+                            if center - delta <= gyrophase < center + delta
+                        ),
+                        num_gyrophase_bins - 1,
+                    )
+
+                    rebinned_summed_by_pa_and_gyro[
+                        i, pitch_angle_bin, gyrophase_bin
+                    ] += intensity
+                    rebinned_count_by_pa_and_gyro[
+                        i, pitch_angle_bin, gyrophase_bin
+                    ] += 1
+
+                    rebinned_summed_counts_by_pa_and_gyro[
+                        i, pitch_angle_bin, gyrophase_bin
+                    ] += count
 
                 rebinned_summed_pa_only[i, pitch_angle_bin] += intensity
                 rebinned_count_pa_only[i, pitch_angle_bin] += 1
                 rebinned_summed_counts_by_pa_only[i, pitch_angle_bin] += count
 
-    rebinned_summed_counts_by_pa_and_gyro[rebinned_summed_counts_by_pa_and_gyro == 0] = np.nan
+    rebinned_summed_counts_by_pa_and_gyro[
+        rebinned_summed_counts_by_pa_and_gyro == 0
+    ] = np.nan
     rebinned_summed_counts_by_pa_only[rebinned_summed_counts_by_pa_only == 0] = np.nan
 
     averaged_rebinned_intensity_by_pa_and_gyro = np.divide(
         rebinned_summed_by_pa_and_gyro,
         rebinned_count_by_pa_and_gyro,
-        out=np.full_like(
-            rebinned_count_by_pa_and_gyro, np.nan),
-        where=rebinned_count_by_pa_and_gyro != 0)
+        out=np.full_like(rebinned_count_by_pa_and_gyro, np.nan),
+        where=rebinned_count_by_pa_and_gyro != 0,
+    )
 
-    averaged_rebinned_intensity_by_pa_only = np.divide(rebinned_summed_pa_only,
-                                                       rebinned_count_pa_only,
-                                                       out=np.full_like(
-                                                           rebinned_count_pa_only,
-                                                           np.nan),
-                                                       where=rebinned_count_pa_only != 0)
+    averaged_rebinned_intensity_by_pa_only = np.divide(
+        rebinned_summed_pa_only,
+        rebinned_count_pa_only,
+        out=np.full_like(rebinned_count_pa_only, np.nan),
+        where=rebinned_count_pa_only != 0,
+    )
 
     uncertainties_by_pa_and_gyro = averaged_rebinned_intensity_by_pa_and_gyro / np.sqrt(
-        rebinned_summed_counts_by_pa_and_gyro)
-    uncertainties_by_pa_only = averaged_rebinned_intensity_by_pa_only / np.sqrt(rebinned_summed_counts_by_pa_only)
+        rebinned_summed_counts_by_pa_and_gyro
+    )
+    uncertainties_by_pa_only = averaged_rebinned_intensity_by_pa_only / np.sqrt(
+        rebinned_summed_counts_by_pa_only
+    )
 
     return (
-        averaged_rebinned_intensity_by_pa_and_gyro, averaged_rebinned_intensity_by_pa_only,
+        averaged_rebinned_intensity_by_pa_and_gyro,
+        averaged_rebinned_intensity_by_pa_only,
         uncertainties_by_pa_and_gyro,
-        uncertainties_by_pa_only)
+        uncertainties_by_pa_only,
+    )
diff --git a/imap_l3_processing/swe/l3/swe_l3_dependencies.py b/imap_l3_processing/swe/l3/swe_l3_dependencies.py
index 1de137846..3137acbe0 100644
--- a/imap_l3_processing/swe/l3/swe_l3_dependencies.py
+++ b/imap_l3_processing/swe/l3/swe_l3_dependencies.py
@@ -7,9 +7,18 @@
 from imap_data_access.processing_input import ProcessingInputCollection
 
 from imap_l3_processing.models import MagData
-from imap_l3_processing.swe.l3.models import SweL2Data, SweConfiguration, SwapiL3aProtonData, SweL1bData
-from imap_l3_processing.swe.l3.utils import read_l2_swe_data, read_l3a_swapi_proton_data, read_swe_config, \
-    read_l1b_swe_data
+from imap_l3_processing.swe.l3.models import (
+    SwapiL3aProtonData,
+    SweConfiguration,
+    SweL1bData,
+    SweL2Data,
+)
+from imap_l3_processing.swe.l3.utils import (
+    read_l1b_swe_data,
+    read_l2_swe_data,
+    read_l3a_swapi_proton_data,
+    read_swe_config,
+)
 from imap_l3_processing.utils import read_mag_data, select_mag_path
 
 MAG_DESPUN_L1D_DESCRIPTOR = "norm-dsrf"
@@ -27,28 +36,40 @@ class SweL3Dependencies:
     mag_is_preliminary: bool = False
 
     @classmethod
-    def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> SweL3Dependencies:
+    def fetch_dependencies(
+        cls, dependencies: ProcessingInputCollection
+    ) -> SweL3Dependencies:
         science_files = dependencies.processing_input
-        swe_config_dependency = dependencies.get_file_paths(source='swe', descriptor=SWE_CONFIG_DESCRIPTOR)[0]
+        swe_config_dependency = dependencies.get_file_paths(
+            source="swe", descriptor=SWE_CONFIG_DESCRIPTOR
+        )[0]
 
         try:
             swe_l2_dependency = next(
-                d.imap_file_paths[0] for d in science_files if d.source == "swe" and d.data_type == "l2")
+                d.imap_file_paths[0]
+                for d in science_files
+                if d.source == "swe" and d.data_type == "l2"
+            )
         except StopIteration:
-            raise ValueError(f"Missing SWE l2 dependency.")
+            raise ValueError("Missing SWE l2 dependency.")
         try:
             swe_l1b_dependency = next(
-                d.imap_file_paths[0] for d in science_files if d.source == "swe" and d.data_type == "l1b")
+                d.imap_file_paths[0]
+                for d in science_files
+                if d.source == "swe" and d.data_type == "l1b"
+            )
         except StopIteration:
-            raise ValueError(f"Missing SWE l1b dependency.")
+            raise ValueError("Missing SWE l1b dependency.")
         mag_file, mag_level = select_mag_path(dependencies, MAG_DESPUN_L1D_DESCRIPTOR)
         if mag_file is None:
             raise ValueError(f"Missing MAG {MAG_DESPUN_L1D_DESCRIPTOR} dependency.")
         mag_is_preliminary = mag_level == "l1d"
         try:
             swapi_dependency = next(
-                d.imap_file_paths[0] for d in science_files if d.source == "swapi"
-                and d.descriptor == SWAPI_L3A_PROTON_DESCRIPTOR)
+                d.imap_file_paths[0]
+                for d in science_files
+                if d.source == "swapi" and d.descriptor == SWAPI_L3A_PROTON_DESCRIPTOR
+            )
         except StopIteration:
             raise ValueError(f"Missing SWAPI {SWAPI_L3A_PROTON_DESCRIPTOR} dependency.")
 
@@ -57,19 +78,36 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> SweL3Dep
         swapi_file = download(swapi_dependency.construct_path())
         swe_config = download(swe_config_dependency)
 
-        return cls.from_file_paths(swe_l2_file, swe_l1b_file, mag_file, swapi_file, swe_config,
-                                   mag_is_preliminary=mag_is_preliminary)
+        return cls.from_file_paths(
+            swe_l2_file,
+            swe_l1b_file,
+            mag_file,
+            swapi_file,
+            swe_config,
+            mag_is_preliminary=mag_is_preliminary,
+        )
 
     @classmethod
-    def from_file_paths(cls, swe_l2_file_path: Path, swe_l1b_file_path: Path, mag_file_path: Path,
-                        swapi_file_path: Path,
-                        configuration_file_path: Path,
-                        mag_is_preliminary: bool = False) -> SweL3Dependencies:
+    def from_file_paths(
+        cls,
+        swe_l2_file_path: Path,
+        swe_l1b_file_path: Path,
+        mag_file_path: Path,
+        swapi_file_path: Path,
+        configuration_file_path: Path,
+        mag_is_preliminary: bool = False,
+    ) -> SweL3Dependencies:
         mag_l1d_data = read_mag_data(mag_file_path)
         swe_l1b_data = read_l1b_swe_data(swe_l1b_file_path)
         swe_l2_data = read_l2_swe_data(swe_l2_file_path)
         swapi_l3a_proton_data = read_l3a_swapi_proton_data(swapi_file_path)
         configuration = read_swe_config(configuration_file_path)
 
-        return cls(swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, configuration,
-                   mag_is_preliminary=mag_is_preliminary)
+        return cls(
+            swe_l2_data,
+            swe_l1b_data,
+            mag_l1d_data,
+            swapi_l3a_proton_data,
+            configuration,
+            mag_is_preliminary=mag_is_preliminary,
+        )
diff --git a/imap_l3_processing/swe/l3/utils.py b/imap_l3_processing/swe/l3/utils.py
index c52b4e0c6..0670fbfbe 100644
--- a/imap_l3_processing/swe/l3/utils.py
+++ b/imap_l3_processing/swe/l3/utils.py
@@ -7,7 +7,12 @@
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
-from imap_l3_processing.swe.l3.models import SweConfiguration, SweL2Data, SwapiL3aProtonData, SweL1bData
+from imap_l3_processing.swe.l3.models import (
+    SwapiL3aProtonData,
+    SweConfiguration,
+    SweL1bData,
+    SweL2Data,
+)
 
 
 def read_l2_swe_data(swe_l2_data: Path) -> SweL2Data:
@@ -20,11 +25,17 @@ def read_l2_swe_data(swe_l2_data: Path) -> SweL2Data:
         inst_az_label = cdf["inst_az_label"][:]
         energy = cdf["energy"][:]
         inst_az_spin_sector = read_numeric_variable(cdf["inst_az_spin_sector"])
-        phase_space_density = read_numeric_variable(cdf["phase_space_density_spin_sector"])
+        phase_space_density = read_numeric_variable(
+            cdf["phase_space_density_spin_sector"]
+        )
         acquisition_time_in_MET = read_numeric_variable(cdf["acquisition_time"])
         valid_times_mask = np.isfinite(acquisition_time_in_MET)
-        converted_valid_times = met_to_datetime64(acquisition_time_in_MET[valid_times_mask].ravel())
-        acquisition_time_dt64 = np.full(acquisition_time_in_MET.shape, np.datetime64("NaT", 'ns'))
+        converted_valid_times = met_to_datetime64(
+            acquisition_time_in_MET[valid_times_mask].ravel()
+        )
+        acquisition_time_dt64 = np.full(
+            acquisition_time_in_MET.shape, np.datetime64("NaT", "ns")
+        )
         acquisition_time_dt64[valid_times_mask] = converted_valid_times
         acquisition_duration = cdf["acq_duration"][...]
         phase_space_density_rebinned = read_numeric_variable(cdf["phase_space_density"])
@@ -52,7 +63,9 @@ def read_l1b_swe_data(swe_l1b_data: Path) -> SweL1bData:
         count_rates = cdf["science_data"][...]
         settle_duration = cdf["settle_duration"][...]
 
-    return SweL1bData(epoch=epoch, count_rates=count_rates, settle_duration=settle_duration)
+    return SweL1bData(
+        epoch=epoch, count_rates=count_rates, settle_duration=settle_duration
+    )
 
 
 def read_l3a_swapi_proton_data(swapi_l3a_data: Path) -> SwapiL3aProtonData:
@@ -65,16 +78,20 @@ def read_l3a_swapi_proton_data(swapi_l3a_data: Path) -> SwapiL3aProtonData:
             epoch_delta = np.repeat(timedelta(seconds=epoch_delta_ns), len(epoch))
         proton_sw_speed = read_numeric_variable(cdf["proton_sw_speed"])
         if "proton_sw_velocity_rtn" in cdf:
-            proton_sw_velocity_rtn = read_numeric_variable(cdf["proton_sw_velocity_rtn"])
+            proton_sw_velocity_rtn = read_numeric_variable(
+                cdf["proton_sw_velocity_rtn"]
+            )
         else:
             proton_sw_velocity_rtn = np.full((len(epoch), 3), np.nan)
         swp_flags = cdf["swp_flags"][...]
 
-    return SwapiL3aProtonData(epoch=epoch,
-                              epoch_delta=epoch_delta,
-                              proton_sw_velocity_rtn=proton_sw_velocity_rtn,
-                              proton_sw_speed=proton_sw_speed,
-                              swp_flags=swp_flags)
+    return SwapiL3aProtonData(
+        epoch=epoch,
+        epoch_delta=epoch_delta,
+        proton_sw_velocity_rtn=proton_sw_velocity_rtn,
+        proton_sw_speed=proton_sw_speed,
+        swp_flags=swp_flags,
+    )
 
 
 def read_swe_config(swe_config: Path) -> SweConfiguration:
@@ -84,14 +101,18 @@ def read_swe_config(swe_config: Path) -> SweConfiguration:
     return config
 
 
-def compute_epoch_delta_in_ns(acq_duration: np.ndarray[float], settle_duration: np.ndarray[float]) -> np.ndarray:
+def compute_epoch_delta_in_ns(
+    acq_duration: np.ndarray[float], settle_duration: np.ndarray[float]
+) -> np.ndarray:
     number_of_energies = acq_duration.shape[1]
     number_of_spins = settle_duration.shape[1]
     number_of_spin_sectors = acq_duration.shape[2]
 
     energies_per_spin = number_of_energies / number_of_spins
 
-    total_settle_duration = np.sum(settle_duration, axis=1) * energies_per_spin * number_of_spin_sectors
+    total_settle_duration = (
+        np.sum(settle_duration, axis=1) * energies_per_spin * number_of_spin_sectors
+    )
     total_acq_duration = np.sum(acq_duration, axis=(1, 2))
     total_duration_in_microseconds = total_settle_duration + total_acq_duration
     return total_duration_in_microseconds / 2 * 1000
diff --git a/imap_l3_processing/swe/quality_flags.py b/imap_l3_processing/swe/quality_flags.py
index 6ac03d5c8..ded60983c 100644
--- a/imap_l3_processing/swe/quality_flags.py
+++ b/imap_l3_processing/swe/quality_flags.py
@@ -1,17 +1,17 @@
-from imap_processing.quality_flags import FlagNameMixin, CommonFlags
+from imap_processing.quality_flags import CommonFlags, FlagNameMixin
 
 
 class SweL3Flags(FlagNameMixin):
     NONE = CommonFlags.NONE
-    FALLBACK_POTENTIAL_ESTIMATE = 2 ** 2
-    BACKUP_SPLINE_UNRESOLVED = 2 ** 3
-    POTENTIAL_FIT_UNCONVERGED = 2 ** 4
-    BREAKPOINT_FIT_UNCONVERGED = 2 ** 5
-    ULTRA_HV_OFF = 2 ** 6
-    TEMPERATURE_OUTLIER = 2 ** 7
-    PRELIMINARY_MAG = 2 ** 8
-    FALLBACK_SWAPI_SPEED = 2 ** 9
-    NEGATIVE_MOMENT = 2 ** 10
-    UNPHYSICAL_PSD = 2 ** 11
+    FALLBACK_POTENTIAL_ESTIMATE = 2**2
+    BACKUP_SPLINE_UNRESOLVED = 2**3
+    POTENTIAL_FIT_UNCONVERGED = 2**4
+    BREAKPOINT_FIT_UNCONVERGED = 2**5
+    ULTRA_HV_OFF = 2**6
+    TEMPERATURE_OUTLIER = 2**7
+    PRELIMINARY_MAG = 2**8
+    FALLBACK_SWAPI_SPEED = 2**9
+    NEGATIVE_MOMENT = 2**10
+    UNPHYSICAL_PSD = 2**11
     FALLBACK_CALIBRATION_EXTRAPOLATED = 2**12
-    PREDICTIVE_EPHEMERIS = 2 ** 15
+    PREDICTIVE_EPHEMERIS = 2**15
diff --git a/imap_l3_processing/swe/swe_processor.py b/imap_l3_processing/swe/swe_processor.py
index 031719d00..e75c8a898 100644
--- a/imap_l3_processing/swe/swe_processor.py
+++ b/imap_l3_processing/swe/swe_processor.py
@@ -12,36 +12,36 @@
 from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
 from imap_l3_processing.processor import Processor
 from imap_l3_processing.swe.l3.models import (
-    SweL3Data,
+    SweConfiguration,
     SweL1bData,
     SweL2Data,
+    SweL3Data,
     SweL3MomentData,
-    SweConfiguration,
 )
 from imap_l3_processing.swe.l3.science.moment_calculations import (
-    compute_maxwellian_weight_factors,
-    rotate_temperature,
+    Moments,
+    ScaleDensityOutput,
     apply_rotation_matrix,
-    rotate_rtn_vectors_to_dps,
+    calculate_primary_eigenvector,
+    compute_maxwellian_weight_factors,
     core_fit_moments_retrying_on_failure,
+    get_dps_to_rtn_rotation_matrix,
     halo_fit_moments_retrying_on_failure,
-    Moments,
     integrate,
+    rotate_rtn_vectors_to_dps,
+    rotate_temperature,
+    rotate_temperature_tensor_to_mag,
+    rotate_vector_to_rtn_spherical_coordinates,
     scale_core_density,
     scale_halo_density,
-    rotate_vector_to_rtn_spherical_coordinates,
-    calculate_primary_eigenvector,
-    ScaleDensityOutput,
-    rotate_temperature_tensor_to_mag,
-    get_dps_to_rtn_rotation_matrix,
 )
 from imap_l3_processing.swe.l3.science.pitch_calculations import (
     average_over_look_directions,
-    mec_breakpoint_finder,
+    calculate_velocity_in_dsp_frame_km_s,
     correct_and_rebin,
     integrate_distribution_to_get_1d_spectrum,
     integrate_distribution_to_get_inbound_and_outbound_1d_spectrum,
-    calculate_velocity_in_dsp_frame_km_s,
+    mec_breakpoint_finder,
     swe_rebin_intensity_by_pitch_angle_and_gyrophase,
 )
 from imap_l3_processing.swe.l3.swe_l3_dependencies import SweL3Dependencies
@@ -73,7 +73,9 @@
 
 
 def _detect_negative(density: np.ndarray, tensor: np.ndarray) -> np.ndarray:
-    return (density < 0) | np.any(tensor[:, TEMPERATURE_TENSOR_DIAGONAL_INDICES] < 0, axis=-1)
+    return (density < 0) | np.any(
+        tensor[:, TEMPERATURE_TENSOR_DIAGONAL_INDICES] < 0, axis=-1
+    )
 
 
 def check_and_mask_negative_moments(moment_data: SweL3MomentData) -> np.ndarray:
@@ -96,23 +98,27 @@ def check_and_mask_negative_moments(moment_data: SweL3MomentData) -> np.ndarray:
     for field_suffix in INTEGRATED_FIELD_SUFFIXES:
         getattr(moment_data, "core" + field_suffix)[core_negative] = np.nan
         getattr(moment_data, "halo" + field_suffix)[halo_negative] = np.nan
-        getattr(moment_data, "total" + field_suffix)[core_negative | halo_negative | total_negative] = np.nan
+        getattr(moment_data, "total" + field_suffix)[
+            core_negative | halo_negative | total_negative
+        ] = np.nan
 
-    flags[core_negative | halo_negative | total_negative] |= np.uint16(SweL3Flags.NEGATIVE_MOMENT)
+    flags[core_negative | halo_negative | total_negative] |= np.uint16(
+        SweL3Flags.NEGATIVE_MOMENT
+    )
     return flags
 
 
 # Temperature Outlier Flag Algorithm
 def check_temperature_outlier_flag(data: np.ndarray):
     # Define window duration that is centered, so best to use an odd number
-    window = 61 # 61 is about 1 hour
+    window = 61  # 61 is about 1 hour
     # Initiate Temperature Outliers to be all NONE value
     TEMPERATURE_OUTLIER = np.zeros(len(data), dtype=np.uint16)
     TEMPERATURE_OUTLIER[:] = SweL3Flags.NONE
     for i in np.arange(len(data)):
         # Get left and right index accounting for edges
-        left_idx = 0 if i - window//2 < 0 else i - window//2
-        right_idx = i + window//2 + 1
+        left_idx = max(i - window // 2, 0)
+        right_idx = i + window // 2 + 1
         # Loop check the current value while removing every point from the window on each iteration
         for k in np.arange(len(data[left_idx:right_idx])):
             temp_data = data[left_idx:right_idx]
@@ -120,7 +126,7 @@ def check_temperature_outlier_flag(data: np.ndarray):
             # Check if current value is beyond 3-sigma of median from data
             median = np.nanmedian(temp_data)
             deviation = np.nanstd(temp_data)
-            if np.abs(median - data[i]) >= deviation*3:
+            if np.abs(median - data[i]) >= deviation * 3:
                 TEMPERATURE_OUTLIER[i] = SweL3Flags.TEMPERATURE_OUTLIER
                 # Break since we already know the current index i is an outlier
                 break
@@ -130,8 +136,8 @@ def check_temperature_outlier_flag(data: np.ndarray):
         # Don't do this is the current value is already an outlier
         if TEMPERATURE_OUTLIER[i] == SweL3Flags.NONE:
             # Get left and right index accounting for edges
-            left_idx = 0 if i - window//2 < 0 else i - window//2
-            right_idx = i + window//2 + 1
+            left_idx = max(i - window // 2, 0)
+            right_idx = i + window // 2 + 1
             # Loop check the current value while removing every point from the window on each iteration
             # This time around, we conditionally slice to avoid including already known outliers from
             #   median and standard devation calculations
@@ -140,9 +146,17 @@ def check_temperature_outlier_flag(data: np.ndarray):
                     continue
                 temp_data = data[left_idx:right_idx]
                 # Check if current value is beyond 3-sigma of median from data
-                median = np.nanmedian(np.delete(temp_data,k)[np.delete(tof_copy[left_idx:right_idx],k)==0])
-                deviation = np.nanstd(np.delete(temp_data,k)[np.delete(tof_copy[left_idx:right_idx],k)==0])
-                if np.abs(median - data[i]) >= deviation * 3.:
+                median = np.nanmedian(
+                    np.delete(temp_data, k)[
+                        np.delete(tof_copy[left_idx:right_idx], k) == 0
+                    ]
+                )
+                deviation = np.nanstd(
+                    np.delete(temp_data, k)[
+                        np.delete(tof_copy[left_idx:right_idx], k) == 0
+                    ]
+                )
+                if np.abs(median - data[i]) >= deviation * 3.0:
                     TEMPERATURE_OUTLIER[i] = SweL3Flags.TEMPERATURE_OUTLIER
                     # Break since we already know the current index i is an outlier
                     break
@@ -185,7 +199,7 @@ def calculate_products(self, dependencies: SweL3Dependencies) -> SweL3Data:
         geometric_fractions = np.array(config["geometric_fractions"])
         for i in range(len(swe_epoch)):
             npts = 7 // 2
-            left_idx = 0 if i - npts < 0 else i - npts
+            left_idx = max(i - npts, 0)
             right_idx = i + (i - left_idx + 1)
             time_avg_psd = np.nanmean(
                 swe_l2_data.phase_space_density[left_idx:right_idx], axis=0
@@ -263,13 +277,21 @@ def calculate_products(self, dependencies: SweL3Dependencies) -> SweL3Data:
                 psd_array > UNPHYSICAL_PSD_THRESHOLD,
                 axis=tuple(range(1, psd_array.ndim)),
             )
-            swe_quality_flags[unphysical_psd_per_epoch] |= np.uint16(SweL3Flags.UNPHYSICAL_PSD)
+            swe_quality_flags[unphysical_psd_per_epoch] |= np.uint16(
+                SweL3Flags.UNPHYSICAL_PSD
+            )
 
         if dependencies.mag_is_preliminary:
-            swe_quality_flags = np.bitwise_or(swe_quality_flags, SweL3Flags.PRELIMINARY_MAG)
+            swe_quality_flags = np.bitwise_or(
+                swe_quality_flags, SweL3Flags.PRELIMINARY_MAG
+            )
 
-        last_cal_interval = (swe_l2_data.data_quality & SweL1bFlags.LAST_CAL_INTERVAL) != 0
-        swe_quality_flags[last_cal_interval] |= np.uint16(SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED)
+        last_cal_interval = (
+            swe_l2_data.data_quality & SweL1bFlags.LAST_CAL_INTERVAL
+        ) != 0
+        swe_quality_flags[last_cal_interval] |= np.uint16(
+            SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED
+        )
 
         rebinned_mask = np.ma.masked_invalid(swe_l2_data.phase_space_density_rebinned)
         dist_by_phi_rebinned = np.average(
@@ -412,11 +434,7 @@ def calculate_moment_products(
                     )
                     continue
 
-                halo_nfit = (
-                    5
-                    if len(swe_l2_data.energy) - jbreak > 5
-                    else len(swe_l2_data.energy) - jbreak
-                )
+                halo_nfit = min(len(swe_l2_data.energy) - jbreak, 5)
 
                 core_moment_fit_result = core_fit_moments_retrying_on_failure(
                     corrected_energy_bins[i],
@@ -432,11 +450,17 @@ def calculate_moment_products(
 
                 predicted_tracker = PredictedEphemerisTracker()
                 try:
-                    dps_to_rtn = predicted_tracker.run(get_dps_to_rtn_rotation_matrix, current_epoch)
+                    dps_to_rtn = predicted_tracker.run(
+                        get_dps_to_rtn_rotation_matrix, current_epoch
+                    )
                 except SpiceyError:
-                    logger.info(f"No IMAP_DPS to IMAP_RTN rotation available at epoch index {i}. Using fill values.")
+                    logger.info(
+                        f"No IMAP_DPS to IMAP_RTN rotation available at epoch index {i}. Using fill values."
+                    )
                     dps_to_rtn = np.full((3, 3), np.nan)
-                quality_flags[i] |= np.uint16(SweL3Flags.PREDICTIVE_EPHEMERIS * predicted_tracker.used_predict)
+                quality_flags[i] |= np.uint16(
+                    SweL3Flags.PREDICTIVE_EPHEMERIS * predicted_tracker.used_predict
+                )
 
                 if core_moment_fit_result is not None:
                     core_moment = core_moment_fit_result.moments
@@ -859,9 +883,10 @@ def calculate_moment_products(
             halo_temperature_perpendicular_to_mag=halo_temperature_perpendicular_to_mag,
             total_temperature_parallel_to_mag=total_temperature_parallel_to_mag,
             total_temperature_perpendicular_to_mag=total_temperature_perpendicular_to_mag,
-            core_temperature_tensor_integrated = core_temperature_tensor_integrated*1e4,
-            halo_temperature_tensor_integrated = halo_temperature_tensor_integrated*1e4,
-            total_temperature_tensor_integrated = total_temperature_tensor_integrated*1e4,
+            core_temperature_tensor_integrated=core_temperature_tensor_integrated * 1e4,
+            halo_temperature_tensor_integrated=halo_temperature_tensor_integrated * 1e4,
+            total_temperature_tensor_integrated=total_temperature_tensor_integrated
+            * 1e4,
             quality_flags=quality_flags,
         )
 
@@ -885,9 +910,11 @@ def calculate_pitch_angle_products(
 
         swapi_l3a_proton_data = dependencies.swapi_l3a_proton_data
         swapi_epoch = swapi_l3a_proton_data.epoch
-        solar_wind_vectors, used_predict_to_rotate_solar_wind = rotate_rtn_vectors_to_dps(
-            swapi_epoch,
-            swapi_l3a_proton_data.proton_sw_velocity_rtn,
+        solar_wind_vectors, used_predict_to_rotate_solar_wind = (
+            rotate_rtn_vectors_to_dps(
+                swapi_epoch,
+                swapi_l3a_proton_data.proton_sw_velocity_rtn,
+            )
         )
         proton_sw_speed = swapi_l3a_proton_data.proton_sw_speed
         fallback_to_speed = np.any(np.isnan(solar_wind_vectors), axis=1) & np.isfinite(
@@ -911,15 +938,22 @@ def calculate_pitch_angle_products(
             maximum_distance=swapi_max_distance,
         )
 
-
         rebinned_solar_wind_vectors = solar_wind_interpolator.interpolate_data()
 
-        rebinned_fallback_to_speed = solar_wind_interpolator.interpolate_flags(fallback_to_speed)
-        rebinned_used_predict_to_rotate_solar_wind = solar_wind_interpolator.interpolate_flags(used_predict_to_rotate_solar_wind)
+        rebinned_fallback_to_speed = solar_wind_interpolator.interpolate_flags(
+            fallback_to_speed
+        )
+        rebinned_used_predict_to_rotate_solar_wind = (
+            solar_wind_interpolator.interpolate_flags(used_predict_to_rotate_solar_wind)
+        )
 
         swe_flags = np.full(len(swe_epoch), SweL3Flags.NONE, dtype=np.uint16)
-        swe_flags[rebinned_fallback_to_speed] |= np.uint16(SweL3Flags.FALLBACK_SWAPI_SPEED)
-        swe_flags[rebinned_used_predict_to_rotate_solar_wind] |= np.uint16(SweL3Flags.PREDICTIVE_EPHEMERIS)
+        swe_flags[rebinned_fallback_to_speed] |= np.uint16(
+            SweL3Flags.FALLBACK_SWAPI_SPEED
+        )
+        swe_flags[rebinned_used_predict_to_rotate_solar_wind] |= np.uint16(
+            SweL3Flags.PREDICTIVE_EPHEMERIS
+        )
 
         counts = dependencies.swe_l1b_data.count_rates * (
             swe_l2_data.acquisition_duration[..., np.newaxis] / 1e6
diff --git a/imap_l3_processing/ultra/models.py b/imap_l3_processing/ultra/models.py
index 43c32b363..7e3d31b0b 100644
--- a/imap_l3_processing/ultra/models.py
+++ b/imap_l3_processing/ultra/models.py
@@ -11,13 +11,15 @@
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
-from imap_l3_processing.constants import TT2000_EPOCH, ONE_SECOND_IN_NANOSECONDS
-from imap_l3_processing.glows.l3e.glows_l3e_hi_model import PROBABILITY_OF_SURVIVAL_VAR_NAME
+from imap_l3_processing.constants import ONE_SECOND_IN_NANOSECONDS, TT2000_EPOCH
+from imap_l3_processing.glows.l3e.glows_l3e_hi_model import (
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+)
 from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import (
-    HEALPIX_INDEX_VAR_NAME,
     ENERGY_VAR_NAME,
     EPOCH_CDF_VAR_NAME,
     GLOWS_FLAGS_VAR_NAME,
+    HEALPIX_INDEX_VAR_NAME,
 )
 
 
@@ -40,8 +42,10 @@ def read_from_path(cls, path_to_cdf: Path) -> UltraGlowsL3eData:
                 repointing=repointing,
                 energy=read_numeric_variable(cdf[ENERGY_VAR_NAME]),
                 healpix_index=cdf[HEALPIX_INDEX_VAR_NAME][...],
-                survival_probability=read_numeric_variable(cdf[PROBABILITY_OF_SURVIVAL_VAR_NAME]),
-                flags=cdf[GLOWS_FLAGS_VAR_NAME][...]
+                survival_probability=read_numeric_variable(
+                    cdf[PROBABILITY_OF_SURVIVAL_VAR_NAME]
+                ),
+                flags=cdf[GLOWS_FLAGS_VAR_NAME][...],
             )
 
 
@@ -67,8 +71,12 @@ def read_from_path(cls, path: Path) -> UltraL1CPSet:
                 epoch_delta=cdf["epoch_delta"][0],
                 energy=read_numeric_variable(cdf[CoordNames.ENERGY_ULTRA_L1C.value]),
                 exposure=read_numeric_variable(cdf["exposure_factor"]),
-                latitude=read_numeric_variable(cdf[CoordNames.ELEVATION_L1C.value]).ravel(),
-                longitude=read_numeric_variable(cdf[CoordNames.AZIMUTH_L1C.value]).ravel(),
+                latitude=read_numeric_variable(
+                    cdf[CoordNames.ELEVATION_L1C.value]
+                ).ravel(),
+                longitude=read_numeric_variable(
+                    cdf[CoordNames.AZIMUTH_L1C.value]
+                ).ravel(),
                 healpix_index=cdf[CoordNames.HEALPIX_INDEX.value][...],
                 sensitivity=read_numeric_variable(cdf["sensitivity"]),
             )
@@ -77,9 +85,11 @@ def to_xarray(self):
         return xr.Dataset(
             {
                 "exposure_factor": (
-                    [CoordNames.TIME.value,
-                     CoordNames.ENERGY_ULTRA_L1C.value,
-                     CoordNames.HEALPIX_INDEX.value],
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_ULTRA_L1C.value,
+                        CoordNames.HEALPIX_INDEX.value,
+                    ],
                     self.exposure,
                 ),
                 "sensitivity": (
@@ -100,9 +110,11 @@ def to_xarray(self):
             },
             coords={
                 CoordNames.TIME.value: [
-                    ((self.epoch - TT2000_EPOCH).total_seconds()) * ONE_SECOND_IN_NANOSECONDS + self.epoch_delta / 2,
+                    ((self.epoch - TT2000_EPOCH).total_seconds())
+                    * ONE_SECOND_IN_NANOSECONDS
+                    + self.epoch_delta / 2,
                 ],
                 CoordNames.ENERGY_ULTRA_L1C.value: self.energy,
                 CoordNames.HEALPIX_INDEX.value: self.healpix_index,
-            }
+            },
         )
diff --git a/imap_l3_processing/ultra/science/ultra_survival_probability.py b/imap_l3_processing/ultra/science/ultra_survival_probability.py
index 1b0fa34ab..b916343ab 100644
--- a/imap_l3_processing/ultra/science/ultra_survival_probability.py
+++ b/imap_l3_processing/ultra/science/ultra_survival_probability.py
@@ -1,10 +1,8 @@
-from typing import Optional
-
 import numpy as np
 import spiceypy
 from astropy.units import Quantity
 from astropy_healpix import HEALPix, npix_to_nside
-from imap_processing.ena_maps.ena_maps import UltraPointingSet, HealpixSkyMap
+from imap_processing.ena_maps.ena_maps import HealpixSkyMap, UltraPointingSet
 from imap_processing.ena_maps.utils.coordinates import CoordNames
 from imap_processing.spice import geometry
 from imap_processing.ultra.l2.ultra_l2 import bin_pset_energy_bins
@@ -13,30 +11,37 @@
 from imap_l3_processing.constants import ONE_SECOND_IN_NANOSECONDS
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.ultra.models import UltraL1CPSet, UltraGlowsL3eData
+from imap_l3_processing.ultra.models import UltraGlowsL3eData, UltraL1CPSet
 
 
 class UltraSurvivalProbability(UltraPointingSet):
-    def __init__(self, l1c_pset: UltraL1CPSet, l3e_glows: UltraGlowsL3eData,
-                 bin_groups: Optional[np.ndarray] = None):
+    def __init__(
+        self,
+        l1c_pset: UltraL1CPSet,
+        l3e_glows: UltraGlowsL3eData,
+        bin_groups: np.ndarray | None = None,
+    ):
         coarse_bins = bin_pset_energy_bins(l1c_pset.to_xarray(), bin_groups)
         super().__init__(coarse_bins, geometry.SpiceFrame.IMAP_DPS)
 
-        predicted_ephemeris = (
-            l3e_glows.flags & GlowsL3Flags.PREDICTIVE_EPHEMERIS > 0
-        )
+        predicted_ephemeris = l3e_glows.flags & GlowsL3Flags.PREDICTIVE_EPHEMERIS > 0
         nominal_alpha_proton_ratio = (
-                l3e_glows.flags & GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO > 0
-        )
-        persisted_last_point = (
-                l3e_glows.flags & GlowsL3Flags.PERSISTED_LAST_POINT > 0
+            l3e_glows.flags & GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO > 0
         )
+        persisted_last_point = l3e_glows.flags & GlowsL3Flags.PERSISTED_LAST_POINT > 0
 
         l1c_epoch_in_et = spiceypy.unitim(
-            self.data.coords[CoordNames.TIME.value].values[0] / ONE_SECOND_IN_NANOSECONDS, "TT", "ET")
-        rotated_az_el_points = geometry.frame_transform_az_el(l1c_epoch_in_et, self.az_el_points,
-                                                              geometry.SpiceFrame.IMAP_DPS,
-                                                              geometry.SpiceFrame.ECLIPJ2000)
+            self.data.coords[CoordNames.TIME.value].values[0]
+            / ONE_SECOND_IN_NANOSECONDS,
+            "TT",
+            "ET",
+        )
+        rotated_az_el_points = geometry.frame_transform_az_el(
+            l1c_epoch_in_et,
+            self.az_el_points,
+            geometry.SpiceFrame.IMAP_DPS,
+            geometry.SpiceFrame.ECLIPJ2000,
+        )
         glows_nside = npix_to_nside(len(l3e_glows.healpix_index))
         glows_healpix = HEALPix(nside=glows_nside)
 
@@ -45,78 +50,105 @@ def __init__(self, l1c_pset: UltraL1CPSet, l3e_glows: UltraGlowsL3eData,
         spatially_interpolated_sp = np.zeros((len(l3e_glows.energy), npixels))
 
         for energy_index in range(len(l3e_glows.energy)):
-            spatially_interpolated_sp[energy_index, :] = glows_healpix.interpolate_bilinear_lonlat(
-                Quantity(rotated_az_el_points[:, 0], unit='deg'),
-                Quantity(rotated_az_el_points[:, 1], unit='deg'),
-                l3e_glows.survival_probability[0, energy_index, :])
+            spatially_interpolated_sp[energy_index, :] = (
+                glows_healpix.interpolate_bilinear_lonlat(
+                    Quantity(rotated_az_el_points[:, 0], unit="deg"),
+                    Quantity(rotated_az_el_points[:, 1], unit="deg"),
+                    l3e_glows.survival_probability[0, energy_index, :],
+                )
+            )
 
-        energy_interpolated_sp = np.zeros((len(coarse_bins.energy_bin_geometric_mean), npixels))
+        energy_interpolated_sp = np.zeros(
+            (len(coarse_bins.energy_bin_geometric_mean), npixels)
+        )
         for healpix_index in range(npixels):
             energy_interpolated_sp[:, healpix_index] = np.interp(
                 np.log10(coarse_bins.energy_bin_geometric_mean),
                 np.log10(l3e_glows.energy),
-                spatially_interpolated_sp[:, healpix_index]
+                spatially_interpolated_sp[:, healpix_index],
             )
 
         self.data["survival_probability_times_exposure"] = (
             [
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
-                CoordNames.HEALPIX_INDEX.value
+                CoordNames.HEALPIX_INDEX.value,
             ],
-            energy_interpolated_sp * coarse_bins.exposure_factor.data
+            energy_interpolated_sp * coarse_bins.exposure_factor.data,
         )
 
         self.data["predicted_ephemeris_flag"] = (
             [
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
-                CoordNames.HEALPIX_INDEX.value
+                CoordNames.HEALPIX_INDEX.value,
             ],
-            predicted_ephemeris * coarse_bins.exposure_factor.data
+            predicted_ephemeris * coarse_bins.exposure_factor.data,
         )
 
         self.data["nominal_alpha_proton_ratio_flag"] = (
             [
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
-                CoordNames.HEALPIX_INDEX.value
+                CoordNames.HEALPIX_INDEX.value,
             ],
-            nominal_alpha_proton_ratio * coarse_bins.exposure_factor.data
+            nominal_alpha_proton_ratio * coarse_bins.exposure_factor.data,
         )
 
         self.data["persisted_last_point_flag"] = (
             [
                 CoordNames.TIME.value,
                 CoordNames.ENERGY_ULTRA_L1C.value,
-                CoordNames.HEALPIX_INDEX.value
+                CoordNames.HEALPIX_INDEX.value,
             ],
-            persisted_last_point * coarse_bins.exposure_factor.data
+            persisted_last_point * coarse_bins.exposure_factor.data,
         )
 
         self.data["epoch_delta"] = [l1c_pset.epoch_delta]
 
 
 class UltraSurvivalProbabilitySkyMap(HealpixSkyMap):
-    def __init__(self, sp: list[UltraSurvivalProbability], spice_frame: geometry.SpiceFrame, nside: int):
+    def __init__(
+        self,
+        sp: list[UltraSurvivalProbability],
+        spice_frame: geometry.SpiceFrame,
+        nside: int,
+    ):
         super().__init__(nside, spice_frame)
         for sp_pset in sp:
-            values_to_project = ["survival_probability_times_exposure", "exposure_factor", "predicted_ephemeris_flag",
-                      "nominal_alpha_proton_ratio_flag", "persisted_last_point_flag"]
-            self.project_pset_values_to_map(sp_pset, values_to_project,
-                                            pset_valid_mask=np.isfinite(
-                                                sp_pset.data["survival_probability_times_exposure"]))
+            values_to_project = [
+                "survival_probability_times_exposure",
+                "exposure_factor",
+                "predicted_ephemeris_flag",
+                "nominal_alpha_proton_ratio_flag",
+                "persisted_last_point_flag",
+            ]
+            self.project_pset_values_to_map(
+                sp_pset,
+                values_to_project,
+                pset_valid_mask=np.isfinite(
+                    sp_pset.data["survival_probability_times_exposure"]
+                ),
+            )
 
         predicted_ephemeris_set = self.data_1d["predicted_ephemeris_flag"] > 0
-        nominal_proton_alpha_ratio_set = self.data_1d["nominal_alpha_proton_ratio_flag"] > 0
+        nominal_proton_alpha_ratio_set = (
+            self.data_1d["nominal_alpha_proton_ratio_flag"] > 0
+        )
         persisted_last_point_set = self.data_1d["persisted_last_point_flag"] > 0
 
-        quality_flags = (predicted_ephemeris_set * MapL3Flags.PREDICTIVE_EPHEMERIS) | \
-                        (nominal_proton_alpha_ratio_set * MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO) | \
-                        (persisted_last_point_set * MapL3Flags.PERSISTED_LAST_POINT)
+        quality_flags = (
+            (predicted_ephemeris_set * MapL3Flags.PREDICTIVE_EPHEMERIS)
+            | (nominal_proton_alpha_ratio_set * MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO)
+            | (persisted_last_point_set * MapL3Flags.PERSISTED_LAST_POINT)
+        )
 
-        self.data_1d = Dataset({
-            "exposure_weighted_survival_probabilities": self.data_1d["survival_probability_times_exposure"] /
-                                                        self.data_1d["exposure_factor"],
-            "quality_flags": quality_flags
-        })
+        self.data_1d = Dataset(
+            {
+                "exposure_weighted_survival_probabilities": self.data_1d[
+                    "survival_probability_times_exposure"
+                ]
+                / self.data_1d["exposure_factor"],
+                "quality_flags": quality_flags,
+            }
+        )
diff --git a/imap_l3_processing/ultra/ultra_combined_nsp_initializer.py b/imap_l3_processing/ultra/ultra_combined_nsp_initializer.py
index 59bed3a3b..b7446352e 100644
--- a/imap_l3_processing/ultra/ultra_combined_nsp_initializer.py
+++ b/imap_l3_processing/ultra/ultra_combined_nsp_initializer.py
@@ -5,8 +5,12 @@
 import imap_data_access
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.maps.map_descriptors import Sensor, map_descriptor_parts_to_string, \
-    parse_map_descriptor, get_duration_from_map_descriptor
+from imap_l3_processing.maps.map_descriptors import (
+    Sensor,
+    get_duration_from_map_descriptor,
+    map_descriptor_parts_to_string,
+    parse_map_descriptor,
+)
 from imap_l3_processing.maps.map_initializer import MapInitializer, PossibleMapToProduce
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.ultra.ultra_sp_initializer import ULTRA_SP_SPICE_KERNELS
@@ -16,37 +20,46 @@
     "ulc-ena-h-hf-nsp-full-hae-2deg-3mo",
     "ulc-ena-h-hf-nsp-full-hae-4deg-3mo",
     "ulc-ena-h-hf-nsp-full-hae-6deg-3mo",
-
     "ulc-ena-h-hf-nsp-full-hae-2deg-6mo",
     "ulc-ena-h-hf-nsp-full-hae-4deg-6mo",
     "ulc-ena-h-hf-nsp-full-hae-6deg-6mo",
-
     "ulc-ena-h-hf-nsp-full-hae-2deg-1yr",
     "ulc-ena-h-hf-nsp-full-hae-4deg-1yr",
     "ulc-ena-h-hf-nsp-full-hae-6deg-1yr",
 ]
 
+
 class UltraCombinedNSPInitializer(MapInitializer):
     def __init__(self):
-        l2_ultra_maps_query_results = imap_data_access.query(instrument="ultra", data_level="l2")
-        self.l2_maps_by_descriptor_and_start = self.get_latest_version_by_descriptor_and_start_date(
-            l2_ultra_maps_query_results)
+        l2_ultra_maps_query_results = imap_data_access.query(
+            instrument="ultra", data_level="l2"
+        )
+        self.l2_maps_by_descriptor_and_start = (
+            self.get_latest_version_by_descriptor_and_start_date(
+                l2_ultra_maps_query_results
+            )
+        )
         energy_bin_group_sizes_files = imap_data_access.query(
             instrument="ultra",
             table="ancillary",
             descriptor="l2-energy-bin-group-sizes",
-            version="latest")
-        self.ancillary_dependencies = [Path(qr['file_path']).name for qr in energy_bin_group_sizes_files]
+            version="latest",
+        )
+        self.ancillary_dependencies = [
+            Path(qr["file_path"]).name for qr in energy_bin_group_sizes_files
+        ]
         super().__init__("ultra")
 
     def furnish_spice_dependencies(self, map_to_produce: PossibleMapToProduce):
         furnish_spice_metakernel(
             start_date=map_to_produce.input_metadata.start_date,
             end_date=map_to_produce.input_metadata.end_date,
-            kernel_types=ULTRA_SP_SPICE_KERNELS
+            kernel_types=ULTRA_SP_SPICE_KERNELS,
         )
 
-    def get_maps_that_can_be_produced(self, descriptor: str) -> list[PossibleMapToProduce]:
+    def get_maps_that_can_be_produced(
+        self, descriptor: str
+    ) -> list[PossibleMapToProduce]:
         descriptor_parts = parse_map_descriptor(descriptor)
         if descriptor_parts is None:
             raise ValueError(f"Invalid map descriptor: {descriptor}")
@@ -55,34 +68,56 @@ def get_maps_that_can_be_produced(self, descriptor: str) -> list[PossibleMapToPr
             dataclasses.replace(descriptor_parts, sensor=Sensor.Ultra45),
             dataclasses.replace(descriptor_parts, sensor=Sensor.Ultra90),
         ]
-        input_map_descriptors = [map_descriptor_parts_to_string(parts) for parts in dependency_descriptors]
+        input_map_descriptors = [
+            map_descriptor_parts_to_string(parts) for parts in dependency_descriptors
+        ]
 
         first_input_descriptor = input_map_descriptors[0]
-        possible_start_dates = self.l2_maps_by_descriptor_and_start[first_input_descriptor].keys()
+        possible_start_dates = self.l2_maps_by_descriptor_and_start[
+            first_input_descriptor
+        ].keys()
 
         possible_maps_to_produce = []
         for str_start_date in possible_start_dates:
             input_map_filenames = []
             for input_descriptor in input_map_descriptors:
-                if str_start_date in self.l2_maps_by_descriptor_and_start[input_descriptor]:
-                    input_map_filenames.append(self.l2_maps_by_descriptor_and_start[input_descriptor][str_start_date])
+                if (
+                    str_start_date
+                    in self.l2_maps_by_descriptor_and_start[input_descriptor]
+                ):
+                    input_map_filenames.append(
+                        self.l2_maps_by_descriptor_and_start[input_descriptor][
+                            str_start_date
+                        ]
+                    )
 
             if len(input_map_filenames) != len(input_map_descriptors):
                 continue
 
-            pset_filenames = [l1c for l2 in input_map_filenames for l1c in self.get_l1c_parents_from_map(l2)]
+            pset_filenames = [
+                l1c
+                for l2 in input_map_filenames
+                for l1c in self.get_l1c_parents_from_map(l2)
+            ]
 
             start_date = datetime.strptime(str_start_date, "%Y%m%d")
-            possible_maps_to_produce.append(PossibleMapToProduce(
-                input_files=set(pset_filenames + input_map_filenames + self.ancillary_dependencies),
-                input_metadata=InputMetadata(
-                    instrument='ultra',
-                    data_level='l3',
-                    start_date=start_date,
-                    end_date=start_date + get_duration_from_map_descriptor(descriptor_parts),
-                    version=VersionMap({},Version(None,1)),
-                    descriptor=descriptor,
+            possible_maps_to_produce.append(
+                PossibleMapToProduce(
+                    input_files=set(
+                        pset_filenames
+                        + input_map_filenames
+                        + self.ancillary_dependencies
+                    ),
+                    input_metadata=InputMetadata(
+                        instrument="ultra",
+                        data_level="l3",
+                        start_date=start_date,
+                        end_date=start_date
+                        + get_duration_from_map_descriptor(descriptor_parts),
+                        version=VersionMap({}, Version(None, 1)),
+                        descriptor=descriptor,
+                    ),
                 )
-            ))
+            )
 
         return possible_maps_to_produce
diff --git a/imap_l3_processing/ultra/ultra_l3_dependencies.py b/imap_l3_processing/ultra/ultra_l3_dependencies.py
index ad6b71bf2..0e38590f9 100644
--- a/imap_l3_processing/ultra/ultra_l3_dependencies.py
+++ b/imap_l3_processing/ultra/ultra_l3_dependencies.py
@@ -6,7 +6,6 @@
 from dataclasses import dataclass, field
 from hashlib import sha256
 from pathlib import Path
-from typing import Optional
 
 import imap_data_access
 import numpy as np
@@ -14,10 +13,16 @@
 from imap_data_access.processing_input import ProcessingInputCollection
 from imap_processing.ultra.l2.ultra_l2 import ultra_l2
 
-from imap_l3_processing.maps.map_models import HealPixIntensityMapData, RectangularIntensityMapData, \
-    SpectralIndexDependencies
-from imap_l3_processing.ultra.models import UltraL1CPSet, UltraGlowsL3eData
-from imap_l3_processing.utils import get_dependency_paths_by_descriptor, get_temp_cache_dir
+from imap_l3_processing.maps.map_models import (
+    HealPixIntensityMapData,
+    RectangularIntensityMapData,
+    SpectralIndexDependencies,
+)
+from imap_l3_processing.ultra.models import UltraGlowsL3eData, UltraL1CPSet
+from imap_l3_processing.utils import (
+    get_dependency_paths_by_descriptor,
+    get_temp_cache_dir,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -28,36 +33,53 @@ class UltraL3Dependencies:
     ultra_l2_rectangular_map: RectangularIntensityMapData
     ultra_l1c_pset: list[UltraL1CPSet]
     glows_l3e_sp: list[UltraGlowsL3eData]
-    energy_bin_group_sizes: Optional[np.ndarray]
+    energy_bin_group_sizes: np.ndarray | None
     dependency_file_paths: list[Path] = field(default_factory=list)
 
     @classmethod
     def fetch_dependencies(cls, deps: ProcessingInputCollection) -> UltraL3Dependencies:
         ultra_l2_names = deps.get_file_paths("ultra", data_type="l2")
-        assert len(ultra_l2_names) == 1, f"Incorrect number of map dependencies: {len(ultra_l2_names)}"
+        assert len(ultra_l2_names) == 1, (
+            f"Incorrect number of map dependencies: {len(ultra_l2_names)}"
+        )
         ultra_l2_name = ultra_l2_names[0]
 
-
         ultra_l1c_names = deps.get_file_paths("ultra", data_type="l1c")
         glows_l3e_names = deps.get_file_paths("glows")
-        energy_bin_group_sizes_names = deps.get_file_paths(data_type='ancillary',
-                                                           descriptor='l2-energy-bin-group-sizes')
+        energy_bin_group_sizes_names = deps.get_file_paths(
+            data_type="ancillary", descriptor="l2-energy-bin-group-sizes"
+        )
 
         l2_map_path = imap_data_access.download(ultra_l2_name)
-        ultra_l1c_downloaded_paths = [imap_data_access.download(l1c) for l1c in ultra_l1c_names]
-        glows_l3e_download_paths = [imap_data_access.download(path) for path in glows_l3e_names]
+        ultra_l1c_downloaded_paths = [
+            imap_data_access.download(l1c) for l1c in ultra_l1c_names
+        ]
+        glows_l3e_download_paths = [
+            imap_data_access.download(path) for path in glows_l3e_names
+        ]
 
         if energy_bin_group_sizes_names:
-            energy_bin_group_sizes_path = imap_data_access.download(energy_bin_group_sizes_names[0])
+            energy_bin_group_sizes_path = imap_data_access.download(
+                energy_bin_group_sizes_names[0]
+            )
         else:
             energy_bin_group_sizes_path = None
 
-        return cls.from_file_paths(l2_map_path, ultra_l1c_downloaded_paths, glows_l3e_download_paths,
-                                   energy_bin_group_sizes_path)
+        return cls.from_file_paths(
+            l2_map_path,
+            ultra_l1c_downloaded_paths,
+            glows_l3e_download_paths,
+            energy_bin_group_sizes_path,
+        )
 
     @classmethod
-    def from_file_paths(cls, l2_map_path: Path, l1c_file_paths: list[Path], glows_file_paths: list[Path],
-                        energy_bin_path: Optional[Path]) -> UltraL3Dependencies:
+    def from_file_paths(
+        cls,
+        l2_map_path: Path,
+        l1c_file_paths: list[Path],
+        glows_file_paths: list[Path],
+        energy_bin_path: Path | None,
+    ) -> UltraL3Dependencies:
         ultra_l1c_data = []
         glows_l3e_data = []
         for file_path in l1c_file_paths:
@@ -68,14 +90,24 @@ def from_file_paths(cls, l2_map_path: Path, l1c_file_paths: list[Path], glows_fi
 
         l2_healpix_map_data = load_or_create_healpix_l2(l2_map_path, l1c_file_paths)
 
-        l2_rectangular_map_data = RectangularIntensityMapData.read_from_path(l2_map_path)
+        l2_rectangular_map_data = RectangularIntensityMapData.read_from_path(
+            l2_map_path
+        )
         energy_bin_group_sizes = None
         if energy_bin_path:
             paths.append(energy_bin_path)
-            energy_bin_group_sizes = np.loadtxt(energy_bin_path, delimiter=',', dtype=np.uint8)
+            energy_bin_group_sizes = np.loadtxt(
+                energy_bin_path, delimiter=",", dtype=np.uint8
+            )
 
-        return cls(ultra_l2_healpix_map=l2_healpix_map_data, ultra_l2_rectangular_map=l2_rectangular_map_data, ultra_l1c_pset=ultra_l1c_data, glows_l3e_sp=glows_l3e_data,
-                   dependency_file_paths=paths, energy_bin_group_sizes=energy_bin_group_sizes)
+        return cls(
+            ultra_l2_healpix_map=l2_healpix_map_data,
+            ultra_l2_rectangular_map=l2_rectangular_map_data,
+            ultra_l1c_pset=ultra_l1c_data,
+            glows_l3e_sp=glows_l3e_data,
+            dependency_file_paths=paths,
+            energy_bin_group_sizes=energy_bin_group_sizes,
+        )
 
 
 @dataclass
@@ -83,18 +115,28 @@ class UltraL3SpectralIndexDependencies(SpectralIndexDependencies):
     fit_energy_ranges: np.ndarray
 
     @classmethod
-    def fetch_dependencies(cls, deps: ProcessingInputCollection) -> UltraL3SpectralIndexDependencies:
-        energy_fit_ranges_ancillary_file_path = deps.get_file_paths(source="ultra", descriptor="spx-energy-ranges")
-        ultra_map_file_paths = deps.get_file_paths(source="ultra", data_type="l3") + deps.get_file_paths(source="ultra", data_type="l2")
+    def fetch_dependencies(
+        cls, deps: ProcessingInputCollection
+    ) -> UltraL3SpectralIndexDependencies:
+        energy_fit_ranges_ancillary_file_path = deps.get_file_paths(
+            source="ultra", descriptor="spx-energy-ranges"
+        )
+        ultra_map_file_paths = deps.get_file_paths(
+            source="ultra", data_type="l3"
+        ) + deps.get_file_paths(source="ultra", data_type="l2")
 
         if len(ultra_map_file_paths) != 1:
-            raise ValueError(f"Expected 1 input map, got {len(ultra_map_file_paths)}: [{', '.join(p.name for p in ultra_map_file_paths)}]")
+            raise ValueError(
+                f"Expected 1 input map, got {len(ultra_map_file_paths)}: [{', '.join(p.name for p in ultra_map_file_paths)}]"
+            )
 
         if len(energy_fit_ranges_ancillary_file_path) != 1:
             raise ValueError("Missing fit energy ranges ancillary file")
 
         map_file_path = imap_data_access.download(ultra_map_file_paths[0].name)
-        energy_ranges_file_path = imap_data_access.download(energy_fit_ranges_ancillary_file_path[0].name)
+        energy_ranges_file_path = imap_data_access.download(
+            energy_fit_ranges_ancillary_file_path[0].name
+        )
 
         return cls.from_file_paths(map_file_path, energy_ranges_file_path)
 
@@ -114,46 +156,72 @@ class UltraL3CombinedDependencies:
     u90_dependencies: UltraL3Dependencies
 
     @classmethod
-    def fetch_dependencies(cls, deps: ProcessingInputCollection) -> UltraL3CombinedDependencies:
+    def fetch_dependencies(
+        cls, deps: ProcessingInputCollection
+    ) -> UltraL3CombinedDependencies:
         descriptors = ["u45", "u90", "45sensor", "90sensor", "survival-probability-ul"]
-        file_paths = get_dependency_paths_by_descriptor(deps=deps, descriptors=descriptors)
-
-        assert len(file_paths['u45']) == 1
-        assert len(file_paths['u90']) == 1
-
-        u45_pset_paths = [imap_data_access.download(pset) for pset in file_paths['45sensor']]
-        u90_pset_paths = [imap_data_access.download(pset) for pset in file_paths['90sensor']]
-        glows_l3e_pset_paths = [imap_data_access.download(pset) for pset in file_paths['survival-probability-ul']]
-        u45_map_path = imap_data_access.download(file_paths['u45'][0])
-        u90_map_path = imap_data_access.download(file_paths['u90'][0])
+        file_paths = get_dependency_paths_by_descriptor(
+            deps=deps, descriptors=descriptors
+        )
 
-        energy_bin_group_sizes_names = deps.get_file_paths(data_type='ancillary',
-                                                           descriptor='l2-energy-bin-group-sizes')
+        assert len(file_paths["u45"]) == 1
+        assert len(file_paths["u90"]) == 1
+
+        u45_pset_paths = [
+            imap_data_access.download(pset) for pset in file_paths["45sensor"]
+        ]
+        u90_pset_paths = [
+            imap_data_access.download(pset) for pset in file_paths["90sensor"]
+        ]
+        glows_l3e_pset_paths = [
+            imap_data_access.download(pset)
+            for pset in file_paths["survival-probability-ul"]
+        ]
+        u45_map_path = imap_data_access.download(file_paths["u45"][0])
+        u90_map_path = imap_data_access.download(file_paths["u90"][0])
+
+        energy_bin_group_sizes_names = deps.get_file_paths(
+            data_type="ancillary", descriptor="l2-energy-bin-group-sizes"
+        )
         if energy_bin_group_sizes_names:
-            energy_bin_group_sizes_path = imap_data_access.download(energy_bin_group_sizes_names[0].name)
+            energy_bin_group_sizes_path = imap_data_access.download(
+                energy_bin_group_sizes_names[0].name
+            )
         else:
             energy_bin_group_sizes_path = None
 
-        return cls.from_file_paths(u45_pset_paths, u90_pset_paths, glows_l3e_pset_paths, u45_map_path, u90_map_path,
-                                   energy_bin_group_sizes_path)
+        return cls.from_file_paths(
+            u45_pset_paths,
+            u90_pset_paths,
+            glows_l3e_pset_paths,
+            u45_map_path,
+            u90_map_path,
+            energy_bin_group_sizes_path,
+        )
 
     @classmethod
-    def from_file_paths(cls, u45_pset_paths: list[Path], u90_pset_paths: list[Path], glows_l3e_pset_paths: list[Path],
-                        u45_map_path: Path, u90_map_path: Path,
-                        energy_bin_group_sizes_path: Optional[Path]) -> UltraL3CombinedDependencies:
+    def from_file_paths(
+        cls,
+        u45_pset_paths: list[Path],
+        u90_pset_paths: list[Path],
+        glows_l3e_pset_paths: list[Path],
+        u45_map_path: Path,
+        u90_map_path: Path,
+        energy_bin_group_sizes_path: Path | None,
+    ) -> UltraL3CombinedDependencies:
 
         return cls(
             u45_dependencies=UltraL3Dependencies.from_file_paths(
                 l2_map_path=u45_map_path,
                 l1c_file_paths=u45_pset_paths,
                 glows_file_paths=glows_l3e_pset_paths,
-                energy_bin_path=energy_bin_group_sizes_path
+                energy_bin_path=energy_bin_group_sizes_path,
             ),
             u90_dependencies=UltraL3Dependencies.from_file_paths(
                 l2_map_path=u90_map_path,
                 l1c_file_paths=u90_pset_paths,
                 glows_file_paths=glows_l3e_pset_paths,
-                energy_bin_path=energy_bin_group_sizes_path
+                energy_bin_path=energy_bin_group_sizes_path,
             ),
         )
 
@@ -176,9 +244,11 @@ def load_or_create_healpix_l2(
     else:
         logger.info("Cached HEALPix L2 not found; building from pointing sets")
         l2_healpix_datasets = ultra_l2(l1c_paths_dict, descriptor=l2_descriptor)
-        l2_healpix_data = HealPixIntensityMapData.read_from_xarray(l2_healpix_datasets[0])
+        l2_healpix_data = HealPixIntensityMapData.read_from_xarray(
+            l2_healpix_datasets[0]
+        )
         logger.info("Saving HEALPix L2 to cache at %s", cache_path)
         with open(cache_path, "wb") as f:
             pickle.dump(l2_healpix_data, f)
 
-    return l2_healpix_data
\ No newline at end of file
+    return l2_healpix_data
diff --git a/imap_l3_processing/ultra/ultra_l3_to_rectangular_dependencies.py b/imap_l3_processing/ultra/ultra_l3_to_rectangular_dependencies.py
index ba8961435..8484637d9 100644
--- a/imap_l3_processing/ultra/ultra_l3_to_rectangular_dependencies.py
+++ b/imap_l3_processing/ultra/ultra_l3_to_rectangular_dependencies.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 from dataclasses import dataclass
 from pathlib import Path
 
@@ -12,7 +13,9 @@ class UltraL3ToRectangularDependencies:
     healpix_map_data: HealPixIntensityMapData
 
     @classmethod
-    def fetch_dependencies(cls, deps: ProcessingInputCollection) -> UltraL3ToRectangularDependencies:
+    def fetch_dependencies(
+        cls, deps: ProcessingInputCollection
+    ) -> UltraL3ToRectangularDependencies:
         [l3_ultra_map] = deps.get_file_paths("ultra", None, "l3")
         l3_map_path = download(l3_ultra_map.name)
         return cls.from_file_paths(l3_map_path)
diff --git a/imap_l3_processing/ultra/ultra_processor.py b/imap_l3_processing/ultra/ultra_processor.py
index b82424615..b5a3ee576 100644
--- a/imap_l3_processing/ultra/ultra_processor.py
+++ b/imap_l3_processing/ultra/ultra_processor.py
@@ -2,11 +2,9 @@
 import pickle
 import re
 from dataclasses import dataclass
-from datetime import datetime, timedelta
 from hashlib import sha256
-from typing import Optional
 
-from imap_data_access import ScienceFilePath, ImapFilePath
+from imap_data_access import ImapFilePath, ScienceFilePath
 from imap_processing.spice.geometry import SpiceFrame
 
 from imap_l3_processing.maps.map_combination import (
@@ -16,36 +14,43 @@
 from imap_l3_processing.maps.map_descriptors import (
     MapDescriptorParts,
     MapQuantity,
-    SurvivalCorrection,
-    parse_map_descriptor,
     PixelSize,
     Sensor,
+    SurvivalCorrection,
+    parse_map_descriptor,
 )
 from imap_l3_processing.maps.map_models import (
+    HealPixCoords,
     HealPixIntensityMapData,
     IntensityMapData,
-    HealPixCoords,
+    RectangularCoords,
     RectangularIntensityDataProduct,
     RectangularIntensityMapData,
-    RectangularCoords,
     RectangularSpectralIndexDataProduct,
     RectangularSpectralIndexMapData,
 )
 from imap_l3_processing.maps.map_processor import MapProcessor
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.maps.spectral_fit import calculate_spectral_index_for_multiple_ranges, \
-    slice_energy_range_by_bin, fit_spectral_index_map
-from imap_l3_processing.maps.survival_probability_processing import combine_glows_l3e_with_l1c_pointing
-from imap_l3_processing.ultra.science.ultra_survival_probability import UltraSurvivalProbabilitySkyMap, \
-    UltraSurvivalProbability
+from imap_l3_processing.maps.spectral_fit import (
+    calculate_spectral_index_for_multiple_ranges,
+    fit_spectral_index_map,
+    slice_energy_range_by_bin,
+)
+from imap_l3_processing.maps.survival_probability_processing import (
+    combine_glows_l3e_with_l1c_pointing,
+)
+from imap_l3_processing.ultra.science.ultra_survival_probability import (
+    UltraSurvivalProbability,
+    UltraSurvivalProbabilitySkyMap,
+)
 from imap_l3_processing.ultra.ultra_l3_dependencies import (
+    UltraL3CombinedDependencies,
     UltraL3Dependencies,
     UltraL3SpectralIndexDependencies,
-    UltraL3CombinedDependencies,
 )
 from imap_l3_processing.utils import (
-    save_data,
     get_temp_cache_dir,
+    save_data,
 )
 
 logger = logging.getLogger(__name__)
@@ -57,34 +62,53 @@ def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000):
         parent_file_names = self.get_parent_file_names()
 
         match parsed_descriptor:
-            case MapDescriptorParts(quantity=MapQuantity.SpectralIndex,
-                                    grid=PixelSize.TwoDegrees | PixelSize.FourDegrees | PixelSize.SixDegrees):
-                ultra_l3_spectral_fit_dependencies = UltraL3SpectralIndexDependencies.fetch_dependencies(
-                    self.dependencies)
+            case MapDescriptorParts(
+                quantity=MapQuantity.SpectralIndex,
+                grid=PixelSize.TwoDegrees
+                | PixelSize.FourDegrees
+                | PixelSize.SixDegrees,
+            ):
+                ultra_l3_spectral_fit_dependencies = (
+                    UltraL3SpectralIndexDependencies.fetch_dependencies(
+                        self.dependencies
+                    )
+                )
                 spectral_index_map_data = self._process_spectral_index(
                     ultra_l3_spectral_fit_dependencies,
-                    parsed_descriptor.spectral_index_energy_range
+                    parsed_descriptor.spectral_index_energy_range,
                 )
-                data_product = RectangularSpectralIndexDataProduct(input_metadata=self.input_metadata,
-                                                                   data=spectral_index_map_data,
-                                                                   spice_frame_name=spice_frame_name)
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    sensor=Sensor.Ultra45 | Sensor.Ultra90,
-                                    grid=PixelSize.TwoDegrees | PixelSize.FourDegrees | PixelSize.SixDegrees):
+                data_product = RectangularSpectralIndexDataProduct(
+                    input_metadata=self.input_metadata,
+                    data=spectral_index_map_data,
+                    spice_frame_name=spice_frame_name,
+                )
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                sensor=Sensor.Ultra45 | Sensor.Ultra90,
+                grid=PixelSize.TwoDegrees
+                | PixelSize.FourDegrees
+                | PixelSize.SixDegrees,
+            ):
                 deps = UltraL3Dependencies.fetch_dependencies(self.dependencies)
                 healpix_intensity_map_data = (
                     correct_healpix_data_for_survival_probability(
                         deps, spice_frame_name
                     )
                 )
-                data_product = self._process_healpix_intensity_to_rectangular(healpix_intensity_map_data,
-                                                                              deps.ultra_l2_rectangular_map,
-                                                                              parsed_descriptor.grid,
-                                                                              spice_frame_name=spice_frame_name)
+                data_product = self._process_healpix_intensity_to_rectangular(
+                    healpix_intensity_map_data,
+                    deps.ultra_l2_rectangular_map,
+                    parsed_descriptor.grid,
+                    spice_frame_name=spice_frame_name,
+                )
                 data_product.add_paths_to_parents(deps.dependency_file_paths)
-            case MapDescriptorParts(survival_correction=SurvivalCorrection.SurvivalCorrected,
-                                    sensor=Sensor.UltraCombined,
-                                    grid=PixelSize.TwoDegrees | PixelSize.FourDegrees | PixelSize.SixDegrees):
+            case MapDescriptorParts(
+                survival_correction=SurvivalCorrection.SurvivalCorrected,
+                sensor=Sensor.UltraCombined,
+                grid=PixelSize.TwoDegrees
+                | PixelSize.FourDegrees
+                | PixelSize.SixDegrees,
+            ):
                 combined_deps = UltraL3CombinedDependencies.fetch_dependencies(
                     self.dependencies
                 )
@@ -106,9 +130,13 @@ def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000):
                     combined_deps.u90_dependencies.dependency_file_paths
                 )
 
-            case MapDescriptorParts(sensor=Sensor.UltraCombined,
-                                    survival_correction=SurvivalCorrection.NotSurvivalCorrected,
-                                    grid=PixelSize.TwoDegrees | PixelSize.FourDegrees | PixelSize.SixDegrees):
+            case MapDescriptorParts(
+                sensor=Sensor.UltraCombined,
+                survival_correction=SurvivalCorrection.NotSurvivalCorrected,
+                grid=PixelSize.TwoDegrees
+                | PixelSize.FourDegrees
+                | PixelSize.SixDegrees,
+            ):
                 deps = UltraL3CombinedDependencies.fetch_dependencies(self.dependencies)
 
                 combination_strategy = ExposureWeightedCombination()
@@ -120,16 +148,27 @@ def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000):
                         ]
                     )
                 )
-                combined_rectangular = combination_strategy.combine_rectangular_intensity_map_data(
-                    [deps.u45_dependencies.ultra_l2_rectangular_map,
-                     deps.u90_dependencies.ultra_l2_rectangular_map])
-
-                data_product = self._process_healpix_intensity_to_rectangular(combined_healpix,
-                                                                              combined_rectangular,
-                                                                              parsed_descriptor.grid,
-                                                                              spice_frame_name=spice_frame_name)
-                data_product.add_paths_to_parents(deps.u45_dependencies.dependency_file_paths)
-                data_product.add_paths_to_parents(deps.u90_dependencies.dependency_file_paths)
+                combined_rectangular = (
+                    combination_strategy.combine_rectangular_intensity_map_data(
+                        [
+                            deps.u45_dependencies.ultra_l2_rectangular_map,
+                            deps.u90_dependencies.ultra_l2_rectangular_map,
+                        ]
+                    )
+                )
+
+                data_product = self._process_healpix_intensity_to_rectangular(
+                    combined_healpix,
+                    combined_rectangular,
+                    parsed_descriptor.grid,
+                    spice_frame_name=spice_frame_name,
+                )
+                data_product.add_paths_to_parents(
+                    deps.u45_dependencies.dependency_file_paths
+                )
+                data_product.add_paths_to_parents(
+                    deps.u90_dependencies.dependency_file_paths
+                )
             case _:
                 raise NotImplementedError
 
@@ -137,27 +176,41 @@ def process(self, spice_frame_name: SpiceFrame = SpiceFrame.ECLIPJ2000):
         return [save_data(data_product)]
 
     def _process_combined_survival_probability(
-            self, deps: UltraL3CombinedDependencies, spice_frame_name: SpiceFrame,
+        self,
+        deps: UltraL3CombinedDependencies,
+        spice_frame_name: SpiceFrame,
     ) -> tuple[HealPixIntensityMapData, RectangularIntensityMapData]:
-        u45_survival_corrected = correct_healpix_data_for_survival_probability(deps.u45_dependencies, spice_frame_name)
-        u90_survival_corrected = correct_healpix_data_for_survival_probability(deps.u90_dependencies, spice_frame_name)
+        u45_survival_corrected = correct_healpix_data_for_survival_probability(
+            deps.u45_dependencies, spice_frame_name
+        )
+        u90_survival_corrected = correct_healpix_data_for_survival_probability(
+            deps.u90_dependencies, spice_frame_name
+        )
 
         combination_strategy = UncertaintyWeightedCombination()
         combined_healpix = combination_strategy.combine_healpix_intensity_map_data(
             [u45_survival_corrected, u90_survival_corrected]
         )
-        combined_rectangular = combination_strategy.combine_rectangular_intensity_map_data(
-            [deps.u45_dependencies.ultra_l2_rectangular_map, deps.u90_dependencies.ultra_l2_rectangular_map]
+        combined_rectangular = (
+            combination_strategy.combine_rectangular_intensity_map_data(
+                [
+                    deps.u45_dependencies.ultra_l2_rectangular_map,
+                    deps.u90_dependencies.ultra_l2_rectangular_map,
+                ]
+            )
         )
         return combined_healpix, combined_rectangular
 
-    def _process_spectral_index(self,
-                                dependencies: UltraL3SpectralIndexDependencies,
-                                spectral_index_range: Optional[tuple[int, int]]
-                                ) -> RectangularSpectralIndexMapData:
+    def _process_spectral_index(
+        self,
+        dependencies: UltraL3SpectralIndexDependencies,
+        spectral_index_range: tuple[int, int] | None,
+    ) -> RectangularSpectralIndexMapData:
         if spectral_index_range is not None:
             start, end = spectral_index_range
-            sliced = slice_energy_range_by_bin(dependencies.map_data.intensity_map_data, start, end)
+            sliced = slice_energy_range_by_bin(
+                dependencies.map_data.intensity_map_data, start, end
+            )
             map_data = fit_spectral_index_map(sliced)
         else:
             map_data = calculate_spectral_index_for_multiple_ranges(
@@ -192,18 +245,28 @@ def _process_healpix_intensity_to_rectangular(
             variables_to_convert_to_rectangular.append("survival_probability")
 
         healpix_map = healpix_map_data.to_healpix_skymap()
-        rectangular_map, _ = healpix_map.to_rectangular_skymap(spacing_deg, variables_to_convert_to_rectangular)
+        rectangular_map, _ = healpix_map.to_rectangular_skymap(
+            spacing_deg, variables_to_convert_to_rectangular
+        )
         rectangular_map_xarray_dataset = rectangular_map.to_dataset()
 
         rect_l2_data = rect_l2_map.intensity_map_data
 
-        predicted_ephemeris_set = rectangular_map_xarray_dataset["predicted_ephemeris_flag"].values > 0
-        nominal_alpha_proton_ratio_set = rectangular_map_xarray_dataset["nominal_alpha_proton_ratio_flag"].values > 0
-        persisted_last_point_set = rectangular_map_xarray_dataset["persisted_last_point_flag"].values > 0
+        predicted_ephemeris_set = (
+            rectangular_map_xarray_dataset["predicted_ephemeris_flag"].values > 0
+        )
+        nominal_alpha_proton_ratio_set = (
+            rectangular_map_xarray_dataset["nominal_alpha_proton_ratio_flag"].values > 0
+        )
+        persisted_last_point_set = (
+            rectangular_map_xarray_dataset["persisted_last_point_flag"].values > 0
+        )
 
-        quality_flags = (predicted_ephemeris_set * MapL3Flags.PREDICTIVE_EPHEMERIS) | \
-                        (nominal_alpha_proton_ratio_set * MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO) | \
-                        (persisted_last_point_set * MapL3Flags.PERSISTED_LAST_POINT)
+        quality_flags = (
+            (predicted_ephemeris_set * MapL3Flags.PREDICTIVE_EPHEMERIS)
+            | (nominal_alpha_proton_ratio_set * MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO)
+            | (persisted_last_point_set * MapL3Flags.PERSISTED_LAST_POINT)
+        )
 
         intensity_map_data = IntensityMapData(
             epoch=rect_l2_data.epoch,
@@ -229,7 +292,9 @@ def _process_healpix_intensity_to_rectangular(
         )
 
         if has_survival_data:
-            intensity_map_data.survival_probability = rectangular_map_xarray_dataset["survival_probability"].values
+            intensity_map_data.survival_probability = rectangular_map_xarray_dataset[
+                "survival_probability"
+            ].values
 
         rect_intensity_map_data = RectangularIntensityMapData(
             intensity_map_data,
@@ -241,13 +306,18 @@ def _process_healpix_intensity_to_rectangular(
             ),
         )
 
-        return RectangularIntensityDataProduct(data=rect_intensity_map_data, input_metadata=self.input_metadata,
-                                               spice_frame_name=spice_frame_name)
+        return RectangularIntensityDataProduct(
+            data=rect_intensity_map_data,
+            input_metadata=self.input_metadata,
+            spice_frame_name=spice_frame_name,
+        )
+
 
 @dataclass
 class UltraMapDescriptorParts:
     grid_size: int
 
+
 def _make_cache_key_for_sp_corrected_data(deps: UltraL3Dependencies) -> str:
     pset_names = []
     glows_names = []
@@ -265,6 +335,7 @@ def _make_cache_key_for_sp_corrected_data(deps: UltraL3Dependencies) -> str:
             pass
     return str(sorted(pset_names)) + str(sorted(glows_names)) + l2_descriptor
 
+
 def correct_healpix_data_for_survival_probability(
     deps: UltraL3Dependencies, spice_frame_name: SpiceFrame
 ) -> HealPixIntensityMapData:
@@ -278,7 +349,9 @@ def correct_healpix_data_for_survival_probability(
 
     logger.info("Cached HEALPix SP-corrected data not found; building from inputs")
 
-    combined_psets = combine_glows_l3e_with_l1c_pointing(deps.glows_l3e_sp, deps.ultra_l1c_pset)
+    combined_psets = combine_glows_l3e_with_l1c_pointing(
+        deps.glows_l3e_sp, deps.ultra_l1c_pset
+    )
     survival_probability_psets = [
         UltraSurvivalProbability(_l1c, _l3e, bin_groups=deps.energy_bin_group_sizes)
         for _l1c, _l3e in combined_psets
@@ -290,7 +363,9 @@ def correct_healpix_data_for_survival_probability(
         survival_probability_psets, spice_frame_name, coords.nside
     )
     skymap_dataset = corrected_skymap.to_dataset()
-    survival_probability_map = skymap_dataset["exposure_weighted_survival_probabilities"].values
+    survival_probability_map = skymap_dataset[
+        "exposure_weighted_survival_probabilities"
+    ].values
 
     healpix_intensity_data = HealPixIntensityMapData(
         intensity_map_data=IntensityMapData(
@@ -324,4 +399,4 @@ def correct_healpix_data_for_survival_probability(
     with open(cache_path, "wb") as f:
         pickle.dump(healpix_intensity_data, f)
 
-    return healpix_intensity_data
\ No newline at end of file
+    return healpix_intensity_data
diff --git a/imap_l3_processing/ultra/ultra_sp_initializer.py b/imap_l3_processing/ultra/ultra_sp_initializer.py
index 2d20430b7..dcee9099e 100644
--- a/imap_l3_processing/ultra/ultra_sp_initializer.py
+++ b/imap_l3_processing/ultra/ultra_sp_initializer.py
@@ -3,11 +3,19 @@
 
 import imap_data_access
 
-from imap_l3_processing.glows.descriptors import GLOWS_L3E_ULTRA_HF_DESCRIPTOR, GLOWS_L3E_ULTRA_SF_DESCRIPTOR
-from imap_l3_processing.maps.map_descriptors import MapDescriptorParts, SurvivalCorrection, Sensor, ReferenceFrame
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+)
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    ReferenceFrame,
+    Sensor,
+    SurvivalCorrection,
+)
 from imap_l3_processing.maps.map_initializer import PossibleMapToProduce
 from imap_l3_processing.maps.sp_map_initializer import SPMapInitializer
-from imap_l3_processing.utils import furnish_spice_metakernel, SpiceKernelTypes
+from imap_l3_processing.utils import SpiceKernelTypes, furnish_spice_metakernel
 
 ULTRA_SP_SPICE_KERNELS = [
     SpiceKernelTypes.Leapseconds,
@@ -23,14 +31,12 @@
     "u45-ena-h-sf-sp-full-hae-4deg-3mo",
     "u45-ena-h-hf-sp-full-hae-6deg-3mo",
     "u45-ena-h-sf-sp-full-hae-6deg-3mo",
-
     "u45-ena-h-hf-sp-full-hae-2deg-6mo",
     "u45-ena-h-sf-sp-full-hae-2deg-6mo",
     "u45-ena-h-hf-sp-full-hae-4deg-6mo",
     "u45-ena-h-sf-sp-full-hae-4deg-6mo",
     "u45-ena-h-hf-sp-full-hae-6deg-6mo",
     "u45-ena-h-sf-sp-full-hae-6deg-6mo",
-
     "u45-ena-h-hf-sp-full-hae-2deg-1yr",
     "u45-ena-h-sf-sp-full-hae-2deg-1yr",
     "u45-ena-h-hf-sp-full-hae-4deg-1yr",
@@ -46,14 +52,12 @@
     "u90-ena-h-sf-sp-full-hae-4deg-3mo",
     "u90-ena-h-hf-sp-full-hae-6deg-3mo",
     "u90-ena-h-sf-sp-full-hae-6deg-3mo",
-
     "u90-ena-h-hf-sp-full-hae-2deg-6mo",
     "u90-ena-h-sf-sp-full-hae-2deg-6mo",
     "u90-ena-h-hf-sp-full-hae-4deg-6mo",
     "u90-ena-h-sf-sp-full-hae-4deg-6mo",
     "u90-ena-h-hf-sp-full-hae-6deg-6mo",
     "u90-ena-h-sf-sp-full-hae-6deg-6mo",
-
     "u90-ena-h-hf-sp-full-hae-2deg-1yr",
     "u90-ena-h-sf-sp-full-hae-2deg-1yr",
     "u90-ena-h-hf-sp-full-hae-4deg-1yr",
@@ -66,11 +70,9 @@
     "ulc-ena-h-hf-sp-full-hae-2deg-3mo",
     "ulc-ena-h-hf-sp-full-hae-4deg-3mo",
     "ulc-ena-h-hf-sp-full-hae-6deg-3mo",
-
     "ulc-ena-h-hf-sp-full-hae-2deg-6mo",
     "ulc-ena-h-hf-sp-full-hae-4deg-6mo",
     "ulc-ena-h-hf-sp-full-hae-6deg-6mo",
-
     "ulc-ena-h-hf-sp-full-hae-2deg-1yr",
     "ulc-ena-h-hf-sp-full-hae-4deg-1yr",
     "ulc-ena-h-hf-sp-full-hae-6deg-1yr",
@@ -79,21 +81,32 @@
 
 class UltraSPInitializer(SPMapInitializer):
     def __init__(self):
-        sf_sp_query_result = imap_data_access.query(instrument='glows', data_level='l3e',
-                                                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR, version="latest")
-        self.sf_glows_psets_by_repointing = {int(r["repointing"]): Path(r["file_path"]).name for r in
-                                             sf_sp_query_result}
-        hf_sp_query_result = imap_data_access.query(instrument='glows', data_level='l3e',
-                                                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR, version="latest")
-        self.hf_glows_psets_by_repointing = {int(r["repointing"]): Path(r["file_path"]).name for r in
-                                             hf_sp_query_result}
+        sf_sp_query_result = imap_data_access.query(
+            instrument="glows",
+            data_level="l3e",
+            descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+            version="latest",
+        )
+        self.sf_glows_psets_by_repointing = {
+            int(r["repointing"]): Path(r["file_path"]).name for r in sf_sp_query_result
+        }
+        hf_sp_query_result = imap_data_access.query(
+            instrument="glows",
+            data_level="l3e",
+            descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+            version="latest",
+        )
+        self.hf_glows_psets_by_repointing = {
+            int(r["repointing"]): Path(r["file_path"]).name for r in hf_sp_query_result
+        }
 
         l2_query_result = imap_data_access.query(instrument="ultra", data_level="l2")
         self._energy_bin_group_sizes_files = imap_data_access.query(
             instrument="ultra",
             table="ancillary",
             descriptor="l2-energy-bin-group-sizes",
-            version="latest")
+            version="latest",
+        )
 
         super().__init__("ultra", l2_query_result)
 
@@ -101,25 +114,43 @@ def furnish_spice_dependencies(self, map_to_produce: PossibleMapToProduce):
         furnish_spice_metakernel(
             start_date=map_to_produce.input_metadata.start_date,
             end_date=map_to_produce.input_metadata.end_date,
-            kernel_types=ULTRA_SP_SPICE_KERNELS
+            kernel_types=ULTRA_SP_SPICE_KERNELS,
         )
 
-    def _collect_glows_psets_by_repoint(self, descriptor: MapDescriptorParts) -> dict[int, str]:
+    def _collect_glows_psets_by_repoint(
+        self, descriptor: MapDescriptorParts
+    ) -> dict[int, str]:
         if descriptor.reference_frame == ReferenceFrame.Heliospheric:
             return self.hf_glows_psets_by_repointing
         elif descriptor.reference_frame == ReferenceFrame.Spacecraft:
             return self.sf_glows_psets_by_repointing
         else:
-            raise NotImplementedError("Reference frame should be either Spacecraft or Heliospheric")
+            raise NotImplementedError(
+                "Reference frame should be either Spacecraft or Heliospheric"
+            )
 
-    def _get_l2_dependencies(self, descriptor: MapDescriptorParts) -> list[MapDescriptorParts]:
+    def _get_l2_dependencies(
+        self, descriptor: MapDescriptorParts
+    ) -> list[MapDescriptorParts]:
         if descriptor.sensor == Sensor.UltraCombined:
-            return [dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected,
-                                        sensor=Sensor.Ultra45),
-                    dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected,
-                                        sensor=Sensor.Ultra90)]
-
-        return [dataclasses.replace(descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected)]
+            return [
+                dataclasses.replace(
+                    descriptor,
+                    survival_correction=SurvivalCorrection.NotSurvivalCorrected,
+                    sensor=Sensor.Ultra45,
+                ),
+                dataclasses.replace(
+                    descriptor,
+                    survival_correction=SurvivalCorrection.NotSurvivalCorrected,
+                    sensor=Sensor.Ultra90,
+                ),
+            ]
+
+        return [
+            dataclasses.replace(
+                descriptor, survival_correction=SurvivalCorrection.NotSurvivalCorrected
+            )
+        ]
 
     def _get_ancillary_files(self) -> list[str]:
         return [Path(f["file_path"]).name for f in self._energy_bin_group_sizes_files]
diff --git a/imap_l3_processing/utils.py b/imap_l3_processing/utils.py
index 0c8c8a891..a205cc2a2 100644
--- a/imap_l3_processing/utils.py
+++ b/imap_l3_processing/utils.py
@@ -3,12 +3,10 @@
 import logging
 from contextlib import contextmanager
 from dataclasses import dataclass
-from datetime import datetime, date, timedelta
+from datetime import date, datetime, timedelta
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Optional, Union
 from urllib.parse import urlparse
-from spiceypy import spiceypy
 
 import imap_data_access
 import requests
@@ -18,26 +16,28 @@
 from imap_data_access.processing_input import ProcessingInputCollection
 from requests import RequestException
 from spacepy.pycdf import CDF
+from spiceypy import spiceypy
 
 import imap_l3_processing
-from imap_l3_processing.cdf.cdf_utils import write_cdf, read_numeric_variable
+from imap_l3_processing.cdf.cdf_utils import read_numeric_variable, write_cdf
 from imap_l3_processing.cdf.imap_attribute_manager import ImapAttributeManager
 from imap_l3_processing.constants import TT2000_EPOCH
 from imap_l3_processing.maps.map_models import (
-    RectangularSpectralIndexMapData,
-    RectangularIntensityMapData,
     HealPixIntensityMapData,
-    RectangularSpectralIndexDataProduct,
-    RectangularIntensityDataProduct,
-    MapDataProduct,
     ISNBackgroundSubtractedDataProduct,
     ISNBackgroundSubtractedMapData,
+    MapDataProduct,
+    RectangularIntensityDataProduct,
+    RectangularIntensityMapData,
+    RectangularSpectralIndexDataProduct,
+    RectangularSpectralIndexMapData,
 )
-from imap_l3_processing.models import DataProduct, MagData, InputMetadata
+from imap_l3_processing.models import DataProduct, InputMetadata, MagData
 from imap_l3_processing.version import VERSION
 
 logger = logging.getLogger(__name__)
-_cache_directory: TemporaryDirectory|None = None
+_cache_directory: TemporaryDirectory | None = None
+
 
 def get_temp_cache_dir() -> Path:
     global _cache_directory
@@ -45,12 +45,14 @@ def get_temp_cache_dir() -> Path:
         _cache_directory = TemporaryDirectory(ignore_cleanup_errors=True)
     return Path(_cache_directory.name)
 
+
 def clear_temp_cache():
     global _cache_directory
     if _cache_directory is not None:
         _cache_directory.cleanup()
     _cache_directory = None
 
+
 class SpiceKernelTypes(enum.Enum):
     Leapseconds = "leapseconds"
     IMAPFrames = "imap_frames"
@@ -64,9 +66,15 @@ class SpiceKernelTypes(enum.Enum):
     PlanetaryConstants = "planetary_constants"
 
 
-def save_data(data: DataProduct, delete_if_present: bool = False, folder_path: Path = None,
-              cr_number=None) -> Path:
-    assert data.input_metadata.repointing is None or cr_number is None, "You cannot call save_data with both a repointing in the metadata while passing in a CR number"
+def save_data(
+    data: DataProduct,
+    delete_if_present: bool = False,
+    folder_path: Path = None,
+    cr_number=None,
+) -> Path:
+    assert data.input_metadata.repointing is None or cr_number is None, (
+        "You cannot call save_data with both a repointing in the metadata while passing in a CR number"
+    )
     formatted_start_date = data.input_metadata.start_date.strftime("%Y%m%d")
     version = data.input_metadata.version.lookup(data.input_metadata.descriptor)
     science_file_path = ScienceFilePath.generate_from_inputs(
@@ -92,11 +100,18 @@ def save_data(data: DataProduct, delete_if_present: bool = False, folder_path: P
         file_path.unlink(missing_ok=True)
 
     attribute_manager = ImapAttributeManager()
-    version = str(data.input_metadata.version.lookup(data.input_metadata.descriptor)).replace('v','')
+    version = str(
+        data.input_metadata.version.lookup(data.input_metadata.descriptor)
+    ).replace("v", "")
     attribute_manager.add_global_attribute("Data_version", version)
-    attribute_manager.add_instrument_attrs(data.input_metadata.instrument, data.input_metadata.data_level,
-                                           data.input_metadata.descriptor)
-    attribute_manager.add_global_attribute("Generation_date", date.today().strftime("%Y%m%d"))
+    attribute_manager.add_instrument_attrs(
+        data.input_metadata.instrument,
+        data.input_metadata.data_level,
+        data.input_metadata.descriptor,
+    )
+    attribute_manager.add_global_attribute(
+        "Generation_date", date.today().strftime("%Y%m%d")
+    )
     attribute_manager.add_global_attribute("Logical_source", logical_source)
     attribute_manager.add_global_attribute("Logical_file_id", logical_file_id)
     attribute_manager.add_global_attribute("ground_software_version", VERSION)
@@ -116,12 +131,22 @@ def save_data(data: DataProduct, delete_if_present: bool = False, folder_path: P
                 attribute_manager.add_global_attribute(key, value)
 
             if attribute_manager.try_load_global_metadata(logical_source) is None:
-                logical_source_global_attrs = generate_global_metadata_for_undefined_logical_source(data.input_metadata)
-                attribute_manager.add_global_attribute(logical_source, logical_source_global_attrs)
-
-            attribute_manager.add_global_attribute("Spice_reference_frame", data.spice_frame_name.name)
+                logical_source_global_attrs = (
+                    generate_global_metadata_for_undefined_logical_source(
+                        data.input_metadata
+                    )
+                )
+                attribute_manager.add_global_attribute(
+                    logical_source, logical_source_global_attrs
+                )
+
+            attribute_manager.add_global_attribute(
+                "Spice_reference_frame", data.spice_frame_name.name
+            )
         else:
-            raise AssertionError(f"Found an unsupported map data product of type: {type(data)}")
+            raise AssertionError(
+                f"Found an unsupported map data product of type: {type(data)}"
+            )
     elif data.parent_file_names:
         attribute_manager.add_global_attribute("Parents", data.parent_file_names)
 
@@ -139,10 +164,10 @@ def generate_map_global_metadata(data_product: MapDataProduct) -> dict:
 
     match data_product.data:
         case (
-        RectangularSpectralIndexMapData(spectral_index_map_data=map_data) |
-        RectangularIntensityMapData(intensity_map_data=map_data) |
-        HealPixIntensityMapData(intensity_map_data=map_data) |
-        ISNBackgroundSubtractedMapData(isn_rate_map_data=map_data)
+            RectangularSpectralIndexMapData(spectral_index_map_data=map_data)
+            | RectangularIntensityMapData(intensity_map_data=map_data)
+            | HealPixIntensityMapData(intensity_map_data=map_data)
+            | ISNBackgroundSubtractedMapData(isn_rate_map_data=map_data)
         ):
             [start_date] = map_data.epoch
             [epoch_delta] = map_data.epoch_delta
@@ -152,7 +177,9 @@ def generate_map_global_metadata(data_product: MapDataProduct) -> dict:
 
             end_date = start_date + timedelta(seconds=epoch_delta / 1e9)
         case _:
-            raise ValueError(f"Found an unsupported map data product of type: {type(data_product.data)}")
+            raise ValueError(
+                f"Found an unsupported map data product of type: {type(data_product.data)}"
+            )
 
     attrs["start_date"] = start_date.isoformat()
     attrs["end_date"] = end_date.isoformat()
@@ -173,15 +200,17 @@ def generate_map_global_metadata(data_product: MapDataProduct) -> dict:
     return attrs
 
 
-def generate_global_metadata_for_undefined_logical_source(input_metadata: InputMetadata) -> dict:
-    level = input_metadata.data_level.replace('l', '')
+def generate_global_metadata_for_undefined_logical_source(
+    input_metadata: InputMetadata,
+) -> dict:
+    level = input_metadata.data_level.replace("l", "")
     data_type_string = f"Level-{level}"
     if "spx" in input_metadata.descriptor:
-        data_type_string += f" Spectral Fit Index Map"
+        data_type_string += " Spectral Fit Index Map"
     elif "ena" in input_metadata.descriptor and "-sp-" in input_metadata.descriptor:
-        data_type_string += f" Survival Corrected"
+        data_type_string += " Survival Corrected"
     elif "ena" in input_metadata.descriptor:
-        data_type_string += f" ENA Intensity"
+        data_type_string += " ENA Intensity"
 
     logical_source_global_attrs = {
         "Data_level": level,
@@ -191,13 +220,13 @@ def generate_global_metadata_for_undefined_logical_source(input_metadata: InputM
     return logical_source_global_attrs
 
 
-def format_time(t: Optional[datetime]) -> Optional[str]:
+def format_time(t: datetime | None) -> str | None:
     if t is not None:
         return t.strftime("%Y%m%d")
     return None
 
 
-def download_external_dependency(dependency_url: str, file_path: Path) -> Optional[Path]:
+def download_external_dependency(dependency_url: str, file_path: Path) -> Path | None:
     try:
         response = requests.get(dependency_url)
         if response.status_code == 200:
@@ -205,38 +234,46 @@ def download_external_dependency(dependency_url: str, file_path: Path) -> Option
                 file.write(response.content)
             return Path(file_path)
         else:
-            logger.error(f"Failed to download {dependency_url} with status code {response.status_code}")
+            logger.error(
+                f"Failed to download {dependency_url} with status code {response.status_code}"
+            )
     except RequestException:
         logger.exception(f"Failed to download {dependency_url}")
     return None
 
 
-def read_mag_data(cdf_path: Union[str, Path]) -> MagData:
+def read_mag_data(cdf_path: str | Path) -> MagData:
     with CDF(str(cdf_path)) as cdf:
         return MagData(
-            epoch=cdf['epoch'][...],
-            mag_data=read_numeric_variable(cdf["b_dsrf"])[:, :3])
+            epoch=cdf["epoch"][...],
+            mag_data=read_numeric_variable(cdf["b_dsrf"])[:, :3],
+        )
 
 
 def select_mag_path(
     dependencies: ProcessingInputCollection,
     descriptor: str,
-) -> tuple[Optional[Path], Optional[str]]:
+) -> tuple[Path | None, str | None]:
     science_files = dependencies.processing_input
     for level in ("l2", "l1d"):
         match = next(
-            (d.imap_file_paths[0] for d in science_files
-             if d.source == "mag"
-             and d.descriptor == descriptor
-             and d.data_type == level),
+            (
+                d.imap_file_paths[0]
+                for d in science_files
+                if d.source == "mag"
+                and d.descriptor == descriptor
+                and d.data_type == level
+            ),
             None,
         )
         if match is not None:
             return download(match.construct_path()), level
     return None, None
 
-def get_dependency_paths_by_descriptor(deps: ProcessingInputCollection, descriptors: list[str]) -> dict[
-    str, list[Path]]:
+
+def get_dependency_paths_by_descriptor(
+    deps: ProcessingInputCollection, descriptors: list[str]
+) -> dict[str, list[Path]]:
     descriptor_to_paths = {key: [] for key in descriptors}
     for input_file in deps.get_science_inputs():
         for descriptor in descriptors:
@@ -250,10 +287,10 @@ def get_dependency_paths_by_descriptor(deps: ProcessingInputCollection, descript
 def furnish_local_spice():
     kernels = Path(imap_l3_processing.__file__).parent.parent.joinpath("spice_kernels")
 
-    total_kernels = spiceypy.ktotal('ALL')
+    total_kernels = spiceypy.ktotal("ALL")
     current_kernels = []
-    for i in range(0, total_kernels):
-        current_kernels.append(Path(spiceypy.kdata(i, 'ALL')[0]).name)
+    for i in range(total_kernels):
+        current_kernels.append(Path(spiceypy.kdata(i, "ALL")[0]).name)
 
     for file in kernels.iterdir():
         if file.name not in current_kernels:
@@ -261,8 +298,8 @@ def furnish_local_spice():
 
 
 def get_spice_parent_file_names() -> list[str]:
-    count = spiceypy.ktotal('ALL')
-    return [Path(spiceypy.kdata(i, 'ALL')[0]).name for i in range(0, count)]
+    count = spiceypy.ktotal("ALL")
+    return [Path(spiceypy.kdata(i, "ALL")[0]).name for i in range(count)]
 
 
 @dataclass
@@ -271,36 +308,56 @@ class FurnishMetakernelOutput:
     spice_kernel_paths: list[Path]
 
 
-def get_spice_kernels_file_names(start_date: datetime, end_date: datetime, kernel_types: list[SpiceKernelTypes]) -> \
-        list[str]:
-    metakernel_url = urlparse(imap_data_access.config['DATA_ACCESS_URL'])._replace(path="metakernel").geturl()
+def get_spice_kernels_file_names(
+    start_date: datetime, end_date: datetime, kernel_types: list[SpiceKernelTypes]
+) -> list[str]:
+    metakernel_url = (
+        urlparse(imap_data_access.config["DATA_ACCESS_URL"])
+        ._replace(path="metakernel")
+        .geturl()
+    )
 
     parameters: dict = {
-        'file_types': [kernel_type.value for kernel_type in kernel_types],
-        'start_time': f"{int((start_date - datetime(2000, 1, 1, 12)).total_seconds())}",
-        'end_time': f"{int((end_date - datetime(2000, 1, 1, 12)).total_seconds())}",
+        "file_types": [kernel_type.value for kernel_type in kernel_types],
+        "start_time": f"{int((start_date - datetime(2000, 1, 1, 12)).total_seconds())}",
+        "end_time": f"{int((end_date - datetime(2000, 1, 1, 12)).total_seconds())}",
     }
 
-    kernels_res = requests.get(metakernel_url, params={**parameters, 'list_files': 'true'})
+    kernels_res = requests.get(
+        metakernel_url, params={**parameters, "list_files": "true"}
+    )
     kernels = json.loads(kernels_res.text)
 
     return kernels
 
 
-def furnish_spice_metakernel(start_date: datetime, end_date: datetime, kernel_types: list[SpiceKernelTypes], metakernel_file_name: str="metakernel.txt") -> FurnishMetakernelOutput:
-    metakernel_path = imap_data_access.config.get("DATA_DIR") / "metakernel" / metakernel_file_name
+def furnish_spice_metakernel(
+    start_date: datetime,
+    end_date: datetime,
+    kernel_types: list[SpiceKernelTypes],
+    metakernel_file_name: str = "metakernel.txt",
+) -> FurnishMetakernelOutput:
+    metakernel_path = (
+        imap_data_access.config.get("DATA_DIR") / "metakernel" / metakernel_file_name
+    )
     kernel_path = imap_data_access.config.get("DATA_DIR") / "imap" / "spice"
 
     parameters: dict = {
-        'spice_path': kernel_path,
-        'file_types': [kernel_type.value for kernel_type in kernel_types],
-        'start_time': str(int((start_date - datetime(2000, 1, 1, 12)).total_seconds())),
-        'end_time': str(int((end_date - datetime(2000, 1, 1, 12)).total_seconds())),
+        "spice_path": kernel_path,
+        "file_types": [kernel_type.value for kernel_type in kernel_types],
+        "start_time": str(int((start_date - datetime(2000, 1, 1, 12)).total_seconds())),
+        "end_time": str(int((end_date - datetime(2000, 1, 1, 12)).total_seconds())),
     }
 
-    metakernel_url = urlparse(imap_data_access.config['DATA_ACCESS_URL'])._replace(path="metakernel").geturl()
+    metakernel_url = (
+        urlparse(imap_data_access.config["DATA_ACCESS_URL"])
+        ._replace(path="metakernel")
+        .geturl()
+    )
 
-    logger.info(f"Getting SPICE Metakernel from: {metakernel_url}, with params: {parameters}")
+    logger.info(
+        f"Getting SPICE Metakernel from: {metakernel_url}, with params: {parameters}"
+    )
 
     metakernel_res = requests.get(metakernel_url, params=parameters)
 
@@ -314,7 +371,10 @@ def furnish_spice_metakernel(start_date: datetime, end_date: datetime, kernel_ty
 
     spiceypy.furnsh(str(metakernel_path))
 
-    return FurnishMetakernelOutput(metakernel_path=metakernel_path, spice_kernel_paths=downloaded_paths)
+    return FurnishMetakernelOutput(
+        metakernel_path=metakernel_path, spice_kernel_paths=downloaded_paths
+    )
+
 
 @contextmanager
 def furnished_metakernel(start_date, end_date, kernel_types):
@@ -323,6 +383,7 @@ def furnished_metakernel(start_date, end_date, kernel_types):
     finally:
         spiceypy.kclear()
 
+
 def read_cdf_parents(server_file_name: str) -> set[str]:
     downloaded_path = imap_data_access.download(server_file_name)
 
@@ -330,6 +391,7 @@ def read_cdf_parents(server_file_name: str) -> set[str]:
         parents = set(cdf.attrs["Parents"])
     return parents
 
+
 def get_version_from_query_result(science_file_query_result):
     if "major_version" in science_file_query_result:
         return Version(
@@ -338,5 +400,3 @@ def get_version_from_query_result(science_file_query_result):
         )
     else:
         return Version.from_version(science_file_query_result["version"])
-
-
diff --git a/imap_l3_processing/version.py b/imap_l3_processing/version.py
index f5c1c29c3..3d3bf67dd 100644
--- a/imap_l3_processing/version.py
+++ b/imap_l3_processing/version.py
@@ -1 +1 @@
-VERSION = 'This will be replaced during docker build'
+VERSION = "This will be replaced during docker build"
diff --git a/run_local.py b/run_local.py
index d68f57874..57f868c2e 100644
--- a/run_local.py
+++ b/run_local.py
@@ -8,67 +8,105 @@
 from collections import defaultdict
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
-from typing import Optional, TypeVar
-from unittest.mock import patch, Mock
+from typing import TypeVar
+from unittest.mock import Mock, patch
 
 import imap_data_access
 import numpy as np
 import spiceypy
 import xarray as xr
-from imap_data_access.processing_input import AncillaryInput, ProcessingInputCollection, ScienceInput, \
-    RepointInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    RepointInput,
+    ScienceInput,
+)
 from imap_processing.spice.geometry import SpiceFrame
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.codice.l3.hi.codice_hi_processor import CodiceHiProcessor
-from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import \
-    CodiceHiL3aDirectEventsDependencies
-from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import CodicePitchAngleDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import \
-    CodiceLoL3b3dDistributionsDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import CodiceLoL3aDirectEventsDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import \
-    CodiceLoL3aPartialDensitiesDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import CodiceLoL3aRatiosDependencies
+from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import (
+    CodiceHiL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import (
+    CodicePitchAngleDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import (
+    CodiceLoL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import (
+    CodiceLoL3aPartialDensitiesDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import (
+    CodiceLoL3aRatiosDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import (
+    CodiceLoL3b3dDistributionsDependencies,
+)
 from imap_l3_processing.codice.l3.lo.codice_lo_processor import CodiceLoProcessor
 from imap_l3_processing.codice.l3.lo.models import CodiceLoL2SWSpeciesData
-from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import MassPerChargeLookup
+from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import (
+    MassPerChargeLookup,
+)
 from imap_l3_processing.constants import ONE_AU_IN_KM, TEMP_CDF_FOLDER_PATH
 from imap_l3_processing.glows.glows_processor import GlowsProcessor
 from imap_l3_processing.glows.l3a.glows_l3a_dependencies import GlowsL3ADependencies
-from imap_l3_processing.glows.l3a.utils import read_l2_glows_data, create_glows_l3a_dictionary_from_cdf
+from imap_l3_processing.glows.l3a.utils import (
+    create_glows_l3a_dictionary_from_cdf,
+    read_l2_glows_data,
+)
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
 from imap_l3_processing.glows.l3d.glows_l3d_dependencies import GlowsL3DDependencies
 from imap_l3_processing.glows.l3e.glows_l3e_dependencies import GlowsL3EDependencies
 from imap_l3_processing.hi.hi_processor import HiProcessor
-from imap_l3_processing.hi.hi_spectral_fit_dependencies import HiSpectralIndexDependencies
-from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import HITL3SectoredDependencies
+from imap_l3_processing.hi.hi_spectral_fit_dependencies import (
+    HiSpectralIndexDependencies,
+)
+from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import (
+    HITL3SectoredDependencies,
+)
 from imap_l3_processing.hit.l3.hit_processor import HitProcessor
 from imap_l3_processing.hit.l3.models import HitL1Data
 from imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies import HitL3PhaDependencies
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import CosineCorrectionLookupTable
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    CosineCorrectionLookupTable,
+)
 from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import GainLookupTable
-from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import HitEventTypeLookup
+from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import (
+    HitEventTypeLookup,
+)
 from imap_l3_processing.hit.l3.pha.science.range_fit_lookup import RangeFitLookup
 from imap_l3_processing.hit.l3.utils import read_l2_hit_data
-from imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies import LoL3SpectralFitDependencies
-from imap_l3_processing.lo.lo_processor import perform_spectral_fit, LoProcessor
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies, \
-    HiL3SingleSensorFullSpinDependencies
-from imap_l3_processing.maps.map_models import RectangularSpectralIndexDataProduct, RectangularIntensityDataProduct, \
-    RectangularIntensityMapData
+from imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies import (
+    LoL3SpectralFitDependencies,
+)
+from imap_l3_processing.lo.lo_processor import LoProcessor, perform_spectral_fit
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiL3SingleSensorFullSpinDependencies,
+    HiLoL3SurvivalDependencies,
+)
+from imap_l3_processing.maps.map_models import (
+    RectangularIntensityDataProduct,
+    RectangularIntensityMapData,
+    RectangularSpectralIndexDataProduct,
+)
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import \
-    DensityOfNeutralHeliumLookupTable
+from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import (
+    DensityOfNeutralHeliumLookupTable,
+)
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
 from imap_l3_processing.swapi.l3a.utils import read_l2_swapi_data
-from imap_l3_processing.swapi.response.efficiency_calibration_table import EfficiencyCalibrationTable
-from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
+from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import (
+    GeometricFactorCalibrationTable,
+)
 from imap_l3_processing.swapi.l3b.swapi_l3b_dependencies import SwapiL3BDependencies
+from imap_l3_processing.swapi.response.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
 from imap_l3_processing.swapi.swapi_processor import SwapiProcessor
 from imap_l3_processing.swe.l3.swe_l3_dependencies import SweL3Dependencies
 from imap_l3_processing.swe.swe_processor import SweProcessor
-from imap_l3_processing.ultra.models import UltraL1CPSet, UltraGlowsL3eData
+from imap_l3_processing.ultra.models import UltraGlowsL3eData, UltraL1CPSet
 from imap_l3_processing.ultra.ultra_l3_dependencies import (
     UltraL3Dependencies,
     UltraL3SpectralIndexDependencies,
@@ -77,32 +115,47 @@
     UltraProcessor,
     correct_healpix_data_for_survival_probability,
 )
-from imap_l3_processing.utils import save_data, read_mag_data, furnish_local_spice
+from imap_l3_processing.utils import furnish_local_spice, read_mag_data, save_data
 from scripts.codice.create_fake_efficiency_ancillary import create_efficiency_lookup
-from scripts.codice.create_more_accurate_l3a_direct_event import create_more_accurate_l3a_direct_events_cdf
-from scripts.codice.create_more_accurate_l3a_direct_event_input import modify_l1a_priority_counts, \
-    modify_l2_direct_events
+from scripts.codice.create_more_accurate_l3a_direct_event import (
+    create_more_accurate_l3a_direct_events_cdf,
+)
+from scripts.codice.create_more_accurate_l3a_direct_event_input import (
+    modify_l1a_priority_counts,
+    modify_l2_direct_events,
+)
 from scripts.hi.create_hi_full_spin_deps import create_hi_full_spin_deps
-from scripts.ultra.create_l1c_l2_and_glows_with_matching_date_range import create_l1c_and_glows_with_matching_date_range
-from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path, try_get_many_run_local_paths, \
-    get_imap_data_dir_path
+from scripts.ultra.create_l1c_l2_and_glows_with_matching_date_range import (
+    create_l1c_and_glows_with_matching_date_range,
+)
+from tests.test_helpers import (
+    get_imap_data_dir_path,
+    get_test_data_path,
+    get_test_instrument_team_data_path,
+    try_get_many_run_local_paths,
+)
 
 
 def create_codice_lo_l3a_partial_densities_cdf():
     codice_lo_l2_data = CodiceLoL2SWSpeciesData.read_from_cdf(
-        get_test_instrument_team_data_path('codice/lo/imap_codice_l2_lo-sw-species_20241110_v002.cdf'))
+        get_test_instrument_team_data_path(
+            "codice/lo/imap_codice_l2_lo-sw-species_20241110_v002.cdf"
+        )
+    )
     mpc_lookup = MassPerChargeLookup.read_from_file(
-        get_test_data_path('codice/imap_codice_mass-per-charge_20241110_v002.csv'))
-    deps = CodiceLoL3aPartialDensitiesDependencies(codice_l2_lo_data=codice_lo_l2_data,
-                                                   mass_per_charge_lookup=mpc_lookup)
+        get_test_data_path("codice/imap_codice_mass-per-charge_20241110_v002.csv")
+    )
+    deps = CodiceLoL3aPartialDensitiesDependencies(
+        codice_l2_lo_data=codice_lo_l2_data, mass_per_charge_lookup=mpc_lookup
+    )
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3a',
+        instrument="codice",
+        data_level="l3a",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor='lo-partial-densities'
+        version="v000",
+        descriptor="lo-partial-densities",
     )
 
     codice_lo_processor = CodiceLoProcessor(ProcessingInputCollection(), input_metadata)
@@ -111,34 +164,51 @@ def create_codice_lo_l3a_partial_densities_cdf():
     return cdf_path
 
 
-def create_codice_lo_l3a_direct_events_cdf(l1a_paths: Optional[tuple[Path, Path]] = None) -> Path:
+def create_codice_lo_l3a_direct_events_cdf(
+    l1a_paths: tuple[Path, Path] | None = None,
+) -> Path:
     codice_lo_l2_direct_event_cdf_path = modify_l2_direct_events(
-        get_test_instrument_team_data_path('codice/lo/imap_codice_l2_lo-direct-events_20241110_v002.cdf'))
+        get_test_instrument_team_data_path(
+            "codice/lo/imap_codice_l2_lo-direct-events_20241110_v002.cdf"
+        )
+    )
 
     if l1a_paths is None:
-        codice_lo_l1a_nsw_priority_path, codice_lo_l1a_sw_priority_path = modify_l1a_priority_counts(
-            get_test_instrument_team_data_path('codice/lo/imap_codice_l1a_lo-nsw-priority_20241110_v002.cdf'),
-            get_test_instrument_team_data_path('codice/lo/imap_codice_l1a_lo-sw-priority_20241110_v002.cdf'))
+        codice_lo_l1a_nsw_priority_path, codice_lo_l1a_sw_priority_path = (
+            modify_l1a_priority_counts(
+                get_test_instrument_team_data_path(
+                    "codice/lo/imap_codice_l1a_lo-nsw-priority_20241110_v002.cdf"
+                ),
+                get_test_instrument_team_data_path(
+                    "codice/lo/imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"
+                ),
+            )
+        )
     else:
         codice_lo_l1a_nsw_priority_path, codice_lo_l1a_sw_priority_path = l1a_paths
 
-    energy_lookup_path = get_test_data_path('codice/imap_codice_lo-energy-per-charge_20241110_v002.csv')
-    mass_coefficient_path = get_test_data_path('codice/imap_codice_mass-coefficient-lookup_20241110_v002.csv')
+    energy_lookup_path = get_test_data_path(
+        "codice/imap_codice_lo-energy-per-charge_20241110_v002.csv"
+    )
+    mass_coefficient_path = get_test_data_path(
+        "codice/imap_codice_mass-coefficient-lookup_20241110_v002.csv"
+    )
 
     deps = CodiceLoL3aDirectEventsDependencies.from_file_paths(
         sw_priority_rates_cdf=codice_lo_l1a_sw_priority_path,
         nsw_priority_rates_cdf=codice_lo_l1a_nsw_priority_path,
         direct_event_path=codice_lo_l2_direct_event_cdf_path,
         mass_coefficients_file_path=mass_coefficient_path,
-        esa_to_energy_per_charge_file_path=energy_lookup_path, )
+        esa_to_energy_per_charge_file_path=energy_lookup_path,
+    )
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3a',
+        instrument="codice",
+        data_level="l3a",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor='lo-direct-events'
+        version="v000",
+        descriptor="lo-direct-events",
     )
 
     codice_lo_processor = CodiceLoProcessor(Mock(), input_metadata)
@@ -152,12 +222,12 @@ def create_codice_lo_l3a_ratios_cdf():
     deps = CodiceLoL3aRatiosDependencies.from_file_paths(partial_densities_file)
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3a',
+        instrument="codice",
+        data_level="l3a",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor='lo-sw-ratios'
+        version="v000",
+        descriptor="lo-sw-ratios",
     )
 
     codice_lo_processor = CodiceLoProcessor(Mock(), input_metadata)
@@ -171,12 +241,12 @@ def create_codice_lo_l3a_abundances_cdf():
     deps = CodiceLoL3aRatiosDependencies.from_file_paths(partial_densities_file)
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3a',
+        instrument="codice",
+        data_level="l3a",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor='lo-sw-charge-state-distributions'
+        version="v000",
+        descriptor="lo-sw-charge-state-distributions",
     )
 
     codice_lo_processor = CodiceLoProcessor(Mock(), input_metadata)
@@ -186,12 +256,20 @@ def create_codice_lo_l3a_abundances_cdf():
 
 def create_codice_lo_l3b_3d_distributions_cdf(species: str):
     l1a_paths = modify_l1a_priority_counts(
-        get_test_instrument_team_data_path('codice/lo/imap_codice_l1a_lo-nsw-priority_20241110_v002.cdf'),
-        get_test_instrument_team_data_path('codice/lo/imap_codice_l1a_lo-sw-priority_20241110_v002.cdf'))
+        get_test_instrument_team_data_path(
+            "codice/lo/imap_codice_l1a_lo-nsw-priority_20241110_v002.cdf"
+        ),
+        get_test_instrument_team_data_path(
+            "codice/lo/imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"
+        ),
+    )
 
-    codice_lo_l3a_direct_event_path = Path(create_codice_lo_l3a_direct_events_cdf(l1a_paths))
-    accurate_codice_lo_l3a_direct_event_path = create_more_accurate_l3a_direct_events_cdf(
-        codice_lo_l3a_direct_event_path)
+    codice_lo_l3a_direct_event_path = Path(
+        create_codice_lo_l3a_direct_events_cdf(l1a_paths)
+    )
+    accurate_codice_lo_l3a_direct_event_path = (
+        create_more_accurate_l3a_direct_events_cdf(codice_lo_l3a_direct_event_path)
+    )
 
     codice_lo_l1a_nsw_priority_path, codice_lo_l1a_sw_priority_path = l1a_paths
 
@@ -199,20 +277,30 @@ def create_codice_lo_l3b_3d_distributions_cdf(species: str):
         l3a_file_path=accurate_codice_lo_l3a_direct_event_path,
         l1a_sw_file_path=codice_lo_l1a_sw_priority_path,
         l1a_nsw_file_path=codice_lo_l1a_nsw_priority_path,
-        mass_species_bin_lut=(get_test_data_path('codice/imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv')),
-        geometric_factors_lut=(get_test_data_path('codice/imap_codice_lo-geometric-factors_20241110_v001.csv')),
+        mass_species_bin_lut=(
+            get_test_data_path(
+                "codice/imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv"
+            )
+        ),
+        geometric_factors_lut=(
+            get_test_data_path(
+                "codice/imap_codice_lo-geometric-factors_20241110_v001.csv"
+            )
+        ),
         efficiency_factors_lut=(create_efficiency_lookup(species)),
-        energy_per_charge_lut=get_test_data_path("codice/imap_codice_lo-energy-per-charge_20241110_v002.csv"),
-        species=species
+        energy_per_charge_lut=get_test_data_path(
+            "codice/imap_codice_lo-energy-per-charge_20241110_v002.csv"
+        ),
+        species=species,
     )
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3b',
+        instrument="codice",
+        data_level="l3b",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor=f'lo-{species}-3d-distribution'
+        version="v000",
+        descriptor=f"lo-{species}-3d-distribution",
     )
 
     codice_lo_processor = CodiceLoProcessor(Mock(), input_metadata)
@@ -220,20 +308,27 @@ def create_codice_lo_l3b_3d_distributions_cdf(species: str):
     return save_data(l3b_3d_distributions, delete_if_present=True)
 
 
-def create_swapi_l3b_cdf(geometric_calibration_file, efficiency_calibration_file, cdf_file):
-    geometric_calibration = GeometricFactorCalibrationTable.from_file(geometric_calibration_file)
+def create_swapi_l3b_cdf(
+    geometric_calibration_file, efficiency_calibration_file, cdf_file
+):
+    geometric_calibration = GeometricFactorCalibrationTable.from_file(
+        geometric_calibration_file
+    )
     efficiency_calibration = EfficiencyCalibrationTable(efficiency_calibration_file)
     swapi_cdf_data = CDF(cdf_file)
     swapi_data = read_l2_swapi_data(swapi_cdf_data)
 
-    swapi_l3_dependencies = SwapiL3BDependencies(swapi_data, geometric_calibration, efficiency_calibration)
+    swapi_l3_dependencies = SwapiL3BDependencies(
+        swapi_data, geometric_calibration, efficiency_calibration
+    )
 
     input_metadata = InputMetadata(
-        instrument='swapi',
-        data_level='l3b',
+        instrument="swapi",
+        data_level="l3b",
         start_date=datetime(2010, 1, 1),
         end_date=datetime(2010, 1, 2),
-        version='v000')
+        version="v000",
+    )
     processor = SwapiProcessor(Mock(), input_metadata)
 
     l3b_combined_vdf = processor.process_l3b(swapi_data, swapi_l3_dependencies)
@@ -242,33 +337,46 @@ def create_swapi_l3b_cdf(geometric_calibration_file, efficiency_calibration_file
 
 
 @patch("imap_l3_processing.swapi.l3a.science.calculate_pickup_ion.spiceypy")
-def create_swapi_l3a_cdf(geometric_factor_calibration_file,
-                         density_of_neutral_helium_calibration_file,
-                         imap_swapi_efficiency_lut_file, cdf_file, mock_spice):
+def create_swapi_l3a_cdf(
+    geometric_factor_calibration_file,
+    density_of_neutral_helium_calibration_file,
+    imap_swapi_efficiency_lut_file,
+    cdf_file,
+    mock_spice,
+):
     ephemeris_time_for_epoch = int(100000 * 1e9)
     mock_spice.unitim.return_value = ephemeris_time_for_epoch
     mock_light_time = 122.0
     mock_spice.spkezr.return_value = (np.array([0, 0, 0, 0, 0, 0]), mock_light_time)
     mock_spice.latrec.return_value = np.array([0, 2, 0])
-    mock_spice.reclat.return_value = np.array([0.99 * ONE_AU_IN_KM, np.deg2rad(255.7), 0.6])
+    mock_spice.reclat.return_value = np.array(
+        [0.99 * ONE_AU_IN_KM, np.deg2rad(255.7), 0.6]
+    )
 
     def mock_sxform(from_frame, to_frame, et):
         if from_frame == "IMAP_SWAPI":
             return np.eye(6)
-        return np.array([
-            [0, 1, 0, 0, 0, 0],
-            [0, 0, 1, 0, 0, 0],
-            [1, 0, 0, 0, 0, 0],
-            [0, 0, 0, 0, 1, 0],
-            [0, 0, 0, 0, 0, 1],
-            [0, 0, 0, 1, 0, 0],
-        ])
+        return np.array(
+            [
+                [0, 1, 0, 0, 0, 0],
+                [0, 0, 1, 0, 0, 0],
+                [1, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 1, 0],
+                [0, 0, 0, 0, 0, 1],
+                [0, 0, 0, 1, 0, 0],
+            ]
+        )
 
     mock_spice.sxform.side_effect = mock_sxform
 
-    efficiency_calibration_table = EfficiencyCalibrationTable(imap_swapi_efficiency_lut_file)
-    density_of_neutral_helium_calibration_table = DensityOfNeutralHeliumLookupTable.from_file(
-        density_of_neutral_helium_calibration_file)
+    efficiency_calibration_table = EfficiencyCalibrationTable(
+        imap_swapi_efficiency_lut_file
+    )
+    density_of_neutral_helium_calibration_table = (
+        DensityOfNeutralHeliumLookupTable.from_file(
+            density_of_neutral_helium_calibration_file
+        )
+    )
     swapi_cdf_data = CDF(cdf_file)
     swapi_data = read_l2_swapi_data(swapi_cdf_data)
     swapi_l3_dependencies = SwapiL3ADependencies(
@@ -281,11 +389,12 @@ def mock_sxform(from_frame, to_frame, et):
     )
 
     input_metadata = InputMetadata(
-        instrument='swapi',
-        data_level='l3a',
+        instrument="swapi",
+        data_level="l3a",
         start_date=datetime(2025, 10, 23),
         end_date=datetime(2025, 10, 24),
-        version='v000')
+        version="v000",
+    )
     processor = SwapiProcessor(Mock(), input_metadata)
 
     l3a_proton_sw = processor.process_l3a_proton(swapi_data, swapi_l3_dependencies)
@@ -299,11 +408,12 @@ def mock_sxform(from_frame, to_frame, et):
 
 def create_swe_product(dependencies: SweL3Dependencies) -> Path:
     input_metadata = InputMetadata(
-        instrument='swe',
-        data_level='l3',
+        instrument="swe",
+        data_level="l3",
         start_date=datetime(2025, 6, 29),
         end_date=datetime(2025, 7, 1),
-        version='v000')
+        version="v000",
+    )
     processor = SweProcessor(Mock(), input_metadata)
     output_data = processor.calculate_products(dependencies)
     cdf_path = save_data(output_data, delete_if_present=True)
@@ -311,20 +421,26 @@ def create_swe_product(dependencies: SweL3Dependencies) -> Path:
 
 
 @patch("imap_l3_processing.swe.l3.science.moment_calculations.spiceypy.pxform")
-def create_swe_product_with_fake_spice(dependencies: SweL3Dependencies, mock_spice_pxform) -> Path:
+def create_swe_product_with_fake_spice(
+    dependencies: SweL3Dependencies, mock_spice_pxform
+) -> Path:
     data = np.loadtxt(get_test_data_path("swe/ace_attitude.dat"), skiprows=1)
 
     def time_to_float(t):
         return (t - datetime(1999, 1, 1, tzinfo=timezone.utc)).total_seconds()
 
-    times = [datetime(1998, 12, 31, tzinfo=timezone.utc) + timedelta(days=row[1], seconds=row[2]) for row in data]
+    times = [
+        datetime(1998, 12, 31, tzinfo=timezone.utc)
+        + timedelta(days=row[1], seconds=row[2])
+        for row in data
+    ]
     times_as_floats = [time_to_float(t) for t in times]
 
     def fake_pxform(from_frame, to_frame, et):
         assert from_frame == "IMAP_DPS"
         assert to_frame == "IMAP_RTN"
         time = spiceypy.et2datetime(et)
-        correction_factor = (datetime(2025, 6, 30) - datetime(1999, 6, 8))
+        correction_factor = datetime(2025, 6, 30) - datetime(1999, 6, 8)
         time_in_1999 = time - correction_factor
         r = np.interp(time_to_float(time_in_1999), times_as_floats, data[:, 4])
         t = np.interp(time_to_float(time_in_1999), times_as_floats, data[:, 5])
@@ -338,77 +454,98 @@ def fake_pxform(from_frame, to_frame, et):
         c2 = np.sqrt(t2 + n2)
         c1 = np.sqrt((t2 + n2) * (t2 + n2) + r2 * t2 + r2 * n2)
 
-        mat = np.array([[(-n2 - t2) / c1, 0, r / c3], [r * t / c1, -n / c2, t / c3], [r * n / c1, t / c2, n / c3]])
+        mat = np.array(
+            [
+                [(-n2 - t2) / c1, 0, r / c3],
+                [r * t / c1, -n / c2, t / c3],
+                [r * n / c1, t / c2, n / c3],
+            ]
+        )
         return mat
 
     mock_spice_pxform.side_effect = fake_pxform
 
     input_metadata = InputMetadata(
-        instrument='swe',
-        data_level='l3',
+        instrument="swe",
+        data_level="l3",
         start_date=datetime(2025, 6, 29),
         end_date=datetime(2025, 7, 1),
-        version='v000')
+        version="v000",
+    )
     processor = SweProcessor(Mock(), input_metadata)
     output_data = processor.calculate_products(dependencies)
     cdf_path = save_data(output_data, delete_if_present=True)
     return cdf_path
 
 
-def create_survival_corrected_full_spin_cdf(dependencies: HiL3SingleSensorFullSpinDependencies) -> Path:
-    input_metadata = InputMetadata(instrument="hi",
-                                   data_level="l3",
-                                   start_date=datetime.now(),
-                                   end_date=datetime.now() + timedelta(days=1),
-                                   version="v000",
-                                   descriptor="h90-ena-h-sf-sp-full-hae-4deg-1yr",
-                                   )
+def create_survival_corrected_full_spin_cdf(
+    dependencies: HiL3SingleSensorFullSpinDependencies,
+) -> Path:
+    input_metadata = InputMetadata(
+        instrument="hi",
+        data_level="l3",
+        start_date=datetime.now(),
+        end_date=datetime.now() + timedelta(days=1),
+        version="v000",
+        descriptor="h90-ena-h-sf-sp-full-hae-4deg-1yr",
+    )
     processor = HiProcessor(Mock(), input_metadata)
-    output_data = processor.process_full_spin_single_sensor(dependencies, SpiceFrame.ECLIPJ2000)
+    output_data = processor.process_full_spin_single_sensor(
+        dependencies, SpiceFrame.ECLIPJ2000
+    )
 
-    data_product = RectangularIntensityDataProduct(data=output_data, input_metadata=input_metadata)
+    data_product = RectangularIntensityDataProduct(
+        data=output_data, input_metadata=input_metadata
+    )
     cdf_path = save_data(data_product, delete_if_present=True)
     return cdf_path
 
 
 def create_hi_spectral_index_cdf(dependencies: HiSpectralIndexDependencies) -> Path:
-    input_metadata = InputMetadata(instrument="hi",
-                                   data_level="l3",
-                                   start_date=datetime.now(),
-                                   end_date=datetime.now() + timedelta(days=1),
-                                   version="v000",
-                                   descriptor="h90-spx-h-hf-sp-full-hae-4deg-6mo",
-                                   )
+    input_metadata = InputMetadata(
+        instrument="hi",
+        data_level="l3",
+        start_date=datetime.now(),
+        end_date=datetime.now() + timedelta(days=1),
+        version="v000",
+        descriptor="h90-spx-h-hf-sp-full-hae-4deg-6mo",
+    )
     processor = HiProcessor(Mock(), input_metadata)
     output_data = processor.process_spectral_fit_index(dependencies)
-    data_product = RectangularSpectralIndexDataProduct(data=output_data, input_metadata=input_metadata)
+    data_product = RectangularSpectralIndexDataProduct(
+        data=output_data, input_metadata=input_metadata
+    )
     cdf_path = save_data(data_product, delete_if_present=True)
     return cdf_path
 
 
 def create_lo_spectral_index_cdf(dependencies: LoL3SpectralFitDependencies) -> Path:
-    input_metadata = InputMetadata(instrument="lo",
-                                   data_level="l3",
-                                   start_date=datetime.now(),
-                                   end_date=datetime.now() + timedelta(days=1),
-                                   version="v000",
-                                   descriptor="l090-spx-h-hf-sp-ram-hae-6deg-1yr",
-                                   )
+    input_metadata = InputMetadata(
+        instrument="lo",
+        data_level="l3",
+        start_date=datetime.now(),
+        end_date=datetime.now() + timedelta(days=1),
+        version="v000",
+        descriptor="l090-spx-h-hf-sp-ram-hae-6deg-1yr",
+    )
 
     output_data = perform_spectral_fit(dependencies.map_data)
-    data_product = RectangularSpectralIndexDataProduct(data=output_data, input_metadata=input_metadata)
+    data_product = RectangularSpectralIndexDataProduct(
+        data=output_data, input_metadata=input_metadata
+    )
     cdf_path = save_data(data_product, delete_if_present=True)
     return cdf_path
 
 
 def create_hit_sectored_cdf(dependencies: HITL3SectoredDependencies) -> Path:
     input_metadata = InputMetadata(
-        instrument='hit',
-        data_level='l3',
-        descriptor='macropixel',
+        instrument="hit",
+        data_level="l3",
+        descriptor="macropixel",
         start_date=datetime(2025, 1, 1),
         end_date=datetime(2025, 1, 2),
-        version='v000')
+        version="v000",
+    )
     processor = HitProcessor(Mock(), input_metadata)
     output_data = processor.process_pitch_angle_product(dependencies)
     cdf_path = save_data(output_data, delete_if_present=True)
@@ -417,42 +554,75 @@ def create_hit_sectored_cdf(dependencies: HITL3SectoredDependencies) -> Path:
 
 def create_hit_direct_event_cdf():
     cosine_table = CosineCorrectionLookupTable(
-        get_test_data_path("hit/pha_events/imap_hit_range-2A-cosine-lookup_20250203_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-2B-cosine-lookup_20250203_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-3A-cosine-lookup_20250203_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-3B-cosine-lookup_20250203_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-4A-cosine-lookup_20250203_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-4B-cosine-lookup_20250203_v000.csv"),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-2A-cosine-lookup_20250203_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-2B-cosine-lookup_20250203_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-3A-cosine-lookup_20250203_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-3B-cosine-lookup_20250203_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-4A-cosine-lookup_20250203_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-4B-cosine-lookup_20250203_v000.csv"
+        ),
     )
     gain_table = GainLookupTable.from_file(
         get_test_data_path("hit/pha_events/imap_hit_hi-gain-lookup_20250203_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_lo-gain-lookup_20250203_v000.csv"))
+        get_test_data_path("hit/pha_events/imap_hit_lo-gain-lookup_20250203_v000.csv"),
+    )
 
     range_fit_lookup = RangeFitLookup.from_files(
-        get_test_data_path("hit/pha_events/imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"),
-        get_test_data_path("hit/pha_events/imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"
+        ),
+        get_test_data_path(
+            "hit/pha_events/imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"
+        ),
     )
 
     event_type_look = HitEventTypeLookup.from_csv(
-        get_test_data_path("hit/pha_events/imap_hit_hit-event-type-lookup_20250228_v000.csv"))
-
-    hit_l1_data = HitL1Data.read_from_cdf("data/imap/hit/l1a/2010/01/imap_hit_l1a_direct-events_20100105_v001.cdf")
+        get_test_data_path(
+            "hit/pha_events/imap_hit_hit-event-type-lookup_20250228_v000.csv"
+        )
+    )
 
-    direct_event_dependencies = HitL3PhaDependencies(hit_l1_data=hit_l1_data, cosine_correction_lookup=cosine_table,
+    hit_l1_data = HitL1Data.read_from_cdf(
+        "data/imap/hit/l1a/2010/01/imap_hit_l1a_direct-events_20100105_v001.cdf"
+    )
 
-                                                     gain_lookup=gain_table, range_fit_lookup=range_fit_lookup,
-                                                     event_type_lookup=event_type_look)
+    direct_event_dependencies = HitL3PhaDependencies(
+        hit_l1_data=hit_l1_data,
+        cosine_correction_lookup=cosine_table,
+        gain_lookup=gain_table,
+        range_fit_lookup=range_fit_lookup,
+        event_type_lookup=event_type_look,
+    )
     input_metadata = InputMetadata(
         instrument="hit",
         data_level="l3",
         start_date=datetime.now(),
         end_date=datetime.now() + timedelta(days=1),
         version="v001",
-        descriptor="direct-events"
+        descriptor="direct-events",
     )
     processor = HitProcessor(Mock(), input_metadata)
 
@@ -461,93 +631,144 @@ def create_hit_direct_event_cdf():
     return file_path
 
 
-@patch('imap_l3_processing.glows.glows_initializer.query')
-@patch('imap_l3_processing.glows.l3bc.glows_l3bc_dependencies.download_dependency_from_path')
-@patch('imap_l3_processing.glows.glows_processor.imap_data_access')
-@patch('imap_l3_processing.glows.l3bc.glows_initializer_ancillary_dependencies.download')
-@patch('imap_l3_processing.glows.l3bc.glows_initializer_ancillary_dependencies.download_external_dependency')
-def run_l3b_initializer(mock_download_external, mock_download, mock_imap_data_access,
-                        mock_download_dependency_from_path, mock_query):
+@patch("imap_l3_processing.glows.glows_initializer.query")
+@patch(
+    "imap_l3_processing.glows.l3bc.glows_l3bc_dependencies.download_dependency_from_path"
+)
+@patch("imap_l3_processing.glows.glows_processor.imap_data_access")
+@patch(
+    "imap_l3_processing.glows.l3bc.glows_initializer_ancillary_dependencies.download"
+)
+@patch(
+    "imap_l3_processing.glows.l3bc.glows_initializer_ancillary_dependencies.download_external_dependency"
+)
+def run_l3b_initializer(
+    mock_download_external,
+    mock_download,
+    mock_imap_data_access,
+    mock_download_dependency_from_path,
+    mock_query,
+):
     local_cdfs: list[str] = os.listdir(get_test_data_path("glows/pipeline/l3a"))
 
-    l3a_dicts = [{'file_path': f"imap/glows/l3a/{file_path}",
-                  'start_date': file_path.split('_')[4].split('-')[0],
-                  'repointing': int(file_path.split('_')[4].split('-repoint')[1])
-                  } for file_path in local_cdfs]
-
-    mock_query.side_effect = [
-        l3a_dicts, []
+    l3a_dicts = [
+        {
+            "file_path": f"imap/glows/l3a/{file_path}",
+            "start_date": file_path.split("_")[4].split("-")[0],
+            "repointing": int(file_path.split("_")[4].split("-repoint")[1]),
+        }
+        for file_path in local_cdfs
     ]
 
+    mock_query.side_effect = [l3a_dicts, []]
+
     mock_download.side_effect = [
-        get_test_data_path('glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json'),
-        get_test_data_path('glows/imap_2026_105_01.repoint.csv'),
+        get_test_data_path(
+            "glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json"
+        ),
+        get_test_data_path("glows/imap_2026_105_01.repoint.csv"),
     ]
 
     mock_download_external.side_effect = [
-        TEMP_CDF_FOLDER_PATH / 'f107_fluxtable.txt',
-        TEMP_CDF_FOLDER_PATH / 'lyman_alpha_composite.nc',
+        TEMP_CDF_FOLDER_PATH / "f107_fluxtable.txt",
+        TEMP_CDF_FOLDER_PATH / "lyman_alpha_composite.nc",
         Path(
-            r'C:\Users\Harrison\Downloads\glows_L3bc_pipeline_2025_04_11\glows_L3bc_pipeline_2025_04_11\external_dependencies\omni2_all_years.dat')
+            r"C:\Users\Harrison\Downloads\glows_L3bc_pipeline_2025_04_11\glows_L3bc_pipeline_2025_04_11\external_dependencies\omni2_all_years.dat"
+        ),
     ]
 
     mock_download_dependency_from_path.side_effect = [
-        get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-        get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-        get_test_data_path('glows/imap_glows_bad-days-list_20100101_v004.dat'),
-        get_test_data_path('glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json'),
-        get_test_data_path('glows/pipeline/l3a/imap_glows_l3a_hist_20250625-repoint01071_v001.cdf'),
-        get_test_data_path('glows/pipeline/l3a/imap_glows_l3a_hist_20250626-repoint01072_v001.cdf'),
-        get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-        get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-        get_test_data_path('glows/imap_glows_bad-days-list_20100101_v004.dat'),
-        get_test_data_path('glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json'),
-        get_test_data_path('glows/pipeline/l3a/imap_glows_l3a_hist_20100521-repoint00289_v001.cdf'),
-        get_test_data_path('glows/pipeline/l3a/imap_glows_l3a_hist_20100522-repoint00290_v001.cdf'),
+        get_test_data_path("glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"),
+        get_test_data_path("glows/imap_glows_WawHelioIonMP_20100101_v002.json"),
+        get_test_data_path("glows/imap_glows_bad-days-list_20100101_v004.dat"),
+        get_test_data_path(
+            "glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json"
+        ),
+        get_test_data_path(
+            "glows/pipeline/l3a/imap_glows_l3a_hist_20250625-repoint01071_v001.cdf"
+        ),
+        get_test_data_path(
+            "glows/pipeline/l3a/imap_glows_l3a_hist_20250626-repoint01072_v001.cdf"
+        ),
+        get_test_data_path("glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"),
+        get_test_data_path("glows/imap_glows_WawHelioIonMP_20100101_v002.json"),
+        get_test_data_path("glows/imap_glows_bad-days-list_20100101_v004.dat"),
+        get_test_data_path(
+            "glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json"
+        ),
+        get_test_data_path(
+            "glows/pipeline/l3a/imap_glows_l3a_hist_20100521-repoint00289_v001.cdf"
+        ),
+        get_test_data_path(
+            "glows/pipeline/l3a/imap_glows_l3a_hist_20100522-repoint00290_v001.cdf"
+        ),
     ]
 
-    bad_days_list = AncillaryInput('imap_glows_bad-days-list_20100101_v004.dat')
-    waw_helio_ion = AncillaryInput('imap_glows_WawHelioIonMP_20100101_v002.json')
-    uv_anisotropy = AncillaryInput('imap_glows_uv-anisotropy-1CR_20100101_v001.json')
-    pipeline_settings = AncillaryInput('imap_glows_pipeline-settings-l3bcde_20250423_v001.json')
+    bad_days_list = AncillaryInput("imap_glows_bad-days-list_20100101_v004.dat")
+    waw_helio_ion = AncillaryInput("imap_glows_WawHelioIonMP_20100101_v002.json")
+    uv_anisotropy = AncillaryInput("imap_glows_uv-anisotropy-1CR_20100101_v001.json")
+    pipeline_settings = AncillaryInput(
+        "imap_glows_pipeline-settings-l3bcde_20250423_v001.json"
+    )
 
-    repointings = RepointInput('imap_2026_105_01.repoint.csv')
+    repointings = RepointInput("imap_2026_105_01.repoint.csv")
 
-    input_collection = ProcessingInputCollection(bad_days_list, waw_helio_ion, uv_anisotropy, pipeline_settings,
-                                                 repointings)
-    input_metadata = InputMetadata(instrument='glows', data_level='l3b',
-                                   start_date=datetime.fromisoformat('2010-01-05T00:00:00'),
-                                   end_date=datetime.fromisoformat('2025-01-06T00:00:00'),
-                                   version='v001')
+    input_collection = ProcessingInputCollection(
+        bad_days_list, waw_helio_ion, uv_anisotropy, pipeline_settings, repointings
+    )
+    input_metadata = InputMetadata(
+        instrument="glows",
+        data_level="l3b",
+        start_date=datetime.fromisoformat("2010-01-05T00:00:00"),
+        end_date=datetime.fromisoformat("2025-01-06T00:00:00"),
+        version="v001",
+    )
 
     processor = GlowsProcessor(input_collection, input_metadata)
 
     print(processor.process())
 
 
-@patch('imap_l3_processing.glows.glows_initializer.query')
+@patch("imap_l3_processing.glows.glows_initializer.query")
 def run_glows_l3bc_processor_and_initializer(mock_query):
     input_metadata = InputMetadata(
-        instrument='glows',
-        data_level='l3b',
+        instrument="glows",
+        data_level="l3b",
         start_date=datetime(2013, 9, 8),
         end_date=datetime(2013, 9, 8),
-        version='v011')
+        version="v011",
+    )
 
-    l3a_files = imap_data_access.query(instrument='glows', version=input_metadata.version, data_level='l3a',
-                                       start_date='20100422', end_date='20100625')
+    l3a_files = imap_data_access.query(
+        instrument="glows",
+        version=input_metadata.version,
+        data_level="l3a",
+        start_date="20100422",
+        end_date="20100625",
+    )
 
-    l3a_files_2 = imap_data_access.query(instrument='glows', version=input_metadata.version, data_level='l3a',
-                                         start_date='20100922', end_date='20101123')
+    l3a_files_2 = imap_data_access.query(
+        instrument="glows",
+        version=input_metadata.version,
+        data_level="l3a",
+        start_date="20100922",
+        end_date="20101123",
+    )
     mock_query.side_effect = [l3a_files + l3a_files_2, []]
 
-    bad_days_list = AncillaryInput('imap_glows_bad-days-list_20100101_v001.dat')
-    waw_helio_ion = AncillaryInput('imap_glows_WawHelioIonMP_20100101_v002.json')
-    uv_anisotropy = AncillaryInput('imap_glows_uv-anisotropy-1CR_20300101_v003.json')
-    pipeline_settings = AncillaryInput('imap_glows_pipeline-settings-l3bcde_20100101_v006.json')
-    input_collection = ProcessingInputCollection(bad_days_list, waw_helio_ion, uv_anisotropy, pipeline_settings)
+    bad_days_list = AncillaryInput("imap_glows_bad-days-list_20100101_v001.dat")
+    waw_helio_ion = AncillaryInput("imap_glows_WawHelioIonMP_20100101_v002.json")
+    uv_anisotropy = AncillaryInput("imap_glows_uv-anisotropy-1CR_20300101_v003.json")
+    pipeline_settings = AncillaryInput(
+        "imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+    )
+    input_collection = ProcessingInputCollection(
+        bad_days_list, waw_helio_ion, uv_anisotropy, pipeline_settings
+    )
 
-    processor = GlowsProcessor(dependencies=input_collection, input_metadata=input_metadata)
+    processor = GlowsProcessor(
+        dependencies=input_collection, input_metadata=input_metadata
+    )
     print(processor.process())
 
 
@@ -555,7 +776,9 @@ def run_glows_l3bc_processor_and_initializer(mock_query):
 @patch("imap_l3_processing.glows.glows_processor.Path")
 @patch("imap_l3_processing.glows.glows_processor.run")
 @patch("imap_l3_processing.glows.glows_processor.get_pointing_date_range")
-def run_glows_l3e_lo_with_mocks(mock_get_repoint_date_range, _, mock_path, mock_l3e_dependencies_class):
+def run_glows_l3e_lo_with_mocks(
+    mock_get_repoint_date_range, _, mock_path, mock_l3e_dependencies_class
+):
     mock_processing_input_collection = Mock()
     mock_processing_input_collection.get_file_paths.return_value = [Path("one path")]
 
@@ -565,56 +788,90 @@ def run_glows_l3e_lo_with_mocks(mock_get_repoint_date_range, _, mock_path, mock_
         Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/EnGridUltra.dat"),
         Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/tessXYZ8.dat"),
         Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/tessAng16.dat"),
-        Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/lyaSeriesV4_2021b.dat"),
         Path(
-            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/solar_uv_anisotropy_NP.1.0_SP.1.0.dat"),
-        Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/speed3D.v01.Legendre.2021b.dat"),
-        Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/density3D.v01.Legendre.2021b.dat"),
-        Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/phion_Hydrogen_T12F107_2021b.dat"),
-        Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/swEqtrElectrons5_2021b.dat"),
-        Path("instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/ionization.files.dat"),
-        pipeline_settings={"executable_dependency_paths": {
-            "energy-grid-lo": "EnGridLo.dat",
-            "energy-grid-hi": "EnGridHi.dat",
-            "energy-grid-ultra": "EnGridUltra.dat",
-            "tess-xyz-8": "tessXYZ8.dat",
-            "tess-ang-16": "tessAng16.dat",
-            "lya-series": "lyaSeriesV4_2021b.dat",
-            "solar-uv-anistropy": "solar_uv_anisotropy_NP.1.0_SP.1.0.dat",
-            "speed-3d": "speed3D.v01.Legendre.2021b.dat",
-            "density-3d": "density3D.v01.Legendre.2021b.dat",
-            "phion-hydrogen": "phion_Hydrogen_T12F107_2021b.dat",
-            "sw-eqtr-electrons": "swEqtrElectrons5_2021b.dat",
-            "ionization-files": "ionization.files.dat",
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/lyaSeriesV4_2021b.dat"
+        ),
+        Path(
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/solar_uv_anisotropy_NP.1.0_SP.1.0.dat"
+        ),
+        Path(
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/speed3D.v01.Legendre.2021b.dat"
+        ),
+        Path(
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/density3D.v01.Legendre.2021b.dat"
+        ),
+        Path(
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/phion_Hydrogen_T12F107_2021b.dat"
+        ),
+        Path(
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/swEqtrElectrons5_2021b.dat"
+        ),
+        Path(
+            "instrument_team_data/glows/GLOWS_L3d_to_L3e_processing/ionization.files.dat"
+        ),
+        pipeline_settings={
+            "executable_dependency_paths": {
+                "energy-grid-lo": "EnGridLo.dat",
+                "energy-grid-hi": "EnGridHi.dat",
+                "energy-grid-ultra": "EnGridUltra.dat",
+                "tess-xyz-8": "tessXYZ8.dat",
+                "tess-ang-16": "tessAng16.dat",
+                "lya-series": "lyaSeriesV4_2021b.dat",
+                "solar-uv-anistropy": "solar_uv_anisotropy_NP.1.0_SP.1.0.dat",
+                "speed-3d": "speed3D.v01.Legendre.2021b.dat",
+                "density-3d": "density3D.v01.Legendre.2021b.dat",
+                "phion-hydrogen": "phion_Hydrogen_T12F107_2021b.dat",
+                "sw-eqtr-electrons": "swEqtrElectrons5_2021b.dat",
+                "ionization-files": "ionization.files.dat",
+            },
+            "start_cr": 2092,
         },
-            "start_cr": 2092},
         elongation=defaultdict(lambda: 90),
         repointing_file=get_test_data_path("fake_1_day_repointing_file.csv"),
     )
 
     mock_l3e_dependencies.copy_dependencies = Mock()
-    mock_l3e_dependencies_class.fetch_dependencies.return_value = (mock_l3e_dependencies, 2094)
+    mock_l3e_dependencies_class.fetch_dependencies.return_value = (
+        mock_l3e_dependencies,
+        2094,
+    )
 
     mock_path.side_effect = [
         # Path(get_test_instrument_team_data_path("glows/probSur.Imap.Lo_20090101_010101_2009.000_60.00.txt")),
-        Path(get_test_instrument_team_data_path("glows/probSur.Imap.Hi_2009.000_135.0.txt")),
-        Path(get_test_instrument_team_data_path("glows/probSur.Imap.Hi_2009.000_90.00.txt")),
-        Path(get_test_instrument_team_data_path("glows/probSur.Imap.Ul_20090101_010101_2009.000.txt")),
+        Path(
+            get_test_instrument_team_data_path(
+                "glows/probSur.Imap.Hi_2009.000_135.0.txt"
+            )
+        ),
+        Path(
+            get_test_instrument_team_data_path(
+                "glows/probSur.Imap.Hi_2009.000_90.00.txt"
+            )
+        ),
+        Path(
+            get_test_instrument_team_data_path(
+                "glows/probSur.Imap.Ul_20090101_010101_2009.000.txt"
+            )
+        ),
     ]
 
     input_metadata = InputMetadata(
-        instrument='glows',
-        data_level='l3e',
+        instrument="glows",
+        data_level="l3e",
         start_date=datetime(2015, 4, 10),
         end_date=datetime(2015, 4, 11),
-        version='v001',
-        descriptor='survival-probability-lo')
+        version="v001",
+        descriptor="survival-probability-lo",
+    )
 
     mock_get_repoint_date_range.return_value = (
         np.datetime64(datetime.fromisoformat("2025-04-20T00:00:00")),
-        np.datetime64(datetime.fromisoformat("2025-04-21T00:00:00")))
+        np.datetime64(datetime.fromisoformat("2025-04-21T00:00:00")),
+    )
 
-    glows_processor: GlowsProcessor = GlowsProcessor(mock_processing_input_collection, input_metadata)
+    glows_processor: GlowsProcessor = GlowsProcessor(
+        mock_processing_input_collection, input_metadata
+    )
 
     glows_processor.process()
 
@@ -624,7 +881,9 @@ def run_glows_l3e_with_less_mocks(mock_spiceypy):
     mock_spiceypy.spkezr = spiceypy.spkezr
     mock_spiceypy.reclat = spiceypy.reclat
     mock_spiceypy.pxform = spiceypy.pxform
-    mock_spiceypy.datetime2et = lambda date: spiceypy.datetime2et(date + timedelta(days=365 * 16 + 4, hours=2))
+    mock_spiceypy.datetime2et = lambda date: spiceypy.datetime2et(
+        date + timedelta(days=365 * 16 + 4, hours=2)
+    )
 
     l3d_file = "imap_glows_l3d_solar-hist_20100101-repoint02092_v002.cdf"
     lo_processing_input_collection = ProcessingInputCollection(
@@ -684,36 +943,64 @@ def run_glows_l3e_with_less_mocks(mock_spiceypy):
         RepointInput("imap_2001_052_001.repoint.csv"),
     )
 
-    version = 'v007'
+    version = "v007"
     start_date = datetime(2010, 1, 1)
     end_date = datetime(2010, 1, 2)
 
-    lo_input_metadata = InputMetadata(instrument='glows', data_level='l3e', start_date=start_date,
-                                      end_date=end_date, version=version,
-                                      descriptor='survival-probability-lo')
+    lo_input_metadata = InputMetadata(
+        instrument="glows",
+        data_level="l3e",
+        start_date=start_date,
+        end_date=end_date,
+        version=version,
+        descriptor="survival-probability-lo",
+    )
 
-    glows_processor: GlowsProcessor = GlowsProcessor(lo_processing_input_collection, lo_input_metadata)
+    glows_processor: GlowsProcessor = GlowsProcessor(
+        lo_processing_input_collection, lo_input_metadata
+    )
     glows_processor.process()
 
-    hi_45_input_metadata = InputMetadata(instrument='glows', data_level='l3e', start_date=start_date,
-                                         end_date=end_date, version=version,
-                                         descriptor='survival-probability-hi-45')
+    hi_45_input_metadata = InputMetadata(
+        instrument="glows",
+        data_level="l3e",
+        start_date=start_date,
+        end_date=end_date,
+        version=version,
+        descriptor="survival-probability-hi-45",
+    )
 
-    glows_processor: GlowsProcessor = GlowsProcessor(hi_45_processing_input_collection, hi_45_input_metadata)
+    glows_processor: GlowsProcessor = GlowsProcessor(
+        hi_45_processing_input_collection, hi_45_input_metadata
+    )
     glows_processor.process()
 
-    hi_90_input_metadata = InputMetadata(instrument='glows', data_level='l3e', start_date=start_date,
-                                         end_date=end_date, version=version,
-                                         descriptor='survival-probability-hi-90')
+    hi_90_input_metadata = InputMetadata(
+        instrument="glows",
+        data_level="l3e",
+        start_date=start_date,
+        end_date=end_date,
+        version=version,
+        descriptor="survival-probability-hi-90",
+    )
 
-    glows_processor: GlowsProcessor = GlowsProcessor(hi_90_processing_input_collection, hi_90_input_metadata)
+    glows_processor: GlowsProcessor = GlowsProcessor(
+        hi_90_processing_input_collection, hi_90_input_metadata
+    )
     glows_processor.process()
 
-    ul_input_metadata = InputMetadata(instrument='glows', data_level='l3e', start_date=start_date,
-                                      end_date=end_date, version=version,
-                                      descriptor='survival-probability-ul')
+    ul_input_metadata = InputMetadata(
+        instrument="glows",
+        data_level="l3e",
+        start_date=start_date,
+        end_date=end_date,
+        version=version,
+        descriptor="survival-probability-ul",
+    )
 
-    glows_processor: GlowsProcessor = GlowsProcessor(ul_processing_input_collection, ul_input_metadata)
+    glows_processor: GlowsProcessor = GlowsProcessor(
+        ul_processing_input_collection, ul_input_metadata
+    )
     glows_processor.process()
 
 
@@ -724,73 +1011,106 @@ def make_glows_l3a_files():
 
 
 def run_glows_l3a(file_path):
-    date_in_path = re.search(r"imap_glows_l2_hist_(\d{8}).*.cdf", file_path.name).group(1)
+    date_in_path = re.search(r"imap_glows_l2_hist_(\d{8}).*.cdf", file_path.name).group(
+        1
+    )
     start_date = datetime.strptime(date_in_path, "%Y%m%d")
     end_date = start_date + timedelta(days=1)
     input_metadata = InputMetadata(
-        instrument='glows',
-        data_level='l3a',
+        instrument="glows",
+        data_level="l3a",
         start_date=start_date,
         end_date=end_date,
-        version='v001'
+        version="v001",
     )
 
     with CDF(str(file_path)) as cdf_data:
         l2_glows_data = read_l2_glows_data(cdf_data)
 
-        repoint_number = int(file_path.name.split('_')[4][-5:])
+        repoint_number = int(file_path.name.split("_")[4][-5:])
         input_metadata.repointing = repoint_number
 
-        dependencies = GlowsL3ADependencies(l2_glows_data, {
-            "calibration_data": get_test_instrument_team_data_path(
-                "glows/imap_glows_calibration-data_20100101_v002.dat"),
-            "settings": get_test_instrument_team_data_path("glows/imap_glows_pipeline-settings_20100101_v001.json"),
-            "time_dependent_bckgrd": get_test_instrument_team_data_path(
-                "glows/imap_glows_time-dep-bckgrd_20100101_v001.dat"),
-            "extra_heliospheric_bckgrd": get_test_instrument_team_data_path(
-                "glows/imap_glows_map-of-extra-helio-bckgrd_20100101_v002.dat"),
-        })
+        dependencies = GlowsL3ADependencies(
+            l2_glows_data,
+            {
+                "calibration_data": get_test_instrument_team_data_path(
+                    "glows/imap_glows_calibration-data_20100101_v002.dat"
+                ),
+                "settings": get_test_instrument_team_data_path(
+                    "glows/imap_glows_pipeline-settings_20100101_v001.json"
+                ),
+                "time_dependent_bckgrd": get_test_instrument_team_data_path(
+                    "glows/imap_glows_time-dep-bckgrd_20100101_v001.dat"
+                ),
+                "extra_heliospheric_bckgrd": get_test_instrument_team_data_path(
+                    "glows/imap_glows_map-of-extra-helio-bckgrd_20100101_v002.dat"
+                ),
+            },
+        )
 
         processor = GlowsProcessor(ProcessingInputCollection(), input_metadata)
 
         l3a_data = processor.process_l3a(dependencies)
 
         glows_l3a_temp_path = TEMP_CDF_FOLDER_PATH / "glows" / "l3a"
-        print(save_data(l3a_data, folder_path=glows_l3a_temp_path, delete_if_present=True))
+        print(
+            save_data(l3a_data, folder_path=glows_l3a_temp_path, delete_if_present=True)
+        )
 
 
 def run_glows_l3bc():
     input_metadata = InputMetadata(
-        instrument='glows',
-        data_level='l3b',
+        instrument="glows",
+        data_level="l3b",
         start_date=datetime(2013, 9, 8),
         end_date=datetime(2013, 9, 8),
-        version='v001')
+        version="v001",
+    )
 
     cr = 2091
     external_files = {
-        'f107_raw_data': get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-        'omni_raw_data': get_test_instrument_team_data_path('glows/omni2_all_years.dat')
+        "f107_raw_data": get_test_instrument_team_data_path("glows/f107_fluxtable.txt"),
+        "omni_raw_data": get_test_instrument_team_data_path(
+            "glows/omni2_all_years.dat"
+        ),
     }
     ancillary_files = {
-        'uv_anisotropy': get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-        'WawHelioIonMP_parameters': get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-        'bad_days_list': get_test_data_path('glows/imap_glows_bad-days-list_v001.dat'),
-        'pipeline_settings': get_test_instrument_team_data_path(
-            'glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json')
+        "uv_anisotropy": get_test_data_path(
+            "glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"
+        ),
+        "WawHelioIonMP_parameters": get_test_data_path(
+            "glows/imap_glows_WawHelioIonMP_20100101_v002.json"
+        ),
+        "bad_days_list": get_test_data_path("glows/imap_glows_bad-days-list_v001.dat"),
+        "pipeline_settings": get_test_instrument_team_data_path(
+            "glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json"
+        ),
     }
-    l3a_data_folder_path = get_test_data_path('glows/l3a_products')
+    l3a_data_folder_path = get_test_data_path("glows/l3a_products")
     l3a_data = []
-    l3a_file_names = [f"imap_glows_l3a_hist_201004{x}-repoint{x + 90:05d}_v012.cdf" for x in range(22, 31)]
-    l3a_file_names += [f"imap_glows_l3a_hist_201005{x:02d}-repoint{x + 120:05d}_v012.cdf" for x in range(1, 19)]
+    l3a_file_names = [
+        f"imap_glows_l3a_hist_201004{x}-repoint{x + 90:05d}_v012.cdf"
+        for x in range(22, 31)
+    ]
+    l3a_file_names += [
+        f"imap_glows_l3a_hist_201005{x:02d}-repoint{x + 120:05d}_v012.cdf"
+        for x in range(1, 19)
+    ]
 
     for name in l3a_file_names:
-        l3a_data.append(create_glows_l3a_dictionary_from_cdf(l3a_data_folder_path / name))
+        l3a_data.append(
+            create_glows_l3a_dictionary_from_cdf(l3a_data_folder_path / name)
+        )
 
-    dependencies = GlowsL3BCDependencies(l3a_data=l3a_data, external_files=external_files,
-                                         ancillary_files=ancillary_files, carrington_rotation_number=cr,
-                                         start_date=datetime(2009, 12, 20), end_date=datetime(2009, 12, 21),
-                                         zip_file_path=Path("fake/path/to/file.zip"))
+    dependencies = GlowsL3BCDependencies(
+        l3a_data=l3a_data,
+        external_files=external_files,
+        ancillary_files=ancillary_files,
+        carrington_rotation_number=cr,
+        start_date=datetime(2009, 12, 20),
+        end_date=datetime(2009, 12, 21),
+        zip_file_path=Path("fake/path/to/file.zip"),
+    )
 
     processor = GlowsProcessor(Mock(), input_metadata)
 
@@ -803,104 +1123,136 @@ def run_glows_l3bc():
     print(save_data(l3c_data_product, delete_if_present=True))
 
 
-@patch('imap_l3_processing.glows.glows_processor.shutil')
+@patch("imap_l3_processing.glows.glows_processor.shutil")
 def run_glows_l3d(mock_shutil):
     mock_shutil.move = shutil.copy
 
     input_metadata = InputMetadata(
-        instrument='glows',
-        data_level='l3d',
+        instrument="glows",
+        data_level="l3d",
         start_date=datetime(2013, 9, 8),
         end_date=datetime(2013, 9, 8),
-        version='v001',
-        descriptor='solar-hist'
+        version="v001",
+        descriptor="solar-hist",
     )
 
     external_files = {
-        'lya_raw_data': get_test_data_path('glows/lyman_alpha_composite.nc'),
+        "lya_raw_data": get_test_data_path("glows/lyman_alpha_composite.nc"),
     }
 
     ancillary_files = {
-        'WawHelioIon': {
-            'speed': get_test_instrument_team_data_path('glows/imap_glows_plasma-speed-2010a_v003.dat'),
-            'p-dens': get_test_instrument_team_data_path('glows/imap_glows_proton-density-2010a_v003.dat'),
-            'uv-anis': get_test_instrument_team_data_path('glows/imap_glows_uv-anisotropy-2010a_v003.dat'),
-            'phion': get_test_instrument_team_data_path('glows/imap_glows_photoion-2010a_v003.dat'),
-            'lya': get_test_instrument_team_data_path('glows/imap_glows_lya-2010a_v003.dat'),
-            'e-dens': get_test_instrument_team_data_path('glows/imap_glows_electron-density-2010a_v003.dat'),
+        "WawHelioIon": {
+            "speed": get_test_instrument_team_data_path(
+                "glows/imap_glows_plasma-speed-2010a_v003.dat"
+            ),
+            "p-dens": get_test_instrument_team_data_path(
+                "glows/imap_glows_proton-density-2010a_v003.dat"
+            ),
+            "uv-anis": get_test_instrument_team_data_path(
+                "glows/imap_glows_uv-anisotropy-2010a_v003.dat"
+            ),
+            "phion": get_test_instrument_team_data_path(
+                "glows/imap_glows_photoion-2010a_v003.dat"
+            ),
+            "lya": get_test_instrument_team_data_path(
+                "glows/imap_glows_lya-2010a_v003.dat"
+            ),
+            "e-dens": get_test_instrument_team_data_path(
+                "glows/imap_glows_electron-density-2010a_v003.dat"
+            ),
         },
-        'pipeline_settings': get_test_data_path(
-            'glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json')
+        "pipeline_settings": get_test_data_path(
+            "glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+        ),
     }
 
     l3b_file_paths = [
-        get_test_data_path('glows/pipeline/l3b/imap_glows_l3b_ion-rate-profile_20100103_v001.cdf'),
-        get_test_data_path('glows/pipeline/l3b/imap_glows_l3b_ion-rate-profile_20100519_v001.cdf')
+        get_test_data_path(
+            "glows/pipeline/l3b/imap_glows_l3b_ion-rate-profile_20100103_v001.cdf"
+        ),
+        get_test_data_path(
+            "glows/pipeline/l3b/imap_glows_l3b_ion-rate-profile_20100519_v001.cdf"
+        ),
     ]
 
     l3c_file_paths = [
-        get_test_data_path('glows/pipeline/l3c/imap_glows_l3c_sw-profile_20100103_v001.cdf'),
-        get_test_data_path('glows/pipeline/l3c/imap_glows_l3c_sw-profile_20100519_v001.cdf'),
+        get_test_data_path(
+            "glows/pipeline/l3c/imap_glows_l3c_sw-profile_20100103_v001.cdf"
+        ),
+        get_test_data_path(
+            "glows/pipeline/l3c/imap_glows_l3c_sw-profile_20100519_v001.cdf"
+        ),
     ]
 
-    l3d_dependencies: GlowsL3DDependencies = GlowsL3DDependencies(external_files=external_files,
-                                                                  ancillary_files=ancillary_files,
-                                                                  l3b_file_paths=l3b_file_paths,
-                                                                  l3c_file_paths=l3c_file_paths)
+    l3d_dependencies: GlowsL3DDependencies = GlowsL3DDependencies(
+        external_files=external_files,
+        ancillary_files=ancillary_files,
+        l3b_file_paths=l3b_file_paths,
+        l3c_file_paths=l3c_file_paths,
+    )
 
     processor = GlowsProcessor(ProcessingInputCollection(), input_metadata)
-    data_product, l3d_txt_paths, last_processed_cr = processor.process_l3d(l3d_dependencies)
+    data_product, l3d_txt_paths, last_processed_cr = processor.process_l3d(
+        l3d_dependencies
+    )
     print("l3d_txts:")
     [print(txt_path) for txt_path in l3d_txt_paths]
     print(save_data(data_product, cr_number=last_processed_cr))
 
 
-def create_empty_hi_l1c_dataset(epoch: datetime, exposures: Optional[np.ndarray] = None,
-                                spin_angles: Optional[np.ndarray] = None,
-                                energies: Optional[np.ndarray] = None):
+def create_empty_hi_l1c_dataset(
+    epoch: datetime,
+    exposures: np.ndarray | None = None,
+    spin_angles: np.ndarray | None = None,
+    energies: np.ndarray | None = None,
+):
     energies = energies if energies is not None else np.geomspace(1, 10000, 9)
-    spin_angles = spin_angles if spin_angles is not None else np.arange(0, 360, 0.1) + 0.05
-    exposures = exposures if exposures is not None else np.ones(shape=(1, len(energies), len(spin_angles)))
+    spin_angles = (
+        spin_angles if spin_angles is not None else np.arange(0, 360, 0.1) + 0.05
+    )
+    exposures = (
+        exposures
+        if exposures is not None
+        else np.ones(shape=(1, len(energies), len(spin_angles)))
+    )
 
-    return xr.Dataset({
-        "exposure_times": (
-            [
-                "epoch",
-                "esa_energy_step",
-                "hi_pset_spin_angle_bin"
-            ],
-            exposures
-        ),
-    },
+    return xr.Dataset(
+        {
+            "exposure_times": (
+                ["epoch", "esa_energy_step", "hi_pset_spin_angle_bin"],
+                exposures,
+            ),
+        },
         coords={
             "epoch": [epoch],
             "esa_energy_step": energies,
             "hi_pset_spin_angle_bin": spin_angles,
-        }
+        },
     )
 
 
-def create_empty_glows_l3e_dataset(epoch: datetime, survival_probabilities: np.ndarray,
-                                   spin_angles: Optional[np.ndarray] = None,
-                                   energies: Optional[np.ndarray] = None):
+def create_empty_glows_l3e_dataset(
+    epoch: datetime,
+    survival_probabilities: np.ndarray,
+    spin_angles: np.ndarray | None = None,
+    energies: np.ndarray | None = None,
+):
     energies = energies or np.geomspace(1, 10000, 16)
     spin_angles = spin_angles or np.arange(0, 360, 1) + 0.5
 
-    return xr.Dataset({
-        "probability_of_survival": (
-            [
-                "epoch",
-                "energy",
-                "spin_angle_bin"
-            ],
-            survival_probabilities
-        )
-    },
+    return xr.Dataset(
+        {
+            "probability_of_survival": (
+                ["epoch", "energy", "spin_angle_bin"],
+                survival_probabilities,
+            )
+        },
         coords={
             "epoch": [epoch],
             "energy": energies,
             "spin_angle_bin": spin_angles,
-        })
+        },
+    )
 
 
 EPOCH = TypeVar("EPOCH")
@@ -910,20 +1262,34 @@ def create_empty_glows_l3e_dataset(epoch: datetime, survival_probabilities: np.n
 
 
 def read_glows_survival_probability_data_from_cdf() -> tuple[np.ndarray, np.ndarray]:
-    l3e = CDF(str(get_test_data_path("glows/imap_glows_l3e_survival-probabilities-hi_20250324_v001.cdf")))
-    return l3e["probability_of_survival"][...][:, 0], l3e["probability_of_survival"][...][:, 1]
+    l3e = CDF(
+        str(
+            get_test_data_path(
+                "glows/imap_glows_l3e_survival-probabilities-hi_20250324_v001.cdf"
+            )
+        )
+    )
+    return l3e["probability_of_survival"][...][:, 0], l3e["probability_of_survival"][
+        ...
+    ][:, 1]
 
 
-@patch('imap_l3_processing.hi.hi_processor.HiLoL3SurvivalDependencies.fetch_dependencies')
-def create_hi_l3_survival_corrected_cdf(survival_dependencies: HiLoL3SurvivalDependencies, spacing_degree: int,
-                                        mock_fetch_dependencies) -> Path:
-    input_metadata = InputMetadata(instrument="hi",
-                                   data_level="l3",
-                                   start_date=datetime(2025, 4, 9),
-                                   end_date=datetime(2025, 4, 10),
-                                   version="v001",
-                                   descriptor="h90-ena-h-sf-sp-ram-hae-4deg-6mo",
-                                   )
+@patch(
+    "imap_l3_processing.hi.hi_processor.HiLoL3SurvivalDependencies.fetch_dependencies"
+)
+def create_hi_l3_survival_corrected_cdf(
+    survival_dependencies: HiLoL3SurvivalDependencies,
+    spacing_degree: int,
+    mock_fetch_dependencies,
+) -> Path:
+    input_metadata = InputMetadata(
+        instrument="hi",
+        data_level="l3",
+        start_date=datetime(2025, 4, 9),
+        end_date=datetime(2025, 4, 10),
+        version="v001",
+        descriptor="h90-ena-h-sf-sp-ram-hae-4deg-6mo",
+    )
 
     mock_fetch_dependencies.return_value = survival_dependencies
 
@@ -933,16 +1299,22 @@ def create_hi_l3_survival_corrected_cdf(survival_dependencies: HiLoL3SurvivalDep
     print(processor.process())
 
 
-@patch('imap_l3_processing.lo.lo_processor.HiLoL3SurvivalDependencies.fetch_dependencies')
-def create_lo_l3_survival_corrected_cdf(survival_dependencies: HiLoL3SurvivalDependencies, spacing_degree: int,
-                                        mock_fetch_dependencies):
-    input_metadata = InputMetadata(instrument="lo",
-                                   data_level="l3",
-                                   start_date=datetime(2025, 4, 9),
-                                   end_date=datetime(2025, 4, 10),
-                                   version="v001",
-                                   descriptor=f"l090-ena-h-sf-sp-ram-hae-{spacing_degree}deg-6mo",
-                                   )
+@patch(
+    "imap_l3_processing.lo.lo_processor.HiLoL3SurvivalDependencies.fetch_dependencies"
+)
+def create_lo_l3_survival_corrected_cdf(
+    survival_dependencies: HiLoL3SurvivalDependencies,
+    spacing_degree: int,
+    mock_fetch_dependencies,
+):
+    input_metadata = InputMetadata(
+        instrument="lo",
+        data_level="l3",
+        start_date=datetime(2025, 4, 9),
+        end_date=datetime(2025, 4, 10),
+        version="v001",
+        descriptor=f"l090-ena-h-sf-sp-ram-hae-{spacing_degree}deg-6mo",
+    )
 
     mock_fetch_dependencies.return_value = survival_dependencies
 
@@ -954,43 +1326,54 @@ def create_lo_l3_survival_corrected_cdf(survival_dependencies: HiLoL3SurvivalDep
 
 def create_codice_hi_l3a_direct_events_cdf():
     codice_hi_de_dependencies = CodiceHiL3aDirectEventsDependencies.from_file_paths(
-        tof_lookup_path=get_test_instrument_team_data_path("codice/hi/imap_codice_tof-lookup_20241110_v002.csv"),
+        tof_lookup_path=get_test_instrument_team_data_path(
+            "codice/hi/imap_codice_tof-lookup_20241110_v002.csv"
+        ),
         codice_l2_hi_cdf=get_test_instrument_team_data_path(
-            "codice/hi/imap_codice_l2_hi-direct-events_20241110_v002.cdf"))
+            "codice/hi/imap_codice_l2_hi-direct-events_20241110_v002.cdf"
+        ),
+    )
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3a',
+        instrument="codice",
+        data_level="l3a",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor='hi-direct-events'
+        version="v000",
+        descriptor="hi-direct-events",
     )
 
     codice_lo_processor = CodiceHiProcessor(ProcessingInputCollection(), input_metadata)
-    direct_event_data = codice_lo_processor.process_l3a_direct_event(codice_hi_de_dependencies)
+    direct_event_data = codice_lo_processor.process_l3a_direct_event(
+        codice_hi_de_dependencies
+    )
     cdf_path = save_data(direct_event_data, delete_if_present=True)
     return cdf_path
 
 
 def create_codice_hi_l3b_pitch_angles_cdf():
     codice_hi_pitch_angle_dependencies = CodicePitchAngleDependencies.from_file_paths(
-        mag_file_path=get_test_data_path("mag/imap_mag_l1d_norm-mago_20250814_v003.cdf"),
+        mag_file_path=get_test_data_path(
+            "mag/imap_mag_l1d_norm-mago_20250814_v003.cdf"
+        ),
         codice_l2_sectored_intensities_path=get_test_instrument_team_data_path(
-            "codice/hi/imap_codice_l2_hi-sectored_20250814_v002.cdf")
+            "codice/hi/imap_codice_l2_hi-sectored_20250814_v002.cdf"
+        ),
     )
 
     input_metadata = InputMetadata(
-        instrument='codice',
-        data_level='l3b',
+        instrument="codice",
+        data_level="l3b",
         start_date=datetime(2024, 11, 10),
         end_date=datetime(2025, 1, 2),
-        version='v000',
-        descriptor='hi-pitch-angle'
+        version="v000",
+        descriptor="hi-pitch-angle",
     )
 
     codice_hi_processor = CodiceHiProcessor(ProcessingInputCollection(), input_metadata)
-    pitch_angle_data = codice_hi_processor.process_l3b(codice_hi_pitch_angle_dependencies)
+    pitch_angle_data = codice_hi_processor.process_l3b(
+        codice_hi_pitch_angle_dependencies
+    )
     cdf_path = save_data(pitch_angle_data, delete_if_present=True)
     return cdf_path
 
@@ -1020,18 +1403,38 @@ def create_codice_hi_l3b_pitch_angles_cdf():
     if "swapi" in sys.argv:
         if "l3a" in sys.argv:
             paths = create_swapi_l3a_cdf(
-                str(get_test_data_path("swapi/imap_swapi_energy-gf-pui-lut_20100101_v001.csv")),
-                str(get_test_data_path(
-                    "swapi/imap_swapi_l2_density-of-neutral-helium-lut-text-not-cdf_20241023_v002.cdf")),
-                str(get_test_data_path("swapi/imap_swapi_efficiency-lut_20241020_v002.dat")),
-                str(get_test_data_path("swapi/imap_swapi_l2_50-sweeps_20250606_v003.cdf")),
+                str(
+                    get_test_data_path(
+                        "swapi/imap_swapi_energy-gf-pui-lut_20100101_v001.csv"
+                    )
+                ),
+                str(
+                    get_test_data_path(
+                        "swapi/imap_swapi_l2_density-of-neutral-helium-lut-text-not-cdf_20241023_v002.cdf"
+                    )
+                ),
+                str(
+                    get_test_data_path(
+                        "swapi/imap_swapi_efficiency-lut_20241020_v002.dat"
+                    )
+                ),
+                str(
+                    get_test_data_path(
+                        "swapi/imap_swapi_l2_50-sweeps_20250606_v003.cdf"
+                    )
+                ),
             )
             print(paths)
         if "l3b" in sys.argv:
             path = create_swapi_l3b_cdf(
                 "tests/test_data/swapi/imap_swapi_energy-gf-sw-lut_20100101_v001.csv",
                 "tests/test_data/swapi/imap_swapi_efficiency-lut_20241020_v002.dat",
-                str(get_test_data_path("swapi/imap_swapi_l2_50-sweeps_20250606_v003.cdf")))
+                str(
+                    get_test_data_path(
+                        "swapi/imap_swapi_l2_50-sweeps_20250606_v003.cdf"
+                    )
+                ),
+            )
             print(path)
     if "glows" in sys.argv:
         if "pre-b" in sys.argv:
@@ -1058,11 +1461,15 @@ def create_codice_hi_l3b_pitch_angles_cdf():
             print(f"hit direct event data product: {path}")
         else:
             mag_data = read_mag_data(
-                r"C:\Users\Petty\Downloads\HIT Validation-20250922T131655Z-1-001\HIT Validation\imap_mag_l1d_norm-dsrf_20100106_v001.cdf")
+                r"C:\Users\Petty\Downloads\HIT Validation-20250922T131655Z-1-001\HIT Validation\imap_mag_l1d_norm-dsrf_20100106_v001.cdf"
+            )
             hit_data = read_l2_hit_data(
-                r"C:\Users\Petty\Downloads\HIT Validation-20250922T131655Z-1-001\HIT Validation\imap_hit_l2_macropixel-intensity_20100106_v001.cdf")
+                r"C:\Users\Petty\Downloads\HIT Validation-20250922T131655Z-1-001\HIT Validation\imap_hit_l2_macropixel-intensity_20100106_v001.cdf"
+            )
             dependencies = HITL3SectoredDependencies(mag_data=mag_data, data=hit_data)
-            print(f"hit macropixel data product: {create_hit_sectored_cdf(dependencies)}")
+            print(
+                f"hit macropixel data product: {create_hit_sectored_cdf(dependencies)}"
+            )
 
     if "swe" in sys.argv:
         dependencies = SweL3Dependencies.from_file_paths(
@@ -1085,21 +1492,30 @@ def create_codice_hi_l3b_pitch_angles_cdf():
         print(create_swe_product_with_fake_spice(dependencies))
 
     if "hi" in sys.argv:
-        hi_targets = ["survival-probability", "spectral-index", "full-spin", "combined-sensors"]
+        hi_targets = [
+            "survival-probability",
+            "spectral-index",
+            "full-spin",
+            "combined-sensors",
+        ]
         do_all = not np.any([t in sys.argv for t in hi_targets])
 
         glows_l3e_folder = get_test_data_path("hi/fake_l3e_survival_probabilities/90")
         glows_l3_paths = list(glows_l3e_folder.iterdir())
-        glows_l3_paths = glows_l3_paths[:len(glows_l3_paths) // 4]
+        glows_l3_paths = glows_l3_paths[: len(glows_l3_paths) // 4]
 
         l2_antiram_90_map_path = get_test_data_path(
-            "hi/validation/l2_maps/imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-1yr_20250415_v001.cdf")
+            "hi/validation/l2_maps/imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-1yr_20250415_v001.cdf"
+        )
         l2_ram_90_map_path = get_test_data_path(
-            "hi/validation/l2_maps/imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf")
+            "hi/validation/l2_maps/imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf"
+        )
         l2_antiram_45_map_path = get_test_data_path(
-            "hi/validation/l2_maps/imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-1yr_20250415_v001.cdf")
+            "hi/validation/l2_maps/imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-1yr_20250415_v001.cdf"
+        )
         l2_ram_45_map_path = get_test_data_path(
-            "hi/validation/l2_maps/imap_hi_l2_h45-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf")
+            "hi/validation/l2_maps/imap_hi_l2_h45-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf"
+        )
 
         l1c_paths = list((get_imap_data_dir_path() / "hi" / "l1c").rglob("*.cdf"))
 
@@ -1108,7 +1524,8 @@ def create_codice_hi_l3b_pitch_angles_cdf():
                 map_file_path=l2_ram_90_map_path,
                 l1c_paths=l1c_paths,
                 glows_l3e_paths=glows_l3_paths,
-                l2_descriptor="h90-ena-h-sf-nsp-ram-hae-4deg-1yr")
+                l2_descriptor="h90-ena-h-sf-nsp-ram-hae-4deg-1yr",
+            )
             create_hi_l3_survival_corrected_cdf(survival_dependencies, 4)
 
         if do_all or "spectral-index" in sys.argv:
@@ -1122,42 +1539,50 @@ def create_codice_hi_l3b_pitch_angles_cdf():
                 map_file_path=l2_ram_90_map_path,
                 l1c_paths=l1c_paths,
                 glows_l3e_paths=glows_l3_paths,
-                l2_descriptor="h90-ena-h-sf-nsp-ram-hae-4deg-6mo")
+                l2_descriptor="h90-ena-h-sf-nsp-ram-hae-4deg-6mo",
+            )
 
             antiram_survival_dependencies = HiLoL3SurvivalDependencies.from_file_paths(
                 map_file_path=l2_antiram_90_map_path,
                 l1c_paths=l1c_paths,
                 glows_l3e_paths=glows_l3_paths,
-                l2_descriptor="h90-ena-h-sf-nsp-anti-hae-4deg-6mo")
+                l2_descriptor="h90-ena-h-sf-nsp-anti-hae-4deg-6mo",
+            )
 
             full_spin_dependencies = HiL3SingleSensorFullSpinDependencies(
                 ram_dependencies=ram_survival_dependencies,
-                antiram_dependencies=antiram_survival_dependencies
+                antiram_dependencies=antiram_survival_dependencies,
             )
 
             print(create_survival_corrected_full_spin_cdf(full_spin_dependencies))
 
     if "lo" in sys.argv:
-        lo_targets = ['spectral-index', 'survival_probabilities']
+        lo_targets = ["spectral-index", "survival_probabilities"]
         do_all = not np.any([t in sys.argv for t in lo_targets])
 
         if do_all or "spectral-index" in sys.argv:
             dependencies = LoL3SpectralFitDependencies(
-                RectangularIntensityMapData.read_from_path(Path(
-                    '/data/imap/lo/l3/2025/04/imap_lo_l3_l090-ena-h-sf-sp-ram-hae-4deg-6mo_20250415_v002.cdf'))
+                RectangularIntensityMapData.read_from_path(
+                    Path(
+                        "/data/imap/lo/l3/2025/04/imap_lo_l3_l090-ena-h-sf-sp-ram-hae-4deg-6mo_20250415_v002.cdf"
+                    )
+                )
             )
 
             print(create_lo_spectral_index_cdf(dependencies))
 
         if do_all or "survival_probabilities" in sys.argv:
-
-            glows_l3e_folder = get_test_data_path("hi/fake_l3e_survival_probabilities/90")
+            glows_l3e_folder = get_test_data_path(
+                "hi/fake_l3e_survival_probabilities/90"
+            )
             glows_l3_paths = list(glows_l3e_folder.iterdir())
 
-            missing_paths, run_local_paths = try_get_many_run_local_paths([
-                "hi/full_spin_deps/l1c",
-                "hi/full_spin_deps/imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v001.cdf",
-            ])
+            missing_paths, run_local_paths = try_get_many_run_local_paths(
+                [
+                    "hi/full_spin_deps/l1c",
+                    "hi/full_spin_deps/imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v001.cdf",
+                ]
+            )
 
             if missing_paths:
                 create_hi_full_spin_deps(sensor="90")
@@ -1170,7 +1595,8 @@ def create_codice_hi_l3b_pitch_angles_cdf():
                     map_file_path=l2_ram_90_map_path,
                     l1c_paths=l1c_paths,
                     glows_l3e_paths=glows_l3_paths,
-                    l2_descriptor="l090-ena-h-sf-nsp-ram-hae-4deg-6mo")
+                    l2_descriptor="l090-ena-h-sf-nsp-ram-hae-4deg-6mo",
+                )
                 create_lo_l3_survival_corrected_cdf(survival_dependencies, 4)
 
     if "ultra" in sys.argv:
@@ -1182,54 +1608,77 @@ def create_codice_hi_l3b_pitch_angles_cdf():
                 end_date=datetime(year=2025, month=9, day=1),
                 data_level="l3",
                 version="v001",
-                descriptor=f"u90-ena-h-sf-sp-full-hae-{spacing_degree}deg-6mo"
+                descriptor=f"u90-ena-h-sf-sp-full-hae-{spacing_degree}deg-6mo",
+            )
+            processor = UltraProcessor(
+                input_metadata=processor_input_metadata, dependencies=Mock()
             )
-            processor = UltraProcessor(input_metadata=processor_input_metadata, dependencies=Mock())
 
             # @formatter:off
-            missing_map_and_pset_paths, [l2_map_path, *l1c_dependency_paths] = try_get_many_run_local_paths([
-                "ultra/20250415-20250419/imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-6mo_20250415_v001.cdf",
-                "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250415-repoint00001_v001.cdf",
-                "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00002_v010.cdf",
-                "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250417-repoint00003_v010.cdf",
-                "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250418-repoint00004_v010.cdf",
-            ])
+            missing_map_and_pset_paths, [l2_map_path, *l1c_dependency_paths] = (
+                try_get_many_run_local_paths(
+                    [
+                        "ultra/20250415-20250419/imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-6mo_20250415_v001.cdf",
+                        "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250415-repoint00001_v001.cdf",
+                        "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00002_v010.cdf",
+                        "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250417-repoint00003_v010.cdf",
+                        "ultra/20250415-20250419/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_20250418-repoint00004_v010.cdf",
+                    ]
+                )
+            )
             # @formatter:on
 
-            missing_glows_paths, [*l3e_glows_paths] = try_get_many_run_local_paths([
-                "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250415_v010.cdf",
-                "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250416_v010.cdf",
-                "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250417_v010.cdf",
-                "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250418_v010.cdf",
-            ])
+            missing_glows_paths, [*l3e_glows_paths] = try_get_many_run_local_paths(
+                [
+                    "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250415_v010.cdf",
+                    "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250416_v010.cdf",
+                    "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250417_v010.cdf",
+                    "ultra/20250415-20250419/glows_l3e/imap_glows_l3e_survival-probability-ultra_20250418_v010.cdf",
+                ]
+            )
             if missing_glows_paths or missing_map_and_pset_paths:
-                create_l1c_and_glows_with_matching_date_range(datetime(2025, 4, 15, 12), datetime(2025, 4, 19, 12))
-
-            l1c_dependency = [UltraL1CPSet.read_from_path(l1c_dependency_path) for l1c_dependency_path in
-                              l1c_dependency_paths]
-            l3e_dependencies = [UltraGlowsL3eData.read_from_path(path) for path in l3e_glows_paths]
+                create_l1c_and_glows_with_matching_date_range(
+                    datetime(2025, 4, 15, 12), datetime(2025, 4, 19, 12)
+                )
+
+            l1c_dependency = [
+                UltraL1CPSet.read_from_path(l1c_dependency_path)
+                for l1c_dependency_path in l1c_dependency_paths
+            ]
+            l3e_dependencies = [
+                UltraGlowsL3eData.read_from_path(path) for path in l3e_glows_paths
+            ]
 
             dependencies = UltraL3Dependencies.from_file_paths(
-                l2_map_path,
-                l1c_dependency_paths,
-                l3e_glows_paths
+                l2_map_path, l1c_dependency_paths, l3e_glows_paths
             )
 
-            healpix_sp_corrected_data = correct_healpix_data_for_survival_probability(deps=dependencies,
-                                                                                spice_frame_name=SpiceFrame.IMAP_HAE)
-            rectangular_sp_data_product = processor._process_healpix_intensity_to_rectangular(healpix_sp_corrected_data,
-                                                                                              spacing_degree,
-                                                                                              spice_frame_name=SpiceFrame.IMAP_HAE)
+            healpix_sp_corrected_data = correct_healpix_data_for_survival_probability(
+                deps=dependencies, spice_frame_name=SpiceFrame.IMAP_HAE
+            )
+            rectangular_sp_data_product = (
+                processor._process_healpix_intensity_to_rectangular(
+                    healpix_sp_corrected_data,
+                    spacing_degree,
+                    spice_frame_name=SpiceFrame.IMAP_HAE,
+                )
+            )
 
-            rectangular_sp_corrected_path = save_data(rectangular_sp_data_product, delete_if_present=True)
+            rectangular_sp_corrected_path = save_data(
+                rectangular_sp_data_product, delete_if_present=True
+            )
             print(rectangular_sp_corrected_path)
 
         if "spectral-index" in sys.argv:
             spacing_degree = 2
 
-            ultra_l3_path = get_test_data_path('ultra/fake_ultra_map_data.cdf')
-            fit_energy_ranges_path = get_test_data_path('ultra/imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat')
-            dependencies = UltraL3SpectralIndexDependencies.from_file_paths(ultra_l3_path, fit_energy_ranges_path)
+            ultra_l3_path = get_test_data_path("ultra/fake_ultra_map_data.cdf")
+            fit_energy_ranges_path = get_test_data_path(
+                "ultra/imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"
+            )
+            dependencies = UltraL3SpectralIndexDependencies.from_file_paths(
+                ultra_l3_path, fit_energy_ranges_path
+            )
 
             processor_input_metadata = InputMetadata(
                 instrument="ultra",
@@ -1237,12 +1686,16 @@ def create_codice_hi_l3b_pitch_angles_cdf():
                 end_date=datetime(year=2025, month=9, day=1),
                 data_level="l3",
                 version="v001",
-                descriptor=f"u90-spx-h-sf-sp-full-hae-{spacing_degree}deg-6mo"
+                descriptor=f"u90-spx-h-sf-sp-full-hae-{spacing_degree}deg-6mo",
             )
 
-            processor = UltraProcessor(input_metadata=processor_input_metadata, dependencies=None)
+            processor = UltraProcessor(
+                input_metadata=processor_input_metadata, dependencies=None
+            )
 
             spectral_index_map_data = processor._process_spectral_index(dependencies)
-            output = processor._process_healpix_spectral_index_to_rectangular(spectral_index_map_data, spacing_degree)
+            output = processor._process_healpix_spectral_index_to_rectangular(
+                spectral_index_map_data, spacing_degree
+            )
 
             print(save_data(output, True))
diff --git a/run_local_with_upload_and_download.py b/run_local_with_upload_and_download.py
index 4327fec09..6bbbb8301 100644
--- a/run_local_with_upload_and_download.py
+++ b/run_local_with_upload_and_download.py
@@ -11,243 +11,545 @@
     match target, subtarget:
         case "hi", "combine-l2":
             version = "v004"
-            subprocess.run([sys.executable,
-                            'imap_l3_data_processor.py', '--instrument', 'hi', '--data-level', 'l3', '--start-date',
-                            '20250415', '--version', version, '--descriptor', 'hic-ena-h-hf-nsp-full-hae-4deg-1yr',
-                            '--dependency',
-                            f"""[{{"type": "science", "files": ["imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-1yr_20250415_{version}.cdf"]}},
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "hi",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20250415",
+                    "--version",
+                    version,
+                    "--descriptor",
+                    "hic-ena-h-hf-nsp-full-hae-4deg-1yr",
+                    "--dependency",
+                    f"""[{{"type": "science", "files": ["imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-1yr_20250415_{version}.cdf"]}},
             {{"type": "science", "files": ["imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-1yr_20250415_{version}.cdf"]}},
             {{"type": "science", "files": ["imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-1yr_20250415_{version}.cdf"]}},
-            {{"type": "science", "files": ["imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-1yr_20250415_{version}.cdf"]}}]"""])
+            {{"type": "science", "files": ["imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-1yr_20250415_{version}.cdf"]}}]""",
+                ]
+            )
         case "lo", "spx":
             version = "v902"
-            subprocess.run([sys.executable,
-                            'imap_l3_data_processor.py', '--instrument', 'lo', '--data-level', 'l3', '--start-date',
-                            '20260101', '--version', version, '--descriptor', 'l090-spx-h-sf-sp-ram-hae-6deg-1yr',
-                            '--dependency',
-                            '[{"type": "science", "files": ["imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-1yr_20260101_v001.cdf"]}]'])
-            subprocess.run([sys.executable,
-                            'imap_l3_data_processor.py', '--instrument', 'lo', '--data-level', 'l3', '--start-date',
-                            '20260101', '--version', version, '--descriptor', 'l090-spx-h-sf-nsp-ram-hae-6deg-1yr',
-                            '--dependency',
-                            '[{"type": "science", "files": ["imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-1yr_20260101_v902.cdf"]}]'])
-            subprocess.run([sys.executable,
-                            'imap_l3_data_processor.py', '--instrument', 'lo', '--data-level', 'l3', '--start-date',
-                            '20260101', '--version', version, '--descriptor', 'l090-spxnbs-h-sf-nsp-ram-hae-6deg-1yr',
-                            '--dependency',
-                            '[{"type": "science", "files": ["imap_lo_l2_l090-enanbs-h-sf-nsp-ram-hae-6deg-1yr_20260101_v900.cdf"]}]'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "lo",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20260101",
+                    "--version",
+                    version,
+                    "--descriptor",
+                    "l090-spx-h-sf-sp-ram-hae-6deg-1yr",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-1yr_20260101_v001.cdf"]}]',
+                ]
+            )
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "lo",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20260101",
+                    "--version",
+                    version,
+                    "--descriptor",
+                    "l090-spx-h-sf-nsp-ram-hae-6deg-1yr",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-1yr_20260101_v902.cdf"]}]',
+                ]
+            )
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "lo",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20260101",
+                    "--version",
+                    version,
+                    "--descriptor",
+                    "l090-spxnbs-h-sf-nsp-ram-hae-6deg-1yr",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_lo_l2_l090-enanbs-h-sf-nsp-ram-hae-6deg-1yr_20260101_v900.cdf"]}]',
+                ]
+            )
         case "hi", "spx":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'hi', '--data-level', 'l3',
-                            '--start-date', '20250415', '--version', 'v002', '--descriptor',
-                            'h90-spx-h-hf-sp-full-hae-4deg-6mo', '--dependency',
-                            '[{"type": "science", "files": ["imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v002.cdf"]}]'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "hi",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20250415",
+                    "--version",
+                    "v002",
+                    "--descriptor",
+                    "h90-spx-h-hf-sp-full-hae-4deg-6mo",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v002.cdf"]}]',
+                ]
+            )
         case "hi", "survival":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'hi', '--data-level', 'l3',
-                            '--start-date', '20250415', '--version', 'v002', '--descriptor',
-                            'h90-ena-h-sf-sp-full-hae-4deg-6mo', '--dependency',
-                            '[{"type": "science", "files": ["imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v001.cdf"]},\n{"type": "science", "files": ["imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20250415_v001.cdf"]}]'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "hi",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20250415",
+                    "--version",
+                    "v002",
+                    "--descriptor",
+                    "h90-ena-h-sf-sp-full-hae-4deg-6mo",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v001.cdf"]},\n{"type": "science", "files": ["imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20250415_v001.cdf"]}]',
+                ]
+            )
         case "hit", "direct":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'hit', '--data-level', 'l3',
-                            '--start-date', '20100106', '--version', 'v001', '--descriptor',
-                            'direct-events', '--dependency',
-                            '[{"type": "science", "files": ["imap_hit_l1a_direct-events_20100106_v001.cdf"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-2A-cosine-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-2B-cosine-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-3A-cosine-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-3B-cosine-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-4A-cosine-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-4B-cosine-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_hi-gain-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_lo-gain-lookup_20250203_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_hit-event-type-lookup_20250228_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"]},' +
-                            '{"type": "ancillary", "files": ["imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"]}]'
-                            ])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "hit",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20100106",
+                    "--version",
+                    "v001",
+                    "--descriptor",
+                    "direct-events",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_hit_l1a_direct-events_20100106_v001.cdf"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-2A-cosine-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-2B-cosine-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-3A-cosine-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-3B-cosine-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-4A-cosine-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-4B-cosine-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_hi-gain-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_lo-gain-lookup_20250203_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_hit-event-type-lookup_20250228_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"]},'
+                    + '{"type": "ancillary", "files": ["imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"]}]',
+                ]
+            )
         case "hit", "macropixel":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'hit', '--data-level', 'l3',
-                            '--start-date', '20100106', '--version', 'v001', '--descriptor',
-                            'macropixel', '--dependency',
-                            '[{"type": "science", "files": ["imap_hit_l2_macropixel-intensity_20100106_v001.cdf"]},' +
-                            '{"type": "science", "files": ["imap_mag_l1d_norm-dsrf_20100106_v001.cdf"]}]'
-                            ])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "hit",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20100106",
+                    "--version",
+                    "v001",
+                    "--descriptor",
+                    "macropixel",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_hit_l2_macropixel-intensity_20100106_v001.cdf"]},'
+                    + '{"type": "science", "files": ["imap_mag_l1d_norm-dsrf_20100106_v001.cdf"]}]',
+                ]
+            )
         case "ultra", "survival":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'ultra', '--data-level', 'l3',
-                            '--start-date', '20250415', '--version', 'v010', '--descriptor',
-                            'u90-ena-h-sf-sp-full-hae-4deg-3mo', '--dependency',
-                            '[{"type": "science", "files": ["imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20250415_v010.cdf"]},' +
-                            '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},' +
-                            '{"type": "spice", "files": ["naif0012.tls"]},' +
-                            '{"type": "spice", "files": ["imap_science_0001.tf"]},' +
-                            '{"type": "spice", "files": ["imap_dps_2025_105_2026_105_01.ah.bc"]}]'
-                            ])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "ultra",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20250415",
+                    "--version",
+                    "v010",
+                    "--descriptor",
+                    "u90-ena-h-sf-sp-full-hae-4deg-3mo",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20250415_v010.cdf"]},'
+                    + '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},'
+                    + '{"type": "spice", "files": ["naif0012.tls"]},'
+                    + '{"type": "spice", "files": ["imap_science_0001.tf"]},'
+                    + '{"type": "spice", "files": ["imap_dps_2025_105_2026_105_01.ah.bc"]}]',
+                ]
+            )
         case "ultra", "spx":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'ultra', '--data-level', 'l3',
-                            '--start-date', '20250415', '--version', 'v010', '--descriptor',
-                            'u90-spx-h-sf-sp-full-hae-4deg-3mo', '--dependency',
-                            '['
-                            '{"type": "science", "files": ["imap_ultra_l3_u90-ena-h-sf-sp-full-hae-4deg-3mo_20250415_v010.cdf"]},'
-                            '{"type": "ancillary", "files": ["imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"]}'
-                            ']'
-                            ])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "ultra",
+                    "--data-level",
+                    "l3",
+                    "--start-date",
+                    "20250415",
+                    "--version",
+                    "v010",
+                    "--descriptor",
+                    "u90-spx-h-sf-sp-full-hae-4deg-3mo",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_ultra_l3_u90-ena-h-sf-sp-full-hae-4deg-3mo_20250415_v010.cdf"]},'
+                    '{"type": "ancillary", "files": ["imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"]}'
+                    "]",
+                ]
+            )
         case "glows", "l3b":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'glows', '--data-level', 'l3b',
-                            '--start-date', '20250415', '--version', 'v025', '--dependency',
-                            '[{"type": "science", "files": ["imap_glows_l3a_hist_20250415-repoint01000_v026.cdf"]},'
-                            '{"type": "ancillary", "files":["imap_glows_bad-days-list_20100101_v001.dat"]},'
-                            '{"type": "ancillary", "files":["imap_glows_WawHelioIonMP_20100101_v002.json"]},'
-                            '{"type": "ancillary", "files":["imap_glows_uv-anisotropy-1CR_20100101_v001.json"]},'
-                            '{"type": "ancillary", "files":["imap_glows_pipeline-settings-l3bcde_19470303_v008.json"]},'
-                            '{"type": "repoint", "files":["imap_2026_105_01.repoint.csv"]}]'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "glows",
+                    "--data-level",
+                    "l3b",
+                    "--start-date",
+                    "20250415",
+                    "--version",
+                    "v025",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_glows_l3a_hist_20250415-repoint01000_v026.cdf"]},'
+                    '{"type": "ancillary", "files":["imap_glows_bad-days-list_20100101_v001.dat"]},'
+                    '{"type": "ancillary", "files":["imap_glows_WawHelioIonMP_20100101_v002.json"]},'
+                    '{"type": "ancillary", "files":["imap_glows_uv-anisotropy-1CR_20100101_v001.json"]},'
+                    '{"type": "ancillary", "files":["imap_glows_pipeline-settings-l3bcde_19470303_v008.json"]},'
+                    '{"type": "repoint", "files":["imap_2026_105_01.repoint.csv"]}]',
+                ]
+            )
         case "glows", "l3d":
-            if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3b'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3b')
-            if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3c'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3c')
-            if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3d'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3d')
-            if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt')
+            if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3b"):
+                shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3b")
+            if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3c"):
+                shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3c")
+            if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3d"):
+                shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3d")
+            if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3d_txt"):
+                shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3d_txt")
 
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'glows', '--data-level', 'l3d',
-                            '--start-date', '20100101', '--version', 'v010', '--descriptor',
-                            'solar-hist', '--dependency',
-                            '[{"type": "science", "files": ["imap_glows_l3b_ion-rate-profile_20100422_v013.cdf"]},'
-                            '{"type": "science", "files": ["imap_glows_l3c_sw-profile_20100422_v012.cdf"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_plasma-speed-2010a_20100101_v003.dat"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_proton-density-2010a_20100101_v003.dat"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_uv-anisotropy-2010a_20100101_v003.dat"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_photoion-2010a_20100101_v003.dat"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_electron-density-2010a_20100101_v003.dat"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_lya-2010a_20100101_v003.dat"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_l3b-archive_20100422_v011.zip"]},'
-                            '{"type": "ancillary", "files": ["imap_glows_pipeline-settings-l3bcde_20100101_v006.json"]}]'
-                            ])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "glows",
+                    "--data-level",
+                    "l3d",
+                    "--start-date",
+                    "20100101",
+                    "--version",
+                    "v010",
+                    "--descriptor",
+                    "solar-hist",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_glows_l3b_ion-rate-profile_20100422_v013.cdf"]},'
+                    '{"type": "science", "files": ["imap_glows_l3c_sw-profile_20100422_v012.cdf"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_plasma-speed-2010a_20100101_v003.dat"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_proton-density-2010a_20100101_v003.dat"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_uv-anisotropy-2010a_20100101_v003.dat"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_photoion-2010a_20100101_v003.dat"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_electron-density-2010a_20100101_v003.dat"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_lya-2010a_20100101_v003.dat"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_l3b-archive_20100422_v011.zip"]},'
+                    '{"type": "ancillary", "files": ["imap_glows_pipeline-settings-l3bcde_20100101_v006.json"]}]',
+                ]
+            )
         case "swapi", "proton":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'swapi', '--data-level', 'l3a',
-                            '--descriptor', 'proton-sw', '--start-date', '20250606', '--version', 'v000',
-                            '--dependency',
-                            '[{"type":"science","files":["imap_swapi_l2_sci_20251208_v006.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
-                            '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_azimuthal-transmission_20260425_v001.csv"]},'
-                            '{"type":"ancillary","files":["imap_swapi_central-effective-area_20260425_v001.csv"]},'
-                            '{"type":"ancillary","files":["imap_swapi_passband-fit-coefficients_20260425_v001.csv"]},'
-                            '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},' +
-                            '{"type": "spice", "files": ["naif0012.tls"]},' +
-                            '{"type": "spice", "files": ["imap_science_108.tf"]},' +
-                            '{"type": "spice", "files": ["imap_001.tf"]},' +
-                            '{"type": "spice", "files": ["imap_2025_105_2026_105_01.ah.bc"]},' +
-                            '{"type": "spice", "files": ["imap_dps_2025_105_2026_105_01.ah.bc"]}'
-                            ']'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "swapi",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "proton-sw",
+                    "--start-date",
+                    "20250606",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    '[{"type":"science","files":["imap_swapi_l2_sci_20251208_v006.cdf"]},'
+                    '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]},'
+                    '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
+                    '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]},'
+                    '{"type":"ancillary","files":["imap_swapi_azimuthal-transmission_20260425_v001.csv"]},'
+                    '{"type":"ancillary","files":["imap_swapi_central-effective-area_20260425_v001.csv"]},'
+                    '{"type":"ancillary","files":["imap_swapi_passband-fit-coefficients_20260425_v001.csv"]},'
+                    '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},'
+                    + '{"type": "spice", "files": ["naif0012.tls"]},'
+                    + '{"type": "spice", "files": ["imap_science_108.tf"]},'
+                    + '{"type": "spice", "files": ["imap_001.tf"]},'
+                    + '{"type": "spice", "files": ["imap_2025_105_2026_105_01.ah.bc"]},'
+                    + '{"type": "spice", "files": ["imap_dps_2025_105_2026_105_01.ah.bc"]}'
+                    "]",
+                ]
+            )
         case "swapi", "alpha":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'swapi', '--data-level', 'l3a',
-                            '--descriptor', 'alpha-sw', '--start-date', '20250606', '--version', 'v000',
-                            '--dependency',
-                            '[{"type":"science","files":["imap_swapi_l2_sci_20250606_v001.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
-                            '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]}'
-                            ']'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "swapi",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "alpha-sw",
+                    "--start-date",
+                    "20250606",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    '[{"type":"science","files":["imap_swapi_l2_sci_20250606_v001.cdf"]},'
+                    '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]},'
+                    '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
+                    '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]}'
+                    "]",
+                ]
+            )
         case "swapi", "pui":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'swapi', '--data-level', 'l3a',
-                            '--descriptor', 'pui-he', '--start-date', '20250606', '--version', 'v000',
-                            '--dependency',
-                            '[{"type":"science","files":["imap_swapi_l2_sci_20250606_v001.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
-                            '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]},'
-                            '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},' +
-                            '{"type": "spice", "files": ["naif0012.tls"]},' +
-                            '{"type": "spice", "files": ["imap_science_0001.tf"]},' +
-                            '{"type": "spice", "files": ["imap_001.tf"]},' +
-                            '{"type": "spice", "files": ["imap_2025_105_2026_105_01.ah.bc"]},' +
-                            '{"type": "spice", "files": ["de440.bsp"]},' +
-                            '{"type": "spice", "files": ["imap_recon_20250415_20260415_v01.bsp"]},' +
-                            '{"type": "spice", "files": ["imap_dps_2025_105_2026_105_01.ah.bc"]}'
-                            ']'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "swapi",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "pui-he",
+                    "--start-date",
+                    "20250606",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    '[{"type":"science","files":["imap_swapi_l2_sci_20250606_v001.cdf"]},'
+                    '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]},'
+                    '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
+                    '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]},'
+                    '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},'
+                    + '{"type": "spice", "files": ["naif0012.tls"]},'
+                    + '{"type": "spice", "files": ["imap_science_0001.tf"]},'
+                    + '{"type": "spice", "files": ["imap_001.tf"]},'
+                    + '{"type": "spice", "files": ["imap_2025_105_2026_105_01.ah.bc"]},'
+                    + '{"type": "spice", "files": ["de440.bsp"]},'
+                    + '{"type": "spice", "files": ["imap_recon_20250415_20260415_v01.bsp"]},'
+                    + '{"type": "spice", "files": ["imap_dps_2025_105_2026_105_01.ah.bc"]}'
+                    "]",
+                ]
+            )
         case "swapi", "l3b":
-            subprocess.run([sys.executable, 'imap_l3_data_processor.py', '--instrument', 'swapi', '--data-level', 'l3b',
-                            '--start-date', '20250606', '--version', 'v003', '--dependency',
-                            '[{"type": "science", "files": ["imap_swapi_l2_sci_20250606_v001.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_energy-gf-sw-lut_20100101_v001.csv"]},'
-                            '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]}]'])
+            subprocess.run(
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "swapi",
+                    "--data-level",
+                    "l3b",
+                    "--start-date",
+                    "20250606",
+                    "--version",
+                    "v003",
+                    "--dependency",
+                    '[{"type": "science", "files": ["imap_swapi_l2_sci_20250606_v001.cdf"]},'
+                    '{"type":"ancillary","files":["imap_swapi_energy-gf-sw-lut_20100101_v001.csv"]},'
+                    '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v002.dat"]}]',
+                ]
+            )
         case "codice", "lo-direct-events":
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3a',
-                 '--descriptor', 'lo-direct-events', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"]},'
-                 '{"type": "science", "files": ["imap_codice_l1a_lo-nsw-priority_20241110_v003.cdf"]},'
-                 '{"type": "science", "files": ["imap_codice_l2_lo-direct-events_20241110_v006.cdf"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_mass-coefficient-lookup_20241110_v002.csv"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_lo-energy-per-charge_20241110_v002.csv"]}]'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "lo-direct-events",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"]},'
+                    '{"type": "science", "files": ["imap_codice_l1a_lo-nsw-priority_20241110_v003.cdf"]},'
+                    '{"type": "science", "files": ["imap_codice_l2_lo-direct-events_20241110_v006.cdf"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_mass-coefficient-lookup_20241110_v002.csv"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_lo-energy-per-charge_20241110_v002.csv"]}]',
+                ]
+            )
         case "codice", ("lo-partial-densities" | "partial-densities"):
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3a',
-                 '--descriptor', 'lo-partial-densities', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l2_lo-sw-species_20241110_v002.cdf"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_mass-per-charge_20241110_v002.csv"]}]'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "lo-partial-densities",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l2_lo-sw-species_20241110_v002.cdf"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_mass-per-charge_20241110_v002.csv"]}]',
+                ]
+            )
         case "codice", ("lo-sw-ratios" | "ratios"):
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3a',
-                 '--descriptor', 'lo-sw-ratios', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l3a_lo-partial-densities_20241110_v000.cdf"]}'
-                 ']'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "lo-sw-ratios",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l3a_lo-partial-densities_20241110_v000.cdf"]}'
+                    "]",
+                ]
+            )
         case "codice", ("lo-sw-charge-state-distributions"):
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3a',
-                 '--descriptor', 'lo-sw-charge-state-distributions', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l3a_lo-partial-densities_20241110_v000.cdf"]}'
-                 ']'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "lo-sw-charge-state-distributions",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l3a_lo-partial-densities_20241110_v000.cdf"]}'
+                    "]",
+                ]
+            )
         case "codice", "hi-direct-events":
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3a',
-                 '--descriptor', 'hi-direct-events', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l2_hi-direct-events_20241110_v002.cdf"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_tof-lookup_20241110_v002.csv"]}]'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    "hi-direct-events",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l2_hi-direct-events_20241110_v002.cdf"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_tof-lookup_20241110_v002.csv"]}]',
+                ]
+            )
         case "codice", "hi-pitch-angle":
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3b',
-                 '--descriptor', 'hi-pitch-angle', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l2_hi-sectored_20241110_v002.cdf"]},'
-                 '{"type": "science", "files": ["imap_mag_l1d_norm-dsrf_20250630_v001.cdf"]}'
-                 ']'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3b",
+                    "--descriptor",
+                    "hi-pitch-angle",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l2_hi-sectored_20241110_v002.cdf"]},'
+                    '{"type": "science", "files": ["imap_mag_l1d_norm-dsrf_20250630_v001.cdf"]}'
+                    "]",
+                ]
+            )
         case "codice", descriptor if "3d-distribution" in descriptor:
             species = descriptor.split("-")[1]
-            assert species in ["hplus", "heplus", "heplus2", "oplus6"], NotImplementedError(
-                "Target instrument or product not implemented")
+            assert species in ["hplus", "heplus", "heplus2", "oplus6"], (
+                NotImplementedError("Target instrument or product not implemented")
+            )
 
             subprocess.run(
-                [sys.executable, 'imap_l3_data_processor.py', '--instrument', 'codice', '--data-level', 'l3a',
-                 '--descriptor', f'lo-{species}-3d-distribution', '--start-date', '20241110', '--version', 'v000',
-                 '--dependency',
-                 '['
-                 '{"type": "science", "files": ["imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"]},'
-                 '{"type": "science", "files": ["imap_codice_l1a_lo-nsw-priority_20241110_v003.cdf"]},'
-                 '{"type": "science", "files": ["imap_codice_l3a_lo-direct-events_20241110_v005.cdf"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_lo-geometric-factors_20241110_v001.csv"]},'
-                 '{"type": "ancillary", "files": ["imap_codice_lo-energy-per-charge_20241110_v002.csv"]},'
-                 f'{{"type": "ancillary", "files": ["imap_codice_lo-{species}-efficiency_20241110_v001.csv"]}}'
-                 ']'
-                 ])
+                [
+                    sys.executable,
+                    "imap_l3_data_processor.py",
+                    "--instrument",
+                    "codice",
+                    "--data-level",
+                    "l3a",
+                    "--descriptor",
+                    f"lo-{species}-3d-distribution",
+                    "--start-date",
+                    "20241110",
+                    "--version",
+                    "v000",
+                    "--dependency",
+                    "["
+                    '{"type": "science", "files": ["imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"]},'
+                    '{"type": "science", "files": ["imap_codice_l1a_lo-nsw-priority_20241110_v003.cdf"]},'
+                    '{"type": "science", "files": ["imap_codice_l3a_lo-direct-events_20241110_v005.cdf"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_lo-geometric-factors_20241110_v001.csv"]},'
+                    '{"type": "ancillary", "files": ["imap_codice_lo-energy-per-charge_20241110_v002.csv"]},'
+                    f'{{"type": "ancillary", "files": ["imap_codice_lo-{species}-efficiency_20241110_v001.csv"]}}'
+                    "]",
+                ]
+            )
         case _:
             raise NotImplementedError("Target instrument or product not implemented")
diff --git a/scripts/batch_download_parents.py b/scripts/batch_download_parents.py
index 2a4d8cbc0..d182b8e62 100644
--- a/scripts/batch_download_parents.py
+++ b/scripts/batch_download_parents.py
@@ -9,6 +9,7 @@
 def download_parents(path: Path, output_dir: Path):
     import imap_data_access
     from spacepy.pycdf import CDF
+
     retries = 10
 
     imap_data_access.config["DATA_DIR"] = output_dir
@@ -19,13 +20,12 @@ def download_parents(path: Path, output_dir: Path):
         for file in path.rglob("*.cdf"):
             print(f"opening {path / file}")
             with CDF(str(file)) as cdf:
-                for parent in cdf.attrs['Parents']:
+                for parent in cdf.attrs["Parents"]:
                     files_to_download.add(parent)
 
         print(f"Downloading {len(files_to_download)} files")
 
         for i, file in enumerate(files_to_download):
-
             print(f"[{i + 1}/{len(files_to_download)}] downloading {file}")
             failures = 0
             while failures < retries:
@@ -35,18 +35,20 @@ def download_parents(path: Path, output_dir: Path):
                 except Exception as e:
                     failures += 1
 
-                    print(f"failed to download file: {file} after {failures} attempts, retrying...")
+                    print(
+                        f"failed to download file: {file} after {failures} attempts, retrying..."
+                    )
                     if failures == retries:
                         raise Exception(
-                            f"failed to download file: {file} after {retries} attempts, exiting").with_traceback(
-                            e.__traceback__)
-                    time.sleep(failures ** 2 * 0.5)
+                            f"failed to download file: {file} after {retries} attempts, exiting"
+                        ).with_traceback(e.__traceback__)
+                    time.sleep(failures**2 * 0.5)
 
     except Exception as e:
         print(e)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     dotenv.load_dotenv(".env")
 
     output_dir = Path(os.getcwd()) / "hi_hf_validation_data"
diff --git a/scripts/clean_metadata.py b/scripts/clean_metadata.py
index 29170224b..935f5c142 100644
--- a/scripts/clean_metadata.py
+++ b/scripts/clean_metadata.py
@@ -15,10 +15,10 @@ def fix_fieldnam():
         with open(variable_yaml, mode="r") as file:
             yaml_data = yaml.safe_load(file)
             for key, item in yaml_data.items():
-                if 'NAME' not in item.keys():
+                if "NAME" not in item.keys():
                     continue
-                if "_" in yaml_data[key]['FIELDNAM']:
-                    yaml_data[key]['FIELDNAM'] = key.replace("_", " ").title()
+                if "_" in item["FIELDNAM"]:
+                    yaml_data[key]["FIELDNAM"] = key.replace("_", " ").title()
                 if len(key) > 30:
                     print("key over 30", variable_yaml, key)
 
@@ -29,8 +29,11 @@ def fix_fieldnam():
 def get_variable_yaml_paths() -> list[Path]:
     yaml_path = Path(cdf.__file__).parent / "config"
 
-    return [yaml_path / str(filename) for filename in os.listdir(yaml_path) if
-            "variable_attrs" in str(filename)]
+    return [
+        yaml_path / str(filename)
+        for filename in os.listdir(yaml_path)
+        if "variable_attrs" in str(filename)
+    ]
 
 
 if __name__ == "__main__":
diff --git a/scripts/codice/create_fake_efficiency_ancillary.py b/scripts/codice/create_fake_efficiency_ancillary.py
index f57941520..e8173bde0 100644
--- a/scripts/codice/create_fake_efficiency_ancillary.py
+++ b/scripts/codice/create_fake_efficiency_ancillary.py
@@ -2,27 +2,36 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.constants import CODICE_LO_NUM_AZIMUTH_BINS, CODICE_LO_NUM_ESA_STEPS
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
+from imap_l3_processing.codice.l3.lo.constants import (
+    CODICE_LO_NUM_AZIMUTH_BINS,
+    CODICE_LO_NUM_ESA_STEPS,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import (
+    MassSpeciesBinLookup,
+)
 from tests.test_helpers import get_run_local_data_path, get_test_data_path
 
 HEADER_TEXT = """# Efficiency Factors for the CoDICE-Lo Instrument for each energy (128 ESA steps) and position (24 values), energy is  assumed to be in ESA step order"""
 
 
-def create_efficiency_lookup(species: str, output_dir: Path = get_run_local_data_path("codice")) -> Path:
+def create_efficiency_lookup(
+    species: str, output_dir: Path = get_run_local_data_path("codice")
+) -> Path:
     output_dir.mkdir(parents=True, exist_ok=True)
     output_path = output_dir / f"imap_codice_lo-{species}-efficiency_20241110_v001.csv"
     open(output_path, "a").close()
 
-    efficiency_data = np.ones((CODICE_LO_NUM_ESA_STEPS, CODICE_LO_NUM_AZIMUTH_BINS), dtype=np.float64)
+    efficiency_data = np.ones(
+        (CODICE_LO_NUM_ESA_STEPS, CODICE_LO_NUM_AZIMUTH_BINS), dtype=np.float64
+    )
     np.savetxt(output_path, efficiency_data, delimiter=",", header=HEADER_TEXT)
 
     return output_path
 
 
 if __name__ == "__main__":
-
     mass_species_bin_path = get_test_data_path(
-        'codice/imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv')
+        "codice/imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv"
+    )
     for species in MassSpeciesBinLookup.read_from_csv(mass_species_bin_path).species:
         print(create_efficiency_lookup(species))
diff --git a/scripts/codice/create_more_accurate_l3a_direct_event.py b/scripts/codice/create_more_accurate_l3a_direct_event.py
index 9e2c40bf0..8606db4bc 100644
--- a/scripts/codice/create_more_accurate_l3a_direct_event.py
+++ b/scripts/codice/create_more_accurate_l3a_direct_event.py
@@ -3,18 +3,27 @@
 import numpy as np
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import SpinAngleLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    SpinAngleLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    EnergyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import (
+    MassSpeciesBinLookup,
+)
 from tests.test_helpers import get_test_data_path
 
 
 def create_more_accurate_l3a_direct_events_cdf(template_cdf: Path):
-    mass_species_path = get_test_data_path("codice/imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv")
+    mass_species_path = get_test_data_path(
+        "codice/imap_codice_lo-mass-species-bin-lookup_20241110_v001.csv"
+    )
     mass_species_bin_lookup = MassSpeciesBinLookup.read_from_csv(mass_species_path)
 
     energy_lookup = EnergyLookup.read_from_csv(
-        get_test_data_path('codice/imap_codice_lo-energy-per-charge_20241110_v002.csv'))
+        get_test_data_path("codice/imap_codice_lo-energy-per-charge_20241110_v002.csv")
+    )
     energies = energy_lookup.bin_centers
 
     spin_angle_lut = SpinAngleLookup()
@@ -27,14 +36,21 @@ def create_more_accurate_l3a_direct_events_cdf(template_cdf: Path):
         position = rng.integers(1, 25, size=event_buffer_shape)
         cdf["position"] = position
 
-        cdf["spin_angle"] = rng.choice(spin_angle_lut.bin_centers, size=event_buffer_shape)
+        cdf["spin_angle"] = rng.choice(
+            spin_angle_lut.bin_centers, size=event_buffer_shape
+        )
 
-        species = rng.integers(0, mass_species_bin_lookup.get_num_species(), size=event_buffer_shape)
+        species = rng.integers(
+            0, mass_species_bin_lookup.get_num_species(), size=event_buffer_shape
+        )
 
         mass_ranges = np.array(mass_species_bin_lookup.mass_ranges)
         lower_masses = mass_ranges[species, 0]
         upper_masses = mass_ranges[species, -1]
-        masses = rng.random(event_buffer_shape) * (upper_masses - lower_masses) + lower_masses
+        masses = (
+            rng.random(event_buffer_shape) * (upper_masses - lower_masses)
+            + lower_masses
+        )
 
         mass_per_charge_ranges = np.array(mass_species_bin_lookup.mass_per_charge)
         lower_mpc = mass_per_charge_ranges[species, 0]
@@ -51,4 +67,5 @@ def create_more_accurate_l3a_direct_events_cdf(template_cdf: Path):
 
 if __name__ == "__main__":
     create_more_accurate_l3a_direct_events_cdf(
-        get_test_data_path("codice/imap_codice_l3a_lo-direct-events_20241110_v000.cdf"))
+        get_test_data_path("codice/imap_codice_l3a_lo-direct-events_20241110_v000.cdf")
+    )
diff --git a/scripts/codice/create_more_accurate_l3a_direct_event_input.py b/scripts/codice/create_more_accurate_l3a_direct_event_input.py
index 53196a765..ec4f19ef8 100644
--- a/scripts/codice/create_more_accurate_l3a_direct_event_input.py
+++ b/scripts/codice/create_more_accurate_l3a_direct_event_input.py
@@ -1,17 +1,24 @@
-from datetime import timedelta, datetime
+from datetime import datetime, timedelta
 from pathlib import Path
 
 import numpy as np
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import PositionToElevationLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    PositionToElevationLookup,
+)
 from imap_l3_processing.codice.l3.lo.models import CODICE_LO_L2_NUM_PRIORITIES
 from imap_l3_processing.constants import ONE_SECOND_IN_NANOSECONDS
 from scripts.time_shift_cdf import convert_epoch_time
-from tests.test_helpers import get_run_local_data_path, get_test_instrument_team_data_path
+from tests.test_helpers import (
+    get_run_local_data_path,
+    get_test_instrument_team_data_path,
+)
 
 
-def extend_priority_counts_to_24_spin_angles(template_cdf_path, priority_count_variables: list[str], output_dir):
+def extend_priority_counts_to_24_spin_angles(
+    template_cdf_path, priority_count_variables: list[str], output_dir
+):
     rng = np.random.default_rng()
 
     output_path = output_dir / template_cdf_path.name
@@ -29,8 +36,12 @@ def extend_priority_counts_to_24_spin_angles(template_cdf_path, priority_count_v
                     cdf[var] = template_cdf[var]
                 cdf[var].attrs = template_cdf[var].attrs
 
-            rgfo_half_spin = rng.integers(0, 33, size=template_cdf["rgfo_half_spin"].shape)
-            randomly_fill_value = rng.choice([False, True], size=template_cdf["rgfo_half_spin"].shape)
+            rgfo_half_spin = rng.integers(
+                0, 33, size=template_cdf["rgfo_half_spin"].shape
+            )
+            randomly_fill_value = rng.choice(
+                [False, True], size=template_cdf["rgfo_half_spin"].shape
+            )
 
             cdf["rgfo_half_spin"] = rgfo_half_spin
             # cdf["rgfo_half_spin"] = np.where(randomly_fill_value, cdf["rgfo_half_spin"].attrs["FILLVAL"],
@@ -38,8 +49,9 @@ def extend_priority_counts_to_24_spin_angles(template_cdf_path, priority_count_v
     return output_path
 
 
-def modify_l1a_priority_counts(instrument_team_l1a_nsw_path: Path, instrument_team_l1a_sw_path: Path) -> tuple[
-    Path, Path]:
+def modify_l1a_priority_counts(
+    instrument_team_l1a_nsw_path: Path, instrument_team_l1a_sw_path: Path
+) -> tuple[Path, Path]:
     output_dir = get_run_local_data_path("codice/lo")
     output_dir.mkdir(parents=True, exist_ok=True)
 
@@ -48,8 +60,9 @@ def modify_l1a_priority_counts(instrument_team_l1a_nsw_path: Path, instrument_te
         "p6_hplus_heplusplus",
     ]
 
-    modified_l1a_nsw_path = extend_priority_counts_to_24_spin_angles(instrument_team_l1a_nsw_path,
-                                                                     l1a_nsw_priority_counts_vars, output_dir)
+    modified_l1a_nsw_path = extend_priority_counts_to_24_spin_angles(
+        instrument_team_l1a_nsw_path, l1a_nsw_priority_counts_vars, output_dir
+    )
 
     l1a_sw_priority_counts_vars = [
         "p0_tcrs",
@@ -59,12 +72,11 @@ def modify_l1a_priority_counts(instrument_team_l1a_nsw_path: Path, instrument_te
         "p4_dcrs",
     ]
 
-    modified_l1a_sw_path = extend_priority_counts_to_24_spin_angles(instrument_team_l1a_sw_path,
-                                                                    l1a_sw_priority_counts_vars,
-                                                                    output_dir)
+    modified_l1a_sw_path = extend_priority_counts_to_24_spin_angles(
+        instrument_team_l1a_sw_path, l1a_sw_priority_counts_vars, output_dir
+    )
 
-    return (modified_l1a_nsw_path,
-            modified_l1a_sw_path)
+    return (modified_l1a_nsw_path, modified_l1a_sw_path)
 
 
 def modify_l2_direct_events(instrument_team_l2_path: Path) -> Path:
@@ -81,7 +93,9 @@ def modify_l2_direct_events(instrument_team_l2_path: Path) -> Path:
 
     with CDF(str(instrument_team_l2_path)) as template_cdf:
         with CDF(str(output_path), masterpath="") as cdf:
-            epoch_delta = timedelta(minutes=2).total_seconds() * ONE_SECOND_IN_NANOSECONDS
+            epoch_delta = (
+                timedelta(minutes=2).total_seconds() * ONE_SECOND_IN_NANOSECONDS
+            )
             cdf["epoch_delta_plus"] = np.full(template_cdf["epoch"].shape, epoch_delta)
             cdf["epoch_delta_minus"] = np.full(template_cdf["epoch"].shape, epoch_delta)
 
@@ -89,14 +103,20 @@ def modify_l2_direct_events(instrument_team_l2_path: Path) -> Path:
                 elevation_var = f"p{priority_i}_elevation"
                 position_var = f"p{priority_i}_position"
 
-                cdf[position_var] = rng.integers(1, 25, size=template_cdf[position_var].shape)
+                cdf[position_var] = rng.integers(
+                    1, 25, size=template_cdf[position_var].shape
+                )
                 cdf[position_var].attrs["FILLVAL"] = int_fillval
 
-                cdf[elevation_var] = rng.choice(elevation_lookup.bin_centers, size=template_cdf[position_var].shape)
+                cdf[elevation_var] = rng.choice(
+                    elevation_lookup.bin_centers, size=template_cdf[position_var].shape
+                )
                 cdf[elevation_var].attrs["FILLVAL"] = float_fillval
 
                 for epoch_i in range(cdf[elevation_var].shape[0]):
-                    number_of_events = template_cdf[f"p{priority_i}_num_events"][epoch_i]
+                    number_of_events = template_cdf[f"p{priority_i}_num_events"][
+                        epoch_i
+                    ]
 
                     filled_position = cdf[position_var][...]
                     filled_position[epoch_i, number_of_events:] = int_fillval
@@ -118,20 +138,31 @@ def modify_l2_direct_events(instrument_team_l2_path: Path) -> Path:
     target_datetime = datetime(year=2024, month=11, day=11)
 
     template_l2_de_path = get_test_instrument_team_data_path(
-        "codice/lo/imap_codice_l2_lo-direct-events_20241110_v002.cdf")
+        "codice/lo/imap_codice_l2_lo-direct-events_20241110_v002.cdf"
+    )
     template_l1a_nsw_path = get_test_instrument_team_data_path(
-        'codice/lo/imap_codice_l1a_lo-nsw-priority_20241110_v002.cdf')
+        "codice/lo/imap_codice_l1a_lo-nsw-priority_20241110_v002.cdf"
+    )
     template_l1a_sw_path = get_test_instrument_team_data_path(
-        'codice/lo/imap_codice_l1a_lo-sw-priority_20241110_v002.cdf')
+        "codice/lo/imap_codice_l1a_lo-sw-priority_20241110_v002.cdf"
+    )
 
     modified_l2_path = modify_l2_direct_events(template_l2_de_path)
-    l1a_nsw_path, l1a_sw_path = modify_l1a_priority_counts(template_l1a_nsw_path, template_l1a_sw_path)
+    l1a_nsw_path, l1a_sw_path = modify_l1a_priority_counts(
+        template_l1a_nsw_path, template_l1a_sw_path
+    )
 
     convert_epoch_time(modified_l2_path, target_datetime)
-    modified_l2_path.rename(modified_l2_path.parent / "imap_codice_l2_lo-direct-events_20241111_v000.cdf")
+    modified_l2_path.rename(
+        modified_l2_path.parent / "imap_codice_l2_lo-direct-events_20241111_v000.cdf"
+    )
 
     convert_epoch_time(l1a_nsw_path, target_datetime)
-    l1a_nsw_path.rename(l1a_nsw_path.parent / "imap_codice_l1a_lo-nsw-priority_20241111_v000.cdf")
+    l1a_nsw_path.rename(
+        l1a_nsw_path.parent / "imap_codice_l1a_lo-nsw-priority_20241111_v000.cdf"
+    )
 
     convert_epoch_time(l1a_sw_path, target_datetime)
-    l1a_sw_path.rename(l1a_sw_path.parent / "imap_codice_l1a_lo-sw-priority_20241111_v000.cdf")
+    l1a_sw_path.rename(
+        l1a_sw_path.parent / "imap_codice_l1a_lo-sw-priority_20241111_v000.cdf"
+    )
diff --git a/scripts/compare_l2_map_metadata.py b/scripts/compare_l2_map_metadata.py
index 2b93601ad..3d7019316 100644
--- a/scripts/compare_l2_map_metadata.py
+++ b/scripts/compare_l2_map_metadata.py
@@ -60,5 +60,3 @@
 
     not_included_keys = [key for key in l3_attrs.keys() if key not in l2_attrs.keys()]
     print(var, not_included_keys)
-
-
diff --git a/scripts/confirm_reprocessing.py b/scripts/confirm_reprocessing.py
index 9034f8a85..66bb94813 100644
--- a/scripts/confirm_reprocessing.py
+++ b/scripts/confirm_reprocessing.py
@@ -7,8 +7,11 @@
 
 def report_oldest_file(**query_args):
     records = imap_data_access.query(**query_args, version="latest")
-    oldest = min(records, key=lambda x:x["ingestion_date"])
-    print(f"Oldest file:\n{oldest["ingestion_date"]}     {Path(oldest["file_path"]).name}")
+    oldest = min(records, key=lambda x: x["ingestion_date"])
+    print(
+        f"Oldest file:\n{oldest['ingestion_date']}     {Path(oldest['file_path']).name}"
+    )
+
 
 def get_unprocessed_files(reference_time_str, **query_args):
     reference_time = datetime.fromisoformat(reference_time_str)
@@ -19,6 +22,7 @@ def get_unprocessed_files(reference_time_str, **query_args):
         if datetime.fromisoformat(r["ingestion_date"]) < reference_time
     ]
 
+
 def analyze(reference_time_str, **query_args):
     files = get_unprocessed_files(reference_time_str, **query_args)
     if len(files) == 0:
@@ -27,6 +31,7 @@ def analyze(reference_time_str, **query_args):
         for date, processed_at in files:
             print(f"File {date} was last processed at {processed_at}")
 
+
 parser = argparse.ArgumentParser()
 parser.add_argument(
     "reprocessing_time",
@@ -38,11 +43,10 @@ def analyze(reference_time_str, **query_args):
 parser.add_argument("--data-level")
 parser.add_argument("--descriptor")
 args = parser.parse_args()
-query_args = dict(instrument=args.instrument,
-            descriptor=args.descriptor,
-            data_level=args.data_level)
+query_args = dict(
+    instrument=args.instrument, descriptor=args.descriptor, data_level=args.data_level
+)
 if args.reprocessing_time is not None:
-    analyze(args.reprocessing_time,
-            **query_args)
+    analyze(args.reprocessing_time, **query_args)
 else:
-    report_oldest_file(**query_args)
\ No newline at end of file
+    report_oldest_file(**query_args)
diff --git a/scripts/convert_hit_event_type_to_csv.py b/scripts/convert_hit_event_type_to_csv.py
index 687b36f96..83a82d0ec 100644
--- a/scripts/convert_hit_event_type_to_csv.py
+++ b/scripts/convert_hit_event_type_to_csv.py
@@ -5,5 +5,6 @@
 fullText = []
 table = doc.tables[0]
 with open("imap_hit_l3_hit-event-types-text-not-cdf_v001.cdf", "w") as file:
-    for row in table.rows:
-        file.write(",".join([cell.text for cell in row.cells]) + "\n")
+    file.writelines(
+        ",".join([cell.text for cell in row.cells]) + "\n" for row in table.rows
+    )
diff --git a/scripts/convert_product_definition_to_yaml.py b/scripts/convert_product_definition_to_yaml.py
index 2ac3548e2..c01b728ee 100644
--- a/scripts/convert_product_definition_to_yaml.py
+++ b/scripts/convert_product_definition_to_yaml.py
@@ -6,30 +6,81 @@
 
 
 def convert_csv_to_yaml(file_path):
-    required_for_support_data = ["NAME", "DATA_TYPE", "CATDESC", "VAR_TYPE", "DEPEND_0", "FIELDNAM", "FORMAT", "UNITS",
-                                 "VALIDMIN", "VALIDMAX", "FILLVAL", "RECORD_VARYING"]
+    required_for_support_data = [
+        "NAME",
+        "DATA_TYPE",
+        "CATDESC",
+        "VAR_TYPE",
+        "DEPEND_0",
+        "FIELDNAM",
+        "FORMAT",
+        "UNITS",
+        "VALIDMIN",
+        "VALIDMAX",
+        "FILLVAL",
+        "RECORD_VARYING",
+    ]
 
     required_for_data = required_for_support_data + ["DISPLAY_TYPE"]
 
-    required_for_metadata = ["NAME", "DATA_TYPE", "CATDESC", "VAR_TYPE", "VAR_TYPE", "FIELDNAM", "FORMAT", ]
-
-    non_required_columns = ["SCALE_TYP", "SCAL_PTR", "VAR_NOTES", "TIME_BASE", "TIME_SCALE", "LEAP_SECONDS_INCLUDED",
-                            "ABSOLUTE_ERROR", "AVG_TYPE", "BIN_LOCATION", "DELTA_PLUS_VAR", "DELTA_MINUS_VAR",
-                            "DERIVN", "DICT_KEY", "MONOTON", "SCALEMIN", "SCALEMAX", "REFERENCE_POSITION",
-                            "RELATIVE_ERROR", "RESOLUTION", "SI_CONVERSION", "DEPEND_1", "DEPEND_2", "DEPEND_3",
-                            "VARIABLE_PURPOSE", "UNIT_PTR", "LABL_PTR_1", "LABL_PTR_2", "LABL_PTR_3", "LABLAXIS"]
+    required_for_metadata = [
+        "NAME",
+        "DATA_TYPE",
+        "CATDESC",
+        "VAR_TYPE",
+        "VAR_TYPE",
+        "FIELDNAM",
+        "FORMAT",
+    ]
+
+    non_required_columns = [
+        "SCALE_TYP",
+        "SCAL_PTR",
+        "VAR_NOTES",
+        "TIME_BASE",
+        "TIME_SCALE",
+        "LEAP_SECONDS_INCLUDED",
+        "ABSOLUTE_ERROR",
+        "AVG_TYPE",
+        "BIN_LOCATION",
+        "DELTA_PLUS_VAR",
+        "DELTA_MINUS_VAR",
+        "DERIVN",
+        "DICT_KEY",
+        "MONOTON",
+        "SCALEMIN",
+        "SCALEMAX",
+        "REFERENCE_POSITION",
+        "RELATIVE_ERROR",
+        "RESOLUTION",
+        "SI_CONVERSION",
+        "DEPEND_1",
+        "DEPEND_2",
+        "DEPEND_3",
+        "VARIABLE_PURPOSE",
+        "UNIT_PTR",
+        "LABL_PTR_1",
+        "LABL_PTR_2",
+        "LABL_PTR_3",
+        "LABLAXIS",
+    ]
     yaml_text = ""
     try:
         with open(file_path, "r") as f:
             csvreader = csv.reader(f)
             row_headers = next(csvreader)
-            header_to_index = {header: index for index, header in enumerate(row_headers)}
+            header_to_index = {
+                header: index for index, header in enumerate(row_headers)
+            }
             for row in csvreader:
                 yaml_text += f"{row[0]}:\n"
 
                 if row[header_to_index["NAME"]].lower() == "epoch":
-                    required_variables = [variable for variable in required_for_support_data if
-                                          variable != "DEPEND_0"]
+                    required_variables = [
+                        variable
+                        for variable in required_for_support_data
+                        if variable != "DEPEND_0"
+                    ]
                 elif row[header_to_index["VAR_TYPE"]] == "support_data":
                     required_variables = required_for_support_data
                 elif row[header_to_index["VAR_TYPE"]] == "metadata":
@@ -37,10 +88,15 @@ def convert_csv_to_yaml(file_path):
                 else:
                     required_variables = required_for_data
 
-                if (("RECORD_VARYING" in required_variables or "RECORD_VARYING" in header_to_index)
-                   and row[header_to_index["RECORD_VARYING"]].lower() == "nrv"):
-                    required_variables = [variable for variable in required_variables if
-                                          variable != "DEPEND_0"]
+                if (
+                    "RECORD_VARYING" in required_variables
+                    or "RECORD_VARYING" in header_to_index
+                ) and row[header_to_index["RECORD_VARYING"]].lower() == "nrv":
+                    required_variables = [
+                        variable
+                        for variable in required_variables
+                        if variable != "DEPEND_0"
+                    ]
 
                 for index, rowitem in enumerate(row):
                     if rowitem == "":
@@ -48,10 +104,16 @@ def convert_csv_to_yaml(file_path):
                     else:
                         display_rowitem = rowitem
 
-                    column_is_empty_and_not_required = (rowitem == "" and row_headers[index] not in required_variables)
+                    column_is_empty_and_not_required = (
+                        rowitem == "" and row_headers[index] not in required_variables
+                    )
                     column_not_listed_in_specified_list = row_headers[index] not in (
-                            required_variables + non_required_columns)
-                    if column_is_empty_and_not_required or column_not_listed_in_specified_list:
+                        required_variables + non_required_columns
+                    )
+                    if (
+                        column_is_empty_and_not_required
+                        or column_not_listed_in_specified_list
+                    ):
                         continue
 
                     yaml_text += f"   {row_headers[index]}: {display_rowitem}\n"
@@ -85,7 +147,7 @@ def process_csv():
         "glows-l3e-hi-45": "imap_l3_processing/cdf/data_product_definition_csv/imap_glows_l3e_survival-probability-hi-45_variable_attrs.csv",
         "glows-l3e-hi-90": "imap_l3_processing/cdf/data_product_definition_csv/imap_glows_l3e_survival-probability-hi-90_variable_attrs.csv",
         "glows-l3e-ul": "imap_l3_processing/cdf/data_product_definition_csv/imap_glows_l3e_survival-probability-ul_variable_attrs.csv",
-        "ultra-l3-survival-probability": "imap_l3_processing/cdf/data_product_definition_csv/imap_ultra_l3_variable_attrs.csv"
+        "ultra-l3-survival-probability": "imap_l3_processing/cdf/data_product_definition_csv/imap_ultra_l3_variable_attrs.csv",
     }
     file_path = instrument_to_file_path[args.instrument]
 
@@ -95,8 +157,12 @@ def process_csv():
     print(filename_without_extension)
     yaml_file_name = f"imap_l3_processing/cdf/config/{filename_without_extension}.yaml"
     if os.path.isfile(yaml_file_name):
-        if input(
-                "The Yaml already exists in the config folder! Are you sure you want to overwrite the other pairs work? [Y/n]") != "n":
+        if (
+            input(
+                "The Yaml already exists in the config folder! Are you sure you want to overwrite the other pairs work? [Y/n]"
+            )
+            != "n"
+        ):
             os.remove(yaml_file_name)
 
     with open(yaml_file_name, "w") as yaml_file:
diff --git a/scripts/convert_variable_yaml_to_csv.py b/scripts/convert_variable_yaml_to_csv.py
index 6a0ee3e5b..e453f4b9a 100644
--- a/scripts/convert_variable_yaml_to_csv.py
+++ b/scripts/convert_variable_yaml_to_csv.py
@@ -11,9 +11,17 @@
     with open(yaml_path) as yaml_file:
         yaml_data = yaml.safe_load(yaml_file)
 
-    all_headers = {key: '' for key in
-                   set(chain.from_iterable([yaml_data[variable].keys() for variable in yaml_data.keys()]))}
-    valid_variables = [yaml_data[var] for var in yaml_data.keys() if 'NAME' in yaml_data[var]]
+    all_headers = {
+        key: ""
+        for key in set(
+            chain.from_iterable(
+                [yaml_data[variable].keys() for variable in yaml_data.keys()]
+            )
+        )
+    }
+    valid_variables = [
+        yaml_data[var] for var in yaml_data.keys() if "NAME" in yaml_data[var]
+    ]
     with open(outpath, "w") as out_file:
         writer = csv.DictWriter(out_file, fieldnames=all_headers.keys())
         writer.writeheader()
diff --git a/scripts/fetch_latest_data.py b/scripts/fetch_latest_data.py
index 11bde547a..b3176416c 100644
--- a/scripts/fetch_latest_data.py
+++ b/scripts/fetch_latest_data.py
@@ -10,8 +10,8 @@
 args = parser.parse_args()
 
 data = imap_data_access.query(instrument=args.instrument, data_level=args.level)
-sorted_data = sorted(data, key=lambda r: r['ingestion_date'])
-paths = [f['file_path'] for f in sorted_data[-int(args.count):]]
+sorted_data = sorted(data, key=lambda r: r["ingestion_date"])
+paths = [f["file_path"] for f in sorted_data[-int(args.count) :]]
 for path in paths:
     imap_data_access.download(path)
     print(path)
diff --git a/scripts/generate_hi_yaml.py b/scripts/generate_hi_yaml.py
index 301fe69c5..c9c3856cf 100644
--- a/scripts/generate_hi_yaml.py
+++ b/scripts/generate_hi_yaml.py
@@ -9,7 +9,7 @@
     "hnu": "Heliospheric Nose Upfield (HNU)",
     "gcs": "Galactic (GCS)",
     "rc": "Ribbon-centered (RC)",
-    "ccs": "custom (CCS)"
+    "ccs": "custom (CCS)",
 }
 
 logical_source_description_parts = {
@@ -18,14 +18,14 @@
     "hic": "Hi Combined",
     "ena": "ENA Intensity",
     "spx": "Spectral Index",
-    'h': "Hydrogen",
-    'sf': "Spacecraft Frame",
-    'hf': "Heliospheric Frame",
-    'sp': "Survival Probability-Corrected",
-    'nsp': "Non-Survival Probability-Corrected",
-    'full': "Full Spin",
-    'ram': "Ram",
-    'anti': "Anti-Ram",
+    "h": "Hydrogen",
+    "sf": "Spacecraft Frame",
+    "hf": "Heliospheric Frame",
+    "sp": "Survival Probability-Corrected",
+    "nsp": "Non-Survival Probability-Corrected",
+    "full": "Full Spin",
+    "ram": "Ram",
+    "anti": "Anti-Ram",
     "hae": "HAE",
     "2deg": "Rectangular 2 degree",
     "4deg": "Rectangular 4 degree",
@@ -81,15 +81,28 @@
 ]
 
 for descriptor in descriptors:
-    descriptor_parts = descriptor.split('-')
+    descriptor_parts = descriptor.split("-")
     sensor = descriptor_parts[0][1:]
-    [quantity, species, frame, sp_corrected, spin_range, coordinate_system, pixelation, time_range] = descriptor_parts[
-                                                                                                      1:]
+    [
+        quantity,
+        species,
+        frame,
+        sp_corrected,
+        spin_range,
+        coordinate_system,
+        pixelation,
+        time_range,
+    ] = descriptor_parts[1:]
 
     products = {
         f"imap_{instrument}_l3_{descriptor}": {
-            "Logical_source_description": "IMAP Hi Instrument Level 3 " + ', '.join(
-                [logical_source_description_parts[part] for part in descriptor.split("-")]),
+            "Logical_source_description": "IMAP Hi Instrument Level 3 "
+            + ", ".join(
+                [
+                    logical_source_description_parts[part]
+                    for part in descriptor.split("-")
+                ]
+            ),
             "Data_level": "3",
             "Data_type": f"L3_{time_range}>Level-3 {time_range}",
             "Map_descriptor": descriptor,
@@ -101,7 +114,9 @@
             "Spin_range": logical_source_description_parts[spin_range],
             "Survival_corrected": "True" if sp_corrected == "sp" else "False",
             "Species": species.capitalize(),
-            "Principal_data_quantity": "ENA Intensity" if quantity == "ena" else "ENA Spectral Index",
+            "Principal_data_quantity": "ENA Intensity"
+            if quantity == "ena"
+            else "ENA Spectral Index",
         }
     }
 
diff --git a/scripts/generate_lo_yaml.py b/scripts/generate_lo_yaml.py
index da4d3c349..b3ccae87e 100644
--- a/scripts/generate_lo_yaml.py
+++ b/scripts/generate_lo_yaml.py
@@ -9,7 +9,7 @@
     "hnu": "Heliospheric Nose Upfield (HNU)",
     "gcs": "Galactic (GCS)",
     "rc": "Ribbon-centered (RC)",
-    "ccs": "custom (CCS)"
+    "ccs": "custom (CCS)",
 }
 
 logical_source_description_parts = {
@@ -17,15 +17,15 @@
     "lc": "Lo Combined",
     "ena": "ENA Intensity",
     "spx": "Spectral Index",
-    'h': "Hydrogen",
-    'sf': "Spacecraft Frame",
-    'hf': "Heliospheric Frame",
-    'hk': "Heliospheric Kinematic Frame",
-    'sp': "Survival Probability-Corrected",
-    'nsp': "Non-Survival Probability-Corrected",
-    'full': "Full Spin",
-    'ram': "Ram",
-    'anti': "Anti-Ram",
+    "h": "Hydrogen",
+    "sf": "Spacecraft Frame",
+    "hf": "Heliospheric Frame",
+    "hk": "Heliospheric Kinematic Frame",
+    "sp": "Survival Probability-Corrected",
+    "nsp": "Non-Survival Probability-Corrected",
+    "full": "Full Spin",
+    "ram": "Ram",
+    "anti": "Anti-Ram",
     "hae": "HAE",
     "2deg": "Rectangular 2 degree",
     "4deg": "Rectangular 4 degree",
@@ -45,28 +45,44 @@
 ]
 
 for descriptor in descriptors:
-    descriptor_parts = descriptor.split('-')
+    descriptor_parts = descriptor.split("-")
     sensor = descriptor_parts[0][1:]
-    [quantity, species, frame, sp_corrected, spin_range, coordinate_system, pixelation, time_range] = descriptor_parts[
-                                                                                                      1:]
+    [
+        quantity,
+        species,
+        frame,
+        sp_corrected,
+        spin_range,
+        coordinate_system,
+        pixelation,
+        time_range,
+    ] = descriptor_parts[1:]
 
     products = {
         f"imap_{instrument}_l3_{descriptor}": {
-            "Logical_source_description": "IMAP Lo Instrument Level 3 " + ', '.join(
-                [logical_source_description_parts[part] for part in descriptor.split("-")]),
+            "Logical_source_description": "IMAP Lo Instrument Level 3 "
+            + ", ".join(
+                [
+                    logical_source_description_parts[part]
+                    for part in descriptor.split("-")
+                ]
+            ),
             "Data_level": "3",
             "Data_type": f"L3_{time_range}>Level-3 {time_range}",
             "Map_descriptor": descriptor,
             "Map_duration": time_range,
             "Instrument": f"Lo {sensor}" if sensor in ["45", "90"] else "Lo",
-            "Reference_frame": "Heliospheric Kinematic (HK)" if frame == "hk" else (
-                "Spacecraft" if frame == "sf" else "Heliospheric (CG)"),
+            "Reference_frame": "Heliospheric Kinematic (HK)"
+            if frame == "hk"
+            else ("Spacecraft" if frame == "sf" else "Heliospheric (CG)"),
             "Coordinate_system": coordinate_systems[coordinate_system],
             "Tessellation_type": "lon, lat" if "deg" in pixelation else "pix index",
             "Spin_range": "Ram",
             "Survival_corrected": "True" if sp_corrected == "sp" else "False",
             "Species": species.capitalize(),
-            "Principal_data_quantity": "ENA Intensity" if quantity == "ena" else "ENA Spectral Index",
+            "Principal_data_quantity": "ENA Intensity"
+            if quantity == "ena"
+            else "ENA Spectral Index",
         }
     }
 
diff --git a/scripts/generate_repointing_lines.py b/scripts/generate_repointing_lines.py
index 3b6371a7b..d84a86e25 100644
--- a/scripts/generate_repointing_lines.py
+++ b/scripts/generate_repointing_lines.py
@@ -14,8 +14,12 @@ def add_repointings():
     first_repointing_inclusive = 1000 - 104
 
     for repointing in range(first_repointing_inclusive, last_repointing_exclusive):
-        start_datetime = repoint1000minus104start + (repointing - (first_repointing_inclusive)) * timedelta(days=1)
-        end_datetime = repoint1000minus104end + (repointing - (first_repointing_inclusive)) * timedelta(days=1)
+        start_datetime = repoint1000minus104start + (
+            repointing - (first_repointing_inclusive)
+        ) * timedelta(days=1)
+        end_datetime = repoint1000minus104end + (
+            repointing - (first_repointing_inclusive)
+        ) * timedelta(days=1)
 
         start_sclk = int((start_datetime - spacecraft_clock_start).total_seconds())
         repoint_start_subsec_sclk = 20
@@ -23,8 +27,8 @@ def add_repointings():
         end_sclk = int((end_datetime - spacecraft_clock_start).total_seconds())
         repoint_end_subsec_sclk = 999980
 
-        formatted_start = start_datetime.strftime('%Y-%m-%d %H:%M:%S.%f')
-        formatted_end = end_datetime.strftime('%Y-%m-%d %H:%M:%S.%f')
+        formatted_start = start_datetime.strftime("%Y-%m-%d %H:%M:%S.%f")
+        formatted_end = end_datetime.strftime("%Y-%m-%d %H:%M:%S.%f")
         csv_line = f"{start_sclk},{repoint_start_subsec_sclk},{end_sclk},{repoint_end_subsec_sclk},{formatted_start},{formatted_end},{repointing}"
         print(csv_line)
 
diff --git a/scripts/generate_ultra_yaml.py b/scripts/generate_ultra_yaml.py
index 4b763e96c..773d04440 100644
--- a/scripts/generate_ultra_yaml.py
+++ b/scripts/generate_ultra_yaml.py
@@ -9,7 +9,7 @@
     "hnu": "Heliospheric Nose Upfield (HNU)",
     "gcs": "Galactic (GCS)",
     "rc": "Ribbon-centered (RC)",
-    "ccs": "custom (CCS)"
+    "ccs": "custom (CCS)",
 }
 
 logical_source_description_parts = {
@@ -18,12 +18,12 @@
     "ulc": "Ultra Combined",
     "ena": "ENA Intensity",
     "spx": "Spectral Index",
-    'h': "Hydrogen",
-    'sf': "Spacecraft Frame",
-    'hf': "Heliospheric Frame",
-    'sp': "Survival Probability-Corrected",
-    'nsp': "Non-Survival Probability-Corrected",
-    'full': "Full Spin",
+    "h": "Hydrogen",
+    "sf": "Spacecraft Frame",
+    "hf": "Heliospheric Frame",
+    "sp": "Survival Probability-Corrected",
+    "nsp": "Non-Survival Probability-Corrected",
+    "full": "Full Spin",
     "hae": "HAE",
     "2deg": "Rectangular 2 degree",
     "4deg": "Rectangular 4 degree",
@@ -136,15 +136,28 @@
 ]
 
 for descriptor in descriptors:
-    descriptor_parts = descriptor.split('-')
+    descriptor_parts = descriptor.split("-")
     sensor = descriptor_parts[0][1:]
-    [quantity, species, frame, sp_corrected, spin_range, coordinate_system, pixelation, time_range] = descriptor_parts[
-                                                                                                      1:]
+    [
+        quantity,
+        species,
+        frame,
+        sp_corrected,
+        spin_range,
+        coordinate_system,
+        pixelation,
+        time_range,
+    ] = descriptor_parts[1:]
 
     products = {
         f"imap_{instrument}_l3_{descriptor}": {
-            "Logical_source_description": "IMAP Ultra Instrument Level 3 " + ', '.join(
-                [logical_source_description_parts[part] for part in descriptor.split("-")]),
+            "Logical_source_description": "IMAP Ultra Instrument Level 3 "
+            + ", ".join(
+                [
+                    logical_source_description_parts[part]
+                    for part in descriptor.split("-")
+                ]
+            ),
             "Data_level": "3",
             "Data_type": f"L3_{time_range}>Level-3 {time_range}",
             "Map_descriptor": descriptor,
@@ -156,7 +169,9 @@
             "Spin_range": "Full Spin",
             "Survival_corrected": "True" if sp_corrected == "sp" else "False",
             "Species": species.capitalize(),
-            "Principal_data_quantity": "ENA Intensity" if quantity == "ena" else "ENA Spectral Index",
+            "Principal_data_quantity": "ENA Intensity"
+            if quantity == "ena"
+            else "ENA Spectral Index",
         }
     }
 
diff --git a/scripts/get_batch_jobs_status.py b/scripts/get_batch_jobs_status.py
index 55cbff752..56d8814bf 100644
--- a/scripts/get_batch_jobs_status.py
+++ b/scripts/get_batch_jobs_status.py
@@ -30,35 +30,56 @@ class Command(enum.Enum):
     Query = "query"
     Logs = "logs"
 
+
 arg_parser = ArgumentParser()
 
 subparsers = arg_parser.add_subparsers()
 
-query_parser = subparsers.add_parser('query')
-queryable_fields = ["status", "instrument", "data_level", "descriptor", "start_date", "version", "repointing",
-                            "job_definition", "job_log_stream_id", "container_image", "container_command",
-                            "started_at", "stopped_at"]
+query_parser = subparsers.add_parser("query")
+queryable_fields = [
+    "status",
+    "instrument",
+    "data_level",
+    "descriptor",
+    "start_date",
+    "version",
+    "repointing",
+    "job_definition",
+    "job_log_stream_id",
+    "container_image",
+    "container_command",
+    "started_at",
+    "stopped_at",
+]
 for field in queryable_fields:
     query_parser.add_argument(f"--{field}", type=str)
 
 
-log_parser = subparsers.add_parser('logs')
-log_parser.add_argument('log_id', type=str)
+log_parser = subparsers.add_parser("logs")
+log_parser.add_argument("log_id", type=str)
 
 args = arg_parser.parse_args()
 print(args)
 
 imap_dev_url = os.getenv("IMAP_DATA_ACCESS_URL")
-headers = {'x-api-key': os.getenv("IMAP_API_KEY")}
+headers = {"x-api-key": os.getenv("IMAP_API_KEY")}
 
 if not "log_id" in vars(args):
-    query_params = { key: val for key, val in vars(args).items() if val is not None and key not in ['command']}
-    response = requests.get(url=f"{imap_dev_url}/processing-jobs", headers=headers, params=query_params)
+    query_params = {
+        key: val
+        for key, val in vars(args).items()
+        if val is not None and key not in ["command"]
+    }
+    response = requests.get(
+        url=f"{imap_dev_url}/processing-jobs", headers=headers, params=query_params
+    )
     processing_jobs = response.json()
     records = []
     for processing_job in processing_jobs:
         if processing_job["started_at"] is not None:
-            processing_job["started_at"] = datetime.fromisoformat(processing_job["started_at"]).strftime("%Y-%m-%d %H:%M:%S")
+            processing_job["started_at"] = datetime.fromisoformat(
+                processing_job["started_at"]
+            ).strftime("%Y-%m-%d %H:%M:%S")
 
         relevant_fields = [
             "status",
@@ -68,18 +89,24 @@ class Command(enum.Enum):
             "start_date",
             "version",
             "started_at",
-            "job_log_stream_id"
+            "job_log_stream_id",
         ]
 
-        records.append({key: val for key, val in processing_job.items() if key in relevant_fields})
+        records.append(
+            {key: val for key, val in processing_job.items() if key in relevant_fields}
+        )
 
-    pd.set_option('display.max_colwidth', None)
+    pd.set_option("display.max_colwidth", None)
     if len(records) > 0:
-        print(pd.DataFrame.from_records(records).sort_values('started_at', na_position='last').tail(50))
+        print(
+            pd.DataFrame.from_records(records)
+            .sort_values("started_at", na_position="last")
+            .tail(50)
+        )
     else:
         print("Found no jobs that matched query params!")
 else:
-    logs = requests.get(url=f"{imap_dev_url}/processing-logs/{args.log_id}", headers=headers)
+    logs = requests.get(
+        url=f"{imap_dev_url}/processing-logs/{args.log_id}", headers=headers
+    )
     print(logs.text)
-
-
diff --git a/scripts/glows/create_example_glows_l3e_survival_probabilities_cdf.py b/scripts/glows/create_example_glows_l3e_survival_probabilities_cdf.py
index 5f3852cf8..426ef25a7 100644
--- a/scripts/glows/create_example_glows_l3e_survival_probabilities_cdf.py
+++ b/scripts/glows/create_example_glows_l3e_survival_probabilities_cdf.py
@@ -8,34 +8,48 @@
 from spacepy import pycdf
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.glows.l3e.glows_l3e_hi_model import PROBABILITY_OF_SURVIVAL_VAR_NAME
+from imap_l3_processing.glows.l3e.glows_l3e_hi_model import (
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+)
 from tests.test_helpers import get_test_data_path
 
 
-def create_survival_probabilities_file(glows_file_path: Path, date_for_file: datetime, sensor: str):
+def create_survival_probabilities_file(
+    glows_file_path: Path, date_for_file: datetime, sensor: str
+):
     filename = f"imap_glows_l3e_survival-probabilities-hi-{sensor}_{date_for_file.strftime('%Y%m%d')}_v001.cdf"
-    cdf_file_path = get_test_data_path(f"hi/fake_l3e_survival_probabilities/{sensor}/{filename}")
+    cdf_file_path = get_test_data_path(
+        f"hi/fake_l3e_survival_probabilities/{sensor}/{filename}"
+    )
 
     cdf_file_path.unlink(missing_ok=True)
     with open(glows_file_path) as input_data:
-        energy_line = [line for line in input_data.readlines() if line.startswith("#energy_grid")]
+        energy_line = [line for line in input_data if line.startswith("#energy_grid")]
         assert len(energy_line) == 1
-        energies = np.array([float("".join(list(t))) for t in itertools.batched(energy_line[0][19:-1], 10)])
-        energy_units = re.search(r'\[(.+)]', energy_line[0]).group(1)
+        energies = np.array(
+            [
+                float("".join(list(t)))
+                for t in itertools.batched(energy_line[0][19:-1], 10)
+            ]
+        )
+        energy_units = re.search(r"\[(.+)]", energy_line[0]).group(1)
 
     spin_angle_and_survival_probabilities = np.loadtxt(glows_file_path, skiprows=200)
     spin_angles = spin_angle_and_survival_probabilities[:, 0]
     survival_probabilities = spin_angle_and_survival_probabilities[:, 1:].T
 
-    with CDF(str(cdf_file_path), '') as c:
+    with CDF(str(cdf_file_path), "") as c:
         c.new("epoch", [date_for_file], pycdf.const.CDF_TIME_TT2000)
         c.new("epoch_delta", [12 * 60 * 60 * 1e9], pycdf.const.CDF_INT8)
         c.new("energy_grid", energies, pycdf.const.CDF_FLOAT, recVary=False)
-        c['energy_grid'].attrs["UNITS"] = energy_units
+        c["energy_grid"].attrs["UNITS"] = energy_units
         c.new("spin_angle", spin_angles, pycdf.const.CDF_FLOAT, recVary=False)
-        c.new(PROBABILITY_OF_SURVIVAL_VAR_NAME, np.array(survival_probabilities)[np.newaxis, ...],
-              pycdf.const.CDF_FLOAT,
-              compress=pycdf.const.GZIP_COMPRESSION)
+        c.new(
+            PROBABILITY_OF_SURVIVAL_VAR_NAME,
+            np.array(survival_probabilities)[np.newaxis, ...],
+            pycdf.const.CDF_FLOAT,
+            compress=pycdf.const.GZIP_COMPRESSION,
+        )
 
         c["energy_grid"].attrs["FILLVAL"] = -1e31
         c["spin_angle"].attrs["FILLVAL"] = -1e31
diff --git a/scripts/glows/create_l2_glows_cdf_from_json.py b/scripts/glows/create_l2_glows_cdf_from_json.py
index eab48bd8c..f048c1989 100644
--- a/scripts/glows/create_l2_glows_cdf_from_json.py
+++ b/scripts/glows/create_l2_glows_cdf_from_json.py
@@ -12,8 +12,9 @@
 from tests.test_helpers import get_test_data_path
 
 
-
-def fill_official_l2_cdf_with_json_values(output_folder: Path, input_path: Path) -> None:
+def fill_official_l2_cdf_with_json_values(
+    output_folder: Path, input_path: Path
+) -> None:
     official_l2_path = get_test_data_path(
         "glows/imap_glows_l2_hist_20260323-repoint00194_v005.cdf"
     )
@@ -27,7 +28,7 @@ def fill_official_l2_cdf_with_json_values(output_folder: Path, input_path: Path)
         epoch_window = end_of_epoch_window - start_of_epoch_window
         epoch = start_of_epoch_window + epoch_window / 2
 
-        repoint_id = int(re.search(r'repoint(\d{5})', input_path.name)[1])
+        repoint_id = int(re.search(r"repoint(\d{5})", input_path.name)[1])
         new_name = ScienceFilePath.generate_from_inputs(
             instrument="glows",
             data_level="l2",
@@ -109,8 +110,12 @@ def fill_official_l2_cdf_with_json_values(output_folder: Path, input_path: Path)
                 "number_of_all_l1b_files"
             ]
             cdf.attrs["pkts_file_name"] = instrument_data["header"]["pkts_file_name"]
-            cdf.attrs["ancillary_data_files"] = instrument_data["header"]["ancillary_data_files"]
-            cdf.attrs["flight_software_version"] = instrument_data["header"]["flight_software_version"]
+            cdf.attrs["ancillary_data_files"] = instrument_data["header"][
+                "ancillary_data_files"
+            ]
+            cdf.attrs["flight_software_version"] = instrument_data["header"][
+                "flight_software_version"
+            ]
 
 
 if __name__ == "__main__":
@@ -123,5 +128,5 @@ def fill_official_l2_cdf_with_json_values(output_folder: Path, input_path: Path)
     for file_path in json_directory.iterdir():
         try:
             fill_official_l2_cdf_with_json_values(output_directory, file_path)
-        except Exception as e:
+        except Exception:
             traceback.print_exc()
diff --git a/scripts/glows/create_l3a_glows_cdf_from_json.py b/scripts/glows/create_l3a_glows_cdf_from_json.py
index ca0457268..c9ae59901 100644
--- a/scripts/glows/create_l3a_glows_cdf_from_json.py
+++ b/scripts/glows/create_l3a_glows_cdf_from_json.py
@@ -16,31 +16,34 @@
 for file in files_to_convert:
     with open(Path(input_file_directory) / file, "r") as l3a_json:
         data = json.load(l3a_json)
-    date = file.split('_')[3][:8]
+    date = file.split("_")[3][:8]
     year = int(date[:4])
     month = int(date[4:6])
     day = int(date[6:8])
     start_date = datetime(year, month, day)
     input_metadata = InputMetadata(
-        instrument='glows',
-        data_level='l3a',
+        instrument="glows",
+        data_level="l3a",
         start_date=start_date,
         end_date=start_date + timedelta(days=1),
-        version='v001',
-        descriptor='hist'
+        version="v001",
+        descriptor="hist",
     )
 
     files = {
-        "settings":
-            Path(scripts.__file__).parent.parent /
-            "instrument_team_data/glows/imap_glows_pipeline-settings_20100101_v001.json"
+        "settings": Path(scripts.__file__).parent.parent
+        / "instrument_team_data/glows/imap_glows_pipeline-settings_20100101_v001.json"
     }
     data_with_spin_angle = GlowsProcessor.add_spin_angle_delta(data, files)
 
-    glows_l3a_lightcurve = create_glows_l3a_from_dictionary(data_with_spin_angle,
-                                                            input_metadata)
+    glows_l3a_lightcurve = create_glows_l3a_from_dictionary(
+        data_with_spin_angle, input_metadata
+    )
 
-    cdf_path = save_data(glows_l3a_lightcurve, delete_if_present=True,
-                         folder_path=get_test_data_path("glows/l3a_products"))
+    cdf_path = save_data(
+        glows_l3a_lightcurve,
+        delete_if_present=True,
+        folder_path=get_test_data_path("glows/l3a_products"),
+    )
 
     print("cdf written to", cdf_path)
diff --git a/scripts/glows/download_l2_prod_data.py b/scripts/glows/download_l2_prod_data.py
index 4b05b4b31..590de2e8d 100644
--- a/scripts/glows/download_l2_prod_data.py
+++ b/scripts/glows/download_l2_prod_data.py
@@ -1,4 +1,3 @@
-
 from datetime import datetime
 from pathlib import Path
 
@@ -13,9 +12,13 @@
 from tests.test_helpers import get_run_local_data_path
 
 GLOWS_L3E_REQUIRED_SPICE_KERNELS: list[SpiceKernelTypes] = [
-    SpiceKernelTypes.ScienceFrames, SpiceKernelTypes.EphemerisReconstructed, SpiceKernelTypes.AttitudeHistory,
-    SpiceKernelTypes.PointingAttitude, SpiceKernelTypes.PlanetaryEphemeris, SpiceKernelTypes.Leapseconds,
-    SpiceKernelTypes.SpacecraftClock
+    SpiceKernelTypes.ScienceFrames,
+    SpiceKernelTypes.EphemerisReconstructed,
+    SpiceKernelTypes.AttitudeHistory,
+    SpiceKernelTypes.PointingAttitude,
+    SpiceKernelTypes.PlanetaryEphemeris,
+    SpiceKernelTypes.Leapseconds,
+    SpiceKernelTypes.SpacecraftClock,
 ]
 
 
@@ -26,7 +29,9 @@ def datetime_to_j2000_seconds(date: datetime) -> int:
 glows_prod_data_path = get_run_local_data_path("glows_prod_data")
 imap_data_access.config["DATA_DIR"] = glows_prod_data_path
 
-l2_query_response = imap_data_access.query(instrument="glows", data_level="l2", version="latest")
+l2_query_response = imap_data_access.query(
+    instrument="glows", data_level="l2", version="latest"
+)
 
 for file in l2_query_response:
     print(f"downloading: {Path(file['file_path']).name}")
@@ -39,7 +44,7 @@ def datetime_to_j2000_seconds(date: datetime) -> int:
     "file_types": [t.value for t in GLOWS_L3E_REQUIRED_SPICE_KERNELS],
     "start_time": datetime_to_j2000_seconds(start_of_l2),
     "end_time": datetime_to_j2000_seconds(end_of_l2),
-    "list_files": "true"
+    "list_files": "true",
 }
 resp = requests.get("https://api.imap-mission.com/metakernel", params=metakernel_params)
 for spice_file in resp.json():
diff --git a/scripts/glows/download_validation_data.py b/scripts/glows/download_validation_data.py
index f2cc0ff73..53effeb5e 100644
--- a/scripts/glows/download_validation_data.py
+++ b/scripts/glows/download_validation_data.py
@@ -5,7 +5,9 @@
 from dotenv import load_dotenv
 
 load_dotenv()
-os.environ["IMAP_DATA_DIR"] = str(Path(__file__).parent.parent.parent / "glows_l3_validation_data")
+os.environ["IMAP_DATA_DIR"] = str(
+    Path(__file__).parent.parent.parent / "glows_l3_validation_data"
+)
 
 import imap_data_access
 
@@ -14,19 +16,16 @@
     ("map-of-extra-helio-bckgrd", "20100101"),
     ("pipeline-settings", "20100101"),
     ("time-dep-bckgrd", "20100101"),
-
     ("uv-anisotropy-1CR", "20100101"),
     ("WawHelioIonMP", "20100101"),
     ("bad-days-list", "20100101"),
     ("pipeline-settings-l3bcde", "20100101"),
-
     ("plasma-speed-2010a", "20100101"),
     ("proton-density-2010a", "20100101"),
     ("uv-anisotropy-2010a", "20100101"),
     ("photoion-2010a", "20100101"),
     ("lya-2010a", "20100101"),
     ("electron-density-2010a", "20100101"),
-
     ("lya", "19470303"),
     ("e-dens", "19470303"),
     ("p-dens", "19470303"),
@@ -41,8 +40,10 @@
         instrument="glows",
         descriptor=descriptor,
         start_date=start_date,
-        end_date=(datetime.strptime(start_date, "%Y%m%d") + timedelta(days=1)).strftime("%Y%m%d"),
-        version="latest"
+        end_date=(datetime.strptime(start_date, "%Y%m%d") + timedelta(days=1)).strftime(
+            "%Y%m%d"
+        ),
+        version="latest",
     )
 
     print(imap_data_access.download(result["file_path"]))
@@ -52,13 +53,20 @@
     ("l3a", "hist"),
     ("l3b", "ion-rate-profile"),
     ("l3c", "sw-profile"),
-    ("l3d", "solar-hist")
+    ("l3d", "solar-hist"),
 ]
 
 for data_level, descriptor in data_files:
-    for qr in imap_data_access.query(instrument="glows", data_level=data_level, descriptor=descriptor, version="latest"):
+    for qr in imap_data_access.query(
+        instrument="glows",
+        data_level=data_level,
+        descriptor=descriptor,
+        version="latest",
+    ):
         print(imap_data_access.download(qr["file_path"]))
 
 # download l3bc archive
-for qr in imap_data_access.query(table="ancillary", instrument="glows", descriptor="l3b-archive", version="latest"):
+for qr in imap_data_access.query(
+    table="ancillary", instrument="glows", descriptor="l3b-archive", version="latest"
+):
     print(imap_data_access.download(qr["file_path"]))
diff --git a/scripts/glows/upload_l3a_ancillary_files.py b/scripts/glows/upload_l3a_ancillary_files.py
index 910ae7b7a..6cb241c34 100644
--- a/scripts/glows/upload_l3a_ancillary_files.py
+++ b/scripts/glows/upload_l3a_ancillary_files.py
@@ -2,9 +2,9 @@
 
 from tests.test_helpers import get_test_instrument_team_data_path
 
-glows_file_dir = get_test_instrument_team_data_path('glows')
+glows_file_dir = get_test_instrument_team_data_path("glows")
 
-upload(glows_file_dir / 'imap_glows_calibration-data_20100101_v002.dat')
-upload(glows_file_dir / 'imap_glows_map-of-extra-helio-bckgrd_20100101_v002.dat')
-upload(glows_file_dir / 'imap_glows_pipeline-settings_20100101_v001.json')
-upload(glows_file_dir / 'imap_glows_time-dep-bckgrd_20100101_v001.dat')
+upload(glows_file_dir / "imap_glows_calibration-data_20100101_v002.dat")
+upload(glows_file_dir / "imap_glows_map-of-extra-helio-bckgrd_20100101_v002.dat")
+upload(glows_file_dir / "imap_glows_pipeline-settings_20100101_v001.json")
+upload(glows_file_dir / "imap_glows_time-dep-bckgrd_20100101_v001.dat")
diff --git a/scripts/hi/create_all_maps_from_inputs.py b/scripts/hi/create_all_maps_from_inputs.py
index 2a777c591..f24ed7579 100644
--- a/scripts/hi/create_all_maps_from_inputs.py
+++ b/scripts/hi/create_all_maps_from_inputs.py
@@ -10,8 +10,13 @@
 from tests.integration.integration_test_helpers import mock_imap_data_access
 
 
-@patch('imap_l3_data_processor._parse_cli_arguments')
-def run_all_maps(mock_parse_cli_arguments, *, output_dir, input_dir,):
+@patch("imap_l3_data_processor._parse_cli_arguments")
+def run_all_maps(
+    mock_parse_cli_arguments,
+    *,
+    output_dir,
+    input_dir,
+):
     intermediate_output = output_dir.parent / f"{output_dir.name}_intermediate"
     mock_arguments = Mock()
     mock_arguments.instrument = "hi"
@@ -23,26 +28,32 @@ def run_all_maps(mock_parse_cli_arguments, *, output_dir, input_dir,):
     mock_arguments.dependency = "[]"
     mock_arguments.upload_to_sdc = False
     mock_parse_cli_arguments.return_value = mock_arguments
-    with mock_imap_data_access(Path(intermediate_output), list(input_dir.rglob('*.*'))):
+    with mock_imap_data_access(Path(intermediate_output), list(input_dir.rglob("*.*"))):
         mock_arguments.descriptor = "sp-maps"
-        logging.basicConfig(force=True, level=logging.INFO,
-                            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-
+        logging.basicConfig(
+            force=True,
+            level=logging.INFO,
+            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        )
 
         imap_l3_data_processor.imap_l3_processor()
     spiceypy.kclear()
-    with mock_imap_data_access(output_dir, list(Path(intermediate_output).rglob('*.*'))):
+    with mock_imap_data_access(
+        output_dir, list(Path(intermediate_output).rglob("*.*"))
+    ):
         mock_arguments.descriptor = "hic-maps"
         imap_l3_data_processor.imap_l3_processor()
     spiceypy.kclear()
 
 
 if __name__ == "__main__":
-    output_dir = Path(f'{datetime.now().strftime('%Y%m%d')}_hi_validation_output')
+    output_dir = Path(f"{datetime.now().strftime('%Y%m%d')}_hi_validation_output")
     try:
         input_dir = Path(sys.argv[1])
     except:
-        input_dir = Path(f'hi_validation_input')
-        print(f"Could not open input file, using default input path of {input_dir} instead")
+        input_dir = Path("hi_validation_input")
+        print(
+            f"Could not open input file, using default input path of {input_dir} instead"
+        )
 
     run_all_maps(output_dir=output_dir, input_dir=input_dir)
diff --git a/scripts/hi/create_cdf_from_ibex.py b/scripts/hi/create_cdf_from_ibex.py
index 309fd14ac..e427d388a 100644
--- a/scripts/hi/create_cdf_from_ibex.py
+++ b/scripts/hi/create_cdf_from_ibex.py
@@ -1,5 +1,5 @@
 import argparse
-from datetime import datetime, timedelta
+from datetime import datetime
 from pathlib import Path
 
 import numpy as np
@@ -16,10 +16,10 @@
 
 
 def extract_data(lines: list[str]):
-    start_row, dimension_string = lines[0][2:10].split(':')
-    data_lines = lines[int(start_row):]
-    data = np.fromstring('\t'.join(data_lines), dtype=float, sep='\t')
-    return data.reshape(tuple(int(x) for x in dimension_string.split('x'))).T
+    start_row, dimension_string = lines[0][2:10].split(":")
+    data_lines = lines[int(start_row) :]
+    data = np.fromstring("\t".join(data_lines), dtype=float, sep="\t")
+    return data.reshape(tuple(int(x) for x in dimension_string.split("x"))).T
 
 
 fluxes = []
@@ -28,17 +28,21 @@ def extract_data(lines: list[str]):
 
 for file in folder.iterdir():
     if file.parts[-1].endswith("flux.txt") and not "mono" in file.parts[-1]:
-        group_file_name_prefix = '-'.join(file.parts[-1].split('-')[:-1])
+        group_file_name_prefix = "-".join(file.parts[-1].split("-")[:-1])
 
-        with open(file.parent / (group_file_name_prefix + "-flux.txt"), "r") as flux_file:
+        with open(
+            file.parent / (group_file_name_prefix + "-flux.txt"), "r"
+        ) as flux_file:
             fluxes.append(extract_data(flux_file.readlines()))
-        with open(file.parent / (group_file_name_prefix + "-fvar.txt"), "r") as variance_file:
+        with open(
+            file.parent / (group_file_name_prefix + "-fvar.txt"), "r"
+        ) as variance_file:
             variances.append(extract_data(variance_file.readlines()))
 
 rng = np.random.default_rng()
 pathname = f"C://Users//Harrison//Development//imap_L3_processing//tests//test_data//{folder.name}.cdf"
 
-with CDF(pathname, '') as cdf:
+with CDF(pathname, "") as cdf:
     cdf.col_major(True)
 
     flux_data = np.stack(fluxes, axis=-1)
diff --git a/scripts/hi/create_cdf_from_instrument_team_data.py b/scripts/hi/create_cdf_from_instrument_team_data.py
index 92ca6df5c..d7dc9207b 100644
--- a/scripts/hi/create_cdf_from_instrument_team_data.py
+++ b/scripts/hi/create_cdf_from_instrument_team_data.py
@@ -9,22 +9,31 @@
 from spacepy import pycdf
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.constants import FIVE_MINUTES_IN_NANOSECONDS, ONE_SECOND_IN_NANOSECONDS, SECONDS_PER_DAY
-from tests.test_helpers import get_run_local_data_path, get_test_instrument_team_data_path
-
-
-def create_l2_map_from_instrument_team(folder: Path, output_dir: Path, pixel_size: float = 4) -> Path:
+from imap_l3_processing.constants import (
+    FIVE_MINUTES_IN_NANOSECONDS,
+    ONE_SECOND_IN_NANOSECONDS,
+    SECONDS_PER_DAY,
+)
+from tests.test_helpers import (
+    get_run_local_data_path,
+    get_test_instrument_team_data_path,
+)
+
+
+def create_l2_map_from_instrument_team(
+    folder: Path, output_dir: Path, pixel_size: float = 4
+) -> Path:
     epoch = np.array([datetime(2025, 5, 1)])
     epoch_delta = np.array([FIVE_MINUTES_IN_NANOSECONDS])
 
-    data = defaultdict(lambda: {})
+    data = defaultdict(dict)
     for sub_folder in folder.iterdir():
         for file in sub_folder.iterdir():
             energy_string = re.search(r"_(\d*p\d*)keV", file.name).group(1)
-            energy = float(energy_string.replace('p', '.'))
+            energy = float(energy_string.replace("p", "."))
             data[sub_folder.name][energy] = np.loadtxt(file, delimiter=",").T
-    assert (data['flux'].keys() == data['sigma'].keys())
-    energy = np.array([key for key in sorted(data['flux'].keys())])
+    assert data["flux"].keys() == data["sigma"].keys()
+    energy = np.array([key for key in sorted(data["flux"].keys())])
     energy_delta_plus = np.full(energy.shape, 0.25)
     energy_delta_minus = np.full(energy.shape, 0.25)
     energy_label = energy.astype(str)
@@ -38,27 +47,36 @@ def create_l2_map_from_instrument_team(folder: Path, output_dir: Path, pixel_siz
     lon_label = [f"{x} deg" for x in lon]
     lat_label = [f"{x} deg" for x in lat]
 
-    intensity = np.array([data['flux'][key] for key in sorted(data['flux'].keys())])
+    intensity = np.array([data["flux"][key] for key in sorted(data["flux"].keys())])
     ena_intensity = intensity.reshape(1, *intensity.shape)
 
-    ena_intensity_stat_uncert = np.array([data['sigma'][key] for key in sorted(data['sigma'].keys())])[np.newaxis, ...]
+    ena_intensity_stat_uncert = np.array(
+        [data["sigma"][key] for key in sorted(data["sigma"].keys())]
+    )[np.newaxis, ...]
 
     # interpolate the original grid onto a new spacing
     if not np.isclose(pixel_size, 4):
         coords = np.stack(np.meshgrid(lon, lat), axis=-1)
 
-        new_ena_intensity = np.full((1, ena_intensity.shape[1], len(lon), len(lat)), -1e31)
-        new_ena_intensity_stat_unc = np.full((1, ena_intensity.shape[1], len(lon), len(lat)), -1e31)
+        new_ena_intensity = np.full(
+            (1, ena_intensity.shape[1], len(lon), len(lat)), -1e31
+        )
+        new_ena_intensity_stat_unc = np.full(
+            (1, ena_intensity.shape[1], len(lon), len(lat)), -1e31
+        )
 
         lon_4_deg = np.arange(0.0, 360.0, 4.0)
         lat_4_deg = np.arange(-88.0, 92.0, 4.0)
 
         for energy_i in range(ena_intensity.shape[1]):
-            interp = scipy.interpolate.RegularGridInterpolator((lon_4_deg, lat_4_deg), ena_intensity[0, energy_i])
+            interp = scipy.interpolate.RegularGridInterpolator(
+                (lon_4_deg, lat_4_deg), ena_intensity[0, energy_i]
+            )
             new_ena_intensity[0, energy_i] = interp(coords).T
 
-            interp = scipy.interpolate.RegularGridInterpolator((lon_4_deg, lat_4_deg),
-                                                               ena_intensity_stat_uncert[0, energy_i])
+            interp = scipy.interpolate.RegularGridInterpolator(
+                (lon_4_deg, lat_4_deg), ena_intensity_stat_uncert[0, energy_i]
+            )
             new_ena_intensity_stat_unc[0, energy_i] = interp(coords).T
 
         ena_intensity = new_ena_intensity
@@ -69,17 +87,19 @@ def create_l2_map_from_instrument_team(folder: Path, output_dir: Path, pixel_siz
     solid_angle = build_solid_angle_map(pixel_size)
 
     output_dir.mkdir(parents=True, exist_ok=True)
-    pathname = output_dir / f'{folder.name}.cdf'
+    pathname = output_dir / f"{folder.name}.cdf"
 
     obs_date = np.full(ena_intensity.shape, datetime.now())
-    obs_date_range = np.full(ena_intensity.shape, ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY * 2)
+    obs_date_range = np.full(
+        ena_intensity.shape, ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY * 2
+    )
 
     exposure_mask = ena_intensity == 0
     exposure_factor = np.full(ena_intensity.shape, 1.0)
     exposure_factor[exposure_mask] = 0
 
     Path(pathname).unlink(missing_ok=True)
-    with CDF(str(pathname), '') as cdf:
+    with CDF(str(pathname), "") as cdf:
         cdf.col_major(True)
 
         cdf.new("epoch", epoch, type=pycdf.const.CDF_TIME_TT2000)
@@ -90,16 +110,42 @@ def create_l2_map_from_instrument_team(folder: Path, output_dir: Path, pixel_siz
         cdf.new("longitude", lon, recVary=False, type=pycdf.const.CDF_FLOAT)
         cdf.new("longitude_delta", lon_delta, recVary=False, type=pycdf.const.CDF_FLOAT)
         cdf.new("longitude_label", lon_label, recVary=False, type=pycdf.const.CDF_CHAR)
-        cdf.new("ena_intensity", ena_intensity, recVary=True, type=pycdf.const.CDF_FLOAT)
-        cdf.new("ena_intensity_stat_uncert", ena_intensity_stat_uncert, recVary=True, type=pycdf.const.CDF_FLOAT)
-        cdf.new("ena_intensity_sys_err", ena_intensity_sys_err, recVary=True, type=pycdf.const.CDF_FLOAT)
-        cdf.new("exposure_factor", exposure_factor, recVary=True, type=pycdf.const.CDF_FLOAT)
+        cdf.new(
+            "ena_intensity", ena_intensity, recVary=True, type=pycdf.const.CDF_FLOAT
+        )
+        cdf.new(
+            "ena_intensity_stat_uncert",
+            ena_intensity_stat_uncert,
+            recVary=True,
+            type=pycdf.const.CDF_FLOAT,
+        )
+        cdf.new(
+            "ena_intensity_sys_err",
+            ena_intensity_sys_err,
+            recVary=True,
+            type=pycdf.const.CDF_FLOAT,
+        )
+        cdf.new(
+            "exposure_factor", exposure_factor, recVary=True, type=pycdf.const.CDF_FLOAT
+        )
         cdf.new("obs_date", obs_date, recVary=True, type=pycdf.const.CDF_TIME_TT2000)
-        cdf.new("obs_date_range", obs_date_range, recVary=True, type=pycdf.const.CDF_INT8)
+        cdf.new(
+            "obs_date_range", obs_date_range, recVary=True, type=pycdf.const.CDF_INT8
+        )
         cdf.new("solid_angle", solid_angle, recVary=True, type=pycdf.const.CDF_FLOAT)
         cdf.new("epoch_delta", epoch_delta, recVary=True, type=pycdf.const.CDF_INT8)
-        cdf.new("energy_delta_plus", energy_delta_plus, recVary=False, type=pycdf.const.CDF_FLOAT)
-        cdf.new("energy_delta_minus", energy_delta_minus, recVary=False, type=pycdf.const.CDF_FLOAT)
+        cdf.new(
+            "energy_delta_plus",
+            energy_delta_plus,
+            recVary=False,
+            type=pycdf.const.CDF_FLOAT,
+        )
+        cdf.new(
+            "energy_delta_minus",
+            energy_delta_minus,
+            recVary=False,
+            type=pycdf.const.CDF_FLOAT,
+        )
         cdf.new("energy_label", energy_label, recVary=False, type=pycdf.const.CDF_CHAR)
 
         for metadata_var_names in ["latitude_label", "longitude_label", "energy_label"]:
@@ -107,16 +153,22 @@ def create_l2_map_from_instrument_team(folder: Path, output_dir: Path, pixel_siz
 
         for var in cdf:
             if cdf[var].type() == pycdf.const.CDF_TIME_TT2000.value:
-                cdf[var].attrs['FILLVAL'] = datetime.fromisoformat("9999-12-31T23:59:59.999999999")
+                cdf[var].attrs["FILLVAL"] = datetime.fromisoformat(
+                    "9999-12-31T23:59:59.999999999"
+                )
             elif cdf[var].type() == pycdf.const.CDF_INT8.value:
-                cdf[var].attrs['FILLVAL'] = -9223372036854775808
+                cdf[var].attrs["FILLVAL"] = -9223372036854775808
             elif cdf[var].type() == pycdf.const.CDF_FLOAT.value:
-                cdf[var].attrs['FILLVAL'] = -1e31
+                cdf[var].attrs["FILLVAL"] = -1e31
     return pathname
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     parent = get_test_instrument_team_data_path("hi")
 
     for folder in parent.iterdir():
-        print(create_l2_map_from_instrument_team(folder, output_dir=get_run_local_data_path("lo"), pixel_size=6))
+        print(
+            create_l2_map_from_instrument_team(
+                folder, output_dir=get_run_local_data_path("lo"), pixel_size=6
+            )
+        )
diff --git a/scripts/hi/create_hi_full_spin_deps.py b/scripts/hi/create_hi_full_spin_deps.py
index 3b36eee3c..ad31b3a6e 100644
--- a/scripts/hi/create_hi_full_spin_deps.py
+++ b/scripts/hi/create_hi_full_spin_deps.py
@@ -7,30 +7,43 @@
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.maps import survival_probability_processing
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiLoL3SurvivalDependencies,
+)
 from imap_l3_processing.maps.map_models import RectangularIntensityDataProduct
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.utils import save_data, furnish_local_spice
-from scripts.hi.create_cdf_from_instrument_team_data import create_l2_map_from_instrument_team
+from imap_l3_processing.utils import furnish_local_spice, save_data
+from scripts.hi.create_cdf_from_instrument_team_data import (
+    create_l2_map_from_instrument_team,
+)
 from scripts.hi.generate_fake_l1c_cdfs import generate_fake_l1c
-from tests.test_helpers import get_test_instrument_team_data_path, get_test_data_path, get_run_local_data_path
+from tests.test_helpers import (
+    get_run_local_data_path,
+    get_test_data_path,
+    get_test_instrument_team_data_path,
+)
 
 
 def create_hi_full_spin_deps(
-        sensor="90",
-        output_dir: Path = get_run_local_data_path("hi/full_spin_deps"),
-        start_date: datetime = datetime(year=2025, month=4, day=16, hour=12),
-        l2_map_dir: Path = None,
-        glows_dir: Path = None):
+    sensor="90",
+    output_dir: Path = get_run_local_data_path("hi/full_spin_deps"),
+    start_date: datetime = datetime(year=2025, month=4, day=16, hour=12),
+    l2_map_dir: Path = None,
+    glows_dir: Path = None,
+):
     if l2_map_dir is None:
         l2_map_dir = get_test_instrument_team_data_path(f"hi/hi{sensor}-6months")
     if glows_dir is None:
-        glows_dir = get_test_data_path(f"hi/fake_l3e_survival_probabilities/90")
+        glows_dir = get_test_data_path("hi/fake_l3e_survival_probabilities/90")
     l1c_folder = output_dir / "l1c"
     l1c_folder.mkdir(parents=True, exist_ok=True)
 
-    l1c_files = [Path(s) for s in generate_fake_l1c(start_date, 2, output_dir=l1c_folder)]
-    original_full_spin_map_path = create_l2_map_from_instrument_team(l2_map_dir, output_dir=output_dir)
+    l1c_files = [
+        Path(s) for s in generate_fake_l1c(start_date, 2, output_dir=l1c_folder)
+    ]
+    original_full_spin_map_path = create_l2_map_from_instrument_team(
+        l2_map_dir, output_dir=output_dir
+    )
     parents = [f.name for f in l1c_files]
     glows_files = list(glows_dir.iterdir())
 
@@ -38,56 +51,89 @@ def create_hi_full_spin_deps(
         original_intensity = cdf["ena_intensity"][...]
 
     ramified_map_deps = HiLoL3SurvivalDependencies.from_file_paths(
-        original_full_spin_map_path, l1c_files, glows_files,
-        f"h{sensor}-ena-h-sf-nsp-ram-hae-4deg-6mo")
+        original_full_spin_map_path,
+        l1c_files,
+        glows_files,
+        f"h{sensor}-ena-h-sf-nsp-ram-hae-4deg-6mo",
+    )
 
     antiramified_map_deps = HiLoL3SurvivalDependencies.from_file_paths(
-        original_full_spin_map_path, l1c_files,
+        original_full_spin_map_path,
+        l1c_files,
         glows_files,
-        f"h{sensor}-ena-h-sf-nsp-anti-hae-4deg-6mo")
-
-    input_metadata = InputMetadata("hi", "l3", datetime(year=2025, month=4, day=15), None, "v001",
-                                   f"h90-ena-h-sf-sp-full-hae-4deg-6mo")
-
-    ram_data = survival_probability_processing.process_survival_probabilities(ramified_map_deps, SpiceFrame.ECLIPJ2000)
-    ram_exposure_is_zero = (np.isnan(ram_data.intensity_map_data.ena_intensity)
-                            | (ram_data.intensity_map_data.ena_intensity == 0))
+        f"h{sensor}-ena-h-sf-nsp-anti-hae-4deg-6mo",
+    )
+
+    input_metadata = InputMetadata(
+        "hi",
+        "l3",
+        datetime(year=2025, month=4, day=15),
+        None,
+        "v001",
+        "h90-ena-h-sf-sp-full-hae-4deg-6mo",
+    )
+
+    ram_data = survival_probability_processing.process_survival_probabilities(
+        ramified_map_deps, SpiceFrame.ECLIPJ2000
+    )
+    ram_exposure_is_zero = np.isnan(ram_data.intensity_map_data.ena_intensity) | (
+        ram_data.intensity_map_data.ena_intensity == 0
+    )
 
     ram_cdf_path = save_data(
         RectangularIntensityDataProduct(input_metadata, ram_data),
-        delete_if_present=True, folder_path=output_dir)
-    ram_logical_source = f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v001"
+        delete_if_present=True,
+        folder_path=output_dir,
+    )
+    ram_logical_source = (
+        f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-ram-hae-4deg-6mo_20250415_v001"
+    )
     with CDF(str(ram_cdf_path), readonly=False) as ram:
-        ram["exposure_factor"] = np.where(ram_exposure_is_zero, 0, ram_data.intensity_map_data.exposure_factor)
-        ram["ena_intensity"] = np.where(ram_exposure_is_zero, ram["ena_intensity"].attrs["FILLVAL"],
-                                        original_intensity)
+        ram["exposure_factor"] = np.where(
+            ram_exposure_is_zero, 0, ram_data.intensity_map_data.exposure_factor
+        )
+        ram["ena_intensity"] = np.where(
+            ram_exposure_is_zero,
+            ram["ena_intensity"].attrs["FILLVAL"],
+            original_intensity,
+        )
         ram.attrs["Parents"] = parents
         ram.attrs["Logical_source"] = ram_logical_source
         ram["ena_intensity"].attrs["CATDESC"] = ram_logical_source
 
     shutil.move(ram_cdf_path, output_dir / f"{ram_logical_source}.cdf")
 
-    antiram_data = survival_probability_processing.process_survival_probabilities(antiramified_map_deps,
-                                                                                  SpiceFrame.ECLIPJ2000)
-    antiram_exposure_is_zero = (np.isnan(antiram_data.intensity_map_data.ena_intensity)
-                                | (antiram_data.intensity_map_data.ena_intensity == 0))
+    antiram_data = survival_probability_processing.process_survival_probabilities(
+        antiramified_map_deps, SpiceFrame.ECLIPJ2000
+    )
+    antiram_exposure_is_zero = np.isnan(
+        antiram_data.intensity_map_data.ena_intensity
+    ) | (antiram_data.intensity_map_data.ena_intensity == 0)
 
     antiram_cdf_path = save_data(
         RectangularIntensityDataProduct(input_metadata, antiram_data),
-        delete_if_present=True, folder_path=output_dir)
-    antiram_logical_source = f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-6mo_20250415_v001"
+        delete_if_present=True,
+        folder_path=output_dir,
+    )
+    antiram_logical_source = (
+        f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-6mo_20250415_v001"
+    )
 
     with CDF(str(antiram_cdf_path), readonly=False) as antiram:
-        antiram["exposure_factor"] = np.where(antiram_exposure_is_zero, 0,
-                                              antiram_data.intensity_map_data.exposure_factor)
-        antiram["ena_intensity"] = np.where(antiram_exposure_is_zero, antiram["ena_intensity"].attrs["FILLVAL"],
-                                            original_intensity)
+        antiram["exposure_factor"] = np.where(
+            antiram_exposure_is_zero, 0, antiram_data.intensity_map_data.exposure_factor
+        )
+        antiram["ena_intensity"] = np.where(
+            antiram_exposure_is_zero,
+            antiram["ena_intensity"].attrs["FILLVAL"],
+            original_intensity,
+        )
         antiram.attrs["Parents"] = parents
         antiram.attrs["Logical_source"] = antiram_logical_source
         antiram["ena_intensity"].attrs["CATDESC"] = antiram_logical_source
     shutil.move(antiram_cdf_path, output_dir / f"{antiram_logical_source}.cdf")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     furnish_local_spice()
     create_hi_full_spin_deps()
diff --git a/scripts/hi/create_l2_from_pointing_sets.py b/scripts/hi/create_l2_from_pointing_sets.py
index 8a9f7a6ec..fbeb1f1f7 100644
--- a/scripts/hi/create_l2_from_pointing_sets.py
+++ b/scripts/hi/create_l2_from_pointing_sets.py
@@ -10,6 +10,7 @@
 
 output_dir = r"C:\Users\Petty\Development\imap_L3_processing\generate_hi_l2"
 
+
 def create_input_file(sensor: str, start: datetime, end: datetime) -> str:
     static_files = [
         "naif0012.tls",
@@ -19,7 +20,7 @@ def create_input_file(sensor: str, start: datetime, end: datetime) -> str:
         "de440.bsp",
         f"imap_hi_{sensor}sensor-cal-prod_20240101_v002.csv",
         f"imap_hi_{sensor}sensor-esa-energies_20240101_v002.csv",
-        f"imap_hi_{sensor}sensor-esa-eta-fit-factors_20240101_v001.csv"
+        f"imap_hi_{sensor}sensor-esa-eta-fit-factors_20240101_v001.csv",
     ]
 
     inputs = ProcessingInputCollection()
@@ -31,7 +32,11 @@ def create_input_file(sensor: str, start: datetime, end: datetime) -> str:
     date = start
     while date < end:
         repointing = int((date - start_repointings) / timedelta(days=1)) + 1000
-        inputs.add(generate_imap_input(f"imap_hi_l1c_{sensor}sensor-pset_{date.strftime('%Y%m%d')}-repoint{repointing:05}_v971.cdf"))
+        inputs.add(
+            generate_imap_input(
+                f"imap_hi_l1c_{sensor}sensor-pset_{date.strftime('%Y%m%d')}-repoint{repointing:05}_v971.cdf"
+            )
+        )
         date += timedelta(days=1)
     dependency_file = f"imap_hi_l2_{sensor}sensor-de-{start.strftime('%Y%m%d')}-{end.strftime('%Y%m%d')}_{start.strftime('%Y%m%d')}_v001.json"
     dependency_path = generate_imap_file_path(dependency_file).construct_path()
@@ -39,8 +44,9 @@ def create_input_file(sensor: str, start: datetime, end: datetime) -> str:
     dependency_path.write_text(inputs.serialize())
     return dependency_file
 
+
 def generate_map(descriptor: str, start: datetime, end: datetime):
-    sensor = descriptor.split('-')[0][1:]
+    sensor = descriptor.split("-")[0][1:]
     dependency_file = create_input_file(sensor, start, end)
 
     env = {
@@ -50,7 +56,7 @@ def generate_map(descriptor: str, start: datetime, end: datetime):
     }
 
     version = "v000"
-    formatted_start_date = start_date.strftime('%Y%m%d')
+    formatted_start_date = start_date.strftime("%Y%m%d")
     output_filename = f"imap_hi_l2_{descriptor}_{formatted_start_date}_{version}.cdf"
     if not generate_imap_file_path(output_filename).construct_path().exists():
         command = f"imap_cli --instrument hi --data-level l2 --descriptor {descriptor} --start-date {formatted_start_date} --version {version} --dependency {dependency_file}"
@@ -58,25 +64,30 @@ def generate_map(descriptor: str, start: datetime, end: datetime):
     else:
         print(f"Skipping generation of {output_filename}, it already exists")
 
+
 if __name__ == "__main__":
     imap_data_access.config["DATA_DIR"] = Path(output_dir)
 
-    duration = '1yr'
+    duration = "1yr"
     start_date = datetime(2025, 4, 15)
     end_date = datetime(2026, 4, 15)
     for sensor in [45, 90]:
-        for side in ['ram', 'anti', 'full']:
+        for side in ["ram", "anti", "full"]:
             for pixelation in [4, 6]:
-                descriptor = f"h{sensor}-ena-h-hf-nsp-{side}-hae-{pixelation}deg-{duration}"
+                descriptor = (
+                    f"h{sensor}-ena-h-hf-nsp-{side}-hae-{pixelation}deg-{duration}"
+                )
                 generate_map(descriptor, start_date, end_date)
 
-    duration = '6mo'
+    duration = "6mo"
     for sensor in [45, 90]:
-        for side in ['ram', 'anti', 'full']:
+        for side in ["ram", "anti", "full"]:
             for pixelation in [4, 6]:
                 for start_date, end_date in [
                     (datetime(2025, 4, 15), datetime(2025, 10, 15)),
-                    (datetime(2025, 10, 15), datetime(2026, 4, 15))
+                    (datetime(2025, 10, 15), datetime(2026, 4, 15)),
                 ]:
-                    descriptor = f"h{sensor}-ena-h-hf-nsp-{side}-hae-{pixelation}deg-{duration}"
+                    descriptor = (
+                        f"h{sensor}-ena-h-hf-nsp-{side}-hae-{pixelation}deg-{duration}"
+                    )
                     generate_map(descriptor, start_date, end_date)
diff --git a/scripts/hi/generate_fake_l1c_cdfs.py b/scripts/hi/generate_fake_l1c_cdfs.py
index 295bf0711..20db0ef08 100644
--- a/scripts/hi/generate_fake_l1c_cdfs.py
+++ b/scripts/hi/generate_fake_l1c_cdfs.py
@@ -7,22 +7,33 @@
 from tests.test_helpers import get_test_data_path
 
 
-def generate_fake_l1c(start_date: datetime, num_days: int, output_dir: Path) -> list[str]:
+def generate_fake_l1c(
+    start_date: datetime, num_days: int, output_dir: Path
+) -> list[str]:
     output_files = []
 
     for i in range(num_days):
         date_to_set = start_date + timedelta(days=i)
-        logical_source = f"imap_hi_l1c_90sensor-pset_{date_to_set.strftime('%Y%m%d')}_v001"
+        logical_source = (
+            f"imap_hi_l1c_90sensor-pset_{date_to_set.strftime('%Y%m%d')}_v001"
+        )
         output_path = output_dir / f"{logical_source}.cdf"
         output_path.unlink(missing_ok=True)
-        with CDF(str(output_path),
-                 masterpath=str(get_test_data_path("hi/imap_hi_l1c_90sensor-pset_20250415_v001.cdf"))) as cdf:
-            del cdf['epoch']
+        with CDF(
+            str(output_path),
+            masterpath=str(
+                get_test_data_path("hi/imap_hi_l1c_90sensor-pset_20250415_v001.cdf")
+            ),
+        ) as cdf:
+            del cdf["epoch"]
             cdf.new("epoch", [date_to_set], type=pycdf.const.CDF_TIME_TT2000)
         output_files.append(str(output_path))
     return output_files
 
 
 if __name__ == "__main__":
-    generate_fake_l1c(datetime(month=4, year=2025, day=16, hour=12), num_days=90,
-                      output_dir=get_test_data_path(f"hi/fake_l1c/90"))
+    generate_fake_l1c(
+        datetime(month=4, year=2025, day=16, hour=12),
+        num_days=90,
+        output_dir=get_test_data_path("hi/fake_l1c/90"),
+    )
diff --git a/scripts/hi/stack_arrays_to_match_number_of_energies.py b/scripts/hi/stack_arrays_to_match_number_of_energies.py
index 6083aab5c..4ee8b0bcb 100644
--- a/scripts/hi/stack_arrays_to_match_number_of_energies.py
+++ b/scripts/hi/stack_arrays_to_match_number_of_energies.py
@@ -1,25 +1,30 @@
 from datetime import date
 
-from spacepy.pycdf import CDF
 import numpy as np
+from spacepy.pycdf import CDF
 
-filename = f"imap_hi_l3_spectral-fit-index_{date.today().strftime("%Y%m%d")}_"
+filename = f"imap_hi_l3_spectral-fit-index_{date.today().strftime('%Y%m%d')}_"
 temp_cdf = CDF(
-    rf"C:\Users\Harrison\Development\imap_L3_processing\temp_cdf_data\{filename}.cdf")
+    rf"C:\Users\Harrison\Development\imap_L3_processing\temp_cdf_data\{filename}.cdf"
+)
 map_cdf = CDF(
-    rf"C:\Users\Harrison\Development\cava\test\test_data\map_files\map_cdfs\{filename}.cdf")
+    rf"C:\Users\Harrison\Development\cava\test\test_data\map_files\map_cdfs\{filename}.cdf"
+)
 
 map_cdf.readonly(False)
-new_spectral_index_values = np.repeat(map_cdf['spectral_fit_index'][...][..., np.newaxis], len(map_cdf["bin"][...]),
-                                      axis=-1)
+new_spectral_index_values = np.repeat(
+    map_cdf["spectral_fit_index"][...][..., np.newaxis],
+    len(map_cdf["bin"][...]),
+    axis=-1,
+)
 
 # new_spectral_index_values = np.stack([map_cdf['spectral_fit_index'], map_cdf['spectral_fit_index'],
 #                                       map_cdf['spectral_fit_index'], map_cdf['spectral_fit_index'],
 #                                       map_cdf['spectral_fit_index']], axis=-1)
-temp_cdf_attrs = temp_cdf['spectral_fit_index'].attrs
-del map_cdf['spectral_fit_index']
-map_cdf['spectral_fit_index'] = new_spectral_index_values
-map_cdf['spectral_fit_index'].attrs = temp_cdf_attrs
+temp_cdf_attrs = temp_cdf["spectral_fit_index"].attrs
+del map_cdf["spectral_fit_index"]
+map_cdf["spectral_fit_index"] = new_spectral_index_values
+map_cdf["spectral_fit_index"].attrs = temp_cdf_attrs
 
 map_cdf.close()
 temp_cdf.close()
diff --git a/scripts/hit/create_l1a_direct-event_data.py b/scripts/hit/create_l1a_direct-event_data.py
index e402e03a4..34c9e3016 100644
--- a/scripts/hit/create_l1a_direct-event_data.py
+++ b/scripts/hit/create_l1a_direct-event_data.py
@@ -1,5 +1,5 @@
 import os
-from datetime import timedelta, datetime
+from datetime import datetime, timedelta
 from pathlib import Path
 
 import numpy as np
@@ -7,7 +7,14 @@
 from spacepy.pycdf import CDF
 
 path = Path(__file__)
-binary_data_path = path.parent.parent.parent / "tests" / "test_data" / "hit" / "pha_events" / "pha_binary"
+binary_data_path = (
+    path.parent.parent.parent
+    / "tests"
+    / "test_data"
+    / "hit"
+    / "pha_events"
+    / "pha_binary"
+)
 
 start_time = datetime(year=2010, month=1, day=6)
 time_delta = timedelta(minutes=10)
@@ -23,7 +30,9 @@
     print(len(binary))
     pha_raw.append(binary)
 
-with CDF("tests/test_data/hit/pha_events/fake-menlo-imap_hit_l1a_pulse-height-events_20100106_v004",
-         masterpath='') as cdf:
+with CDF(
+    "tests/test_data/hit/pha_events/fake-menlo-imap_hit_l1a_pulse-height-events_20100106_v004",
+    masterpath="",
+) as cdf:
     cdf.new("epoch", np.array(epoch))
     cdf.new("pha_raw", pha_raw)
diff --git a/scripts/hit/create_l2_hit_sectored_rates_cdf_from_csv.py b/scripts/hit/create_l2_hit_sectored_rates_cdf_from_csv.py
index e10e217fa..31ebf1974 100644
--- a/scripts/hit/create_l2_hit_sectored_rates_cdf_from_csv.py
+++ b/scripts/hit/create_l2_hit_sectored_rates_cdf_from_csv.py
@@ -9,10 +9,11 @@
 
 
 def create_l2_hit_sectored_rates_cdf_from_csv(csv_file_path: str, output_filename: str):
-    with CDF(output_filename, '') as cdf:
+    with CDF(output_filename, "") as cdf:
         cdf.col_major(True)
-        raw_data = np.loadtxt(csv_file_path, delimiter=",", skiprows=1,
-                              usecols=range(1, 121))
+        raw_data = np.loadtxt(
+            csv_file_path, delimiter=",", skiprows=1, usecols=range(1, 121)
+        )
         flux_data = raw_data.reshape((-1, 8, 15))
         epoch_count = 1
         start_time = datetime(2025, 1, 1, 0, 5)
@@ -78,10 +79,21 @@ def create_l2_hit_sectored_rates_cdf_from_csv(csv_file_path: str, output_filenam
 
 if __name__ == "__main__":
     path = Path(__file__)
-    input_path = path.parent.parent.parent / "instrument_team_data" / "hit" / "hit_l2_sectored_sample1.csv"
-    cdf_file_path = path.parent.parent.parent / "tests" / "test_data" / "hit" / "imap_hit_l2_macropixel-intensity_20250101_v002.cdf"
+    input_path = (
+        path.parent.parent.parent
+        / "instrument_team_data"
+        / "hit"
+        / "hit_l2_sectored_sample1.csv"
+    )
+    cdf_file_path = (
+        path.parent.parent.parent
+        / "tests"
+        / "test_data"
+        / "hit"
+        / "imap_hit_l2_macropixel-intensity_20250101_v002.cdf"
+    )
 
     try:
         create_l2_hit_sectored_rates_cdf_from_csv(str(input_path), str(cdf_file_path))
-    except Exception as e:
+    except Exception:
         traceback.print_exc()
diff --git a/scripts/hit/create_mag_from_csv.py b/scripts/hit/create_mag_from_csv.py
index 51d0e39a8..fdcd60cf0 100644
--- a/scripts/hit/create_mag_from_csv.py
+++ b/scripts/hit/create_mag_from_csv.py
@@ -11,20 +11,22 @@ def create_mag(csv_path, l2_path, output_path, offset):
     epoch_len = len(epoch)
     csv_data = np.loadtxt(csv_path, delimiter=",", skiprows=1)
 
-    mag_data = csv_data[offset:offset+epoch_len, -3:]
-    mag_sclk = csv_data[offset:offset+epoch_len, 6]
-    mag_epochs = [spiceypy.et2datetime(spiceypy.scs2e(-43, str(sclk))) for sclk in mag_sclk]
+    mag_data = csv_data[offset : offset + epoch_len, -3:]
+    mag_sclk = csv_data[offset : offset + epoch_len, 6]
+    mag_epochs = [
+        spiceypy.et2datetime(spiceypy.scs2e(-43, str(sclk))) for sclk in mag_sclk
+    ]
     print(mag_sclk[0])
     print(mag_epochs[:5])
     print(epoch[:5])
 
     with CDF(output_path, create=True) as output_cdf:
-        output_cdf['epoch'] = epoch
-        output_cdf['vectors'] = mag_data
-        output_cdf['vectors'].attrs["FILLVAL"] = -1e31
+        output_cdf["epoch"] = epoch
+        output_cdf["vectors"] = mag_data
+        output_cdf["vectors"].attrs["FILLVAL"] = -1e31
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     spiceypy.furnsh("spice_kernels/imap_sclk_0000.tsc")
     spiceypy.furnsh("spice_kernels/naif0012.tls")
     # USAGE: python create_mag_from_csv.py <path to csv> <path to l2 cdf> <output path> <offset>
@@ -32,4 +34,4 @@ def create_mag(csv_path, l2_path, output_path, offset):
     create_mag(csv_path, l2_path, output_path, int(offset))
 
     # python scripts/hit/create_mag_from_csv.py instrument_team_data/hit/hit_l1a_sample2_fakeMAG.csv tests/test_data/hit/imap_hit_l2_macropixel-intensity_20100106_v001.cdf tests/test_data/hit/mag_data_for_hit_20100106 900
-    # python scripts/hit/create_mag_from_csv.py instrument_team_data/hit/hit_l1a_sample2_fakeMAG.csv tests/test_data/hit/imap_hit_l2_macropixel-intensity_20100107_v001.cdf tests/test_data/hit/mag_data_for_hit_20100107 2340
\ No newline at end of file
+    # python scripts/hit/create_mag_from_csv.py instrument_team_data/hit/hit_l1a_sample2_fakeMAG.csv tests/test_data/hit/imap_hit_l2_macropixel-intensity_20100107_v001.cdf tests/test_data/hit/mag_data_for_hit_20100107 2340
diff --git a/scripts/hit/hit_plots.py b/scripts/hit/hit_plots.py
index 136cc32ca..e392678ba 100644
--- a/scripts/hit/hit_plots.py
+++ b/scripts/hit/hit_plots.py
@@ -8,11 +8,10 @@
 xs = []
 ys = []
 
-for i in range(0, len(de['energy'][...])):
-
-    if de['range'][i] == 3 and de['side'][i] == 1 and de['stim_tag'][i] != 1:
-        ys.append(de['delta_e'][i])
-        xs.append(de['e_prime'][i])
+for i in range(len(de["energy"][...])):
+    if de["range"][i] == 3 and de["side"][i] == 1 and de["stim_tag"][i] != 1:
+        ys.append(de["delta_e"][i])
+        xs.append(de["e_prime"][i])
 
 #
 # for delta_e, e_prime, d_range in zip(de["delta_e"], de["e_prime"], de["range"], s):
@@ -21,6 +20,6 @@
 #         ys.append(delta_e)
 
 plt.scatter(xs, ys)
-plt.xscale('log')
-plt.yscale('log')
+plt.xscale("log")
+plt.yscale("log")
 plt.show()
diff --git a/scripts/lo/combine_existing_maps_to_fix_frozen_data.py b/scripts/lo/combine_existing_maps_to_fix_frozen_data.py
index 63321188f..7471194ef 100644
--- a/scripts/lo/combine_existing_maps_to_fix_frozen_data.py
+++ b/scripts/lo/combine_existing_maps_to_fix_frozen_data.py
@@ -1,37 +1,19 @@
-import abc
-import dataclasses
 import logging
-import re
 import shutil
-from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
 from pathlib import Path
-from typing import Self, Optional, override, Literal
 
 import imap_data_access
-import numpy as np
-import pandas
-import xarray as xr
 from imap_data_access import ProcessingInputCollection, ScienceFilePath
 from imap_data_access.file_validation import Version
 from imap_data_access.processing_input import ScienceInput
-from imap_processing.cdf.utils import write_cdf as write_l2_cdf
-from imap_processing.ena_maps.ena_maps import RectangularSkyMap
-from imap_processing.hit.l1b.constants import FILLVAL_INT64
-from imap_processing.spice.geometry import SpiceFrame
-from spacepy.pycdf import CDF
 
-from imap_l3_processing.constants import TT2000_EPOCH
-from imap_l3_processing.lo.l3.lo_sp_initializer import LO_SP_MAP_KERNELS
 from imap_l3_processing.lo.lo_processor import LoProcessor
 from imap_l3_processing.models import InputMetadata, VersionMap
-from imap_l3_processing.utils import furnished_metakernel
 from tests.test_helpers import get_run_local_data_path
 
 
-def copy_to_output_directory(
-        release_directory: Path, output_maps: list[Path]
-):
+def copy_to_output_directory(release_directory: Path, output_maps: list[Path]):
     release_directory.mkdir(parents=True, exist_ok=True)
 
     for generated_path in output_maps:
@@ -39,8 +21,11 @@ def copy_to_output_directory(
 
 
 if __name__ == "__main__":
-
-    logging.basicConfig(force=True, level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    logging.basicConfig(
+        force=True,
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
 
     output_data_path = get_run_local_data_path("lo_txt_pipeline")
     shutil.rmtree(output_data_path / "imap" / "lo" / "l2", ignore_errors=True)
@@ -108,22 +93,21 @@ def copy_to_output_directory(
             input_files[sfp.descriptor] = file.name
     output_maps = []
     for combined_descriptor, combined_dependencies in maps_needed_for_combination:
-
         [combined_sp_map] = LoProcessor(
-            dependencies=ProcessingInputCollection(*[ScienceInput(input_files[desc]) for desc in combined_dependencies]),
+            dependencies=ProcessingInputCollection(
+                *[ScienceInput(input_files[desc]) for desc in combined_dependencies]
+            ),
             input_metadata=InputMetadata(
                 instrument="lo",
                 data_level="l3",
-                start_date=datetime(2025,11,25),
+                start_date=datetime(2025, 11, 25),
                 end_date=None,
                 version=VersionMap({}, Version(None, 2)),
-                descriptor=combined_descriptor
-            )
+                descriptor=combined_descriptor,
+            ),
         ).process()
 
-        output_maps.extend([
-            combined_sp_map
-        ])
+        output_maps.extend([combined_sp_map])
 
     release_directory = get_run_local_data_path("IMAP-Lo July 31st 2026 Maps")
     copy_to_output_directory(release_directory, output_maps)
diff --git a/scripts/lo/create_l2_from_pointing_sets.py b/scripts/lo/create_l2_from_pointing_sets.py
index 5a14304ea..0b633d35d 100644
--- a/scripts/lo/create_l2_from_pointing_sets.py
+++ b/scripts/lo/create_l2_from_pointing_sets.py
@@ -1,7 +1,7 @@
 import os
 import shutil
 import subprocess
-from datetime import datetime, timedelta
+from datetime import datetime
 from pathlib import Path
 
 import imap_data_access
@@ -28,14 +28,14 @@ def create_input_file() -> str:
         "imap_lo_l1c_pset_20250420-repoint01005_v002.cdf",
         "imap_lo_l1c_pset_20250424-repoint01009_v002.cdf",
         "imap_lo_l1c_pset_20250429-repoint01014_v002.cdf",
-        "imap_lo_l1c_pset_20250504-repoint01019_v002.cdf"
+        "imap_lo_l1c_pset_20250504-repoint01019_v002.cdf",
     ]
 
     inputs = ProcessingInputCollection()
 
     inputs.add([generate_imap_input(f) for f in static_files])
 
-    dependency_file = f"imap_lo_l2_90sensor-de-20250415-20250504_20250415_v001.json"
+    dependency_file = "imap_lo_l2_90sensor-de-20250415-20250504_20250415_v001.json"
     dependency_path = generate_imap_file_path(dependency_file).construct_path()
     dependency_path.parent.mkdir(parents=True, exist_ok=True)
     dependency_path.write_text(inputs.serialize())
@@ -52,7 +52,7 @@ def generate_map(descriptor: str, start: datetime):
     }
 
     version = "v000"
-    formatted_start_date = start.strftime('%Y%m%d')
+    formatted_start_date = start.strftime("%Y%m%d")
     output_filename = f"imap_lo_l2_{descriptor}_{formatted_start_date}_{version}.cdf"
     command = f"imap_cli --instrument lo --data-level l2 --descriptor {descriptor} --start-date {formatted_start_date} --version {version} --dependency {dependency_file}"
     print(command)
@@ -72,10 +72,10 @@ def generate_map(descriptor: str, start: datetime):
 
     start_date = datetime(2025, 4, 15)
     descriptors = [
-        f"l090-ena-h-hf-nsp-ram-hae-6deg-1yr",
-        f"l090-ena-h-sf-nsp-ram-hae-6deg-1yr",
-        f"l090-enanbs-h-hf-nsp-ram-hae-6deg-1yr",
-        f"l090-enanbs-h-sf-nsp-ram-hae-6deg-1yr",
+        "l090-ena-h-hf-nsp-ram-hae-6deg-1yr",
+        "l090-ena-h-sf-nsp-ram-hae-6deg-1yr",
+        "l090-enanbs-h-hf-nsp-ram-hae-6deg-1yr",
+        "l090-enanbs-h-sf-nsp-ram-hae-6deg-1yr",
     ]
     for descriptor in descriptors:
-        generate_map(descriptor, start_date)
\ No newline at end of file
+        generate_map(descriptor, start_date)
diff --git a/scripts/make_cdf_all_fill.py b/scripts/make_cdf_all_fill.py
index c35144514..5f2f8cfdb 100644
--- a/scripts/make_cdf_all_fill.py
+++ b/scripts/make_cdf_all_fill.py
@@ -13,8 +13,11 @@
 shutil.copy(args.template_cdf, output_file_name)
 with CDF(output_file_name, readonly=False) as cdf:
     for var in cdf:
-        if var not in ["Epoch", "epoch"] and cdf[var].attrs.get("VAR_TYPE") != "metadata":
+        if (
+            var not in ["Epoch", "epoch"]
+            and cdf[var].attrs.get("VAR_TYPE") != "metadata"
+        ):
             if "FILLVAL" in cdf[var].attrs:
                 cdf[var] = np.full_like(cdf[var], cdf[var].attrs["FILLVAL"])
             else:
-                print(f'The var {var} has no FILLVAL attribute.')
+                print(f"The var {var} has no FILLVAL attribute.")
diff --git a/scripts/poll_batch_jobs_status.py b/scripts/poll_batch_jobs_status.py
index 9162c8d45..24b3cbee1 100644
--- a/scripts/poll_batch_jobs_status.py
+++ b/scripts/poll_batch_jobs_status.py
@@ -7,17 +7,24 @@
 
 
 def format_job(job) -> str:
-    dependencies_passed = job["container_command"].split("--dependency ")[1].split(" --upload-to-sdc")[0]
+    dependencies_passed = (
+        job["container_command"].split("--dependency ")[1].split(" --upload-to-sdc")[0]
+    )
 
     input_collection = ProcessingInputCollection()
     input_collection.deserialize(dependencies_passed)
 
-    processing_input = "\n".join([f"\t\t{type(pi)}: {pi.filename_list}" for pi in input_collection.processing_input])
+    processing_input = "\n".join(
+        [
+            f"\t\t{type(pi)}: {pi.filename_list}"
+            for pi in input_collection.processing_input
+        ]
+    )
 
     return f"""
         descriptor: {job["descriptor"]}
         start_date: {job["start_date"]}
-        version: {job['version']}
+        version: {job["version"]}
         status: {job["status"]}
         processing_input:
 {processing_input}
@@ -29,15 +36,20 @@ def format_job(job) -> str:
 
 AFTER_DATE = datetime.now(tz=timezone.utc)
 
-url = 'https://api.dev.imap-mission.com/batch-job'
+url = "https://api.dev.imap-mission.com/batch-job"
 
 while True:
-    response = requests.get(url=url, params={'instrument': INSTRUMENT, 'data_level': DATA_LEVEL})
+    response = requests.get(
+        url=url, params={"instrument": INSTRUMENT, "data_level": DATA_LEVEL}
+    )
     logs = json.loads(json.dumps(response.json()))
-    relevant_logs = [log for log in logs if
-                     log['started_at'] is not None and
-                     datetime.fromisoformat(log['started_at']).replace(tzinfo=timezone.utc) > AFTER_DATE
-                     ]
+    relevant_logs = [
+        log
+        for log in logs
+        if log["started_at"] is not None
+        and datetime.fromisoformat(log["started_at"]).replace(tzinfo=timezone.utc)
+        > AFTER_DATE
+    ]
 
     print("\n".join([format_job(job) for job in relevant_logs]))
 
diff --git a/scripts/reprocess_all_in_situ.py b/scripts/reprocess_all_in_situ.py
index 77a55294a..e0dbf33ca 100644
--- a/scripts/reprocess_all_in_situ.py
+++ b/scripts/reprocess_all_in_situ.py
@@ -4,38 +4,33 @@
 
 LAUNCH_DATE = "20250924"
 
-GLOWS_PRODUCTS= "glows", {
-    "l3a": [
-        "hist"
-    ]
-}
-SWAPI_PRODUCTS= "swapi",{
-    "l3a": [
-        "proton-sw",
-        "alpha-sw",
-        "pui-he",
-    ],
-    "l3b":[
-        "combined"
-    ]
-}
-HIT_PRODUCTS = "hit", {
-    "l3": [
-        "direct-events",
-        "macropixel"
-    ]
-}
+GLOWS_PRODUCTS = "glows", {"l3a": ["hist"]}
+SWAPI_PRODUCTS = (
+    "swapi",
+    {
+        "l3a": [
+            "proton-sw",
+            "alpha-sw",
+            "pui-he",
+        ],
+        "l3b": ["combined"],
+    },
+)
+HIT_PRODUCTS = "hit", {"l3": ["direct-events", "macropixel"]}
 
-CODICE_PRODUCTS= "codice",{
-    "l3a": [
-        # "lo-partial-densities",
-        # "lo-direct-events",
-        "hi-direct-events",
-    ],
-    # "l3b": [
-    #     "hi-pitch-angle"
-    # ]
-}
+CODICE_PRODUCTS = (
+    "codice",
+    {
+        "l3a": [
+            # "lo-partial-densities",
+            # "lo-direct-events",
+            "hi-direct-events",
+        ],
+        # "l3b": [
+        #     "hi-pitch-angle"
+        # ]
+    },
+)
 
 ALL_PRODUCTS = [GLOWS_PRODUCTS, SWAPI_PRODUCTS, HIT_PRODUCTS, CODICE_PRODUCTS]
 
@@ -46,9 +41,9 @@
         for data_level, descriptors in descriptors_by_level.items():
             for descriptor in descriptors:
                 imap_data_access.reprocess(
-                    instrument = instrument,
-                    data_level = data_level,
-                    descriptor = descriptor,
-                    start_date = LAUNCH_DATE,
-                    end_date = TODAY
-                )
\ No newline at end of file
+                    instrument=instrument,
+                    data_level=data_level,
+                    descriptor=descriptor,
+                    start_date=LAUNCH_DATE,
+                    end_date=TODAY,
+                )
diff --git a/scripts/run_spectral_index.py b/scripts/run_spectral_index.py
index 02fb0128b..00f8363b0 100644
--- a/scripts/run_spectral_index.py
+++ b/scripts/run_spectral_index.py
@@ -4,19 +4,19 @@
 from pathlib import Path
 
 from imap_data_access import (
-    ScienceFilePath,
+    AncillaryInput,
     ProcessingInputCollection,
+    ScienceFilePath,
     ScienceInput,
-    AncillaryInput,
 )
 from imap_data_access.file_validation import Version
 
 from imap_l3_processing.hi.hi_processor import HiProcessor
 from imap_l3_processing.lo.lo_processor import LoProcessor
 from imap_l3_processing.maps.map_descriptors import (
-    parse_map_descriptor,
     MapQuantity,
     map_descriptor_parts_to_string,
+    parse_map_descriptor,
 )
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.ultra.ultra_processor import UltraProcessor
@@ -29,12 +29,16 @@ def run_spectral_index(input: Path):
     parsed_descriptor = parse_map_descriptor(parsed.descriptor)
 
     output_descriptor = map_descriptor_parts_to_string(
-        dataclasses.replace(parsed_descriptor, quantity=MapQuantity.SpectralIndex, quantity_suffix="")
+        dataclasses.replace(
+            parsed_descriptor, quantity=MapQuantity.SpectralIndex, quantity_suffix=""
+        )
     )
 
     dependencies = ProcessingInputCollection(ScienceInput(input.name))
-    if parsed.instrument == 'ultra':
-        dependencies.add(AncillaryInput("imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"))
+    if parsed.instrument == "ultra":
+        dependencies.add(
+            AncillaryInput("imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat")
+        )
 
     data_path = parsed.construct_path()
     data_path.parent.mkdir(parents=True, exist_ok=True)
@@ -56,6 +60,7 @@ def run_spectral_index(input: Path):
 if __name__ == "__main__":
     outputs = []
     for file in Path(
-            '/Users/harrison/Development/imap_L3_processing/run_local_input_data/lo/integration_data/imap/lo/l3/2025/04').iterdir():
+        "/Users/harrison/Development/imap_L3_processing/run_local_input_data/lo/integration_data/imap/lo/l3/2025/04"
+    ).iterdir():
         outputs.extend(run_spectral_index(file))
     print(outputs)
diff --git a/scripts/swapi/determine_swapi_files_with_outdated_spice.py b/scripts/swapi/determine_swapi_files_with_outdated_spice.py
index da8537ee0..b890df0ba 100644
--- a/scripts/swapi/determine_swapi_files_with_outdated_spice.py
+++ b/scripts/swapi/determine_swapi_files_with_outdated_spice.py
@@ -5,7 +5,12 @@
 descriptors = ["pui-he"]
 
 for descriptor in descriptors:
-    query_results = imap_data_access.query(instrument="swapi", data_level=data_level, descriptor=descriptor, version="latest")
+    query_results = imap_data_access.query(
+        instrument="swapi",
+        data_level=data_level,
+        descriptor=descriptor,
+        version="latest",
+    )
 
     dates_that_used_predict = []
     for result in sorted(query_results, key=lambda r: r["start_date"]):
diff --git a/scripts/swapi/fake_alpha_sw_temperature_density_lut_generator.py b/scripts/swapi/fake_alpha_sw_temperature_density_lut_generator.py
index 71a6c5f2e..359c90a1f 100644
--- a/scripts/swapi/fake_alpha_sw_temperature_density_lut_generator.py
+++ b/scripts/swapi/fake_alpha_sw_temperature_density_lut_generator.py
@@ -9,7 +9,9 @@
 densities = np.arange(1, 6.5, 0.5)
 temps = np.arange(1e4, 1.6e5, 1e4)
 
-with open("imap_swapi_l2_alpha-density-temperature-lut-text-not-cdf_20240905_v002.cdf", 'w') as f:
+with open(
+    "imap_swapi_l2_alpha-density-temperature-lut-text-not-cdf_20240905_v002.cdf", "w"
+) as f:
     f.write("#\n")
     f.write(f"# Created on: {today()}\n")
     f.write("#\n")
@@ -17,6 +19,7 @@
     f.write("#\n")
     f.write("#   u_sw     n     density (cm^-3)     T     temperature (K)\n")
 
-    for u_sw, density, temp in itertools.product(u_sw_values, densities, temps):
-        f.write(
-            f"{u_sw}  {density}  {density * 1.021:.4f}  {temp:.4e}  {temp / 1.025:0.4e}\n")
+    f.writelines(
+        f"{u_sw}  {density}  {density * 1.021:.4f}  {temp:.4e}  {temp / 1.025:0.4e}\n"
+        for u_sw, density, temp in itertools.product(u_sw_values, densities, temps)
+    )
diff --git a/scripts/swapi/fake_data_generator.py b/scripts/swapi/fake_data_generator.py
index 51fe72923..3aafb9655 100644
--- a/scripts/swapi/fake_data_generator.py
+++ b/scripts/swapi/fake_data_generator.py
@@ -1,4 +1,4 @@
-from random import random, randint
+from random import randint
 
 import numpy as np
 
@@ -20,17 +20,19 @@ def generate_sweep_data(center):
 
 def generate_sweeps(n=5):
     time_base = 315576112184000000  # Jan 1, 2010 00:00:46
-    start_times_in_seconds = time_base + np.arange(n)*12
+    start_times_in_seconds = time_base + np.arange(n) * 12
     seed = randint(0, 359)
-    angles = [seed - 72*i for i in range(5)]
-    center_points = 1050 + 20*np.sin(np.deg2rad(angles))
+    angles = [seed - 72 * i for i in range(5)]
+    center_points = 1050 + 20 * np.sin(np.deg2rad(angles))
     sweeps = [generate_sweep_data(c) for c in center_points]
     return list(zip(start_times_in_seconds, sweeps))
 
 
 def generate_sweep_energies():
     coarse_sweep_energies = np.geomspace(100, 19000, 62)
-    return np.concatenate((np.array([np.nan]), coarse_sweep_energies, np.array([np.nan] * 9)))
+    return np.concatenate(
+        (np.array([np.nan]), coarse_sweep_energies, np.array([np.nan] * 9))
+    )
 
 
 # K Factor may come from a lookup table in the future
@@ -42,11 +44,12 @@ def get_k_factor():
 def get_spin_phase_using_spice(time):
     arbitrary_offset = 0.4
     rotation_time = 15
-    rotations = arbitrary_offset + time/rotation_time
+    rotations = arbitrary_offset + time / rotation_time
     fractional, integral = np.modf(rotations)
-    return fractional*360
+    return fractional * 360
 
 
 def generate_peak(energies, height, center, narrowness):
-    return np.exp(np.log(height) - narrowness * np.square(np.log(energies) - np.log(center)))
-
+    return np.exp(
+        np.log(height) - narrowness * np.square(np.log(energies) - np.log(center))
+    )
diff --git a/scripts/swapi/fake_uncertainties_for_cdf.py b/scripts/swapi/fake_uncertainties_for_cdf.py
index 999bfb877..98959d057 100644
--- a/scripts/swapi/fake_uncertainties_for_cdf.py
+++ b/scripts/swapi/fake_uncertainties_for_cdf.py
@@ -1,6 +1,7 @@
 import numpy as np
 from spacepy import pycdf
 
+
 def fake_uncertainties(cdf_path):
     original_cdf = pycdf.CDF(cdf_path, readonly=False)
 
@@ -8,4 +9,4 @@ def fake_uncertainties(cdf_path):
     uncertainties = np.sqrt(6 * coincidence_count_rates)
     original_cdf["swp_coin_unc"] = uncertainties
 
-    original_cdf.save()
\ No newline at end of file
+    original_cdf.save()
diff --git a/scripts/swapi/fit_and_plot_alpha.py b/scripts/swapi/fit_and_plot_alpha.py
index 43ab988fd..43cff380c 100644
--- a/scripts/swapi/fit_and_plot_alpha.py
+++ b/scripts/swapi/fit_and_plot_alpha.py
@@ -27,7 +27,11 @@
 import imap_data_access
 import matplotlib.pyplot as plt
 import numpy as np
-from imap_data_access import DependencyFilePath, ProcessingInputCollection, ScienceFilePath
+from imap_data_access import (
+    DependencyFilePath,
+    ProcessingInputCollection,
+    ScienceFilePath,
+)
 from imap_data_access.processing_input import generate_imap_input
 from spacepy.pycdf import CDF
 
@@ -65,9 +69,18 @@ def fetch_wind_swe_2min_alphas(compact_date_string, cache_directory):
         print(f"Downloading WIND SWE 2-min {year_month_string}...", end=" ", flush=True)
         try:
             subprocess.run(
-                ["curl", "--ssl-reqd", "-fsS",
-                 "--connect-timeout", "15", "--max-time", "300",
-                 "-o", str(cache_file_path), url],
+                [
+                    "curl",
+                    "--ssl-reqd",
+                    "-fsS",
+                    "--connect-timeout",
+                    "15",
+                    "--max-time",
+                    "300",
+                    "-o",
+                    str(cache_file_path),
+                    url,
+                ],
                 check=True,
             )
             print("done")
@@ -93,13 +106,17 @@ def fetch_wind_swe_2min_alphas(compact_date_string, cache_directory):
         for y in np.unique(year_column)
     }
     year_start_nanoseconds_column = np.array(
-        [year_starts_nanoseconds[int(y)] for y in year_column], dtype="int64")
+        [year_starts_nanoseconds[int(y)] for y in year_column], dtype="int64"
+    )
     epoch_nanoseconds = year_start_nanoseconds_column + (
-        (fractional_day_of_year_column - 1.0) * 86400.0 * 1e9).astype("int64")
+        (fractional_day_of_year_column - 1.0) * 86400.0 * 1e9
+    ).astype("int64")
     epoch = epoch_nanoseconds.view("datetime64[ns]")
 
     day_start = np.datetime64(
-        f"{compact_date_string[:4]}-{compact_date_string[4:6]}-{compact_date_string[6:8]}", "ns")
+        f"{compact_date_string[:4]}-{compact_date_string[4:6]}-{compact_date_string[6:8]}",
+        "ns",
+    )
     day_end = day_start + np.timedelta64(1, "D")
     day_mask = (epoch >= day_start) & (epoch < day_end)
     if not np.any(day_mask):
@@ -119,10 +136,15 @@ def fetch_wind_swe_2min_alphas(compact_date_string, cache_directory):
     alpha_density_sigma = row[:, 38]
 
     # T = c * W^2 -> sigma_T = 2 * c * W * sigma_W (linear propagation, W >= 0).
-    alpha_temperature = WIND_ALPHA_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2 * alpha_thermal_speed**2
+    alpha_temperature = (
+        WIND_ALPHA_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2 * alpha_thermal_speed**2
+    )
     alpha_temperature_sigma = (
-        2.0 * WIND_ALPHA_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2
-        * np.abs(alpha_thermal_speed) * alpha_thermal_speed_sigma)
+        2.0
+        * WIND_ALPHA_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2
+        * np.abs(alpha_thermal_speed)
+        * alpha_thermal_speed_sigma
+    )
 
     # GSE -> RTN at L1, Sun-Earth-line approximation.
     return {
@@ -147,8 +169,13 @@ def build_dependency_collection(target_date, compact_date):
     them with the static SWAPI ancillary template into a ProcessingInputCollection.
     """
     science_query_results = imap_data_access.query(
-        instrument="swapi", data_level="l2", descriptor="sci",
-        start_date=compact_date, end_date=compact_date, version="latest")
+        instrument="swapi",
+        data_level="l2",
+        descriptor="sci",
+        start_date=compact_date,
+        end_date=compact_date,
+        version="latest",
+    )
     if not science_query_results:
         sys.exit(f"No SWAPI L2 sci file at the SDC for {compact_date}")
 
@@ -158,8 +185,13 @@ def build_dependency_collection(target_date, compact_date):
         os.path.basename(row["file_path"])
         for level in ("l2", "l1d")
         for row in imap_data_access.query(
-            instrument="mag", data_level=level, descriptor="norm-rtn",
-            start_date=compact_date, end_date=compact_date, version="latest")
+            instrument="mag",
+            data_level=level,
+            descriptor="norm-rtn",
+            start_date=compact_date,
+            end_date=compact_date,
+            version="latest",
+        )
     ]
     if not magnetic_field_filenames:
         sys.exit(f"No MAG norm-rtn file (L2 or L1D) at the SDC for {compact_date}")
@@ -168,22 +200,30 @@ def build_dependency_collection(target_date, compact_date):
     # window around the target date. Spacecraft ephemeris kernels need a wide
     # lookback because the reconstructed kernel ends weeks before the target
     # date and the predicted kernel covers the gap onward.
-    ephemeris_kernel_types = [SpiceKernelTypes.EphemerisReconstructed,
-                              SpiceKernelTypes.EphemerisPredicted]
-    non_ephemeris_kernel_types = [t for t in SpiceKernelTypes
-                                  if t not in ephemeris_kernel_types]
-    spice_kernel_filenames = list(dict.fromkeys(
-        os.path.basename(path) for path in (
-            get_spice_kernels_file_names(
-                target_date - timedelta(days=1),
-                target_date + timedelta(days=2),
-                non_ephemeris_kernel_types)
-            + get_spice_kernels_file_names(
-                target_date - timedelta(days=90),
-                target_date + timedelta(days=2),
-                ephemeris_kernel_types)
+    ephemeris_kernel_types = [
+        SpiceKernelTypes.EphemerisReconstructed,
+        SpiceKernelTypes.EphemerisPredicted,
+    ]
+    non_ephemeris_kernel_types = [
+        t for t in SpiceKernelTypes if t not in ephemeris_kernel_types
+    ]
+    spice_kernel_filenames = list(
+        dict.fromkeys(
+            os.path.basename(path)
+            for path in (
+                get_spice_kernels_file_names(
+                    target_date - timedelta(days=1),
+                    target_date + timedelta(days=2),
+                    non_ephemeris_kernel_types,
+                )
+                + get_spice_kernels_file_names(
+                    target_date - timedelta(days=90),
+                    target_date + timedelta(days=2),
+                    ephemeris_kernel_types,
+                )
+            )
         )
-    ))
+    )
 
     dynamic_filenames = [
         os.path.basename(science_query_results[0]["file_path"]),
@@ -191,18 +231,24 @@ def build_dependency_collection(target_date, compact_date):
         *spice_kernel_filenames,
     ]
     ancillary_template_path = (
-        Path(__file__).parent / "imap_swapi_l3a_proton-sw_dependency_template.json")
+        Path(__file__).parent / "imap_swapi_l3a_proton-sw_dependency_template.json"
+    )
     processing_input_collection = ProcessingInputCollection(
-        *[generate_imap_input(filename) for filename in dynamic_filenames])
+        *[generate_imap_input(filename) for filename in dynamic_filenames]
+    )
     processing_input_collection.deserialize(ancillary_template_path.read_text())
     return processing_input_collection
 
 
 argument_parser = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+)
 argument_parser.add_argument("date", help="UTC date in YYYY-MM-DD format")
-argument_parser.add_argument("--use-cache", action="store_true",
-                             help="Skip download/processing if the alpha-sw CDF already exists in IMAP_DATA_DIR.")
+argument_parser.add_argument(
+    "--use-cache",
+    action="store_true",
+    help="Skip download/processing if the alpha-sw CDF already exists in IMAP_DATA_DIR.",
+)
 arguments = argument_parser.parse_args()
 
 if "IMAP_API_KEY" not in os.environ:
@@ -218,27 +264,38 @@ def build_dependency_collection(target_date, compact_date):
 imap_data_access.config["DATA_DIR"] = DATA_DIR
 
 output_cdf_path = ScienceFilePath(
-    f"imap_swapi_l3a_alpha-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.cdf").construct_path()
+    f"imap_swapi_l3a_alpha-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.cdf"
+).construct_path()
 
 if not arguments.use_cache or not output_cdf_path.exists():
     processing_input_collection = build_dependency_collection(target_date, compact_date)
     processing_input_collection.download_all_files()
 
     dependency_filename = (
-        f"imap_swapi_l3a_alpha-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.json")
+        f"imap_swapi_l3a_alpha-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.json"
+    )
     dependency_path = DependencyFilePath(dependency_filename).construct_path()
     dependency_path.parent.mkdir(parents=True, exist_ok=True)
     dependency_path.write_text(processing_input_collection.serialize())
 
     output_cdf_path.unlink(missing_ok=True)
     result = subprocess.run(
-        [sys.executable, "imap_l3_data_processor.py",
-         "--instrument", "swapi",
-         "--data-level", "l3a",
-         "--descriptor", "alpha-sw",
-         "--start-date", compact_date,
-         "--version", PROCESSOR_OUTPUT_VERSION,
-         "--dependency", dependency_filename],
+        [
+            sys.executable,
+            "imap_l3_data_processor.py",
+            "--instrument",
+            "swapi",
+            "--data-level",
+            "l3a",
+            "--descriptor",
+            "alpha-sw",
+            "--start-date",
+            compact_date,
+            "--version",
+            PROCESSOR_OUTPUT_VERSION,
+            "--dependency",
+            dependency_filename,
+        ],
         cwd=L3_PROCESSING_ROOT,
     )
     if result.returncode != 0:
@@ -250,14 +307,19 @@ def build_dependency_collection(target_date, compact_date):
     timestamps = alpha_cdf["epoch"][...]
     quality_flags = alpha_cdf["swp_flags"][...]
     swapi_density = read_numeric_variable(alpha_cdf["alpha_sw_density"])
-    swapi_density_uncertainty = read_numeric_variable(alpha_cdf["alpha_sw_density_uncert"])
+    swapi_density_uncertainty = read_numeric_variable(
+        alpha_cdf["alpha_sw_density_uncert"]
+    )
     swapi_temperature = read_numeric_variable(alpha_cdf["alpha_sw_temperature"])
-    swapi_temperature_uncertainty = read_numeric_variable(alpha_cdf["alpha_sw_temperature_uncert"])
+    swapi_temperature_uncertainty = read_numeric_variable(
+        alpha_cdf["alpha_sw_temperature_uncert"]
+    )
     swapi_speed = read_numeric_variable(alpha_cdf["alpha_sw_speed"])
     swapi_speed_uncertainty = read_numeric_variable(alpha_cdf["alpha_sw_speed_uncert"])
     swapi_velocity_rtn = read_numeric_variable(alpha_cdf["alpha_sw_velocity_rtn_sun"])
     swapi_velocity_rtn_covariance = read_numeric_variable(
-        alpha_cdf["alpha_sw_velocity_rtn_covariance"])
+        alpha_cdf["alpha_sw_velocity_rtn_covariance"]
+    )
 
 # Drop flagged records so fill values don't drag the y-axes.
 good_record_mask = quality_flags == 0
@@ -272,42 +334,93 @@ def build_dependency_collection(target_date, compact_date):
 swapi_velocity_rtn_covariance = swapi_velocity_rtn_covariance[good_record_mask]
 
 # Diagonal of the RTN velocity covariance gives variances on (vR, vT, vN).
-swapi_velocity_rtn_sigma = np.sqrt(np.diagonal(
-    swapi_velocity_rtn_covariance, axis1=1, axis2=2))
+swapi_velocity_rtn_sigma = np.sqrt(
+    np.diagonal(swapi_velocity_rtn_covariance, axis1=1, axis2=2)
+)
 
 wind_alphas = fetch_wind_swe_2min_alphas(compact_date, DATA_DIR / "wind") or {}
 
 panels = [
-    ("Density [cm$^{-3}$]", "log",
-     swapi_density, swapi_density_uncertainty,
-     wind_alphas.get("density"), wind_alphas.get("density_sigma")),
-    ("Temperature [K]", "log",
-     swapi_temperature, swapi_temperature_uncertainty,
-     wind_alphas.get("temperature"), wind_alphas.get("temperature_sigma")),
-    ("Bulk speed [km/s]", "linear",
-     swapi_speed, swapi_speed_uncertainty,
-     wind_alphas.get("speed"), wind_alphas.get("speed_sigma")),
-    ("$v_R$ [km/s]", "linear",
-     swapi_velocity_rtn[:, 0], swapi_velocity_rtn_sigma[:, 0],
-     wind_alphas.get("velocity_r"), wind_alphas.get("velocity_r_sigma")),
-    ("$v_T$ [km/s]", "linear",
-     swapi_velocity_rtn[:, 1], swapi_velocity_rtn_sigma[:, 1],
-     wind_alphas.get("velocity_t"), wind_alphas.get("velocity_t_sigma")),
-    ("$v_N$ [km/s]", "linear",
-     swapi_velocity_rtn[:, 2], swapi_velocity_rtn_sigma[:, 2],
-     wind_alphas.get("velocity_n"), wind_alphas.get("velocity_n_sigma")),
+    (
+        "Density [cm$^{-3}$]",
+        "log",
+        swapi_density,
+        swapi_density_uncertainty,
+        wind_alphas.get("density"),
+        wind_alphas.get("density_sigma"),
+    ),
+    (
+        "Temperature [K]",
+        "log",
+        swapi_temperature,
+        swapi_temperature_uncertainty,
+        wind_alphas.get("temperature"),
+        wind_alphas.get("temperature_sigma"),
+    ),
+    (
+        "Bulk speed [km/s]",
+        "linear",
+        swapi_speed,
+        swapi_speed_uncertainty,
+        wind_alphas.get("speed"),
+        wind_alphas.get("speed_sigma"),
+    ),
+    (
+        "$v_R$ [km/s]",
+        "linear",
+        swapi_velocity_rtn[:, 0],
+        swapi_velocity_rtn_sigma[:, 0],
+        wind_alphas.get("velocity_r"),
+        wind_alphas.get("velocity_r_sigma"),
+    ),
+    (
+        "$v_T$ [km/s]",
+        "linear",
+        swapi_velocity_rtn[:, 1],
+        swapi_velocity_rtn_sigma[:, 1],
+        wind_alphas.get("velocity_t"),
+        wind_alphas.get("velocity_t_sigma"),
+    ),
+    (
+        "$v_N$ [km/s]",
+        "linear",
+        swapi_velocity_rtn[:, 2],
+        swapi_velocity_rtn_sigma[:, 2],
+        wind_alphas.get("velocity_n"),
+        wind_alphas.get("velocity_n_sigma"),
+    ),
 ]
 wind_epoch = wind_alphas.get("epoch")
 
 figure, axes = plt.subplots(len(panels), 1, sharex=True, figsize=(10, 14))
-for axis, (ylabel, yscale, swapi_values, swapi_uncertainties,
-           wind_values, wind_uncertainties) in zip(axes, panels):
-    axis.errorbar(timestamps, swapi_values, yerr=swapi_uncertainties,
-                  fmt=".", capsize=2, color="tab:blue", label="SWAPI L3a")
+for axis, (
+    ylabel,
+    yscale,
+    swapi_values,
+    swapi_uncertainties,
+    wind_values,
+    wind_uncertainties,
+) in zip(axes, panels):
+    axis.errorbar(
+        timestamps,
+        swapi_values,
+        yerr=swapi_uncertainties,
+        fmt=".",
+        capsize=2,
+        color="tab:blue",
+        label="SWAPI L3a",
+    )
     if wind_values is not None and wind_epoch is not None:
-        axis.errorbar(wind_epoch, wind_values, yerr=wind_uncertainties,
-                      fmt=".", capsize=2, color="tab:orange", alpha=0.6,
-                      label="WIND SWE 2-min")
+        axis.errorbar(
+            wind_epoch,
+            wind_values,
+            yerr=wind_uncertainties,
+            fmt=".",
+            capsize=2,
+            color="tab:orange",
+            alpha=0.6,
+            label="WIND SWE 2-min",
+        )
     axis.set_ylabel(ylabel)
     axis.set_yscale(yscale)
 axes[0].legend(loc="best")
diff --git a/scripts/swapi/fit_and_plot_proton.py b/scripts/swapi/fit_and_plot_proton.py
index 7a6602c59..543b00260 100644
--- a/scripts/swapi/fit_and_plot_proton.py
+++ b/scripts/swapi/fit_and_plot_proton.py
@@ -27,7 +27,11 @@
 import imap_data_access
 import matplotlib.pyplot as plt
 import numpy as np
-from imap_data_access import DependencyFilePath, ProcessingInputCollection, ScienceFilePath
+from imap_data_access import (
+    DependencyFilePath,
+    ProcessingInputCollection,
+    ScienceFilePath,
+)
 from imap_data_access.processing_input import generate_imap_input
 from spacepy.pycdf import CDF
 
@@ -65,9 +69,18 @@ def fetch_wind_swe_2min_protons(compact_date_string, cache_directory):
         print(f"Downloading WIND SWE 2-min {year_month_string}...", end=" ", flush=True)
         try:
             subprocess.run(
-                ["curl", "--ssl-reqd", "-fsS",
-                 "--connect-timeout", "15", "--max-time", "300",
-                 "-o", str(cache_file_path), url],
+                [
+                    "curl",
+                    "--ssl-reqd",
+                    "-fsS",
+                    "--connect-timeout",
+                    "15",
+                    "--max-time",
+                    "300",
+                    "-o",
+                    str(cache_file_path),
+                    url,
+                ],
                 check=True,
             )
             print("done")
@@ -92,13 +105,17 @@ def fetch_wind_swe_2min_protons(compact_date_string, cache_directory):
         for y in np.unique(year_column)
     }
     year_start_nanoseconds_column = np.array(
-        [year_starts_nanoseconds[int(y)] for y in year_column], dtype="int64")
+        [year_starts_nanoseconds[int(y)] for y in year_column], dtype="int64"
+    )
     epoch_nanoseconds = year_start_nanoseconds_column + (
-        (fractional_day_of_year_column - 1.0) * 86400.0 * 1e9).astype("int64")
+        (fractional_day_of_year_column - 1.0) * 86400.0 * 1e9
+    ).astype("int64")
     epoch = epoch_nanoseconds.view("datetime64[ns]")
 
     day_start = np.datetime64(
-        f"{compact_date_string[:4]}-{compact_date_string[4:6]}-{compact_date_string[6:8]}", "ns")
+        f"{compact_date_string[:4]}-{compact_date_string[4:6]}-{compact_date_string[6:8]}",
+        "ns",
+    )
     day_end = day_start + np.timedelta64(1, "D")
     day_mask = (epoch >= day_start) & (epoch < day_end)
     if not np.any(day_mask):
@@ -118,10 +135,15 @@ def fetch_wind_swe_2min_protons(compact_date_string, cache_directory):
     proton_thermal_speed, proton_thermal_speed_sigma = row[:, 11], row[:, 12]
     proton_density, proton_density_sigma = row[:, 21], row[:, 22]
 
-    proton_temperature = WIND_PROTON_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2 * proton_thermal_speed**2
+    proton_temperature = (
+        WIND_PROTON_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2 * proton_thermal_speed**2
+    )
     proton_temperature_sigma = (
-        2.0 * WIND_PROTON_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2
-        * np.abs(proton_thermal_speed) * proton_thermal_speed_sigma)
+        2.0
+        * WIND_PROTON_TEMPERATURE_FACTOR_K_PER_KM2_PER_S2
+        * np.abs(proton_thermal_speed)
+        * proton_thermal_speed_sigma
+    )
 
     return {
         "epoch": epoch_day,
@@ -145,8 +167,13 @@ def build_dependency_collection(target_date, compact_date):
     them with the static SWAPI ancillary template into a ProcessingInputCollection.
     """
     science_query_results = imap_data_access.query(
-        instrument="swapi", data_level="l2", descriptor="sci",
-        start_date=compact_date, end_date=compact_date, version="latest")
+        instrument="swapi",
+        data_level="l2",
+        descriptor="sci",
+        start_date=compact_date,
+        end_date=compact_date,
+        version="latest",
+    )
     if not science_query_results:
         sys.exit(f"No SWAPI L2 sci file at the SDC for {compact_date}")
 
@@ -154,28 +181,41 @@ def build_dependency_collection(target_date, compact_date):
         os.path.basename(row["file_path"])
         for level in ("l2", "l1d")
         for row in imap_data_access.query(
-            instrument="mag", data_level=level, descriptor="norm-rtn",
-            start_date=compact_date, end_date=compact_date, version="latest")
+            instrument="mag",
+            data_level=level,
+            descriptor="norm-rtn",
+            start_date=compact_date,
+            end_date=compact_date,
+            version="latest",
+        )
     ]
     if not magnetic_field_filenames:
         sys.exit(f"No MAG norm-rtn file (L2 or L1D) at the SDC for {compact_date}")
 
-    ephemeris_kernel_types = [SpiceKernelTypes.EphemerisReconstructed,
-                              SpiceKernelTypes.EphemerisPredicted]
-    non_ephemeris_kernel_types = [t for t in SpiceKernelTypes
-                                  if t not in ephemeris_kernel_types]
-    spice_kernel_filenames = list(dict.fromkeys(
-        os.path.basename(path) for path in (
-            get_spice_kernels_file_names(
-                target_date - timedelta(days=1),
-                target_date + timedelta(days=2),
-                non_ephemeris_kernel_types)
-            + get_spice_kernels_file_names(
-                target_date - timedelta(days=90),
-                target_date + timedelta(days=2),
-                ephemeris_kernel_types)
+    ephemeris_kernel_types = [
+        SpiceKernelTypes.EphemerisReconstructed,
+        SpiceKernelTypes.EphemerisPredicted,
+    ]
+    non_ephemeris_kernel_types = [
+        t for t in SpiceKernelTypes if t not in ephemeris_kernel_types
+    ]
+    spice_kernel_filenames = list(
+        dict.fromkeys(
+            os.path.basename(path)
+            for path in (
+                get_spice_kernels_file_names(
+                    target_date - timedelta(days=1),
+                    target_date + timedelta(days=2),
+                    non_ephemeris_kernel_types,
+                )
+                + get_spice_kernels_file_names(
+                    target_date - timedelta(days=90),
+                    target_date + timedelta(days=2),
+                    ephemeris_kernel_types,
+                )
+            )
         )
-    ))
+    )
 
     dynamic_filenames = [
         os.path.basename(science_query_results[0]["file_path"]),
@@ -183,18 +223,24 @@ def build_dependency_collection(target_date, compact_date):
         *spice_kernel_filenames,
     ]
     ancillary_template_path = (
-        Path(__file__).parent / "imap_swapi_l3a_proton-sw_dependency_template.json")
+        Path(__file__).parent / "imap_swapi_l3a_proton-sw_dependency_template.json"
+    )
     processing_input_collection = ProcessingInputCollection(
-        *[generate_imap_input(filename) for filename in dynamic_filenames])
+        *[generate_imap_input(filename) for filename in dynamic_filenames]
+    )
     processing_input_collection.deserialize(ancillary_template_path.read_text())
     return processing_input_collection
 
 
 argument_parser = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+)
 argument_parser.add_argument("date", help="UTC date in YYYY-MM-DD format")
-argument_parser.add_argument("--use-cache", action="store_true",
-                             help="Skip download/processing if the proton-sw CDF already exists in IMAP_DATA_DIR.")
+argument_parser.add_argument(
+    "--use-cache",
+    action="store_true",
+    help="Skip download/processing if the proton-sw CDF already exists in IMAP_DATA_DIR.",
+)
 arguments = argument_parser.parse_args()
 
 if "IMAP_API_KEY" not in os.environ:
@@ -208,27 +254,38 @@ def build_dependency_collection(target_date, compact_date):
 imap_data_access.config["DATA_DIR"] = DATA_DIR
 
 output_cdf_path = ScienceFilePath(
-    f"imap_swapi_l3a_proton-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.cdf").construct_path()
+    f"imap_swapi_l3a_proton-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.cdf"
+).construct_path()
 
 if not arguments.use_cache or not output_cdf_path.exists():
     processing_input_collection = build_dependency_collection(target_date, compact_date)
     processing_input_collection.download_all_files()
 
     dependency_filename = (
-        f"imap_swapi_l3a_proton-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.json")
+        f"imap_swapi_l3a_proton-sw_{compact_date}_{PROCESSOR_OUTPUT_VERSION}.json"
+    )
     dependency_path = DependencyFilePath(dependency_filename).construct_path()
     dependency_path.parent.mkdir(parents=True, exist_ok=True)
     dependency_path.write_text(processing_input_collection.serialize())
 
     output_cdf_path.unlink(missing_ok=True)
     result = subprocess.run(
-        [sys.executable, "imap_l3_data_processor.py",
-         "--instrument", "swapi",
-         "--data-level", "l3a",
-         "--descriptor", "proton-sw",
-         "--start-date", compact_date,
-         "--version", PROCESSOR_OUTPUT_VERSION,
-         "--dependency", dependency_filename],
+        [
+            sys.executable,
+            "imap_l3_data_processor.py",
+            "--instrument",
+            "swapi",
+            "--data-level",
+            "l3a",
+            "--descriptor",
+            "proton-sw",
+            "--start-date",
+            compact_date,
+            "--version",
+            PROCESSOR_OUTPUT_VERSION,
+            "--dependency",
+            dependency_filename,
+        ],
         cwd=L3_PROCESSING_ROOT,
     )
     if result.returncode != 0:
@@ -240,14 +297,21 @@ def build_dependency_collection(target_date, compact_date):
     timestamps = proton_cdf["epoch"][...]
     quality_flags = proton_cdf["swp_flags"][...]
     swapi_density = read_numeric_variable(proton_cdf["proton_sw_density"])
-    swapi_density_uncertainty = read_numeric_variable(proton_cdf["proton_sw_density_uncert"])
+    swapi_density_uncertainty = read_numeric_variable(
+        proton_cdf["proton_sw_density_uncert"]
+    )
     swapi_temperature = read_numeric_variable(proton_cdf["proton_sw_temperature"])
-    swapi_temperature_uncertainty = read_numeric_variable(proton_cdf["proton_sw_temperature_uncert"])
+    swapi_temperature_uncertainty = read_numeric_variable(
+        proton_cdf["proton_sw_temperature_uncert"]
+    )
     swapi_speed = read_numeric_variable(proton_cdf["proton_sw_speed"])
-    swapi_speed_uncertainty = read_numeric_variable(proton_cdf["proton_sw_speed_uncert"])
+    swapi_speed_uncertainty = read_numeric_variable(
+        proton_cdf["proton_sw_speed_uncert"]
+    )
     swapi_velocity_rtn = read_numeric_variable(proton_cdf["proton_sw_velocity_rtn_sun"])
     swapi_velocity_rtn_covariance = read_numeric_variable(
-        proton_cdf["proton_sw_velocity_rtn_covariance"])
+        proton_cdf["proton_sw_velocity_rtn_covariance"]
+    )
 
 good_record_mask = quality_flags == 0
 timestamps = timestamps[good_record_mask]
@@ -260,42 +324,93 @@ def build_dependency_collection(target_date, compact_date):
 swapi_velocity_rtn = swapi_velocity_rtn[good_record_mask]
 swapi_velocity_rtn_covariance = swapi_velocity_rtn_covariance[good_record_mask]
 
-swapi_velocity_rtn_sigma = np.sqrt(np.diagonal(
-    swapi_velocity_rtn_covariance, axis1=1, axis2=2))
+swapi_velocity_rtn_sigma = np.sqrt(
+    np.diagonal(swapi_velocity_rtn_covariance, axis1=1, axis2=2)
+)
 
 wind_protons = fetch_wind_swe_2min_protons(compact_date, DATA_DIR / "wind") or {}
 
 panels = [
-    ("Density [cm$^{-3}$]", "log",
-     swapi_density, swapi_density_uncertainty,
-     wind_protons.get("density"), wind_protons.get("density_sigma")),
-    ("Temperature [K]", "log",
-     swapi_temperature, swapi_temperature_uncertainty,
-     wind_protons.get("temperature"), wind_protons.get("temperature_sigma")),
-    ("Bulk speed [km/s]", "linear",
-     swapi_speed, swapi_speed_uncertainty,
-     wind_protons.get("speed"), wind_protons.get("speed_sigma")),
-    ("$v_R$ [km/s]", "linear",
-     swapi_velocity_rtn[:, 0], swapi_velocity_rtn_sigma[:, 0],
-     wind_protons.get("velocity_r"), wind_protons.get("velocity_r_sigma")),
-    ("$v_T$ [km/s]", "linear",
-     swapi_velocity_rtn[:, 1], swapi_velocity_rtn_sigma[:, 1],
-     wind_protons.get("velocity_t"), wind_protons.get("velocity_t_sigma")),
-    ("$v_N$ [km/s]", "linear",
-     swapi_velocity_rtn[:, 2], swapi_velocity_rtn_sigma[:, 2],
-     wind_protons.get("velocity_n"), wind_protons.get("velocity_n_sigma")),
+    (
+        "Density [cm$^{-3}$]",
+        "log",
+        swapi_density,
+        swapi_density_uncertainty,
+        wind_protons.get("density"),
+        wind_protons.get("density_sigma"),
+    ),
+    (
+        "Temperature [K]",
+        "log",
+        swapi_temperature,
+        swapi_temperature_uncertainty,
+        wind_protons.get("temperature"),
+        wind_protons.get("temperature_sigma"),
+    ),
+    (
+        "Bulk speed [km/s]",
+        "linear",
+        swapi_speed,
+        swapi_speed_uncertainty,
+        wind_protons.get("speed"),
+        wind_protons.get("speed_sigma"),
+    ),
+    (
+        "$v_R$ [km/s]",
+        "linear",
+        swapi_velocity_rtn[:, 0],
+        swapi_velocity_rtn_sigma[:, 0],
+        wind_protons.get("velocity_r"),
+        wind_protons.get("velocity_r_sigma"),
+    ),
+    (
+        "$v_T$ [km/s]",
+        "linear",
+        swapi_velocity_rtn[:, 1],
+        swapi_velocity_rtn_sigma[:, 1],
+        wind_protons.get("velocity_t"),
+        wind_protons.get("velocity_t_sigma"),
+    ),
+    (
+        "$v_N$ [km/s]",
+        "linear",
+        swapi_velocity_rtn[:, 2],
+        swapi_velocity_rtn_sigma[:, 2],
+        wind_protons.get("velocity_n"),
+        wind_protons.get("velocity_n_sigma"),
+    ),
 ]
 wind_epoch = wind_protons.get("epoch")
 
 figure, axes = plt.subplots(len(panels), 1, sharex=True, figsize=(10, 14))
-for axis, (ylabel, yscale, swapi_values, swapi_uncertainties,
-           wind_values, wind_uncertainties) in zip(axes, panels):
-    axis.errorbar(timestamps, swapi_values, yerr=swapi_uncertainties,
-                  fmt=".", capsize=2, color="tab:blue", label="SWAPI L3a")
+for axis, (
+    ylabel,
+    yscale,
+    swapi_values,
+    swapi_uncertainties,
+    wind_values,
+    wind_uncertainties,
+) in zip(axes, panels):
+    axis.errorbar(
+        timestamps,
+        swapi_values,
+        yerr=swapi_uncertainties,
+        fmt=".",
+        capsize=2,
+        color="tab:blue",
+        label="SWAPI L3a",
+    )
     if wind_values is not None and wind_epoch is not None:
-        axis.errorbar(wind_epoch, wind_values, yerr=wind_uncertainties,
-                      fmt=".", capsize=2, color="tab:orange", alpha=0.6,
-                      label="WIND SWE 2-min")
+        axis.errorbar(
+            wind_epoch,
+            wind_values,
+            yerr=wind_uncertainties,
+            fmt=".",
+            capsize=2,
+            color="tab:orange",
+            alpha=0.6,
+            label="WIND SWE 2-min",
+        )
     axis.set_ylabel(ylabel)
     axis.set_yscale(yscale)
 axes[0].legend(loc="best")
diff --git a/scripts/swapi/fit_and_plot_pui.py b/scripts/swapi/fit_and_plot_pui.py
index 880bb13e2..d6a99e153 100644
--- a/scripts/swapi/fit_and_plot_pui.py
+++ b/scripts/swapi/fit_and_plot_pui.py
@@ -35,11 +35,19 @@
 
 from imap_l3_processing.constants import (
     HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E,
-    ONE_AU_IN_KM,
     ONE_SECOND_IN_NANOSECONDS,
 )
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.swapi.constants import SWAPI_COARSE_SWEEP_BINS, SWAPI_L2_K_FACTOR
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+)
+from imap_l3_processing.swapi.l3a.chunk_fits import (
+    ParallelChunkRunner,
+    ProtonChunkFitter,
+    PuiChunkFitter,
+)
+from imap_l3_processing.swapi.l3a.models import SwapiL3PickupIonData
 from imap_l3_processing.swapi.l3a.science.pickup_ion.calculate_coincidence_rate import (
     calculate_coincidence_rate,
 )
@@ -49,46 +57,45 @@
 from imap_l3_processing.swapi.l3a.science.pickup_ion.collapsed_response_grid import (
     build_chunk_collapsed_response,
 )
+from imap_l3_processing.swapi.l3a.science.pickup_ion.utils import (
+    rotate_rtn_velocity_to_swapi_per_bin,
+)
 from imap_l3_processing.swapi.l3a.science.pickup_ion.vasyliunas_siscoe_distribution import (
     FittingParameters,
     build_vasyliunas_siscoe_distribution,
 )
-from imap_l3_processing.swapi.l3a.chunk_fits import (
-    ParallelChunkRunner,
-    ProtonChunkFitter,
-    PuiChunkFitter,
-)
-from imap_l3_processing.swapi.l3a.models import SwapiL3PickupIonData
-from imap_l3_processing.swapi.l3a.science.pickup_ion.utils import (
-    rotate_rtn_velocity_to_swapi_per_bin,
-)
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
 from imap_l3_processing.swapi.l3a.utils import chunk_l2_data
 from imap_l3_processing.utils import SpiceKernelTypes, get_spice_kernels_file_names
 
-
 _HELIUM_MASS_PER_CHARGE_M_P_PER_E = HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E
 _COARSE_SWEEP_LEN = 62
 
 
-def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
-                          sw_velocity_rtn_kms, fit_params):
+def replay_chunk_spectrum(
+    dependencies, data_chunk, center_of_epoch, sw_velocity_rtn_kms, fit_params
+):
     """Re-evaluate the forward model on the fit window for one 50-sweep chunk.
 
     Mirrors the geometry path in calculate_pickup_ion_values so the energies
     and modeled rate match production exactly.
     """
     chunk_ephemeris_time = spiceypy.unitim(
-        center_of_epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET")
+        center_of_epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET"
+    )
     sw_velocity_kms = float(np.linalg.norm(sw_velocity_rtn_kms))
 
-    voltages_flat = (data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS]
-                     / SWAPI_L2_K_FACTOR).flatten()
+    voltages_flat = (
+        data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS] / SWAPI_L2_K_FACTOR
+    ).flatten()
     count_rates_flat = data_chunk.coincidence_count_rate[
-        :, SWAPI_COARSE_SWEEP_BINS].flatten()
+        :, SWAPI_COARSE_SWEEP_BINS
+    ].flatten()
     central_effective_area_scale = (
-        dependencies.efficiency_calibration_table
-        .central_effective_area_scale_for(center_of_epoch, "helium"))
+        dependencies.efficiency_calibration_table.central_effective_area_scale_for(
+            center_of_epoch, "helium"
+        )
+    )
 
     voltages_2d = voltages_flat.reshape(-1, _COARSE_SWEEP_LEN)
     count_rates_2d = count_rates_flat.reshape(-1, _COARSE_SWEEP_LEN)
@@ -97,11 +104,13 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
     )
 
     lower_energy_cutoff, upper_energy_cutoff = calculate_pickup_ion_fit_energy_range(
-        sw_velocity_kms)
+        sw_velocity_kms
+    )
     voltages_per_step = np.mean(voltages_2d, axis=0)
     energies_per_step = np.abs(voltages_per_step) * SWAPI_L2_K_FACTOR
-    bin_mask = ((energies_per_step > lower_energy_cutoff)
-                & (energies_per_step < upper_energy_cutoff))
+    bin_mask = (energies_per_step > lower_energy_cutoff) & (
+        energies_per_step < upper_energy_cutoff
+    )
 
     extracted_voltages = voltages_per_step[bin_mask]
     extracted_rates = count_rates_2d[:, bin_mask]
@@ -114,7 +123,8 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
         0.0,
     )
     vasyliunas_siscoe_distribution = build_vasyliunas_siscoe_distribution(
-        chunk_ephemeris_time, sw_velocity_rtn_kms,
+        chunk_ephemeris_time,
+        sw_velocity_rtn_kms,
         dependencies.density_of_neutral_helium_calibration_table,
         dependencies.helium_inflow_vector,
     )
@@ -127,7 +137,8 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
         central_effective_area_scale=central_effective_area_scale,
     )
     modeled_per_sweep = calculate_coincidence_rate(
-        chunk_response, vasyliunas_siscoe_distribution, fit_params_no_uncertainty)
+        chunk_response, vasyliunas_siscoe_distribution, fit_params_no_uncertainty
+    )
     return {
         "energies_ev": extracted_voltages * SWAPI_L2_K_FACTOR,
         "observed_rate_per_sweep": extracted_rates,
@@ -138,12 +149,20 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
 
 if __name__ == "__main__":
     argument_parser = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
     argument_parser.add_argument("date", help="UTC date in YYYY-MM-DD format")
-    argument_parser.add_argument("--use-cache", action="store_true",
-                                 help="Skip download and refit; load the cached fit from /tmp.")
-    argument_parser.add_argument("--output-dir", type=Path, default=None,
-                                 help="If set, save the figure as pui_fit_<YYYYMMDD>.png in this directory and skip plt.show().")
+    argument_parser.add_argument(
+        "--use-cache",
+        action="store_true",
+        help="Skip download and refit; load the cached fit from /tmp.",
+    )
+    argument_parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=None,
+        help="If set, save the figure as pui_fit_<YYYYMMDD>.png in this directory and skip plt.show().",
+    )
     arguments = argument_parser.parse_args()
 
     if "IMAP_API_KEY" not in os.environ:
@@ -156,49 +175,70 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
     imap_data_access.config["DATA_DIR"] = Path("/tmp/swapi_fit_and_plot_data")
     imap_data_access.config["DATA_DIR"].mkdir(parents=True, exist_ok=True)
 
-    ancillary_template_path = (Path(__file__).parent
-                               / "imap_swapi_l3a_proton-sw_dependency_template.json")
+    ancillary_template_path = (
+        Path(__file__).parent / "imap_swapi_l3a_proton-sw_dependency_template.json"
+    )
 
     science_query_results = imap_data_access.query(
-        instrument="swapi", data_level="l2", descriptor="sci",
-        start_date=compact_date, end_date=compact_date, version="latest")
+        instrument="swapi",
+        data_level="l2",
+        descriptor="sci",
+        start_date=compact_date,
+        end_date=compact_date,
+        version="latest",
+    )
     if not science_query_results:
         sys.exit(f"No SWAPI L2 sci file at the SDC for {arguments.date}")
 
     published_l3a_query_results = imap_data_access.query(
-        instrument="swapi", data_level="l3a", descriptor="pui-he",
-        start_date=compact_date, end_date=compact_date, version="latest")
+        instrument="swapi",
+        data_level="l3a",
+        descriptor="pui-he",
+        start_date=compact_date,
+        end_date=compact_date,
+        version="latest",
+    )
     if published_l3a_query_results:
         published_l3a_cdf_path = imap_data_access.download(
-            published_l3a_query_results[0]["file_path"])
+            published_l3a_query_results[0]["file_path"]
+        )
         print(f"Downloaded published L3A pui-he from SDC: {published_l3a_cdf_path}")
     else:
         published_l3a_cdf_path = None
         print(f"No published SWAPI L3A pui-he file at the SDC for {arguments.date}")
 
-    ephemeris_kernel_types = [SpiceKernelTypes.EphemerisReconstructed,
-                              SpiceKernelTypes.EphemerisPredicted]
-    non_ephemeris_kernel_types = [t for t in SpiceKernelTypes
-                                  if t not in ephemeris_kernel_types]
-    spice_kernel_filenames = list(dict.fromkeys(
-        os.path.basename(path) for path in (
-            get_spice_kernels_file_names(
-                target_date - timedelta(days=1),
-                target_date + timedelta(days=2),
-                non_ephemeris_kernel_types)
-            + get_spice_kernels_file_names(
-                target_date - timedelta(days=90),
-                target_date + timedelta(days=2),
-                ephemeris_kernel_types)
+    ephemeris_kernel_types = [
+        SpiceKernelTypes.EphemerisReconstructed,
+        SpiceKernelTypes.EphemerisPredicted,
+    ]
+    non_ephemeris_kernel_types = [
+        t for t in SpiceKernelTypes if t not in ephemeris_kernel_types
+    ]
+    spice_kernel_filenames = list(
+        dict.fromkeys(
+            os.path.basename(path)
+            for path in (
+                get_spice_kernels_file_names(
+                    target_date - timedelta(days=1),
+                    target_date + timedelta(days=2),
+                    non_ephemeris_kernel_types,
+                )
+                + get_spice_kernels_file_names(
+                    target_date - timedelta(days=90),
+                    target_date + timedelta(days=2),
+                    ephemeris_kernel_types,
+                )
+            )
         )
-    ))
+    )
 
     dynamic_filenames = [
         os.path.basename(science_query_results[0]["file_path"]),
         *spice_kernel_filenames,
     ]
     processing_input_collection = ProcessingInputCollection(
-        *[generate_imap_input(filename) for filename in dynamic_filenames])
+        *[generate_imap_input(filename) for filename in dynamic_filenames]
+    )
     processing_input_collection.deserialize(ancillary_template_path.read_text())
     processing_input_collection.download_all_files()
 
@@ -208,18 +248,25 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
     dependencies = SwapiL3ADependencies.fetch_dependencies(processing_input_collection)
 
     input_metadata = InputMetadata(
-        instrument="swapi", data_level="l3a",
-        start_date=target_date, end_date=target_date,
-        version="v001", descriptor="pui-he",
+        instrument="swapi",
+        data_level="l3a",
+        start_date=target_date,
+        end_date=target_date,
+        version="v001",
+        descriptor="pui-he",
     )
 
     if not arguments.use_cache or not cache_path.exists():
-        dependencies.swapi_response.warm_cache(dependencies.data.energy / SWAPI_L2_K_FACTOR)
+        dependencies.swapi_response.warm_cache(
+            dependencies.data.energy / SWAPI_L2_K_FACTOR
+        )
         runner = ParallelChunkRunner(
-            dependencies.swapi_response, dependencies.efficiency_calibration_table)
+            dependencies.swapi_response, dependencies.efficiency_calibration_table
+        )
         pui_chunks = list(chunk_l2_data(dependencies.data, 50))
         proton_results = runner.run(
-            list(chunk_l2_data(dependencies.data, 5)), ProtonChunkFitter())
+            list(chunk_l2_data(dependencies.data, 5)), ProtonChunkFitter()
+        )
         pui_fitter = PuiChunkFitter(
             density_of_neutral_helium_lookup_table=dependencies.density_of_neutral_helium_calibration_table,
             hydrogen_inflow_vector=dependencies.hydrogen_inflow_vector,
@@ -240,14 +287,22 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
         pickup_ion_data, sw_velocity_rtn_by_chunk_epoch = pickle.load(cache_file)
 
     epochs_tt2000 = np.asarray(pickup_ion_data.epoch, dtype=np.int64)
-    timestamps = np.array([cdf_library.tt2000_to_datetime(int(t)) for t in epochs_tt2000])
+    timestamps = np.array(
+        [cdf_library.tt2000_to_datetime(int(t)) for t in epochs_tt2000]
+    )
 
-    spectrogram_cache_path = Path(f"/tmp/swapi_pui_spectrograms_per_sweep_{compact_date}.pkl")
+    spectrogram_cache_path = Path(
+        f"/tmp/swapi_pui_spectrograms_per_sweep_{compact_date}.pkl"
+    )
     all_chunks = list(chunk_l2_data(dependencies.data, 50))
 
     if not arguments.use_cache or not spectrogram_cache_path.exists():
-        print("Building per-sweep observed and modeled spectrograms (one replay per chunk)...")
-        dependencies.swapi_response.warm_cache(dependencies.data.energy / SWAPI_L2_K_FACTOR)
+        print(
+            "Building per-sweep observed and modeled spectrograms (one replay per chunk)..."
+        )
+        dependencies.swapi_response.warm_cache(
+            dependencies.data.energy / SWAPI_L2_K_FACTOR
+        )
 
         chunk_count = min(len(all_chunks), len(epochs_tt2000))
         per_sweep_voltages = []
@@ -257,24 +312,31 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
 
         for chunk_index in range(chunk_count):
             data_chunk = all_chunks[chunk_index]
-            voltages_2d = (data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS]
-                           / SWAPI_L2_K_FACTOR)
+            voltages_2d = (
+                data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS] / SWAPI_L2_K_FACTOR
+            )
             rates_2d = data_chunk.coincidence_count_rate[:, SWAPI_COARSE_SWEEP_BINS]
             per_sweep_voltages.append(voltages_2d)
             per_sweep_observed.append(rates_2d)
             per_sweep_epoch_tt2000.append(
-                np.asarray(data_chunk.sci_start_time, dtype=np.int64))
+                np.asarray(data_chunk.sci_start_time, dtype=np.int64)
+            )
 
             model_rates_for_chunk = np.full_like(rates_2d, np.nan, dtype=float)
 
             chunk_epoch = int(epochs_tt2000[chunk_index])
             sw_velocity_rtn = sw_velocity_rtn_by_chunk_epoch.get(chunk_epoch)
-            if (sw_velocity_rtn is not None
-                    and not np.any(np.isnan(sw_velocity_rtn))
-                    and np.isfinite(pickup_ion_data.cooling_index[chunk_index].n)):
+            if (
+                sw_velocity_rtn is not None
+                and not np.any(np.isnan(sw_velocity_rtn))
+                and np.isfinite(pickup_ion_data.cooling_index[chunk_index].n)
+            ):
                 try:
                     replay = replay_chunk_spectrum(
-                        dependencies, data_chunk, chunk_epoch, sw_velocity_rtn,
+                        dependencies,
+                        data_chunk,
+                        chunk_epoch,
+                        sw_velocity_rtn,
                         FittingParameters(
                             pickup_ion_data.cooling_index[chunk_index],
                             pickup_ion_data.ionization_rate[chunk_index],
@@ -285,24 +347,37 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
                 except Exception as replay_error:
                     print(f"chunk {chunk_index} replay failed: {replay_error}")
                 else:
-                    model_rates_for_chunk[:, replay["bin_mask"]] = replay["modeled_rate_per_sweep"]
+                    model_rates_for_chunk[:, replay["bin_mask"]] = replay[
+                        "modeled_rate_per_sweep"
+                    ]
             per_sweep_model.append(model_rates_for_chunk)
 
-        energies_per_sweep_ev = np.concatenate(per_sweep_voltages, axis=0) * SWAPI_L2_K_FACTOR
+        energies_per_sweep_ev = (
+            np.concatenate(per_sweep_voltages, axis=0) * SWAPI_L2_K_FACTOR
+        )
         observed_spectrogram = np.concatenate(per_sweep_observed, axis=0)
         model_spectrogram = np.concatenate(per_sweep_model, axis=0)
         sweep_epochs_tt2000 = np.concatenate(per_sweep_epoch_tt2000, axis=0)
 
         with spectrogram_cache_path.open("wb") as spectrogram_file:
             pickle.dump(
-                (energies_per_sweep_ev, observed_spectrogram, model_spectrogram,
-                 sweep_epochs_tt2000),
-                spectrogram_file)
+                (
+                    energies_per_sweep_ev,
+                    observed_spectrogram,
+                    model_spectrogram,
+                    sweep_epochs_tt2000,
+                ),
+                spectrogram_file,
+            )
         print(f"Cached spectrograms to {spectrogram_cache_path}")
 
     with spectrogram_cache_path.open("rb") as spectrogram_file:
-        (energies_per_sweep_ev, observed_spectrogram, model_spectrogram,
-         sweep_epochs_tt2000) = pickle.load(spectrogram_file)
+        (
+            energies_per_sweep_ev,
+            observed_spectrogram,
+            model_spectrogram,
+            sweep_epochs_tt2000,
+        ) = pickle.load(spectrogram_file)
 
     import matplotlib.pyplot as plt
     from matplotlib.colors import LogNorm
@@ -313,27 +388,32 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
     observed_sorted = observed_spectrogram[:, energy_sort_order]
     model_sorted = model_spectrogram[:, energy_sort_order]
 
-    positive_rate_values = np.concatenate([
-        array[np.isfinite(array) & (array > 0)]
-        for array in (observed_sorted, model_sorted)
-    ])
+    positive_rate_values = np.concatenate(
+        [
+            array[np.isfinite(array) & (array > 0)]
+            for array in (observed_sorted, model_sorted)
+        ]
+    )
     if positive_rate_values.size:
         rate_vmin = max(float(np.percentile(positive_rate_values, 1)), 1e-3)
         rate_vmax = float(positive_rate_values.max())
     else:
         rate_vmin, rate_vmax = 1e-3, 1.0
 
-    sweep_timestamps = np.array([
-        cdf_library.tt2000_to_datetime(int(t)) for t in sweep_epochs_tt2000])
+    sweep_timestamps = np.array(
+        [cdf_library.tt2000_to_datetime(int(t)) for t in sweep_epochs_tt2000]
+    )
 
     figure = plt.figure(figsize=(11, 16), layout="constrained")
     grid_spec = figure.add_gridspec(8, 1, height_ratios=[1.6, 1.6, 1, 1, 1, 1, 1, 1])
 
     observed_axis = figure.add_subplot(grid_spec[0])
     observed_mesh = observed_axis.pcolormesh(
-        sweep_timestamps, mean_energies_ev_sorted,
+        sweep_timestamps,
+        mean_energies_ev_sorted,
         np.ma.masked_invalid(observed_sorted).T,
-        shading="nearest", cmap="viridis",
+        shading="nearest",
+        cmap="viridis",
         norm=LogNorm(vmin=rate_vmin, vmax=rate_vmax),
     )
     observed_axis.set_yscale("log")
@@ -342,31 +422,55 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
     figure.colorbar(observed_mesh, ax=observed_axis, label="rate / s$^{-1}$")
     plt.setp(observed_axis.get_xticklabels(), visible=False)
 
-    model_axis = figure.add_subplot(grid_spec[1], sharex=observed_axis, sharey=observed_axis)
+    model_axis = figure.add_subplot(
+        grid_spec[1], sharex=observed_axis, sharey=observed_axis
+    )
     model_mesh = model_axis.pcolormesh(
-        sweep_timestamps, mean_energies_ev_sorted,
+        sweep_timestamps,
+        mean_energies_ev_sorted,
         np.ma.masked_invalid(model_sorted).T,
-        shading="nearest", cmap="viridis",
+        shading="nearest",
+        cmap="viridis",
         norm=LogNorm(vmin=rate_vmin, vmax=rate_vmax),
     )
     model_axis.set_yscale("log")
     model_axis.set_ylabel("Energy / eV")
-    model_axis.set_title("Fitted helium PUI model coincidence count rate (per 12 s sweep)")
+    model_axis.set_title(
+        "Fitted helium PUI model coincidence count rate (per 12 s sweep)"
+    )
     figure.colorbar(model_mesh, ax=model_axis, label="rate / s$^{-1}$")
     plt.setp(model_axis.get_xticklabels(), visible=False)
 
-    sw_speed_kms = np.array([
-        np.linalg.norm(sw_velocity_rtn_by_chunk_epoch[int(epoch)])
-        if int(epoch) in sw_velocity_rtn_by_chunk_epoch else np.nan
-        for epoch in epochs_tt2000
-    ])
+    sw_speed_kms = np.array(
+        [
+            np.linalg.norm(sw_velocity_rtn_by_chunk_epoch[int(epoch)])
+            if int(epoch) in sw_velocity_rtn_by_chunk_epoch
+            else np.nan
+            for epoch in epochs_tt2000
+        ]
+    )
 
     CUTOFF_SPEED_PANEL_INDEX = 2
     timeseries_panels = [
         ("Cooling index", "linear", pickup_ion_data.cooling_index, "pui_cooling_index"),
-        ("Ionization rate [s$^{-1}$]", "log", pickup_ion_data.ionization_rate, "pui_ionization_rate"),
-        ("Cutoff speed [km/s]", "linear", pickup_ion_data.cutoff_speed, "pui_cutoff_speed"),
-        ("Background rate [s$^{-1}$]", "linear", pickup_ion_data.background_rate, "pui_background_count_rate"),
+        (
+            "Ionization rate [s$^{-1}$]",
+            "log",
+            pickup_ion_data.ionization_rate,
+            "pui_ionization_rate",
+        ),
+        (
+            "Cutoff speed [km/s]",
+            "linear",
+            pickup_ion_data.cutoff_speed,
+            "pui_cutoff_speed",
+        ),
+        (
+            "Background rate [s$^{-1}$]",
+            "linear",
+            pickup_ion_data.background_rate,
+            "pui_background_count_rate",
+        ),
         ("Density [cm$^{-3}$]", "log", pickup_ion_data.density, "pui_density"),
         ("Temperature [K]", "log", pickup_ion_data.temperature, "pui_temperature"),
     ]
@@ -376,52 +480,91 @@ def replay_chunk_spectrum(dependencies, data_chunk, center_of_epoch,
     if published_l3a_cdf_path is not None:
         with CDF(str(published_l3a_cdf_path)) as published_cdf:
             published_epoch_tt2000 = np.asarray(
-                published_cdf.raw_var("epoch")[...], dtype=np.int64)
-            published_l3a_timestamps = np.array([
-                cdf_library.tt2000_to_datetime(int(t)) for t in published_epoch_tt2000
-            ])
+                published_cdf.raw_var("epoch")[...], dtype=np.int64
+            )
+            published_l3a_timestamps = np.array(
+                [cdf_library.tt2000_to_datetime(int(t)) for t in published_epoch_tt2000]
+            )
             for _, _, _, cdf_var_name in timeseries_panels:
                 published_l3a_values_by_var[cdf_var_name] = (
                     np.asarray(published_cdf[cdf_var_name][...], dtype=float),
-                    np.asarray(published_cdf[f"{cdf_var_name}_uncert"][...], dtype=float),
+                    np.asarray(
+                        published_cdf[f"{cdf_var_name}_uncert"][...], dtype=float
+                    ),
                 )
 
     local_flags = np.asarray(pickup_ion_data.quality_flags, dtype=np.int64)
     local_flagged = local_flags != 0
 
-    for panel_index, (ylabel, yscale, values, cdf_var_name) in enumerate(timeseries_panels):
+    for panel_index, (ylabel, yscale, values, cdf_var_name) in enumerate(
+        timeseries_panels
+    ):
         axis = figure.add_subplot(grid_spec[panel_index + 2], sharex=observed_axis)
         nominal_values_array = unumpy.nominal_values(values)
         std_devs_array = unumpy.std_devs(values)
-        show_legend = (panel_index == CUTOFF_SPEED_PANEL_INDEX
-                       or published_l3a_timestamps is not None)
-        axis.errorbar(timestamps,
-                      nominal_values_array,
-                      yerr=std_devs_array,
-                      fmt=".", capsize=2, color="tab:blue",
-                      label="This run" if show_legend else None)
+        show_legend = (
+            panel_index == CUTOFF_SPEED_PANEL_INDEX
+            or published_l3a_timestamps is not None
+        )
+        axis.errorbar(
+            timestamps,
+            nominal_values_array,
+            yerr=std_devs_array,
+            fmt=".",
+            capsize=2,
+            color="tab:blue",
+            label="This run" if show_legend else None,
+        )
         if np.any(local_flagged):
-            axis.scatter(timestamps[local_flagged],
-                         nominal_values_array[local_flagged],
-                         s=60, facecolors="none", edgecolors="red",
-                         linewidths=1.2, zorder=3,
-                         label="flag != 0" if show_legend else None)
+            axis.scatter(
+                timestamps[local_flagged],
+                nominal_values_array[local_flagged],
+                s=60,
+                facecolors="none",
+                edgecolors="red",
+                linewidths=1.2,
+                zorder=3,
+                label="flag != 0" if show_legend else None,
+            )
         if published_l3a_timestamps is not None:
-            published_nominal, published_uncert = published_l3a_values_by_var[cdf_var_name]
-            valid = np.isfinite(published_nominal) & (published_uncert >= 0) & np.isfinite(published_uncert)
-            axis.errorbar(published_l3a_timestamps[valid],
-                          published_nominal[valid],
-                          yerr=published_uncert[valid],
-                          fmt="x", capsize=2, color="red",
-                          label="SDC L3A")
+            published_nominal, published_uncert = published_l3a_values_by_var[
+                cdf_var_name
+            ]
+            valid = (
+                np.isfinite(published_nominal)
+                & (published_uncert >= 0)
+                & np.isfinite(published_uncert)
+            )
+            axis.errorbar(
+                published_l3a_timestamps[valid],
+                published_nominal[valid],
+                yerr=published_uncert[valid],
+                fmt="x",
+                capsize=2,
+                color="red",
+                label="SDC L3A",
+            )
         if panel_index == CUTOFF_SPEED_PANEL_INDEX:
-            axis.plot(timestamps, sw_speed_kms,
-                      color="tab:orange", linewidth=1.0, label="Proton SW speed")
-            axis.fill_between(timestamps, 0.8 * sw_speed_kms, 1.2 * sw_speed_kms,
-                              color="tab:orange", alpha=0.15, linewidth=0,
-                              label=r"$\pm 20\%$ SW speed")
-        if (panel_index == CUTOFF_SPEED_PANEL_INDEX
-                or published_l3a_timestamps is not None):
+            axis.plot(
+                timestamps,
+                sw_speed_kms,
+                color="tab:orange",
+                linewidth=1.0,
+                label="Proton SW speed",
+            )
+            axis.fill_between(
+                timestamps,
+                0.8 * sw_speed_kms,
+                1.2 * sw_speed_kms,
+                color="tab:orange",
+                alpha=0.15,
+                linewidth=0,
+                label=r"$\pm 20\%$ SW speed",
+            )
+        if (
+            panel_index == CUTOFF_SPEED_PANEL_INDEX
+            or published_l3a_timestamps is not None
+        ):
             axis.legend(loc="best", fontsize="small")
         axis.set_ylabel(ylabel)
         axis.set_yscale(yscale)
diff --git a/scripts/swapi/generate_cdf_from_dat.py b/scripts/swapi/generate_cdf_from_dat.py
index bd0fd6369..81337a627 100644
--- a/scripts/swapi/generate_cdf_from_dat.py
+++ b/scripts/swapi/generate_cdf_from_dat.py
@@ -5,14 +5,21 @@
 from spacepy.pycdf.const import CDF_TIME_TT2000
 
 import imap_l3_processing
-from scripts.swapi.swapi_alpha_sw_speed_temperature_density_demo import read_l2_data_from_dat
+from scripts.swapi.swapi_alpha_sw_speed_temperature_density_demo import (
+    read_l2_data_from_dat,
+)
 
 
 def generate_cdf_from_dat(dat_file_path: str, output_filename: str):
     dat_data = read_l2_data_from_dat(dat_file_path)
-    cdf_file_path = Path(imap_l3_processing.__file__).parent.parent / 'swapi' / 'test_data' / output_filename
-
-    with CDF(str(cdf_file_path), '') as cdf:
+    cdf_file_path = (
+        Path(imap_l3_processing.__file__).parent.parent
+        / "swapi"
+        / "test_data"
+        / output_filename
+    )
+
+    with CDF(str(cdf_file_path), "") as cdf:
         cdf.col_major(True)
 
         cdf.new("energy", dat_data.energy, recVary=False)
@@ -23,5 +30,9 @@ def generate_cdf_from_dat(dat_file_path: str, output_filename: str):
 
 
 if __name__ == "__main__":
-    generate_cdf_from_dat(os.path.abspath("../../instrument_team_data/swapi/swapi_test_data_50_sweeps.dat"),
-                          "imap_swapi_l2_sci_20100101_v001")
+    generate_cdf_from_dat(
+        os.path.abspath(
+            "../../instrument_team_data/swapi/swapi_test_data_50_sweeps.dat"
+        ),
+        "imap_swapi_l2_sci_20100101_v001",
+    )
diff --git a/scripts/swapi/generate_collapsed_response_grid_reference.py b/scripts/swapi/generate_collapsed_response_grid_reference.py
index a850b2645..0222fb928 100644
--- a/scripts/swapi/generate_collapsed_response_grid_reference.py
+++ b/scripts/swapi/generate_collapsed_response_grid_reference.py
@@ -38,7 +38,11 @@
 
 _REPO_ROOT = Path(__file__).resolve().parents[2]
 _OUTPUT_PATH = (
-    _REPO_ROOT / "tests" / "test_data" / "swapi" / "collapsed_response_grid_reference.csv"
+    _REPO_ROOT
+    / "tests"
+    / "test_data"
+    / "swapi"
+    / "collapsed_response_grid_reference.csv"
 )
 
 _N_POINTS = 256
@@ -67,17 +71,20 @@ def shell_integral_h(
     central_speed = float(response_grid.central_speed)
 
     # SWAPI convention: hat{v}(θ,φ) = (-cosθ sinφ, -cosθ cosφ, -sinθ).
-    bulk_direction = np.array([
-        -math.cos(bulk_elevation) * math.sin(bulk_azimuth),
-        -math.cos(bulk_elevation) * math.cos(bulk_azimuth),
-        -math.sin(bulk_elevation),
-    ])
+    bulk_direction = np.array(
+        [
+            -math.cos(bulk_elevation) * math.sin(bulk_azimuth),
+            -math.cos(bulk_elevation) * math.cos(bulk_azimuth),
+            -math.sin(bulk_elevation),
+        ]
+    )
     velocity = bulk_speed * bulk_direction
 
     # cos-α band from |v|² = v_sw² + v'² + 2 v_sw v' cos α (law of cosines).
     cos_alpha_bounds = (
         (np.array([_SPEED_RATIO_MIN, _SPEED_RATIO_MAX]) * central_speed) ** 2
-        - bulk_speed**2 - v_prime**2
+        - bulk_speed**2
+        - v_prime**2
     ) / (2.0 * bulk_speed * v_prime)
     if cos_alpha_bounds[0] >= 1.0 or cos_alpha_bounds[1] <= -1.0:
         return 0.0
@@ -85,16 +92,29 @@ def shell_integral_h(
 
     # Right-handed orthonormal frame with bulk_direction as polar axis.
     e_in_xy = np.array([-math.cos(bulk_azimuth), math.sin(bulk_azimuth), 0.0])
-    rotation = np.column_stack([e_in_xy, np.cross(bulk_direction, e_in_xy), bulk_direction])
+    rotation = np.column_stack(
+        [e_in_xy, np.cross(bulk_direction, e_in_xy), bulk_direction]
+    )
 
     def integrate(passband, azimuth_min_deg, azimuth_max_deg):
         return dblquad(
             lambda alpha, beta: _shell_integrand(
-                alpha, beta, velocity, rotation, v_prime, response_grid,
-                passband, azimuth_min_deg, azimuth_max_deg,
+                alpha,
+                beta,
+                velocity,
+                rotation,
+                v_prime,
+                response_grid,
+                passband,
+                azimuth_min_deg,
+                azimuth_max_deg,
             ),
-            0.0, 2.0 * math.pi, alpha_min, alpha_max,
-            epsabs=1e-13, epsrel=1e-3,
+            0.0,
+            2.0 * math.pi,
+            alpha_min,
+            alpha_max,
+            epsabs=1e-13,
+            epsrel=1e-3,
         )[0]
 
     return (
@@ -106,8 +126,15 @@ def integrate(passband, azimuth_min_deg, azimuth_max_deg):
 
 @numba.njit
 def _shell_integrand(
-    alpha, beta, velocity, rotation, v_prime, response_grid,
-    passband, azimuth_min_deg, azimuth_max_deg,
+    alpha,
+    beta,
+    velocity,
+    rotation,
+    v_prime,
+    response_grid,
+    passband,
+    azimuth_min_deg,
+    azimuth_max_deg,
 ):
     # `alpha`: polar angle on the shell from bulk-velocity direction;
     #   caller restricts to the α-band where the shell crosses
@@ -119,21 +146,33 @@ def _shell_integrand(
     local_x = sin_alpha * math.cos(beta)
     local_y = sin_alpha * math.sin(beta)
     local_z = math.cos(alpha)
-    shell_x = rotation[0, 0] * local_x + rotation[0, 1] * local_y + rotation[0, 2] * local_z
-    shell_y = rotation[1, 0] * local_x + rotation[1, 1] * local_y + rotation[1, 2] * local_z
-    shell_z = rotation[2, 0] * local_x + rotation[2, 1] * local_y + rotation[2, 2] * local_z
+    shell_x = (
+        rotation[0, 0] * local_x + rotation[0, 1] * local_y + rotation[0, 2] * local_z
+    )
+    shell_y = (
+        rotation[1, 0] * local_x + rotation[1, 1] * local_y + rotation[1, 2] * local_z
+    )
+    shell_z = (
+        rotation[2, 0] * local_x + rotation[2, 1] * local_y + rotation[2, 2] * local_z
+    )
     vx = velocity[0] + v_prime * shell_x
     vy = velocity[1] + v_prime * shell_y
     vz = velocity[2] + v_prime * shell_z
     speed = math.sqrt(vx * vx + vy * vy + vz * vz)
-    azimuth_deg, elevation_deg = velocity_components_to_angles_in_instrument_frame(vx, vy, vz)
+    azimuth_deg, elevation_deg = velocity_components_to_angles_in_instrument_frame(
+        vx, vy, vz
+    )
     if not (azimuth_min_deg <= azimuth_deg <= azimuth_max_deg):
         return 0.0
     effective_area = (
         response_grid.central_effective_area
         * _EFFECTIVE_AREA_CM2_TO_KM2
-        * interpolate_passband(passband, elevation_deg, speed / response_grid.central_speed)
-        * interpolate_azimuthal_transmission(response_grid.azimuthal_transmission, azimuth_deg)
+        * interpolate_passband(
+            passband, elevation_deg, speed / response_grid.central_speed
+        )
+        * interpolate_azimuthal_transmission(
+            response_grid.azimuthal_transmission, azimuth_deg
+        )
     )
     # H is an angular integral (docs convention); dα dβ comes from dblquad, so
     # only the sin α solid-angle factor is multiplied in here.
@@ -160,10 +199,10 @@ def _worker_compute(v_prime):
 
 
 def main():
-    print(f"Loading SWAPI response and warming cache at V={_ESA_VOLTAGE}...", flush=True)
-    swapi_response = load_swapi_response(
-        warm_cache_voltages=np.array([_ESA_VOLTAGE])
+    print(
+        f"Loading SWAPI response and warming cache at V={_ESA_VOLTAGE}...", flush=True
     )
+    swapi_response = load_swapi_response(warm_cache_voltages=np.array([_ESA_VOLTAGE]))
     response_grid = swapi_response.get_response_grid(
         esa_voltage=_ESA_VOLTAGE,
         mass_per_charge_m_p_per_e=_MASS_PER_CHARGE,
diff --git a/scripts/swapi/generate_reference_integrals.py b/scripts/swapi/generate_reference_integrals.py
index 18b0ba21b..c97714def 100644
--- a/scripts/swapi/generate_reference_integrals.py
+++ b/scripts/swapi/generate_reference_integrals.py
@@ -32,6 +32,7 @@
 
 import numpy as np
 import pandas as pd
+from figure_utils import ANCHOR_ROTATION_SWAPI_TO_RTN
 
 from imap_l3_processing.constants import (
     METERS_PER_KILOMETER,
@@ -39,19 +40,16 @@
     PROTON_MASS_KG,
     PROTON_MASS_PER_CHARGE_M_P_PER_E,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
 from scripts.swapi.reference_integral import reference_integrals_batch
-from figure_utils import ANCHOR_ROTATION_SWAPI_TO_RTN
 
 _INSTRUMENT_DATA = _REPO_ROOT / "instrument_team_data" / "swapi"
 _WIND_SAMPLES_PATH = (
     _REPO_ROOT / "docs" / "swapi" / "figure_src" / "wind_solar_wind_samples_2025.csv"
 )
-_OUTPUT_PATH = (
-    _REPO_ROOT / "tests" / "test_data" / "swapi" / "reference_integrals.csv"
-)
+_OUTPUT_PATH = _REPO_ROOT / "tests" / "test_data" / "swapi" / "reference_integrals.csv"
 
 
 def _peak_voltage(bulk_speed_km_s: float) -> float:
@@ -90,9 +88,7 @@ def main():
         )
         for i in range(n_samples)
     ]
-    rotation_matrices = np.broadcast_to(
-        ANCHOR_ROTATION_SWAPI_TO_RTN, (n_samples, 3, 3)
-    )
+    rotation_matrices = np.broadcast_to(ANCHOR_ROTATION_SWAPI_TO_RTN, (n_samples, 3, 3))
     voltages = [_peak_voltage(float(v)) for v in bulk_speed]
     swapi_response.warm_cache(voltages)
     response_grids = [
diff --git a/scripts/swapi/generate_with_predicted_ephemeris.py b/scripts/swapi/generate_with_predicted_ephemeris.py
index 818a301e4..a1b962612 100644
--- a/scripts/swapi/generate_with_predicted_ephemeris.py
+++ b/scripts/swapi/generate_with_predicted_ephemeris.py
@@ -7,7 +7,7 @@
 from imap_data_access import DependencyFilePath, ProcessingInputCollection
 from imap_data_access.processing_input import generate_imap_input
 
-from imap_l3_processing.utils import get_spice_kernels_file_names, SpiceKernelTypes
+from imap_l3_processing.utils import SpiceKernelTypes, get_spice_kernels_file_names
 
 SPICE_KERNELS = [
     SpiceKernelTypes.Leapseconds,
@@ -18,29 +18,37 @@
     SpiceKernelTypes.PointingAttitude,
     SpiceKernelTypes.SpacecraftClock,
     SpiceKernelTypes.EphemerisPredicted,
-    SpiceKernelTypes.PlanetaryEphemeris
+    SpiceKernelTypes.PlanetaryEphemeris,
 ]
 
 
 def generate_swapi_for_given_day(descriptor: str, day: datetime):
-    spice_kernel_files = get_spice_kernels_file_names(day, day + timedelta(days=1), SPICE_KERNELS)
-    day_as_string = day.strftime('%Y%m%d')
-    l2_file_path = imap_data_access.query(instrument='swapi', data_level='l2', start_date=day_as_string,
-                                          end_date=day_as_string, version='latest')[0]['file_path']
-
-    template_json = Path('scripts/swapi/imap_swapi_l3a_proton-sw_dependency_template.json')
-
-    input_files = [
-        *spice_kernel_files,
-        Path(l2_file_path).name
-    ]
-
-    input_collection = ProcessingInputCollection(*[generate_imap_input(i) for i in input_files])
+    spice_kernel_files = get_spice_kernels_file_names(
+        day, day + timedelta(days=1), SPICE_KERNELS
+    )
+    day_as_string = day.strftime("%Y%m%d")
+    l2_file_path = imap_data_access.query(
+        instrument="swapi",
+        data_level="l2",
+        start_date=day_as_string,
+        end_date=day_as_string,
+        version="latest",
+    )[0]["file_path"]
+
+    template_json = Path(
+        "scripts/swapi/imap_swapi_l3a_proton-sw_dependency_template.json"
+    )
+
+    input_files = [*spice_kernel_files, Path(l2_file_path).name]
+
+    input_collection = ProcessingInputCollection(
+        *[generate_imap_input(i) for i in input_files]
+    )
     input_collection.deserialize(template_json.read_text())
 
     input_collection.download_all_files()
 
-    dependency_file_name = f'imap_swapi_l3a_{descriptor}_{day_as_string}_v002.json'
+    dependency_file_name = f"imap_swapi_l3a_{descriptor}_{day_as_string}_v002.json"
     output_filepath = DependencyFilePath(dependency_file_name)
 
     fullpath = output_filepath.construct_path()
@@ -48,23 +56,27 @@ def generate_swapi_for_given_day(descriptor: str, day: datetime):
 
     fullpath.write_text(input_collection.serialize())
 
-    args_string = (f"imap_l3_data_processor.py "
-                   f"--instrument swapi --data-level l3a --descriptor {descriptor} "
-                   f"--start-date {day_as_string} --version v003 "
-                   f"--dependency {dependency_file_name}")
+    args_string = (
+        f"imap_l3_data_processor.py "
+        f"--instrument swapi --data-level l3a --descriptor {descriptor} "
+        f"--start-date {day_as_string} --version v003 "
+        f"--dependency {dependency_file_name}"
+    )
 
     return subprocess.Popen([sys.executable, *args_string.split(" ")])
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     if len(sys.argv) > 1:
         dates = [datetime.strptime(arg, "%Y%m%d") for arg in sys.argv[1:]]
     else:
-        dates = [datetime(2025, 12, 15),
-                 datetime(2025, 12, 16),
-                 datetime(2025, 12, 17),
-                 datetime(2025, 12, 18)]
-    pids = [generate_swapi_for_given_day('alpha-sw', date) for date in dates]
+        dates = [
+            datetime(2025, 12, 15),
+            datetime(2025, 12, 16),
+            datetime(2025, 12, 17),
+            datetime(2025, 12, 18),
+        ]
+    pids = [generate_swapi_for_given_day("alpha-sw", date) for date in dates]
 
     for pid in pids:
         pid.wait()
diff --git a/scripts/swapi/pui_xarray_reference.py b/scripts/swapi/pui_xarray_reference.py
index 35189bb8a..3f9f34ce7 100644
--- a/scripts/swapi/pui_xarray_reference.py
+++ b/scripts/swapi/pui_xarray_reference.py
@@ -63,6 +63,7 @@
     \end{pmatrix}
 $$
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -71,7 +72,7 @@
 import xarray as xr
 
 from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E
-from tests.test_helpers import get_test_instrument_team_data_path, get_test_data_path
+from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
 OUTPUT_PATH = get_test_data_path("swapi/pui_count_rate_reference.csv")
 
@@ -82,23 +83,37 @@
 
 K_FACTOR = Q(1.89, "eV/V/e")  # E_beam = K · |V|; eV carries the elementary charge
 ONE_AU = Q(1.0, "au")
-HE_PLUS_MASS_PER_CHARGE = Q(HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E, "m_p/e")  # He+ nucleus, q = +1 e
+HE_PLUS_MASS_PER_CHARGE = Q(
+    HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E, "m_p/e"
+)  # He+ nucleus, q = +1 e
 HELIUM_EFFICIENCY_RATIO = 1.05
 
 """## Load response files"""
 
 central_effective_area = HELIUM_EFFICIENCY_RATIO * (
-    pd.read_csv(get_test_instrument_team_data_path("swapi/imap_swapi_central-effective-area_20260425_v001.csv"))
-    .set_index("esa_voltage").effective_area.to_xarray().rename(esa_voltage="V")
+    pd.read_csv(
+        get_test_instrument_team_data_path(
+            "swapi/imap_swapi_central-effective-area_20260425_v001.csv"
+        )
+    )
+    .set_index("esa_voltage")
+    .effective_area.to_xarray()
+    .rename(esa_voltage="V")
     .pint.quantify("cm**2")
 )
 _voltages_mag = central_effective_area["V"].values
 voltages = xr.DataArray(
-    Q(_voltages_mag, "V"), dims="V", coords={"V": _voltages_mag},
+    Q(_voltages_mag, "V"),
+    dims="V",
+    coords={"V": _voltages_mag},
 )
 
 passband_coefficients = (
-    pd.read_csv(get_test_instrument_team_data_path("swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"))
+    pd.read_csv(
+        get_test_instrument_team_data_path(
+            "swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"
+        )
+    )
     .set_index(["region", "energy_ratio", "elevation"])[["0", "1", "2"]]
     .rename_axis(columns="degree")
     .to_xarray()
@@ -107,20 +122,38 @@
     .pipe(lambda da: da.assign_coords(degree=da["degree"].astype(int)))
 )
 source_speed_ratio = np.sqrt(
-    passband_coefficients["energy_ratio"].values / K_FACTOR.to('eV/V/e').magnitude
+    passband_coefficients["energy_ratio"].values / K_FACTOR.to("eV/V/e").magnitude
 )
 passband_coefficients = (
-    passband_coefficients.assign_coords(speed_ratio=("energy_ratio", source_speed_ratio))
-    .swap_dims({"energy_ratio": "speed_ratio"}).drop_vars("energy_ratio")
+    passband_coefficients.assign_coords(
+        speed_ratio=("energy_ratio", source_speed_ratio)
+    )
+    .swap_dims({"energy_ratio": "speed_ratio"})
+    .drop_vars("energy_ratio")
 )
 
 az_transmission_native = (
-    pd.read_csv(get_test_instrument_team_data_path("swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"))
-    .fillna(0).set_index("abs_azimuth").transmission.to_xarray()
-).coarsen(abs_azimuth=10, boundary='trim').mean()
-print("azimuth spacing:", az_transmission_native.abs_azimuth.to_series().diff().dropna().round(4).unique())
+    (
+        pd.read_csv(
+            get_test_instrument_team_data_path(
+                "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+            )
+        )
+        .fillna(0)
+        .set_index("abs_azimuth")
+        .transmission.to_xarray()
+    )
+    .coarsen(abs_azimuth=10, boundary="trim")
+    .mean()
+)
+print(
+    "azimuth spacing:",
+    az_transmission_native.abs_azimuth.to_series().diff().dropna().round(4).unique(),
+)
 
-density_data = np.loadtxt(get_test_instrument_team_data_path("swapi/density-of-neutral-helium-lut.dat"))
+density_data = np.loadtxt(
+    get_test_instrument_team_data_path("swapi/density-of-neutral-helium-lut.dat")
+)
 psi_axis, r_axis = np.unique(density_data[:, 0]), np.unique(density_data[:, 1])
 density_values = density_data[:, 2].reshape(len(psi_axis), len(r_axis))
 
@@ -129,33 +162,42 @@
 
 density_table = xr.DataArray(
     density_values,
-    dims=("psi", "r"), coords={"psi": psi_axis, "r": r_axis},
+    dims=("psi", "r"),
+    coords={"psi": psi_axis, "r": r_axis},
 ).pint.quantify("1/cm**3")
 
 """## Integration grid (native to the calibration files)"""
 
 elevation_deg = passband_coefficients["elevation_deg"]
-speed_ratio   = passband_coefficients["speed_ratio"]
+speed_ratio = passband_coefficients["speed_ratio"]
 
 # Mirror az_transmission_native (abs_azimuth 0..180) into signed [-180, 180].
 _abs_az = az_transmission_native["abs_azimuth"].values
-_trans  = az_transmission_native.values
+_trans = az_transmission_native.values
 _az_signed = np.concatenate([-_abs_az[:0:-1], _abs_az])
-azimuth_deg = xr.DataArray(_az_signed, dims="azimuth_deg", coords={"azimuth_deg": _az_signed})
+azimuth_deg = xr.DataArray(
+    _az_signed, dims="azimuth_deg", coords={"azimuth_deg": _az_signed}
+)
 azimuthal_transmission = xr.DataArray(
     np.concatenate([_trans[:0:-1], _trans]),
-    dims="azimuth_deg", coords={"azimuth_deg": _az_signed},
+    dims="azimuth_deg",
+    coords={"azimuth_deg": _az_signed},
 )
 
 azimuth_rad = np.deg2rad(azimuth_deg)
 elevation_rad = np.deg2rad(elevation_deg)
-direction = xr.concat([
-    -np.cos(elevation_rad) * np.sin(azimuth_rad),
-    -np.cos(elevation_rad) * np.cos(azimuth_rad),
-    -np.sin(elevation_rad) * xr.ones_like(azimuth_rad),
-], dim="cartesian").assign_coords(cartesian=["x", "y", "z"])
-
-central_instrument_speed = np.sqrt(2 * K_FACTOR * voltages / HE_PLUS_MASS_PER_CHARGE).pint.to("km/s")
+direction = xr.concat(
+    [
+        -np.cos(elevation_rad) * np.sin(azimuth_rad),
+        -np.cos(elevation_rad) * np.cos(azimuth_rad),
+        -np.sin(elevation_rad) * xr.ones_like(azimuth_rad),
+    ],
+    dim="cartesian",
+).assign_coords(cartesian=["x", "y", "z"])
+
+central_instrument_speed = np.sqrt(
+    2 * K_FACTOR * voltages / HE_PLUS_MASS_PER_CHARGE
+).pint.to("km/s")
 
 """## Evaluate passband at each ESA voltage"""
 
@@ -166,22 +208,24 @@
 # A_0 and P share the SIMION normalization at (θ=0, k*=1.89 eV/V/e); enforce
 # P(elevation_deg=0, speed_ratio=1) = 1 to match the production passband grid.
 passband_per_region = passband_per_region / passband_per_region.interp(
-    elevation_deg=0.0, speed_ratio=1.0,
+    elevation_deg=0.0,
+    speed_ratio=1.0,
 )
 az_is_sg = np.abs(azimuth_deg) <= 20
-passband_full = xr.where(az_is_sg, passband_per_region.sel(region="SG"),
-                                   passband_per_region.sel(region="OA"))
+passband_full = xr.where(
+    az_is_sg, passband_per_region.sel(region="SG"), passband_per_region.sel(region="OA")
+)
 
 """## Parameters"""
 
 SW_SPEED_KMS = 450.0
 SW_AZ_DEG = 0.0
 SW_EL_DEG = -10.0
-COOLING_INDEX = 2.0       # α
+COOLING_INDEX = 2.0  # α
 CUTOFF_SPEED_KMS = 450.0  # v_b
-IONIZATION_RATE_HZ = 2e-7 # β_E at 1 AU
-HELIO_DIST_AU = 1.0       # r
-INFLOW_PSI_DEG = 75.0     # ψ — IMAP-to-helium-inflow longitude offset
+IONIZATION_RATE_HZ = 2e-7  # β_E at 1 AU
+HELIO_DIST_AU = 1.0  # r
+INFLOW_PSI_DEG = 75.0  # ψ — IMAP-to-helium-inflow longitude offset
 SW_SPEED_INERTIAL_KMS = 450.0  # |v_sw| in ECLIPJ2000 (≈ SW_SPEED + 25)
 
 """## Bulk-SW vector in SWAPI coords
@@ -190,24 +234,26 @@
 
 sw_az_rad = np.deg2rad(SW_AZ_DEG)
 sw_el_rad = np.deg2rad(SW_EL_DEG)
-v_sw_vec = Q(SW_SPEED_KMS, "km/s") * np.array([
-    -np.cos(sw_el_rad) * np.sin(sw_az_rad),
-    -np.cos(sw_el_rad) * np.cos(sw_az_rad),
-    -np.sin(sw_el_rad),
-])
+v_sw_vec = Q(SW_SPEED_KMS, "km/s") * np.array(
+    [
+        -np.cos(sw_el_rad) * np.sin(sw_az_rad),
+        -np.cos(sw_el_rad) * np.cos(sw_az_rad),
+        -np.sin(sw_el_rad),
+    ]
+)
 v_sw = xr.DataArray(v_sw_vec, dims="cartesian", coords={"cartesian": ["x", "y", "z"]})
 
 """## Per-V speed grid and SW-frame speed"""
 
-speed_grid = (
-    speed_ratio * central_instrument_speed
-).transpose('V', 'speed_ratio')
-v_sw_speed = np.sqrt((v_sw**2).sum("cartesian"))            # scalar
-v_dot_vsw  = xr.dot(direction, v_sw, dim="cartesian")       # (az, el)
-speed_sw   = np.sqrt(np.maximum(
-    speed_grid**2 + v_sw_speed**2 - 2 * speed_grid * v_dot_vsw,
-    Q(0, "km**2/s**2"),
-))
+speed_grid = (speed_ratio * central_instrument_speed).transpose("V", "speed_ratio")
+v_sw_speed = np.sqrt((v_sw**2).sum("cartesian"))  # scalar
+v_dot_vsw = xr.dot(direction, v_sw, dim="cartesian")  # (az, el)
+speed_sw = np.sqrt(
+    np.maximum(
+        speed_grid**2 + v_sw_speed**2 - 2 * speed_grid * v_dot_vsw,
+        Q(0, "km**2/s**2"),
+    )
+)
 
 r"""## V-S PUI VDF
 
@@ -244,10 +290,14 @@
 term3 = w ** (cooling_index - 3)
 
 # Term 4  `r·w^α`
-term4 = density_table.pint.interp(
-    psi=(psi % Q(360, "deg")).magnitude,
-    r=(r * w**cooling_index).pint.to("au").pint.dequantify(),
-).drop_vars(["psi", "r"]).fillna(0)
+term4 = (
+    density_table.pint.interp(
+        psi=(psi % Q(360, "deg")).magnitude,
+        r=(r * w**cooling_index).pint.to("au").pint.dequantify(),
+    )
+    .drop_vars(["psi", "r"])
+    .fillna(0)
+)
 
 # Term 5  Θ(1 − w)
 term5 = xr.where((w < 1), 1, 0)
@@ -256,20 +306,22 @@
 
 """## Coincidence count rate per ESA voltage"""
 
-effective_area = (central_effective_area * passband_full * azimuthal_transmission).clip(min=Q(0, "cm**2"))
+effective_area = (central_effective_area * passband_full * azimuthal_transmission).clip(
+    min=Q(0, "cm**2")
+)
 
 flux = f_PUI * speed_grid
 
 deg2_to_sr = (np.pi / 180.0) ** 2
 cos_elevation = np.cos(np.deg2rad(elevation_deg))
-d3v = speed_grid ** 2 * deg2_to_sr * cos_elevation
+d3v = speed_grid**2 * deg2_to_sr * cos_elevation
 
 integrand = effective_area * flux * d3v
 
 integral = (
     integrand.integrate("azimuth_deg")
-             .integrate("elevation_deg")
-             .integrate("speed_ratio")
+    .integrate("elevation_deg")
+    .integrate("speed_ratio")
 )
 count_rate = (integral * central_instrument_speed).pint.to("counts/s")
 
@@ -288,10 +340,7 @@
 ax0.set_xlabel("ESA voltage [V]")
 ax0.set_ylabel("Coincidence Rate [Hz]")
 ax0.set_ylim(1e-1, 1e2)
-ax0.set_title(
-    f"|v_sw|={SW_SPEED_KMS:g} km/s  "
-    f"az={SW_AZ_DEG:g}°  el={SW_EL_DEG:g}°"
-)
+ax0.set_title(f"|v_sw|={SW_SPEED_KMS:g} km/s  az={SW_AZ_DEG:g}°  el={SW_EL_DEG:g}°")
 ax0.grid(True, alpha=0.3)
 
 ax1.semilogy(v_flat_order / cutoff_speed, f_flat_order)
diff --git a/scripts/swapi/pui_xarray_reference_50sweep.py b/scripts/swapi/pui_xarray_reference_50sweep.py
index d201bb9cb..43e4a5aca 100644
--- a/scripts/swapi/pui_xarray_reference_50sweep.py
+++ b/scripts/swapi/pui_xarray_reference_50sweep.py
@@ -6,12 +6,12 @@
 - `evaluate_pui_sweep_xarray(...)` evaluates the V-S PUI count rate for one
   sweep at fitted (or truth) parameters.
 """
+
 import os
 import sys
 from dataclasses import dataclass
 from datetime import datetime, timedelta
 from pathlib import Path
-from typing import Union
 
 import numpy as np
 import pandas as pd
@@ -48,10 +48,10 @@ class PuiXArrayContext:
 
 def build_pui_xarray_context(
     *,
-    azimuthal_transmission_path: Union[str, Path],
-    central_effective_area_path: Union[str, Path],
-    passband_fit_coefficients_path: Union[str, Path],
-    density_of_neutral_helium_lut_path: Union[str, Path],
+    azimuthal_transmission_path: str | Path,
+    central_effective_area_path: str | Path,
+    passband_fit_coefficients_path: str | Path,
+    density_of_neutral_helium_lut_path: str | Path,
     helium_efficiency_ratio: float = HELIUM_EFFICIENCY_RATIO,
 ) -> PuiXArrayContext:
     """Load SWAPI calibration CSVs/DAT and assemble the xarray integration
@@ -80,8 +80,7 @@ def build_pui_xarray_context(
         .pipe(lambda da: da.assign_coords(degree=da["degree"].astype(int)))
     )
     source_speed_ratio = np.sqrt(
-        passband_coefficients["energy_ratio"].values
-        / K_FACTOR.to("eV/V/e").magnitude
+        passband_coefficients["energy_ratio"].values / K_FACTOR.to("eV/V/e").magnitude
     )
     passband_coefficients = (
         passband_coefficients.assign_coords(
@@ -92,11 +91,15 @@ def build_pui_xarray_context(
     )
 
     az_transmission_native = (
-        pd.read_csv(azimuthal_transmission_path)
-        .fillna(0)
-        .set_index("abs_azimuth")
-        .transmission.to_xarray()
-    ).coarsen(abs_azimuth=10, boundary="trim").mean()
+        (
+            pd.read_csv(azimuthal_transmission_path)
+            .fillna(0)
+            .set_index("abs_azimuth")
+            .transmission.to_xarray()
+        )
+        .coarsen(abs_azimuth=10, boundary="trim")
+        .mean()
+    )
 
     density_data = np.loadtxt(density_of_neutral_helium_lut_path)
     psi_axis, r_axis = np.unique(density_data[:, 0]), np.unique(density_data[:, 1])
@@ -116,9 +119,7 @@ def build_pui_xarray_context(
 
     abs_azimuth_axis = az_transmission_native["abs_azimuth"].values
     transmission_values = az_transmission_native.values
-    signed_azimuth_axis = np.concatenate(
-        [-abs_azimuth_axis[:0:-1], abs_azimuth_axis]
-    )
+    signed_azimuth_axis = np.concatenate([-abs_azimuth_axis[:0:-1], abs_azimuth_axis])
     azimuth_deg = xr.DataArray(
         signed_azimuth_axis,
         dims="azimuth_deg",
@@ -152,7 +153,8 @@ def build_pui_xarray_context(
         xr.polyval(log_beam_energy, passband_coefficients, degree_dim="degree")
     ).fillna(0)
     passband_per_region = passband_per_region / passband_per_region.interp(
-        elevation_deg=0.0, speed_ratio=1.0,
+        elevation_deg=0.0,
+        speed_ratio=1.0,
     )
     azimuth_is_sg = np.abs(azimuth_deg) <= 20
     passband_full = xr.where(
@@ -219,9 +221,7 @@ def evaluate_pui_sweep_xarray(
     v_dot_vsw = xr.dot(context.direction, v_sw, dim="cartesian")
     speed_sw = np.sqrt(
         np.maximum(
-            context.speed_grid**2
-            + v_sw_speed**2
-            - 2 * context.speed_grid * v_dot_vsw,
+            context.speed_grid**2 + v_sw_speed**2 - 2 * context.speed_grid * v_dot_vsw,
             Q(0, "km**2/s**2"),
         )
     )
@@ -231,7 +231,9 @@ def evaluate_pui_sweep_xarray(
     term4 = (
         context.density_table.pint.interp(
             psi=(psi % Q(360, "deg")).magnitude,
-            r=(heliocentric_distance * w**cooling_index).pint.to("au").pint.dequantify(),
+            r=(heliocentric_distance * w**cooling_index)
+            .pint.to("au")
+            .pint.dequantify(),
         )
         .drop_vars(["psi", "r"])
         .fillna(0)
@@ -273,7 +275,10 @@ def evaluate_pui_sweep_xarray(
     from imap_l3_processing.swapi.response.deadtime import deadtime_factor
     from imap_l3_processing.utils import SpiceKernelTypes, furnish_spice_metakernel
     from tests.swapi._helpers import load_swapi_response
-    from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
+    from tests.test_helpers import (
+        get_test_data_path,
+        get_test_instrument_team_data_path,
+    )
 
     OUTPUT_PATH = get_test_data_path("swapi/pui_count_rate_reference_50sweep.h5")
 
@@ -291,9 +296,7 @@ def evaluate_pui_sweep_xarray(
     # that window so the spin phase advances across the steps.
     TOTAL_COARSE_DURATION_S = SWEEP_DURATION_S * N_ESA_STEPS_PER_SWEEP / 72
     STEP_DURATION_S = TOTAL_COARSE_DURATION_S / N_ESA_STEPS_PER_SWEEP
-    END_TIME_UTC = START_TIME_UTC + timedelta(
-        seconds=SWEEP_DURATION_S * N_SWEEPS + 60
-    )
+    END_TIME_UTC = START_TIME_UTC + timedelta(seconds=SWEEP_DURATION_S * N_SWEEPS + 60)
 
     # Truth values are chosen well inside every fit bound so frozen-value
     # integration tests are not sensitive to LM termination next to a wall:
@@ -397,7 +400,9 @@ def evaluate_pui_sweep_xarray(
             solar_wind_speed_inertial_kms=SW_SPEED_INERTIAL_KMS,
         )
 
-    print("Computing proton + alpha Maxwellian shoulder via production forward model...")
+    print(
+        "Computing proton + alpha Maxwellian shoulder via production forward model..."
+    )
     voltage_repeated = np.broadcast_to(
         pui_context.voltages_v, (N_SWEEPS, N_ESA_STEPS_PER_SWEEP)
     ).ravel()
diff --git a/scripts/swapi/report_out_of_range_uncertainties.py b/scripts/swapi/report_out_of_range_uncertainties.py
index 6e269790e..fb34edd18 100644
--- a/scripts/swapi/report_out_of_range_uncertainties.py
+++ b/scripts/swapi/report_out_of_range_uncertainties.py
@@ -51,4 +51,4 @@ def get_all_swapi_l3a():
 
 # report_out_of_range_uncertainties(
 #     r"C:\Users\Petty\Development\imap_L3_processing\data\imap\swapi\l3a\2026\04\imap_swapi_l3a_pui-he_20260419_v001.0033.cdf"
-# )
\ No newline at end of file
+# )
diff --git a/scripts/swapi/swapi_alpha_sw_speed_temperature_density_demo.py b/scripts/swapi/swapi_alpha_sw_speed_temperature_density_demo.py
index 3cf62aa1c..426438db3 100644
--- a/scripts/swapi/swapi_alpha_sw_speed_temperature_density_demo.py
+++ b/scripts/swapi/swapi_alpha_sw_speed_temperature_density_demo.py
@@ -3,24 +3,31 @@
 
 import numpy as np
 import scipy
+from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import (
+    calculate_alpha_solar_wind_speed,
+    calculate_combined_sweeps,
+    get_alpha_peak_indices,
+)
+from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import (
+    AlphaTemperatureDensityCalibrationTable,
+    alpha_count_rate_model,
+    calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps,
+)
 from matplotlib import pyplot as plt
 from numpy import ndarray
 from spacepy.pycdf import CDF
-from uncertainties import ufloat, correlated_values
-from uncertainties.unumpy import uarray, nominal_values, std_devs
+from uncertainties import correlated_values, ufloat
+from uncertainties.unumpy import nominal_values, std_devs, uarray
 
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import calculate_alpha_solar_wind_speed, \
-    calculate_combined_sweeps, get_alpha_peak_indices
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import \
-    calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps, AlphaTemperatureDensityCalibrationTable, \
-    alpha_count_rate_model
 
 
 def read_l2_data_from_dat(file_path: str) -> SwapiL2Data:
     data = np.loadtxt(file_path)
     data = data.reshape((-1, 72, 8))
-    start_time = int(np.datetime64("2010-01-01", "ns") - np.datetime64("2000-01-01T12:00", "ns"))
+    start_time = int(
+        np.datetime64("2010-01-01", "ns") - np.datetime64("2000-01-01T12:00", "ns")
+    )
     twelve_seconds_in_nanoseconds = 12_000_000_000
     epochs = start_time + twelve_seconds_in_nanoseconds * np.arange(len(data))
 
@@ -32,20 +39,24 @@ def read_l2_data_from_dat(file_path: str) -> SwapiL2Data:
     coincident_count_rates = data[..., 7]
 
     fake_coincident_count_rate_uncertainties = np.sqrt(6 * coincident_count_rates)
-    return SwapiL2Data(epochs,
-                       energy_for_first_sweep,
-                       coincident_count_rates,
-                       spin_angles,
-                       fake_coincident_count_rate_uncertainties)
+    return SwapiL2Data(
+        epochs,
+        energy_for_first_sweep,
+        coincident_count_rates,
+        spin_angles,
+        fake_coincident_count_rate_uncertainties,
+    )
 
 
 def read_l2_data(cdf_path: str) -> SwapiL2Data:
     cdf = CDF(cdf_path)
-    return SwapiL2Data(cdf.raw_var("epoch")[...],
-                       cdf["energy"][...],
-                       cdf["swp_coin_rate"][...],
-                       cdf["spin_angles"][...],
-                       cdf["swp_coin_unc"][...])
+    return SwapiL2Data(
+        cdf.raw_var("epoch")[...],
+        cdf["energy"][...],
+        cdf["swp_coin_rate"][...],
+        cdf["spin_angles"][...],
+        cdf["swp_coin_unc"][...],
+    )
 
 
 fig = plt.figure()
@@ -54,28 +65,35 @@ def read_l2_data(cdf_path: str) -> SwapiL2Data:
 def plot_sweeps(data):
     for i in range(len(data.sci_start_time)):
         axes = fig.add_subplot(len(data.sci_start_time) + 1, 1, i + 1)
-        axes.loglog(data.energy, data.coincidence_count_rate[i, :], marker='.', linestyle="None")
+        axes.loglog(
+            data.energy, data.coincidence_count_rate[i, :], marker=".", linestyle="None"
+        )
         axes.set(xlabel="Energy", ylabel="Count Rate")
 
 
 def run_example_dat_files():
     data = read_l2_data_from_dat("swapi/test_data/swapi_test_data_v5.dat")
-    coincident_count_rate = uarray(data.coincidence_count_rate, data.coincidence_count_rate_uncertainty)
+    coincident_count_rate = uarray(
+        data.coincidence_count_rate, data.coincidence_count_rate_uncertainty
+    )
     energy = data.energy
     alpha_speed = calculate_alpha_solar_wind_speed(coincident_count_rate, energy)
 
     temperature_density_lut = AlphaTemperatureDensityCalibrationTable.from_file(
-        r"swapi/test_data/imap_swapi_alpha-density-temperature-lut_20240920_v000.dat")
-    alpha_temperature, alpha_density = calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-        temperature_density_lut, alpha_speed,
-        coincident_count_rate, energy)
+        r"swapi/test_data/imap_swapi_alpha-density-temperature-lut_20240920_v000.dat"
+    )
+    alpha_temperature, alpha_density = (
+        calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
+            temperature_density_lut, alpha_speed, coincident_count_rate, energy
+        )
+    )
 
 
 def plot_and_calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-        table: AlphaTemperatureDensityCalibrationTable,
-        alpha_sw_speed: ufloat,
-        count_rates: uarray,
-        energies: ndarray,
+    table: AlphaTemperatureDensityCalibrationTable,
+    alpha_sw_speed: ufloat,
+    count_rates: uarray,
+    energies: ndarray,
 ):
     average_count_rates, energies = calculate_combined_sweeps(count_rates, energies)
 
@@ -85,15 +103,22 @@ def plot_and_calculate_alpha_solar_wind_temperature_and_density_for_combined_swe
     peak_average_alpha_count_rates = average_count_rates[alpha_particle_peak_slice]
 
     initial_parameter_guess = [0.15, 3.6e5, nominal_values(alpha_sw_speed)]
-    values, covariance = scipy.optimize.curve_fit(alpha_count_rate_model,
-                                                  peak_energies,
-                                                  nominal_values(peak_average_alpha_count_rates),
-                                                  sigma=std_devs(peak_average_alpha_count_rates),
-                                                  absolute_sigma=True,
-                                                  bounds=[[0, 0, 0], [np.inf, np.inf, np.inf]],
-                                                  p0=initial_parameter_guess)
-    residual = abs(alpha_count_rate_model(peak_energies, *values) - nominal_values(peak_average_alpha_count_rates))
-    reduced_chisq = np.sum(np.square(residual / std_devs(peak_average_alpha_count_rates))) / (len(peak_energies) - 3)
+    values, covariance = scipy.optimize.curve_fit(
+        alpha_count_rate_model,
+        peak_energies,
+        nominal_values(peak_average_alpha_count_rates),
+        sigma=std_devs(peak_average_alpha_count_rates),
+        absolute_sigma=True,
+        bounds=[[0, 0, 0], [np.inf, np.inf, np.inf]],
+        p0=initial_parameter_guess,
+    )
+    residual = abs(
+        alpha_count_rate_model(peak_energies, *values)
+        - nominal_values(peak_average_alpha_count_rates)
+    )
+    reduced_chisq = np.sum(
+        np.square(residual / std_devs(peak_average_alpha_count_rates))
+    ) / (len(peak_energies) - 3)
     if reduced_chisq > 10:
         raise ValueError("Failed to fit - chi-squared too large", reduced_chisq)
     density, temperature, speed = correlated_values(values, covariance)
@@ -120,25 +145,33 @@ def main(file_path):
     else:
         raise Exception("The demo can only load data from .dat or .cdf files!")
 
-    coincident_count_rate = uarray(data.coincidence_count_rate, data.coincidence_count_rate_uncertainty)
+    coincident_count_rate = uarray(
+        data.coincidence_count_rate, data.coincidence_count_rate_uncertainty
+    )
     energy = data.energy
     alpha_speed = calculate_alpha_solar_wind_speed(coincident_count_rate, energy)
 
     temperature_density_lut = AlphaTemperatureDensityCalibrationTable.from_file(
-        r"../../tests/test_data/swapi/imap_swapi_alpha-density-temperature-lut_20240920_v000.dat")
-    alpha_temperature, alpha_density = plot_and_calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-        temperature_density_lut, alpha_speed,
-        coincident_count_rate, energy)
+        r"../../tests/test_data/swapi/imap_swapi_alpha-density-temperature-lut_20240920_v000.dat"
+    )
+    alpha_temperature, alpha_density = (
+        plot_and_calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
+            temperature_density_lut, alpha_speed, coincident_count_rate, energy
+        )
+    )
 
     print(f"SW He++ speed: {alpha_speed.n} +/- {alpha_speed.s}")
     print(f"SW He++ temperature: {alpha_temperature.n} +/- {alpha_temperature.s}")
     print(f"SW He++ density: {alpha_density.n} +/- {alpha_density.s}")
 
     if __name__ == "__main__":
-        fig.legend([f"SW He++ speed: {alpha_speed :.3f}",
-                    f"SW He++ temperature: {alpha_temperature :.3f}",
-                    f"SW He++ density: {alpha_density :.3f}"],
-                   )
+        fig.legend(
+            [
+                f"SW He++ speed: {alpha_speed:.3f}",
+                f"SW He++ temperature: {alpha_temperature:.3f}",
+                f"SW He++ density: {alpha_density:.3f}",
+            ],
+        )
 
         fig.set_figheight(10)
         plt.show()
diff --git a/scripts/swapi/upload_swapi_l3_dependencies.py b/scripts/swapi/upload_swapi_l3_dependencies.py
index 2510bef1a..3d96b6667 100644
--- a/scripts/swapi/upload_swapi_l3_dependencies.py
+++ b/scripts/swapi/upload_swapi_l3_dependencies.py
@@ -1,20 +1,25 @@
 import shutil
-from pathlib import Path
 
 import imap_data_access
 from imap_data_access.file_validation import generate_imap_file_path
 
 from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
-_swapi = get_test_data_path('swapi')
+_swapi = get_test_data_path("swapi")
 files_to_upload = [
     _swapi / "imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat",
     _swapi / "imap_swapi_efficiency-lut_20241020_v000.dat",
     _swapi / "imap_swapi_energy-gf-pui-lut_20100101_v001.csv",
     _swapi / "imap_swapi_energy-gf-sw-lut_20100101_v001.csv",
-    get_test_instrument_team_data_path("swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"),
-    get_test_instrument_team_data_path("swapi/imap_swapi_central-effective-area_20260425_v001.csv"),
-    get_test_instrument_team_data_path("swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"),
+    get_test_instrument_team_data_path(
+        "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+    ),
+    get_test_instrument_team_data_path(
+        "swapi/imap_swapi_central-effective-area_20260425_v001.csv"
+    ),
+    get_test_instrument_team_data_path(
+        "swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"
+    ),
 ]
 upload_to_local_dir = False
 for file in files_to_upload:
diff --git a/scripts/swapi/view_one_pui_spectrum.py b/scripts/swapi/view_one_pui_spectrum.py
index 20c1a4d37..ec9949b5f 100644
--- a/scripts/swapi/view_one_pui_spectrum.py
+++ b/scripts/swapi/view_one_pui_spectrum.py
@@ -16,6 +16,7 @@
 caches. Pass --offline to resolve the L2 file and SPICE kernels from the local
 DATA_DIR cache (populated by a prior online run) instead of contacting the SDC.
 """
+
 import argparse
 import os
 import pickle
@@ -27,15 +28,17 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import spiceypy
-from matplotlib.colors import LogNorm
 from imap_data_access import ProcessingInputCollection
 from imap_data_access.processing_input import generate_imap_input
-from spacepy.pycdf import lib as cdf_library
-
 from imap_processing.swapi.l2 import swapi_l2
+from matplotlib.colors import LogNorm
+from spacepy.pycdf import lib as cdf_library
 
 from imap_l3_processing.constants import ONE_AU_IN_KM, ONE_SECOND_IN_NANOSECONDS
-from imap_l3_processing.swapi.constants import SWAPI_COARSE_SWEEP_BINS, SWAPI_L2_K_FACTOR
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+)
 from imap_l3_processing.swapi.descriptors import (
     AZIMUTHAL_TRANSMISSION_DESCRIPTOR,
     CENTRAL_EFFECTIVE_AREA_DESCRIPTOR,
@@ -56,19 +59,28 @@
     evaluate_pui_sweep_xarray,
 )
 
-
 argument_parser = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+)
 argument_parser.add_argument("date", help="UTC date in YYYY-MM-DD format")
-argument_parser.add_argument("time",
-                             help="UTC time in HH:MM[:SS] format; picks the 50-sweep "
-                                  "chunk whose central epoch is nearest.")
-argument_parser.add_argument("--output-path", type=Path, default=None,
-                             help="If set, save the figure to this path and skip plt.show().")
-argument_parser.add_argument("--offline", action="store_true",
-                             help="Resolve the L2 file and SPICE kernels from the local "
-                                  "DATA_DIR cache instead of querying/downloading from the "
-                                  "SDC. Requires a prior online run to have populated the cache.")
+argument_parser.add_argument(
+    "time",
+    help="UTC time in HH:MM[:SS] format; picks the 50-sweep "
+    "chunk whose central epoch is nearest.",
+)
+argument_parser.add_argument(
+    "--output-path",
+    type=Path,
+    default=None,
+    help="If set, save the figure to this path and skip plt.show().",
+)
+argument_parser.add_argument(
+    "--offline",
+    action="store_true",
+    help="Resolve the L2 file and SPICE kernels from the local "
+    "DATA_DIR cache instead of querying/downloading from the "
+    "SDC. Requires a prior online run to have populated the cache.",
+)
 arguments = argument_parser.parse_args()
 
 
@@ -80,47 +92,67 @@ def _parse_hh_mm_ss(time_string: str):
             continue
     raise SystemExit(f"Could not parse time {time_string!r}; expected HH:MM[:SS].")
 
+
 if not arguments.offline and "IMAP_API_KEY" not in os.environ:
     sys.exit("IMAP_API_KEY environment variable is required.")
 
 target_date = datetime.strptime(arguments.date, "%Y-%m-%d")
-target_datetime_utc = datetime.combine(target_date.date(), _parse_hh_mm_ss(arguments.time))
+target_datetime_utc = datetime.combine(
+    target_date.date(), _parse_hh_mm_ss(arguments.time)
+)
 compact_date = target_date.strftime("%Y%m%d")
 fit_cache_path = Path(f"/tmp/swapi_pui_fit_{compact_date}.pkl")
 spectrogram_cache_path = Path(
-    f"/tmp/swapi_pui_spectrograms_per_sweep_{compact_date}.pkl")
+    f"/tmp/swapi_pui_spectrograms_per_sweep_{compact_date}.pkl"
+)
 if not fit_cache_path.exists() or not spectrogram_cache_path.exists():
-    sys.exit(f"Missing pickle caches for {arguments.date}. "
-             f"Run scripts/swapi/fit_and_plot_pui.py {arguments.date} first.")
+    sys.exit(
+        f"Missing pickle caches for {arguments.date}. "
+        f"Run scripts/swapi/fit_and_plot_pui.py {arguments.date} first."
+    )
 
 with fit_cache_path.open("rb") as cache_file:
     pickup_ion_data, sw_velocity_rtn_by_chunk_epoch = pickle.load(cache_file)
 with spectrogram_cache_path.open("rb") as spectrogram_file:
-    (energies_per_sweep_ev, observed_spectrogram, model_spectrogram,
-     _sweep_epochs_tt2000) = pickle.load(spectrogram_file)
+    (
+        energies_per_sweep_ev,
+        observed_spectrogram,
+        model_spectrogram,
+        _sweep_epochs_tt2000,
+    ) = pickle.load(spectrogram_file)
 
 imap_data_access.config["DATA_DIR"] = Path("/tmp/swapi_fit_and_plot_data")
 imap_data_access.config["DATA_DIR"].mkdir(parents=True, exist_ok=True)
 
-ancillary_template_path = (Path(__file__).parent
-                           / "imap_swapi_l3a_proton-sw_dependency_template.json")
+ancillary_template_path = (
+    Path(__file__).parent / "imap_swapi_l3a_proton-sw_dependency_template.json"
+)
 data_dir = imap_data_access.config["DATA_DIR"]
 
 
 def resolve_l2_filename_from_cache() -> str:
-    matches = sorted((data_dir / "imap" / "swapi" / "l2").rglob(
-        f"imap_swapi_l2_sci_{compact_date}_v*.cdf"))
+    matches = sorted(
+        (data_dir / "imap" / "swapi" / "l2").rglob(
+            f"imap_swapi_l2_sci_{compact_date}_v*.cdf"
+        )
+    )
     if not matches:
-        sys.exit(f"No cached SWAPI L2 sci file for {arguments.date} under {data_dir}. "
-                 f"Run once without --offline to populate the cache.")
+        sys.exit(
+            f"No cached SWAPI L2 sci file for {arguments.date} under {data_dir}. "
+            f"Run once without --offline to populate the cache."
+        )
     return matches[-1].name
 
 
 def resolve_spice_filenames_from_cache() -> list[str]:
-    names = sorted(p.name for p in (data_dir / "imap" / "spice").rglob("*") if p.is_file())
+    names = sorted(
+        p.name for p in (data_dir / "imap" / "spice").rglob("*") if p.is_file()
+    )
     if not names:
-        sys.exit(f"No cached SPICE kernels under {data_dir}. "
-                 f"Run once without --offline to populate the cache.")
+        sys.exit(
+            f"No cached SPICE kernels under {data_dir}. "
+            f"Run once without --offline to populate the cache."
+        )
     return names
 
 
@@ -129,32 +161,46 @@ def resolve_spice_filenames_from_cache() -> list[str]:
     spice_kernel_filenames = resolve_spice_filenames_from_cache()
 else:
     science_query_results = imap_data_access.query(
-        instrument="swapi", data_level="l2", descriptor="sci",
-        start_date=compact_date, end_date=compact_date, version="latest")
+        instrument="swapi",
+        data_level="l2",
+        descriptor="sci",
+        start_date=compact_date,
+        end_date=compact_date,
+        version="latest",
+    )
     if not science_query_results:
         sys.exit(f"No SWAPI L2 sci file at the SDC for {arguments.date}")
     l2_science_filename = os.path.basename(science_query_results[0]["file_path"])
 
-    ephemeris_kernel_types = [SpiceKernelTypes.EphemerisReconstructed,
-                              SpiceKernelTypes.EphemerisPredicted]
-    non_ephemeris_kernel_types = [t for t in SpiceKernelTypes
-                                  if t not in ephemeris_kernel_types]
-    spice_kernel_filenames = list(dict.fromkeys(
-        os.path.basename(path) for path in (
-            get_spice_kernels_file_names(
-                target_date - timedelta(days=1),
-                target_date + timedelta(days=2),
-                non_ephemeris_kernel_types)
-            + get_spice_kernels_file_names(
-                target_date - timedelta(days=90),
-                target_date + timedelta(days=2),
-                ephemeris_kernel_types)
+    ephemeris_kernel_types = [
+        SpiceKernelTypes.EphemerisReconstructed,
+        SpiceKernelTypes.EphemerisPredicted,
+    ]
+    non_ephemeris_kernel_types = [
+        t for t in SpiceKernelTypes if t not in ephemeris_kernel_types
+    ]
+    spice_kernel_filenames = list(
+        dict.fromkeys(
+            os.path.basename(path)
+            for path in (
+                get_spice_kernels_file_names(
+                    target_date - timedelta(days=1),
+                    target_date + timedelta(days=2),
+                    non_ephemeris_kernel_types,
+                )
+                + get_spice_kernels_file_names(
+                    target_date - timedelta(days=90),
+                    target_date + timedelta(days=2),
+                    ephemeris_kernel_types,
+                )
+            )
         )
-    ))
+    )
 
 dynamic_filenames = [l2_science_filename, *spice_kernel_filenames]
 processing_input_collection = ProcessingInputCollection(
-    *[generate_imap_input(filename) for filename in dynamic_filenames])
+    *[generate_imap_input(filename) for filename in dynamic_filenames]
+)
 processing_input_collection.deserialize(ancillary_template_path.read_text())
 processing_input_collection.download_all_files()
 
@@ -166,31 +212,44 @@ def resolve_spice_filenames_from_cache() -> list[str]:
 
 def _swapi_ancillary_path(descriptor: str) -> Path:
     paths = processing_input_collection.get_file_paths(
-        source="swapi", descriptor=descriptor)
+        source="swapi", descriptor=descriptor
+    )
     return imap_data_access.download(paths[0])
 
 
 pui_xarray_context = build_pui_xarray_context(
-    azimuthal_transmission_path=_swapi_ancillary_path(AZIMUTHAL_TRANSMISSION_DESCRIPTOR),
-    central_effective_area_path=_swapi_ancillary_path(CENTRAL_EFFECTIVE_AREA_DESCRIPTOR),
-    passband_fit_coefficients_path=_swapi_ancillary_path(PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR),
-    density_of_neutral_helium_lut_path=_swapi_ancillary_path(DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR),
+    azimuthal_transmission_path=_swapi_ancillary_path(
+        AZIMUTHAL_TRANSMISSION_DESCRIPTOR
+    ),
+    central_effective_area_path=_swapi_ancillary_path(
+        CENTRAL_EFFECTIVE_AREA_DESCRIPTOR
+    ),
+    passband_fit_coefficients_path=_swapi_ancillary_path(
+        PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR
+    ),
+    density_of_neutral_helium_lut_path=_swapi_ancillary_path(
+        DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR
+    ),
 )
 
 chunks = list(chunk_l2_data(dependencies.data, 50))
-chunk_central_datetimes = np.array([
-    cdf_library.tt2000_to_datetime(int(t)) for t in pickup_ion_data.epoch
-])
-seconds_from_target = np.array([
-    abs((central - target_datetime_utc).total_seconds())
-    for central in chunk_central_datetimes
-])
+chunk_central_datetimes = np.array(
+    [cdf_library.tt2000_to_datetime(int(t)) for t in pickup_ion_data.epoch]
+)
+seconds_from_target = np.array(
+    [
+        abs((central - target_datetime_utc).total_seconds())
+        for central in chunk_central_datetimes
+    ]
+)
 chunk_index = int(np.argmin(seconds_from_target))
 data_chunk = chunks[chunk_index]
-print(f"Selected chunk {chunk_index} (central epoch "
-      f"{chunk_central_datetimes[chunk_index].isoformat()}, "
-      f"{seconds_from_target[chunk_index]:.1f} s from requested "
-      f"{target_datetime_utc.isoformat()})")
+print(
+    f"Selected chunk {chunk_index} (central epoch "
+    f"{chunk_central_datetimes[chunk_index].isoformat()}, "
+    f"{seconds_from_target[chunk_index]:.1f} s from requested "
+    f"{target_datetime_utc.isoformat()})"
+)
 
 chunk_epoch_tt2000 = int(pickup_ion_data.epoch[chunk_index])
 sw_velocity_rtn_kms = sw_velocity_rtn_by_chunk_epoch.get(chunk_epoch_tt2000)
@@ -200,14 +259,17 @@ def _swapi_ancillary_path(descriptor: str) -> Path:
     sys.exit(f"Chunk {chunk_index} has no successful PUI fit.")
 
 chunk_ephemeris_time = spiceypy.unitim(
-    chunk_epoch_tt2000 / ONE_SECOND_IN_NANOSECONDS, "TT", "ET")
+    chunk_epoch_tt2000 / ONE_SECOND_IN_NANOSECONDS, "TT", "ET"
+)
 vasyliunas_siscoe = build_vasyliunas_siscoe_distribution(
-    chunk_ephemeris_time, sw_velocity_rtn_kms,
+    chunk_ephemeris_time,
+    sw_velocity_rtn_kms,
     dependencies.density_of_neutral_helium_calibration_table,
     dependencies.helium_inflow_vector,
 )
 bulk_sw_per_bin_swapi_kms = rotate_rtn_velocity_to_swapi_per_bin(
-    data_chunk, sw_velocity_rtn_kms)
+    data_chunk, sw_velocity_rtn_kms
+)
 
 # Production averages per-sweep rates computed with the per-bin bulk SW
 # vector. At high E the f_PUI azimuthal window is narrow (~tens of degrees),
@@ -225,15 +287,17 @@ def _swapi_ancillary_path(descriptor: str) -> Path:
 fit_background_rate_hz = float(pickup_ion_data.background_rate[chunk_index].n)
 # A rejected background is stored as NaN (see _set_background_to_fill_if_too_high);
 # adding it would wipe out the model lines, so fall back to no offset.
-background_offset_hz = fit_background_rate_hz if np.isfinite(fit_background_rate_hz) else 0.0
+background_offset_hz = (
+    fit_background_rate_hz if np.isfinite(fit_background_rate_hz) else 0.0
+)
 
 xarray_rate_per_sweep = np.empty(bulk_sw_per_bin_swapi_kms.shape[:2])
 n_sweeps_in_chunk = bulk_sw_per_bin_swapi_kms.shape[0]
 print(f"Evaluating xarray reference per-sweep ({n_sweeps_in_chunk} sweeps)...")
 for sweep_index_in_chunk in range(n_sweeps_in_chunk):
-    sweep_bulk_sw_swapi_kms = (
-        bulk_sw_per_bin_swapi_kms[sweep_index_in_chunk][ascending_voltage_order]
-    )
+    sweep_bulk_sw_swapi_kms = bulk_sw_per_bin_swapi_kms[sweep_index_in_chunk][
+        ascending_voltage_order
+    ]
     xarray_rate_per_sweep[sweep_index_in_chunk] = evaluate_pui_sweep_xarray(
         pui_xarray_context,
         sweep_bulk_sw_swapi_kms,
@@ -253,21 +317,25 @@ def _swapi_ancillary_path(descriptor: str) -> Path:
 
 chunk_observed_mean = np.nanmean(chunk_observed_per_sweep, axis=0)
 chunk_model_mean = np.nanmean(chunk_model_per_sweep, axis=0)
-chunk_energies_mean_ev = np.nanmean(chunk_voltages_per_sweep, axis=0) * SWAPI_L2_K_FACTOR
+chunk_energies_mean_ev = (
+    np.nanmean(chunk_voltages_per_sweep, axis=0) * SWAPI_L2_K_FACTOR
+)
 
 # Poisson uncertainty on the chunk-mean rate at each bin: the mean of N
 # independent counting measurements over a livetime each, so total counts
 # = N · mean_rate · livetime and σ(mean_rate) = sqrt(mean_rate / (N · livetime)).
 valid_sweeps_per_bin = np.sum(np.isfinite(chunk_observed_per_sweep), axis=0)
 chunk_observed_uncertainty = np.sqrt(
-    chunk_observed_mean / (valid_sweeps_per_bin * swapi_l2.SWAPI_LIVETIME))
+    chunk_observed_mean / (valid_sweeps_per_bin * swapi_l2.SWAPI_LIVETIME)
+)
 
 pickup_ion_window_bin_mask = ~np.all(np.isnan(chunk_model_per_sweep), axis=0)
 
 xarray_energies_ev = pui_xarray_context.voltages_v * SWAPI_L2_K_FACTOR
 
 mean_energies_ev_per_step = np.nanmean(
-    chunk_voltages_per_sweep * SWAPI_L2_K_FACTOR, axis=0)
+    chunk_voltages_per_sweep * SWAPI_L2_K_FACTOR, axis=0
+)
 pickup_ion_bin_indices = np.where(pickup_ion_window_bin_mask)[0]
 fit_window_energies_ev = mean_energies_ev_per_step[pickup_ion_bin_indices]
 energy_sort_order_within_window = np.argsort(fit_window_energies_ev)
@@ -276,13 +344,14 @@ def _swapi_ancillary_path(descriptor: str) -> Path:
 observed_spectrogram_sorted = chunk_observed_per_sweep[:, sorted_pickup_ion_bin_indices]
 model_spectrogram_sorted = chunk_model_per_sweep[:, sorted_pickup_ion_bin_indices]
 
-positive_spectrogram_values = np.concatenate([
-    array[np.isfinite(array) & (array > 0)]
-    for array in (observed_spectrogram_sorted, model_spectrogram_sorted)
-])
+positive_spectrogram_values = np.concatenate(
+    [
+        array[np.isfinite(array) & (array > 0)]
+        for array in (observed_spectrogram_sorted, model_spectrogram_sorted)
+    ]
+)
 if positive_spectrogram_values.size:
-    spectrogram_vmin = max(
-        float(np.percentile(positive_spectrogram_values, 1)), 1e-3)
+    spectrogram_vmin = max(float(np.percentile(positive_spectrogram_values, 1)), 1e-3)
     spectrogram_vmax = float(positive_spectrogram_values.max())
 else:
     spectrogram_vmin, spectrogram_vmax = 1e-3, 1.0
@@ -293,17 +362,33 @@ def _swapi_ancillary_path(descriptor: str) -> Path:
 line_axis = figure.add_subplot(grid_spec[0])
 observed_spectrogram_axis = figure.add_subplot(grid_spec[1])
 model_spectrogram_axis = figure.add_subplot(
-    grid_spec[2], sharex=observed_spectrogram_axis,
-    sharey=observed_spectrogram_axis)
-
-line_axis.errorbar(chunk_energies_mean_ev, chunk_observed_mean,
-                   yerr=chunk_observed_uncertainty,
-                   fmt=".", color="tab:blue", elinewidth=0.8, capsize=2,
-                   label="Observed (chunk mean)")
-line_axis.plot(chunk_energies_mean_ev, chunk_model_mean + background_offset_hz,
-               "x-", color="tab:orange", label="Model (Optimized) + background")
-line_axis.plot(xarray_energies_ev, xarray_rate_per_step + background_offset_hz,
-               "+-", color="tab:green", label="Model (Reference) + background")
+    grid_spec[2], sharex=observed_spectrogram_axis, sharey=observed_spectrogram_axis
+)
+
+line_axis.errorbar(
+    chunk_energies_mean_ev,
+    chunk_observed_mean,
+    yerr=chunk_observed_uncertainty,
+    fmt=".",
+    color="tab:blue",
+    elinewidth=0.8,
+    capsize=2,
+    label="Observed (chunk mean)",
+)
+line_axis.plot(
+    chunk_energies_mean_ev,
+    chunk_model_mean + background_offset_hz,
+    "x-",
+    color="tab:orange",
+    label="Model (Optimized) + background",
+)
+line_axis.plot(
+    xarray_energies_ev,
+    xarray_rate_per_step + background_offset_hz,
+    "+-",
+    color="tab:green",
+    label="Model (Reference) + background",
+)
 line_axis.set_xscale("log")
 line_axis.set_yscale("log")
 line_axis.set_xlabel("Energy / eV")
@@ -314,36 +399,52 @@ def _swapi_ancillary_path(descriptor: str) -> Path:
     f"α={fit_cooling_index:.2f}  "
     f"v_b={fit_cutoff_speed_kms:.0f} km/s  "
     f"β_E={fit_ionization_rate_hz:.2e} 1/s  "
-    + (f"bg={fit_background_rate_hz:.3f} Hz"
-       if np.isfinite(fit_background_rate_hz) else "bg=nan (rejected)"))
+    + (
+        f"bg={fit_background_rate_hz:.3f} Hz"
+        if np.isfinite(fit_background_rate_hz)
+        else "bg=nan (rejected)"
+    )
+)
 line_axis.grid(True, which="both", alpha=0.3)
 line_axis.legend()
 
 sweep_indices_in_chunk = np.arange(n_sweeps_in_chunk)
 for axis_for_spectrogram, spectrogram_values, label in (
-    (observed_spectrogram_axis, observed_spectrogram_sorted,
-     "Observed (50 sweeps × PUI fit window)"),
+    (
+        observed_spectrogram_axis,
+        observed_spectrogram_sorted,
+        "Observed (50 sweeps × PUI fit window)",
+    ),
     (model_spectrogram_axis, model_spectrogram_sorted, "Model"),
 ):
     mesh = axis_for_spectrogram.pcolormesh(
-        sweep_indices_in_chunk, mean_energies_ev_sorted,
+        sweep_indices_in_chunk,
+        mean_energies_ev_sorted,
         np.ma.masked_invalid(spectrogram_values).T,
-        shading="nearest", cmap="viridis", norm=spectrogram_norm,
+        shading="nearest",
+        cmap="viridis",
+        norm=spectrogram_norm,
         rasterized=True,
     )
     axis_for_spectrogram.set_yscale("log")
     axis_for_spectrogram.set_ylabel("Energy / eV")
     axis_for_spectrogram.text(
-        0.01, 0.95, label,
+        0.01,
+        0.95,
+        label,
         transform=axis_for_spectrogram.transAxes,
-        ha="left", va="top", fontsize=10,
+        ha="left",
+        va="top",
+        fontsize=10,
         bbox=dict(boxstyle="round,pad=0.3", fc="white", alpha=0.85, ec="none"),
     )
 plt.setp(observed_spectrogram_axis.get_xticklabels(), visible=False)
 model_spectrogram_axis.set_xlabel("Sweep index within chunk")
 figure.colorbar(
-    mesh, ax=[observed_spectrogram_axis, model_spectrogram_axis],
-    label="Coincidence rate [Hz]")
+    mesh,
+    ax=[observed_spectrogram_axis, model_spectrogram_axis],
+    label="Coincidence rate [Hz]",
+)
 
 if arguments.output_path is not None:
     arguments.output_path.parent.mkdir(parents=True, exist_ok=True)
diff --git a/scripts/swe/calculate_rebin_by_pitch_angle.py b/scripts/swe/calculate_rebin_by_pitch_angle.py
index 1898e0dc8..99d7e7219 100644
--- a/scripts/swe/calculate_rebin_by_pitch_angle.py
+++ b/scripts/swe/calculate_rebin_by_pitch_angle.py
@@ -5,16 +5,27 @@
 import spiceypy
 from imap_data_access import SPICEFilePath
 
-from imap_l3_processing.swe.l3.science.pitch_calculations import calculate_velocity_in_dsp_frame_km_s, \
-    swe_rebin_intensity_by_pitch_angle_and_gyrophase, correct_and_rebin
+from imap_l3_processing.swe.l3.science.pitch_calculations import (
+    calculate_velocity_in_dsp_frame_km_s,
+    correct_and_rebin,
+    swe_rebin_intensity_by_pitch_angle_and_gyrophase,
+)
 from imap_l3_processing.swe.l3.swe_l3_dependencies import SweL3Dependencies
 from imap_l3_processing.swe.swe_processor import SweProcessor
 
-swe_l2_file_path = Path(r'data/imap/swe/l2/2025/12/imap_swe_l2_sci_20251217_v002.cdf')
-swe_l1b_file_path = Path(r'data/imap/swe/l1b/2025/12/imap_swe_l1b_sci_20251217_v004.cdf')
-mag_file_path = Path(r'data/imap/mag/l1d/2025/12/imap_mag_l1d_norm-dsrf_20251217_v001.cdf')
-swapi_file_path = Path(r'data/imap/swapi/l3a/2025/12/imap_swapi_l3a_proton-sw_20251217_v002.cdf')
-configuration_file_path = Path(r'data/imap/ancillary/swe/imap_swe_config_20251119_v002.json')
+swe_l2_file_path = Path(r"data/imap/swe/l2/2025/12/imap_swe_l2_sci_20251217_v002.cdf")
+swe_l1b_file_path = Path(
+    r"data/imap/swe/l1b/2025/12/imap_swe_l1b_sci_20251217_v004.cdf"
+)
+mag_file_path = Path(
+    r"data/imap/mag/l1d/2025/12/imap_mag_l1d_norm-dsrf_20251217_v001.cdf"
+)
+swapi_file_path = Path(
+    r"data/imap/swapi/l3a/2025/12/imap_swapi_l3a_proton-sw_20251217_v002.cdf"
+)
+configuration_file_path = Path(
+    r"data/imap/ancillary/swe/imap_swe_config_20251119_v002.json"
+)
 
 spice_files = [
     "naif0012.tls",
@@ -26,44 +37,82 @@
     "imap_pred_od013_20251201_20260112_v01.bsp",
     "imap_dps_2025_350_2025_351_001.ah.bc",
     "imap_dps_2025_350_2025_352_001.ah.bc",
-    "imap_dps_2025_351_2025_353_001.ah.bc"
+    "imap_dps_2025_351_2025_353_001.ah.bc",
 ]
 
 for spice in spice_files:
     spiceypy.furnsh(str(SPICEFilePath(spice).construct_path()))
 
-swe_l3_dependencies = SweL3Dependencies.from_file_paths(swe_l2_file_path, swe_l1b_file_path, mag_file_path,
-                                                        swapi_file_path, configuration_file_path)
+swe_l3_dependencies = SweL3Dependencies.from_file_paths(
+    swe_l2_file_path,
+    swe_l1b_file_path,
+    mag_file_path,
+    swapi_file_path,
+    configuration_file_path,
+)
 
 swe_processor = SweProcessor(sentinel.pic, sentinel.input_metadata)
-corrected_energy_bins = np.array([[-3.2314079545247543, -2.1864079545247543, -0.7614079545247536,
-                                   1.281092045475246, 4.083592045475245, 7.978592045475245, 13.393592045475241,
-                                   20.898592045475244, 31.396092045475246, 45.978592045475246,
-                                   66.26109204547525, 94.47609204547524, 133.71109204547523, 188.28859204547524,
-                                   264.2410920454752, 369.8335920454752, 516.7510920454753, 721.0960920454753,
-                                   1005.3360920454752, 1400.7735920454752, 1950.7760920454753,
-                                   2715.811092045475, 3780.001092045475, 5260.243592045475]])
+corrected_energy_bins = np.array(
+    [
+        [
+            -3.2314079545247543,
+            -2.1864079545247543,
+            -0.7614079545247536,
+            1.281092045475246,
+            4.083592045475245,
+            7.978592045475245,
+            13.393592045475241,
+            20.898592045475244,
+            31.396092045475246,
+            45.978592045475246,
+            66.26109204547525,
+            94.47609204547524,
+            133.71109204547523,
+            188.28859204547524,
+            264.2410920454752,
+            369.8335920454752,
+            516.7510920454753,
+            721.0960920454753,
+            1005.3360920454752,
+            1400.7735920454752,
+            1950.7760920454753,
+            2715.811092045475,
+            3780.001092045475,
+            5260.243592045475,
+        ]
+    ]
+)
 # swe_processor.calculate_pitch_angle_products(swe_l3_dependencies, corrected_energy_bins)
 swe_l2_data = swe_l3_dependencies.swe_l2_data
-counts = swe_l3_dependencies.swe_l1b_data.count_rates * (swe_l2_data.acquisition_duration[..., np.newaxis] / 1e6)
-rebinned_mag_data = np.array([-0.91321456, 0.96044633, 6.76249351])[np.newaxis, np.newaxis, np.newaxis, :]
+counts = swe_l3_dependencies.swe_l1b_data.count_rates * (
+    swe_l2_data.acquisition_duration[..., np.newaxis] / 1e6
+)
+rebinned_mag_data = np.array([-0.91321456, 0.96044633, 6.76249351])[
+    np.newaxis, np.newaxis, np.newaxis, :
+]
 
 
 def calculate(i):
-    dsp_velocities = calculate_velocity_in_dsp_frame_km_s(corrected_energy_bins[i], swe_l2_data.inst_el,
-                                                          swe_l2_data.inst_az_spin_sector[i])
+    dsp_velocities = calculate_velocity_in_dsp_frame_km_s(
+        corrected_energy_bins[i],
+        swe_l2_data.inst_el,
+        swe_l2_data.inst_az_spin_sector[i],
+    )
 
-    rebinned_psd, rebinned_psd_by_pa_and_gyro = correct_and_rebin(swe_l2_data.phase_space_density[i],
-                                                                  np.array([0, 0, -400]),
-                                                                  dsp_velocities,
-                                                                  rebinned_mag_data[i],
-                                                                  swe_l3_dependencies.configuration)
+    rebinned_psd, rebinned_psd_by_pa_and_gyro = correct_and_rebin(
+        swe_l2_data.phase_space_density[i],
+        np.array([0, 0, -400]),
+        dsp_velocities,
+        rebinned_mag_data[i],
+        swe_l3_dependencies.configuration,
+    )
     swe_rebin_intensity_by_pitch_angle_and_gyrophase(
         swe_l2_data.flux[i],
         counts[i],
         dsp_velocities,
         rebinned_mag_data,
-        swe_l3_dependencies.configuration)
+        swe_l3_dependencies.configuration,
+    )
 
 
 calculate(0)
diff --git a/scripts/swe/compare_outputs.py b/scripts/swe/compare_outputs.py
index 2258c8567..b1251f365 100644
--- a/scripts/swe/compare_outputs.py
+++ b/scripts/swe/compare_outputs.py
@@ -22,11 +22,13 @@ def canonicalize(thetas, phis):
 
 def read_numeric_variable(var: pycdf.Var) -> np.ndarray:
     assert np.issubdtype(var.dtype, np.number)
-    return np.where(var[...] == var.attrs['FILLVAL'], np.nan, var[...])
+    return np.where(var[...] == var.attrs["FILLVAL"], np.nan, var[...])
 
 
-OUTPUT_PATH = Path(__file__).parent.parent.parent / 'comparisons' / 'interpolated_calibration'
-HERITAGE_PATH = r'instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf'
+OUTPUT_PATH = (
+    Path(__file__).parent.parent.parent / "comparisons" / "interpolated_calibration"
+)
+HERITAGE_PATH = r"instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf"
 
 variable_mapping = {
     "spacecraft_potential": "potential",
@@ -102,21 +104,25 @@ def get_heritage_variable(path, variable_spec):
     l3_swe_e = l3_vs.attach("swepam_e")
     match variable_spec:
         case (heritage_name, index):
-            heritage_data = np.array([x[l3_swe_e.field(heritage_name)._index][index] for x in l3_swe_e[:]])
+            heritage_data = np.array(
+                [x[l3_swe_e.field(heritage_name)._index][index] for x in l3_swe_e[:]]
+            )
         case heritage_name:
-            heritage_data = np.array([x[l3_swe_e.field(heritage_name)._index] for x in l3_swe_e[:]])
+            heritage_data = np.array(
+                [x[l3_swe_e.field(heritage_name)._index] for x in l3_swe_e[:]]
+            )
     return heritage_data
 
 
 def plot_heritage_variable(name):
     heritage_data = get_heritage_variable(HERITAGE_PATH, variable_mapping[name])
-    plt.plot(heritage_data, label=f'Heritage: {name}')
+    plt.plot(heritage_data, label=f"Heritage: {name}")
 
 
 def plot_modern_variable(name):
-    l3_output_cdf = CDF('data/imap/swe/l3/2025/06/imap_swe_l3_sci_20250629_v000.cdf')
+    l3_output_cdf = CDF("data/imap/swe/l3/2025/06/imap_swe_l3_sci_20250629_v000.cdf")
     modern_data = read_numeric_variable(l3_output_cdf[name])
-    plt.plot(modern_data, label=f'Modern: {name}')
+    plt.plot(modern_data, label=f"Modern: {name}")
 
 
 def compare_parallel_perp():
@@ -136,22 +142,25 @@ def compare_parallel_perp():
 compare_parallel_perp()
 
 for modern_name, heritage_info in variable_mapping.items():
-
-    l3_hdf = HDF(r'instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf')
+    l3_hdf = HDF(r"instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf")
     l3_vs = l3_hdf.vstart()
     l3_swe_e = l3_vs.attach("swepam_e")
     match heritage_info:
         case (heritage_name, index):
-            heritage_data = np.array([x[l3_swe_e.field(heritage_name)._index][index] for x in l3_swe_e[:]])
+            heritage_data = np.array(
+                [x[l3_swe_e.field(heritage_name)._index][index] for x in l3_swe_e[:]]
+            )
         case heritage_name:
-            heritage_data = np.array([x[l3_swe_e.field(heritage_info)._index] for x in l3_swe_e[:]])
-    l3_output_cdf = CDF('data/imap/swe/l3/2025/06/imap_swe_l3_sci_20250629_v000.cdf')
+            heritage_data = np.array(
+                [x[l3_swe_e.field(heritage_info)._index] for x in l3_swe_e[:]]
+            )
+    l3_output_cdf = CDF("data/imap/swe/l3/2025/06/imap_swe_l3_sci_20250629_v000.cdf")
 
     modern_data = read_numeric_variable(l3_output_cdf[modern_name])
     if "tensor" in modern_name:
         for i in range(6):
-            plt.plot(heritage_data[:, i], label=f'Heritage: {heritage_name} {i}')
-            plt.plot(modern_data[:, i], label=f'Modern: {modern_name} {i}')
+            plt.plot(heritage_data[:, i], label=f"Heritage: {heritage_name} {i}")
+            plt.plot(modern_data[:, i], label=f"Modern: {modern_name} {i}")
 
             plt.legend()
             plt.savefig(OUTPUT_PATH / f"{modern_name}_{i}.png")
@@ -164,8 +173,8 @@ def compare_parallel_perp():
                 continue
             heritage_data = np.convolve(heritage_data, window_size, mode="same")
             modern_data = np.convolve(modern_data, window_size, mode="same")
-        plt.plot(heritage_data, label=f'Heritage: {heritage_name}')
-        plt.plot(modern_data, label=f'Modern: {modern_name}')
+        plt.plot(heritage_data, label=f"Heritage: {heritage_name}")
+        plt.plot(modern_data, label=f"Modern: {modern_name}")
 
         plt.legend()
         plt.savefig(OUTPUT_PATH / f"{modern_name}.png")
@@ -174,17 +183,17 @@ def compare_parallel_perp():
             ratio = heritage_data / modern_data
             smooth_ratio = np.convolve(ratio, np.ones(5) / 5, mode="same")
             plt.plot(smooth_ratio)
-            plt.savefig(OUTPUT_PATH / f"density_ratio.png")
+            plt.savefig(OUTPUT_PATH / "density_ratio.png")
             plt.clf()
     l3_output_cdf.close()
 
 
 def compute_canonicalized_data(population: str):
-    l3_hdf = HDF(r'instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf')
+    l3_hdf = HDF(r"instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf")
     l3_vs = l3_hdf.vstart()
     l3_swe_e = l3_vs.attach("swepam_e")
 
-    l3_output_cdf = CDF('data/imap/swe/l3/2025/06/imap_swe_l3_sci_20250629_v000.cdf')
+    l3_output_cdf = CDF("data/imap/swe/l3/2025/06/imap_swe_l3_sci_20250629_v000.cdf")
 
     if population == "core":
         theta_rtn_integrated = "core_temperature_theta_rtn_integrated"
@@ -206,26 +215,40 @@ def compute_canonicalized_data(population: str):
     temp_phi = read_numeric_variable(l3_output_cdf[phi_rtn_integrated])
 
     heritage_theta_rtn_integrated_data = np.array(
-        [x[l3_swe_e.field(heritage_theta_rtn_integrated)._index] for x in l3_swe_e[:]])
+        [x[l3_swe_e.field(heritage_theta_rtn_integrated)._index] for x in l3_swe_e[:]]
+    )
     heritage_phi_rtn_integrated_data = np.array(
-        [x[l3_swe_e.field(heritage_phi_rtn_integrated)._index] for x in l3_swe_e[:]])
+        [x[l3_swe_e.field(heritage_phi_rtn_integrated)._index] for x in l3_swe_e[:]]
+    )
 
     canonical_theta, canonical_phi = canonicalize(temp_theta, temp_phi)
-    heritage_canonical_theta, heritage_canonical_phi = canonicalize(heritage_theta_rtn_integrated_data,
-                                                                    heritage_phi_rtn_integrated_data)
-
-    plt.plot(heritage_canonical_theta, '.', label=f'Heritage: {heritage_theta_rtn_integrated}')
-    plt.plot(canonical_theta, '.', label=f'Modern {theta_rtn_integrated}')
+    heritage_canonical_theta, heritage_canonical_phi = canonicalize(
+        heritage_theta_rtn_integrated_data, heritage_phi_rtn_integrated_data
+    )
+
+    plt.plot(
+        heritage_canonical_theta,
+        ".",
+        label=f"Heritage: {heritage_theta_rtn_integrated}",
+    )
+    plt.plot(canonical_theta, ".", label=f"Modern {theta_rtn_integrated}")
     plt.legend()
     plt.savefig(OUTPUT_PATH / f"{population}_temperature_rtn_theta.png")
     plt.clf()
 
-    plt.plot(heritage_canonical_phi, '.', label=f'Heritage: {heritage_phi_rtn_integrated}')
-    plt.plot(canonical_phi, '.', label=f'Modern {phi_rtn_integrated}')
+    plt.plot(
+        heritage_canonical_phi, ".", label=f"Heritage: {heritage_phi_rtn_integrated}"
+    )
+    plt.plot(canonical_phi, ".", label=f"Modern {phi_rtn_integrated}")
     plt.legend()
     plt.savefig(OUTPUT_PATH / f"{population}_temperature_rtn_phi.png")
     plt.clf()
-    return canonical_theta, canonical_phi, heritage_theta_rtn_integrated_data, heritage_phi_rtn_integrated_data
+    return (
+        canonical_theta,
+        canonical_phi,
+        heritage_theta_rtn_integrated_data,
+        heritage_phi_rtn_integrated_data,
+    )
 
 
 def compare_eigenvector_directions_with_canonicalization():
diff --git a/scripts/swe/create_fake_l2_cdf.py b/scripts/swe/create_fake_l2_cdf.py
index c3fa5480e..3bdaf4c66 100644
--- a/scripts/swe/create_fake_l2_cdf.py
+++ b/scripts/swe/create_fake_l2_cdf.py
@@ -11,68 +11,76 @@
 from spacepy.pycdf import CDF
 
 
-def create_fake_swe_l1b_and_l2_cdf(l1_hdf_path: str, l2_hdf_path: str, output_l1b_swe_cdf_file_path: str,
-                                   output_l2_swe_cdf_file_path: str):
+def create_fake_swe_l1b_and_l2_cdf(
+    l1_hdf_path: str,
+    l2_hdf_path: str,
+    output_l1b_swe_cdf_file_path: str,
+    output_l2_swe_cdf_file_path: str,
+):
     l1_hdf = pyhdf.SD.SD(l1_hdf_path)
     counts = l1_hdf.select("DNSWE_COUNT")[:]
     total_number_of_energies = 20
     spin_spectors = 30
     apertures = 7
-    counts_in_swe_shape = np.moveaxis(counts, 3, 2).reshape((-1, total_number_of_energies, spin_spectors, apertures))
+    counts_in_swe_shape = np.moveaxis(counts, 3, 2).reshape(
+        (-1, total_number_of_energies, spin_spectors, apertures)
+    )
 
     l2_swepam_electron_file = HDF(l2_hdf_path)
     vs_electron = l2_swepam_electron_file.vstart()
     vd_electron = vs_electron.attach("swepam_e")
     epochs = get_epochs_from_output_file(vd_electron)
-    fields = ["chisq_c",
-              "chisq_h",
-              "n_fc",
-              "n_fh",
-              "n_ic",
-              "n_ih",
-              "n_i",
-              "q_flux_ic",
-              "q_flux_ih",
-              "q_flux_i",
-              "q_flux_phi_ic",
-              "q_flux_phi_ih",
-              "q_flux_phi_i",
-              "q_flux_theta_ic",
-              "q_flux_theta_ih",
-              "q_flux_theta_i",
-              "t_para_fc",
-              "t_para_fh",
-              "t_para_ic",
-              "t_para_ih",
-              "t_para_i",
-              "t_perp_fc",
-              "t_perp_fh",
-              "t_perp_ic",
-              "t_perp_ih",
-              "t_perp_i",
-              "t_mat_ic",
-              "t_mat_ih",
-              "t_mat_i",
-              "t_phi_fc",
-              "t_phi_fh",
-              "t_phi_ic",
-              "t_phi_ih",
-              "t_phi_i",
-              "t_theta_fc",
-              "t_theta_fh",
-              "t_theta_ic",
-              "t_theta_ih",
-              "t_theta_i",
-              "v_fc",
-              "v_fh",
-              "v_ic",
-              "v_ih",
-              "v_i",
-              "v_rtn_fc",
-              "v_rtn_fh",
-              "v_rtn_ic",
-              "v_rtn_ih",
-              "v_rtn_i"]
+    fields = [
+        "chisq_c",
+        "chisq_h",
+        "n_fc",
+        "n_fh",
+        "n_ic",
+        "n_ih",
+        "n_i",
+        "q_flux_ic",
+        "q_flux_ih",
+        "q_flux_i",
+        "q_flux_phi_ic",
+        "q_flux_phi_ih",
+        "q_flux_phi_i",
+        "q_flux_theta_ic",
+        "q_flux_theta_ih",
+        "q_flux_theta_i",
+        "t_para_fc",
+        "t_para_fh",
+        "t_para_ic",
+        "t_para_ih",
+        "t_para_i",
+        "t_perp_fc",
+        "t_perp_fh",
+        "t_perp_ic",
+        "t_perp_ih",
+        "t_perp_i",
+        "t_mat_ic",
+        "t_mat_ih",
+        "t_mat_i",
+        "t_phi_fc",
+        "t_phi_fh",
+        "t_phi_ic",
+        "t_phi_ih",
+        "t_phi_i",
+        "t_theta_fc",
+        "t_theta_fh",
+        "t_theta_ic",
+        "t_theta_ih",
+        "t_theta_i",
+        "v_fc",
+        "v_fh",
+        "v_ic",
+        "v_ih",
+        "v_i",
+        "v_rtn_fc",
+        "v_rtn_fh",
+        "v_rtn_ic",
+        "v_rtn_ih",
+        "v_rtn_i",
+    ]
     create_expected_cdf(vd_electron, fields, "swe_moments")
 
     with CDF(output_l2_swe_cdf_file_path, create=True) as cdf:
@@ -92,13 +100,17 @@ def create_fake_swe_l1b_and_l2_cdf(l1_hdf_path: str, l2_hdf_path: str, output_l1
         cdf.new("inst_el", [-63, -42, -21, 0, 21, 42, 63], recVary=False)
 
         shape_without_aperture_axis = psd.shape[:-1]
-        placeholder_insta_az_spin_sector = np.random.random(shape_without_aperture_axis) * 360
+        placeholder_insta_az_spin_sector = (
+            np.random.random(shape_without_aperture_axis) * 360
+        )
         create_variable(cdf, "inst_az_spin_sector", placeholder_insta_az_spin_sector)
 
         minutes = len(counts_in_swe_shape)
         measurement_count = placeholder_insta_az_spin_sector.size
         measurement_times = np.linspace(0, minutes * 60, measurement_count)
-        placeholder_acquisition_time = measurement_times.reshape(shape_without_aperture_axis)
+        placeholder_acquisition_time = measurement_times.reshape(
+            shape_without_aperture_axis
+        )
         create_variable(cdf, "acquisition_time", placeholder_acquisition_time)
 
         acquisition_duration = np.full((minutes, 20, 30), 80000)
@@ -109,14 +121,14 @@ def create_fake_swe_l1b_and_l2_cdf(l1_hdf_path: str, l2_hdf_path: str, output_l1
         cdf["settle_duration"] = np.full((minutes, 4), 133333.3333333334)
 
 
-def create_fake_swapi_l3a_cdf(l2_swepam_ion_file_path: str,
-                              output_swapi_file_path: str):
+def create_fake_swapi_l3a_cdf(
+    l2_swepam_ion_file_path: str, output_swapi_file_path: str
+):
     l2_swepam_ion_file = HDF(l2_swepam_ion_file_path)
     vs_ion = l2_swepam_ion_file.vstart()
     vd_ion = vs_ion.attach("swepam_i")
 
-    sw_velocity_index = vd_ion.field(
-        "vel_p_rtn")._index
+    sw_velocity_index = vd_ion.field("vel_p_rtn")._index
     sw_velocity_data = np.array([x[sw_velocity_index] for x in vd_ion[:]])
     sw_speed = np.linalg.norm(sw_velocity_data, axis=1)
 
@@ -126,10 +138,10 @@ def create_fake_swapi_l3a_cdf(l2_swepam_ion_file_path: str,
 
     with CDF(output_swapi_file_path, readonly=False, create=True) as cdf:
         cdf.compress(pycdf.const.GZIP_COMPRESSION)
-        cdf['epoch'] = epochs
-        cdf['epoch_delta'] = epoch_deltas
-        cdf['proton_sw_speed'] = sw_speed
-        cdf['proton_sw_speed'].attrs["FILLVAL"] = -1e31
+        cdf["epoch"] = epochs
+        cdf["epoch_delta"] = epoch_deltas
+        cdf["proton_sw_speed"] = sw_speed
+        cdf["proton_sw_speed"].attrs["FILLVAL"] = -1e31
 
 
 ATTITUDE_DATA = """Year	DOY	Secofday	Flag	RTN_r		RTN_t		RTN_n		J2GCI_x		J2GCI_y		J2GCI_z		GSE_x		GSE_y		GSE_z
@@ -162,15 +174,18 @@ def create_fake_swapi_l3a_cdf(l2_swepam_ion_file_path: str,
 1999	160	    3600	0	-0.98089	-0.17395	-0.08711	 0.02904	 0.94059	 0.33831	 0.98116	 0.18238	-0.06375"""
 
 
-def create_fake_mag_l1d_cdf(l2_swepam_electron_file_path: str,
-                            output_mag_file_path: str):
+def create_fake_mag_l1d_cdf(
+    l2_swepam_electron_file_path: str, output_mag_file_path: str
+):
     buffer = io.StringIO(ATTITUDE_DATA)
     data = np.loadtxt(buffer, skiprows=1)
 
     def time_to_float(ts):
         return [(t - datetime(1999, 1, 1)).total_seconds() for t in ts]
 
-    times = [datetime(1998, 12, 31) + timedelta(days=row[1], seconds=row[2]) for row in data]
+    times = [
+        datetime(1998, 12, 31) + timedelta(days=row[1], seconds=row[2]) for row in data
+    ]
 
     l2_swepam_electron_file_path = HDF(l2_swepam_electron_file_path)
     vs_electron = l2_swepam_electron_file_path.vstart()
@@ -178,11 +193,17 @@ def time_to_float(ts):
 
     mag_epochs = get_epochs_from_output_file(vd_electron)
 
-    correction_factor = (datetime(2025, 6, 30) - datetime(1999, 6, 8))
+    correction_factor = datetime(2025, 6, 30) - datetime(1999, 6, 8)
     mag_epochs_uncorrected = mag_epochs - correction_factor
-    r_att = np.interp(time_to_float(mag_epochs_uncorrected), time_to_float(times), data[:, 4])
-    t_att = np.interp(time_to_float(mag_epochs_uncorrected), time_to_float(times), data[:, 5])
-    n_att = np.interp(time_to_float(mag_epochs_uncorrected), time_to_float(times), data[:, 6])
+    r_att = np.interp(
+        time_to_float(mag_epochs_uncorrected), time_to_float(times), data[:, 4]
+    )
+    t_att = np.interp(
+        time_to_float(mag_epochs_uncorrected), time_to_float(times), data[:, 5]
+    )
+    n_att = np.interp(
+        time_to_float(mag_epochs_uncorrected), time_to_float(times), data[:, 6]
+    )
 
     rtn_att = np.stack((r_att, t_att, n_att), axis=-1)
 
@@ -212,7 +233,13 @@ def convert_rtn_to_instrument(input_vector, att_vector):
     c2 = np.sqrt(t2 + n2)
     c1 = np.sqrt((t2 + n2) * (t2 + n2) + r2 * t2 + r2 * n2)
 
-    mat = np.array([[(-n2 - t2) / c1, 0, r / c3], [r * t / c1, -n / c2, t / c3], [r * n / c1, t / c2, n / c3]])
+    mat = np.array(
+        [
+            [(-n2 - t2) / c1, 0, r / c3],
+            [r * t / c1, -n / c2, t / c3],
+            [r * n / c1, t / c2, n / c3],
+        ]
+    )
 
     return np.linalg.inv(mat) @ input_vector
 
@@ -230,14 +257,29 @@ def get_epochs_from_output_file(dataset: VD) -> np.array:
     min_index = dataset.field("min")._index
     sec_index = dataset.field("sec")._index
 
-    correction_factor = (datetime(2025, 6, 30) - datetime(1999, 6, 8))
+    correction_factor = datetime(2025, 6, 30) - datetime(1999, 6, 8)
 
-    return np.array([datetime(year=x[years_index], month=x[month_index], day=x[day_index], hour=x[hour_index],
-                              minute=x[min_index], second=x[sec_index]) + correction_factor for x in dataset[:]])
+    return np.array(
+        [
+            datetime(
+                year=x[years_index],
+                month=x[month_index],
+                day=x[day_index],
+                hour=x[hour_index],
+                minute=x[min_index],
+                second=x[sec_index],
+            )
+            + correction_factor
+            for x in dataset[:]
+        ]
+    )
 
 
 def create_expected_cdf(dataset: VD, fields: list[str], file_name):
-    with pycdf.CDF(str(path.parent.parent.parent / f'temp_cdf_data/expected_{file_name}.cdf'), masterpath="") as file:
+    with pycdf.CDF(
+        str(path.parent.parent.parent / f"temp_cdf_data/expected_{file_name}.cdf"),
+        masterpath="",
+    ) as file:
         for field in fields:
             index = dataset.field(field)._index
             data = np.array([x[index] for x in dataset[:]])
@@ -246,14 +288,47 @@ def create_expected_cdf(dataset: VD, fields: list[str], file_name):
 
 if __name__ == "__main__":
     path = Path(__file__)
-    l1_hdf_path = path.parent.parent.parent / "instrument_team_data/swe/ACE_LV1_1999-159.swepam.hdf"
-    l2_electron_hdf_path = path.parent.parent.parent / "instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf"
-    l2_ion_hdf_path = path.parent.parent.parent / "instrument_team_data/swe/swepam-swi-1999-159.v2-01.hdf"
-
-    l2_swe_cdf_file_path = path.parent.parent.parent / "tests" / "test_data" / "swe" / "imap_swe_l2_sci-with-ace-data_20250630_v002.cdf"
-    l1b_swe_cdf_file_path = path.parent.parent.parent / "tests" / "test_data" / "swe" / "imap_swe_l1b_sci-with-ace-data_20250630_v002.cdf"
-    mag_file_path = path.parent.parent.parent / "tests" / "test_data" / "swe" / "imap_mag_l1d_norm-dsrf_20250630_v001.cdf"
-    swapi_file_path = path.parent.parent.parent / "tests" / "test_data" / "swe" / "imap_swapi_l3a_proton-sw_20250630_v001.cdf"
+    l1_hdf_path = (
+        path.parent.parent.parent
+        / "instrument_team_data/swe/ACE_LV1_1999-159.swepam.hdf"
+    )
+    l2_electron_hdf_path = (
+        path.parent.parent.parent
+        / "instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf"
+    )
+    l2_ion_hdf_path = (
+        path.parent.parent.parent
+        / "instrument_team_data/swe/swepam-swi-1999-159.v2-01.hdf"
+    )
+
+    l2_swe_cdf_file_path = (
+        path.parent.parent.parent
+        / "tests"
+        / "test_data"
+        / "swe"
+        / "imap_swe_l2_sci-with-ace-data_20250630_v002.cdf"
+    )
+    l1b_swe_cdf_file_path = (
+        path.parent.parent.parent
+        / "tests"
+        / "test_data"
+        / "swe"
+        / "imap_swe_l1b_sci-with-ace-data_20250630_v002.cdf"
+    )
+    mag_file_path = (
+        path.parent.parent.parent
+        / "tests"
+        / "test_data"
+        / "swe"
+        / "imap_mag_l1d_norm-dsrf_20250630_v001.cdf"
+    )
+    swapi_file_path = (
+        path.parent.parent.parent
+        / "tests"
+        / "test_data"
+        / "swe"
+        / "imap_swapi_l3a_proton-sw_20250630_v001.cdf"
+    )
 
     mag_file_path.unlink(missing_ok=True)
     swapi_file_path.unlink(missing_ok=True)
diff --git a/scripts/swe/filter_good_times_from_l1b_sdc_cdf.py b/scripts/swe/filter_good_times_from_l1b_sdc_cdf.py
index b1a1e5a8f..be6e53c62 100644
--- a/scripts/swe/filter_good_times_from_l1b_sdc_cdf.py
+++ b/scripts/swe/filter_good_times_from_l1b_sdc_cdf.py
@@ -5,52 +5,90 @@
 
 l1b_cdf = CDF(sys.argv[1])
 
-with CDF("imap_swe_l1b_sci-3-11-good-times_20100101_v002", masterpath="", readonly=False) as cdf:
-    cdf["acq_duration"] = np.array([l1b_cdf["acq_duration"][2], l1b_cdf["acq_duration"][4]])
+with CDF(
+    "imap_swe_l1b_sci-3-11-good-times_20100101_v002", masterpath="", readonly=False
+) as cdf:
+    cdf["acq_duration"] = np.array(
+        [l1b_cdf["acq_duration"][2], l1b_cdf["acq_duration"][4]]
+    )
     cdf["acq_duration"].attrs = l1b_cdf["acq_duration"].attrs
-    cdf["acq_start_coarse"] = np.array([l1b_cdf["acq_start_coarse"][2], l1b_cdf["acq_start_coarse"][4]])
+    cdf["acq_start_coarse"] = np.array(
+        [l1b_cdf["acq_start_coarse"][2], l1b_cdf["acq_start_coarse"][4]]
+    )
     cdf["acq_start_coarse"].attrs = l1b_cdf["acq_start_coarse"].attrs
-    cdf["acq_start_fine"] = np.array([l1b_cdf["acq_start_fine"][2], l1b_cdf["acq_start_fine"][4]])
+    cdf["acq_start_fine"] = np.array(
+        [l1b_cdf["acq_start_fine"][2], l1b_cdf["acq_start_fine"][4]]
+    )
     cdf["acq_start_fine"].attrs = l1b_cdf["acq_start_fine"].attrs
-    cdf["acquisition_time"] = np.array([l1b_cdf["acquisition_time"][2], l1b_cdf["acquisition_time"][4]])
+    cdf["acquisition_time"] = np.array(
+        [l1b_cdf["acquisition_time"][2], l1b_cdf["acquisition_time"][4]]
+    )
     cdf["acquisition_time"].attrs = l1b_cdf["acquisition_time"].attrs
-    cdf["cem_nominal_only"] = np.array([l1b_cdf["cem_nominal_only"][2], l1b_cdf["cem_nominal_only"][4]])
+    cdf["cem_nominal_only"] = np.array(
+        [l1b_cdf["cem_nominal_only"][2], l1b_cdf["cem_nominal_only"][4]]
+    )
     cdf["cem_nominal_only"].attrs = l1b_cdf["cem_nominal_only"].attrs
     cdf["cksum"] = np.array([l1b_cdf["cksum"][2], l1b_cdf["cksum"][4]])
     cdf["cksum"].attrs = l1b_cdf["cksum"].attrs
     cdf["epoch"] = np.array([l1b_cdf["epoch"][2], l1b_cdf["epoch"][4]])
     cdf["epoch"].attrs = l1b_cdf["epoch"].attrs
-    cdf["esa_acq_cfg"] = np.array([l1b_cdf["esa_acq_cfg"][2], l1b_cdf["esa_acq_cfg"][4]])
+    cdf["esa_acq_cfg"] = np.array(
+        [l1b_cdf["esa_acq_cfg"][2], l1b_cdf["esa_acq_cfg"][4]]
+    )
     cdf["esa_acq_cfg"].attrs = l1b_cdf["esa_acq_cfg"].attrs
-    cdf["esa_table_num"] = np.array([l1b_cdf["esa_table_num"][2], l1b_cdf["esa_table_num"][4]])
+    cdf["esa_table_num"] = np.array(
+        [l1b_cdf["esa_table_num"][2], l1b_cdf["esa_table_num"][4]]
+    )
     cdf["esa_table_num"].attrs = l1b_cdf["esa_table_num"].attrs
     cdf["high_count"] = np.array([l1b_cdf["high_count"][2], l1b_cdf["high_count"][4]])
     cdf["high_count"].attrs = l1b_cdf["high_count"].attrs
-    cdf["quarter_cycle"] = np.array([l1b_cdf["quarter_cycle"][2], l1b_cdf["quarter_cycle"][4]])
+    cdf["quarter_cycle"] = np.array(
+        [l1b_cdf["quarter_cycle"][2], l1b_cdf["quarter_cycle"][4]]
+    )
     cdf["quarter_cycle"].attrs = l1b_cdf["quarter_cycle"].attrs
-    cdf["repoint_warning"] = np.array([l1b_cdf["repoint_warning"][2], l1b_cdf["repoint_warning"][4]])
+    cdf["repoint_warning"] = np.array(
+        [l1b_cdf["repoint_warning"][2], l1b_cdf["repoint_warning"][4]]
+    )
     cdf["repoint_warning"].attrs = l1b_cdf["repoint_warning"].attrs
-    cdf["science_data"] = np.array([l1b_cdf["science_data"][2], l1b_cdf["science_data"][4]])
+    cdf["science_data"] = np.array(
+        [l1b_cdf["science_data"][2], l1b_cdf["science_data"][4]]
+    )
     cdf["science_data"].attrs = l1b_cdf["science_data"].attrs
-    cdf["settle_duration"] = np.array([l1b_cdf["settle_duration"][2], l1b_cdf["settle_duration"][4]])
+    cdf["settle_duration"] = np.array(
+        [l1b_cdf["settle_duration"][2], l1b_cdf["settle_duration"][4]]
+    )
     cdf["settle_duration"].attrs = l1b_cdf["settle_duration"].attrs
     cdf["shcoarse"] = np.array([l1b_cdf["shcoarse"][2], l1b_cdf["shcoarse"][4]])
     cdf["shcoarse"].attrs = l1b_cdf["shcoarse"].attrs
-    cdf["spin_period"] = np.array([l1b_cdf["spin_period"][2], l1b_cdf["spin_period"][4]])
+    cdf["spin_period"] = np.array(
+        [l1b_cdf["spin_period"][2], l1b_cdf["spin_period"][4]]
+    )
     cdf["spin_period"].attrs = l1b_cdf["spin_period"].attrs
-    cdf["spin_period_source"] = np.array([l1b_cdf["spin_period_source"][2], l1b_cdf["spin_period_source"][4]])
+    cdf["spin_period_source"] = np.array(
+        [l1b_cdf["spin_period_source"][2], l1b_cdf["spin_period_source"][4]]
+    )
     cdf["spin_period_source"].attrs = l1b_cdf["spin_period_source"].attrs
-    cdf["spin_period_validity"] = np.array([l1b_cdf["spin_period_validity"][2], l1b_cdf["spin_period_validity"][4]])
+    cdf["spin_period_validity"] = np.array(
+        [l1b_cdf["spin_period_validity"][2], l1b_cdf["spin_period_validity"][4]]
+    )
     cdf["spin_period_validity"].attrs = l1b_cdf["spin_period_validity"].attrs
     cdf["spin_phase"] = np.array([l1b_cdf["spin_phase"][2], l1b_cdf["spin_phase"][4]])
     cdf["spin_phase"].attrs = l1b_cdf["spin_phase"].attrs
-    cdf["spin_phase_validity"] = np.array([l1b_cdf["spin_phase_validity"][2], l1b_cdf["spin_phase_validity"][4]])
+    cdf["spin_phase_validity"] = np.array(
+        [l1b_cdf["spin_phase_validity"][2], l1b_cdf["spin_phase_validity"][4]]
+    )
     cdf["spin_phase_validity"].attrs = l1b_cdf["spin_phase_validity"].attrs
-    cdf["stim_cfg_reg"] = np.array([l1b_cdf["stim_cfg_reg"][2], l1b_cdf["stim_cfg_reg"][4]])
+    cdf["stim_cfg_reg"] = np.array(
+        [l1b_cdf["stim_cfg_reg"][2], l1b_cdf["stim_cfg_reg"][4]]
+    )
     cdf["stim_cfg_reg"].attrs = l1b_cdf["stim_cfg_reg"].attrs
-    cdf["stim_enabled"] = np.array([l1b_cdf["stim_enabled"][2], l1b_cdf["stim_enabled"][4]])
+    cdf["stim_enabled"] = np.array(
+        [l1b_cdf["stim_enabled"][2], l1b_cdf["stim_enabled"][4]]
+    )
     cdf["stim_enabled"].attrs = l1b_cdf["stim_enabled"].attrs
-    cdf["threshold_dac"] = np.array([l1b_cdf["threshold_dac"][2], l1b_cdf["threshold_dac"][4]])
+    cdf["threshold_dac"] = np.array(
+        [l1b_cdf["threshold_dac"][2], l1b_cdf["threshold_dac"][4]]
+    )
     cdf["threshold_dac"].attrs = l1b_cdf["threshold_dac"].attrs
 
     cdf["cycle"] = l1b_cdf["cycle"]
diff --git a/scripts/swe/filter_good_times_from_l2_sdc_cdf.py b/scripts/swe/filter_good_times_from_l2_sdc_cdf.py
index 3763ec44b..d3dab9488 100644
--- a/scripts/swe/filter_good_times_from_l2_sdc_cdf.py
+++ b/scripts/swe/filter_good_times_from_l2_sdc_cdf.py
@@ -8,21 +8,35 @@
 with CDF("imap_swe_l2_sci-fake_20240510_v002.cdf", masterpath="", readonly=False) as w:
     w["epoch"] = np.array([bad_time_cdf["epoch"][2], bad_time_cdf["epoch"][4]])
     w["epoch"].attrs = bad_time_cdf["epoch"].attrs
-    w["acq_duration"] = np.array([bad_time_cdf["acq_duration"][2], bad_time_cdf["acq_duration"][4]])
+    w["acq_duration"] = np.array(
+        [bad_time_cdf["acq_duration"][2], bad_time_cdf["acq_duration"][4]]
+    )
     w["acq_duration"].attrs = bad_time_cdf["acq_duration"].attrs
-    w["acquisition_time"] = np.array([bad_time_cdf["acquisition_time"][2], bad_time_cdf["acquisition_time"][4]])
+    w["acquisition_time"] = np.array(
+        [bad_time_cdf["acquisition_time"][2], bad_time_cdf["acquisition_time"][4]]
+    )
     w["acquisition_time"].attrs = bad_time_cdf["acquisition_time"].attrs
-    w["flux_spin_sector"] = np.array([bad_time_cdf["flux_spin_sector"][2], bad_time_cdf["flux_spin_sector"][4]])
+    w["flux_spin_sector"] = np.array(
+        [bad_time_cdf["flux_spin_sector"][2], bad_time_cdf["flux_spin_sector"][4]]
+    )
     w["flux_spin_sector"].attrs = bad_time_cdf["flux_spin_sector"].attrs
     w["inst_az_spin_sector"] = np.array(
-        [bad_time_cdf["inst_az_spin_sector"][2], bad_time_cdf["inst_az_spin_sector"][4]])
+        [bad_time_cdf["inst_az_spin_sector"][2], bad_time_cdf["inst_az_spin_sector"][4]]
+    )
     w["inst_az_spin_sector"].attrs = bad_time_cdf["inst_az_spin_sector"].attrs
     w["phase_space_density"] = np.array(
-        [bad_time_cdf["phase_space_density"][2], bad_time_cdf["phase_space_density"][4]])
+        [bad_time_cdf["phase_space_density"][2], bad_time_cdf["phase_space_density"][4]]
+    )
     w["phase_space_density"].attrs = bad_time_cdf["phase_space_density"].attrs
     w["phase_space_density_spin_sector"] = np.array(
-        [bad_time_cdf["phase_space_density_spin_sector"][2], bad_time_cdf["phase_space_density_spin_sector"][4]])
-    w["phase_space_density_spin_sector"].attrs = bad_time_cdf["phase_space_density_spin_sector"].attrs
+        [
+            bad_time_cdf["phase_space_density_spin_sector"][2],
+            bad_time_cdf["phase_space_density_spin_sector"][4],
+        ]
+    )
+    w["phase_space_density_spin_sector"].attrs = bad_time_cdf[
+        "phase_space_density_spin_sector"
+    ].attrs
     w["flux"] = np.array([bad_time_cdf["flux"][2], bad_time_cdf["flux"][4]])
     w["flux"].attrs = bad_time_cdf["flux"].attrs
 
diff --git a/scripts/swe/generate_sci_using_swapi_with_predicted_ephemeris.py b/scripts/swe/generate_sci_using_swapi_with_predicted_ephemeris.py
index 8c38e17db..32f8fc375 100644
--- a/scripts/swe/generate_sci_using_swapi_with_predicted_ephemeris.py
+++ b/scripts/swe/generate_sci_using_swapi_with_predicted_ephemeris.py
@@ -6,7 +6,7 @@
 from imap_data_access import DependencyFilePath, ProcessingInputCollection
 from imap_data_access.processing_input import generate_imap_input
 
-from imap_l3_processing.utils import get_spice_kernels_file_names, SpiceKernelTypes
+from imap_l3_processing.utils import SpiceKernelTypes, get_spice_kernels_file_names
 
 SPICE_KERNELS = [
     SpiceKernelTypes.Leapseconds,
@@ -22,20 +22,37 @@
 
 
 def generate_swe_for_given_day(day: datetime):
-    spice_kernel_files = get_spice_kernels_file_names(day, day + timedelta(days=1), SPICE_KERNELS)
-    day_as_string = day.strftime('%Y%m%d')
-    l2_file_name = imap_data_access.query(instrument='swe', data_level='l2', start_date=day_as_string,
-                                          end_date=day_as_string, version='latest')[0]['file_path']
-
-    mag_l1d_file_name = imap_data_access.query(instrument='mag', data_level='l1d', start_date=day_as_string,
-                                               end_date=day_as_string, version='latest', descriptor='norm-dsrf')[0][
-        'file_path']
-
-    swapi_l3a_file_name = f'imap_swapi_l3a_proton-sw_{day_as_string}_v003.cdf'
-
-    swe_l1b_file_name = imap_data_access.query(instrument='swe', data_level='l1b', start_date=day_as_string,
-                                               end_date=day_as_string, version='latest', descriptor='sci')[0][
-        'file_path']
+    spice_kernel_files = get_spice_kernels_file_names(
+        day, day + timedelta(days=1), SPICE_KERNELS
+    )
+    day_as_string = day.strftime("%Y%m%d")
+    l2_file_name = imap_data_access.query(
+        instrument="swe",
+        data_level="l2",
+        start_date=day_as_string,
+        end_date=day_as_string,
+        version="latest",
+    )[0]["file_path"]
+
+    mag_l1d_file_name = imap_data_access.query(
+        instrument="mag",
+        data_level="l1d",
+        start_date=day_as_string,
+        end_date=day_as_string,
+        version="latest",
+        descriptor="norm-dsrf",
+    )[0]["file_path"]
+
+    swapi_l3a_file_name = f"imap_swapi_l3a_proton-sw_{day_as_string}_v003.cdf"
+
+    swe_l1b_file_name = imap_data_access.query(
+        instrument="swe",
+        data_level="l1b",
+        start_date=day_as_string,
+        end_date=day_as_string,
+        version="latest",
+        descriptor="sci",
+    )[0]["file_path"]
 
     swe_ancillary_file_name = "imap_swe_config_20251119_v002.json"
 
@@ -45,13 +62,15 @@ def generate_swe_for_given_day(day: datetime):
         swe_l1b_file_name,
         mag_l1d_file_name,
         swapi_l3a_file_name,
-        swe_ancillary_file_name
+        swe_ancillary_file_name,
     ]
 
-    input_collection = ProcessingInputCollection(*[generate_imap_input(i) for i in input_files])
+    input_collection = ProcessingInputCollection(
+        *[generate_imap_input(i) for i in input_files]
+    )
     input_collection.download_all_files()
 
-    dependency_file_name = f'imap_swe_l3_sci_{day_as_string}_v002.json'
+    dependency_file_name = f"imap_swe_l3_sci_{day_as_string}_v002.json"
     output_filepath = DependencyFilePath(dependency_file_name)
 
     fullpath = output_filepath.construct_path()
@@ -59,15 +78,17 @@ def generate_swe_for_given_day(day: datetime):
 
     fullpath.write_text(input_collection.serialize())
 
-    args_string = (f"imap_l3_data_processor.py "
-                   f"--instrument swe --data-level l3 --descriptor sci "
-                   f"--start-date {day_as_string} --version v004 "
-                   f"--dependency {dependency_file_name}")
+    args_string = (
+        f"imap_l3_data_processor.py "
+        f"--instrument swe --data-level l3 --descriptor sci "
+        f"--start-date {day_as_string} --version v004 "
+        f"--dependency {dependency_file_name}"
+    )
 
     return subprocess.Popen([sys.executable, *args_string.split(" ")])
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     if len(sys.argv) > 1:
         dates = [datetime.strptime(arg, "%Y%m%d") for arg in sys.argv[1:]]
     else:
diff --git a/scripts/swe/plot_swe.py b/scripts/swe/plot_swe.py
index c8e977022..37aba7760 100644
--- a/scripts/swe/plot_swe.py
+++ b/scripts/swe/plot_swe.py
@@ -6,15 +6,9 @@
 raw = cdf["phase_space_density"][...]
 rebinned = np.where(raw == -1e31, np.nan, raw)
 
-geometric_fractions = np.array([
-    0.0781351,
-    0.151448,
-    0.204686,
-    0.181759,
-    0.175125,
-    0.138312,
-    0.0697327
-])
+geometric_fractions = np.array(
+    [0.0781351, 0.151448, 0.204686, 0.181759, 0.175125, 0.138312, 0.0697327]
+)
 
 rebinned_ma = np.ma.masked_invalid(rebinned)
 
@@ -24,5 +18,7 @@
 
 (fig, (ax1, ax2)) = plt.subplots(2, 1)
 ax1.pcolormesh(dist_1d.filled(np.nan).T, norm="log")
-ax2.pcolormesh(np.ma.mean(rebinned_by_phi[:, :, 0:4], axis=-1).filled(np.nan).T, norm="log")
+ax2.pcolormesh(
+    np.ma.mean(rebinned_by_phi[:, :, 0:4], axis=-1).filled(np.nan).T, norm="log"
+)
 plt.show()
diff --git a/scripts/swe/read_ace.py b/scripts/swe/read_ace.py
index 56518d3b1..c5351a9a1 100644
--- a/scripts/swe/read_ace.py
+++ b/scripts/swe/read_ace.py
@@ -1,10 +1,10 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Mar 11 16:12:45 2025
 
 @author: hafijulislam
 """
+
 import shutil
 import sys
 from datetime import datetime
@@ -15,6 +15,7 @@
 from matplotlib.colors import LogNorm
 from pyhdf.HDF import *
 from pyhdf.SD import SD, SDC
+
 # import pyhdf.SD
 from pyhdf.VS import *
 from spacepy.pycdf import CDF
@@ -69,9 +70,9 @@ def decompressed_counts(cem_count: int) -> int:
     # the quotient
 
     return (
-            decompress_table[index]["base"]
-            + (multi * decompress_table[index]["step_size"])
-            + ((decompress_table[index]["step_size"] - 1) // 2)
+        decompress_table[index]["base"]
+        + (multi * decompress_table[index]["step_size"])
+        + ((decompress_table[index]["step_size"] - 1) // 2)
     )
 
 
@@ -140,13 +141,19 @@ def calculate_phase(phase, spin_period):
             sss = i // N_ENERGIES_LEVEL1
             f1 = N_PHI_LEVEL1 * sss + k + 1
             f2 = 120 * sss + N_ENERGIES_LEVEL1 * k + rrr + 1
-            offset = (0.0547645 + f1 * 0.0575027 + (f2 - 0.5) * K_FOR_THIS_MODE) * 12.0 / spin_period
+            offset = (
+                (0.0547645 + f1 * 0.0575027 + (f2 - 0.5) * K_FOR_THIS_MODE)
+                * 12.0
+                / spin_period
+            )
             phi[i][k] = phase + offset
     CONVERT_PHI_TO_IMAP_AZIMUTH = 270
     return (phi + CONVERT_PHI_TO_IMAP_AZIMUTH - SWE_MOUNTING_ANGLE) % 360
 
 
-def calculate_acquisition_time(phase: float, spin_period: float, epoch: datetime) -> np.ndarray:
+def calculate_acquisition_time(
+    phase: float, spin_period: float, epoch: datetime
+) -> np.ndarray:
     met = get_met_from_epoch(epoch)
 
     met_rounded = (met // 15) * 15
@@ -164,22 +171,26 @@ def plot_dnswe_data_from_hdf(file_path):
     # Open the HDF4 file for reading
     hdf = SD(file_path, SDC.READ)
     # Select the DNSWE_COUNT dataset
-    data = hdf.select('DNSWE_COUNT')[:]
+    data = hdf.select("DNSWE_COUNT")[:]
 
     # Print the shape and data for reference
     print(f"Shape of DNSWE_COUNT dataset: {data.shape}")
     print(f"Data:\n{data}")
 
     # For simplicity, let's plot the first 2D slice (you can change this depending on your needs)
-    data_slice = data[:, 0, 0, 0, :]  # Select a slice (e.g., first 2D slice from the 5D array)
+    data_slice = data[
+        :, 0, 0, 0, :
+    ]  # Select a slice (e.g., first 2D slice from the 5D array)
 
     # Plot the data
     plt.figure(figsize=(10, 6))
-    plt.imshow(data_slice, norm=LogNorm(), aspect='auto', cmap='viridis', origin='lower')
-    plt.colorbar(label='DNSWE Values')
-    plt.title('Plot of DNSWE_COUNT dataset slice')
-    plt.xlabel('Dimension 1')
-    plt.ylabel('Dimension 2')
+    plt.imshow(
+        data_slice, norm=LogNorm(), aspect="auto", cmap="viridis", origin="lower"
+    )
+    plt.colorbar(label="DNSWE Values")
+    plt.title("Plot of DNSWE_COUNT dataset slice")
+    plt.xlabel("Dimension 1")
+    plt.ylabel("Dimension 2")
     plt.show()
 
 
@@ -207,11 +218,22 @@ def get_epochs_from_output_file(filepath: str) -> tuple[np.array, np.array]:
 
     sc_data = np.array([x[sct_index] for x in dataset[:]])
 
-    correction_factor = (datetime(2025, 6, 30) - datetime(1999, 6, 8))
+    correction_factor = datetime(2025, 6, 30) - datetime(1999, 6, 8)
 
-    return np.array([datetime(year=x[years_index], month=x[month_index], day=x[day_index], hour=x[hour_index],
-                              minute=x[min_index], second=x[sec_index]) + correction_factor for x in
-                     dataset[:]]), sc_data
+    return np.array(
+        [
+            datetime(
+                year=x[years_index],
+                month=x[month_index],
+                day=x[day_index],
+                hour=x[hour_index],
+                minute=x[min_index],
+                second=x[sec_index],
+            )
+            + correction_factor
+            for x in dataset[:]
+        ]
+    ), sc_data
 
 
 initial_epoch = datetime.fromisoformat("2025-06-30T12:00:00")
@@ -219,24 +241,30 @@ def get_epochs_from_output_file(filepath: str) -> tuple[np.array, np.array]:
 # File path to the HDF4 file
 file_path = "instrument_team_data/swe/ACE_LV1_1999-159.swepam.hdf"
 xarray_data = hdf4_to_xarray(file_path)
-new_ds = xarray_data['DNSWE_COUNT'].transpose('fakeDim32', 'fakeDim33', 'fakeDim35', 'fakeDim34', 'fakeDim36')
+new_ds = xarray_data["DNSWE_COUNT"].transpose(
+    "fakeDim32", "fakeDim33", "fakeDim35", "fakeDim34", "fakeDim36"
+)
 new_ds = new_ds.stack(merged_dim=("fakeDim33", "fakeDim35"))
 new_ds = new_ds.assign_coords({"merged_dim": range(20)})
-new_ds = new_ds.transpose('fakeDim32', 'merged_dim', 'fakeDim34', 'fakeDim36')
+new_ds = new_ds.transpose("fakeDim32", "merged_dim", "fakeDim34", "fakeDim36")
 
-epochs, sc_times = get_epochs_from_output_file("instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf")
+epochs, sc_times = get_epochs_from_output_file(
+    "instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf"
+)
 
 ds_expanded = xr.DataArray(
     new_ds.values,
     dims=("dim1", "dim2", "dim3", "dim4"),
     coords={
-        "dim1": 'epoch',  # Keep original coordinates
-        "dim2": 'energy',  # Update dim2 to be 0-23
-        "dim3": 'spin',
-        "dim4": 'cem'
-    }
+        "dim1": "epoch",  # Keep original coordinates
+        "dim2": "energy",  # Update dim2 to be 0-23
+        "dim3": "spin",
+        "dim4": "cem",
+    },
+)
+decompress_table = decompression_table = np.array(
+    [decompressed_counts(i) for i in range(256)]
 )
-decompress_table = decompression_table = np.array([decompressed_counts(i) for i in range(256)])
 counts = decompress_table[ds_expanded.values.astype(int)]
 sample_time = 0.10031
 sample_time_microseconds = int(sample_time * 1e6)
@@ -245,32 +273,22 @@ def get_epochs_from_output_file(filepath: str) -> tuple[np.array, np.array]:
 
 t0 = 55990787
 t1 = 56586115
-cal_t0, cal_t1 = np.array([
-    [
-        1.359617,
-        1.856468,
-        1.981586,
-        2.068891,
-        1.965609,
-        1.889478,
-        1.105694
-    ],
+cal_t0, cal_t1 = np.array(
     [
-        1.290341,
-        1.800907,
-        1.931721,
-        2.060242,
-        1.992930,
-        1.981554,
-        1.117602
+        [1.359617, 1.856468, 1.981586, 2.068891, 1.965609, 1.889478, 1.105694],
+        [1.290341, 1.800907, 1.931721, 2.060242, 1.992930, 1.981554, 1.117602],
     ]
-])
+)
 
-calibration = cal_t0 + ((sc_times - t0) / (t1 - t0))[:, np.newaxis, np.newaxis, np.newaxis] * (cal_t1 - cal_t0)
+calibration = cal_t0 + ((sc_times - t0) / (t1 - t0))[
+    :, np.newaxis, np.newaxis, np.newaxis
+] * (cal_t1 - cal_t0)
 
 rates *= calibration
 
-phase, spin = get_phase_and_spin('instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf')
+phase, spin = get_phase_and_spin(
+    "instrument_team_data/swe/swepam-nswe-1999-159.v1-02.hdf"
+)
 
 acquisition_time = []
 
@@ -283,15 +301,36 @@ def get_epochs_from_output_file(filepath: str) -> tuple[np.array, np.array]:
     truncate_to = int(sys.argv[1])
 else:
     truncate_to = len(epochs)
-settle_duration_needed_to_fill_time_between_points = time_between_data_points / (20 * 30) - sample_time_microseconds
+settle_duration_needed_to_fill_time_between_points = (
+    time_between_data_points / (20 * 30) - sample_time_microseconds
+)
 output_path = "tests/test_data/swe/imap_swe_l1b_sci_20250630_v003.cdf"
 shutil.copy("tests/test_data/swe/imap_swe_l1b_sci_20240510_v002.cdf", output_path)
 
-energy = np.array([2.55714286, 3.65142857, 5.16, 7.30571429,
-                   10.32857143, 14.34285714, 19.95714286, 27.42857143,
-                   38.37142857, 52.82857143, 73.32857143, 102.0,
-                   142.14285714, 196.57142857, 272., 372.71428571,
-                   519.0, 712.57142857, 987.14285714, 1370.0])
+energy = np.array(
+    [
+        2.55714286,
+        3.65142857,
+        5.16,
+        7.30571429,
+        10.32857143,
+        14.34285714,
+        19.95714286,
+        27.42857143,
+        38.37142857,
+        52.82857143,
+        73.32857143,
+        102.0,
+        142.14285714,
+        196.57142857,
+        272.0,
+        372.71428571,
+        519.0,
+        712.57142857,
+        987.14285714,
+        1370.0,
+    ]
+)
 
 esa_energy = np.full((truncate_to, 20, 30), np.nan)
 
@@ -299,21 +338,23 @@ def get_epochs_from_output_file(filepath: str) -> tuple[np.array, np.array]:
 
 energy = energy[:, np.newaxis, np.newaxis]
 with CDF(output_path, readonly=False) as cdf:
-    del cdf['science_data']
-    del cdf['acquisition_time']
-    del cdf['acq_duration']
-    del cdf['settle_duration']
-    del cdf['esa_step']
-    cdf['epoch'] = epochs[:truncate_to]
-    cdf['epoch'].attrs['VAR_TYPE'] = 'support_data'
-    del cdf['epoch'].attrs['DEPEND_0']
-    cdf['science_data'] = rates[:truncate_to, :, :, ::-1]
-    cdf['acquisition_time'] = np.array(acquisition_time[:truncate_to])
-    cdf['acq_duration'] = np.full((truncate_to, 20, 30), sample_time_microseconds)
-    cdf['settle_duration'] = np.full((truncate_to, 4), round(settle_duration_needed_to_fill_time_between_points))
-    cdf['esa_table_num'] = np.full((truncate_to, 4), 0)
-    cdf.new('esa_energy', esa_energy)
-    cdf.new('esa_step', np.arange(20), recVary=False)
-    cdf['esa_energy'].attrs['VAR_TYPE'] = 'support_data'
-    cdf['esa_step'].attrs['VAR_TYPE'] = 'support_data'
-    cdf['counts_stat_uncert'] = np.full(rates.shape, 1)
+    del cdf["science_data"]
+    del cdf["acquisition_time"]
+    del cdf["acq_duration"]
+    del cdf["settle_duration"]
+    del cdf["esa_step"]
+    cdf["epoch"] = epochs[:truncate_to]
+    cdf["epoch"].attrs["VAR_TYPE"] = "support_data"
+    del cdf["epoch"].attrs["DEPEND_0"]
+    cdf["science_data"] = rates[:truncate_to, :, :, ::-1]
+    cdf["acquisition_time"] = np.array(acquisition_time[:truncate_to])
+    cdf["acq_duration"] = np.full((truncate_to, 20, 30), sample_time_microseconds)
+    cdf["settle_duration"] = np.full(
+        (truncate_to, 4), round(settle_duration_needed_to_fill_time_between_points)
+    )
+    cdf["esa_table_num"] = np.full((truncate_to, 4), 0)
+    cdf.new("esa_energy", esa_energy)
+    cdf.new("esa_step", np.arange(20), recVary=False)
+    cdf["esa_energy"].attrs["VAR_TYPE"] = "support_data"
+    cdf["esa_step"].attrs["VAR_TYPE"] = "support_data"
+    cdf["counts_stat_uncert"] = np.full(rates.shape, 1)
diff --git a/scripts/swe/swe_l2.py b/scripts/swe/swe_l2.py
index 1637982ff..1dc942a5d 100644
--- a/scripts/swe/swe_l2.py
+++ b/scripts/swe/swe_l2.py
@@ -7,8 +7,7 @@
 import imap_processing.tests.conftest
 import pandas as pd
 from imap_processing import imap_module_directory
-from imap_processing.cdf.utils import load_cdf
-from imap_processing.cdf.utils import write_cdf
+from imap_processing.cdf.utils import load_cdf, write_cdf
 from imap_processing.spice.spin import set_global_spin_table_paths
 from imap_processing.swe.l1a.swe_l1a import swe_l1a
 from imap_processing.swe.l1b.swe_l1b import swe_l1b
@@ -38,15 +37,16 @@ def fake_cal():
 use_l0_data = False
 # Fake the spin data
 if use_l0_data:
-    data_start_time = 4.53051293e+08
+    data_start_time = 4.53051293e08
 else:
     data_start_time = 488937480
 
 for spice_file in Path("spice_kernels").iterdir():
     spiceypy.furnsh(str(spice_file))
 data_end_time = data_start_time + 60 * 1500
-spin_df = imap_processing.tests.conftest.generate_spin_data.__wrapped__()(data_start_time, end_met=data_end_time,
-                                                                          spin_period=15.0)
+spin_df = imap_processing.tests.conftest.generate_spin_data.__wrapped__()(
+    data_start_time, end_met=data_end_time, spin_period=15.0
+)
 spin_csv_file_path = "spin_data.spin.csv"
 spin_df.to_csv(spin_csv_file_path, index=False)
 os.environ["SPIN_DATA_FILEPATH"] = str(spin_csv_file_path)
@@ -61,7 +61,8 @@ def fake_cal():
     loaded_l1b_dataset = load_cdf(l1b_path)
 else:
     loaded_l1b_dataset = load_cdf(
-        "tests/test_data/swe/imap_swe_l1b_sci_20250630_v003.cdf")  # .isel(epoch=slice(None, 20))
+        "tests/test_data/swe/imap_swe_l1b_sci_20250630_v003.cdf"
+    )  # .isel(epoch=slice(None, 20))
 l2_dataset = swe_l2(loaded_l1b_dataset)
 phase_space_density = l2_dataset["phase_space_density_spin_sector"].values
 
diff --git a/scripts/swe/swe_l2_old.py b/scripts/swe/swe_l2_old.py
index 326057921..5c5d6eb45 100644
--- a/scripts/swe/swe_l2_old.py
+++ b/scripts/swe/swe_l2_old.py
@@ -7,8 +7,8 @@
 import imap_processing.tests.conftest
 import pandas as pd
 from imap_processing import imap_module_directory
-from imap_processing.cdf.utils import load_cdf
-from imap_processing.cdf.utils import write_cdf
+from imap_processing.cdf.utils import load_cdf, write_cdf
+
 # from imap_processing.spice.spin import set_global_spin_table_paths
 from imap_processing.swe.l1a.swe_l1a import swe_l1a
 from imap_processing.swe.l1b.swe_l1b import swe_l1b
@@ -38,15 +38,17 @@ def fake_cal():
 use_l0_data = False
 # Fake the spin data
 if use_l0_data:
-    data_start_time = 4.53051293e+08
+    data_start_time = 4.53051293e08
 else:
     data_start_time = 488937480
 
 for spice_file in Path("spice_kernels").iterdir():
     spiceypy.furnsh(str(spice_file))
 data_end_time = data_start_time + 60 * 1500
-spin_df = imap_processing.tests.conftest.generate_spin_data.__wrapped__()(data_start_time, end_met=data_end_time,
-                                                                          )  # spin_period=15.0)
+spin_df = imap_processing.tests.conftest.generate_spin_data.__wrapped__()(
+    data_start_time,
+    end_met=data_end_time,
+)  # spin_period=15.0)
 spin_csv_file_path = "spin_data.spin.csv"
 spin_df.to_csv(spin_csv_file_path, index=False)
 os.environ["SPIN_DATA_FILEPATH"] = str(spin_csv_file_path)
@@ -61,7 +63,8 @@ def fake_cal():
     loaded_l1b_dataset = load_cdf(l1b_path)
 else:
     loaded_l1b_dataset = load_cdf(
-        "tests/test_data/swe/imap_swe_l1b_sci_20250630_v003.cdf")  # .isel(epoch=slice(None, 20))
+        "tests/test_data/swe/imap_swe_l1b_sci_20250630_v003.cdf"
+    )  # .isel(epoch=slice(None, 20))
 l2_dataset = swe_l2(loaded_l1b_dataset, "v001")
 phase_space_density = l2_dataset["phase_space_density_spin_sector"].values
 
diff --git a/scripts/time_shift_cdf.py b/scripts/time_shift_cdf.py
index cd5d00981..fd059dc39 100644
--- a/scripts/time_shift_cdf.py
+++ b/scripts/time_shift_cdf.py
@@ -1,20 +1,23 @@
 import sys
 from datetime import datetime
 from pathlib import Path
-from typing import Optional, Any
+from typing import Any
 
-from imap_data_access.file_validation import ScienceFilePath, ImapFilePath
+from imap_data_access.file_validation import ImapFilePath, ScienceFilePath
 from spacepy.pycdf import CDF
 
 
-def convert_epoch_time(filename, target=datetime(2026, 10, 20, 12),
-                       new_science_file_parts: Optional[dict[str, Any]] = None) -> Optional[Path]:
+def convert_epoch_time(
+    filename,
+    target=datetime(2026, 10, 20, 12),
+    new_science_file_parts: dict[str, Any] | None = None,
+) -> Path | None:
     file_path = Path(filename)
     new_science_file_parts = new_science_file_parts or {}
 
     with CDF(str(file_path), readonly=False) as cdf:
-        delta = target - cdf['epoch'][0]
-        cdf['epoch'][...] += delta
+        delta = target - cdf["epoch"][0]
+        cdf["epoch"][...] += delta
 
     try:
         science_file_path = ScienceFilePath(file_path)
@@ -27,20 +30,24 @@ def convert_epoch_time(filename, target=datetime(2026, 10, 20, 12),
             "version": science_file_path.version,
             "extension": science_file_path.extension,
             "repointing": science_file_path.repointing,
-            "cr": science_file_path.cr
+            "cr": science_file_path.cr,
         }
 
-        new_science_file_path = ScienceFilePath.generate_from_inputs(**{**science_file_args, **new_science_file_parts})
+        new_science_file_path = ScienceFilePath.generate_from_inputs(
+            **{**science_file_args, **new_science_file_parts}
+        )
 
         new_name = new_science_file_path.construct_path().name
         new_path = file_path.rename(file_path.parent / new_name)
 
     except ImapFilePath.InvalidImapFileError:
-        print("Did not rename the file because it could not be converted into a science file path!")
+        print(
+            "Did not rename the file because it could not be converted into a science file path!"
+        )
         return None
 
     return new_path
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     convert_epoch_time(sys.argv[1])
diff --git a/scripts/truncate_cdf.py b/scripts/truncate_cdf.py
index 91e50ab18..910364a43 100644
--- a/scripts/truncate_cdf.py
+++ b/scripts/truncate_cdf.py
@@ -14,7 +14,9 @@ def truncate_cdf(cdf_path: Path, number_of_epochs: int = 2) -> Path:
             cdf.attrs[attr_name] = list(original_cdf.attrs[attr_name])
 
         for var in original_cdf:
-            depends_on_epoch = var == "epoch" or original_cdf[var].attrs.get("DEPEND_0") == "epoch"
+            depends_on_epoch = (
+                var == "epoch" or original_cdf[var].attrs.get("DEPEND_0") == "epoch"
+            )
             if depends_on_epoch:
                 cdf.clone(original_cdf[var], var, data=False)
                 cdf[var][:] = original_cdf[var][...][:number_of_epochs]
@@ -28,7 +30,9 @@ def truncate_cdf(cdf_path: Path, number_of_epochs: int = 2) -> Path:
     parser = argparse.ArgumentParser(
         description="Truncate one or more CDF files to a smaller number of epochs along the epoch axis."
     )
-    parser.add_argument("cdf_paths", nargs="+", type=Path, help="One or more CDF files to truncate.")
+    parser.add_argument(
+        "cdf_paths", nargs="+", type=Path, help="One or more CDF files to truncate."
+    )
     parser.add_argument(
         "-n",
         "--number-of-epochs",
diff --git a/scripts/ultra/create_example_ultra_l1c_pset.py b/scripts/ultra/create_example_ultra_l1c_pset.py
index 020187d65..2ec11a78f 100644
--- a/scripts/ultra/create_example_ultra_l1c_pset.py
+++ b/scripts/ultra/create_example_ultra_l1c_pset.py
@@ -17,6 +17,7 @@
 DEFAULT_RECT_SPACING_DEG_L1C = 0.5
 DEFAULT_HEALPIX_NSIDE_L1C = 128
 
+
 # TODO: Add ability to mock with/without energy dim to exposure_factor
 # The Helio frame L1C will have the energy dimension, but the spacecraft frame will not.
 def create_example_ultra_l1c_pset(
@@ -222,9 +223,12 @@ def create_example_ultra_l1c_pset(
 
 
 def _write_ultra_l1c_cdf_with_parents(
-        out_path: Path = get_run_local_data_path("ultra/fake_l1c_psets/test_pset.cdf"),
-        date: str = "2025-09-01T00:00:00"):
-    out_xarray = create_example_ultra_l1c_pset(nside=1, timestr=date, energy_dependent_exposure=True)
+    out_path: Path = get_run_local_data_path("ultra/fake_l1c_psets/test_pset.cdf"),
+    date: str = "2025-09-01T00:00:00",
+):
+    out_xarray = create_example_ultra_l1c_pset(
+        nside=1, timestr=date, energy_dependent_exposure=True
+    )
     out_path.parent.mkdir(parents=True, exist_ok=True)
     out_path.unlink(missing_ok=True)
 
@@ -235,39 +239,88 @@ def _write_ultra_l1c_cdf_with_parents(
         cdf.new("observation_time", np.full_like(out_xarray["counts"], 1))
         cdf.new("quality_flags", out_xarray["quality_flags"].values)
         cdf.new("sensitivity", out_xarray["sensitivity"].values)
-        cdf.new(CoordNames.ELEVATION_L1C.value, out_xarray[CoordNames.ELEVATION_L1C.value].values, recVary=False)
-        cdf.new(CoordNames.AZIMUTH_L1C.value, out_xarray[CoordNames.AZIMUTH_L1C.value].values, recVary=False)
-        cdf.new("epoch", out_xarray[CoordNames.TIME.value].values, type=pycdf.const.CDF_TIME_TT2000.value)
-        cdf.new(CoordNames.ENERGY_ULTRA_L1C.value, out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, recVary=False)
-        cdf.new("energy_bin_delta", np.full_like(out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, 1),
-                recVary=False)
-        cdf.new(CoordNames.HEALPIX_INDEX.value, out_xarray[CoordNames.HEALPIX_INDEX.value].values, recVary=False)
+        cdf.new(
+            CoordNames.ELEVATION_L1C.value,
+            out_xarray[CoordNames.ELEVATION_L1C.value].values,
+            recVary=False,
+        )
+        cdf.new(
+            CoordNames.AZIMUTH_L1C.value,
+            out_xarray[CoordNames.AZIMUTH_L1C.value].values,
+            recVary=False,
+        )
+        cdf.new(
+            "epoch",
+            out_xarray[CoordNames.TIME.value].values,
+            type=pycdf.const.CDF_TIME_TT2000.value,
+        )
+        cdf.new(
+            CoordNames.ENERGY_ULTRA_L1C.value,
+            out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values,
+            recVary=False,
+        )
+        cdf.new(
+            "energy_bin_delta",
+            np.full_like(out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, 1),
+            recVary=False,
+        )
+        cdf.new(
+            CoordNames.HEALPIX_INDEX.value,
+            out_xarray[CoordNames.HEALPIX_INDEX.value].values,
+            recVary=False,
+        )
 
         cdf[CoordNames.ENERGY_ULTRA_L1C.value].attrs["VAR_TYPE"] = "support_data"
         cdf[CoordNames.HEALPIX_INDEX.value].attrs["VAR_TYPE"] = "support_data"
-        _add_depends(cdf["counts"], [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value], "epoch")
-        _add_depends(cdf["exposure_factor"], [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
-                     "epoch")
-        _add_depends(cdf["sensitivity"], [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value], "epoch")
-        _add_depends(cdf["background_rates"], [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
-                     "epoch")
-        _add_depends(cdf["observation_time"], [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
-                     "epoch")
-        _add_depends(cdf["quality_flags"], [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value], "epoch")
+        _add_depends(
+            cdf["counts"],
+            [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
+            "epoch",
+        )
+        _add_depends(
+            cdf["exposure_factor"],
+            [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
+            "epoch",
+        )
+        _add_depends(
+            cdf["sensitivity"],
+            [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
+            "epoch",
+        )
+        _add_depends(
+            cdf["background_rates"],
+            [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
+            "epoch",
+        )
+        _add_depends(
+            cdf["observation_time"],
+            [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
+            "epoch",
+        )
+        _add_depends(
+            cdf["quality_flags"],
+            [CoordNames.ENERGY_ULTRA_L1C.value, CoordNames.HEALPIX_INDEX.value],
+            "epoch",
+        )
         _add_depends(cdf["energy_bin_delta"], [CoordNames.ENERGY_ULTRA_L1C.value])
 
         for var in cdf:
             if cdf[var].type() == pycdf.const.CDF_TIME_TT2000.value:
-                cdf[var].attrs['FILLVAL'] = datetime.datetime.fromisoformat("9999-12-31T23:59:59.999999999")
+                cdf[var].attrs["FILLVAL"] = datetime.datetime.fromisoformat(
+                    "9999-12-31T23:59:59.999999999"
+                )
             elif cdf[var].type() == pycdf.const.CDF_INT8.value:
-                cdf[var].attrs['FILLVAL'] = -9223372036854775808
-            elif cdf[var].type() == pycdf.const.CDF_FLOAT.value or pycdf.const.CDF_DOUBLE.value:
-                cdf[var].attrs['FILLVAL'] = -1e31
+                cdf[var].attrs["FILLVAL"] = -9223372036854775808
+            elif (
+                cdf[var].type() == pycdf.const.CDF_FLOAT.value
+                or pycdf.const.CDF_DOUBLE.value
+            ):
+                cdf[var].attrs["FILLVAL"] = -1e31
 
 
 def _add_depends(var: Var, depends: list[str], depend_0: str = None):
     if depend_0 is not None:
-        var.attrs[f"DEPEND_0"] = depend_0
+        var.attrs["DEPEND_0"] = depend_0
     for i, dep in enumerate(depends):
         var.attrs[f"DEPEND_{i + 1}"] = dep
 
diff --git a/scripts/ultra/create_example_ultra_l2_map.py b/scripts/ultra/create_example_ultra_l2_map.py
index bccf05208..c3bb007ae 100644
--- a/scripts/ultra/create_example_ultra_l2_map.py
+++ b/scripts/ultra/create_example_ultra_l2_map.py
@@ -16,14 +16,14 @@
 DEFAULT_HEALPIX_NSIDE_L1C = 16
 
 
-def create_example_ultra_l2_map(  # noqa: PLR0913
-        nside: int = DEFAULT_HEALPIX_NSIDE_L1C,
-        stripe_center_lat: int = 0,
-        width_scale: float = 180.0,
-        counts_scaling_params: tuple[int, float] = (100, 0.01),
-        peak_exposure: float = 1000.0,
-        timestr: str = "2025-09-01T00:00:00",
-        head: str = "45",
+def create_example_ultra_l2_map(
+    nside: int = DEFAULT_HEALPIX_NSIDE_L1C,
+    stripe_center_lat: int = 0,
+    width_scale: float = 180.0,
+    counts_scaling_params: tuple[int, float] = (100, 0.01),
+    peak_exposure: float = 1000.0,
+    timestr: str = "2025-09-01T00:00:00",
+    head: str = "45",
 ) -> xr.Dataset:
     """
     Mock the L1C PSET product with recognizable but unrealistic counts.
@@ -94,7 +94,7 @@ def create_example_ultra_l2_map(  # noqa: PLR0913
     # Calculate probability based on distance from target latitude
     lat_diff = np.abs(lat_pix - stripe_center_lat)
     prob_scaling_factor = counts_scaling_params[1] * np.exp(
-        -(lat_diff ** 2) / (2 * width_scale ** 2)
+        -(lat_diff**2) / (2 * width_scale**2)
     )
     # Generate counts using binomial distribution
     rng = np.random.default_rng(seed=42)
@@ -134,7 +134,7 @@ def create_example_ultra_l2_map(  # noqa: PLR0913
                 [
                     CoordNames.TIME.value,
                     CoordNames.ENERGY_ULTRA_L1C.value,
-                    CoordNames.HEALPIX_INDEX.value
+                    CoordNames.HEALPIX_INDEX.value,
                 ],
                 exposure_time,
             ),
@@ -156,9 +156,7 @@ def create_example_ultra_l2_map(  # noqa: PLR0913
             ),
         },
         coords={
-            CoordNames.TIME.value: [
-                tt_j2000ns
-            ],
+            CoordNames.TIME.value: [tt_j2000ns],
             CoordNames.ENERGY_ULTRA_L1C.value: energy_bin_midpoints,
             CoordNames.HEALPIX_INDEX.value: pix_indices,
         },
@@ -168,7 +166,7 @@ def create_example_ultra_l2_map(  # noqa: PLR0913
                 f"{timestr[5:7]}{timestr[8:10]}-repointNNNNN_vNNN"
             ),
             "Logical_source": f"imap_ultra_l2_{head}sensor-map",
-            "Data_version": "v001"
+            "Data_version": "v001",
         },
     )
 
@@ -179,13 +177,19 @@ def create_example_ultra_l2_map(  # noqa: PLR0913
     pset_product[CoordNames.ELEVATION_L1C.value].attrs["VAR_TYPE"] = "data"
 
     pset_product.coords[CoordNames.TIME.value].attrs["VAR_TYPE"] = "support_data"
-    pset_product.coords[CoordNames.ENERGY_ULTRA_L1C.value].attrs["VAR_TYPE"] = "support_data"
-    pset_product.coords[CoordNames.HEALPIX_INDEX.value].attrs["VAR_TYPE"] = "support_data"
+    pset_product.coords[CoordNames.ENERGY_ULTRA_L1C.value].attrs["VAR_TYPE"] = (
+        "support_data"
+    )
+    pset_product.coords[CoordNames.HEALPIX_INDEX.value].attrs["VAR_TYPE"] = (
+        "support_data"
+    )
 
     return pset_product
 
 
-def _write_ultra_l2_cdf_with_parents(out_path=get_run_local_data_path("ultra/fake_l2_maps/test_l2_map.cdf")):
+def _write_ultra_l2_cdf_with_parents(
+    out_path=get_run_local_data_path("ultra/fake_l2_maps/test_l2_map.cdf"),
+):
     out_xarray = create_example_ultra_l2_map(nside=2)
     out_path.parent.mkdir(parents=True, exist_ok=True)
     out_path.unlink(missing_ok=True)
@@ -194,32 +198,75 @@ def _write_ultra_l2_cdf_with_parents(out_path=get_run_local_data_path("ultra/fak
         cdf.new("ena_intensity", out_xarray["counts"].values)
         cdf.new("exposure_factor", out_xarray["exposure_time"].values)
         cdf.new("sensitivity", out_xarray["sensitivity"].values)
-        cdf.new("latitude", out_xarray[CoordNames.ELEVATION_L1C.value].values, recVary=False)
-        cdf.new("longitude", out_xarray[CoordNames.AZIMUTH_L1C.value].values, recVary=False)
-        cdf.new("epoch", out_xarray[CoordNames.TIME.value].values, recVary=False,
-                type=pycdf.const.CDF_TIME_TT2000.value)
-        cdf.new("energy", out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, recVary=False)
-        cdf.new("pixel_index", out_xarray[CoordNames.HEALPIX_INDEX.value].values, recVary=False)
+        cdf.new(
+            "latitude", out_xarray[CoordNames.ELEVATION_L1C.value].values, recVary=False
+        )
+        cdf.new(
+            "longitude", out_xarray[CoordNames.AZIMUTH_L1C.value].values, recVary=False
+        )
+        cdf.new(
+            "epoch",
+            out_xarray[CoordNames.TIME.value].values,
+            recVary=False,
+            type=pycdf.const.CDF_TIME_TT2000.value,
+        )
+        cdf.new(
+            "energy",
+            out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values,
+            recVary=False,
+        )
+        cdf.new(
+            "pixel_index",
+            out_xarray[CoordNames.HEALPIX_INDEX.value].values,
+            recVary=False,
+        )
         cdf.new("epoch_delta", np.array([0]))
-        cdf.new("energy_delta_plus", np.full_like(out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, 1))
-        cdf.new("energy_delta_minus", np.full_like(out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, 1))
-        cdf.new("energy_label", [str(val) for val in out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values])
-        cdf.new("obs_date", np.full(out_xarray["counts"].shape,
-                                    spiceypy.unitim(datetime.datetime.now().timestamp(), "ET", "TT") * 1e9),
-                type=pycdf.const.CDF_TIME_TT2000.value)
+        cdf.new(
+            "energy_delta_plus",
+            np.full_like(out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, 1),
+        )
+        cdf.new(
+            "energy_delta_minus",
+            np.full_like(out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values, 1),
+        )
+        cdf.new(
+            "energy_label",
+            [str(val) for val in out_xarray[CoordNames.ENERGY_ULTRA_L1C.value].values],
+        )
+        cdf.new(
+            "obs_date",
+            np.full(
+                out_xarray["counts"].shape,
+                spiceypy.unitim(datetime.datetime.now().timestamp(), "ET", "TT") * 1e9,
+            ),
+            type=pycdf.const.CDF_TIME_TT2000.value,
+        )
         cdf.new("obs_date_range", np.full_like(out_xarray["counts"].values, 1))
-        cdf.new("solid_angle", np.full_like(out_xarray[CoordNames.HEALPIX_INDEX.value].values, 1))
-        cdf.new("ena_intensity_stat_uncert", np.full_like(out_xarray["counts"].values, 1))
+        cdf.new(
+            "solid_angle",
+            np.full_like(out_xarray[CoordNames.HEALPIX_INDEX.value].values, 1),
+        )
+        cdf.new(
+            "ena_intensity_stat_uncert", np.full_like(out_xarray["counts"].values, 1)
+        )
         cdf.new("ena_intensity_sys_err", np.full_like(out_xarray["counts"].values, 1))
-        cdf.new("pixel_index_label", [str(val) for val in out_xarray[CoordNames.HEALPIX_INDEX.value].values])
+        cdf.new(
+            "pixel_index_label",
+            [str(val) for val in out_xarray[CoordNames.HEALPIX_INDEX.value].values],
+        )
 
         for var in cdf:
             if cdf[var].type() == pycdf.const.CDF_TIME_TT2000.value:
-                cdf[var].attrs['FILLVAL'] = datetime.datetime.fromisoformat("9999-12-31T23:59:59.999999999")
+                cdf[var].attrs["FILLVAL"] = datetime.datetime.fromisoformat(
+                    "9999-12-31T23:59:59.999999999"
+                )
             elif cdf[var].type() == pycdf.const.CDF_INT8.value:
-                cdf[var].attrs['FILLVAL'] = -9223372036854775808
-            elif cdf[var].type() == pycdf.const.CDF_FLOAT.value or pycdf.const.CDF_DOUBLE.value:
-                cdf[var].attrs['FILLVAL'] = -1e31
+                cdf[var].attrs["FILLVAL"] = -9223372036854775808
+            elif (
+                cdf[var].type() == pycdf.const.CDF_FLOAT.value
+                or pycdf.const.CDF_DOUBLE.value
+            ):
+                cdf[var].attrs["FILLVAL"] = -1e31
 
 
 if __name__ == "__main__":
diff --git a/scripts/ultra/create_example_ultra_l2_map_with_power_law.py b/scripts/ultra/create_example_ultra_l2_map_with_power_law.py
index 30512098c..48040e639 100644
--- a/scripts/ultra/create_example_ultra_l2_map_with_power_law.py
+++ b/scripts/ultra/create_example_ultra_l2_map_with_power_law.py
@@ -9,7 +9,9 @@
 from tests.test_helpers import get_test_data_path
 
 
-def _create_example_ultra_l2_map_with_power_law(out_path: Path, parents: list[str] = None):
+def _create_example_ultra_l2_map_with_power_law(
+    out_path: Path, parents: list[str] = None
+):
     out_path.parent.mkdir(parents=True, exist_ok=True)
     out_path.unlink(missing_ok=True)
     nside = 2
@@ -27,9 +29,15 @@ def _create_example_ultra_l2_map_with_power_law(out_path: Path, parents: list[st
     energy_breakpoint = 15  # 15 keV
     for i in range(num_lon):
         for j in range(num_lat):
-            breakpoint_index = np.searchsorted(energies, energy_breakpoint, side="right")
-            ena_intensities[0, :breakpoint_index, i, j] = power_law_1(energies[:breakpoint_index])
-            ena_intensities[0, breakpoint_index:, i, j] = power_law_2(energies[breakpoint_index:])
+            breakpoint_index = np.searchsorted(
+                energies, energy_breakpoint, side="right"
+            )
+            ena_intensities[0, :breakpoint_index, i, j] = power_law_1(
+                energies[:breakpoint_index]
+            )
+            ena_intensities[0, breakpoint_index:, i, j] = power_law_2(
+                energies[breakpoint_index:]
+            )
 
     ena_intensities_delta = np.full_like(ena_intensities, 0.0001)
 
@@ -41,15 +49,21 @@ def _create_example_ultra_l2_map_with_power_law(out_path: Path, parents: list[st
         cdf.new("latitude_delta", np.full_like(lat, 2), recVary=False)
         cdf.new("longitude", lon, recVary=False)
         cdf.new("longitude_delta", np.full_like(lon, 2), recVary=False)
-        cdf.new("epoch", np.array([datetime.now()]), type=pycdf.const.CDF_TIME_TT2000.value)
+        cdf.new(
+            "epoch", np.array([datetime.now()]), type=pycdf.const.CDF_TIME_TT2000.value
+        )
         cdf.new("energy", energies, recVary=False)
         cdf.new("epoch_delta", np.array([1]))
         cdf.new("energy_delta_plus", np.full_like(energies, 0.1))
         cdf.new("energy_delta_minus", np.full_like(energies, 0.1))
-        cdf.new("energy_label", [str(val) for val in energies]),
-        cdf.new("obs_date",
-                np.full_like(ena_intensities, (datetime.now() - TT2000_EPOCH).total_seconds() * 1e9),
-                type=pycdf.const.CDF_TIME_TT2000.value)
+        (cdf.new("energy_label", [str(val) for val in energies]),)
+        cdf.new(
+            "obs_date",
+            np.full_like(
+                ena_intensities, (datetime.now() - TT2000_EPOCH).total_seconds() * 1e9
+            ),
+            type=pycdf.const.CDF_TIME_TT2000.value,
+        )
         cdf.new("obs_date_range", np.full_like(ena_intensities, 1))
         cdf.new("solid_angle", np.full_like(ena_intensities, 1), recVary=False)
         cdf.new("ena_intensity_stat_uncert", ena_intensities_delta)
@@ -58,16 +72,23 @@ def _create_example_ultra_l2_map_with_power_law(out_path: Path, parents: list[st
         cdf.new("longitude_label", lon.astype(str), recVary=False)
 
         if parents is not None:
-            cdf.attrs['Parents'] = parents
+            cdf.attrs["Parents"] = parents
 
         for var in cdf:
             if cdf[var].type() == pycdf.const.CDF_TIME_TT2000.value:
-                cdf[var].attrs['FILLVAL'] = datetime.fromisoformat("9999-12-31T23:59:59.999999999")
+                cdf[var].attrs["FILLVAL"] = datetime.fromisoformat(
+                    "9999-12-31T23:59:59.999999999"
+                )
             elif cdf[var].type() == pycdf.const.CDF_INT8.value:
-                cdf[var].attrs['FILLVAL'] = -9223372036854775808
-            elif cdf[var].type() == pycdf.const.CDF_FLOAT.value or pycdf.const.CDF_DOUBLE.value:
-                cdf[var].attrs['FILLVAL'] = -1e31
+                cdf[var].attrs["FILLVAL"] = -9223372036854775808
+            elif (
+                cdf[var].type() == pycdf.const.CDF_FLOAT.value
+                or pycdf.const.CDF_DOUBLE.value
+            ):
+                cdf[var].attrs["FILLVAL"] = -1e31
 
 
 if __name__ == "__main__":
-    _create_example_ultra_l2_map_with_power_law(get_test_data_path('ultra') / 'fake_ultra_map_data.cdf')
+    _create_example_ultra_l2_map_with_power_law(
+        get_test_data_path("ultra") / "fake_ultra_map_data.cdf"
+    )
diff --git a/scripts/ultra/create_example_ultra_l3_survival_probabilities_cdf.py b/scripts/ultra/create_example_ultra_l3_survival_probabilities_cdf.py
index 795b005c1..0f08fcfda 100644
--- a/scripts/ultra/create_example_ultra_l3_survival_probabilities_cdf.py
+++ b/scripts/ultra/create_example_ultra_l3_survival_probabilities_cdf.py
@@ -7,16 +7,21 @@
 from spacepy import pycdf
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import HEALPIX_INDEX_VAR_NAME, PROBABILITY_OF_SURVIVAL_VAR_NAME
+from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import (
+    HEALPIX_INDEX_VAR_NAME,
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+)
 from tests.test_helpers import get_test_data_path
 
 
-def create_survival_probabilities_file(glows_file_path: Path, date_for_file: datetime, cdf_file_path: Path):
+def create_survival_probabilities_file(
+    glows_file_path: Path, date_for_file: datetime, cdf_file_path: Path
+):
     with open(glows_file_path) as input_data:
-        energy_line = [line for line in input_data.readlines() if line.startswith("#energy_grid")]
+        energy_line = [line for line in input_data if line.startswith("#energy_grid")]
         assert len(energy_line) == 1
         match = [float(x) for x in re.findall(r"\s+([0-9.]+)", energy_line[0])]
-        energy_units = re.search(r'\[(.+)]', energy_line[0]).group(1)
+        energy_units = re.search(r"\[(.+)]", energy_line[0]).group(1)
 
         energies = np.array(match)
 
@@ -30,9 +35,11 @@ def create_survival_probabilities_file(glows_file_path: Path, date_for_file: dat
     latitude = lats.value
     longitude = lons.value
     skips = 0
-    for i in range(0, 3072):
+    for i in range(3072):
         if i in healpix_index:
-            output_survival_probabilities[:, i] = spin_angle_and_survival_probabilities[i - skips, 3:-1].T
+            output_survival_probabilities[:, i] = spin_angle_and_survival_probabilities[
+                i - skips, 3:-1
+            ].T
         else:
             skips += 1
             output_survival_probabilities[:, i] = 0
@@ -40,16 +47,24 @@ def create_survival_probabilities_file(glows_file_path: Path, date_for_file: dat
     # Removing last column as it appears to be extra
     survival_probabilities = spin_angle_and_survival_probabilities[:, 3:-1].T
 
-    with CDF(str(cdf_file_path), '') as c:
+    with CDF(str(cdf_file_path), "") as c:
         c.new("epoch", [date_for_file], pycdf.const.CDF_TIME_TT2000)
         c.new("epoch_delta", [12 * 60 * 60 * 1e9], pycdf.const.CDF_INT8)
         c.new("energy_grid", energies, pycdf.const.CDF_FLOAT, recVary=False)
-        c['energy_grid'].attrs["UNITS"] = energy_units
+        c["energy_grid"].attrs["UNITS"] = energy_units
         c.new("latitude", latitude, pycdf.const.CDF_FLOAT, recVary=False)
         c.new("longitude", longitude, pycdf.const.CDF_FLOAT, recVary=False)
-        c.new(HEALPIX_INDEX_VAR_NAME, output_healpix_index, pycdf.const.CDF_INT2, recVary=False)
-        c.new(PROBABILITY_OF_SURVIVAL_VAR_NAME, np.array(output_survival_probabilities)[np.newaxis, ...],
-              pycdf.const.CDF_FLOAT)
+        c.new(
+            HEALPIX_INDEX_VAR_NAME,
+            output_healpix_index,
+            pycdf.const.CDF_INT2,
+            recVary=False,
+        )
+        c.new(
+            PROBABILITY_OF_SURVIVAL_VAR_NAME,
+            np.array(output_survival_probabilities)[np.newaxis, ...],
+            pycdf.const.CDF_FLOAT,
+        )
 
         c["energy_grid"].attrs["FILLVAL"] = -1e31
         c["latitude"].attrs["FILLVAL"] = -1e31
@@ -62,7 +77,12 @@ def create_survival_probabilities_file(glows_file_path: Path, date_for_file: dat
 
 if __name__ == "__main__":
     path = Path(__file__)
-    input_file_path = path.parent.parent.parent / "instrument_team_data" / "glows" / "probSur.Imap.Ul.V0_2009.000.dat"
+    input_file_path = (
+        path.parent.parent.parent
+        / "instrument_team_data"
+        / "glows"
+        / "probSur.Imap.Ul.V0_2009.000.dat"
+    )
 
     start_date = datetime(year=2025, month=4, day=15, hour=12)
     num_psets_to_generate = 4
@@ -70,7 +90,9 @@ def create_survival_probabilities_file(glows_file_path: Path, date_for_file: dat
     for i in range(num_psets_to_generate):
         date_to_set = start_date + timedelta(days=i)
         filename = f"imap_glows_l3e_survival-probability-ul_{date_to_set.strftime('%Y%m%d')}_v015.cdf"
-        cdf_file_path = get_test_data_path(f"ultra/fake_l3e_survival_probabilities/{filename}")
+        cdf_file_path = get_test_data_path(
+            f"ultra/fake_l3e_survival_probabilities/{filename}"
+        )
 
         cdf_file_path.unlink(missing_ok=True)
         create_survival_probabilities_file(input_file_path, date_to_set, cdf_file_path)
diff --git a/scripts/ultra/create_glows_l3e_cdf_from_dat.py b/scripts/ultra/create_glows_l3e_cdf_from_dat.py
index 0caec3fa2..860d1d254 100644
--- a/scripts/ultra/create_glows_l3e_cdf_from_dat.py
+++ b/scripts/ultra/create_glows_l3e_cdf_from_dat.py
@@ -11,18 +11,20 @@
 
 version = 0
 
-glows_dat_dir = Path(r'C:\Users\Harrison\Downloads\timeshifted_glows_data\fake_l3_validation')
-glows_dat_files = list(glows_dat_dir.glob('*'))
-output_dir = Path(r'C:\Users\Harrison\Downloads\timeshifted_glows_data\cdf')
+glows_dat_dir = Path(
+    r"C:\Users\Harrison\Downloads\timeshifted_glows_data\fake_l3_validation"
+)
+glows_dat_files = list(glows_dat_dir.glob("*"))
+output_dir = Path(r"C:\Users\Harrison\Downloads\timeshifted_glows_data\cdf")
 output_dir.mkdir(exist_ok=True)
 
 for glows_dat in glows_dat_files:
-    full_date_str = glows_dat.name.split('_')[1]
-    year_str, decimal_fractional_date_str = full_date_str.split('.')
-    fractional_year = float(f'0.{decimal_fractional_date_str}')
+    full_date_str = glows_dat.name.split("_")[1]
+    year_str, decimal_fractional_date_str = full_date_str.split(".")
+    fractional_year = float(f"0.{decimal_fractional_date_str}")
     start_date = datetime(int(year_str), 1, 1) + 365 * timedelta(fractional_year)
 
-    repointing = int(glows_dat.name.split('_')[2][:-4])
+    repointing = int(glows_dat.name.split("_")[2][:-4])
 
     input_metadata = InputMetadata(
         instrument="glows",
@@ -35,8 +37,8 @@
     )
 
     call_args_object = GlowsL3eCallArguments(
-        formatted_date='',
-        decimal_date='',
+        formatted_date="",
+        decimal_date="",
         spacecraft_radius=np.float32(0.0),
         spacecraft_longitude=np.float32(0.0),
         spacecraft_latitude=np.float32(0.0),
@@ -48,10 +50,11 @@
         elongation=0,
     )
 
-    glows_l3e = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(input_metadata,
+    glows_l3e = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(
+        input_metadata,
         glows_dat,
         start_date,
         call_args_object,
     )
 
-    ul_cdf = save_data(glows_l3e, folder_path=output_dir)
\ No newline at end of file
+    ul_cdf = save_data(glows_l3e, folder_path=output_dir)
diff --git a/scripts/ultra/create_l1c_l2_and_glows_with_matching_date_range.py b/scripts/ultra/create_l1c_l2_and_glows_with_matching_date_range.py
index 32becda3e..205fbdf91 100644
--- a/scripts/ultra/create_l1c_l2_and_glows_with_matching_date_range.py
+++ b/scripts/ultra/create_l1c_l2_and_glows_with_matching_date_range.py
@@ -3,19 +3,30 @@
 
 import imap_data_access
 
-from scripts.ultra.create_example_ultra_l1c_pset import _write_ultra_l1c_cdf_with_parents
-from scripts.ultra.create_example_ultra_l2_map_with_power_law import _create_example_ultra_l2_map_with_power_law
-from scripts.ultra.create_example_ultra_l3_survival_probabilities_cdf import create_survival_probabilities_file
-from tests.test_helpers import get_test_instrument_team_data_path, get_run_local_data_path
-
-
-def create_l1c_and_glows_with_matching_date_range(start_date: datetime, end_date: datetime):
+from scripts.ultra.create_example_ultra_l1c_pset import (
+    _write_ultra_l1c_cdf_with_parents,
+)
+from scripts.ultra.create_example_ultra_l2_map_with_power_law import (
+    _create_example_ultra_l2_map_with_power_law,
+)
+from scripts.ultra.create_example_ultra_l3_survival_probabilities_cdf import (
+    create_survival_probabilities_file,
+)
+from tests.test_helpers import (
+    get_run_local_data_path,
+    get_test_instrument_team_data_path,
+)
+
+
+def create_l1c_and_glows_with_matching_date_range(
+    start_date: datetime, end_date: datetime
+):
     process_date = start_date
     repoint_counter = 0
     l1c_filepaths = []
     all_paths = []
 
-    base_path = f'ultra/{start_date:%Y%m%d}-{end_date:%Y%m%d}'
+    base_path = f"ultra/{start_date:%Y%m%d}-{end_date:%Y%m%d}"
     shutil.rmtree(get_run_local_data_path(base_path), ignore_errors=True)
 
     version_number = 10
@@ -24,26 +35,33 @@ def create_l1c_and_glows_with_matching_date_range(start_date: datetime, end_date
         repoint_counter += 1
 
         l1c_file_path = get_run_local_data_path(
-            f'{base_path}/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_{process_date:%Y%m%d}-repoint{repoint_counter:05}_v{version_number:03}.cdf'
+            f"{base_path}/ultra_l1c/imap_ultra_l1c_45sensor-spacecraftpset_{process_date:%Y%m%d}-repoint{repoint_counter:05}_v{version_number:03}.cdf"
         )
         l1c_file_path.parent.mkdir(parents=True, exist_ok=True)
-        _write_ultra_l1c_cdf_with_parents(date=process_date.isoformat(), out_path=l1c_file_path)
+        _write_ultra_l1c_cdf_with_parents(
+            date=process_date.isoformat(), out_path=l1c_file_path
+        )
         l1c_filepaths.append(str(l1c_file_path))
         all_paths.append(str(l1c_file_path))
 
         glows_l3e_file_path = get_run_local_data_path(
-            f'{base_path}/glows_l3e/imap_glows_l3e_survival-probability-ultra_{process_date:%Y%m%d}_v{version_number:03}.cdf'
+            f"{base_path}/glows_l3e/imap_glows_l3e_survival-probability-ultra_{process_date:%Y%m%d}_v{version_number:03}.cdf"
         )
         glows_l3e_file_path.parent.mkdir(parents=True, exist_ok=True)
 
-        glows_input_path = get_test_instrument_team_data_path('glows/probSur.Imap.Ul.V0_2009.000.dat')
-        create_survival_probabilities_file(glows_file_path=glows_input_path, date_for_file=process_date,
-                                           cdf_file_path=get_run_local_data_path(glows_l3e_file_path))
+        glows_input_path = get_test_instrument_team_data_path(
+            "glows/probSur.Imap.Ul.V0_2009.000.dat"
+        )
+        create_survival_probabilities_file(
+            glows_file_path=glows_input_path,
+            date_for_file=process_date,
+            cdf_file_path=get_run_local_data_path(glows_l3e_file_path),
+        )
         all_paths.append(str(glows_l3e_file_path))
         process_date = process_date + timedelta(days=1)
 
     l2_out_path = get_run_local_data_path(
-        f'{base_path}/imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-6mo_{start_date:%Y%m%d}_v{version_number:03}.cdf'
+        f"{base_path}/imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-6mo_{start_date:%Y%m%d}_v{version_number:03}.cdf"
     )
     l2_out_path.parent.mkdir(parents=True, exist_ok=True)
 
@@ -58,6 +76,8 @@ def create_example_ultra_l2_with_parents(out_path, parents: list[str]):
 
 
 if __name__ == "__main__":
-    paths = create_l1c_and_glows_with_matching_date_range(datetime(2025, 4, 15, 12), datetime(2025, 4, 19, 12))
+    paths = create_l1c_and_glows_with_matching_date_range(
+        datetime(2025, 4, 15, 12), datetime(2025, 4, 19, 12)
+    )
     for path in paths:
         imap_data_access.upload(path)
diff --git a/scripts/ultra/create_l2_cdf_from_instrument_team_data.py b/scripts/ultra/create_l2_cdf_from_instrument_team_data.py
index 3017afd86..5a057f43c 100644
--- a/scripts/ultra/create_l2_cdf_from_instrument_team_data.py
+++ b/scripts/ultra/create_l2_cdf_from_instrument_team_data.py
@@ -9,7 +9,11 @@
 from spacepy import pycdf
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.constants import FIVE_MINUTES_IN_NANOSECONDS, ONE_SECOND_IN_NANOSECONDS, SECONDS_PER_DAY
+from imap_l3_processing.constants import (
+    FIVE_MINUTES_IN_NANOSECONDS,
+    ONE_SECOND_IN_NANOSECONDS,
+    SECONDS_PER_DAY,
+)
 
 parser = argparse.ArgumentParser()
 parser.add_argument("map_folder")
@@ -18,21 +22,23 @@
 
 parent = Path(args.map_folder)
 
-data = defaultdict(lambda: {})
+data = defaultdict(dict)
 for folder in parent.iterdir():
     for sub_folder in folder.iterdir():
         for file in sub_folder.iterdir():
             energy_string = re.search(r"_(\d*p\d*)keV", file.name).group(1)
-            energy = float(energy_string.replace('p', '.'))
+            energy = float(energy_string.replace("p", "."))
             data[sub_folder.name][energy] = np.loadtxt(file, delimiter=",").T
-    assert (data['flux'].keys() == data['sigma'].keys())
-    energy = np.array([key for key in sorted(data['flux'].keys())])
-    intensity = np.array([data['flux'][key] for key in sorted(data['flux'].keys())])
+    assert data["flux"].keys() == data["sigma"].keys()
+    energy = np.array([key for key in sorted(data["flux"].keys())])
+    intensity = np.array([data["flux"][key] for key in sorted(data["flux"].keys())])
     ena_intensity = intensity.reshape(1, *intensity.shape)
     energy_delta_plus = np.full(energy.shape, 1.0)
     energy_delta_minus = np.full(energy.shape, 1.0)
     energy_label = energy.astype(str)
-    ena_intensity_stat_uncert = np.array([data['sigma'][key] for key in sorted(data['sigma'].keys())])[np.newaxis, ...]
+    ena_intensity_stat_uncert = np.array(
+        [data["sigma"][key] for key in sorted(data["sigma"].keys())]
+    )[np.newaxis, ...]
     ena_intensity_sys_err = ena_intensity_stat_uncert / 2
 
     epoch = np.array([datetime.now()])
@@ -44,40 +50,75 @@
     pixel_index_label = [f"Pixel index {x}" for x in pixel_index]
 
     obs_date = np.full(ena_intensity.shape, datetime.now())
-    obs_date_range = np.full(ena_intensity.shape, ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY * 2)
+    obs_date_range = np.full(
+        ena_intensity.shape, ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY * 2
+    )
 
     solid_angle = build_solid_angle_map(4)
 
     pathname = f"/Users/harrison/Development/imap_L3_processing/tests/test_data/ultra/fake_l2_maps/{folder.name}.cdf"
     Path(pathname).unlink(missing_ok=True)
-    with CDF(pathname, '') as cdf:
+    with CDF(pathname, "") as cdf:
         cdf.col_major(True)
 
         cdf.new("epoch", epoch, type=pycdf.const.CDF_TIME_TT2000)
         cdf.new("energy", energy, recVary=False, type=pycdf.const.CDF_FLOAT)
         cdf.new("latitude", lat, recVary=False, type=pycdf.const.CDF_FLOAT)
         cdf.new("longitude", lon, recVary=False, type=pycdf.const.CDF_FLOAT)
-        cdf.new("ena_intensity", ena_intensity, recVary=True, type=pycdf.const.CDF_FLOAT)
-        cdf.new("ena_intensity_stat_uncert", ena_intensity_stat_uncert, recVary=True, type=pycdf.const.CDF_FLOAT)
-        cdf.new("ena_intensity_sys_err", ena_intensity_sys_err, recVary=True, type=pycdf.const.CDF_FLOAT)
-        cdf.new("exposure_factor", exposure_factor, recVary=True, type=pycdf.const.CDF_FLOAT)
+        cdf.new(
+            "ena_intensity", ena_intensity, recVary=True, type=pycdf.const.CDF_FLOAT
+        )
+        cdf.new(
+            "ena_intensity_stat_uncert",
+            ena_intensity_stat_uncert,
+            recVary=True,
+            type=pycdf.const.CDF_FLOAT,
+        )
+        cdf.new(
+            "ena_intensity_sys_err",
+            ena_intensity_sys_err,
+            recVary=True,
+            type=pycdf.const.CDF_FLOAT,
+        )
+        cdf.new(
+            "exposure_factor", exposure_factor, recVary=True, type=pycdf.const.CDF_FLOAT
+        )
         cdf.new("obs_date", obs_date, recVary=True, type=pycdf.const.CDF_TIME_TT2000)
-        cdf.new("obs_date_range", obs_date_range, recVary=True, type=pycdf.const.CDF_INT8)
+        cdf.new(
+            "obs_date_range", obs_date_range, recVary=True, type=pycdf.const.CDF_INT8
+        )
         cdf.new("solid_angle", solid_angle, recVary=True, type=pycdf.const.CDF_FLOAT)
         cdf.new("epoch_delta", epoch_delta, recVary=True, type=pycdf.const.CDF_INT8)
-        cdf.new("energy_delta_plus", energy_delta_plus, recVary=False, type=pycdf.const.CDF_FLOAT)
-        cdf.new("energy_delta_minus", energy_delta_minus, recVary=False, type=pycdf.const.CDF_FLOAT)
+        cdf.new(
+            "energy_delta_plus",
+            energy_delta_plus,
+            recVary=False,
+            type=pycdf.const.CDF_FLOAT,
+        )
+        cdf.new(
+            "energy_delta_minus",
+            energy_delta_minus,
+            recVary=False,
+            type=pycdf.const.CDF_FLOAT,
+        )
         cdf.new("energy_label", energy_label, recVary=False, type=pycdf.const.CDF_CHAR)
         cdf.new("pixel_index", pixel_index, recVary=False, type=pycdf.const.CDF_INT8)
-        cdf.new("pixel_index_label", pixel_index_label, recVary=False, type=pycdf.const.CDF_CHAR)
+        cdf.new(
+            "pixel_index_label",
+            pixel_index_label,
+            recVary=False,
+            type=pycdf.const.CDF_CHAR,
+        )
 
         for metadata_var_names in ["pixel_index_label", "energy_label"]:
             cdf[metadata_var_names].attrs["VAR_TYPE"] = "metadata"
 
         for var in cdf:
             if cdf[var].type() == pycdf.const.CDF_TIME_TT2000.value:
-                cdf[var].attrs['FILLVAL'] = datetime.fromisoformat("9999-12-31T23:59:59.999999999")
+                cdf[var].attrs["FILLVAL"] = datetime.fromisoformat(
+                    "9999-12-31T23:59:59.999999999"
+                )
             elif cdf[var].type() == pycdf.const.CDF_INT8.value:
-                cdf[var].attrs['FILLVAL'] = -9223372036854775808
+                cdf[var].attrs["FILLVAL"] = -9223372036854775808
             elif cdf[var].type() == pycdf.const.CDF_FLOAT.value:
-                cdf[var].attrs['FILLVAL'] = -1e31
+                cdf[var].attrs["FILLVAL"] = -1e31
diff --git a/scripts/ultra/create_l2_dependency_files.py b/scripts/ultra/create_l2_dependency_files.py
index e047c644f..413667ba0 100644
--- a/scripts/ultra/create_l2_dependency_files.py
+++ b/scripts/ultra/create_l2_dependency_files.py
@@ -1,4 +1,4 @@
-from datetime import timedelta, datetime
+from datetime import datetime
 from pathlib import Path
 
 import imap_data_access
@@ -7,61 +7,71 @@
 from imap_data_access.file_validation import generate_imap_file_path
 from imap_data_access.processing_input import generate_imap_input
 
-from imap_l3_processing.utils import get_spice_kernels_file_names, SpiceKernelTypes
+from imap_l3_processing.utils import SpiceKernelTypes
 
-REFERENCE_FRAMES = ['sf', 'hf']
-POINTING_SET_DESCRIPTORS_FROM_REF_FRAME = {
-    'sf':'spacecraftpset',
-    'hf':'heliopset'
-}
-SENSORS = ['u90', 'u45']
+REFERENCE_FRAMES = ["sf", "hf"]
+POINTING_SET_DESCRIPTORS_FROM_REF_FRAME = {"sf": "spacecraftpset", "hf": "heliopset"}
+SENSORS = ["u90", "u45"]
 START_DATE = datetime(2025, 9, 29)
-END_DATE = datetime(2025,12,29)
+END_DATE = datetime(2025, 12, 29)
 
-SENSOR_TO_SENSORNAME = {
-    'u90':'90sensor',
-    'u45':'45sensor'
-}
+SENSOR_TO_SENSORNAME = {"u90": "90sensor", "u45": "45sensor"}
 
 SPICE_KERNELS = [
     SpiceKernelTypes.SpacecraftClock,
     SpiceKernelTypes.Leapseconds,
     SpiceKernelTypes.IMAPFrames,
     SpiceKernelTypes.PointingAttitude,
-    SpiceKernelTypes.ScienceFrames
+    SpiceKernelTypes.ScienceFrames,
 ]
 
+
 def create_l2_dependency_files(sensor, reference_frame):
     inputs = ProcessingInputCollection()
-    pointing_set_descriptor = f'{SENSOR_TO_SENSORNAME[sensor]}-{POINTING_SET_DESCRIPTORS_FROM_REF_FRAME[reference_frame]}'
-    dependency_file_descriptor = f'{sensor}-{reference_frame}'
+    pointing_set_descriptor = f"{SENSOR_TO_SENSORNAME[sensor]}-{POINTING_SET_DESCRIPTORS_FROM_REF_FRAME[reference_frame]}"
+    dependency_file_descriptor = f"{sensor}-{reference_frame}"
 
     spice_files = []
     for kernel_type in SPICE_KERNELS:
         response = requests.get(
-            imap_data_access.config["DATA_ACCESS_URL"] + f"/spice-query?type={kernel_type.value}&start_time=0",
+            imap_data_access.config["DATA_ACCESS_URL"]
+            + f"/spice-query?type={kernel_type.value}&start_time=0",
         )
         response.raise_for_status()
         file_json = response.json()
         for spice_file in file_json:
-            spice_start_date = datetime.strptime(spice_file["min_date_datetime"], "%Y-%m-%d, %H:%M:%S")
-            spice_end_date = datetime.strptime(spice_file["max_date_datetime"], "%Y-%m-%d, %H:%M:%S")
+            spice_start_date = datetime.strptime(
+                spice_file["min_date_datetime"], "%Y-%m-%d, %H:%M:%S"
+            )
+            spice_end_date = datetime.strptime(
+                spice_file["max_date_datetime"], "%Y-%m-%d, %H:%M:%S"
+            )
             if spice_start_date <= END_DATE and START_DATE < spice_end_date:
                 spice_files.append(Path(spice_file["file_name"]).name)
     query_results = imap_data_access.query(
         descriptor=pointing_set_descriptor,
-        instrument='ultra',
-        data_level='l1c',
-        start_date=START_DATE.strftime('%Y%m%d'),
-        end_date=END_DATE.strftime('%Y%m%d'),
-        version='latest'
+        instrument="ultra",
+        data_level="l1c",
+        start_date=START_DATE.strftime("%Y%m%d"),
+        end_date=END_DATE.strftime("%Y%m%d"),
+        version="latest",
     )
 
-    desired_repointings = [*range(21,28+1), 31, 32, *range(51,57+1), *range(62, 66+1)]
+    desired_repointings = [
+        *range(21, 28 + 1),
+        31,
+        32,
+        *range(51, 57 + 1),
+        *range(62, 66 + 1),
+    ]
 
-    pset_files = [Path(file['file_path']).name for file in query_results if file['repointing'] in desired_repointings]
+    pset_files = [
+        Path(file["file_path"]).name
+        for file in query_results
+        if file["repointing"] in desired_repointings
+    ]
 
-    inputs.add([generate_imap_input(f) for f in spice_files+pset_files])
+    inputs.add([generate_imap_input(f) for f in spice_files + pset_files])
 
     dependency_file = f"imap_ultra_l2_{dependency_file_descriptor}_{START_DATE.strftime('%Y%m%d')}_v001.json"
     dependency_path = generate_imap_file_path(dependency_file).construct_path()
@@ -71,7 +81,7 @@ def create_l2_dependency_files(sensor, reference_frame):
     return dependency_file
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     for sensor in SENSORS:
         for reference_frame in REFERENCE_FRAMES:
             create_l2_dependency_files(sensor, reference_frame)
diff --git a/scripts/ultra/create_l2_from_pointing_sets.py b/scripts/ultra/create_l2_from_pointing_sets.py
index 4d7568154..69ddb3254 100644
--- a/scripts/ultra/create_l2_from_pointing_sets.py
+++ b/scripts/ultra/create_l2_from_pointing_sets.py
@@ -32,8 +32,11 @@ def create_input_file(sensor: str, start: datetime, end: datetime) -> str:
     date = start
     while date < end:
         repointing = int((date - start_repointings) / timedelta(days=1))
-        inputs.add(generate_imap_input(
-            f"imap_ultra_l1c_{sensor}sensor-heliopset_{date.strftime('%Y%m%d')}-repoint{repointing:05}_v000.cdf"))
+        inputs.add(
+            generate_imap_input(
+                f"imap_ultra_l1c_{sensor}sensor-heliopset_{date.strftime('%Y%m%d')}-repoint{repointing:05}_v000.cdf"
+            )
+        )
         date += timedelta(days=1)
     dependency_file = f"imap_ultra_l2_{sensor}sensor-de-{start.strftime('%Y%m%d')}-{end.strftime('%Y%m%d')}_{start.strftime('%Y%m%d')}_v001.json"
     dependency_path = generate_imap_file_path(dependency_file).construct_path()
@@ -43,7 +46,7 @@ def create_input_file(sensor: str, start: datetime, end: datetime) -> str:
 
 
 def generate_map(descriptor: str, start: datetime, end: datetime):
-    sensor = descriptor.split('-')[0][1:]
+    sensor = descriptor.split("-")[0][1:]
     dependency_file = create_input_file(sensor, start, end)
 
     env = {
@@ -53,7 +56,7 @@ def generate_map(descriptor: str, start: datetime, end: datetime):
     }
 
     version = "v000"
-    formatted_start_date = start_date.strftime('%Y%m%d')
+    formatted_start_date = start_date.strftime("%Y%m%d")
     output_filename = f"imap_ultra_l2_{descriptor}_{formatted_start_date}_{version}.cdf"
     command = f"imap_cli --instrument ultra --data-level l2 --descriptor {descriptor} --start-date {formatted_start_date} --version {version} --dependency {dependency_file}"
     print(command)
@@ -69,8 +72,8 @@ def generate_map(descriptor: str, start: datetime, end: datetime):
     start_date = datetime(2025, 4, 16)
     end_date = datetime(2025, 10, 15)
 
-    duration = '6mo'
-    descriptor = f"u90-ena-h-hf-nsp-full-hae-2deg-6mo"
+    duration = "6mo"
+    descriptor = "u90-ena-h-hf-nsp-full-hae-2deg-6mo"
     generate_map(descriptor, start_date, end_date)
     # for sensor in [45, 90]:
     #     for side in ['ram', 'anti', 'full']:
diff --git a/scripts/ultra/plot_l2_uncertainties.py b/scripts/ultra/plot_l2_uncertainties.py
index 3f27cf2ec..2a8631652 100644
--- a/scripts/ultra/plot_l2_uncertainties.py
+++ b/scripts/ultra/plot_l2_uncertainties.py
@@ -12,12 +12,12 @@
 
 ultra_imap_data_dir = get_run_local_data_path("ultra/integration_data")
 ultra_path_90 = Path(
-    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\ULTRA-90sensor_helio-psets_20251201")
+    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\ULTRA-90sensor_helio-psets_20251201"
+)
 ultra_path_45 = Path(
     r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\ULTRA-45sensor_helio-psets_20251201"
 )
-glows_path = Path(
-    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\glows")
+glows_path = Path(r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\glows")
 
 ancil_files = [
     INTEGRATION_TEST_DATA_PATH / "spice" / "naif020.tls",
@@ -30,24 +30,38 @@
 u90_paths = [Path(f) for f in list(ultra_path_90.glob("*.cdf"))]
 glows_paths = [Path(f) for f in list(glows_path.glob("*.cdf"))]
 l2_u90 = Path(
-    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-2deg-6mo_20250416_v000.cdf")
+    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-2deg-6mo_20250416_v000.cdf"
+)
 l2_u45 = Path(
-    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-2deg-6mo_20250416_v000.cdf")
+    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-2deg-6mo_20250416_v000.cdf"
+)
 start_date = datetime(2025, 4, 16)
 descriptor = "ulc-ena-h-hf-nsp-full-hae-2deg-6mo"
 
 for f in ancil_files:
     spiceypy.furnsh(str(f))
 
-ultra_nsp_combined_dep = UltraL3CombinedDependencies.from_file_paths(u45_pset_paths=u45_paths, u90_pset_paths=u90_paths,
-                                                                     glows_l3e_pset_paths=glows_paths,
-                                                                     u45_map_path=l2_u45, u90_map_path=l2_u90,
-                                                                     energy_bin_group_sizes_path=get_test_data_path(
-                                                                         "ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"))
-print(ultra_nsp_combined_dep.u45_l2_map.intensity_map_data.ena_intensity_stat_uncert.shape)
+ultra_nsp_combined_dep = UltraL3CombinedDependencies.from_file_paths(
+    u45_pset_paths=u45_paths,
+    u90_pset_paths=u90_paths,
+    glows_l3e_pset_paths=glows_paths,
+    u45_map_path=l2_u45,
+    u90_map_path=l2_u90,
+    energy_bin_group_sizes_path=get_test_data_path(
+        "ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+    ),
+)
+print(
+    ultra_nsp_combined_dep.u45_l2_map.intensity_map_data.ena_intensity_stat_uncert.shape
+)
 
 hp32 = HEALPix(32)
 lon, lat = hp32.healpix_to_lonlat(np.arange(hp32.npix))
-plt.scatter(lon, lat,
-            c=ultra_nsp_combined_dep.u45_l2_map.intensity_map_data.ena_intensity_stat_uncert[0].reshape(12, -1)[0])
+plt.scatter(
+    lon,
+    lat,
+    c=ultra_nsp_combined_dep.u45_l2_map.intensity_map_data.ena_intensity_stat_uncert[
+        0
+    ].reshape(12, -1)[0],
+)
 plt.show()
diff --git a/scripts/ultra/validation/create_L2_validation_maps.py b/scripts/ultra/validation/create_L2_validation_maps.py
index bb5415152..edea0e1a0 100644
--- a/scripts/ultra/validation/create_L2_validation_maps.py
+++ b/scripts/ultra/validation/create_L2_validation_maps.py
@@ -6,20 +6,21 @@
 from imap_processing.cli import Ultra
 
 L2_DESCRIPTORS = [
-    'u90-ena-h-hf-nsp-full-hae-2deg-1mo',
-    'u90-ena-h-sf-nsp-full-hae-2deg-1mo',
-    'u45-ena-h-hf-nsp-full-hae-2deg-1mo',
-    'u45-ena-h-sf-nsp-full-hae-2deg-1mo',
-    'u90-ena-h-hf-nsp-full-hae-4deg-1mo',
-    'u90-ena-h-sf-nsp-full-hae-4deg-1mo',
-    'u45-ena-h-hf-nsp-full-hae-4deg-1mo',
-    'u45-ena-h-sf-nsp-full-hae-4deg-1mo',
-    'u90-ena-h-hf-nsp-full-hae-6deg-1mo',
-    'u90-ena-h-sf-nsp-full-hae-6deg-1mo',
-    'u45-ena-h-hf-nsp-full-hae-6deg-1mo',
-    'u45-ena-h-sf-nsp-full-hae-6deg-1mo',
+    "u90-ena-h-hf-nsp-full-hae-2deg-1mo",
+    "u90-ena-h-sf-nsp-full-hae-2deg-1mo",
+    "u45-ena-h-hf-nsp-full-hae-2deg-1mo",
+    "u45-ena-h-sf-nsp-full-hae-2deg-1mo",
+    "u90-ena-h-hf-nsp-full-hae-4deg-1mo",
+    "u90-ena-h-sf-nsp-full-hae-4deg-1mo",
+    "u45-ena-h-hf-nsp-full-hae-4deg-1mo",
+    "u45-ena-h-sf-nsp-full-hae-4deg-1mo",
+    "u90-ena-h-hf-nsp-full-hae-6deg-1mo",
+    "u90-ena-h-sf-nsp-full-hae-6deg-1mo",
+    "u45-ena-h-hf-nsp-full-hae-6deg-1mo",
+    "u45-ena-h-sf-nsp-full-hae-6deg-1mo",
 ]
 
+
 def generate_l2_files():
     groups = defaultdict(list)
     for descriptor in L2_DESCRIPTORS:
@@ -28,20 +29,26 @@ def generate_l2_files():
         for group, descriptors in groups.items():
             pool.submit(make_maps, descriptors)
 
+
 def make_maps(descriptors):
     print("making", descriptors)
     for descriptor in descriptors:
-        sensor, _, _, frame, *_ = descriptor.split('-')
+        sensor, _, _, frame, *_ = descriptor.split("-")
         dependency_filename = f"imap_ultra_l2_{sensor}-{frame}_20250929_v001.json"
-        dependency = (Path(__file__).parent / "l2_dependency_files" / dependency_filename).read_text()
+        dependency = (
+            Path(__file__).parent / "l2_dependency_files" / dependency_filename
+        ).read_text()
 
         version = "v000"
-        start_date = '20251018'
+        start_date = "20251018"
         output_filename = f"imap_ultra_l2_{descriptor}_{start_date}_{version}.cdf"
         if not generate_imap_file_path(output_filename).construct_path().exists():
-            Ultra("l2", descriptor, dependency, start_date, None, "v000", False).process()
+            Ultra(
+                "l2", descriptor, dependency, start_date, None, "v000", False
+            ).process()
         else:
             print(f"Skipping generation of {output_filename}, it already exists")
 
-if __name__ == '__main__':
-    generate_l2_files()
\ No newline at end of file
+
+if __name__ == "__main__":
+    generate_l2_files()
diff --git a/scripts/ultra/validation/create_l3_validation_maps.py b/scripts/ultra/validation/create_l3_validation_maps.py
index ec3276922..5fd7666da 100644
--- a/scripts/ultra/validation/create_l3_validation_maps.py
+++ b/scripts/ultra/validation/create_l3_validation_maps.py
@@ -10,8 +10,13 @@
 from tests.integration.integration_test_helpers import mock_imap_data_access
 
 
-@patch('imap_l3_data_processor._parse_cli_arguments')
-def run_all_maps(mock_parse_cli_arguments, *, output_dir, input_dir,):
+@patch("imap_l3_data_processor._parse_cli_arguments")
+def run_all_maps(
+    mock_parse_cli_arguments,
+    *,
+    output_dir,
+    input_dir,
+):
     mock_arguments = Mock()
     mock_arguments.instrument = "ultra"
     mock_arguments.data_level = "l3"
@@ -22,9 +27,12 @@ def run_all_maps(mock_parse_cli_arguments, *, output_dir, input_dir,):
     mock_arguments.dependency = "[]"
     mock_arguments.upload_to_sdc = False
     mock_parse_cli_arguments.return_value = mock_arguments
-    logging.basicConfig(force=True, level=logging.INFO,
-                        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-    with mock_imap_data_access(output_dir, list(input_dir.rglob('*.*'))):
+    logging.basicConfig(
+        force=True,
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+    with mock_imap_data_access(output_dir, list(input_dir.rglob("*.*"))):
         mock_arguments.descriptor = "u45-maps"
         imap_l3_data_processor.imap_l3_processor()
         spiceypy.kclear()
@@ -39,14 +47,14 @@ def run_all_maps(mock_parse_cli_arguments, *, output_dir, input_dir,):
         spiceypy.kclear()
 
 
-
-
 if __name__ == "__main__":
-    output_dir = Path(f'{datetime.now().strftime('%Y%m%d')}_ultra_validation_output')
+    output_dir = Path(f"{datetime.now().strftime('%Y%m%d')}_ultra_validation_output")
     try:
         input_dir = Path(sys.argv[1])
     except:
-        input_dir = Path(fr'C:\Users\Harrison\AppData\Local\cava\cava\data')
-        print(f"Could not open input file, using default input path of {input_dir} instead")
+        input_dir = Path(r"C:\Users\Harrison\AppData\Local\cava\cava\data")
+        print(
+            f"Could not open input file, using default input path of {input_dir} instead"
+        )
 
-    run_all_maps(output_dir=output_dir, input_dir=input_dir)
\ No newline at end of file
+    run_all_maps(output_dir=output_dir, input_dir=input_dir)
diff --git a/scripts/ultra/validation/prepare_glows_for_ultra_validation.py b/scripts/ultra/validation/prepare_glows_for_ultra_validation.py
index 8e8bd6522..b8d4e3784 100644
--- a/scripts/ultra/validation/prepare_glows_for_ultra_validation.py
+++ b/scripts/ultra/validation/prepare_glows_for_ultra_validation.py
@@ -6,12 +6,13 @@
 output_dir = Path(r"C:\Users\Harrison\Downloads\glows_l3e_for_ultra_validation")
 output_dir.mkdir(exist_ok=True)
 input_dir = Path(r"C:\Users\Harrison\Downloads\glows_l3e_from_validation\l3e")
-for i, file in enumerate(input_dir.rglob('imap_glows_l3e_survival-probability-ul*.cdf')):
+for i, file in enumerate(
+    input_dir.rglob("imap_glows_l3e_survival-probability-ul*.cdf")
+):
     if i > 184:
         break
-    new_repoint = re.sub(r'repoint\d{5}', f'repoint{i:05}', file.name)
-    new_epoch = datetime(2025,4,16)+timedelta(days=i)
-    new_name = re.sub(r'\d{8}', new_epoch.strftime('%Y%m%d'), new_repoint)
-    print(f'renaming {file.name} to {new_name}')
-    shutil.copy(file, output_dir/new_name)
-
+    new_repoint = re.sub(r"repoint\d{5}", f"repoint{i:05}", file.name)
+    new_epoch = datetime(2025, 4, 16) + timedelta(days=i)
+    new_name = re.sub(r"\d{8}", new_epoch.strftime("%Y%m%d"), new_repoint)
+    print(f"renaming {file.name} to {new_name}")
+    shutil.copy(file, output_dir / new_name)
diff --git a/scripts/ultra/validation/prepare_ul_repoint_files_for_ultra_validation.py b/scripts/ultra/validation/prepare_ul_repoint_files_for_ultra_validation.py
index 286aea19d..bc1cec2d8 100644
--- a/scripts/ultra/validation/prepare_ul_repoint_files_for_ultra_validation.py
+++ b/scripts/ultra/validation/prepare_ul_repoint_files_for_ultra_validation.py
@@ -6,11 +6,13 @@
 output_dir = Path(r"C:\Users\Harrison\Downloads\glows_l3e_for_ultra_validation")
 output_dir.mkdir(exist_ok=True)
 input_dir = Path(r"C:\Users\Harrison\Downloads\\")
-for i, file in enumerate(input_dir.rglob('imap_glows_l3e_survival-probability-ul*.cdf')):
+for i, file in enumerate(
+    input_dir.rglob("imap_glows_l3e_survival-probability-ul*.cdf")
+):
     if i > 184:
         break
-    new_repoint = re.sub(r'repoint\d{5}', f'repoint{i:05}', file.name)
-    new_epoch = datetime(2025,4,16)+timedelta(days=i)
-    new_name = re.sub(r'\d{8}', new_epoch.strftime('%Y%m%d'), new_repoint)
-    print(f'renaming {file.name} to {new_name}')
-    shutil.copy(file, output_dir/new_name)
\ No newline at end of file
+    new_repoint = re.sub(r"repoint\d{5}", f"repoint{i:05}", file.name)
+    new_epoch = datetime(2025, 4, 16) + timedelta(days=i)
+    new_name = re.sub(r"\d{8}", new_epoch.strftime("%Y%m%d"), new_repoint)
+    print(f"renaming {file.name} to {new_name}")
+    shutil.copy(file, output_dir / new_name)
diff --git a/scripts/ultra/validation/upload_files.py b/scripts/ultra/validation/upload_files.py
index f39a0cf0c..00b78e201 100644
--- a/scripts/ultra/validation/upload_files.py
+++ b/scripts/ultra/validation/upload_files.py
@@ -5,23 +5,26 @@
 
 import imap_data_access
 
-imap_data_access.config['DATA_ACCESS_URL']='https://api.dev.imap-mission.com/api-key'
-imap_data_access.config['API_KEY']='e6378327cc4fe2d2eb35373a9b39de3f85a89f896bec3010e0476709868d1590'
+imap_data_access.config["DATA_ACCESS_URL"] = "https://api.dev.imap-mission.com/api-key"
+imap_data_access.config["API_KEY"] = (
+    "e6378327cc4fe2d2eb35373a9b39de3f85a89f896bec3010e0476709868d1590"
+)
 
 l1_45_folder = Path(
-    r"C:\Users\Harrison\Downloads\ULTRA_45sensor_spacecraft-psets_20251113-20251125T164844Z-1-001\ULTRA_45sensor_spacecraft-psets_20251113")
+    r"C:\Users\Harrison\Downloads\ULTRA_45sensor_spacecraft-psets_20251113-20251125T164844Z-1-001\ULTRA_45sensor_spacecraft-psets_20251113"
+)
 l1_90_folder = Path(
     r"C:\Users\Harrison\Downloads\ULTRA_90sensor_spacecraft-psets_20251113"
 )
-glows_folder = Path(
-    r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\glows"
-)
+glows_folder = Path(r"C:\Users\Harrison\Downloads\ultra_combined_hf_validation\glows")
 l2_45_folder = Path(r"C:\Users\Harrison\Downloads\u45_l2")
 l2_90_folder = Path(r"C:\Users\Harrison\Downloads\u90_l2")
 retries = 10
 
 uploaded = []
 failed = []
+
+
 def upload_file(f):
     failures = 0
     while failures < retries:
@@ -30,7 +33,7 @@ def upload_file(f):
             uploaded.append(f.name)
             print("uploaded", f.name)
             return
-        except Exception as e:
+        except Exception:
             failures += 1
 
             print(f"failed to upload file: {f} after {failures} attempts, retrying...")
@@ -38,7 +41,8 @@ def upload_file(f):
                 print(f"failed to upload file: {f} after {retries} attempts")
                 failed.append(f)
                 return
-            time.sleep(failures ** 2 * 0.5)
+            time.sleep(failures**2 * 0.5)
+
 
 with ThreadPoolExecutor() as executor:
     for f in itertools.chain(glows_folder.rglob("*.cdf")):
@@ -47,5 +51,3 @@ def upload_file(f):
 
 print("uploaded", uploaded)
 print("failed", failed)
-
-
diff --git a/tests/cdf/test_cdf_utils.py b/tests/cdf/test_cdf_utils.py
index ae8ed73ed..f6444a158 100644
--- a/tests/cdf/test_cdf_utils.py
+++ b/tests/cdf/test_cdf_utils.py
@@ -6,9 +6,18 @@
 from imap_data_access.file_validation import Version
 from spacepy import pycdf
 
-from imap_l3_processing.cdf.cdf_utils import write_cdf, read_variable_and_mask_fill_values, read_numeric_variable
+from imap_l3_processing.cdf.cdf_utils import (
+    read_numeric_variable,
+    read_variable_and_mask_fill_values,
+    write_cdf,
+)
 from imap_l3_processing.cdf.imap_attribute_manager import ImapAttributeManager
-from imap_l3_processing.models import DataProduct, DataProductVariable, InputMetadata, VersionMap
+from imap_l3_processing.models import (
+    DataProduct,
+    DataProductVariable,
+    InputMetadata,
+    VersionMap,
+)
 from tests.temp_file_test_case import TempFileTestCase
 
 
@@ -16,84 +25,185 @@ class TestCdfUtils(TempFileTestCase):
     def test_write_cdf(self):
         path = str(self.temp_directory / "write_cdf.cdf")
         data = TestDataProduct()
-        var_without_explicit_type, int_var, float_var, time_var, non_rec_varying_var = data.to_data_product_variables()
+        var_without_explicit_type, int_var, float_var, time_var, non_rec_varying_var = (
+            data.to_data_product_variables()
+        )
         attribute_manager = Mock(spec=ImapAttributeManager)
-        attribute_manager.get_global_attributes.return_value = {"global1": "global_val1", "global2": "global_val2"}
+        attribute_manager.get_global_attributes.return_value = {
+            "global1": "global_val1",
+            "global2": "global_val2",
+        }
         attribute_manager.get_variable_attributes.side_effect = [
-            {"variable_attr1": "var_val1", "DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "rV"},
-            {"variable_attr1": "var_val1", "FILLVAL": -9223372036854775808, "DATA_TYPE": "CDF_INT8",
-             "RECORD_VARYING": "RV"},
-            {"variable_attr1": "var_val1", "FILLVAL": -1e31, "DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "RV"},
-            {"variable_attr3": "var_val3", "FILLVAL": -9223372036854775808, "DATA_TYPE": "CDF_TIME_TT2000",
-             "RECORD_VARYING": "NRV"},
-            {"variable_attr5": "var_val5", "variable_attr6": "var_val6", "DATA_TY"
-                                                                         "PE": "CDF_INT4",
-             "RECORD_VARYING": "NRV"},
+            {
+                "variable_attr1": "var_val1",
+                "DATA_TYPE": "CDF_REAL4",
+                "RECORD_VARYING": "rV",
+            },
+            {
+                "variable_attr1": "var_val1",
+                "FILLVAL": -9223372036854775808,
+                "DATA_TYPE": "CDF_INT8",
+                "RECORD_VARYING": "RV",
+            },
+            {
+                "variable_attr1": "var_val1",
+                "FILLVAL": -1e31,
+                "DATA_TYPE": "CDF_REAL4",
+                "RECORD_VARYING": "RV",
+            },
+            {
+                "variable_attr3": "var_val3",
+                "FILLVAL": -9223372036854775808,
+                "DATA_TYPE": "CDF_TIME_TT2000",
+                "RECORD_VARYING": "NRV",
+            },
+            {
+                "variable_attr5": "var_val5",
+                "variable_attr6": "var_val6",
+                "DATA_TYPE": "CDF_INT4",
+                "RECORD_VARYING": "NRV",
+            },
         ]
 
         write_cdf(path, data, attribute_manager)
 
         expected_data_product_logical_source = "imap_glows_l3_descriptor"
-        attribute_manager.get_global_attributes.assert_called_once_with(expected_data_product_logical_source)
+        attribute_manager.get_global_attributes.assert_called_once_with(
+            expected_data_product_logical_source
+        )
         attribute_manager.get_variable_attributes.assert_has_calls(
             [call(var.name) for var in data.to_data_product_variables()]
         )
 
         with pycdf.CDF(path) as actual_cdf:
             self.assertTrue(actual_cdf.col_major())
-            self.assertEqual('global_val1', actual_cdf.attrs['global1'][...][0])
-            self.assertEqual('global_val2', actual_cdf.attrs['global2'][...][0])
-
-            np.testing.assert_array_equal(var_without_explicit_type.value,
-                                          actual_cdf[var_without_explicit_type.name][...])
-            self.assertEqual('var_val1', actual_cdf[var_without_explicit_type.name].attrs['variable_attr1'])
-            self.assertEqual(pycdf.const.CDF_REAL4.value, actual_cdf[var_without_explicit_type.name].type())
+            self.assertEqual("global_val1", actual_cdf.attrs["global1"][...][0])
+            self.assertEqual("global_val2", actual_cdf.attrs["global2"][...][0])
+
+            np.testing.assert_array_equal(
+                var_without_explicit_type.value,
+                actual_cdf[var_without_explicit_type.name][...],
+            )
+            self.assertEqual(
+                "var_val1",
+                actual_cdf[var_without_explicit_type.name].attrs["variable_attr1"],
+            )
+            self.assertEqual(
+                pycdf.const.CDF_REAL4.value,
+                actual_cdf[var_without_explicit_type.name].type(),
+            )
             self.assertTrue(actual_cdf[var_without_explicit_type.name].rv())
 
-            np.testing.assert_array_equal(int_var.value, actual_cdf.raw_var(int_var.name))
-            self.assertEqual('var_val1', actual_cdf[int_var.name].attrs['variable_attr1'])
-            self.assertEqual(-9223372036854775808, actual_cdf[int_var.name].attrs['FILLVAL'])
-            self.assertEqual(pycdf.const.CDF_INT8.value, actual_cdf[int_var.name].attrs.type("FILLVAL"))
-            self.assertEqual(pycdf.const.CDF_INT8.value, actual_cdf[int_var.name].type())
+            np.testing.assert_array_equal(
+                int_var.value, actual_cdf.raw_var(int_var.name)
+            )
+            self.assertEqual(
+                "var_val1", actual_cdf[int_var.name].attrs["variable_attr1"]
+            )
+            self.assertEqual(
+                -9223372036854775808, actual_cdf[int_var.name].attrs["FILLVAL"]
+            )
+            self.assertEqual(
+                pycdf.const.CDF_INT8.value,
+                actual_cdf[int_var.name].attrs.type("FILLVAL"),
+            )
+            self.assertEqual(
+                pycdf.const.CDF_INT8.value, actual_cdf[int_var.name].type()
+            )
             self.assertTrue(actual_cdf[int_var.name].rv())
 
-            np.testing.assert_array_equal(float_var.value, actual_cdf.raw_var(float_var.name))
-            self.assertEqual('var_val1', actual_cdf[float_var.name].attrs['variable_attr1'])
-            self.assertEqual(-1e31, actual_cdf[float_var.name].attrs['FILLVAL'])
-            self.assertEqual(pycdf.const.CDF_REAL4.value, actual_cdf[float_var.name].attrs.type("FILLVAL"))
-            self.assertEqual(pycdf.const.CDF_REAL4.value, actual_cdf[float_var.name].type())
+            np.testing.assert_array_equal(
+                float_var.value, actual_cdf.raw_var(float_var.name)
+            )
+            self.assertEqual(
+                "var_val1", actual_cdf[float_var.name].attrs["variable_attr1"]
+            )
+            self.assertEqual(-1e31, actual_cdf[float_var.name].attrs["FILLVAL"])
+            self.assertEqual(
+                pycdf.const.CDF_REAL4.value,
+                actual_cdf[float_var.name].attrs.type("FILLVAL"),
+            )
+            self.assertEqual(
+                pycdf.const.CDF_REAL4.value, actual_cdf[float_var.name].type()
+            )
             self.assertTrue(actual_cdf[float_var.name].rv())
 
-            np.testing.assert_array_equal(time_var.value, actual_cdf.raw_var(time_var.name))
-            self.assertEqual('var_val3', actual_cdf[time_var.name].attrs['variable_attr3'])
-            self.assertEqual(datetime(9999, 12, 31, 23, 59, 59, 999999), actual_cdf[time_var.name].attrs['FILLVAL'])
-            self.assertEqual(pycdf.const.CDF_TIME_TT2000.value, actual_cdf[time_var.name].attrs.type("FILLVAL"))
-            self.assertEqual(pycdf.const.CDF_TIME_TT2000.value, actual_cdf[time_var.name].type())
+            np.testing.assert_array_equal(
+                time_var.value, actual_cdf.raw_var(time_var.name)
+            )
+            self.assertEqual(
+                "var_val3", actual_cdf[time_var.name].attrs["variable_attr3"]
+            )
+            self.assertEqual(
+                datetime(9999, 12, 31, 23, 59, 59, 999999),
+                actual_cdf[time_var.name].attrs["FILLVAL"],
+            )
+            self.assertEqual(
+                pycdf.const.CDF_TIME_TT2000.value,
+                actual_cdf[time_var.name].attrs.type("FILLVAL"),
+            )
+            self.assertEqual(
+                pycdf.const.CDF_TIME_TT2000.value, actual_cdf[time_var.name].type()
+            )
             self.assertFalse(actual_cdf[time_var.name].rv())
 
-            self.assertEqual('var_val5', actual_cdf[non_rec_varying_var.name].attrs['variable_attr5'])
-            self.assertEqual('var_val6', actual_cdf[non_rec_varying_var.name].attrs['variable_attr6'])
-            self.assertEqual(non_rec_varying_var.value, actual_cdf[non_rec_varying_var.name][...])
-            self.assertEqual(pycdf.const.CDF_INT4.value, actual_cdf[non_rec_varying_var.name].type())
+            self.assertEqual(
+                "var_val5", actual_cdf[non_rec_varying_var.name].attrs["variable_attr5"]
+            )
+            self.assertEqual(
+                "var_val6", actual_cdf[non_rec_varying_var.name].attrs["variable_attr6"]
+            )
+            self.assertEqual(
+                non_rec_varying_var.value, actual_cdf[non_rec_varying_var.name][...]
+            )
+            self.assertEqual(
+                pycdf.const.CDF_INT4.value, actual_cdf[non_rec_varying_var.name].type()
+            )
             self.assertFalse(actual_cdf[non_rec_varying_var.name].rv())
 
-            actual_var_attributes = [attribute for k, v in actual_cdf.items() for attribute in v.attrs]
+            actual_var_attributes = [
+                attribute for k, v in actual_cdf.items() for attribute in v.attrs
+            ]
             self.assertNotIn("DATA_TYPE", actual_var_attributes)
             self.assertNotIn("RECORD_VARYING", actual_var_attributes)
 
     @patch("imap_l3_processing.cdf.cdf_utils.CDF")
     def test_write_cdf_compresses_variables(self, mock_new_cdf):
         attribute_manager = Mock(spec=ImapAttributeManager)
-        attribute_manager.get_global_attributes.return_value = {"global1": "global_val1", "global2": "global_val2"}
+        attribute_manager.get_global_attributes.return_value = {
+            "global1": "global_val1",
+            "global2": "global_val2",
+        }
         attribute_manager.get_variable_attributes.side_effect = [
-            {"variable_attr1": "var_val1", "DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "rV"},
-            {"variable_attr1": "var_val1", "FILLVAL": -9223372036854775808, "DATA_TYPE": "CDF_INT8",
-             "RECORD_VARYING": "RV"},
-            {"variable_attr1": "var_val1", "FILLVAL": -1e31, "DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "RV"},
-            {"variable_attr3": "var_val3", "FILLVAL": -9223372036854775808, "DATA_TYPE": "CDF_TIME_TT2000",
-             "RECORD_VARYING": "NRV"},
-            {"variable_attr5": "var_val5", "variable_attr6": "var_val6", "DATA_TYPE": "CDF_INT4",
-             "RECORD_VARYING": "NRV"},
+            {
+                "variable_attr1": "var_val1",
+                "DATA_TYPE": "CDF_REAL4",
+                "RECORD_VARYING": "rV",
+            },
+            {
+                "variable_attr1": "var_val1",
+                "FILLVAL": -9223372036854775808,
+                "DATA_TYPE": "CDF_INT8",
+                "RECORD_VARYING": "RV",
+            },
+            {
+                "variable_attr1": "var_val1",
+                "FILLVAL": -1e31,
+                "DATA_TYPE": "CDF_REAL4",
+                "RECORD_VARYING": "RV",
+            },
+            {
+                "variable_attr3": "var_val3",
+                "FILLVAL": -9223372036854775808,
+                "DATA_TYPE": "CDF_TIME_TT2000",
+                "RECORD_VARYING": "NRV",
+            },
+            {
+                "variable_attr5": "var_val5",
+                "variable_attr6": "var_val6",
+                "DATA_TYPE": "CDF_INT4",
+                "RECORD_VARYING": "NRV",
+            },
         ]
 
         expected_data_product = TestDataProduct()
@@ -102,43 +212,91 @@ def test_write_cdf_compresses_variables(self, mock_new_cdf):
 
         mock_new_cdf.return_value.__enter__.return_value.new.assert_has_calls(
             [
-                call('var_without_explicit_type', mock.ANY, recVary=True, type=pycdf.const.CDF_REAL4, dims=mock.ANY,
-                     compress=pycdf.const.GZIP_COMPRESSION, compress_param=7),
-                call('int_var', mock.ANY, recVary=True, type=pycdf.const.CDF_INT8, dims=mock.ANY,
-                     compress=pycdf.const.GZIP_COMPRESSION, compress_param=7),
-                call('float_var', mock.ANY, recVary=True, type=pycdf.const.CDF_REAL4, dims=mock.ANY,
-                     compress=pycdf.const.GZIP_COMPRESSION, compress_param=7),
-                call('time_var', mock.ANY, recVary=False, type=pycdf.const.CDF_TIME_TT2000, dims=mock.ANY,
-                     compress=None, compress_param=None),
-                call('non_record_varying', mock.ANY, recVary=False, type=pycdf.const.CDF_INT4, dims=mock.ANY,
-                     compress=None, compress_param=None),
+                call(
+                    "var_without_explicit_type",
+                    mock.ANY,
+                    recVary=True,
+                    type=pycdf.const.CDF_REAL4,
+                    dims=mock.ANY,
+                    compress=pycdf.const.GZIP_COMPRESSION,
+                    compress_param=7,
+                ),
+                call(
+                    "int_var",
+                    mock.ANY,
+                    recVary=True,
+                    type=pycdf.const.CDF_INT8,
+                    dims=mock.ANY,
+                    compress=pycdf.const.GZIP_COMPRESSION,
+                    compress_param=7,
+                ),
+                call(
+                    "float_var",
+                    mock.ANY,
+                    recVary=True,
+                    type=pycdf.const.CDF_REAL4,
+                    dims=mock.ANY,
+                    compress=pycdf.const.GZIP_COMPRESSION,
+                    compress_param=7,
+                ),
+                call(
+                    "time_var",
+                    mock.ANY,
+                    recVary=False,
+                    type=pycdf.const.CDF_TIME_TT2000,
+                    dims=mock.ANY,
+                    compress=None,
+                    compress_param=None,
+                ),
+                call(
+                    "non_record_varying",
+                    mock.ANY,
+                    recVary=False,
+                    type=pycdf.const.CDF_INT4,
+                    dims=mock.ANY,
+                    compress=None,
+                    compress_param=None,
+                ),
             ]
         )
 
-    def test_write_cdf_trims_numbers_in_logical_source_when_fetching_global_metadata(self):
+    def test_write_cdf_trims_numbers_in_logical_source_when_fetching_global_metadata(
+        self,
+    ):
         path = str(self.temp_directory / "write_cdf.cdf")
         data = TestDataProduct()
         attribute_manager = Mock(spec=ImapAttributeManager)
-        attribute_manager.get_global_attributes.side_effect = [KeyError("Logical Source Not Found"),
-                                                               {"global1": "global_val1", "global2": "global_val2"}]
-        attribute_manager.get_variable_attributes.return_value = {"DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "NRV"}
+        attribute_manager.get_global_attributes.side_effect = [
+            KeyError("Logical Source Not Found"),
+            {"global1": "global_val1", "global2": "global_val2"},
+        ]
+        attribute_manager.get_variable_attributes.return_value = {
+            "DATA_TYPE": "CDF_REAL4",
+            "RECORD_VARYING": "NRV",
+        }
         expected_data_product_logical_source = "imap_glows_l3_descriptor-10100"
         data.input_metadata.descriptor = "descriptor-10100"
 
         write_cdf(path, data, attribute_manager)
 
         attribute_manager.get_global_attributes.assert_has_calls(
-            [call(expected_data_product_logical_source), call("imap_glows_l3_descriptor-")])
+            [
+                call(expected_data_product_logical_source),
+                call("imap_glows_l3_descriptor-"),
+            ]
+        )
 
         with pycdf.CDF(path) as actual_cdf:
             self.assertTrue(actual_cdf.col_major())
-            self.assertEqual('global_val1', str(actual_cdf.attrs['global1']))
-            self.assertEqual('global_val2', str(actual_cdf.attrs['global2']))
+            self.assertEqual("global_val1", str(actual_cdf.attrs["global1"]))
+            self.assertEqual("global_val2", str(actual_cdf.attrs["global2"]))
 
     def test_write_cdf_replaces_nan_inf_and_overflowing_floats_with_fill_value(self):
         epoch_fillval = datetime.fromisoformat("9999-12-31T23:59:59.999999")
 
-        epoch_data = np.ma.masked_array([datetime(2025, 3, 7, 17, 0), None], mask=[False, True])
+        epoch_data = np.ma.masked_array(
+            [datetime(2025, 3, 7, 17, 0), None], mask=[False, True]
+        )
         expected_cdf_epoch_data = np.array([datetime(2025, 3, 7, 17, 0), epoch_fillval])
 
         float_var_input = np.array(
@@ -156,9 +314,12 @@ def __init__(self):
 
             def to_data_product_variables(self) -> list[DataProductVariable]:
                 return [
-                    DataProductVariable("epoch", epoch_data, pycdf.const.CDF_TIME_TT2000),
-                    DataProductVariable("float_var", float_var_input,
-                                        pycdf.const.CDF_REAL4),
+                    DataProductVariable(
+                        "epoch", epoch_data, pycdf.const.CDF_TIME_TT2000
+                    ),
+                    DataProductVariable(
+                        "float_var", float_var_input, pycdf.const.CDF_REAL4
+                    ),
                 ]
 
         path = str(self.temp_directory / "write_cdf.cdf")
@@ -166,9 +327,18 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         attribute_manager = Mock(spec=ImapAttributeManager)
         attribute_manager.get_global_attributes.return_value = {}
         attribute_manager.get_variable_attributes.side_effect = [
-            {"VAR_NAME": "epoch", "FILLVAL": epoch_fillval,
-             "DATA_TYPE": "CDF_TIME_TT2000", "RECORD_VARYING": "NRV"},
-            {"VAR_NAME": "float_var", "FILLVAL": -1e31, "DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "NRV"},
+            {
+                "VAR_NAME": "epoch",
+                "FILLVAL": epoch_fillval,
+                "DATA_TYPE": "CDF_TIME_TT2000",
+                "RECORD_VARYING": "NRV",
+            },
+            {
+                "VAR_NAME": "float_var",
+                "FILLVAL": -1e31,
+                "DATA_TYPE": "CDF_REAL4",
+                "RECORD_VARYING": "NRV",
+            },
         ]
 
         write_cdf(path, data, attribute_manager)
@@ -176,9 +346,12 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             actual_float_var = actual_cdf["float_var"][...]
             self.assertFalse(np.any(np.isnan(actual_float_var)))
             self.assertFalse(np.any(np.isinf(actual_float_var)))
-            np.testing.assert_array_equal(expected_float_var, actual_float_var, strict=True)
-            np.testing.assert_array_equal(actual_cdf["epoch"][...], expected_cdf_epoch_data, strict=True)
-
+            np.testing.assert_array_equal(
+                expected_float_var, actual_float_var, strict=True
+            )
+            np.testing.assert_array_equal(
+                actual_cdf["epoch"][...], expected_cdf_epoch_data, strict=True
+            )
 
     def test_can_write_empty_epoch_variable(self):
         epoch_data = []
@@ -190,7 +363,9 @@ def __init__(self):
 
             def to_data_product_variables(self) -> list[DataProductVariable]:
                 return [
-                    DataProductVariable("epoch", epoch_data, pycdf.const.CDF_TIME_TT2000),
+                    DataProductVariable(
+                        "epoch", epoch_data, pycdf.const.CDF_TIME_TT2000
+                    ),
                 ]
 
         path = str(self.temp_directory / "write_cdf.cdf")
@@ -198,33 +373,57 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
         attribute_manager = Mock(spec=ImapAttributeManager)
         attribute_manager.get_global_attributes.return_value = {}
         attribute_manager.get_variable_attributes.side_effect = [
-            {"VAR_NAME": "epoch", "FILLVAL": epoch_fillval,
-             "DATA_TYPE": "CDF_TIME_TT2000", "RECORD_VARYING": "RV"},
-            {"VAR_NAME": "float_var", "FILLVAL": -1e31, "DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "NRV"},
+            {
+                "VAR_NAME": "epoch",
+                "FILLVAL": epoch_fillval,
+                "DATA_TYPE": "CDF_TIME_TT2000",
+                "RECORD_VARYING": "RV",
+            },
+            {
+                "VAR_NAME": "float_var",
+                "FILLVAL": -1e31,
+                "DATA_TYPE": "CDF_REAL4",
+                "RECORD_VARYING": "NRV",
+            },
         ]
 
         write_cdf(path, data, attribute_manager)
         with pycdf.CDF(path) as actual_cdf:
-            np.testing.assert_array_equal(actual_cdf["epoch"][...], np.array([], dtype=object), strict=True)
-
-
+            np.testing.assert_array_equal(
+                actual_cdf["epoch"][...], np.array([], dtype=object), strict=True
+            )
 
     def test_can_write_empty_variables_of_various_shapes(self):
-        cases=[
-            (np.zeros((0,5), dtype=object), "CDF_TIME_TT2000", "RV", datetime.fromisoformat("9999-12-31T23:59:59.999999")),
-            (np.zeros((0), dtype=object), "CDF_TIME_TT2000", "RV", datetime.fromisoformat("9999-12-31T23:59:59.999999")),
-            (np.zeros((0,5), dtype=float), "CDF_REAL8", "RV", -1e31),
+        cases = [
+            (
+                np.zeros((0, 5), dtype=object),
+                "CDF_TIME_TT2000",
+                "RV",
+                datetime.fromisoformat("9999-12-31T23:59:59.999999"),
+            ),
+            (
+                np.zeros((0), dtype=object),
+                "CDF_TIME_TT2000",
+                "RV",
+                datetime.fromisoformat("9999-12-31T23:59:59.999999"),
+            ),
+            (np.zeros((0, 5), dtype=float), "CDF_REAL8", "RV", -1e31),
             (np.zeros((0), dtype=float), "CDF_REAL8", "RV", -1e31),
         ]
         for input_data, data_type, record_varying, fill_val in cases:
-            with self.subTest(input_data=input_data, data_type=data_type, rv=record_varying):
+            with self.subTest(
+                input_data=input_data, data_type=data_type, rv=record_varying
+            ):
+
                 class DataProductWithEmptyEpoch(DataProduct):
                     def __init__(self):
                         self.input_metadata = Mock()
 
                     def to_data_product_variables(self) -> list[DataProductVariable]:
                         return [
-                            DataProductVariable("var", input_data, pycdf.const.CDF_REAL8),
+                            DataProductVariable(
+                                "var", input_data, pycdf.const.CDF_REAL8
+                            ),
                         ]
 
                 path = self.temp_directory / "write_cdf.cdf"
@@ -232,22 +431,33 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
                 attribute_manager = Mock(spec=ImapAttributeManager)
                 attribute_manager.get_global_attributes.return_value = {}
                 attribute_manager.get_variable_attributes.side_effect = [
-                    {"VAR_NAME": "var", "FILLVAL": fill_val, "DATA_TYPE": data_type, "RECORD_VARYING": record_varying},
+                    {
+                        "VAR_NAME": "var",
+                        "FILLVAL": fill_val,
+                        "DATA_TYPE": data_type,
+                        "RECORD_VARYING": record_varying,
+                    },
                 ]
 
                 write_cdf(str(path), data, attribute_manager)
                 with pycdf.CDF(str(path)) as actual_cdf:
-                    np.testing.assert_array_equal(actual_cdf["var"][...], input_data, strict=True)
+                    np.testing.assert_array_equal(
+                        actual_cdf["var"][...], input_data, strict=True
+                    )
 
                 path.unlink()
 
-
     def test_does_not_write_variable_attribute_if_value_is_empty_string(self):
         path = str(self.temp_directory / "write_cdf.cdf")
         data = TestDataProduct()
-        _, _, regular_var, time_var, non_rec_varying_var = data.to_data_product_variables()
+        _, _, regular_var, time_var, non_rec_varying_var = (
+            data.to_data_product_variables()
+        )
         attribute_manager = Mock(spec=ImapAttributeManager)
-        attribute_manager.get_global_attributes.return_value = {"global1": "global_val1", "global2": "global_val2"}
+        attribute_manager.get_global_attributes.return_value = {
+            "global1": "global_val1",
+            "global2": "global_val2",
+        }
         attribute_manager.get_variable_attributes.side_effect = [
             {"DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "NRV"},
             {"DATA_TYPE": "CDF_REAL4", "RECORD_VARYING": "NRV"},
@@ -259,41 +469,55 @@ def test_does_not_write_variable_attribute_if_value_is_empty_string(self):
         write_cdf(path, data, attribute_manager)
 
         with pycdf.CDF(path) as actual_cdf:
-            np.testing.assert_array_equal(regular_var.value, actual_cdf[regular_var.name][...])
-            self.assertEqual('epoch', actual_cdf[regular_var.name].attrs['DEPEND_0'])
-            self.assertFalse('CATDESC' in actual_cdf[time_var.name].attrs)
-            self.assertFalse('DEPEND_0' in actual_cdf[non_rec_varying_var.name].attrs)
+            np.testing.assert_array_equal(
+                regular_var.value, actual_cdf[regular_var.name][...]
+            )
+            self.assertEqual("epoch", actual_cdf[regular_var.name].attrs["DEPEND_0"])
+            self.assertFalse("CATDESC" in actual_cdf[time_var.name].attrs)
+            self.assertFalse("DEPEND_0" in actual_cdf[non_rec_varying_var.name].attrs)
 
     def test_read_variable_and_mask_fill_values(self):
         path = str(self.temp_directory / "cdf.cdf")
         with pycdf.CDF(path, create=True) as actual_cdf:
             datetime_fill = datetime(9999, 12, 31, 23, 59, 59, 999999)
-            actual_cdf['date_var'] = [datetime(2025, 4, 16), datetime_fill]
-            actual_cdf['date_var'].attrs['FILLVAL'] = datetime_fill
+            actual_cdf["date_var"] = [datetime(2025, 4, 16), datetime_fill]
+            actual_cdf["date_var"].attrs["FILLVAL"] = datetime_fill
 
             int_fillval = -9223372036854775808
-            actual_cdf['int_var'] = np.array([-1, 2, int_fillval, int_fillval + 1], dtype=np.int64)
-            actual_cdf['int_var'].attrs['FILLVAL'] = int_fillval
-
-            masked_datetime_data: np.ma.masked_array = read_variable_and_mask_fill_values(actual_cdf['date_var'])
-            masked_int_data: np.ma.masked_array = read_variable_and_mask_fill_values(actual_cdf['int_var'])
-        np.testing.assert_array_equal(masked_datetime_data.data,
-                                      [datetime(2025, 4, 16), datetime_fill])
+            actual_cdf["int_var"] = np.array(
+                [-1, 2, int_fillval, int_fillval + 1], dtype=np.int64
+            )
+            actual_cdf["int_var"].attrs["FILLVAL"] = int_fillval
+
+            masked_datetime_data: np.ma.masked_array = (
+                read_variable_and_mask_fill_values(actual_cdf["date_var"])
+            )
+            masked_int_data: np.ma.masked_array = read_variable_and_mask_fill_values(
+                actual_cdf["int_var"]
+            )
+        np.testing.assert_array_equal(
+            masked_datetime_data.data, [datetime(2025, 4, 16), datetime_fill]
+        )
         np.testing.assert_equal(masked_datetime_data.mask, [False, True])
 
-        np.testing.assert_equal(masked_int_data.data, np.array([-1, 2, int_fillval, int_fillval + 1], dtype=np.int64))
-        np.testing.assert_equal(masked_int_data.mask, np.array([False, False, True, False]))
+        np.testing.assert_equal(
+            masked_int_data.data,
+            np.array([-1, 2, int_fillval, int_fillval + 1], dtype=np.int64),
+        )
+        np.testing.assert_equal(
+            masked_int_data.mask, np.array([False, False, True, False])
+        )
 
     def test_read_variable_replaces_fill_values_with_nan(self):
         path = str(self.temp_directory / "cdf.cdf")
         with pycdf.CDF(path, create=True) as actual_cdf:
-            actual_cdf['var'] = np.array([1, 2, -1e31, 4, 5], dtype=np.float64)
-            actual_cdf['var'].attrs['FILLVAL'] = -1e31
-            actual_cdf['int_var'] = np.array([1, 2, 3, 4, 5], dtype=np.int32)
-            actual_cdf['int_var'].attrs['FILLVAL'] = 5
+            actual_cdf["var"] = np.array([1, 2, -1e31, 4, 5], dtype=np.float64)
+            actual_cdf["var"].attrs["FILLVAL"] = -1e31
+            actual_cdf["int_var"] = np.array([1, 2, 3, 4, 5], dtype=np.int32)
+            actual_cdf["int_var"].attrs["FILLVAL"] = 5
 
-            data = read_numeric_variable(actual_cdf['var'])
-            int_data = read_numeric_variable(actual_cdf['int_var'])
+            data = read_numeric_variable(actual_cdf["var"])
+            int_data = read_numeric_variable(actual_cdf["int_var"])
 
         np.testing.assert_equal(data, np.array([1, 2, np.nan, 4, 5]))
         np.testing.assert_equal(int_data, np.array([1, 2, 3, 4, np.nan]))
@@ -301,9 +525,14 @@ def test_read_variable_replaces_fill_values_with_nan(self):
     def test_write_map_cdf(self):
         class TestMapDataProduct(DataProduct):
             def __init__(self):
-                self.input_metadata = InputMetadata("hi", "l3", datetime(year=2025, month=1, day=1),
-                                                    datetime(year=2025, month=5, day=31), "v001",
-                                                    "h45-ena-h-sf-sp-ram-hae-6deg-6mo")
+                self.input_metadata = InputMetadata(
+                    "hi",
+                    "l3",
+                    datetime(year=2025, month=1, day=1),
+                    datetime(year=2025, month=5, day=31),
+                    "v001",
+                    "h45-ena-h-sf-sp-ram-hae-6deg-6mo",
+                )
                 self.parent_file_names = []
 
             def to_data_product_variables(self) -> list[DataProductVariable]:
@@ -311,35 +540,60 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
                     DataProductVariable("ena_intensity", np.arange(0, 10)),
                     DataProductVariable("ena_intensity_stat_uncert", np.arange(0, 10)),
                 ]
+
         path = str(self.temp_directory / "write_cdf.cdf")
         data = TestMapDataProduct()
         attribute_manager = Mock(spec=ImapAttributeManager)
-        attribute_manager.get_global_attributes.return_value = {"global1": "global_val1", "global2": "global_val2"}
-
-        variable_metadata = {"CATDESC": "This should be ignored", "DATA_TYPE": "CDF_REAL4", "FILLVAL": -1e31, "RECORD_VARYING": "rV"}
-        attribute_manager.get_variable_attributes.side_effect = lambda _: variable_metadata.copy()
+        attribute_manager.get_global_attributes.return_value = {
+            "global1": "global_val1",
+            "global2": "global_val2",
+        }
+
+        variable_metadata = {
+            "CATDESC": "This should be ignored",
+            "DATA_TYPE": "CDF_REAL4",
+            "FILLVAL": -1e31,
+            "RECORD_VARYING": "rV",
+        }
+        attribute_manager.get_variable_attributes.side_effect = lambda _: (
+            variable_metadata.copy()
+        )
 
         write_cdf(path, data, attribute_manager)
 
-        expected_data_product_logical_source = "imap_hi_l3_h45-ena-h-sf-sp-ram-hae-6deg-6mo"
-        attribute_manager.get_global_attributes.assert_called_once_with(expected_data_product_logical_source)
+        expected_data_product_logical_source = (
+            "imap_hi_l3_h45-ena-h-sf-sp-ram-hae-6deg-6mo"
+        )
+        attribute_manager.get_global_attributes.assert_called_once_with(
+            expected_data_product_logical_source
+        )
         attribute_manager.get_variable_attributes.assert_has_calls(
             [call(var.name) for var in data.to_data_product_variables()]
         )
 
         with pycdf.CDF(path) as actual_cdf:
-            self.assertEqual("IMAP Hi45 H Inten, HAE SC Frame, Surv Corr, Ram, 6 deg, 6 Mon",
-                             actual_cdf['ena_intensity'].attrs['CATDESC'])
-            self.assertEqual("IMAP Hi45 H Inten Stat. Unc., HAE SC Frame, Surv Corr, Ram, 6 deg, 6 Mon",
-                             actual_cdf['ena_intensity_stat_uncert'].attrs['CATDESC'])
+            self.assertEqual(
+                "IMAP Hi45 H Inten, HAE SC Frame, Surv Corr, Ram, 6 deg, 6 Mon",
+                actual_cdf["ena_intensity"].attrs["CATDESC"],
+            )
+            self.assertEqual(
+                "IMAP Hi45 H Inten Stat. Unc., HAE SC Frame, Surv Corr, Ram, 6 deg, 6 Mon",
+                actual_cdf["ena_intensity_stat_uncert"].attrs["CATDESC"],
+            )
 
 
 class TestDataProduct(DataProduct):
     def __init__(self):
         super().__init__(
-            input_metadata=InputMetadata("glows", "l3", datetime(year=2025, month=5, day=10),
-                                            datetime(year=2025, month=5, day=12), VersionMap({'descriptor': Version(None, 3)}), "descriptor"),
-            parent_file_names=[]
+            input_metadata=InputMetadata(
+                "glows",
+                "l3",
+                datetime(year=2025, month=5, day=10),
+                datetime(year=2025, month=5, day=12),
+                VersionMap({"descriptor": Version(None, 3)}),
+                "descriptor",
+            ),
+            parent_file_names=[],
         )
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
@@ -348,5 +602,5 @@ def to_data_product_variables(self) -> list[DataProductVariable]:
             DataProductVariable("int_var", np.arange(0, 10)),
             DataProductVariable("float_var", np.arange(0, 10)),
             DataProductVariable("time_var", np.arange(10, 20)),
-            DataProductVariable("non_record_varying", 100)
+            DataProductVariable("non_record_varying", 100),
         ]
diff --git a/tests/cdf/test_imap_attribute_manager.py b/tests/cdf/test_imap_attribute_manager.py
index f43ad1025..9f0dc62da 100644
--- a/tests/cdf/test_imap_attribute_manager.py
+++ b/tests/cdf/test_imap_attribute_manager.py
@@ -9,93 +9,178 @@
 
 class TestImapCdfManager(TestCase):
     def setUp(self):
-        self.config_folder_path = Path(imap_l3_processing.__file__).parent.resolve() / 'cdf/config'
+        self.config_folder_path = (
+            Path(imap_l3_processing.__file__).parent.resolve() / "cdf/config"
+        )
         self.base_manager = CdfAttributeManager(
-            variable_schema_layers=[self.config_folder_path / 'imap_l3_variable_cdf_attrs_schema.yaml',
-                                    self.config_folder_path / 'default_variable_cdf_attrs_schema.yaml'],
-            global_schema_layers=[self.config_folder_path / 'default_global_cdf_attrs_schema.yaml'],
-            use_defaults=True)
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_default_global_cdf_attrs.yaml')
+            variable_schema_layers=[
+                self.config_folder_path / "imap_l3_variable_cdf_attrs_schema.yaml",
+                self.config_folder_path / "default_variable_cdf_attrs_schema.yaml",
+            ],
+            global_schema_layers=[
+                self.config_folder_path / "default_global_cdf_attrs_schema.yaml"
+            ],
+            use_defaults=True,
+        )
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_default_global_cdf_attrs.yaml"
+        )
 
     def test_constructor(self):
         manager = ImapAttributeManager()
         self.assertIsInstance(manager, CdfAttributeManager)
 
-        self.assertEqual(self.base_manager.get_global_attributes(), manager.get_global_attributes())
+        self.assertEqual(
+            self.base_manager.get_global_attributes(), manager.get_global_attributes()
+        )
 
     def test_load_instrument_and_variable_attributes_with_level(self):
         manager = ImapAttributeManager()
-        manager.add_instrument_attrs('swapi', 'l3a', "descriptor")
-        manager.add_global_attribute('ground_software_version', 'test version')
-
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_swapi_global_cdf_attrs.yaml')
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_swapi_l3a_global_cdf_attrs.yaml')
-        self.base_manager.load_variable_attributes(self.config_folder_path / 'imap_swapi_l3a_variable_attrs.yaml')
-        self.base_manager.add_global_attribute('ground_software_version', 'test version')
-
-        self.assertEqual(self.base_manager.get_global_attributes(), manager.get_global_attributes())
-        self.assertEqual(self.base_manager.get_variable_attributes('epoch'), manager.get_variable_attributes('epoch'))
-        self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_speed_uncert'),
-                         manager.get_variable_attributes('proton_sw_speed_uncert'))
-        self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_speed'),
-                         manager.get_variable_attributes('proton_sw_speed'))
-
-        self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_speed'),
-                         manager.get_variable_attributes('alpha_sw_speed'))
-        self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_speed_uncert'),
-                         manager.get_variable_attributes('alpha_sw_speed_uncert'))
-
-        self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_density'),
-                         manager.get_variable_attributes('alpha_sw_density'))
-        self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_density_uncert'),
-                         manager.get_variable_attributes('alpha_sw_density_uncert'))
-
-        self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_temperature'),
-                         manager.get_variable_attributes('alpha_sw_temperature'))
-        self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_temperature_uncert'),
-                         manager.get_variable_attributes('alpha_sw_temperature_uncert'))
+        manager.add_instrument_attrs("swapi", "l3a", "descriptor")
+        manager.add_global_attribute("ground_software_version", "test version")
+
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_swapi_global_cdf_attrs.yaml"
+        )
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_swapi_l3a_global_cdf_attrs.yaml"
+        )
+        self.base_manager.load_variable_attributes(
+            self.config_folder_path / "imap_swapi_l3a_variable_attrs.yaml"
+        )
+        self.base_manager.add_global_attribute(
+            "ground_software_version", "test version"
+        )
+
+        self.assertEqual(
+            self.base_manager.get_global_attributes(), manager.get_global_attributes()
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("epoch"),
+            manager.get_variable_attributes("epoch"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("proton_sw_speed_uncert"),
+            manager.get_variable_attributes("proton_sw_speed_uncert"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("proton_sw_speed"),
+            manager.get_variable_attributes("proton_sw_speed"),
+        )
+
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("alpha_sw_speed"),
+            manager.get_variable_attributes("alpha_sw_speed"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("alpha_sw_speed_uncert"),
+            manager.get_variable_attributes("alpha_sw_speed_uncert"),
+        )
+
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("alpha_sw_density"),
+            manager.get_variable_attributes("alpha_sw_density"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("alpha_sw_density_uncert"),
+            manager.get_variable_attributes("alpha_sw_density_uncert"),
+        )
+
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("alpha_sw_temperature"),
+            manager.get_variable_attributes("alpha_sw_temperature"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("alpha_sw_temperature_uncert"),
+            manager.get_variable_attributes("alpha_sw_temperature_uncert"),
+        )
 
     def test_l3b_metadata_configuration(self):
         manager = ImapAttributeManager()
-        manager.add_instrument_attrs('swapi', 'l3b', "descriptor")
-
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_swapi_global_cdf_attrs.yaml')
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_swapi_l3b_global_cdf_attrs.yaml')
-        self.base_manager.load_variable_attributes(self.config_folder_path / 'imap_swapi_l3b_variable_attrs.yaml')
-        self.assertEqual(self.base_manager.get_global_attributes(), manager.get_global_attributes())
-        self.assertEqual(self.base_manager.get_variable_attributes('epoch'), manager.get_variable_attributes('epoch'))
-
-        self.assertEqual(self.base_manager.get_variable_attributes('combined_energy'),
-                         manager.get_variable_attributes('combined_energy'))
-        self.assertEqual(self.base_manager.get_variable_attributes('combined_energy_stat_uncert_minus'),
-                         manager.get_variable_attributes('combined_energy_stat_uncert_minus'))
-        self.assertEqual(self.base_manager.get_variable_attributes('combined_energy_stat_uncert_plus'),
-                         manager.get_variable_attributes('combined_energy_stat_uncert_plus'))
-
-        self.assertEqual(self.base_manager.get_variable_attributes('combined_differential_flux'),
-                         manager.get_variable_attributes('combined_differential_flux'))
-        self.assertEqual(self.base_manager.get_variable_attributes('combined_differential_flux_stat_uncert'),
-                         manager.get_variable_attributes('combined_differential_flux_stat_uncert'))
+        manager.add_instrument_attrs("swapi", "l3b", "descriptor")
+
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_swapi_global_cdf_attrs.yaml"
+        )
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_swapi_l3b_global_cdf_attrs.yaml"
+        )
+        self.base_manager.load_variable_attributes(
+            self.config_folder_path / "imap_swapi_l3b_variable_attrs.yaml"
+        )
+        self.assertEqual(
+            self.base_manager.get_global_attributes(), manager.get_global_attributes()
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("epoch"),
+            manager.get_variable_attributes("epoch"),
+        )
+
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("combined_energy"),
+            manager.get_variable_attributes("combined_energy"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes(
+                "combined_energy_stat_uncert_minus"
+            ),
+            manager.get_variable_attributes("combined_energy_stat_uncert_minus"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes(
+                "combined_energy_stat_uncert_plus"
+            ),
+            manager.get_variable_attributes("combined_energy_stat_uncert_plus"),
+        )
+
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("combined_differential_flux"),
+            manager.get_variable_attributes("combined_differential_flux"),
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes(
+                "combined_differential_flux_stat_uncert"
+            ),
+            manager.get_variable_attributes("combined_differential_flux_stat_uncert"),
+        )
 
     def test_load_instrument_and_variable_attributes_with_level_and_descriptor(self):
         manager = ImapAttributeManager()
-        manager.add_instrument_attrs('hit', 'l3', 'macropixel')
+        manager.add_instrument_attrs("hit", "l3", "macropixel")
 
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_hit_global_cdf_attrs.yaml')
         self.base_manager.load_global_attributes(
-            self.config_folder_path / 'imap_hit_l3_macropixel_global_cdf_attrs.yaml')
+            self.config_folder_path / "imap_hit_global_cdf_attrs.yaml"
+        )
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_hit_l3_macropixel_global_cdf_attrs.yaml"
+        )
         self.base_manager.load_variable_attributes(
-            self.config_folder_path / 'imap_hit_l3_macropixel_variable_attrs.yaml')
-
-        self.assertEqual(self.base_manager.get_global_attributes(), manager.get_global_attributes())
-        self.assertEqual(self.base_manager.get_variable_attributes('epoch'), manager.get_variable_attributes('epoch'))
-
-    def test_gets_global_attrs_from_global_file_when_descriptor_specific_variable_file_exists_and_global_does_not(self):
+            self.config_folder_path / "imap_hit_l3_macropixel_variable_attrs.yaml"
+        )
+
+        self.assertEqual(
+            self.base_manager.get_global_attributes(), manager.get_global_attributes()
+        )
+        self.assertEqual(
+            self.base_manager.get_variable_attributes("epoch"),
+            manager.get_variable_attributes("epoch"),
+        )
+
+    def test_gets_global_attrs_from_global_file_when_descriptor_specific_variable_file_exists_and_global_does_not(
+        self,
+    ):
         manager = ImapAttributeManager()
-        manager.add_instrument_attrs('hi', 'l3', '45sensor-spacecraft-survival-full-4deg-map')
+        manager.add_instrument_attrs(
+            "hi", "l3", "45sensor-spacecraft-survival-full-4deg-map"
+        )
 
-        self.base_manager.load_global_attributes(self.config_folder_path / 'imap_hi_l3_global_cdf_attrs.yaml')
         self.base_manager.load_global_attributes(
-            self.config_folder_path / 'imap_hi_global_cdf_attrs.yaml')
+            self.config_folder_path / "imap_hi_l3_global_cdf_attrs.yaml"
+        )
+        self.base_manager.load_global_attributes(
+            self.config_folder_path / "imap_hi_global_cdf_attrs.yaml"
+        )
 
-        self.assertEqual(self.base_manager.get_global_attributes(), manager.get_global_attributes())
+        self.assertEqual(
+            self.base_manager.get_global_attributes(), manager.get_global_attributes()
+        )
diff --git a/tests/cdf/test_yaml_var_metadata.py b/tests/cdf/test_yaml_var_metadata.py
index 3ab501f75..ea8e7833a 100644
--- a/tests/cdf/test_yaml_var_metadata.py
+++ b/tests/cdf/test_yaml_var_metadata.py
@@ -266,10 +266,7 @@ def test_all_record_varying_data_depends_on_epoch(self):
     def _epoch_meets_schema(self, yaml_data: dict):
         epoch_delta_types = ["epoch_delta", "epoch_delta_plus", "epoch_delta_minus"]
         if any(
-            [
-                epoch_delta_type in yaml_data.keys()
-                for epoch_delta_type in epoch_delta_types
-            ]
+            [epoch_delta_type in yaml_data for epoch_delta_type in epoch_delta_types]
         ):
             self.assertEqual(20, len(yaml_data["epoch"].keys()))
         else:
diff --git a/tests/codice/l3/hi/direct_event/test_codice_hi_mass_correction_lookup_table.py b/tests/codice/l3/hi/direct_event/test_codice_hi_mass_correction_lookup_table.py
index 968d09989..8d69b47ec 100644
--- a/tests/codice/l3/hi/direct_event/test_codice_hi_mass_correction_lookup_table.py
+++ b/tests/codice/l3/hi/direct_event/test_codice_hi_mass_correction_lookup_table.py
@@ -1,29 +1,57 @@
 import unittest
 
-import numpy as np
-
-from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_mass_correction_lookup_table import \
-    CodiceHiMassCorrectionLookupTable
+from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_mass_correction_lookup_table import (
+    CodiceHiMassCorrectionLookupTable,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestCodiceHiMassCorrectionLookupTable(unittest.TestCase):
     def test_from_file(self):
-        file_path = get_test_data_path("codice/imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx")
+        file_path = get_test_data_path(
+            "codice/imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx"
+        )
         mass_correction_lookup = CodiceHiMassCorrectionLookupTable.from_file(file_path)
 
         self.assertIsInstance(mass_correction_lookup, CodiceHiMassCorrectionLookupTable)
 
     def test_lookup_correction_factor(self):
-        file_path = get_test_data_path("codice/imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx")
+        file_path = get_test_data_path(
+            "codice/imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx"
+        )
         mass_correction_lookup = CodiceHiMassCorrectionLookupTable.from_file(file_path)
 
-        self.assertEqual(0.753777, mass_correction_lookup.lookup_correction_factor(ssd_id=7, energy_channel=40, tof=6))
-
-        self.assertEqual(7.47142, mass_correction_lookup.lookup_correction_factor(ssd_id=3, energy_channel=24, tof=0))
-        self.assertEqual(1, mass_correction_lookup.lookup_correction_factor(ssd_id=3, energy_channel=24, tof=1))
-        self.assertEqual(12.8534, mass_correction_lookup.lookup_correction_factor(ssd_id=3, energy_channel=24, tof=6))
+        self.assertEqual(
+            0.753777,
+            mass_correction_lookup.lookup_correction_factor(
+                ssd_id=7, energy_channel=40, tof=6
+            ),
+        )
+
+        self.assertEqual(
+            7.47142,
+            mass_correction_lookup.lookup_correction_factor(
+                ssd_id=3, energy_channel=24, tof=0
+            ),
+        )
+        self.assertEqual(
+            1,
+            mass_correction_lookup.lookup_correction_factor(
+                ssd_id=3, energy_channel=24, tof=1
+            ),
+        )
+        self.assertEqual(
+            12.8534,
+            mass_correction_lookup.lookup_correction_factor(
+                ssd_id=3, energy_channel=24, tof=6
+            ),
+        )
 
         missing_ssds = [2, 6, 8, 9, 10, 12, 13, 14, 15]
         for ssd_id in missing_ssds:
-            self.assertEqual(1.0, mass_correction_lookup.lookup_correction_factor(ssd_id, energy_channel=24, tof=0))
+            self.assertEqual(
+                1.0,
+                mass_correction_lookup.lookup_correction_factor(
+                    ssd_id, energy_channel=24, tof=0
+                ),
+            )
diff --git a/tests/codice/l3/hi/direct_event/test_codice_l3a_direct_events_dependencies.py b/tests/codice/l3/hi/direct_event/test_codice_l3a_direct_events_dependencies.py
index e8415ccfe..42d22d287 100644
--- a/tests/codice/l3/hi/direct_event/test_codice_l3a_direct_events_dependencies.py
+++ b/tests/codice/l3/hi/direct_event/test_codice_l3a_direct_events_dependencies.py
@@ -3,55 +3,103 @@
 from unittest.mock import call, patch
 
 import imap_data_access
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
-from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import \
-    CodiceHiL3aDirectEventsDependencies
+from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import (
+    CodiceHiL3aDirectEventsDependencies,
+)
 
 
 class TestCodiceHiL3aDirectEventDependencies(unittest.TestCase):
-
-    @patch("imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies.download")
     @patch(
-        "imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies.CodiceHiL3aDirectEventsDependencies.from_file_paths")
+        "imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies.download"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies.CodiceHiL3aDirectEventsDependencies.from_file_paths"
+    )
     def test_fetch_dependencies(self, mock_from_file_paths, mock_download):
         input_collection = ProcessingInputCollection()
 
-        expected_codice_l2_direct_event_science_file_download_path = "imap/codice/l2/2010/01/imap_codice_l2_hi-direct-events_20100105_v010.cdf"
-        codice_l2_direct_event_input_file_name = "imap_codice_l2_hi-direct-events_20100105_v010.cdf"
+        expected_codice_l2_direct_event_science_file_download_path = (
+            "imap/codice/l2/2010/01/imap_codice_l2_hi-direct-events_20100105_v010.cdf"
+        )
+        codice_l2_direct_event_input_file_name = (
+            "imap_codice_l2_hi-direct-events_20100105_v010.cdf"
+        )
 
-        expected_codice_l1a_direct_event_science_file_download_path = "imap/codice/l1a/2010/10/imap_codice_l1a_hi-direct-events_20101005_v010.cdf"
-        codice_l1a_direct_event_input_file_name = "imap_codice_l1a_hi-direct-events_20101005_v010.cdf"
+        expected_codice_l1a_direct_event_science_file_download_path = (
+            "imap/codice/l1a/2010/10/imap_codice_l1a_hi-direct-events_20101005_v010.cdf"
+        )
+        codice_l1a_direct_event_input_file_name = (
+            "imap_codice_l1a_hi-direct-events_20101005_v010.cdf"
+        )
 
-        expected_codice_hi_mass_correction_lut_ancillary_file_download_path = "codice/imap_codice_l3-hi-mass-correction-lut_20100105_v010.xlsx"
-        codice_hi_mass_correction_lut_input_file_name = "imap_codice_l3-hi-mass-correction-lut_20100105_v010.xlsx"
+        expected_codice_hi_mass_correction_lut_ancillary_file_download_path = (
+            "codice/imap_codice_l3-hi-mass-correction-lut_20100105_v010.xlsx"
+        )
+        codice_hi_mass_correction_lut_input_file_name = (
+            "imap_codice_l3-hi-mass-correction-lut_20100105_v010.xlsx"
+        )
 
-        codice_l2_direct_event_science_input = ScienceInput(codice_l2_direct_event_input_file_name)
-        codice_l1a_direct_event_science_input = ScienceInput(codice_l1a_direct_event_input_file_name)
-        codice_hi_mass_correction_lut_ancillary_input = AncillaryInput(codice_hi_mass_correction_lut_input_file_name)
+        codice_l2_direct_event_science_input = ScienceInput(
+            codice_l2_direct_event_input_file_name
+        )
+        codice_l1a_direct_event_science_input = ScienceInput(
+            codice_l1a_direct_event_input_file_name
+        )
+        codice_hi_mass_correction_lut_ancillary_input = AncillaryInput(
+            codice_hi_mass_correction_lut_input_file_name
+        )
 
         non_codice_ancillary_input = AncillaryInput(
-            "imap/ancillary/hit/imap_codice_range-2A-cosine-lookup_20250203_v001.csv")
-        non_codice_science_input = ScienceInput("imap/hit/l3/2010/01/imap_hit_l3_hi-direct-events_20100105_v010.cdf")
-        non_l2_codice_science_input = ScienceInput("imap_codice_l3_hi-direct-events_20100105_v010.cdf")
+            "imap/ancillary/hit/imap_codice_range-2A-cosine-lookup_20250203_v001.csv"
+        )
+        non_codice_science_input = ScienceInput(
+            "imap/hit/l3/2010/01/imap_hit_l3_hi-direct-events_20100105_v010.cdf"
+        )
+        non_l2_codice_science_input = ScienceInput(
+            "imap_codice_l3_hi-direct-events_20100105_v010.cdf"
+        )
 
-        input_collection.add([codice_l1a_direct_event_science_input, codice_l2_direct_event_science_input, codice_hi_mass_correction_lut_ancillary_input, non_codice_ancillary_input, non_codice_science_input,
-                              non_l2_codice_science_input])
+        input_collection.add(
+            [
+                codice_l1a_direct_event_science_input,
+                codice_l2_direct_event_science_input,
+                codice_hi_mass_correction_lut_ancillary_input,
+                non_codice_ancillary_input,
+                non_codice_science_input,
+                non_l2_codice_science_input,
+            ]
+        )
 
-        codice_l3_dependencies = CodiceHiL3aDirectEventsDependencies.fetch_dependencies(input_collection)
+        codice_l3_dependencies = CodiceHiL3aDirectEventsDependencies.fetch_dependencies(
+            input_collection
+        )
 
         data_dir = imap_data_access.config["DATA_DIR"]
-        expected_download_codice_l2_direct_event_science_path = data_dir / expected_codice_l2_direct_event_science_file_download_path
-        expected_download_codice_l1a_direct_event_science_path = data_dir / expected_codice_l1a_direct_event_science_file_download_path
-        expected_download_codice_hi_mass_correction_lut_ancillary_path = data_dir / "imap/ancillary" / expected_codice_hi_mass_correction_lut_ancillary_file_download_path
-
-
-        mock_download.assert_has_calls([
-            call(expected_download_codice_l2_direct_event_science_path),
-            call(expected_download_codice_l1a_direct_event_science_path),
-            call(expected_download_codice_hi_mass_correction_lut_ancillary_path),
-        ])
+        expected_download_codice_l2_direct_event_science_path = (
+            data_dir / expected_codice_l2_direct_event_science_file_download_path
+        )
+        expected_download_codice_l1a_direct_event_science_path = (
+            data_dir / expected_codice_l1a_direct_event_science_file_download_path
+        )
+        expected_download_codice_hi_mass_correction_lut_ancillary_path = (
+            data_dir
+            / "imap/ancillary"
+            / expected_codice_hi_mass_correction_lut_ancillary_file_download_path
+        )
 
+        mock_download.assert_has_calls(
+            [
+                call(expected_download_codice_l2_direct_event_science_path),
+                call(expected_download_codice_l1a_direct_event_science_path),
+                call(expected_download_codice_hi_mass_correction_lut_ancillary_path),
+            ]
+        )
 
         mock_from_file_paths.assert_called_with(
             expected_download_codice_l2_direct_event_science_path,
@@ -61,20 +109,47 @@ def test_fetch_dependencies(self, mock_from_file_paths, mock_download):
 
         self.assertEqual(mock_from_file_paths.return_value, codice_l3_dependencies)
 
-    @patch("imap_l3_processing.codice.l3.hi.models.CodiceL2HiDirectEventData.read_from_cdf")
-    @patch("imap_l3_processing.codice.l3.hi.models.CodiceL1aHiDirectEvents.read_from_cdf")
-    @patch("imap_l3_processing.codice.l3.hi.direct_event.codice_hi_mass_correction_lookup_table.CodiceHiMassCorrectionLookupTable.from_file")
-    def test_can_load_from_files(self, mock_from_file_mass_correction_lut_ancillary, mock_l1a_read_from_cdf, mock_l2_read_from_cdf):
+    @patch(
+        "imap_l3_processing.codice.l3.hi.models.CodiceL2HiDirectEventData.read_from_cdf"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.models.CodiceL1aHiDirectEvents.read_from_cdf"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.direct_event.codice_hi_mass_correction_lookup_table.CodiceHiMassCorrectionLookupTable.from_file"
+    )
+    def test_can_load_from_files(
+        self,
+        mock_from_file_mass_correction_lut_ancillary,
+        mock_l1a_read_from_cdf,
+        mock_l2_read_from_cdf,
+    ):
         codice_l2_cdf_file = Path("CodiceL2CDF")
         codice_l1a_cdf_file = Path("CodiceL1aCDF")
-        codice_mass_correction_lut_ancillary_file = Path("CodiceMassCorrectionLutAncillary")
+        codice_mass_correction_lut_ancillary_file = Path(
+            "CodiceMassCorrectionLutAncillary"
+        )
 
-        codice_l3_dependencies = CodiceHiL3aDirectEventsDependencies.from_file_paths(codice_l2_cdf_file, codice_l1a_cdf_file, codice_mass_correction_lut_ancillary_file)
+        codice_l3_dependencies = CodiceHiL3aDirectEventsDependencies.from_file_paths(
+            codice_l2_cdf_file,
+            codice_l1a_cdf_file,
+            codice_mass_correction_lut_ancillary_file,
+        )
 
         mock_l2_read_from_cdf.assert_called_with(codice_l2_cdf_file)
         mock_l1a_read_from_cdf.assert_called_with(codice_l1a_cdf_file)
-        mock_from_file_mass_correction_lut_ancillary.assert_called_with(codice_mass_correction_lut_ancillary_file)
+        mock_from_file_mass_correction_lut_ancillary.assert_called_with(
+            codice_mass_correction_lut_ancillary_file
+        )
 
-        self.assertEqual(mock_l2_read_from_cdf.return_value, codice_l3_dependencies.codice_l2_hi_data)
-        self.assertEqual(mock_l1a_read_from_cdf.return_value, codice_l3_dependencies.codice_l1a_hi_data)
-        self.assertEqual(mock_from_file_mass_correction_lut_ancillary.return_value, codice_l3_dependencies.codice_hi_mass_correction_lut_ancillary)
+        self.assertEqual(
+            mock_l2_read_from_cdf.return_value, codice_l3_dependencies.codice_l2_hi_data
+        )
+        self.assertEqual(
+            mock_l1a_read_from_cdf.return_value,
+            codice_l3_dependencies.codice_l1a_hi_data,
+        )
+        self.assertEqual(
+            mock_from_file_mass_correction_lut_ancillary.return_value,
+            codice_l3_dependencies.codice_hi_mass_correction_lut_ancillary,
+        )
diff --git a/tests/codice/l3/hi/pitch_angle/test_codice_pitch_angle_dependencies.py b/tests/codice/l3/hi/pitch_angle/test_codice_pitch_angle_dependencies.py
index 626cc1a90..97de13e93 100644
--- a/tests/codice/l3/hi/pitch_angle/test_codice_pitch_angle_dependencies.py
+++ b/tests/codice/l3/hi/pitch_angle/test_codice_pitch_angle_dependencies.py
@@ -1,52 +1,82 @@
 import unittest
-from unittest.mock import patch, call, sentinel
+from unittest.mock import call, patch, sentinel
 
 import imap_data_access
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import CodicePitchAngleDependencies
+from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import (
+    CodicePitchAngleDependencies,
+)
 
 
 class TestCodicePitchAngleDependencies(unittest.TestCase):
-
     @patch(
-        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.CodicePitchAngleDependencies.from_file_paths")
-    @patch("imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.imap_data_access.download")
+        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.CodicePitchAngleDependencies.from_file_paths"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.imap_data_access.download"
+    )
     def test_fetch_dependencies(self, mock_download, mock_from_files):
-        expected_codice_science_file_download_path = "imap/codice/l2/2010/01/imap_codice_l2_hi-sectored_20100105_v010.cdf"
-        expected_mag_download_path = "imap/mag/l1d/2010/01/imap_mag_l1d_norm-dsrf_20100105_v010.cdf"
+        expected_codice_science_file_download_path = (
+            "imap/codice/l2/2010/01/imap_codice_l2_hi-sectored_20100105_v010.cdf"
+        )
+        expected_mag_download_path = (
+            "imap/mag/l1d/2010/01/imap_mag_l1d_norm-dsrf_20100105_v010.cdf"
+        )
 
-        science_input_codice_l2 = ScienceInput("imap_codice_l2_hi-sectored_20100105_v010.cdf")
+        science_input_codice_l2 = ScienceInput(
+            "imap_codice_l2_hi-sectored_20100105_v010.cdf"
+        )
         science_input_mag_l1d = ScienceInput("imap_mag_l1d_norm-dsrf_20100105_v010.cdf")
 
         process_input_collection = ProcessingInputCollection()
         process_input_collection.add(science_input_codice_l2)
         process_input_collection.add(science_input_mag_l1d)
 
-        dependencies = CodicePitchAngleDependencies.fetch_dependencies(process_input_collection)
+        dependencies = CodicePitchAngleDependencies.fetch_dependencies(
+            process_input_collection
+        )
 
         data_dir = imap_data_access.config["DATA_DIR"]
 
-        expected_codice_science_file_path = data_dir / expected_codice_science_file_download_path
+        expected_codice_science_file_path = (
+            data_dir / expected_codice_science_file_download_path
+        )
         expected_mag_science_file_path = data_dir / expected_mag_download_path
 
-        mock_download.assert_has_calls([
-            call(expected_codice_science_file_path),
-            call(expected_mag_science_file_path)
-        ])
+        mock_download.assert_has_calls(
+            [
+                call(expected_codice_science_file_path),
+                call(expected_mag_science_file_path),
+            ]
+        )
 
-        mock_from_files.assert_called_with(expected_mag_science_file_path, expected_codice_science_file_path,
-                                           mag_is_preliminary=True)
+        mock_from_files.assert_called_with(
+            expected_mag_science_file_path,
+            expected_codice_science_file_path,
+            mag_is_preliminary=True,
+        )
         self.assertEqual(mock_from_files.return_value, dependencies)
 
     @patch(
-        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.CodicePitchAngleDependencies.from_file_paths")
-    @patch("imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.imap_data_access.download")
-    def test_fetch_dependencies_uses_mag_l2_if_available(self, mock_download, mock_from_files):
-        expected_codice_science_file_download_path = "imap/codice/l2/2010/01/imap_codice_l2_hi-sectored_20100105_v010.cdf"
-        expected_mag_download_path = "imap/mag/l2/2010/01/imap_mag_l2_norm-dsrf_20100105_v010.cdf"
+        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.CodicePitchAngleDependencies.from_file_paths"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.imap_data_access.download"
+    )
+    def test_fetch_dependencies_uses_mag_l2_if_available(
+        self, mock_download, mock_from_files
+    ):
+        expected_codice_science_file_download_path = (
+            "imap/codice/l2/2010/01/imap_codice_l2_hi-sectored_20100105_v010.cdf"
+        )
+        expected_mag_download_path = (
+            "imap/mag/l2/2010/01/imap_mag_l2_norm-dsrf_20100105_v010.cdf"
+        )
 
-        science_input_codice_l2 = ScienceInput("imap_codice_l2_hi-sectored_20100105_v010.cdf")
+        science_input_codice_l2 = ScienceInput(
+            "imap_codice_l2_hi-sectored_20100105_v010.cdf"
+        )
         science_input_mag_l2 = ScienceInput("imap_mag_l2_norm-dsrf_20100105_v010.cdf")
         science_input_mag_l1d = ScienceInput("imap_mag_l1d_norm-dsrf_20100105_v010.cdf")
 
@@ -56,34 +86,51 @@ def test_fetch_dependencies_uses_mag_l2_if_available(self, mock_download, mock_f
             science_input_mag_l2,
         )
 
-        dependencies = CodicePitchAngleDependencies.fetch_dependencies(process_input_collection)
+        dependencies = CodicePitchAngleDependencies.fetch_dependencies(
+            process_input_collection
+        )
 
         data_dir = imap_data_access.config["DATA_DIR"]
 
-        expected_codice_science_file_path = data_dir / expected_codice_science_file_download_path
+        expected_codice_science_file_path = (
+            data_dir / expected_codice_science_file_download_path
+        )
         expected_mag_science_file_path = data_dir / expected_mag_download_path
 
-        mock_download.assert_has_calls([
-            call(expected_codice_science_file_path),
-            call(expected_mag_science_file_path)
-        ])
+        mock_download.assert_has_calls(
+            [
+                call(expected_codice_science_file_path),
+                call(expected_mag_science_file_path),
+            ]
+        )
 
-        mock_from_files.assert_called_with(expected_mag_science_file_path, expected_codice_science_file_path,
-                                           mag_is_preliminary=False)
+        mock_from_files.assert_called_with(
+            expected_mag_science_file_path,
+            expected_codice_science_file_path,
+            mag_is_preliminary=False,
+        )
         self.assertEqual(mock_from_files.return_value, dependencies)
 
-    @patch('imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.read_mag_data')
     @patch(
-        'imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.CodiceHiL2SectoredIntensitiesData.read_from_cdf')
+        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.read_mag_data"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies.CodiceHiL2SectoredIntensitiesData.read_from_cdf"
+    )
     def test_from_file_paths(self, mock_codice_l2_data, mock_read_mag_data):
         codice_file_path = sentinel.codice_file_path
         mag_file_path = sentinel.mag_file_path
-        dependencies = CodicePitchAngleDependencies.from_file_paths(mag_file_path, codice_file_path)
+        dependencies = CodicePitchAngleDependencies.from_file_paths(
+            mag_file_path, codice_file_path
+        )
 
         mock_codice_l2_data.assert_called_once_with(codice_file_path)
         mock_read_mag_data.assert_called_once_with(mag_file_path)
 
-        self.assertEqual(mock_codice_l2_data.return_value, dependencies.codice_sectored_intensities_data)
+        self.assertEqual(
+            mock_codice_l2_data.return_value,
+            dependencies.codice_sectored_intensities_data,
+        )
         self.assertEqual(mock_read_mag_data.return_value, dependencies.mag_data)
         self.assertFalse(dependencies.mag_is_preliminary)
 
diff --git a/tests/codice/l3/hi/test_codice_hi_processor.py b/tests/codice/l3/hi/test_codice_hi_processor.py
index e1a143e84..d9355e695 100644
--- a/tests/codice/l3/hi/test_codice_hi_processor.py
+++ b/tests/codice/l3/hi/test_codice_hi_processor.py
@@ -2,35 +2,50 @@
 import unittest
 from datetime import datetime, timedelta
 from pathlib import Path
-from unittest.mock import patch, sentinel, Mock, call, MagicMock
+from unittest.mock import MagicMock, Mock, call, patch, sentinel
 
 import numpy as np
 
 from imap_l3_processing.codice.l3.hi.codice_hi_processor import CodiceHiProcessor
-from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import \
-    CodiceHiL3aDirectEventsDependencies
-from imap_l3_processing.codice.l3.hi.models import CodiceL1aHiDirectEvents, CodiceL2HiDirectEventData, CodiceHiL2SectoredIntensitiesData, \
-    CodiceL3HiDirectEvents
-from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import CodicePitchAngleDependencies
-from imap_l3_processing.codice.l3.lo.constants import CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET
+from imap_l3_processing.codice.l3.hi.direct_event.codice_hi_l3a_direct_events_dependencies import (
+    CodiceHiL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.hi.models import (
+    CodiceHiL2SectoredIntensitiesData,
+    CodiceL1aHiDirectEvents,
+    CodiceL2HiDirectEventData,
+    CodiceL3HiDirectEvents,
+)
+from imap_l3_processing.codice.l3.hi.pitch_angle.codice_pitch_angle_dependencies import (
+    CodicePitchAngleDependencies,
+)
+from imap_l3_processing.codice.l3.lo.constants import (
+    CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET,
+)
 from imap_l3_processing.codice.quality_flags import CodiceL3Flags
 from imap_l3_processing.models import InputMetadata
 from tests.test_helpers import NumpyArrayMatcher, get_test_data_path
 
 
 class TestCodiceHiProcessor(unittest.TestCase):
-    @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.CodiceHiL3aDirectEventsDependencies.fetch_dependencies")
-    @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.CodiceHiProcessor.process_l3a_direct_event")
+    @patch(
+        "imap_l3_processing.codice.l3.hi.codice_hi_processor.CodiceHiL3aDirectEventsDependencies.fetch_dependencies"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.codice_hi_processor.CodiceHiProcessor.process_l3a_direct_event"
+    )
     @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.save_data")
-    def test_process_l3a(self, mock_save_data, mock_process_l3a, mock_fetch_dependencies):
+    def test_process_l3a(
+        self, mock_save_data, mock_process_l3a, mock_fetch_dependencies
+    ):
         start_date = datetime(2024, 10, 7, 10, 00, 00)
         end_date = datetime(2024, 10, 8, 10, 00, 00)
-        input_metadata = InputMetadata('codice', "l3a", start_date, end_date, 'v02')
+        input_metadata = InputMetadata("codice", "l3a", start_date, end_date, "v02")
         mock_processed_direct_events = Mock()
         mock_process_l3a.return_value = mock_processed_direct_events
 
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1')]
+        input_collection.get_file_paths.return_value = [Path("path/to/parent_file_1")]
 
         processor = CodiceHiProcessor(input_collection, input_metadata)
         product = processor.process()
@@ -39,13 +54,33 @@ def test_process_l3a(self, mock_save_data, mock_process_l3a, mock_fetch_dependen
         mock_process_l3a.assert_called_with(mock_fetch_dependencies.return_value)
         mock_save_data.assert_called_with(mock_processed_direct_events)
         self.assertEqual([mock_save_data.return_value], product)
-        self.assertEqual(['parent_file_1'], mock_processed_direct_events.parent_file_names)
+        self.assertEqual(
+            ["parent_file_1"], mock_processed_direct_events.parent_file_names
+        )
 
     def test_process_l3a_with_small_dataset(self):
         high_gain_events = {
-            (0, 1, 0): {"ssd_id": 11, "energy_channel": 1, "tof": 21, "l2_energy": 5, "factor": 0.5},
-            (0, 1, 1): {"ssd_id": 12, "energy_channel": 2, "tof": 22, "l2_energy": 6, "factor": 1.0},
-            (1, 0, 2): {"ssd_id": 13, "energy_channel": 3, "tof": 23, "l2_energy": 3, "factor": 2.0},
+            (0, 1, 0): {
+                "ssd_id": 11,
+                "energy_channel": 1,
+                "tof": 21,
+                "l2_energy": 5,
+                "factor": 0.5,
+            },
+            (0, 1, 1): {
+                "ssd_id": 12,
+                "energy_channel": 2,
+                "tof": 22,
+                "l2_energy": 6,
+                "factor": 1.0,
+            },
+            (1, 0, 2): {
+                "ssd_id": 13,
+                "energy_channel": 3,
+                "tof": 23,
+                "l2_energy": 3,
+                "factor": 2.0,
+            },
         }
         expected_factors = {
             (event["ssd_id"], event["energy_channel"], event["tof"]): event["factor"]
@@ -61,8 +96,9 @@ def lookup_factor(ssd_id, energy_channel, tof):
         mock_codice_hi_mass_correction_lut_ancillary = Mock()
         mock_codice_hi_mass_correction_lut_ancillary.lookup_correction_factor.side_effect = lookup_factor
 
-        codice_hi_processor = CodiceHiProcessor(sentinel.processing_input_collection,
-                                                input_metadata=sentinel.input_metadata)
+        codice_hi_processor = CodiceHiProcessor(
+            sentinel.processing_input_collection, input_metadata=sentinel.input_metadata
+        )
 
         number_of_events = np.array([[0, 2], [4, 3]])
         shape = (2, 2, 4)
@@ -118,7 +154,7 @@ def lookup_factor(ssd_id, energy_channel, tof):
         dependencies = CodiceHiL3aDirectEventsDependencies(
             codice_l2_hi_data=l2_data,
             codice_l1a_hi_data=l1a_data,
-            codice_hi_mass_correction_lut_ancillary=mock_codice_hi_mass_correction_lut_ancillary
+            codice_hi_mass_correction_lut_ancillary=mock_codice_hi_mass_correction_lut_ancillary,
         )
 
         expected_incident_ion_energy = np.full(shape, np.nan)
@@ -150,41 +186,41 @@ def lookup_factor(ssd_id, energy_channel, tof):
             spin_sector=sentinel.l2_spin_sector,
         )
 
-        actual_output = codice_hi_processor.process_l3a_direct_event(dependencies=dependencies)
+        actual_output = codice_hi_processor.process_l3a_direct_event(
+            dependencies=dependencies
+        )
         for field in dataclasses.fields(CodiceL3HiDirectEvents):
-            np.testing.assert_equal(getattr(actual_output, field.name), getattr(expected_output, field.name))
+            np.testing.assert_equal(
+                getattr(actual_output, field.name), getattr(expected_output, field.name)
+            )
 
         self.assertEqual(0, len(expected_factors))
 
     def test_process_l3a_only_calculates_events_within_num_events(self):
-        codice_hi_processor = CodiceHiProcessor(sentinel.processing_input_collection,
-                                                input_metadata=sentinel.input_metadata)
+        codice_hi_processor = CodiceHiProcessor(
+            sentinel.processing_input_collection, input_metadata=sentinel.input_metadata
+        )
 
-        expected_number_of_events = np.array([
-            [0, 2], [4, 3]
-        ])
+        expected_number_of_events = np.array([[0, 2], [4, 3]])
 
-        expected_ssd_energy = np.ma.masked_equal(np.array([
-            [
-                [255, 255, 255, 255],
-                [5, 6, 255, 255]
-            ],
-            [
-                [1, 2, 3, 4],
-                [5, 6, 2, 255]
-            ]
-        ], dtype=float), 255)
+        expected_ssd_energy = np.ma.masked_equal(
+            np.array(
+                [
+                    [[255, 255, 255, 255], [5, 6, 255, 255]],
+                    [[1, 2, 3, 4], [5, 6, 2, 255]],
+                ],
+                dtype=float,
+            ),
+            255,
+        )
 
-        expected_energy_per_nuc = np.array([
+        expected_energy_per_nuc = np.array(
             [
-                [255, 255, 255, 255],
-                [15, 16, 255, 255]
+                [[255, 255, 255, 255], [15, 16, 255, 255]],
+                [[11, 12, 13, 14], [15, 16, 12, 255]],
             ],
-            [
-                [11, 12, 13, 14],
-                [15, 16, 12, 255]
-            ]
-        ], dtype=float)
+            dtype=float,
+        )
 
         l2_gain = np.ones_like(expected_ssd_energy)
         l1a_data = CodiceL1aHiDirectEvents(
@@ -231,40 +267,51 @@ def test_process_l3a_only_calculates_events_within_num_events(self):
             tof=sentinel.expected_time_of_flight,
             type=sentinel.expected_type,
             energy_per_nuc=expected_energy_per_nuc,
-            estimated_mass=np.array([
-                [
-                    [np.nan, np.nan, np.nan, np.nan],
-                    [5 / 15, 6 / 16, np.nan, np.nan]
-                ],
+            estimated_mass=np.array(
                 [
-                    [1 / 11, 2 / 12, 3 / 13, 4 / 14],
-                    [5 / 15, 6 / 16, 2 / 12, np.nan]
+                    [
+                        [np.nan, np.nan, np.nan, np.nan],
+                        [5 / 15, 6 / 16, np.nan, np.nan],
+                    ],
+                    [
+                        [1 / 11, 2 / 12, 3 / 13, 4 / 14],
+                        [5 / 15, 6 / 16, 2 / 12, np.nan],
+                    ],
                 ]
-            ]),
+            ),
             elevation_angle=sentinel.l2_elevation_angle,
             gain=l2_gain,
             spin_sector=sentinel.l2_spin_sector,
         )
 
-        actual_output = codice_hi_processor.process_l3a_direct_event(dependencies=dependencies)
+        actual_output = codice_hi_processor.process_l3a_direct_event(
+            dependencies=dependencies
+        )
         mock_lut.lookup_correction_factor.assert_not_called()
         for field in dataclasses.fields(CodiceL3HiDirectEvents):
-            np.testing.assert_equal(getattr(actual_output, field.name), getattr(expected_output, field.name))
+            np.testing.assert_equal(
+                getattr(actual_output, field.name), getattr(expected_output, field.name)
+            )
 
-    @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.CodicePitchAngleDependencies.fetch_dependencies")
-    @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.CodiceHiProcessor.process_l3b")
+    @patch(
+        "imap_l3_processing.codice.l3.hi.codice_hi_processor.CodicePitchAngleDependencies.fetch_dependencies"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.hi.codice_hi_processor.CodiceHiProcessor.process_l3b"
+    )
     @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.save_data")
-    def test_process_l3b_saves(self, mock_save_data, mock_process_l3b,
-                               mock_fetch_dependencies):
+    def test_process_l3b_saves(
+        self, mock_save_data, mock_process_l3b, mock_fetch_dependencies
+    ):
         start_date = datetime(2024, 10, 7, 10, 00, 00)
         end_date = datetime(2024, 10, 8, 10, 00, 00)
-        input_metadata = InputMetadata('codice', "l3b", start_date, end_date, 'v02')
+        input_metadata = InputMetadata("codice", "l3b", start_date, end_date, "v02")
 
         mock_processed_pitch_angles = Mock()
         mock_process_l3b.return_value = mock_processed_pitch_angles
 
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_2')]
+        input_collection.get_file_paths.return_value = [Path("path/to/parent_file_2")]
 
         processor = CodiceHiProcessor(input_collection, input_metadata)
         product = processor.process()
@@ -273,10 +320,12 @@ def test_process_l3b_saves(self, mock_save_data, mock_process_l3b,
         mock_process_l3b.assert_called_with(mock_fetch_dependencies.return_value)
         mock_save_data.assert_called_with(mock_processed_pitch_angles)
         self.assertEqual([mock_save_data.return_value], product)
-        self.assertEqual(['parent_file_2'], mock_processed_pitch_angles.parent_file_names)
+        self.assertEqual(
+            ["parent_file_2"], mock_processed_pitch_angles.parent_file_names
+        )
 
     def test_raises_exception_on_non_l3_input_metadata(self):
-        input_metadata = InputMetadata('codice', "L2a", Mock(), Mock(), 'v02')
+        input_metadata = InputMetadata("codice", "L2a", Mock(), Mock(), "v02")
 
         processor = CodiceHiProcessor(Mock(), input_metadata)
         with self.assertRaises(NotImplementedError) as context:
@@ -284,32 +333,64 @@ def test_raises_exception_on_non_l3_input_metadata(self):
         self.assertEqual("Unknown data level for CoDICE: L2a", str(context.exception))
 
     @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.calculate_unit_vector")
-    @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.get_sector_unit_vectors")
+    @patch(
+        "imap_l3_processing.codice.l3.hi.codice_hi_processor.get_sector_unit_vectors"
+    )
     @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.calculate_pitch_angle")
     @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.calculate_gyrophase")
-    @patch("imap_l3_processing.codice.l3.hi.codice_hi_processor.rebin_by_pitch_angle_and_gyrophase")
-    def test_process_l3b_with_mocks(self, mock_rebin_by_pitch_angle_and_gyrophase, mock_calculate_gyrophases,
-                                    mock_calculate_pitch_angles, mock_get_sector_unit_vectors,
-                                    mock_calculate_unit_vector):
+    @patch(
+        "imap_l3_processing.codice.l3.hi.codice_hi_processor.rebin_by_pitch_angle_and_gyrophase"
+    )
+    def test_process_l3b_with_mocks(
+        self,
+        mock_rebin_by_pitch_angle_and_gyrophase,
+        mock_calculate_gyrophases,
+        mock_calculate_pitch_angles,
+        mock_get_sector_unit_vectors,
+        mock_calculate_unit_vector,
+    ):
         rng = np.random.default_rng()
         epoch_1 = datetime(2025, 2, 5)
         epoch_2 = datetime(2025, 2, 6)
         epoch = np.array([epoch_1, epoch_2])
-        epoch_delta = np.full(epoch.shape, timedelta(days=.5))
+        epoch_delta = np.full(epoch.shape, timedelta(days=0.5))
 
         rebinned_mag_data = rng.random((len(epoch), 3))
         mag_l1d_data = Mock()
         mag_l1d_data.rebin_to.return_value = rebinned_mag_data
 
-        h_intensity = rng.random((len(epoch), len(np.array([1.11, 1.17]) * 1.4), len(np.array([270, 15])), len(
-            np.array([15, 45, 75, 105, 135, 165]))))
-        he3he4_intensity = rng.random((len(epoch), len(np.array([1.11, 1.17]) * 1.4), len(np.array([270, 15])), len(
-            np.array([15, 45, 75, 105, 135, 165]))))
-        cno_intensity = rng.random((len(epoch), len(np.array([1.11, 1.17, 1.25])), len(np.array([270, 15])), len(
-            np.array([15, 45, 75, 105, 135, 165]))))
+        h_intensity = rng.random(
+            (
+                len(epoch),
+                len(np.array([1.11, 1.17]) * 1.4),
+                len(np.array([270, 15])),
+                len(np.array([15, 45, 75, 105, 135, 165])),
+            )
+        )
+        he3he4_intensity = rng.random(
+            (
+                len(epoch),
+                len(np.array([1.11, 1.17]) * 1.4),
+                len(np.array([270, 15])),
+                len(np.array([15, 45, 75, 105, 135, 165])),
+            )
+        )
+        cno_intensity = rng.random(
+            (
+                len(epoch),
+                len(np.array([1.11, 1.17, 1.25])),
+                len(np.array([270, 15])),
+                len(np.array([15, 45, 75, 105, 135, 165])),
+            )
+        )
         fe_intensity = rng.random(
-            (len(epoch), len(np.array([1.11, 1.17]) * 1.3), len(np.array([270, 15])),
-             len(np.array([15, 45, 75, 105, 135, 165]))))
+            (
+                len(epoch),
+                len(np.array([1.11, 1.17]) * 1.3),
+                len(np.array([270, 15])),
+                len(np.array([15, 45, 75, 105, 135, 165])),
+            )
+        )
 
         codice_l2_data = CodiceHiL2SectoredIntensitiesData(
             epoch=epoch,
@@ -335,16 +416,18 @@ def test_process_l3b_with_mocks(self, mock_rebin_by_pitch_angle_and_gyrophase, m
             energy_he3he4_minus=(np.repeat(1.6, len(np.array([1.11, 1.17])))),
         )
 
-        dependencies = CodicePitchAngleDependencies(mag_data=mag_l1d_data,
-                                                    codice_sectored_intensities_data=codice_l2_data,
-                                                    mag_is_preliminary=True)
+        dependencies = CodicePitchAngleDependencies(
+            mag_data=mag_l1d_data,
+            codice_sectored_intensities_data=codice_l2_data,
+            mag_is_preliminary=True,
+        )
 
         expected_pitch_angles = np.linspace(15, 165, 6)
         expected_gyrophase = np.linspace(15, 345, 12)
 
         expected_pitch_angle_delta = np.repeat(15, 6)
         expected_gyrophase_delta = np.repeat(15, 12)
-        sector_vectors = rng.random((2,6,3))
+        sector_vectors = rng.random((2, 6, 3))
         sector_unit_vectors = rng.random((6, 2, 3))
         mag_unit_vectors = rng.random((2, 3))
         mock_calculate_unit_vector.side_effect = [mag_unit_vectors, sector_unit_vectors]
@@ -373,36 +456,126 @@ def test_process_l3b_with_mocks(self, mock_rebin_by_pitch_angle_and_gyrophase, m
         expected_parents = []
 
         mock_rebin_by_pitch_angle_and_gyrophase.side_effect = [
-            (expected_h_intensity_binned_by_pa_and_gyro[0], 0, 0, expected_h_intensity_binned_by_pa[0], 0, 0),
-            (expected_he4_intensity_binned_by_pa_and_gyro[0], 0, 0, expected_he4_intensity_binned_by_pa[0], 0, 0),
-            (expected_cno_intensity_binned_by_pa_and_gyro[0], 0, 0, expected_cno_intensity_binned_by_pa[0], 0, 0),
-            (expected_fe_intensity_binned_by_pa_and_gyro[0], 0, 0, expected_fe_intensity_binned_by_pa[0], 0, 0),
-            (expected_h_intensity_binned_by_pa_and_gyro[1], 0, 0, expected_h_intensity_binned_by_pa[1], 0, 0),
-            (expected_he4_intensity_binned_by_pa_and_gyro[1], 0, 0, expected_he4_intensity_binned_by_pa[1], 0, 0),
-            (expected_cno_intensity_binned_by_pa_and_gyro[1], 0, 0, expected_cno_intensity_binned_by_pa[1], 0, 0),
-            (expected_fe_intensity_binned_by_pa_and_gyro[1], 0, 0, expected_fe_intensity_binned_by_pa[1], 0, 0),
+            (
+                expected_h_intensity_binned_by_pa_and_gyro[0],
+                0,
+                0,
+                expected_h_intensity_binned_by_pa[0],
+                0,
+                0,
+            ),
+            (
+                expected_he4_intensity_binned_by_pa_and_gyro[0],
+                0,
+                0,
+                expected_he4_intensity_binned_by_pa[0],
+                0,
+                0,
+            ),
+            (
+                expected_cno_intensity_binned_by_pa_and_gyro[0],
+                0,
+                0,
+                expected_cno_intensity_binned_by_pa[0],
+                0,
+                0,
+            ),
+            (
+                expected_fe_intensity_binned_by_pa_and_gyro[0],
+                0,
+                0,
+                expected_fe_intensity_binned_by_pa[0],
+                0,
+                0,
+            ),
+            (
+                expected_h_intensity_binned_by_pa_and_gyro[1],
+                0,
+                0,
+                expected_h_intensity_binned_by_pa[1],
+                0,
+                0,
+            ),
+            (
+                expected_he4_intensity_binned_by_pa_and_gyro[1],
+                0,
+                0,
+                expected_he4_intensity_binned_by_pa[1],
+                0,
+                0,
+            ),
+            (
+                expected_cno_intensity_binned_by_pa_and_gyro[1],
+                0,
+                0,
+                expected_cno_intensity_binned_by_pa[1],
+                0,
+                0,
+            ),
+            (
+                expected_fe_intensity_binned_by_pa_and_gyro[1],
+                0,
+                0,
+                expected_fe_intensity_binned_by_pa[1],
+                0,
+                0,
+            ),
         ]
 
-        codice_processor = CodiceHiProcessor(dependencies=Mock(), input_metadata=sentinel.input_metadata)
+        codice_processor = CodiceHiProcessor(
+            dependencies=Mock(), input_metadata=sentinel.input_metadata
+        )
         codice_hi_data_product = codice_processor.process_l3b(dependencies=dependencies)
 
         mock_get_sector_unit_vectors.assert_called_once_with(
             codice_l2_data.elevation_angle,
-            NumpyArrayMatcher((codice_l2_data.spin_angles.T + CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET) % 360))
+            NumpyArrayMatcher(
+                (
+                    codice_l2_data.spin_angles.T
+                    + CODICE_INSTRUMENT_TO_SC_FRAME_SPIN_ANGLE_OFFSET
+                )
+                % 360
+            ),
+        )
         mock_calculate_unit_vector.assert_has_calls(
-            [call(NumpyArrayMatcher(rebinned_mag_data)), call(NumpyArrayMatcher(sector_vectors.transpose(1,0,2)))])
+            [
+                call(NumpyArrayMatcher(rebinned_mag_data)),
+                call(NumpyArrayMatcher(sector_vectors.transpose(1, 0, 2))),
+            ]
+        )
 
-        mock_calculate_pitch_angles.assert_has_calls([
-            call(NumpyArrayMatcher(-1 * sector_unit_vectors), NumpyArrayMatcher(mag_unit_vectors[0])),
-            call(NumpyArrayMatcher(-1 * sector_unit_vectors), NumpyArrayMatcher(mag_unit_vectors[1])),
-        ])
+        mock_calculate_pitch_angles.assert_has_calls(
+            [
+                call(
+                    NumpyArrayMatcher(-1 * sector_unit_vectors),
+                    NumpyArrayMatcher(mag_unit_vectors[0]),
+                ),
+                call(
+                    NumpyArrayMatcher(-1 * sector_unit_vectors),
+                    NumpyArrayMatcher(mag_unit_vectors[1]),
+                ),
+            ]
+        )
 
-        mock_calculate_gyrophases.assert_has_calls([
-            call(NumpyArrayMatcher(-1 * sector_unit_vectors), NumpyArrayMatcher(mag_unit_vectors[0])),
-            call(NumpyArrayMatcher(-1 * sector_unit_vectors), NumpyArrayMatcher(mag_unit_vectors[1])),
-        ])
+        mock_calculate_gyrophases.assert_has_calls(
+            [
+                call(
+                    NumpyArrayMatcher(-1 * sector_unit_vectors),
+                    NumpyArrayMatcher(mag_unit_vectors[0]),
+                ),
+                call(
+                    NumpyArrayMatcher(-1 * sector_unit_vectors),
+                    NumpyArrayMatcher(mag_unit_vectors[1]),
+                ),
+            ]
+        )
 
-        species_intensities = [h_intensity, he3he4_intensity, cno_intensity, fe_intensity]
+        species_intensities = [
+            h_intensity,
+            he3he4_intensity,
+            cno_intensity,
+            fe_intensity,
+        ]
 
         species_uncertainties = [
             np.zeros_like(h_intensity),
@@ -412,27 +585,34 @@ def test_process_l3b_with_mocks(self, mock_rebin_by_pitch_angle_and_gyrophase, m
         ]
 
         expected_rebin_calls = []
-        for species_intensity, species_uncertainty in zip(species_intensities, species_uncertainties):
+        for species_intensity, species_uncertainty in zip(
+            species_intensities, species_uncertainties
+        ):
             expected_rebin_calls.append(
-                call(intensity_data=NumpyArrayMatcher(species_intensity[0]),
-                     intensity_delta_plus=NumpyArrayMatcher(species_uncertainty[0]),
-                     intensity_delta_minus=NumpyArrayMatcher(species_uncertainty[0]),
-                     pitch_angles=sentinel.epoch1_pitch_angle,
-                     gyrophases=sentinel.epoch1_gyrophase,
-                     number_of_pitch_angle_bins=6, number_of_gyrophase_bins=12
-                     )
-
+                call(
+                    intensity_data=NumpyArrayMatcher(species_intensity[0]),
+                    intensity_delta_plus=NumpyArrayMatcher(species_uncertainty[0]),
+                    intensity_delta_minus=NumpyArrayMatcher(species_uncertainty[0]),
+                    pitch_angles=sentinel.epoch1_pitch_angle,
+                    gyrophases=sentinel.epoch1_gyrophase,
+                    number_of_pitch_angle_bins=6,
+                    number_of_gyrophase_bins=12,
+                )
             )
 
-        for species_intensity, species_uncertainty in zip(species_intensities, species_uncertainties):
+        for species_intensity, species_uncertainty in zip(
+            species_intensities, species_uncertainties
+        ):
             expected_rebin_calls.append(
-                call(intensity_data=NumpyArrayMatcher(species_intensity[1]),
-                     intensity_delta_plus=NumpyArrayMatcher(species_uncertainty[1]),
-                     intensity_delta_minus=NumpyArrayMatcher(species_uncertainty[1]),
-                     pitch_angles=sentinel.epoch2_pitch_angle,
-                     gyrophases=sentinel.epoch2_gyrophase,
-                     number_of_pitch_angle_bins=6, number_of_gyrophase_bins=12
-                     )
+                call(
+                    intensity_data=NumpyArrayMatcher(species_intensity[1]),
+                    intensity_delta_plus=NumpyArrayMatcher(species_uncertainty[1]),
+                    intensity_delta_minus=NumpyArrayMatcher(species_uncertainty[1]),
+                    pitch_angles=sentinel.epoch2_pitch_angle,
+                    gyrophases=sentinel.epoch2_gyrophase,
+                    number_of_pitch_angle_bins=6,
+                    number_of_gyrophase_bins=12,
+                )
             )
 
         mock_rebin_by_pitch_angle_and_gyrophase.assert_has_calls(expected_rebin_calls)
@@ -441,63 +621,129 @@ def test_process_l3b_with_mocks(self, mock_rebin_by_pitch_angle_and_gyrophase, m
         np.testing.assert_array_equal(epoch, codice_hi_data_product.epoch)
         np.testing.assert_array_equal(epoch_delta, codice_hi_data_product.epoch_delta)
 
-        np.testing.assert_array_equal(codice_hi_data_product.energy_h, codice_l2_data.energy_h)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_h_plus, codice_l2_data.energy_h_plus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_h_minus, codice_l2_data.energy_h_minus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_cno, codice_l2_data.energy_cno)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_cno_plus, codice_l2_data.energy_cno_plus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_cno_minus, codice_l2_data.energy_cno_minus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_fe, codice_l2_data.energy_fe)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_fe_plus, codice_l2_data.energy_fe_plus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_fe_minus, codice_l2_data.energy_fe_minus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_he3he4, codice_l2_data.energy_he3he4)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_he3he4_plus, codice_l2_data.energy_he3he4_plus)
-        np.testing.assert_array_equal(codice_hi_data_product.energy_he3he4_minus, codice_l2_data.energy_he3he4_minus)
-
-        np.testing.assert_array_almost_equal(expected_pitch_angles, codice_hi_data_product.pitch_angle)
-        np.testing.assert_array_equal(expected_pitch_angle_delta, codice_hi_data_product.pitch_angle_delta)
-        np.testing.assert_array_almost_equal(expected_gyrophase, codice_hi_data_product.gyrophase)
-        np.testing.assert_array_equal(expected_gyrophase_delta, codice_hi_data_product.gyrophase_delta)
-
-        np.testing.assert_allclose(codice_hi_data_product.h_intensity_by_pitch_angle, expected_h_intensity_binned_by_pa)
-
-        np.testing.assert_array_equal(codice_hi_data_product.he3he4_intensity_by_pitch_angle,
-                                      expected_he4_intensity_binned_by_pa)
-        np.testing.assert_array_equal(codice_hi_data_product.cno_intensity_by_pitch_angle,
-                                      expected_cno_intensity_binned_by_pa)
-        np.testing.assert_array_equal(codice_hi_data_product.fe_intensity_by_pitch_angle,
-                                      expected_fe_intensity_binned_by_pa)
-
-        np.testing.assert_array_equal(codice_hi_data_product.h_intensity_by_pitch_angle_and_gyrophase,
-                                      expected_h_intensity_binned_by_pa_and_gyro)
-        np.testing.assert_array_equal(codice_hi_data_product.he3he4_intensity_by_pitch_angle_and_gyrophase,
-                                      expected_he4_intensity_binned_by_pa_and_gyro)
-        np.testing.assert_array_equal(codice_hi_data_product.cno_intensity_by_pitch_angle_and_gyrophase,
-                                      expected_cno_intensity_binned_by_pa_and_gyro)
-        np.testing.assert_array_equal(codice_hi_data_product.fe_intensity_by_pitch_angle_and_gyrophase,
-                                      expected_fe_intensity_binned_by_pa_and_gyro)
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_h, codice_l2_data.energy_h
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_h_plus, codice_l2_data.energy_h_plus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_h_minus, codice_l2_data.energy_h_minus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_cno, codice_l2_data.energy_cno
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_cno_plus, codice_l2_data.energy_cno_plus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_cno_minus, codice_l2_data.energy_cno_minus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_fe, codice_l2_data.energy_fe
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_fe_plus, codice_l2_data.energy_fe_plus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_fe_minus, codice_l2_data.energy_fe_minus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_he3he4, codice_l2_data.energy_he3he4
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_he3he4_plus, codice_l2_data.energy_he3he4_plus
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.energy_he3he4_minus,
+            codice_l2_data.energy_he3he4_minus,
+        )
+
+        np.testing.assert_array_almost_equal(
+            expected_pitch_angles, codice_hi_data_product.pitch_angle
+        )
+        np.testing.assert_array_equal(
+            expected_pitch_angle_delta, codice_hi_data_product.pitch_angle_delta
+        )
+        np.testing.assert_array_almost_equal(
+            expected_gyrophase, codice_hi_data_product.gyrophase
+        )
+        np.testing.assert_array_equal(
+            expected_gyrophase_delta, codice_hi_data_product.gyrophase_delta
+        )
+
+        np.testing.assert_allclose(
+            codice_hi_data_product.h_intensity_by_pitch_angle,
+            expected_h_intensity_binned_by_pa,
+        )
+
+        np.testing.assert_array_equal(
+            codice_hi_data_product.he3he4_intensity_by_pitch_angle,
+            expected_he4_intensity_binned_by_pa,
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.cno_intensity_by_pitch_angle,
+            expected_cno_intensity_binned_by_pa,
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.fe_intensity_by_pitch_angle,
+            expected_fe_intensity_binned_by_pa,
+        )
+
+        np.testing.assert_array_equal(
+            codice_hi_data_product.h_intensity_by_pitch_angle_and_gyrophase,
+            expected_h_intensity_binned_by_pa_and_gyro,
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.he3he4_intensity_by_pitch_angle_and_gyrophase,
+            expected_he4_intensity_binned_by_pa_and_gyro,
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.cno_intensity_by_pitch_angle_and_gyrophase,
+            expected_cno_intensity_binned_by_pa_and_gyro,
+        )
+        np.testing.assert_array_equal(
+            codice_hi_data_product.fe_intensity_by_pitch_angle_and_gyrophase,
+            expected_fe_intensity_binned_by_pa_and_gyro,
+        )
 
         np.testing.assert_array_equal(
             codice_hi_data_product.codice_flags,
             np.array([CodiceL3Flags.PRELIMINARY_MAG, CodiceL3Flags.PRELIMINARY_MAG]),
         )
 
-        np.testing.assert_array_equal(codice_hi_data_product.parent_file_names, expected_parents)
+        np.testing.assert_array_equal(
+            codice_hi_data_product.parent_file_names, expected_parents
+        )
 
     def test_integration_test(self):
-        l2_direct_event_sci_path = get_test_data_path("codice/imap_codice_l2_hi-direct-events_20260831_v001.0003.cdf")
-        l1a_direct_event_sci_path = get_test_data_path("codice/imap_codice_l1a_hi-direct-events_20260831_v001.0003.cdf")
-        mass_correction_lut_ancillary_path = get_test_data_path("codice/imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx")
-
-        codice_hi_dependencies = CodiceHiL3aDirectEventsDependencies.from_file_paths(l2_direct_event_sci_path, l1a_direct_event_sci_path, mass_correction_lut_ancillary_path)
-
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor='hi-direct-events')
-        processor = CodiceHiProcessor(dependencies=Mock(), input_metadata=input_metadata)
+        l2_direct_event_sci_path = get_test_data_path(
+            "codice/imap_codice_l2_hi-direct-events_20260831_v001.0003.cdf"
+        )
+        l1a_direct_event_sci_path = get_test_data_path(
+            "codice/imap_codice_l1a_hi-direct-events_20260831_v001.0003.cdf"
+        )
+        mass_correction_lut_ancillary_path = get_test_data_path(
+            "codice/imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx"
+        )
+
+        codice_hi_dependencies = CodiceHiL3aDirectEventsDependencies.from_file_paths(
+            l2_direct_event_sci_path,
+            l1a_direct_event_sci_path,
+            mass_correction_lut_ancillary_path,
+        )
+
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor="hi-direct-events",
+        )
+        processor = CodiceHiProcessor(
+            dependencies=Mock(), input_metadata=input_metadata
+        )
 
         try:
             processor.process_l3a_direct_event(codice_hi_dependencies)
diff --git a/tests/codice/l3/hi/test_models.py b/tests/codice/l3/hi/test_models.py
index aa0f16ad2..2774e4feb 100644
--- a/tests/codice/l3/hi/test_models.py
+++ b/tests/codice/l3/hi/test_models.py
@@ -8,24 +8,37 @@
 import numpy as np
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.codice.l3.hi.models import CodiceL2HiDirectEventData, CodiceL3HiDirectEvents, \
-    CodiceHiL2SectoredIntensitiesData, CodiceHiL3PitchAngleDataProduct, CodiceL1aHiDirectEvents
-from tests.test_helpers import get_test_instrument_team_data_path, get_test_data_path, with_tempdir
+from imap_l3_processing.codice.l3.hi.models import (
+    CodiceHiL2SectoredIntensitiesData,
+    CodiceHiL3PitchAngleDataProduct,
+    CodiceL1aHiDirectEvents,
+    CodiceL2HiDirectEventData,
+    CodiceL3HiDirectEvents,
+)
+from tests.test_helpers import (
+    get_test_data_path,
+    get_test_instrument_team_data_path,
+    with_tempdir,
+)
 
 
 class TestModels(unittest.TestCase):
     def setUp(self):
-        if os.path.exists('test_cdf.cdf'):
-            os.remove('test_cdf.cdf')
+        if os.path.exists("test_cdf.cdf"):
+            os.remove("test_cdf.cdf")
 
     def tearDown(self):
-        if os.path.exists('test_cdf.cdf'):
-            os.remove('test_cdf.cdf')
+        if os.path.exists("test_cdf.cdf"):
+            os.remove("test_cdf.cdf")
 
     @with_tempdir
     def test_codice_hi_l2_data_read_from_instrument_team_cdf(self, tempdir):
-        l2_path = get_test_data_path("codice/imap_codice_l2_hi-direct-events_20250814_v001.cdf")
-        expanded_l2_path = TestModels._create_expanded_l2_hi_de_dataset(l2_path, tempdir)
+        l2_path = get_test_data_path(
+            "codice/imap_codice_l2_hi-direct-events_20250814_v001.cdf"
+        )
+        expanded_l2_path = TestModels._create_expanded_l2_hi_de_dataset(
+            l2_path, tempdir
+        )
 
         test_cases = [
             ("6 priorities", l2_path),
@@ -34,46 +47,80 @@ def test_codice_hi_l2_data_read_from_instrument_team_cdf(self, tempdir):
 
         for name, input_path in test_cases:
             with self.subTest(name):
-                l2_direct_event_data = CodiceL2HiDirectEventData.read_from_cdf(input_path)
+                l2_direct_event_data = CodiceL2HiDirectEventData.read_from_cdf(
+                    input_path
+                )
 
                 with CDF(str(l2_path)) as cdf:
                     # @formatter:off
-                    np.testing.assert_array_equal(l2_direct_event_data.epoch, cdf["epoch"])
-                    np.testing.assert_array_equal(l2_direct_event_data.epoch_delta_plus, cdf["epoch_delta_plus"])
-                    np.testing.assert_array_equal(l2_direct_event_data.ssd_energy, cdf[f"ssd_energy"])
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.epoch, cdf["epoch"]
+                    )
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.epoch_delta_plus, cdf["epoch_delta_plus"]
+                    )
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.ssd_energy, cdf["ssd_energy"]
+                    )
                     expected_ssd_id = cdf["ssd_id"][...]
-                    np.testing.assert_array_equal(l2_direct_event_data.ssd_id, expected_ssd_id)
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.ssd_id, expected_ssd_id
+                    )
 
                     expected_energy_per_nuc = cdf["energy_per_nuc"][...]
-                    expected_energy_per_nuc[expected_energy_per_nuc==-1e31] = np.nan
-                    np.testing.assert_array_equal(l2_direct_event_data.energy_per_nuc, expected_energy_per_nuc)
+                    expected_energy_per_nuc[expected_energy_per_nuc == -1e31] = np.nan
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.energy_per_nuc, expected_energy_per_nuc
+                    )
 
                     expected_spin_angle = cdf["spin_angle"][...]
-                    expected_spin_angle[expected_spin_angle==-1e31] = np.nan
-                    np.testing.assert_array_equal(l2_direct_event_data.spin_angle, expected_spin_angle)
+                    expected_spin_angle[expected_spin_angle == -1e31] = np.nan
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.spin_angle, expected_spin_angle
+                    )
 
                     expected_spin_number = cdf["spin_number"][...]
-                    np.testing.assert_array_equal(l2_direct_event_data.spin_number, expected_spin_number)
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.spin_number, expected_spin_number
+                    )
 
                     expected_time_of_flight = cdf["tof"][...]
-                    expected_time_of_flight[expected_time_of_flight==-1e31] = np.nan
-                    np.testing.assert_array_equal(l2_direct_event_data.time_of_flight, expected_time_of_flight)
-
-                    np.testing.assert_array_equal(l2_direct_event_data.number_of_events, cdf["num_events"])
-                    np.testing.assert_array_equal(l2_direct_event_data.data_quality, cdf["data_quality"])
-                    np.testing.assert_array_equal(l2_direct_event_data.multi_flag, cdf["multi_flag"])
-
-                    np.testing.assert_array_equal(l2_direct_event_data.type, cdf[f"type"])
+                    expected_time_of_flight[expected_time_of_flight == -1e31] = np.nan
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.time_of_flight, expected_time_of_flight
+                    )
+
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.number_of_events, cdf["num_events"]
+                    )
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.data_quality, cdf["data_quality"]
+                    )
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.multi_flag, cdf["multi_flag"]
+                    )
+
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.type, cdf["type"]
+                    )
 
                     expected_elevation_angle = cdf["elevation_angle"][...]
                     expected_elevation_angle[expected_elevation_angle == -1e31] = np.nan
-                    np.testing.assert_array_equal(l2_direct_event_data.elevation_angle, expected_elevation_angle)
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.elevation_angle, expected_elevation_angle
+                    )
 
                     self.assertIsInstance(l2_direct_event_data.gain, np.ma.masked_array)
-                    np.testing.assert_array_equal(l2_direct_event_data.gain.data, cdf["gain"])
-
-                    self.assertIsInstance(l2_direct_event_data.spin_sector, np.ma.masked_array)
-                    np.testing.assert_array_equal(l2_direct_event_data.spin_sector.data, cdf["spin_sector"])
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.gain.data, cdf["gain"]
+                    )
+
+                    self.assertIsInstance(
+                        l2_direct_event_data.spin_sector, np.ma.masked_array
+                    )
+                    np.testing.assert_array_equal(
+                        l2_direct_event_data.spin_sector.data, cdf["spin_sector"]
+                    )
                     # @formatter:on
 
     def test_codice_l3_hi_direct_event_data_products(self):
@@ -84,12 +131,20 @@ def test_codice_l3_hi_direct_event_data_products(self):
         number_of_priority_events = 6
         event_buffer_size = 3
 
-        event_data_shape = (len(expected_epoch), number_of_priority_events, event_buffer_size)
+        event_data_shape = (
+            len(expected_epoch),
+            number_of_priority_events,
+            event_buffer_size,
+        )
 
         expected_priority_index = np.arange(number_of_priority_events)
         expected_event_index = np.arange(event_buffer_size)
-        expected_priority_index_label = np.array([str(i) for i in range(number_of_priority_events)])
-        expected_event_index_label = np.array([str(i) for i in range(event_buffer_size)])
+        expected_priority_index_label = np.array(
+            [str(i) for i in range(number_of_priority_events)]
+        )
+        expected_event_index_label = np.array(
+            [str(i) for i in range(event_buffer_size)]
+        )
 
         l3_data_product = CodiceL3HiDirectEvents(
             Mock(),
@@ -113,19 +168,30 @@ def test_codice_l3_hi_direct_event_data_products(self):
         )
 
         data_product_variables = l3_data_product.to_data_product_variables()
-        non_parent_fields = [f for f in fields(CodiceL3HiDirectEvents) if
-                             f.name in CodiceL3HiDirectEvents.__annotations__]
+        non_parent_fields = [
+            f
+            for f in fields(CodiceL3HiDirectEvents)
+            if f.name in CodiceL3HiDirectEvents.__annotations__
+        ]
 
         self.assertEqual(len(data_product_variables), len(non_parent_fields))
 
-        np.testing.assert_array_equal(l3_data_product.priority_index, expected_priority_index)
+        np.testing.assert_array_equal(
+            l3_data_product.priority_index, expected_priority_index
+        )
         np.testing.assert_array_equal(l3_data_product.event_index, expected_event_index)
-        np.testing.assert_array_equal(l3_data_product.priority_index_label, expected_priority_index_label)
-        np.testing.assert_array_equal(l3_data_product.event_index_label, expected_event_index_label)
+        np.testing.assert_array_equal(
+            l3_data_product.priority_index_label, expected_priority_index_label
+        )
+        np.testing.assert_array_equal(
+            l3_data_product.event_index_label, expected_event_index_label
+        )
 
         for data_product_variable in data_product_variables:
-            np.testing.assert_array_equal(data_product_variable.value,
-                                          getattr(l3_data_product, data_product_variable.name))
+            np.testing.assert_array_equal(
+                data_product_variable.value,
+                getattr(l3_data_product, data_product_variable.name),
+            )
 
     def test_codice_hi_l3_pitch_angle_to_data_product(self):
         epoch_data = np.array([datetime.now()])
@@ -133,7 +199,9 @@ def test_codice_hi_l3_pitch_angle_to_data_product(self):
         pitch_angle = np.array([100, 200])
         gyrophase = np.array([100, 200])
         pitch_angle_size = len(epoch_data) * len(energy_data) * len(pitch_angle)
-        pitch_angle_and_gyrophase_size = len(epoch_data) * len(energy_data) * len(pitch_angle) * len(gyrophase)
+        pitch_angle_and_gyrophase_size = (
+            len(epoch_data) * len(energy_data) * len(pitch_angle) * len(gyrophase)
+        )
 
         energy_h_data = np.array([101, 103])
         energy_cno_data = np.array([101, 103]) + 2
@@ -158,109 +226,205 @@ def test_codice_hi_l3_pitch_angle_to_data_product(self):
             "pitch_angle_delta": np.array([100, 200]),
             "gyrophase": gyrophase,
             "gyrophase_delta": np.array([100, 200]),
-            "h_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(len(epoch_data), len(energy_data),
-                                                                              len(pitch_angle)) + 1,
-            "h_intensity_by_pitch_angle_and_gyrophase": np.arange(pitch_angle_and_gyrophase_size).reshape(
-                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)) + 2,
-            "he3he4_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(len(epoch_data), len(energy_data),
-                                                                                   len(pitch_angle)),
-            "he3he4_intensity_by_pitch_angle_and_gyrophase": np.arange(pitch_angle_and_gyrophase_size).reshape(
-                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)) + 3,
-            "cno_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(len(epoch_data), len(energy_data),
-                                                                                len(pitch_angle)) + 4,
-            "cno_intensity_by_pitch_angle_and_gyrophase": np.arange(pitch_angle_and_gyrophase_size).reshape(
-                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)) + 5,
-            "fe_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(len(epoch_data), len(energy_data),
-                                                                               len(pitch_angle)) + 6,
-            "fe_intensity_by_pitch_angle_and_gyrophase": np.arange(pitch_angle_and_gyrophase_size).reshape(
-                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)) + 7,
+            "h_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle)
+            )
+            + 1,
+            "h_intensity_by_pitch_angle_and_gyrophase": np.arange(
+                pitch_angle_and_gyrophase_size
+            ).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)
+            )
+            + 2,
+            "he3he4_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle)
+            ),
+            "he3he4_intensity_by_pitch_angle_and_gyrophase": np.arange(
+                pitch_angle_and_gyrophase_size
+            ).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)
+            )
+            + 3,
+            "cno_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle)
+            )
+            + 4,
+            "cno_intensity_by_pitch_angle_and_gyrophase": np.arange(
+                pitch_angle_and_gyrophase_size
+            ).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)
+            )
+            + 5,
+            "fe_intensity_by_pitch_angle": np.arange(pitch_angle_size).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle)
+            )
+            + 6,
+            "fe_intensity_by_pitch_angle_and_gyrophase": np.arange(
+                pitch_angle_and_gyrophase_size
+            ).reshape(
+                len(epoch_data), len(energy_data), len(pitch_angle), len(gyrophase)
+            )
+            + 7,
             "codice_flags": np.zeros(len(epoch_data), dtype=np.uint16),
         }
 
         data_product = CodiceHiL3PitchAngleDataProduct(
-            input_metadata=Mock(),
-            **inputted_data_product_kwargs
+            input_metadata=Mock(), **inputted_data_product_kwargs
         )
 
-        np.testing.assert_array_equal(data_product.energy_h_label, np.array([str(v) for v in energy_h_data]))
-        np.testing.assert_array_equal(data_product.energy_cno_label, np.array([str(v) for v in energy_cno_data]))
-        np.testing.assert_array_equal(data_product.energy_fe_label, np.array([str(v) for v in energy_fe_data]))
-        np.testing.assert_array_equal(data_product.energy_he3he4_label, np.array([str(v) for v in energy_he3he4_data]))
-        np.testing.assert_array_equal(data_product.pitch_angle_label, np.array([str(v) for v in pitch_angle]))
-        np.testing.assert_array_equal(data_product.gyrophase_label, np.array([str(v) for v in gyrophase]))
+        np.testing.assert_array_equal(
+            data_product.energy_h_label, np.array([str(v) for v in energy_h_data])
+        )
+        np.testing.assert_array_equal(
+            data_product.energy_cno_label, np.array([str(v) for v in energy_cno_data])
+        )
+        np.testing.assert_array_equal(
+            data_product.energy_fe_label, np.array([str(v) for v in energy_fe_data])
+        )
+        np.testing.assert_array_equal(
+            data_product.energy_he3he4_label,
+            np.array([str(v) for v in energy_he3he4_data]),
+        )
+        np.testing.assert_array_equal(
+            data_product.pitch_angle_label, np.array([str(v) for v in pitch_angle])
+        )
+        np.testing.assert_array_equal(
+            data_product.gyrophase_label, np.array([str(v) for v in gyrophase])
+        )
 
         actual_data_product_variables = data_product.to_data_product_variables()
 
-        non_parent_fields = [f for f in fields(CodiceHiL3PitchAngleDataProduct)
-                             if f.name in CodiceHiL3PitchAngleDataProduct.__annotations__]
+        non_parent_fields = [
+            f
+            for f in fields(CodiceHiL3PitchAngleDataProduct)
+            if f.name in CodiceHiL3PitchAngleDataProduct.__annotations__
+        ]
         data_product_fields = [f.name for f in non_parent_fields]
 
         self.assertEqual(len(actual_data_product_variables), len(non_parent_fields))
         for data_product_var in actual_data_product_variables:
             self.assertIn(data_product_var.name, data_product_fields)
-            if data_product_var.name != 'epoch_delta':
-                np.testing.assert_array_equal(data_product_var.value, getattr(data_product, data_product_var.name))
+            if data_product_var.name != "epoch_delta":
+                np.testing.assert_array_equal(
+                    data_product_var.value, getattr(data_product, data_product_var.name)
+                )
             else:
-                np.testing.assert_array_equal(data_product_var.value, np.array([10 * 1e9]))
+                np.testing.assert_array_equal(
+                    data_product_var.value, np.array([10 * 1e9])
+                )
 
     def test_codice_hi_l1a_direct_event_read_from_sdc_cdf(self):
-        l1a_path = get_test_data_path("codice/imap_codice_l1a_hi-direct-events_20260831_v001.0003.cdf")
+        l1a_path = get_test_data_path(
+            "codice/imap_codice_l1a_hi-direct-events_20260831_v001.0003.cdf"
+        )
         l1a_hi_direct_events = CodiceL1aHiDirectEvents.read_from_cdf(l1a_path)
 
         with CDF(str(l1a_path)) as cdf:
-            np.testing.assert_array_equal(l1a_hi_direct_events.epoch, cdf['epoch'][...])
-            np.testing.assert_array_equal(l1a_hi_direct_events.ssd_energy, cdf['ssd_energy'][...])
-            np.testing.assert_array_equal(l1a_hi_direct_events.tof, cdf['tof'][...])
+            np.testing.assert_array_equal(l1a_hi_direct_events.epoch, cdf["epoch"][...])
+            np.testing.assert_array_equal(
+                l1a_hi_direct_events.ssd_energy, cdf["ssd_energy"][...]
+            )
+            np.testing.assert_array_equal(l1a_hi_direct_events.tof, cdf["tof"][...])
 
     def test_l2_sectored_intensities_read_from_sdc_cdf(self):
-        l2_path = get_test_instrument_team_data_path("codice/hi/imap_codice_l2_hi-sectored_20251206_v002.cdf")
+        l2_path = get_test_instrument_team_data_path(
+            "codice/hi/imap_codice_l2_hi-sectored_20251206_v002.cdf"
+        )
         l2_sectored_data = CodiceHiL2SectoredIntensitiesData.read_from_cdf(l2_path)
 
         with CDF(str(l2_path)) as cdf:
             np.testing.assert_array_equal(l2_sectored_data.epoch, cdf["epoch"])
-            expected_epoch_delta = np.array([timedelta(seconds=ns / 1e9) for ns in cdf["epoch_delta_plus"][...]])
-            np.testing.assert_array_equal(l2_sectored_data.epoch_delta_plus, expected_epoch_delta)
-            np.testing.assert_array_equal(l2_sectored_data.spin_angles, cdf['spin_angle'][...])
-            np.testing.assert_array_equal(l2_sectored_data.elevation_angle, cdf['elevation_angle'])
-
-            np.testing.assert_array_equal(l2_sectored_data.data_quality, cdf['data_quality'])
-
-            np.testing.assert_array_equal(l2_sectored_data.h_intensities, cdf['h'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_h, cdf['energy_h'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_h_plus, cdf['energy_h_plus'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_h_minus, cdf['energy_h_minus'])
-
-            np.testing.assert_array_equal(l2_sectored_data.cno_intensities, cdf['cno'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_cno, cdf['energy_cno'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_cno_plus, cdf['energy_cno_plus'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_cno_minus, cdf['energy_cno_minus'])
-
-            np.testing.assert_array_equal(l2_sectored_data.fe_intensities, cdf['fe'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_fe, cdf['energy_fe'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_fe_plus, cdf['energy_fe_plus'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_fe_minus, cdf['energy_fe_minus'])
-
-            np.testing.assert_array_equal(l2_sectored_data.he3he4_intensities, cdf['he3he4'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_he3he4, cdf['energy_he3he4'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_he3he4_plus, cdf['energy_he3he4_plus'])
-            np.testing.assert_array_equal(l2_sectored_data.energy_he3he4_minus, cdf['energy_he3he4_minus'])
-
-    def test_l2_sectored_intensities_read_from_instrument_team_cdf_handles_fill_values(self):
-        all_fill_l2_sectored_intensities_path = \
-            get_test_data_path('codice/imap_codice_l2_hi-sectored_20250814_v002-all-fill.cdf')
-        l2_sectored_intensities = CodiceHiL2SectoredIntensitiesData.read_from_cdf(all_fill_l2_sectored_intensities_path)
+            expected_epoch_delta = np.array(
+                [timedelta(seconds=ns / 1e9) for ns in cdf["epoch_delta_plus"][...]]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.epoch_delta_plus, expected_epoch_delta
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.spin_angles, cdf["spin_angle"][...]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.elevation_angle, cdf["elevation_angle"]
+            )
+
+            np.testing.assert_array_equal(
+                l2_sectored_data.data_quality, cdf["data_quality"]
+            )
+
+            np.testing.assert_array_equal(l2_sectored_data.h_intensities, cdf["h"])
+            np.testing.assert_array_equal(l2_sectored_data.energy_h, cdf["energy_h"])
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_h_plus, cdf["energy_h_plus"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_h_minus, cdf["energy_h_minus"]
+            )
+
+            np.testing.assert_array_equal(l2_sectored_data.cno_intensities, cdf["cno"])
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_cno, cdf["energy_cno"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_cno_plus, cdf["energy_cno_plus"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_cno_minus, cdf["energy_cno_minus"]
+            )
+
+            np.testing.assert_array_equal(l2_sectored_data.fe_intensities, cdf["fe"])
+            np.testing.assert_array_equal(l2_sectored_data.energy_fe, cdf["energy_fe"])
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_fe_plus, cdf["energy_fe_plus"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_fe_minus, cdf["energy_fe_minus"]
+            )
+
+            np.testing.assert_array_equal(
+                l2_sectored_data.he3he4_intensities, cdf["he3he4"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_he3he4, cdf["energy_he3he4"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_he3he4_plus, cdf["energy_he3he4_plus"]
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_data.energy_he3he4_minus, cdf["energy_he3he4_minus"]
+            )
+
+    def test_l2_sectored_intensities_read_from_instrument_team_cdf_handles_fill_values(
+        self,
+    ):
+        all_fill_l2_sectored_intensities_path = get_test_data_path(
+            "codice/imap_codice_l2_hi-sectored_20250814_v002-all-fill.cdf"
+        )
+        l2_sectored_intensities = CodiceHiL2SectoredIntensitiesData.read_from_cdf(
+            all_fill_l2_sectored_intensities_path
+        )
         with CDF(str(all_fill_l2_sectored_intensities_path)) as cdf:
-            np.testing.assert_array_equal(l2_sectored_intensities.cno_intensities, np.full_like(cdf['cno'], np.nan))
-            np.testing.assert_array_equal(l2_sectored_intensities.fe_intensities, np.full_like(cdf['fe'], np.nan))
-            np.testing.assert_array_equal(l2_sectored_intensities.h_intensities, np.full_like(cdf['h'], np.nan))
-            np.testing.assert_array_equal(l2_sectored_intensities.he3he4_intensities,
-                                          np.full_like(cdf['he3he4'], np.nan))
+            np.testing.assert_array_equal(
+                l2_sectored_intensities.cno_intensities,
+                np.full_like(cdf["cno"], np.nan),
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_intensities.fe_intensities, np.full_like(cdf["fe"], np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_intensities.h_intensities, np.full_like(cdf["h"], np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_sectored_intensities.he3he4_intensities,
+                np.full_like(cdf["he3he4"], np.nan),
+            )
 
     @staticmethod
     def _create_expanded_l2_hi_de_dataset(unexpanded_cdf: Path, tempdir: Path) -> Path:
         expanded_cdf_path = tempdir / "expanded_hi_l2_direct_events.cdf"
 
-        with CDF(str(expanded_cdf_path), masterpath=str(unexpanded_cdf), readonly=False) as expanded_cdf:
+        with CDF(
+            str(expanded_cdf_path), masterpath=str(unexpanded_cdf), readonly=False
+        ) as expanded_cdf:
             old_priority_attrs = expanded_cdf["priority"].attrs
             del expanded_cdf["priority"]
             expanded_cdf["priority"] = np.arange(8)
@@ -284,7 +448,7 @@ def _create_expanded_l2_hi_de_dataset(unexpanded_cdf: Path, tempdir: Path) -> Pa
                 "ssd_energy",
                 "ssd_id",
                 "tof",
-                "type"
+                "type",
             ]
 
             for var in event_based_vars:
diff --git a/tests/codice/l3/lo/direct_events/science/test_angle_lookups.py b/tests/codice/l3/lo/direct_events/science/test_angle_lookups.py
index c5271affe..5125d33b7 100644
--- a/tests/codice/l3/lo/direct_events/science/test_angle_lookups.py
+++ b/tests/codice/l3/lo/direct_events/science/test_angle_lookups.py
@@ -2,8 +2,10 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import SpinAngleLookup, \
-    PositionToElevationLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    PositionToElevationLookup,
+    SpinAngleLookup,
+)
 
 
 class TestCoDICEAngleLookup(unittest.TestCase):
@@ -45,7 +47,32 @@ def test_apd_to_elevation_index(self):
         actual_indices = []
         for apd_id in range(1, 25):
             actual_indices.append(elevation_angle_lut.apd_to_elevation_index(apd_id))
-        expected_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
+        expected_indices = [
+            0,
+            1,
+            2,
+            3,
+            4,
+            5,
+            6,
+            7,
+            8,
+            9,
+            10,
+            11,
+            12,
+            11,
+            10,
+            9,
+            8,
+            7,
+            6,
+            5,
+            4,
+            3,
+            2,
+            1,
+        ]
         np.testing.assert_array_equal(actual_indices, expected_indices)
 
     def test_apd_to_elevation(self):
@@ -53,6 +80,30 @@ def test_apd_to_elevation(self):
         actual_elevations = []
         for apd_id in range(1, 25):
             actual_elevations.append(elevation_angle_lut.apd_to_elevation(apd_id))
-        expected_elevations = [0, 15, 30, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 165, 150, 135, 120, 105, 90, 75,
-                               60, 45, 30, 15]
+        expected_elevations = [
+            0,
+            15,
+            30,
+            45,
+            60,
+            75,
+            90,
+            105,
+            120,
+            135,
+            150,
+            165,
+            180,
+            165,
+            150,
+            135,
+            120,
+            105,
+            90,
+            75,
+            60,
+            45,
+            30,
+            15,
+        ]
         np.testing.assert_array_equal(actual_elevations, expected_elevations)
diff --git a/tests/codice/l3/lo/direct_events/science/test_efficiency_lookup.py b/tests/codice/l3/lo/direct_events/science/test_efficiency_lookup.py
index 7210e943c..1288791d7 100644
--- a/tests/codice/l3/lo/direct_events/science/test_efficiency_lookup.py
+++ b/tests/codice/l3/lo/direct_events/science/test_efficiency_lookup.py
@@ -5,7 +5,9 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import EfficiencyLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import (
+    EfficiencyLookup,
+)
 
 
 class TestEfficiencyLookup(unittest.TestCase):
@@ -22,22 +24,28 @@ def test_read_from_csv(self):
             with open(output_csv, "w") as csvfile:
                 csv_writer = csv.writer(csvfile)
 
-                csv_writer.writerow(["species","product","esa_step"]+[f"position_{i}" for i in range(num_positions)])
+                csv_writer.writerow(
+                    ["species", "product", "esa_step"]
+                    + [f"position_{i}" for i in range(num_positions)]
+                )
                 for i in range(num_energies):
-                    csv_writer.writerow([
-                        "hplus","sw",i, *expected_efficiency_data[i, :]
-                    ])
+                    csv_writer.writerow(
+                        ["hplus", "sw", i, *expected_efficiency_data[i, :]]
+                    )
                 for i in range(num_energies):
-                    csv_writer.writerow([
-                        "cplus6","sw",i, *expected_efficiency_data[i, :]*6
-                    ])
+                    csv_writer.writerow(
+                        ["cplus6", "sw", i, *expected_efficiency_data[i, :] * 6]
+                    )
                 for i in range(num_energies):
-                    csv_writer.writerow([
-                        "hplus","nsw",i, *expected_efficiency_data[i, :]*42
-                    ])
-
+                    csv_writer.writerow(
+                        ["hplus", "nsw", i, *expected_efficiency_data[i, :] * 42]
+                    )
 
             efficiency_lookup = EfficiencyLookup.read_from_csv(output_csv, "hplus")
 
-            np.testing.assert_almost_equal(efficiency_lookup.efficiency_data, expected_efficiency_data)
-            self.assertEqual(efficiency_lookup.efficiency_data.shape, (num_energies, num_positions))
+            np.testing.assert_almost_equal(
+                efficiency_lookup.efficiency_data, expected_efficiency_data
+            )
+            self.assertEqual(
+                efficiency_lookup.efficiency_data.shape, (num_energies, num_positions)
+            )
diff --git a/tests/codice/l3/lo/direct_events/science/test_energy_lookup.py b/tests/codice/l3/lo/direct_events/science/test_energy_lookup.py
index dbdee21de..117df203e 100644
--- a/tests/codice/l3/lo/direct_events/science/test_energy_lookup.py
+++ b/tests/codice/l3/lo/direct_events/science/test_energy_lookup.py
@@ -2,18 +2,33 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    EnergyLookup,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestEnergyLookup(unittest.TestCase):
     def test_read_from_csv(self):
-        input_file = get_test_data_path("codice") / "imap_codice_lo-energy-per-charge_20241110_v002.csv"
+        input_file = (
+            get_test_data_path("codice")
+            / "imap_codice_lo-energy-per-charge_20241110_v002.csv"
+        )
         actual_energy_lookup = EnergyLookup.read_from_csv(input_file)
 
         np.testing.assert_array_equal(actual_energy_lookup.bin_centers[0], 81.216)
-        np.testing.assert_array_equal(actual_energy_lookup.bin_centers[127], 0.507357072)
-        np.testing.assert_array_equal(actual_energy_lookup.delta_minus[0], 81.216 - 79.60339125)
-        np.testing.assert_array_equal(actual_energy_lookup.delta_minus[127], 0.507357072 - 0.497319345)
-        np.testing.assert_array_equal(actual_energy_lookup.delta_plus[0], 82.855305 - 81.216)
-        np.testing.assert_array_equal(actual_energy_lookup.delta_plus[127], 0.517446956 - 0.507357072)
+        np.testing.assert_array_equal(
+            actual_energy_lookup.bin_centers[127], 0.507357072
+        )
+        np.testing.assert_array_equal(
+            actual_energy_lookup.delta_minus[0], 81.216 - 79.60339125
+        )
+        np.testing.assert_array_equal(
+            actual_energy_lookup.delta_minus[127], 0.507357072 - 0.497319345
+        )
+        np.testing.assert_array_equal(
+            actual_energy_lookup.delta_plus[0], 82.855305 - 81.216
+        )
+        np.testing.assert_array_equal(
+            actual_energy_lookup.delta_plus[127], 0.517446956 - 0.507357072
+        )
diff --git a/tests/codice/l3/lo/direct_events/science/test_geometric_factor_lookup.py b/tests/codice/l3/lo/direct_events/science/test_geometric_factor_lookup.py
index d21698bc6..6d510fa2c 100644
--- a/tests/codice/l3/lo/direct_events/science/test_geometric_factor_lookup.py
+++ b/tests/codice/l3/lo/direct_events/science/test_geometric_factor_lookup.py
@@ -4,48 +4,115 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup import GeometricFactorLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup import (
+    GeometricFactorLookup,
+)
 from tests.test_helpers import get_test_data_folder
 
 
 class TestGeometricFactorLookup(TestCase):
-
     def test_default_esa_step_end_index(self):
         geometric_factor_lookup = GeometricFactorLookup(2, 0.5)
-        expected_esa_step_end_index = \
-            np.array(
-                [0, 1, 2, 3, 5, 7, 9, 11, 14, 17, 20, 23, 27, 31, 35, 39, 44, 49, 54, 59, 64, 69, 74, 79, 85, 91, 97,
-                 103, 109, 115, 121, 127])
-        np.testing.assert_array_equal(geometric_factor_lookup._esa_step_end_index, expected_esa_step_end_index)
+        expected_esa_step_end_index = np.array(
+            [
+                0,
+                1,
+                2,
+                3,
+                5,
+                7,
+                9,
+                11,
+                14,
+                17,
+                20,
+                23,
+                27,
+                31,
+                35,
+                39,
+                44,
+                49,
+                54,
+                59,
+                64,
+                69,
+                74,
+                79,
+                85,
+                91,
+                97,
+                103,
+                109,
+                115,
+                121,
+                127,
+            ]
+        )
+        np.testing.assert_array_equal(
+            geometric_factor_lookup._esa_step_end_index, expected_esa_step_end_index
+        )
 
     def test_read_from_csv(self):
         expected_num_esa_steps = 128
         expected_num_positions = 24
 
-        path = get_test_data_folder() / 'codice' / 'imap_codice_l2-lo-gfactor_20251212_v003.csv'
+        path = (
+            get_test_data_folder()
+            / "codice"
+            / "imap_codice_l2-lo-gfactor_20251212_v003.csv"
+        )
         lookup = GeometricFactorLookup.read_from_csv(path)
 
         self.assertIsInstance(lookup, GeometricFactorLookup)
 
-        self.assertEqual((expected_num_esa_steps, expected_num_positions), lookup._full_factor.shape)
-        self.assertEqual((expected_num_esa_steps, expected_num_positions), lookup._reduced_factor.shape)
+        self.assertEqual(
+            (expected_num_esa_steps, expected_num_positions), lookup._full_factor.shape
+        )
+        self.assertEqual(
+            (expected_num_esa_steps, expected_num_positions),
+            lookup._reduced_factor.shape,
+        )
         self.assertEqual(8.71567e-05, lookup._full_factor[0, 0])
         self.assertEqual(9.7615504e-06, lookup._reduced_factor[0, 0])
 
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS", 3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
-           3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
-           2)
-    def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_half_spin_first_era(self):
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
+        2,
+    )
+    def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_half_spin_first_era(
+        self,
+    ):
         rgfo_half_spin_threshold = 5
         below_half_spin_threshold = 3
         above_half_spin_threshold = 7
-        rgfo_half_spin = np.ma.masked_array(data=np.array([rgfo_half_spin_threshold, rgfo_half_spin_threshold]))
-        half_spin = np.ma.masked_array(data=np.array([
-            [below_half_spin_threshold, above_half_spin_threshold, rgfo_half_spin_threshold],
-            [above_half_spin_threshold, below_half_spin_threshold, above_half_spin_threshold],
-        ]))
+        rgfo_half_spin = np.ma.masked_array(
+            data=np.array([rgfo_half_spin_threshold, rgfo_half_spin_threshold])
+        )
+        half_spin = np.ma.masked_array(
+            data=np.array(
+                [
+                    [
+                        below_half_spin_threshold,
+                        above_half_spin_threshold,
+                        rgfo_half_spin_threshold,
+                    ],
+                    [
+                        above_half_spin_threshold,
+                        below_half_spin_threshold,
+                        above_half_spin_threshold,
+                    ],
+                ]
+            )
+        )
         full_factors = np.array([[10, 20], [30, 40], [50, 60]])
         reduced_factors = np.array([[1, 2], [3, 4], [5, 6]])
         geometric_factor_lookup = GeometricFactorLookup(
@@ -57,7 +124,7 @@ def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_ha
             rgfo_spin_sector=np.zeros(2),
             rgfo_esa_step=np.zeros(2),
             half_spin=half_spin,
-            epoch=date(2025,11, 23),
+            epoch=date(2025, 11, 23),
         )
         all_spin_sectors_and_positions = (3, 2)
 
@@ -69,27 +136,54 @@ def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_ha
             (1, 1, full_factors),
             (1, 2, reduced_factors),
         ]
-        for epoch, esa_step, expected_factors in epoch_esa_step_index_pairs_and_expected_factors:
+        for (
+            epoch,
+            esa_step,
+            expected_factors,
+        ) in epoch_esa_step_index_pairs_and_expected_factors:
             np.testing.assert_array_equal(
                 actual[epoch, esa_step, :, :],
-                np.broadcast_to(expected_factors[esa_step], all_spin_sectors_and_positions),
-                f"Mismatch at epoch:{epoch}, esa:{esa_step}"
+                np.broadcast_to(
+                    expected_factors[esa_step], all_spin_sectors_and_positions
+                ),
+                f"Mismatch at epoch:{epoch}, esa:{esa_step}",
             )
 
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS", 3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
-           3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
-           2)
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
+        2,
+    )
     def test_get_geometric_factors_gets_all_full_geometric_factors_for_second_era(self):
         rgfo_half_spin_threshold = 5
         below_half_spin_threshold = 3
         above_half_spin_threshold = 7
-        rgfo_half_spin = np.ma.masked_array(data=np.array([rgfo_half_spin_threshold, rgfo_half_spin_threshold]))
-        half_spin = np.ma.masked_array(data=np.array([
-            [below_half_spin_threshold, above_half_spin_threshold, rgfo_half_spin_threshold],
-            [above_half_spin_threshold, below_half_spin_threshold, above_half_spin_threshold],
-        ]))
+        rgfo_half_spin = np.ma.masked_array(
+            data=np.array([rgfo_half_spin_threshold, rgfo_half_spin_threshold])
+        )
+        half_spin = np.ma.masked_array(
+            data=np.array(
+                [
+                    [
+                        below_half_spin_threshold,
+                        above_half_spin_threshold,
+                        rgfo_half_spin_threshold,
+                    ],
+                    [
+                        above_half_spin_threshold,
+                        below_half_spin_threshold,
+                        above_half_spin_threshold,
+                    ],
+                ]
+            )
+        )
         full_factors = np.array([[10, 20], [30, 40], [50, 60]])
         reduced_factors = np.array([[1, 2], [3, 4], [5, 6]])
 
@@ -102,33 +196,56 @@ def test_get_geometric_factors_gets_all_full_geometric_factors_for_second_era(se
             rgfo_spin_sector=np.zeros(2),
             rgfo_esa_step=np.zeros(2),
             half_spin=half_spin,
-            epoch=date(2025,11, 24),
+            epoch=date(2025, 11, 24),
         )
         actual_end_second_era = geometric_factor_lookup.get_geometric_factors(
             rgfo_half_spin=rgfo_half_spin,
             rgfo_spin_sector=np.zeros(2),
             rgfo_esa_step=np.zeros(2),
             half_spin=half_spin,
-            epoch=date(2026,1, 28),
+            epoch=date(2026, 1, 28),
         )
-        expected = np.broadcast_to(full_factors[None, :, None, :], (2,3,3,2))
+        expected = np.broadcast_to(full_factors[None, :, None, :], (2, 3, 3, 2))
         np.testing.assert_array_equal(actual_start_second_era, expected)
         np.testing.assert_array_equal(actual_end_second_era, expected)
 
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS", 3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
-           3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
-           2)
-    def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_half_spin_third_era(self):
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
+        2,
+    )
+    def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_half_spin_third_era(
+        self,
+    ):
         rgfo_half_spin_threshold = 5
         below_half_spin_threshold = 3
         above_half_spin_threshold = 7
-        rgfo_half_spin = np.ma.masked_array(data=np.array([rgfo_half_spin_threshold, rgfo_half_spin_threshold]))
-        half_spin = np.ma.masked_array(data=np.array([
-            [below_half_spin_threshold, above_half_spin_threshold, above_half_spin_threshold],
-            [above_half_spin_threshold, below_half_spin_threshold, above_half_spin_threshold],
-        ]))
+        rgfo_half_spin = np.ma.masked_array(
+            data=np.array([rgfo_half_spin_threshold, rgfo_half_spin_threshold])
+        )
+        half_spin = np.ma.masked_array(
+            data=np.array(
+                [
+                    [
+                        below_half_spin_threshold,
+                        above_half_spin_threshold,
+                        above_half_spin_threshold,
+                    ],
+                    [
+                        above_half_spin_threshold,
+                        below_half_spin_threshold,
+                        above_half_spin_threshold,
+                    ],
+                ]
+            )
+        )
         full_factors = np.array([[10, 20], [30, 40], [50, 60]])
         reduced_factors = np.array([[1, 2], [3, 4], [5, 6]])
         geometric_factor_lookup = GeometricFactorLookup(
@@ -140,7 +257,7 @@ def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_ha
             rgfo_spin_sector=np.zeros(2),
             rgfo_esa_step=np.zeros(2),
             half_spin=half_spin,
-            epoch=date(2026,1,29),
+            epoch=date(2026, 1, 29),
         )
         all_spin_sectors_and_positions = (3, 2)
 
@@ -152,22 +269,42 @@ def test_get_geometric_factors_gets_reduced_where_half_spin_greater_than_rgfo_ha
             (1, 1, full_factors),
             (1, 2, reduced_factors),
         ]
-        for epoch, esa_step, expected_factors in epoch_esa_step_index_pairs_and_expected_factors:
+        for (
+            epoch,
+            esa_step,
+            expected_factors,
+        ) in epoch_esa_step_index_pairs_and_expected_factors:
             np.testing.assert_array_equal(
                 actual[epoch, esa_step, :, :],
-                np.broadcast_to(expected_factors[esa_step], all_spin_sectors_and_positions))
-
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS", 3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
-           24)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
-           2)
-    def test_get_geometric_factors_gets_reduced_where_equal_half_spin_greater_spin_sector_third_era(self):
+                np.broadcast_to(
+                    expected_factors[esa_step], all_spin_sectors_and_positions
+                ),
+            )
+
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
+        24,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
+        2,
+    )
+    def test_get_geometric_factors_gets_reduced_where_equal_half_spin_greater_spin_sector_third_era(
+        self,
+    ):
         rgfo_half_spin = np.ma.masked_array(data=np.array([2, 2]))
-        half_spin = np.ma.masked_array(data=np.array([
-            [2, 2, 2],
-            [2, 2, 2],
-        ]))
+        half_spin = np.ma.masked_array(
+            data=np.array(
+                [
+                    [2, 2, 2],
+                    [2, 2, 2],
+                ]
+            )
+        )
         rgfo_spin_sector = np.ma.masked_array(data=np.array([6, 8]))
         rgfo_esa_step = np.ma.masked_array(data=np.array([3, 3]))
 
@@ -182,7 +319,7 @@ def test_get_geometric_factors_gets_reduced_where_equal_half_spin_greater_spin_s
             rgfo_spin_sector=rgfo_spin_sector,
             rgfo_esa_step=rgfo_esa_step,
             half_spin=half_spin,
-            epoch=date(2026,1, 29),
+            epoch=date(2026, 1, 29),
         )
 
         sector_mod_12 = np.arange(24) % 12
@@ -195,18 +332,31 @@ def test_get_geometric_factors_gets_reduced_where_equal_half_spin_greater_spin_s
         )
         np.testing.assert_array_equal(actual, expected)
 
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS", 3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
-           3)
-    @patch("imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
-           2)
-    def test_get_geometric_factors_gets_reduced_where_equal_half_spin_equal_spin_sector_greater_esa_step_third_era(self):
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_ESA_STEPS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_SPIN_SECTORS",
+        3,
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup.CODICE_LO_NUM_AZIMUTH_BINS",
+        2,
+    )
+    def test_get_geometric_factors_gets_reduced_where_equal_half_spin_equal_spin_sector_greater_esa_step_third_era(
+        self,
+    ):
         rgfo_half_spin = np.ma.masked_array(data=np.array([2, 2]))
 
-        half_spin = np.ma.masked_array(data=np.array([
-            [2, 2, 2],
-            [2, 2, 2],
-        ]))
+        half_spin = np.ma.masked_array(
+            data=np.array(
+                [
+                    [2, 2, 2],
+                    [2, 2, 2],
+                ]
+            )
+        )
 
         rgfo_spin_sector = np.ma.masked_array(data=np.array([1, 1]))
         rgfo_esa_step = np.ma.masked_array(data=np.array([0, 1]))
@@ -221,7 +371,7 @@ def test_get_geometric_factors_gets_reduced_where_equal_half_spin_equal_spin_sec
             rgfo_spin_sector=rgfo_spin_sector,
             rgfo_esa_step=rgfo_esa_step,
             half_spin=half_spin,
-            epoch=date(2026,1, 29),
+            epoch=date(2026, 1, 29),
         )
 
         epoch_esa_step_index_and_expected_factors = [
@@ -232,8 +382,11 @@ def test_get_geometric_factors_gets_reduced_where_equal_half_spin_equal_spin_sec
             (1, 1, full_factors),
             (1, 2, reduced_factors),
         ]
-        for epoch, esa_step, expected_factors in epoch_esa_step_index_and_expected_factors:
+        for (
+            epoch,
+            esa_step,
+            expected_factors,
+        ) in epoch_esa_step_index_and_expected_factors:
             np.testing.assert_array_equal(
-                actual[epoch, esa_step, 1, :],
-                expected_factors[esa_step]
+                actual[epoch, esa_step, 1, :], expected_factors[esa_step]
             )
diff --git a/tests/codice/l3/lo/direct_events/science/test_mass_coefficient_lookup.py b/tests/codice/l3/lo/direct_events/science/test_mass_coefficient_lookup.py
index 24d5bcec9..698c450d4 100644
--- a/tests/codice/l3/lo/direct_events/science/test_mass_coefficient_lookup.py
+++ b/tests/codice/l3/lo/direct_events/science/test_mass_coefficient_lookup.py
@@ -3,13 +3,22 @@
 import unittest
 from pathlib import Path
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import MassCoefficientLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import (
+    MassCoefficientLookup,
+)
 
 
 class TestMassCoefficientLookup(unittest.TestCase):
     def test_mass_coefficient_lookup(self):
         mass_coefficient_lookup_csv_path = "mass_coefficient_lookup.csv"
-        coefficients = [[5.29633], [-1.5053], [-2.86483], [0.473693], [0.0900633], [0.0783456]]
+        coefficients = [
+            [5.29633],
+            [-1.5053],
+            [-2.86483],
+            [0.473693],
+            [0.0900633],
+            [0.0783456],
+        ]
 
         with tempfile.TemporaryDirectory() as tmpdir:
             temp_csv_file = Path(tmpdir) / mass_coefficient_lookup_csv_path
diff --git a/tests/codice/l3/lo/direct_events/science/test_mass_species_bin_lookup.py b/tests/codice/l3/lo/direct_events/science/test_mass_species_bin_lookup.py
index f79576482..7eab888f1 100644
--- a/tests/codice/l3/lo/direct_events/science/test_mass_species_bin_lookup.py
+++ b/tests/codice/l3/lo/direct_events/science/test_mass_species_bin_lookup.py
@@ -2,13 +2,17 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import (
+    MassSpeciesBinLookup,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestMassSpeciesBinLookup(unittest.TestCase):
     def test_read_csv(self):
-        csv_path = get_test_data_path("codice/imap_codice_lo-mass-species-bin-lookup_20250309_v003.csv")
+        csv_path = get_test_data_path(
+            "codice/imap_codice_lo-mass-species-bin-lookup_20250309_v003.csv"
+        )
         mass_species_bin_lookup = MassSpeciesBinLookup.read_from_csv(csv_path)
 
         expected_species = ["hplus", "heplusplus", "oplus6", "heplus"]
@@ -16,8 +20,12 @@ def test_read_csv(self):
         expected_mass_range = [(0.5, 1.5), (2.0, 7.5), (14.0, 18.0), (1.0, 7.5)]
 
         np.testing.assert_array_equal(mass_species_bin_lookup.species, expected_species)
-        np.testing.assert_array_equal(mass_species_bin_lookup.mass_per_charge, expected_mass_per_charge)
-        np.testing.assert_array_equal(mass_species_bin_lookup.mass_ranges, expected_mass_range)
+        np.testing.assert_array_equal(
+            mass_species_bin_lookup.mass_per_charge, expected_mass_per_charge
+        )
+        np.testing.assert_array_equal(
+            mass_species_bin_lookup.mass_ranges, expected_mass_range
+        )
 
     def test_get_species(self):
         lookup = MassSpeciesBinLookup(
@@ -36,15 +44,14 @@ def test_get_species(self):
         self.assertEqual(4, lookup.get_num_species())
 
     def test_get_species_index(self):
-        csv_path = get_test_data_path("codice/imap_codice_lo-mass-species-bin-lookup_20250309_v003.csv")
+        csv_path = get_test_data_path(
+            "codice/imap_codice_lo-mass-species-bin-lookup_20250309_v003.csv"
+        )
         mass_species_bin_lookup = MassSpeciesBinLookup.read_from_csv(csv_path)
 
-        test_cases = [
-            ("hplus", 0),
-            ("heplusplus", 1),
-            ("oplus6", 2),
-            ("heplus", 3)
-        ]
+        test_cases = [("hplus", 0), ("heplusplus", 1), ("oplus6", 2), ("heplus", 3)]
         for species, index in test_cases:
             with self.subTest(species):
-                self.assertEqual(index, mass_species_bin_lookup.get_species_index(species))
+                self.assertEqual(
+                    index, mass_species_bin_lookup.get_species_index(species)
+                )
diff --git a/tests/codice/l3/lo/science/test_codice_lo_calculations.py b/tests/codice/l3/lo/science/test_codice_lo_calculations.py
index dd677731e..ae1143810 100644
--- a/tests/codice/l3/lo/science/test_codice_lo_calculations.py
+++ b/tests/codice/l3/lo/science/test_codice_lo_calculations.py
@@ -4,53 +4,86 @@
 
 import numpy as np
 
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import PositionToElevationLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import MassCoefficientLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import MassSpeciesBinLookup
-from imap_l3_processing.codice.l3.lo.models import EnergyAndSpinAngle, CodiceLoDirectEventData
-from imap_l3_processing.codice.l3.lo.science.codice_lo_calculations import calculate_partial_densities, \
-    calculate_total_number_of_events, calculate_mass, calculate_mass_per_charge, \
-    rebin_to_counts_by_species_elevation_and_spin_sector, rebin_direct_events_by_energy_and_spin_sector, \
-    CODICE_LO_NUM_AZIMUTH_BINS, combine_priorities_for_species_and_calculate_count_uncertainty, \
-    rebin_3d_distribution_azimuth_to_elevation, convert_counts_to_intensity, rebin_direct_events_for_normalization, \
-    calculate_normalization_factor, lookup_normalization_per_event
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    PositionToElevationLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    EnergyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_coefficient_lookup import (
+    MassCoefficientLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.mass_species_bin_lookup import (
+    MassSpeciesBinLookup,
+)
+from imap_l3_processing.codice.l3.lo.models import (
+    CodiceLoDirectEventData,
+)
+from imap_l3_processing.codice.l3.lo.science.codice_lo_calculations import (
+    CODICE_LO_NUM_AZIMUTH_BINS,
+    calculate_mass,
+    calculate_mass_per_charge,
+    calculate_normalization_factor,
+    calculate_partial_densities,
+    calculate_total_number_of_events,
+    combine_priorities_for_species_and_calculate_count_uncertainty,
+    convert_counts_to_intensity,
+    lookup_normalization_per_event,
+    rebin_3d_distribution_azimuth_to_elevation,
+    rebin_direct_events_by_energy_and_spin_sector,
+    rebin_direct_events_for_normalization,
+    rebin_to_counts_by_species_elevation_and_spin_sector,
+)
 
 
 class TestCodiceLoCalculations(unittest.TestCase):
-
     def test_calculate_total_number_of_events(self):
-        priority_0_tcrs = np.array([
-            [[1, 2, 3],
-             [4, 5, 6],
-             [7, 8, 9],
-             [10, 11, 12]],
-            [[2, 2, 3],
-             [4, 5, 6],
-             [7, 8, 9],
-             [10, 11, 12]]])
+        priority_0_tcrs = np.array(
+            [
+                [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
+                [[2, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
+            ]
+        )
         acquisition_time = np.array([1, 2, 3, 4]) * 1_000_000
 
         expected_total_number_of_events = [6 + 30 + 72 + 132, 7 + 30 + 72 + 132]
 
-        actual_total_number_of_events = calculate_total_number_of_events(priority_0_tcrs, acquisition_time)
-        np.testing.assert_array_equal(actual_total_number_of_events, expected_total_number_of_events)
+        actual_total_number_of_events = calculate_total_number_of_events(
+            priority_0_tcrs, acquisition_time
+        )
+        np.testing.assert_array_equal(
+            actual_total_number_of_events, expected_total_number_of_events
+        )
 
     def test_calculate_mass(self):
         apd_energy = np.array([[np.exp(1)], [np.exp(2)]])
         tof = np.array([[np.exp(50)], [np.exp(60)]])
 
-        lookup = MassCoefficientLookup(np.array([10e-1, 10e-2, 10e-3, 10e-4, 10e-5, 10e-6]))
+        lookup = MassCoefficientLookup(
+            np.array([10e-1, 10e-2, 10e-3, 10e-4, 10e-5, 10e-6])
+        )
         actual_mass = calculate_mass(apd_energy, tof, lookup)
 
-        expected_mass_1 = lookup[0] + lookup[1] * 1 + lookup[2] * 50 + lookup[3] * 1 * 50 + lookup[4] * 1 + lookup[
-            5] * np.power(50, 3)
-        expected_mass_2 = lookup[0] + lookup[1] * 2 + lookup[2] * 60 + lookup[3] * 2 * 60 + lookup[4] * np.power(2, 2) + \
-                          lookup[5] * np.power(60, 3)
+        expected_mass_1 = (
+            lookup[0]
+            + lookup[1] * 1
+            + lookup[2] * 50
+            + lookup[3] * 1 * 50
+            + lookup[4] * 1
+            + lookup[5] * np.power(50, 3)
+        )
+        expected_mass_2 = (
+            lookup[0]
+            + lookup[1] * 2
+            + lookup[2] * 60
+            + lookup[3] * 2 * 60
+            + lookup[4] * np.power(2, 2)
+            + lookup[5] * np.power(60, 3)
+        )
 
-        expected_mass_calculation = np.array([
-            [np.e ** expected_mass_1],
-            [np.e ** expected_mass_2]])
+        expected_mass_calculation = np.array(
+            [[np.e**expected_mass_1], [np.e**expected_mass_2]]
+        )
 
         np.testing.assert_array_equal(actual_mass, expected_mass_calculation)
 
@@ -62,17 +95,27 @@ def test_calculate_mass_per_charge(self):
         ENERGY_LOST_IN_CARBON_FOIL = 0
         CONVERSION_CONSTANT_K = 1.692e-5
 
-        mass_per_charge_1 = (1 + POST_ACCELERATION_VOLTAGE_IN_KV - ENERGY_LOST_IN_CARBON_FOIL) * (
-                5 ** 2) * CONVERSION_CONSTANT_K
+        mass_per_charge_1 = (
+            (1 + POST_ACCELERATION_VOLTAGE_IN_KV - ENERGY_LOST_IN_CARBON_FOIL)
+            * (5**2)
+            * CONVERSION_CONSTANT_K
+        )
 
-        mass_per_charge_2 = (2 + POST_ACCELERATION_VOLTAGE_IN_KV - ENERGY_LOST_IN_CARBON_FOIL) * (
-                6 ** 2) * CONVERSION_CONSTANT_K
+        mass_per_charge_2 = (
+            (2 + POST_ACCELERATION_VOLTAGE_IN_KV - ENERGY_LOST_IN_CARBON_FOIL)
+            * (6**2)
+            * CONVERSION_CONSTANT_K
+        )
 
         actual_mass_per_charge = calculate_mass_per_charge(energy_per_charge, tof)
-        np.testing.assert_array_equal(actual_mass_per_charge, np.array([[mass_per_charge_1], [mass_per_charge_2]]))
+        np.testing.assert_array_equal(
+            actual_mass_per_charge, np.array([[mass_per_charge_1], [mass_per_charge_2]])
+        )
 
     def test_calculate_mass_per_charge_handles_fill_value(self):
-        tof = np.ma.masked_array([[432, 234], [434, 347]], mask=[[False, True], [True, False]])
+        tof = np.ma.masked_array(
+            [[432, 234], [434, 347]], mask=[[False, True], [True, False]]
+        )
         apd_energy = np.array([[np.nan, 2342.2], [4324.8, np.nan]])
 
         actual_mass_per_charge = calculate_mass_per_charge(apd_energy, tof)
@@ -86,7 +129,9 @@ def test_calculate_mass_handles_fill_value(self):
         rng = np.random.default_rng()
         mass_coefficients = MassCoefficientLookup(coefficients=rng.random(size=6))
 
-        tof = np.ma.masked_array([[432, 234], [434, 347]], mask=[[False, True], [True, False]])
+        tof = np.ma.masked_array(
+            [[432, 234], [434, 347]], mask=[[False, True], [True, False]]
+        )
         apd_energy = np.array([[np.nan, 2342.2], [4324.8, np.nan]])
 
         actual_mass = calculate_mass(apd_energy, tof, mass_coefficients)
@@ -103,28 +148,40 @@ def test_calculate_partial_densities(self):
         intensities = rng.random((len(epochs), len(energy_steps), 1))
         mass_per_charge = 10
 
-        partial_densities = calculate_partial_densities(intensities, energy_steps, mass_per_charge)
+        partial_densities = calculate_partial_densities(
+            intensities, energy_steps, mass_per_charge
+        )
 
         expected_partial_densities = np.sum(
-            2.283e-8 * np.deg2rad(15) * np.deg2rad(15) * .4 * intensities * np.sqrt(
-                energy_steps[np.newaxis, :, np.newaxis]) * np.sqrt(mass_per_charge), axis=(1, 2))
+            2.283e-8
+            * np.deg2rad(15)
+            * np.deg2rad(15)
+            * 0.4
+            * intensities
+            * np.sqrt(energy_steps[np.newaxis, :, np.newaxis])
+            * np.sqrt(mass_per_charge),
+            axis=(1, 2),
+        )
 
         np.testing.assert_array_equal(expected_partial_densities, partial_densities)
 
     def test_calculate_partial_densities_ignores_nan_intensities(self):
         energy_steps = np.array([1, 100, 10000])
-        intensities = np.array([
-            [[1], [2], [3]],
-            [[4], [5], [np.nan]]
-        ])
+        intensities = np.array([[[1], [2], [3]], [[4], [5], [np.nan]]])
         mass_per_charge = 10
 
-        partial_densities = calculate_partial_densities(intensities, energy_steps, mass_per_charge)
+        partial_densities = calculate_partial_densities(
+            intensities, energy_steps, mass_per_charge
+        )
 
-        expected_partial_densities = 2.283e-8 * np.deg2rad(15) * np.deg2rad(15) * .4 * np.array([
-            1 + 2 * 10 + 3 * 100,
-            4 + 5 * 10
-        ]) * np.sqrt(mass_per_charge)
+        expected_partial_densities = (
+            2.283e-8
+            * np.deg2rad(15)
+            * np.deg2rad(15)
+            * 0.4
+            * np.array([1 + 2 * 10 + 3 * 100, 4 + 5 * 10])
+            * np.sqrt(mass_per_charge)
+        )
 
         np.testing.assert_allclose(expected_partial_densities, partial_densities)
 
@@ -137,59 +194,83 @@ def test_rebin_to_counts_by_species_elevation_and_spin_sector(self):
 
         # [[He+, Fe, He+, no call], [O+5, no call, no call, no call]],
         # [[Mg, no call, None, no call], [no call, no call, no call, no call]]
-        expected_species_returned = [
-            "He+", "Fe", "He+", "O+5",
-            "Mg", None
-        ]
+        expected_species_returned = ["He+", "Fe", "He+", "O+5", "Mg", None]
         mock_species_mass_range_lookup = Mock(spec=MassSpeciesBinLookup)
-        mock_species_mass_range_lookup.get_species.side_effect = expected_species_returned
+        mock_species_mass_range_lookup.get_species.side_effect = (
+            expected_species_returned
+        )
         mock_species_mass_range_lookup.get_num_species.return_value = num_species
 
         def mock_get_species_index(species):
-            return_values = {"He+": 0, "Fe": 1, "Mg": 2, "O+5": 3, }
+            return_values = {
+                "He+": 0,
+                "Fe": 1,
+                "Mg": 2,
+                "O+5": 3,
+            }
             return return_values[species]
 
         mock_species_mass_range_lookup.get_species_index = mock_get_species_index
 
-        num_events = np.array([[4, 2],
-                               [3, 1]])
-        num_events = np.ma.masked_array(num_events, mask=[[False, False], [False, True]])
-
-        normalized_per_event = np.array([
-            [[100.78, 30.9, 200.1, np.nan], [50.458, np.nan, np.nan, np.nan]],
-            [[60.22, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan]]
-        ])
-
-        spin_sector_indices = np.array([
-            [[2, 1, 2, 0], [0, 0, 0, 0]],
-            [[2, 2, 2, 0], [0, 0, 0, 0]]
-        ])
-
-        apd_id = np.ma.masked_array(data=np.array([
-            [[2, 24, 2, 0], [1, 25, 255, 255]],
-            [[9, 9, 9, 255], [1, 255, 255, 255]]
-        ]), mask=np.array([
-            [[False, False, False, False], [False, False, True, True]],
-            [[False, True, False, True], [False, True, True, True]],
-        ]))
-
-        energy_step = np.ma.masked_array(data=np.array([
-            [[0, 100, 0, 0], [0, 100, 255, 255]],
-            [[127, 255, 127, 255], [0, 255, 255, 255]]
-        ]), mask=np.array([
-            [[False, False, False, False], [False, False, True, True]],
-            [[False, True, False, True], [False, True, True, True]],
-        ]))
-
-        mass = np.array([
-            [[6, 5, 4, np.nan], [7, 5, np.nan, np.nan]],
-            [[3, 3, 3, np.nan], [7, np.nan, np.nan, np.nan]],
-        ])
-
-        mass_per_charge = np.array([
-            [[1, 2, 3, np.nan], [8, 2, np.nan, np.nan]],
-            [[4, 4, 4, np.nan], [9, np.nan, np.nan, np.nan]],
-        ])
+        num_events = np.array([[4, 2], [3, 1]])
+        num_events = np.ma.masked_array(
+            num_events, mask=[[False, False], [False, True]]
+        )
+
+        normalized_per_event = np.array(
+            [
+                [[100.78, 30.9, 200.1, np.nan], [50.458, np.nan, np.nan, np.nan]],
+                [[60.22, np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan, np.nan]],
+            ]
+        )
+
+        spin_sector_indices = np.array(
+            [[[2, 1, 2, 0], [0, 0, 0, 0]], [[2, 2, 2, 0], [0, 0, 0, 0]]]
+        )
+
+        apd_id = np.ma.masked_array(
+            data=np.array(
+                [
+                    [[2, 24, 2, 0], [1, 25, 255, 255]],
+                    [[9, 9, 9, 255], [1, 255, 255, 255]],
+                ]
+            ),
+            mask=np.array(
+                [
+                    [[False, False, False, False], [False, False, True, True]],
+                    [[False, True, False, True], [False, True, True, True]],
+                ]
+            ),
+        )
+
+        energy_step = np.ma.masked_array(
+            data=np.array(
+                [
+                    [[0, 100, 0, 0], [0, 100, 255, 255]],
+                    [[127, 255, 127, 255], [0, 255, 255, 255]],
+                ]
+            ),
+            mask=np.array(
+                [
+                    [[False, False, False, False], [False, False, True, True]],
+                    [[False, True, False, True], [False, True, True, True]],
+                ]
+            ),
+        )
+
+        mass = np.array(
+            [
+                [[6, 5, 4, np.nan], [7, 5, np.nan, np.nan]],
+                [[3, 3, 3, np.nan], [7, np.nan, np.nan, np.nan]],
+            ]
+        )
+
+        mass_per_charge = np.array(
+            [
+                [[1, 2, 3, np.nan], [8, 2, np.nan, np.nan]],
+                [[4, 4, 4, np.nan], [9, np.nan, np.nan, np.nan]],
+            ]
+        )
 
         direct_event_data = Mock(spec=CodiceLoDirectEventData)
         direct_event_data.mass = mass
@@ -205,29 +286,35 @@ def mock_get_species_index(species):
             mock_species_mass_range_lookup,
         )
 
-        mock_species_mass_range_lookup.get_species.assert_has_calls([
-            call(mass[0, 0, 0], mass_per_charge[0, 0, 0]),
-            call(mass[0, 0, 1], mass_per_charge[0, 0, 1]),
-            call(mass[0, 0, 2], mass_per_charge[0, 0, 2]),
-
-            call(mass[0, 1, 0], mass_per_charge[0, 1, 0]),
-
-            call(mass[1, 0, 0], mass_per_charge[1, 0, 0]),
-            call(mass[1, 0, 2], mass_per_charge[1, 0, 2]),
-        ])
+        mock_species_mass_range_lookup.get_species.assert_has_calls(
+            [
+                call(mass[0, 0, 0], mass_per_charge[0, 0, 0]),
+                call(mass[0, 0, 1], mass_per_charge[0, 0, 1]),
+                call(mass[0, 0, 2], mass_per_charge[0, 0, 2]),
+                call(mass[0, 1, 0], mass_per_charge[0, 1, 0]),
+                call(mass[1, 0, 0], mass_per_charge[1, 0, 0]),
+                call(mass[1, 0, 2], mass_per_charge[1, 0, 2]),
+            ]
+        )
 
         self.assertIsInstance(actual_counts_3d_data, np.ndarray)
 
-        output_shape = [num_species, num_epochs, num_priorities, num_esa_steps, num_spin_sectors,
-                        CODICE_LO_NUM_AZIMUTH_BINS]
-        self.assertEqual(
-            tuple(output_shape),
-            actual_counts_3d_data.shape)
+        output_shape = [
+            num_species,
+            num_epochs,
+            num_priorities,
+            num_esa_steps,
+            num_spin_sectors,
+            CODICE_LO_NUM_AZIMUTH_BINS,
+        ]
+        self.assertEqual(tuple(output_shape), actual_counts_3d_data.shape)
 
         rebinned_shape = tuple(output_shape[1:])
         expected_he_plus_counts = np.zeros(rebinned_shape)
         expected_he_plus_counts[0, 0, 0, 2, 1] = 300.88
-        np.testing.assert_array_equal(actual_counts_3d_data[0, ...], expected_he_plus_counts)
+        np.testing.assert_array_equal(
+            actual_counts_3d_data[0, ...], expected_he_plus_counts
+        )
 
         expected_fe_counts = np.zeros(rebinned_shape)
         expected_fe_counts[0, 0, 100, 1, 23] = 30.9
@@ -247,19 +334,28 @@ def test_rebin_direct_events_by_energy_and_spin_sector(self):
         num_priorities = 3
         num_epochs = 1
         event_buffer_len = 15
-        num_events = np.ma.masked_array(np.array([[1, 2, 4]]), mask=[[False, True, False]])
-        spin_sector = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=np.uint8)
+        num_events = np.ma.masked_array(
+            np.array([[1, 2, 4]]), mask=[[False, True, False]]
+        )
+        spin_sector = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=np.uint8
+        )
         spin_sector[0, 0, :1] = [3]
         spin_sector[0, 2, :4] = [5, 6, 5, 7]
 
-        energy_step = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=np.uint8)
+        energy_step = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=np.uint8
+        )
         energy_step[0, 0, :1] = [7]
         energy_step[0, 2, :4] = [1, 3, 1, 4]
 
-        result = rebin_direct_events_by_energy_and_spin_sector(num_events, spin_sector, energy_step,
-                                                               num_spin_angle_bins, num_energy_bins)
+        result = rebin_direct_events_by_energy_and_spin_sector(
+            num_events, spin_sector, energy_step, num_spin_angle_bins, num_energy_bins
+        )
 
-        expected_rebinned_counts = np.zeros((num_epochs, num_priorities, num_energy_bins, num_spin_angle_bins))
+        expected_rebinned_counts = np.zeros(
+            (num_epochs, num_priorities, num_energy_bins, num_spin_angle_bins)
+        )
         expected_rebinned_counts[0, 0, 7, 3] = 1
         expected_rebinned_counts[0, 2, 1, 5] = 2
         expected_rebinned_counts[0, 2, 3, 6] = 1
@@ -267,28 +363,36 @@ def test_rebin_direct_events_by_energy_and_spin_sector(self):
 
         np.testing.assert_array_equal(result, expected_rebinned_counts)
 
-
     def test_rebin_direct_events_by_energy_and_spin_sector_ignores_masked_events(self):
         num_energy_bins = 30
         num_spin_angle_bins = 20
         num_priorities = 3
         num_epochs = 1
         event_buffer_len = 15
-        num_events = np.ma.masked_array(np.array([[1, 2, 4]]), mask=[[False, True, False]])
-        spin_sector = np.ma.zeros((num_epochs, num_priorities, event_buffer_len), dtype=np.uint8)
+        num_events = np.ma.masked_array(
+            np.array([[1, 2, 4]]), mask=[[False, True, False]]
+        )
+        spin_sector = np.ma.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=np.uint8
+        )
         spin_sector[0, 0, :1] = [3]
         spin_sector[0, 2, :4] = [5, 6, 255, 7]
         spin_sector[0, 2, 2] = np.ma.masked
 
-        energy_step = np.ma.zeros((num_epochs, num_priorities, event_buffer_len), dtype=np.uint8)
+        energy_step = np.ma.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=np.uint8
+        )
         energy_step[0, 0, :1] = [7]
         energy_step[0, 2, :4] = [1, 255, 1, 4]
         energy_step[0, 2, 1] = np.ma.masked
 
-        result = rebin_direct_events_by_energy_and_spin_sector(num_events, spin_sector, energy_step,
-                                                               num_spin_angle_bins, num_energy_bins)
+        result = rebin_direct_events_by_energy_and_spin_sector(
+            num_events, spin_sector, energy_step, num_spin_angle_bins, num_energy_bins
+        )
 
-        expected_rebinned_counts = np.zeros((num_epochs, num_priorities, num_energy_bins, num_spin_angle_bins))
+        expected_rebinned_counts = np.zeros(
+            (num_epochs, num_priorities, num_energy_bins, num_spin_angle_bins)
+        )
         expected_rebinned_counts[0, 0, 7, 3] = 1
         expected_rebinned_counts[0, 2, 1, 5] = 1
         expected_rebinned_counts[0, 2, 3, 6] = 0
@@ -302,19 +406,28 @@ def test_rebin_direct_events_for_normalization(self):
         num_priorities = 3
         num_epochs = 1
         event_buffer_len = 15
-        num_events = np.ma.masked_array(np.array([[2, 2, 4]]), mask=[[False, True, False]])
-        spin_sector = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=np.uint8)
+        num_events = np.ma.masked_array(
+            np.array([[2, 2, 4]]), mask=[[False, True, False]]
+        )
+        spin_sector = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=np.uint8
+        )
         spin_sector[0, 0, :2] = [3, 3 + 12]
         spin_sector[0, 2, :4] = [5, 6, 5, 7 + 12]
 
-        energy_step = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=np.uint8)
+        energy_step = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=np.uint8
+        )
         energy_step[0, 0, :2] = [7, 7]
         energy_step[0, 2, :4] = [1, 3, 1, 4]
 
-        result = rebin_direct_events_for_normalization(num_events, spin_sector, energy_step, num_spin_sectors,
-                                                       num_energy_bins)
+        result = rebin_direct_events_for_normalization(
+            num_events, spin_sector, energy_step, num_spin_sectors, num_energy_bins
+        )
 
-        expected_rebinned_counts = np.zeros((num_epochs, num_priorities, num_energy_bins, num_spin_sectors))
+        expected_rebinned_counts = np.zeros(
+            (num_epochs, num_priorities, num_energy_bins, num_spin_sectors)
+        )
         expected_rebinned_counts[0, 0, 7, 3] = 2
         expected_rebinned_counts[0, 2, 1, 5] = 2
         expected_rebinned_counts[0, 2, 3, 6] = 1
@@ -333,23 +446,35 @@ def test_calculate_normalization_factor(self):
         num_esa_steps = 128
         num_l1_spin_sectors = 12
         num_l2_spin_sectors = 2 * num_l1_spin_sectors
-        priority_counts = np.zeros((num_epochs, num_priorities, num_esa_steps, num_l1_spin_sectors))
+        priority_counts = np.zeros(
+            (num_epochs, num_priorities, num_esa_steps, num_l1_spin_sectors)
+        )
         priority_counts[0, 0, 0, 0] = 4
         priority_counts[0, 0, 0, 2] = 20
 
         event_buffer_len = 15
         num_events = np.zeros((num_epochs, num_priorities), dtype=int)
-        spin_sectors = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=int)
-        energy_steps = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=int)
-
+        spin_sectors = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=int
+        )
+        energy_steps = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=int
+        )
 
         num_events[0, 0] = 4
         spin_sectors[0, 0, :4] = [0, 2, 12, 0]
         energy_steps[0, 0, :4] = [0, 0, 0, 0]
 
-        result = calculate_normalization_factor(priority_counts, num_events, energy_steps, spin_sectors)
+        result = calculate_normalization_factor(
+            priority_counts, num_events, energy_steps, spin_sectors
+        )
 
-        expected_shape = (num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors)
+        expected_shape = (
+            num_epochs,
+            num_priorities,
+            num_esa_steps,
+            num_l2_spin_sectors,
+        )
         expected = np.full(expected_shape, 0.0)
         expected[0, 0, 0, 0] = 4 / 3
         expected[0, 0, 0, 0 + 12] = 4 / 3
@@ -364,22 +489,35 @@ def test_calculate_normalization_factor_floor_is_one(self):
         num_esa_steps = 128
         num_l1_spin_sectors = 12
         num_l2_spin_sectors = 2 * num_l1_spin_sectors
-        priority_counts = np.zeros((num_epochs, num_priorities, num_esa_steps, num_l1_spin_sectors))
-        priority_counts[0, 0, 0, 0] = 1.
-        priority_counts[0, 0, 0, 2] = 0.
+        priority_counts = np.zeros(
+            (num_epochs, num_priorities, num_esa_steps, num_l1_spin_sectors)
+        )
+        priority_counts[0, 0, 0, 0] = 1.0
+        priority_counts[0, 0, 0, 2] = 0.0
 
         event_buffer_len = 15
         num_events = np.zeros((num_epochs, num_priorities), dtype=int)
-        spin_sectors = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=int)
-        energy_steps = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=int)
+        spin_sectors = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=int
+        )
+        energy_steps = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=int
+        )
 
         num_events[0, 0] = 3
         spin_sectors[0, 0, :3] = [0, 12, 2]
         energy_steps[0, 0, :3] = [0, 0, 0]
 
-        result = calculate_normalization_factor(priority_counts, num_events, energy_steps, spin_sectors)
+        result = calculate_normalization_factor(
+            priority_counts, num_events, energy_steps, spin_sectors
+        )
 
-        expected_shape = (num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors)
+        expected_shape = (
+            num_epochs,
+            num_priorities,
+            num_esa_steps,
+            num_l2_spin_sectors,
+        )
         expected = np.full(expected_shape, 0.0)
         self.assertEqual(1.0, result[0, 0, 0, 0])
         self.assertEqual(1.0, result[0, 0, 0, 0 + 12])
@@ -395,18 +533,31 @@ def test_normalization_factor_is_fill_when_priority_nonzero_and_direct_zero(self
         num_esa_steps = 128
         num_l1_spin_sectors = 12
         num_l2_spin_sectors = 2 * num_l1_spin_sectors
-        priority_counts = np.zeros((num_epochs, num_priorities, num_esa_steps, num_l1_spin_sectors))
-        priority_counts[0, 0, 0, 0] = 1.
-        priority_counts[0, 0, 0, 2] = 50.
+        priority_counts = np.zeros(
+            (num_epochs, num_priorities, num_esa_steps, num_l1_spin_sectors)
+        )
+        priority_counts[0, 0, 0, 0] = 1.0
+        priority_counts[0, 0, 0, 2] = 50.0
 
         event_buffer_len = 15
         num_events = np.zeros((num_epochs, num_priorities), dtype=int)
-        spin_sectors = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=int)
-        energy_steps = np.zeros((num_epochs, num_priorities, event_buffer_len), dtype=int)
+        spin_sectors = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=int
+        )
+        energy_steps = np.zeros(
+            (num_epochs, num_priorities, event_buffer_len), dtype=int
+        )
 
-        result = calculate_normalization_factor(priority_counts, num_events, energy_steps, spin_sectors)
+        result = calculate_normalization_factor(
+            priority_counts, num_events, energy_steps, spin_sectors
+        )
 
-        expected_shape = (num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors)
+        expected_shape = (
+            num_epochs,
+            num_priorities,
+            num_esa_steps,
+            num_l2_spin_sectors,
+        )
         expected = np.full(expected_shape, 0.0)
         np.testing.assert_equal(result[0, 0, 0, 0], np.nan)
         np.testing.assert_equal(result[0, 0, 0, 0 + 12], np.nan)
@@ -423,28 +574,44 @@ def test_lookup_normalization_per_event(self):
         num_l1_spin_sectors = 12
         num_l2_spin_sectors = 2 * num_l1_spin_sectors
 
-        normalization = np.zeros((num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors))
-        normalization[0, 0, 0, 0] = 123.
-        normalization[0, 0, 0, 12] = 456.
-        normalization[0, 0, 1, 2] = 789.
+        normalization = np.zeros(
+            (num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors)
+        )
+        normalization[0, 0, 0, 0] = 123.0
+        normalization[0, 0, 0, 12] = 456.0
+        normalization[0, 0, 1, 2] = 789.0
 
         event_buffer_len = 15
         num_events = np.zeros((num_epochs, num_priorities), dtype=int)
-        spin_sectors = np.ma.array(np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int), mask=True)
-        energy_steps = np.ma.array(np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int), mask=True)
+        spin_sectors = np.ma.array(
+            np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int),
+            mask=True,
+        )
+        energy_steps = np.ma.array(
+            np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int),
+            mask=True,
+        )
 
         num_events[0, 0] = 3
         spin_sectors[0, 0, :3] = [0, 12, 2]
         energy_steps[0, 0, :3] = [0, 0, 1]
 
-        normalization_per_event = lookup_normalization_per_event(normalization, num_events, energy_steps, spin_sectors)
+        normalization_per_event = lookup_normalization_per_event(
+            normalization, num_events, energy_steps, spin_sectors
+        )
 
-        np.testing.assert_array_equal(normalization_per_event[0, 0, :3], [123, 456, 789])
+        np.testing.assert_array_equal(
+            normalization_per_event[0, 0, :3], [123, 456, 789]
+        )
 
-        expected_normalization_per_event = np.full((num_epochs, num_priorities, event_buffer_len), np.nan)
+        expected_normalization_per_event = np.full(
+            (num_epochs, num_priorities, event_buffer_len), np.nan
+        )
         expected_normalization_per_event[0, 0, :3] = [123, 456, 789]
 
-        np.testing.assert_array_equal(normalization_per_event, expected_normalization_per_event)
+        np.testing.assert_array_equal(
+            normalization_per_event, expected_normalization_per_event
+        )
 
     def test_lookup_normalization_per_event_skips_masked_num_events(self):
         num_epochs = 2
@@ -453,20 +620,36 @@ def test_lookup_normalization_per_event_skips_masked_num_events(self):
         num_l1_spin_sectors = 12
         num_l2_spin_sectors = 2 * num_l1_spin_sectors
 
-        normalization = np.zeros((num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors))
-        normalization[0, 0, 0, 0] = 123.
-        normalization[0, 0, 0, 12] = 456.
-        normalization[0, 0, 1, 2] = 789.
+        normalization = np.zeros(
+            (num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors)
+        )
+        normalization[0, 0, 0, 0] = 123.0
+        normalization[0, 0, 0, 12] = 456.0
+        normalization[0, 0, 1, 2] = 789.0
 
         event_buffer_len = 15
-        num_events = np.ma.array(np.full((num_epochs, num_priorities), 65535), mask=True)
-        spin_sectors = np.ma.array(np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int), mask=True)
-        energy_steps = np.ma.array(np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int), mask=True)
+        num_events = np.ma.array(
+            np.full((num_epochs, num_priorities), 65535), mask=True
+        )
+        spin_sectors = np.ma.array(
+            np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int),
+            mask=True,
+        )
+        energy_steps = np.ma.array(
+            np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int),
+            mask=True,
+        )
 
-        normalization_per_event = lookup_normalization_per_event(normalization, num_events, energy_steps, spin_sectors)
+        normalization_per_event = lookup_normalization_per_event(
+            normalization, num_events, energy_steps, spin_sectors
+        )
 
-        expected_normalization_per_event = np.full((num_epochs, num_priorities, event_buffer_len), np.nan)
-        np.testing.assert_array_equal(normalization_per_event, expected_normalization_per_event)
+        expected_normalization_per_event = np.full(
+            (num_epochs, num_priorities, event_buffer_len), np.nan
+        )
+        np.testing.assert_array_equal(
+            normalization_per_event, expected_normalization_per_event
+        )
 
     def test_lookup_normalization_per_event_ignores_masked_events(self):
         num_epochs = 2
@@ -475,15 +658,23 @@ def test_lookup_normalization_per_event_ignores_masked_events(self):
         num_l1_spin_sectors = 12
         num_l2_spin_sectors = 2 * num_l1_spin_sectors
 
-        normalization = np.zeros((num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors))
-        normalization[0, 0, 0, 0] = 123.
-        normalization[0, 0, 0, 12] = 456.
-        normalization[0, 0, 1, 2] = 789.
+        normalization = np.zeros(
+            (num_epochs, num_priorities, num_esa_steps, num_l2_spin_sectors)
+        )
+        normalization[0, 0, 0, 0] = 123.0
+        normalization[0, 0, 0, 12] = 456.0
+        normalization[0, 0, 1, 2] = 789.0
 
         event_buffer_len = 15
         num_events = np.zeros((num_epochs, num_priorities), dtype=int)
-        spin_sectors = np.ma.array(np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int), mask=True)
-        energy_steps = np.ma.array(np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int), mask=True)
+        spin_sectors = np.ma.array(
+            np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int),
+            mask=True,
+        )
+        energy_steps = np.ma.array(
+            np.full((num_epochs, num_priorities, event_buffer_len), 255, dtype=int),
+            mask=True,
+        )
 
         num_events[0, 0] = 4
         energy_steps[0, 0, 1] = 0
@@ -492,14 +683,22 @@ def test_lookup_normalization_per_event_ignores_masked_events(self):
         spin_sectors[0, 0, :4] = np.ma.masked_equal([255, 255, 0, 12], 255)
         energy_steps[0, 0, :4] = np.ma.masked_equal([255, 0, 255, 0], 255)
 
-        normalization_per_event = lookup_normalization_per_event(normalization, num_events, energy_steps, spin_sectors)
+        normalization_per_event = lookup_normalization_per_event(
+            normalization, num_events, energy_steps, spin_sectors
+        )
 
-        np.testing.assert_array_equal(normalization_per_event[0, 0, :4], [np.nan, np.nan, np.nan, 456])
+        np.testing.assert_array_equal(
+            normalization_per_event[0, 0, :4], [np.nan, np.nan, np.nan, 456]
+        )
 
-        expected_normalization_per_event = np.full((num_epochs, num_priorities, event_buffer_len), np.nan)
+        expected_normalization_per_event = np.full(
+            (num_epochs, num_priorities, event_buffer_len), np.nan
+        )
         expected_normalization_per_event[0, 0, :4] = [np.nan, np.nan, np.nan, 456]
 
-        np.testing.assert_array_equal(normalization_per_event, expected_normalization_per_event)
+        np.testing.assert_array_equal(
+            normalization_per_event, expected_normalization_per_event
+        )
 
     def test_combine_priorities_for_species_and_calculate_count_uncertainty(self):
         num_epochs = 2
@@ -508,20 +707,32 @@ def test_combine_priorities_for_species_and_calculate_count_uncertainty(self):
         num_azimuth_bins = 5
 
         rng = np.random.default_rng()
-        priority_1 = rng.random((num_epochs, num_energies, num_spin_sectors, num_azimuth_bins))
-        priority_2 = rng.random((num_epochs, num_energies, num_spin_sectors, num_azimuth_bins))
-        priority_3 = rng.random((num_epochs, num_energies, num_spin_sectors, num_azimuth_bins))
+        priority_1 = rng.random(
+            (num_epochs, num_energies, num_spin_sectors, num_azimuth_bins)
+        )
+        priority_2 = rng.random(
+            (num_epochs, num_energies, num_spin_sectors, num_azimuth_bins)
+        )
+        priority_3 = rng.random(
+            (num_epochs, num_energies, num_spin_sectors, num_azimuth_bins)
+        )
 
         counts = np.stack((priority_1, priority_2, priority_3), axis=1)
 
-        actual_counts, actual_count_uncertainty = combine_priorities_for_species_and_calculate_count_uncertainty(counts)
+        actual_counts, actual_count_uncertainty = (
+            combine_priorities_for_species_and_calculate_count_uncertainty(counts)
+        )
         expected_summed_counts = priority_1 + priority_2 + priority_3
 
-        self.assertEqual((num_epochs, num_energies, num_spin_sectors, num_azimuth_bins),
-                         actual_counts.shape)
+        self.assertEqual(
+            (num_epochs, num_energies, num_spin_sectors, num_azimuth_bins),
+            actual_counts.shape,
+        )
 
         np.testing.assert_array_equal(actual_counts, expected_summed_counts)
-        np.testing.assert_array_equal(actual_count_uncertainty, np.sqrt(expected_summed_counts))
+        np.testing.assert_array_equal(
+            actual_count_uncertainty, np.sqrt(expected_summed_counts)
+        )
 
     def test_convert_count_rate_to_intensity(self):
         num_epochs = 3
@@ -530,26 +741,47 @@ def test_convert_count_rate_to_intensity(self):
         num_energies = 6
 
         rng = np.random.default_rng()
-        counts = rng.random((num_epochs, num_energies, num_spin_angles, num_position_bins))
-        acquisition_times = rng.random((num_epochs, num_energies,))
-        energy_per_charge = EnergyLookup(bin_centers=rng.random(num_energies),
-                                         delta_plus=rng.random(num_energies),
-                                         delta_minus=rng.random(num_energies))
-        geometric_factor = rng.random((num_epochs, num_energies, num_spin_angles, num_position_bins))
+        counts = rng.random(
+            (num_epochs, num_energies, num_spin_angles, num_position_bins)
+        )
+        acquisition_times = rng.random(
+            (
+                num_epochs,
+                num_energies,
+            )
+        )
+        energy_per_charge = EnergyLookup(
+            bin_centers=rng.random(num_energies),
+            delta_plus=rng.random(num_energies),
+            delta_minus=rng.random(num_energies),
+        )
+        geometric_factor = rng.random(
+            (num_epochs, num_energies, num_spin_angles, num_position_bins)
+        )
 
         efficiency = rng.random((num_energies, num_position_bins))
 
         mock_efficiency_lookup = Mock()
         mock_efficiency_lookup.efficiency_data = efficiency
 
-        intensity_data = convert_counts_to_intensity(counts, acquisition_times, energy_per_charge, mock_efficiency_lookup,
-                                                         geometric_factor)
+        intensity_data = convert_counts_to_intensity(
+            counts,
+            acquisition_times,
+            energy_per_charge,
+            mock_efficiency_lookup,
+            geometric_factor,
+        )
 
-        expected_denominator = (energy_per_charge.bin_centers[np.newaxis, :, np.newaxis, np.newaxis]
-                                * geometric_factor
-                                * efficiency[np.newaxis, :, np.newaxis, :])
+        expected_denominator = (
+            energy_per_charge.bin_centers[np.newaxis, :, np.newaxis, np.newaxis]
+            * geometric_factor
+            * efficiency[np.newaxis, :, np.newaxis, :]
+        )
 
-        np.testing.assert_array_almost_equal(intensity_data * expected_denominator, counts/acquisition_times[:,:,np.newaxis, np.newaxis])
+        np.testing.assert_array_almost_equal(
+            intensity_data * expected_denominator,
+            counts / acquisition_times[:, :, np.newaxis, np.newaxis],
+        )
 
     def test_rebin_azimuth_to_elevation(self):
         num_epochs = 3
@@ -557,7 +789,9 @@ def test_rebin_azimuth_to_elevation(self):
         num_spin_angles = 24
         num_azimuth_bins = 24
 
-        intensity_data = np.zeros((num_epochs, num_energies, num_spin_angles, num_azimuth_bins))
+        intensity_data = np.zeros(
+            (num_epochs, num_energies, num_spin_angles, num_azimuth_bins)
+        )
         azimuth_1_intensity = 1
         azimuth_2_intensity = 2
         azimuth_3_intensity = 3
@@ -574,19 +808,32 @@ def test_rebin_azimuth_to_elevation(self):
         mock_position_to_elevation_lookup = Mock(spec=PositionToElevationLookup)
         mock_position_to_elevation_lookup.num_bins = num_elevation_bins
         mock_position_to_elevation_lookup.bin_centers = np.arange(num_elevation_bins)
-        mock_position_to_elevation_lookup.apd_to_elevation_index.return_value = np.array([1, 0, 2, 2])
+        mock_position_to_elevation_lookup.apd_to_elevation_index.return_value = (
+            np.array([1, 0, 2, 2])
+        )
 
         half_spin_per_esa = np.zeros((num_epochs, num_energies))
-        actual_rebinned = rebin_3d_distribution_azimuth_to_elevation(intensity_data,
-                                                                     azimuths,
-                                                                     mock_position_to_elevation_lookup,
-                                                                     half_spin_per_esa)
+        actual_rebinned = rebin_3d_distribution_azimuth_to_elevation(
+            intensity_data,
+            azimuths,
+            mock_position_to_elevation_lookup,
+            half_spin_per_esa,
+        )
 
-        mock_position_to_elevation_lookup.apd_to_elevation_index.assert_called_once_with(azimuths)
+        mock_position_to_elevation_lookup.apd_to_elevation_index.assert_called_once_with(
+            azimuths
+        )
 
-        self.assertEqual((num_epochs, num_energies, num_spin_angles, num_elevation_bins),
-                         actual_rebinned.shape)
+        self.assertEqual(
+            (num_epochs, num_energies, num_spin_angles, num_elevation_bins),
+            actual_rebinned.shape,
+        )
 
         self.assertTrue(np.all(actual_rebinned[:, :, :, 0] == azimuth_2_intensity))
         self.assertTrue(np.all(actual_rebinned[:, :, :, 1] == azimuth_1_intensity))
-        self.assertTrue(np.all(actual_rebinned[:, :, :, 2] == (azimuth_3_intensity + azimuth_4_intensity)))
+        self.assertTrue(
+            np.all(
+                actual_rebinned[:, :, :, 2]
+                == (azimuth_3_intensity + azimuth_4_intensity)
+            )
+        )
diff --git a/tests/codice/l3/lo/sectored_intensities/test_mass_per_charge_lookup.py b/tests/codice/l3/lo/sectored_intensities/test_mass_per_charge_lookup.py
index 0be392c9f..ca56e1d18 100644
--- a/tests/codice/l3/lo/sectored_intensities/test_mass_per_charge_lookup.py
+++ b/tests/codice/l3/lo/sectored_intensities/test_mass_per_charge_lookup.py
@@ -1,14 +1,20 @@
 import unittest
 
-from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import MassPerChargeLookup
+from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import (
+    MassPerChargeLookup,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestMassPerChargeLookup(unittest.TestCase):
     def test_read_from_file(self):
-        mass_per_charge_csv_path = get_test_data_path("codice/imap_codice_mass-per-charge_20241110_v002.csv")
+        mass_per_charge_csv_path = get_test_data_path(
+            "codice/imap_codice_mass-per-charge_20241110_v002.csv"
+        )
 
-        mass_per_charge_lookup = MassPerChargeLookup.read_from_file(mass_per_charge_csv_path)
+        mass_per_charge_lookup = MassPerChargeLookup.read_from_file(
+            mass_per_charge_csv_path
+        )
 
         self.assertEqual(1, mass_per_charge_lookup.hplus)
         self.assertEqual(2, mass_per_charge_lookup.heplusplus)
@@ -26,5 +32,5 @@ def test_read_from_file(self):
         self.assertEqual(7.25, mass_per_charge_lookup.fe_hiq)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/codice/l3/lo/test_codice_lo_l3a_direct_events_dependencies.py b/tests/codice/l3/lo/test_codice_lo_l3a_direct_events_dependencies.py
index c1b7966a1..3acc62dc1 100644
--- a/tests/codice/l3/lo/test_codice_lo_l3a_direct_events_dependencies.py
+++ b/tests/codice/l3/lo/test_codice_lo_l3a_direct_events_dependencies.py
@@ -1,26 +1,45 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, call, sentinel
-
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
-
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import \
-    CodiceLoL3aDirectEventsDependencies, DIRECT_EVENTS_DESCRIPTOR, SW_PRIORITY_DESCRIPTOR, NSW_PRIORITY_DESCRIPTOR, \
-    MASS_COEFFICIENT_DESCRIPTOR
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import \
-    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR
+from unittest.mock import call, patch, sentinel
+
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
+
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import (
+    DIRECT_EVENTS_DESCRIPTOR,
+    MASS_COEFFICIENT_DESCRIPTOR,
+    NSW_PRIORITY_DESCRIPTOR,
+    SW_PRIORITY_DESCRIPTOR,
+    CodiceLoL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR,
+)
 
 
 class TestCodiceLoL3aDirectEventsDependencies(unittest.TestCase):
-
-    @patch("imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.download")
     @patch(
-        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL3aDirectEventsDependencies.from_file_paths")
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.download"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL3aDirectEventsDependencies.from_file_paths"
+    )
     def test_fetch_dependencies(self, mock_from_file_paths, mock_download):
-        direct_events_science_file_name = f"imap_codice_l2_{DIRECT_EVENTS_DESCRIPTOR}_20100105_v010.cdf"
-        lo_sw_priority_science_file_name = f"imap_codice_l1a_{SW_PRIORITY_DESCRIPTOR}_20100105_v010.cdf"
-        lo_nsw_priority_science_file_name = f"imap_codice_l1a_{NSW_PRIORITY_DESCRIPTOR}_20100105_v010.cdf"
-        mass_coefficient_file_name = f"imap_codice_{MASS_COEFFICIENT_DESCRIPTOR}_20100105_v001.csv"
+        direct_events_science_file_name = (
+            f"imap_codice_l2_{DIRECT_EVENTS_DESCRIPTOR}_20100105_v010.cdf"
+        )
+        lo_sw_priority_science_file_name = (
+            f"imap_codice_l1a_{SW_PRIORITY_DESCRIPTOR}_20100105_v010.cdf"
+        )
+        lo_nsw_priority_science_file_name = (
+            f"imap_codice_l1a_{NSW_PRIORITY_DESCRIPTOR}_20100105_v010.cdf"
+        )
+        mass_coefficient_file_name = (
+            f"imap_codice_{MASS_COEFFICIENT_DESCRIPTOR}_20100105_v001.csv"
+        )
         energy_lookup_file_name = f"imap_codice_{ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR}_20100105_v001.csv"
 
         mock_download.side_effect = [
@@ -28,7 +47,7 @@ def test_fetch_dependencies(self, mock_from_file_paths, mock_download):
             Path("some_local_folder") / lo_nsw_priority_science_file_name,
             Path("some_local_folder") / direct_events_science_file_name,
             Path("some_local_folder") / mass_coefficient_file_name,
-            Path("some_local_folder") / energy_lookup_file_name
+            Path("some_local_folder") / energy_lookup_file_name,
         ]
 
         processing_input_collection = ProcessingInputCollection(
@@ -37,67 +56,107 @@ def test_fetch_dependencies(self, mock_from_file_paths, mock_download):
             ScienceInput(lo_nsw_priority_science_file_name),
             ScienceInput(lo_sw_priority_science_file_name),
             ScienceInput(direct_events_science_file_name),
-
-            AncillaryInput("imap/ancillary/hit/imap_codice_range-2A-cosine-lookup_20250203_v001.csv"),
-            ScienceInput("imap/hit/l3/2010/01/imap_hit_l3_direct-events_20100105_v010.cdf"),
-            ScienceInput("imap_codice_l1_direct-events_20100105_v010.cdf")
+            AncillaryInput(
+                "imap/ancillary/hit/imap_codice_range-2A-cosine-lookup_20250203_v001.csv"
+            ),
+            ScienceInput(
+                "imap/hit/l3/2010/01/imap_hit_l3_direct-events_20100105_v010.cdf"
+            ),
+            ScienceInput("imap_codice_l1_direct-events_20100105_v010.cdf"),
         )
 
-        codice_l3_dependencies = CodiceLoL3aDirectEventsDependencies.fetch_dependencies(processing_input_collection)
+        codice_l3_dependencies = CodiceLoL3aDirectEventsDependencies.fetch_dependencies(
+            processing_input_collection
+        )
 
-        mock_download.assert_has_calls([
-            call(lo_sw_priority_science_file_name),
-            call(lo_nsw_priority_science_file_name),
-            call(direct_events_science_file_name),
-            call(mass_coefficient_file_name),
-            call(energy_lookup_file_name)
-        ], any_order=True)
+        mock_download.assert_has_calls(
+            [
+                call(lo_sw_priority_science_file_name),
+                call(lo_nsw_priority_science_file_name),
+                call(direct_events_science_file_name),
+                call(mass_coefficient_file_name),
+                call(energy_lookup_file_name),
+            ],
+            any_order=True,
+        )
 
         mock_from_file_paths.assert_called_with(
             Path("some_local_folder") / lo_sw_priority_science_file_name,
             Path("some_local_folder") / lo_nsw_priority_science_file_name,
             Path("some_local_folder") / direct_events_science_file_name,
             Path("some_local_folder") / mass_coefficient_file_name,
-            Path("some_local_folder") / energy_lookup_file_name
+            Path("some_local_folder") / energy_lookup_file_name,
         )
         self.assertEqual(mock_from_file_paths.return_value, codice_l3_dependencies)
 
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.EnergyLookup.read_from_csv')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.EnergyLookup.read_from_csv"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL2DirectEventData.read_from_cdf')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL2DirectEventData.read_from_cdf"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.MassCoefficientLookup.read_from_csv')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.MassCoefficientLookup.read_from_csv"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL1aSWPriorityRates.read_from_cdf')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL1aSWPriorityRates.read_from_cdf"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL1aNSWPriorityRates.read_from_cdf')
-    def test_from_file_paths(self, mock_codice_lo_l1a_nsw_priority_rates,
-                             mock_codice_lo_l1a_sw_priority_rates,
-                             mock_mass_coefficient_lookup,
-                             mock_direct_event_read_from_cdf, mock_energy_lookup_read_from_csv):
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies.CodiceLoL1aNSWPriorityRates.read_from_cdf"
+    )
+    def test_from_file_paths(
+        self,
+        mock_codice_lo_l1a_nsw_priority_rates,
+        mock_codice_lo_l1a_sw_priority_rates,
+        mock_mass_coefficient_lookup,
+        mock_direct_event_read_from_cdf,
+        mock_energy_lookup_read_from_csv,
+    ):
         actual_dependencies = CodiceLoL3aDirectEventsDependencies.from_file_paths(
             sentinel.sw_priority,
             sentinel.nsw_priority,
             sentinel.direct_events_cdf_path,
             sentinel.mass_coefficient_lookup_path,
-            sentinel.energy_lookup_path
+            sentinel.energy_lookup_path,
         )
 
-        mock_direct_event_read_from_cdf.assert_called_once_with(sentinel.direct_events_cdf_path)
-        mock_mass_coefficient_lookup.assert_called_once_with(sentinel.mass_coefficient_lookup_path)
-        mock_codice_lo_l1a_sw_priority_rates.assert_called_once_with(sentinel.sw_priority)
-        mock_codice_lo_l1a_nsw_priority_rates.assert_called_once_with(sentinel.nsw_priority)
-        mock_energy_lookup_read_from_csv.assert_called_once_with(sentinel.energy_lookup_path)
+        mock_direct_event_read_from_cdf.assert_called_once_with(
+            sentinel.direct_events_cdf_path
+        )
+        mock_mass_coefficient_lookup.assert_called_once_with(
+            sentinel.mass_coefficient_lookup_path
+        )
+        mock_codice_lo_l1a_sw_priority_rates.assert_called_once_with(
+            sentinel.sw_priority
+        )
+        mock_codice_lo_l1a_nsw_priority_rates.assert_called_once_with(
+            sentinel.nsw_priority
+        )
+        mock_energy_lookup_read_from_csv.assert_called_once_with(
+            sentinel.energy_lookup_path
+        )
 
-        self.assertEqual(mock_direct_event_read_from_cdf.return_value, actual_dependencies.codice_l2_direct_events)
-        self.assertEqual(mock_mass_coefficient_lookup.return_value, actual_dependencies.mass_coefficient_lookup)
-        self.assertEqual(mock_codice_lo_l1a_sw_priority_rates.return_value,
-                         actual_dependencies.codice_lo_l1a_sw_priority_rates)
-        self.assertEqual(mock_codice_lo_l1a_nsw_priority_rates.return_value,
-                         actual_dependencies.codice_lo_l1a_nsw_priority_rates)
-        self.assertEqual(mock_energy_lookup_read_from_csv.return_value, actual_dependencies.energy_lookup)
+        self.assertEqual(
+            mock_direct_event_read_from_cdf.return_value,
+            actual_dependencies.codice_l2_direct_events,
+        )
+        self.assertEqual(
+            mock_mass_coefficient_lookup.return_value,
+            actual_dependencies.mass_coefficient_lookup,
+        )
+        self.assertEqual(
+            mock_codice_lo_l1a_sw_priority_rates.return_value,
+            actual_dependencies.codice_lo_l1a_sw_priority_rates,
+        )
+        self.assertEqual(
+            mock_codice_lo_l1a_nsw_priority_rates.return_value,
+            actual_dependencies.codice_lo_l1a_nsw_priority_rates,
+        )
+        self.assertEqual(
+            mock_energy_lookup_read_from_csv.return_value,
+            actual_dependencies.energy_lookup,
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/codice/l3/lo/test_codice_lo_l3a_partial_densities_dependencies.py b/tests/codice/l3/lo/test_codice_lo_l3a_partial_densities_dependencies.py
index f5be265e4..b8285e5e5 100644
--- a/tests/codice/l3/lo/test_codice_lo_l3a_partial_densities_dependencies.py
+++ b/tests/codice/l3/lo/test_codice_lo_l3a_partial_densities_dependencies.py
@@ -1,74 +1,121 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, call, sentinel
+from unittest.mock import call, patch, sentinel
 
 import imap_data_access
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import \
-    CodiceLoL3aPartialDensitiesDependencies, SW_SPECIES_DESCRIPTOR, MASS_PER_CHARGE_DESCRIPTOR
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import (
+    MASS_PER_CHARGE_DESCRIPTOR,
+    SW_SPECIES_DESCRIPTOR,
+    CodiceLoL3aPartialDensitiesDependencies,
+)
 
 
 class TestCodiceLoL3aPartialDensitiesDependencies(unittest.TestCase):
-
-    @patch("imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.imap_data_access.download")
     @patch(
-        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.CodiceLoL3aPartialDensitiesDependencies.from_file_paths")
-    def test_fetch_dependencies(self, mock_from_file_paths, mock_imap_data_access_download):
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.CodiceLoL3aPartialDensitiesDependencies.from_file_paths"
+    )
+    def test_fetch_dependencies(
+        self, mock_from_file_paths, mock_imap_data_access_download
+    ):
         expected_codice_science_file_download_path = f"imap/codice/l2/2010/01/imap_codice_l2_{SW_SPECIES_DESCRIPTOR}_20100105_v010.cdf"
         expected_mass_per_charge_file_download_path = f"imap/ancillary/codice/imap_codice_{MASS_PER_CHARGE_DESCRIPTOR}_20100105_v001.csv"
 
-        sectored_science_input = ScienceInput(f"imap_codice_l2_{SW_SPECIES_DESCRIPTOR}_20100105_v010.cdf")
-        ancillary_input = AncillaryInput(f"imap_codice_{MASS_PER_CHARGE_DESCRIPTOR}_20100105_v001.csv")
-
-        non_codice_ancillary_input = AncillaryInput("imap_codice_range-2A-cosine-lookup_20250203_v001.csv")
-        non_codice_science_input = ScienceInput("imap/hit/l3/2010/01/imap_hit_l3_direct-events_20100105_v010.cdf")
-        non_l2_codice_science_input = ScienceInput("imap_codice_l1_direct-events_20100105_v010.cdf")
+        sectored_science_input = ScienceInput(
+            f"imap_codice_l2_{SW_SPECIES_DESCRIPTOR}_20100105_v010.cdf"
+        )
+        ancillary_input = AncillaryInput(
+            f"imap_codice_{MASS_PER_CHARGE_DESCRIPTOR}_20100105_v001.csv"
+        )
+
+        non_codice_ancillary_input = AncillaryInput(
+            "imap_codice_range-2A-cosine-lookup_20250203_v001.csv"
+        )
+        non_codice_science_input = ScienceInput(
+            "imap/hit/l3/2010/01/imap_hit_l3_direct-events_20100105_v010.cdf"
+        )
+        non_l2_codice_science_input = ScienceInput(
+            "imap_codice_l1_direct-events_20100105_v010.cdf"
+        )
 
         processing_input_collection = ProcessingInputCollection(
-            sectored_science_input, ancillary_input,
-            non_codice_ancillary_input, non_codice_science_input,
-            non_l2_codice_science_input)
-
-        expected_local_codice_science_file_path = Path("some_local_folder") / expected_codice_science_file_download_path
-        expected_local_mass_per_charge_file_download_path = Path(
-            "some_local_folder") / expected_mass_per_charge_file_download_path
+            sectored_science_input,
+            ancillary_input,
+            non_codice_ancillary_input,
+            non_codice_science_input,
+            non_l2_codice_science_input,
+        )
+
+        expected_local_codice_science_file_path = (
+            Path("some_local_folder") / expected_codice_science_file_download_path
+        )
+        expected_local_mass_per_charge_file_download_path = (
+            Path("some_local_folder") / expected_mass_per_charge_file_download_path
+        )
 
         mock_imap_data_access_download.side_effect = [
             expected_local_codice_science_file_path,
-            expected_local_mass_per_charge_file_download_path
+            expected_local_mass_per_charge_file_download_path,
         ]
 
-        codice_l3_dependencies = CodiceLoL3aPartialDensitiesDependencies.fetch_dependencies(processing_input_collection)
+        codice_l3_dependencies = (
+            CodiceLoL3aPartialDensitiesDependencies.fetch_dependencies(
+                processing_input_collection
+            )
+        )
 
         data_dir = imap_data_access.config["DATA_DIR"]
-        expected_download_sectored_science_path = data_dir / expected_codice_science_file_download_path
-        expected_download_ancillary_path = data_dir / expected_mass_per_charge_file_download_path
-
-        mock_imap_data_access_download.assert_has_calls([
-            call(expected_download_sectored_science_path.name),
-            call(expected_download_ancillary_path.name),
-
-        ], any_order=True)
-
-        mock_from_file_paths.assert_called_with(expected_local_codice_science_file_path,
-                                                expected_local_mass_per_charge_file_download_path)
+        expected_download_sectored_science_path = (
+            data_dir / expected_codice_science_file_download_path
+        )
+        expected_download_ancillary_path = (
+            data_dir / expected_mass_per_charge_file_download_path
+        )
+
+        mock_imap_data_access_download.assert_has_calls(
+            [
+                call(expected_download_sectored_science_path.name),
+                call(expected_download_ancillary_path.name),
+            ],
+            any_order=True,
+        )
+
+        mock_from_file_paths.assert_called_with(
+            expected_local_codice_science_file_path,
+            expected_local_mass_per_charge_file_download_path,
+        )
         self.assertEqual(mock_from_file_paths.return_value, codice_l3_dependencies)
 
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.CodiceLoL2SWSpeciesData.read_from_cdf')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.CodiceLoL2SWSpeciesData.read_from_cdf"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.MassPerChargeLookup.read_from_file')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies.MassPerChargeLookup.read_from_file"
+    )
     def test_from_file_paths(self, mock_mpc_read_from_file, mock_read_from_cdf):
         actual_dependencies = CodiceLoL3aPartialDensitiesDependencies.from_file_paths(
-            sentinel.cdf_path, sentinel.mpc_lookup_path)
+            sentinel.cdf_path, sentinel.mpc_lookup_path
+        )
 
         mock_mpc_read_from_file.assert_called_once_with(sentinel.mpc_lookup_path)
         mock_read_from_cdf.assert_called_once_with(sentinel.cdf_path)
 
-        self.assertEqual(mock_read_from_cdf.return_value, actual_dependencies.codice_l2_lo_data)
-        self.assertEqual(mock_mpc_read_from_file.return_value, actual_dependencies.mass_per_charge_lookup)
+        self.assertEqual(
+            mock_read_from_cdf.return_value, actual_dependencies.codice_l2_lo_data
+        )
+        self.assertEqual(
+            mock_mpc_read_from_file.return_value,
+            actual_dependencies.mass_per_charge_lookup,
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/codice/l3/lo/test_codice_lo_l3a_ratios_dependencies.py b/tests/codice/l3/lo/test_codice_lo_l3a_ratios_dependencies.py
index 4767993b6..bc119f6df 100644
--- a/tests/codice/l3/lo/test_codice_lo_l3a_ratios_dependencies.py
+++ b/tests/codice/l3/lo/test_codice_lo_l3a_ratios_dependencies.py
@@ -3,30 +3,42 @@
 
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import CodiceLoL3aRatiosDependencies
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import (
+    CodiceLoL3aRatiosDependencies,
+)
 
 
 class TestCodiceLoL3aRatiosDependencies(unittest.TestCase):
-
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies.CodiceLoPartialDensityData.read_from_cdf')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies.CodiceLoPartialDensityData.read_from_cdf"
+    )
     def test_from_file_paths(self, mock_read_from_cdf):
-        actual_dependencies = CodiceLoL3aRatiosDependencies.from_file_paths(sentinel.cdf_path)
+        actual_dependencies = CodiceLoL3aRatiosDependencies.from_file_paths(
+            sentinel.cdf_path
+        )
 
         mock_read_from_cdf.assert_called_once_with(sentinel.cdf_path)
 
-        self.assertEqual(mock_read_from_cdf.return_value, actual_dependencies.partial_density_data)
+        self.assertEqual(
+            mock_read_from_cdf.return_value, actual_dependencies.partial_density_data
+        )
 
     @patch(
-        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies.CodiceLoPartialDensityData.read_from_cdf")
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies.CodiceLoPartialDensityData.read_from_cdf"
+    )
     @patch(
-        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies.imap_data_access.download")
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies.imap_data_access.download"
+    )
     def test_fetch_dependencies(self, mock_download, mock_read_from_cdf):
         expected_file_name = "imap_codice_l3a_lo-partial-densities_20200507_v002.cdf"
         science_input = ScienceInput(expected_file_name)
         processing_inputs = ProcessingInputCollection(science_input)
 
-        dependencies = CodiceLoL3aRatiosDependencies.fetch_dependencies(processing_inputs)
+        dependencies = CodiceLoL3aRatiosDependencies.fetch_dependencies(
+            processing_inputs
+        )
 
         mock_download.assert_called_once_with(expected_file_name)
-        self.assertEqual(mock_read_from_cdf.return_value, dependencies.partial_density_data)
+        self.assertEqual(
+            mock_read_from_cdf.return_value, dependencies.partial_density_data
+        )
diff --git a/tests/codice/l3/lo/test_codice_lo_l3b_3d_distributions_dependencies.py b/tests/codice/l3/lo/test_codice_lo_l3b_3d_distributions_dependencies.py
index 75529dd73..14baadd63 100644
--- a/tests/codice/l3/lo/test_codice_lo_l3b_3d_distributions_dependencies.py
+++ b/tests/codice/l3/lo/test_codice_lo_l3b_3d_distributions_dependencies.py
@@ -1,13 +1,21 @@
 import unittest
-from unittest.mock import patch, sentinel, call
+from unittest.mock import call, patch, sentinel
 
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
-from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import \
-    CodiceLoL3b3dDistributionsDependencies, MASS_SPECIES_BIN_LOOKUP_DESCRIPTOR, GEOMETRIC_FACTOR_LOOKUP_DESCRIPTOR, \
-    EFFICIENCY_FACTOR_LOOKUP_DESCRIPTOR
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import \
-    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR
+from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import (
+    EFFICIENCY_FACTOR_LOOKUP_DESCRIPTOR,
+    GEOMETRIC_FACTOR_LOOKUP_DESCRIPTOR,
+    MASS_SPECIES_BIN_LOOKUP_DESCRIPTOR,
+    CodiceLoL3b3dDistributionsDependencies,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR,
+)
 
 
 class TestCodiceLoL3b3dDistributions(unittest.TestCase):
@@ -17,25 +25,41 @@ def test_fetch_dependencies_multiple_species(self):
                 self._test_fetch_dependencies(species)
 
     @patch(
-        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.imap_data_access.download")
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.imap_data_access.download"
+    )
     @patch(
-        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.CodiceLoL3b3dDistributionsDependencies.from_file_paths")
-    def _test_fetch_dependencies(self, species: str, mock_3d_distribution_deps_from_file_paths,
-                                 mock_data_access_download):
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.CodiceLoL3b3dDistributionsDependencies.from_file_paths"
+    )
+    def _test_fetch_dependencies(
+        self,
+        species: str,
+        mock_3d_distribution_deps_from_file_paths,
+        mock_data_access_download,
+    ):
         l3a_direct_event_name = "imap_codice_l3a_lo-direct-events_20100105_v010.cdf"
-        mass_species_lut_name = f"imap_codice_{MASS_SPECIES_BIN_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
-        geometric_factors_lut_name = f"imap_codice_{GEOMETRIC_FACTOR_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
-        efficiency_factors_lut_name = f"imap_codice_{EFFICIENCY_FACTOR_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
+        mass_species_lut_name = (
+            f"imap_codice_{MASS_SPECIES_BIN_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
+        )
+        geometric_factors_lut_name = (
+            f"imap_codice_{GEOMETRIC_FACTOR_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
+        )
+        efficiency_factors_lut_name = (
+            f"imap_codice_{EFFICIENCY_FACTOR_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
+        )
         energy_per_charge_lut_name = f"imap_codice_{ESA_TO_ENERGY_PER_CHARGE_LOOKUP_DESCRIPTOR}_20100105_v010.csv"
 
-        unused_science_input = ScienceInput(f"imap_codice_l2_lo-direct-events_20100105_v010.cdf")
+        unused_science_input = ScienceInput(
+            "imap_codice_l2_lo-direct-events_20100105_v010.cdf"
+        )
 
-        processing_input_collection = ProcessingInputCollection(AncillaryInput(mass_species_lut_name),
-                                                                AncillaryInput(geometric_factors_lut_name),
-                                                                AncillaryInput(efficiency_factors_lut_name),
-                                                                AncillaryInput(energy_per_charge_lut_name),
-                                                                ScienceInput(l3a_direct_event_name),
-                                                                unused_science_input)
+        processing_input_collection = ProcessingInputCollection(
+            AncillaryInput(mass_species_lut_name),
+            AncillaryInput(geometric_factors_lut_name),
+            AncillaryInput(efficiency_factors_lut_name),
+            AncillaryInput(energy_per_charge_lut_name),
+            ScienceInput(l3a_direct_event_name),
+            unused_science_input,
+        )
 
         mock_data_access_download.side_effect = [
             sentinel.l3a_direct_event_downloaded_path,
@@ -45,15 +69,19 @@ def _test_fetch_dependencies(self, species: str, mock_3d_distribution_deps_from_
             sentinel.energy_per_charge_downloaded_path,
         ]
 
-        CodiceLoL3b3dDistributionsDependencies.fetch_dependencies(processing_input_collection, species)
+        CodiceLoL3b3dDistributionsDependencies.fetch_dependencies(
+            processing_input_collection, species
+        )
 
-        mock_data_access_download.assert_has_calls([
-            call(l3a_direct_event_name),
-            call(mass_species_lut_name),
-            call(geometric_factors_lut_name),
-            call(efficiency_factors_lut_name),
-            call(energy_per_charge_lut_name)
-        ])
+        mock_data_access_download.assert_has_calls(
+            [
+                call(l3a_direct_event_name),
+                call(mass_species_lut_name),
+                call(geometric_factors_lut_name),
+                call(efficiency_factors_lut_name),
+                call(energy_per_charge_lut_name),
+            ]
+        )
 
         mock_3d_distribution_deps_from_file_paths.assert_called_once_with(
             l3a_file_path=sentinel.l3a_direct_event_downloaded_path,
@@ -64,30 +92,48 @@ def _test_fetch_dependencies(self, species: str, mock_3d_distribution_deps_from_
             species=species,
         )
 
-    @patch('imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.EfficiencyLookup.read_from_csv')
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.GeometricFactorLookup.read_from_csv')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.EfficiencyLookup.read_from_csv"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.MassSpeciesBinLookup.read_from_csv')
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.GeometricFactorLookup.read_from_csv"
+    )
     @patch(
-        'imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.CodiceLoDirectEventData.read_from_cdf')
-    @patch('imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.EnergyLookup.read_from_csv')
-    def test_from_file_paths(self, mock_energy_lookup_from_csv, mock_l3a_direct_event_read_from_cdf,
-                             mock_mass_species_from_csv, mock_geometric_factor_from_csv,
-                             mock_efficiency_lut_read_from_csv):
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.MassSpeciesBinLookup.read_from_csv"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.CodiceLoDirectEventData.read_from_cdf"
+    )
+    @patch(
+        "imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies.EnergyLookup.read_from_csv"
+    )
+    def test_from_file_paths(
+        self,
+        mock_energy_lookup_from_csv,
+        mock_l3a_direct_event_read_from_cdf,
+        mock_mass_species_from_csv,
+        mock_geometric_factor_from_csv,
+        mock_efficiency_lut_read_from_csv,
+    ):
         actual = CodiceLoL3b3dDistributionsDependencies.from_file_paths(
             l3a_file_path=sentinel.l3a_path,
             mass_species_bin_lut=sentinel.mass_species_path,
             geometric_factors_lut=sentinel.geometric_factor_path,
             efficiency_factors_lut=sentinel.efficiency_factor_path,
             energy_per_charge_lut=sentinel.energy_per_charge_path,
-            species="some-species"
+            species="some-species",
         )
         mock_l3a_direct_event_read_from_cdf.assert_called_once_with(sentinel.l3a_path)
         mock_mass_species_from_csv.assert_called_once_with(sentinel.mass_species_path)
-        mock_geometric_factor_from_csv.assert_called_once_with(sentinel.geometric_factor_path)
-        mock_efficiency_lut_read_from_csv.assert_called_once_with(sentinel.efficiency_factor_path, "some-species")
-        mock_energy_lookup_from_csv.assert_called_once_with(sentinel.energy_per_charge_path)
+        mock_geometric_factor_from_csv.assert_called_once_with(
+            sentinel.geometric_factor_path
+        )
+        mock_efficiency_lut_read_from_csv.assert_called_once_with(
+            sentinel.efficiency_factor_path, "some-species"
+        )
+        mock_energy_lookup_from_csv.assert_called_once_with(
+            sentinel.energy_per_charge_path
+        )
 
         expected_dependencies = CodiceLoL3b3dDistributionsDependencies(
             l3a_direct_event_data=mock_l3a_direct_event_read_from_cdf.return_value,
@@ -95,5 +141,6 @@ def test_from_file_paths(self, mock_energy_lookup_from_csv, mock_l3a_direct_even
             geometric_factors_lookup=mock_geometric_factor_from_csv.return_value,
             efficiency_factors_lut=mock_efficiency_lut_read_from_csv.return_value,
             energy_per_charge_lut=mock_energy_lookup_from_csv.return_value,
-            species="some-species")
+            species="some-species",
+        )
         self.assertEqual(expected_dependencies, actual)
diff --git a/tests/codice/l3/lo/test_codice_lo_processor.py b/tests/codice/l3/lo/test_codice_lo_processor.py
index 25cf7bd38..838b89c30 100644
--- a/tests/codice/l3/lo/test_codice_lo_processor.py
+++ b/tests/codice/l3/lo/test_codice_lo_processor.py
@@ -1,33 +1,61 @@
 import unittest
 import warnings
 from dataclasses import fields
-from datetime import datetime, timedelta, date
+from datetime import date, datetime, timedelta
 from pathlib import Path
-from unittest.mock import Mock, patch, call, sentinel, MagicMock
+from unittest.mock import MagicMock, Mock, call, patch, sentinel
 
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection
-from imap_processing.spice.geometry import get_spacecraft_to_instrument_spin_phase_offset
-
-from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import \
-    CodiceLoL3b3dDistributionsDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import CodiceLoL3aDirectEventsDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import \
-    CodiceLoL3aPartialDensitiesDependencies
-from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import CodiceLoL3aRatiosDependencies
+
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_direct_events_dependencies import (
+    CodiceLoL3aDirectEventsDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_partial_densities_dependencies import (
+    CodiceLoL3aPartialDensitiesDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3a_ratios_dependencies import (
+    CodiceLoL3aRatiosDependencies,
+)
+from imap_l3_processing.codice.l3.lo.codice_lo_l3b_3d_distributions_dependencies import (
+    CodiceLoL3b3dDistributionsDependencies,
+)
 from imap_l3_processing.codice.l3.lo.codice_lo_processor import CodiceLoProcessor
-from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import SpinAngleLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import EfficiencyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import EnergyLookup
-from imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup import GeometricFactorLookup
-from imap_l3_processing.codice.l3.lo.models import CodiceLoL3aPartialDensityDataProduct, CodiceLoL2DirectEventData, \
-    CodiceLoL3aDirectEventDataProduct, CodiceLoL2SWSpeciesData, \
-    CodiceLoL1aSWPriorityRates, CodiceLoL1aNSWPriorityRates, CodiceLoPartialDensityData, CodiceLoL3aRatiosDataProduct, \
-    CODICE_LO_L2_NUM_PRIORITIES, CodiceLoL3ChargeStateDistributionsDataProduct, CodiceLoL3b3dDistributionDataProduct
-from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import MassPerChargeLookup
+from imap_l3_processing.codice.l3.lo.direct_events.science.angle_lookup import (
+    SpinAngleLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.efficiency_lookup import (
+    EfficiencyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.energy_lookup import (
+    EnergyLookup,
+)
+from imap_l3_processing.codice.l3.lo.direct_events.science.geometric_factor_lookup import (
+    GeometricFactorLookup,
+)
+from imap_l3_processing.codice.l3.lo.models import (
+    CODICE_LO_L2_NUM_PRIORITIES,
+    CodiceLoL1aNSWPriorityRates,
+    CodiceLoL1aSWPriorityRates,
+    CodiceLoL2DirectEventData,
+    CodiceLoL2SWSpeciesData,
+    CodiceLoL3aDirectEventDataProduct,
+    CodiceLoL3aPartialDensityDataProduct,
+    CodiceLoL3aRatiosDataProduct,
+    CodiceLoL3b3dDistributionDataProduct,
+    CodiceLoL3ChargeStateDistributionsDataProduct,
+    CodiceLoPartialDensityData,
+)
+from imap_l3_processing.codice.l3.lo.sectored_intensities.science.mass_per_charge_lookup import (
+    MassPerChargeLookup,
+)
 from imap_l3_processing.models import InputMetadata
 from imap_l3_processing.processor import Processor
-from tests.test_helpers import create_dataclass_mock, get_test_data_path, NumpyArrayMatcher
+from tests.test_helpers import (
+    NumpyArrayMatcher,
+    create_dataclass_mock,
+    get_test_data_path,
+)
 
 MODULE = "imap_l3_processing.codice.l3.lo.codice_lo_processor"
 
@@ -37,97 +65,154 @@ def test_implements_processor(self):
         processor = CodiceLoProcessor(Mock(), Mock())
         self.assertIsInstance(processor, Processor)
 
-    @patch(f'{MODULE}.CodiceLoL3aPartialDensitiesDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.CodiceLoProcessor.process_l3a_partial_densities')
-    @patch(f'{MODULE}.save_data')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_partial_densities(self, mock_spiceypy, mock_save_data, mock_process_l3a_partial_densities,
-                                       mock_fetch_dependencies):
+    @patch(f"{MODULE}.CodiceLoL3aPartialDensitiesDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.CodiceLoProcessor.process_l3a_partial_densities")
+    @patch(f"{MODULE}.save_data")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_partial_densities(
+        self,
+        mock_spiceypy,
+        mock_save_data,
+        mock_process_l3a_partial_densities,
+        mock_fetch_dependencies,
+    ):
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1'), Path('path/to/parent_file_2')]
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor='lo-partial-densities')
+        input_collection.get_file_paths.return_value = [
+            Path("path/to/parent_file_1"),
+            Path("path/to/parent_file_2"),
+        ]
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor="lo-partial-densities",
+        )
         mock_spiceypy.ktotal.return_value = 0
 
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         product = processor.process()
 
         mock_fetch_dependencies.assert_called_once_with(processor.dependencies)
-        mock_process_l3a_partial_densities.assert_called_once_with(mock_fetch_dependencies.return_value)
+        mock_process_l3a_partial_densities.assert_called_once_with(
+            mock_fetch_dependencies.return_value
+        )
 
-        mock_save_data.assert_called_once_with(mock_process_l3a_partial_densities.return_value)
+        mock_save_data.assert_called_once_with(
+            mock_process_l3a_partial_densities.return_value
+        )
 
-        self.assertEqual(['parent_file_1', 'parent_file_2'],
-                         mock_process_l3a_partial_densities.return_value.parent_file_names)
+        self.assertEqual(
+            ["parent_file_1", "parent_file_2"],
+            mock_process_l3a_partial_densities.return_value.parent_file_names,
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch(f'{MODULE}.CodiceLoL3aRatiosDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.CodiceLoProcessor.process_l3a_ratios')
-    @patch(f'{MODULE}.save_data')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_ratios(self, mock_spiceypy, mock_save_data, mock_process_l3a_ratios,
-                            mock_fetch_dependencies):
+    @patch(f"{MODULE}.CodiceLoL3aRatiosDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.CodiceLoProcessor.process_l3a_ratios")
+    @patch(f"{MODULE}.save_data")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_ratios(
+        self,
+        mock_spiceypy,
+        mock_save_data,
+        mock_process_l3a_ratios,
+        mock_fetch_dependencies,
+    ):
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1')]
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor='lo-sw-ratios')
+        input_collection.get_file_paths.return_value = [Path("path/to/parent_file_1")]
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor="lo-sw-ratios",
+        )
         mock_spiceypy.ktotal.return_value = 0
 
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         product = processor.process()
 
         mock_fetch_dependencies.assert_called_once_with(processor.dependencies)
-        mock_process_l3a_ratios.assert_called_once_with(mock_fetch_dependencies.return_value)
+        mock_process_l3a_ratios.assert_called_once_with(
+            mock_fetch_dependencies.return_value
+        )
 
         mock_save_data.assert_called_once_with(mock_process_l3a_ratios.return_value)
 
-        self.assertEqual(['parent_file_1'],
-                         mock_process_l3a_ratios.return_value.parent_file_names)
+        self.assertEqual(
+            ["parent_file_1"], mock_process_l3a_ratios.return_value.parent_file_names
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch(f'{MODULE}.CodiceLoL3aRatiosDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.CodiceLoProcessor.process_l3a_charge_state_distributions')
-    @patch(f'{MODULE}.save_data')
-    @patch(f'imap_l3_processing.processor.spiceypy')
-    def test_process_abundances(self, mock_spiceypy, mock_save_data, mock_process_l3a_abundances,
-                                mock_fetch_dependencies):
+    @patch(f"{MODULE}.CodiceLoL3aRatiosDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.CodiceLoProcessor.process_l3a_charge_state_distributions")
+    @patch(f"{MODULE}.save_data")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_abundances(
+        self,
+        mock_spiceypy,
+        mock_save_data,
+        mock_process_l3a_abundances,
+        mock_fetch_dependencies,
+    ):
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1')]
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor='lo-sw-charge-state-distributions')
+        input_collection.get_file_paths.return_value = [Path("path/to/parent_file_1")]
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor="lo-sw-charge-state-distributions",
+        )
         mock_spiceypy.ktotal.return_value = 0
 
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         product = processor.process()
 
         mock_fetch_dependencies.assert_called_once_with(processor.dependencies)
-        mock_process_l3a_abundances.assert_called_once_with(mock_fetch_dependencies.return_value)
+        mock_process_l3a_abundances.assert_called_once_with(
+            mock_fetch_dependencies.return_value
+        )
 
         mock_save_data.assert_called_once_with(mock_process_l3a_abundances.return_value)
 
-        self.assertEqual(['parent_file_1'],
-                         mock_process_l3a_abundances.return_value.parent_file_names)
+        self.assertEqual(
+            ["parent_file_1"],
+            mock_process_l3a_abundances.return_value.parent_file_names,
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
     def test_process_ratios_calculate_abundance_ratios(self):
         now = datetime.now()
         data = CodiceLoPartialDensityData(
             epoch=np.array(
-                [now, now + timedelta(minutes=4), now + timedelta(minutes=8),
-                 now + timedelta(minutes=12), now + timedelta(minutes=16)]),
-            epoch_delta=np.array([120_000_000_000, 120_000_000_000, 120_000_000_000, 120_000_000_000, 120_000_000_000]),
+                [
+                    now,
+                    now + timedelta(minutes=4),
+                    now + timedelta(minutes=8),
+                    now + timedelta(minutes=12),
+                    now + timedelta(minutes=16),
+                ]
+            ),
+            epoch_delta=np.array(
+                [
+                    120_000_000_000,
+                    120_000_000_000,
+                    120_000_000_000,
+                    120_000_000_000,
+                    120_000_000_000,
+                ]
+            ),
             fe_hiq_partial_density=np.array([1, 2, 3, 10, 12]),
             fe_loq_partial_density=np.array([4, 5, 6, 11, 13]),
             oplus5_partial_density=np.array([1, 2, 4, 12, 14]),
@@ -146,32 +231,59 @@ def test_process_ratios_calculate_abundance_ratios(self):
 
         dependency = CodiceLoL3aRatiosDependencies(data)
         input_metadata = Mock()
-        processor = CodiceLoProcessor(dependencies=Mock(), input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=Mock(), input_metadata=input_metadata
+        )
 
-        ratios_data_product: CodiceLoL3aRatiosDataProduct = processor.process_l3a_ratios(dependency)
+        ratios_data_product: CodiceLoL3aRatiosDataProduct = (
+            processor.process_l3a_ratios(dependency)
+        )
         self.assertEqual(input_metadata, ratios_data_product.input_metadata)
-        np.testing.assert_array_equal(ratios_data_product.epoch,
-                                      [now + timedelta(minutes=4), now + timedelta(minutes=14)])
-        np.testing.assert_array_equal(ratios_data_product.epoch_delta, [360_000_000_000, 240_000_000_000])
-        np.testing.assert_array_almost_equal(ratios_data_product.fe_to_o_ratio,
-                                             np.array(
-                                                 [(2 + 5) / (7 / 3 + 6 + 9 + 12), (11 + 12) / (13 + 14 + 15 + 16)]))
-        np.testing.assert_array_almost_equal(ratios_data_product.mg_to_o_ratio,
-                                             np.array([15 / (7 / 3 + 6 + 9 + 12), 17 / (13 + 14 + 15 + 16)]))
-        np.testing.assert_array_almost_equal(ratios_data_product.c_to_o_ratio, np.array(
-            [(18 + 21 + 24) / (7 / 3 + 6 + 9 + 12), (18 + 19 + 20) / (13 + 14 + 15 + 16)]))
-
-        np.testing.assert_array_almost_equal(ratios_data_product.c6_to_c4_ratio, np.array([24 / 18, 20 / 18]))
-        np.testing.assert_array_almost_equal(ratios_data_product.c6_to_c5_ratio, np.array([24 / 21, 20 / 19]))
-
-        np.testing.assert_array_almost_equal(ratios_data_product.o7_to_o6_ratio, np.array([9 / 6, 15 / 14]))
-        np.testing.assert_array_almost_equal(ratios_data_product.felo_to_fehi_ratio, np.array([5 / 2, 12 / 11]))
+        np.testing.assert_array_equal(
+            ratios_data_product.epoch,
+            [now + timedelta(minutes=4), now + timedelta(minutes=14)],
+        )
+        np.testing.assert_array_equal(
+            ratios_data_product.epoch_delta, [360_000_000_000, 240_000_000_000]
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.fe_to_o_ratio,
+            np.array([(2 + 5) / (7 / 3 + 6 + 9 + 12), (11 + 12) / (13 + 14 + 15 + 16)]),
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.mg_to_o_ratio,
+            np.array([15 / (7 / 3 + 6 + 9 + 12), 17 / (13 + 14 + 15 + 16)]),
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.c_to_o_ratio,
+            np.array(
+                [
+                    (18 + 21 + 24) / (7 / 3 + 6 + 9 + 12),
+                    (18 + 19 + 20) / (13 + 14 + 15 + 16),
+                ]
+            ),
+        )
+
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.c6_to_c4_ratio, np.array([24 / 18, 20 / 18])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.c6_to_c5_ratio, np.array([24 / 21, 20 / 19])
+        )
+
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.o7_to_o6_ratio, np.array([9 / 6, 15 / 14])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.felo_to_fehi_ratio, np.array([5 / 2, 12 / 11])
+        )
 
     def test_process_ratios_calculate_abundance_ratios_using_safe_divide(self):
         now = datetime.now()
         data = CodiceLoPartialDensityData(
             epoch=np.array(
-                [now, now + timedelta(minutes=4), now + timedelta(minutes=8)]),
+                [now, now + timedelta(minutes=4), now + timedelta(minutes=8)]
+            ),
             epoch_delta=np.array([120_000_000_000, 120_000_000_000, 120_000_000_000]),
             fe_hiq_partial_density=np.array([0, 0, 0]),
             fe_loq_partial_density=np.array([0, 0, 0]),
@@ -191,37 +303,78 @@ def test_process_ratios_calculate_abundance_ratios_using_safe_divide(self):
 
         dependency = CodiceLoL3aRatiosDependencies(data)
         input_metadata = Mock()
-        processor = CodiceLoProcessor(dependencies=Mock(), input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=Mock(), input_metadata=input_metadata
+        )
 
         with warnings.catch_warnings(record=True) as w:
             ratios_data_product = processor.process_l3a_ratios(dependency)
         self.assertEqual(0, len(w))
 
         self.assertEqual(input_metadata, ratios_data_product.input_metadata)
-        np.testing.assert_array_equal(ratios_data_product.epoch,
-                                      [now + timedelta(minutes=4)])
-        np.testing.assert_array_equal(ratios_data_product.epoch_delta, [360_000_000_000])
-        np.testing.assert_array_almost_equal(ratios_data_product.fe_to_o_ratio, np.array([np.nan]))
-        np.testing.assert_array_almost_equal(ratios_data_product.mg_to_o_ratio, np.array([np.nan]))
-        np.testing.assert_array_almost_equal(ratios_data_product.c_to_o_ratio, np.array([np.nan]))
-        np.testing.assert_array_almost_equal(ratios_data_product.c6_to_c4_ratio, np.array([np.nan]))
-        np.testing.assert_array_almost_equal(ratios_data_product.c6_to_c5_ratio, np.array([np.nan]))
-        np.testing.assert_array_almost_equal(ratios_data_product.o7_to_o6_ratio, np.array([np.nan]))
-        np.testing.assert_array_almost_equal(ratios_data_product.felo_to_fehi_ratio, np.array([np.nan]))
+        np.testing.assert_array_equal(
+            ratios_data_product.epoch, [now + timedelta(minutes=4)]
+        )
+        np.testing.assert_array_equal(
+            ratios_data_product.epoch_delta, [360_000_000_000]
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.fe_to_o_ratio, np.array([np.nan])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.mg_to_o_ratio, np.array([np.nan])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.c_to_o_ratio, np.array([np.nan])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.c6_to_c4_ratio, np.array([np.nan])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.c6_to_c5_ratio, np.array([np.nan])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.o7_to_o6_ratio, np.array([np.nan])
+        )
+        np.testing.assert_array_almost_equal(
+            ratios_data_product.felo_to_fehi_ratio, np.array([np.nan])
+        )
 
     def test_process_abundances_calculates_abundance_ratios(self):
         now = datetime.now()
         data = CodiceLoPartialDensityData(
             epoch=np.array(
-                [now, now + timedelta(minutes=4), now + timedelta(minutes=8),
-                 now + timedelta(minutes=12), now + timedelta(minutes=16)]),
-            epoch_delta=np.array([120_000_000_000, 120_000_000_000, 120_000_000_000, 120_000_000_000, 120_000_000_000]),
+                [
+                    now,
+                    now + timedelta(minutes=4),
+                    now + timedelta(minutes=8),
+                    now + timedelta(minutes=12),
+                    now + timedelta(minutes=16),
+                ]
+            ),
+            epoch_delta=np.array(
+                [
+                    120_000_000_000,
+                    120_000_000_000,
+                    120_000_000_000,
+                    120_000_000_000,
+                    120_000_000_000,
+                ]
+            ),
             fe_hiq_partial_density=np.arange(5),
             fe_loq_partial_density=np.arange(5),
             oplus5_partial_density=np.array([0, 1, 2, 2, 2]),
             oplus6_partial_density=np.array([5, 5, 5, 6, 6]),
             oplus7_partial_density=np.array([8, 8, 8, 9, 9]),
-            oplus8_partial_density=np.array([11, 11, 11, 12, 12, ]),
+            oplus8_partial_density=np.array(
+                [
+                    11,
+                    11,
+                    11,
+                    12,
+                    12,
+                ]
+            ),
             mg_partial_density=np.arange(5),
             cplus4_partial_density=np.array([16, 17, 18, 18, 18]),
             cplus5_partial_density=np.array([20, 20, 20, 21, 21]),
@@ -234,38 +387,59 @@ def test_process_abundances_calculates_abundance_ratios(self):
 
         dependency = CodiceLoL3aRatiosDependencies(data)
         input_metadata = Mock()
-        processor = CodiceLoProcessor(dependencies=Mock(), input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=Mock(), input_metadata=input_metadata
+        )
 
-        abundances_data_product = processor.process_l3a_charge_state_distributions(dependency)
-        self.assertIsInstance(abundances_data_product, CodiceLoL3ChargeStateDistributionsDataProduct)
+        abundances_data_product = processor.process_l3a_charge_state_distributions(
+            dependency
+        )
+        self.assertIsInstance(
+            abundances_data_product, CodiceLoL3ChargeStateDistributionsDataProduct
+        )
         self.assertEqual(input_metadata, abundances_data_product.input_metadata)
-        np.testing.assert_array_equal(abundances_data_product.epoch,
-                                      [now + timedelta(minutes=4), now + timedelta(minutes=14)])
-        np.testing.assert_array_equal(abundances_data_product.epoch_delta, [360_000_000_000, 240_000_000_000])
-
-        np.testing.assert_array_almost_equal(abundances_data_product.oxygen_charge_state_distribution,
-                                             np.array(
-                                                 [
-                                                     [1 / (1 + 5 + 8 + 11), 5 / (1 + 5 + 8 + 11), 8 / (1 + 5 + 8 + 11),
-                                                      11 / (1 + 5 + 8 + 11), ],
-                                                     [2 / (2 + 6 + 9 + 12), 6 / (2 + 6 + 9 + 12), 9 / (2 + 6 + 9 + 12),
-                                                      12 / (2 + 6 + 9 + 12), ],
-                                                 ])
-                                             )
-        np.testing.assert_array_almost_equal(abundances_data_product.carbon_charge_state_distribution,
-                                             np.array(
-                                                 [
-                                                     [17 / (17 + 20 + 23), 20 / (17 + 20 + 23), 23 / (17 + 20 + 23)],
-                                                     [18 / (18 + 21 + 24), 21 / (18 + 21 + 24), 24 / (18 + 21 + 24)],
-                                                 ]
-                                             ))
+        np.testing.assert_array_equal(
+            abundances_data_product.epoch,
+            [now + timedelta(minutes=4), now + timedelta(minutes=14)],
+        )
+        np.testing.assert_array_equal(
+            abundances_data_product.epoch_delta, [360_000_000_000, 240_000_000_000]
+        )
+
+        np.testing.assert_array_almost_equal(
+            abundances_data_product.oxygen_charge_state_distribution,
+            np.array(
+                [
+                    [
+                        1 / (1 + 5 + 8 + 11),
+                        5 / (1 + 5 + 8 + 11),
+                        8 / (1 + 5 + 8 + 11),
+                        11 / (1 + 5 + 8 + 11),
+                    ],
+                    [
+                        2 / (2 + 6 + 9 + 12),
+                        6 / (2 + 6 + 9 + 12),
+                        9 / (2 + 6 + 9 + 12),
+                        12 / (2 + 6 + 9 + 12),
+                    ],
+                ]
+            ),
+        )
+        np.testing.assert_array_almost_equal(
+            abundances_data_product.carbon_charge_state_distribution,
+            np.array(
+                [
+                    [17 / (17 + 20 + 23), 20 / (17 + 20 + 23), 23 / (17 + 20 + 23)],
+                    [18 / (18 + 21 + 24), 21 / (18 + 21 + 24), 24 / (18 + 21 + 24)],
+                ]
+            ),
+        )
 
     def test_process_abundances_uses_safe_divide(self):
 
         now = datetime.now()
         data = CodiceLoPartialDensityData(
-            epoch=np.array(
-                [now]),
+            epoch=np.array([now]),
             epoch_delta=np.array([120_000_000_000]),
             fe_hiq_partial_density=np.array([1]),
             fe_loq_partial_density=np.array([1]),
@@ -284,81 +458,114 @@ def test_process_abundances_uses_safe_divide(self):
         )
         dependency = CodiceLoL3aRatiosDependencies(data)
         input_metadata = Mock()
-        processor = CodiceLoProcessor(dependencies=Mock(), input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=Mock(), input_metadata=input_metadata
+        )
 
         with warnings.catch_warnings(record=True) as w:
-            abundances_data_product = processor.process_l3a_charge_state_distributions(dependency)
+            abundances_data_product = processor.process_l3a_charge_state_distributions(
+                dependency
+            )
         self.assertEqual(0, len(w))
 
-        self.assertIsInstance(abundances_data_product, CodiceLoL3ChargeStateDistributionsDataProduct)
+        self.assertIsInstance(
+            abundances_data_product, CodiceLoL3ChargeStateDistributionsDataProduct
+        )
         self.assertEqual(input_metadata, abundances_data_product.input_metadata)
-        np.testing.assert_array_equal(abundances_data_product.epoch,
-                                      [now])
-        np.testing.assert_array_equal(abundances_data_product.epoch_delta,
-                                      [120_000_000_000])
-
-        np.testing.assert_array_almost_equal(abundances_data_product.oxygen_charge_state_distribution,
-                                             np.array(
-                                                 [
-                                                     [np.nan, np.nan, np.nan, np.nan]
-                                                 ])
-                                             )
-        np.testing.assert_array_almost_equal(abundances_data_product.carbon_charge_state_distribution,
-                                             np.array(
-                                                 [
-                                                     [np.nan, np.nan, np.nan]
-                                                 ]
-                                             ))
-
-    @patch(f'{MODULE}.CodiceLoL3aDirectEventsDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.CodiceLoProcessor.process_l3a_direct_event_data_product')
-    @patch(f'{MODULE}.save_data')
-    @patch(f'imap_l3_processing.processor.spiceypy')
-    def test_process_direct_events(self, mock_spiceypy, mock_save_data, mock_process_direct_event,
-                                   mock_fetch_dependencies):
+        np.testing.assert_array_equal(abundances_data_product.epoch, [now])
+        np.testing.assert_array_equal(
+            abundances_data_product.epoch_delta, [120_000_000_000]
+        )
+
+        np.testing.assert_array_almost_equal(
+            abundances_data_product.oxygen_charge_state_distribution,
+            np.array([[np.nan, np.nan, np.nan, np.nan]]),
+        )
+        np.testing.assert_array_almost_equal(
+            abundances_data_product.carbon_charge_state_distribution,
+            np.array([[np.nan, np.nan, np.nan]]),
+        )
+
+    @patch(f"{MODULE}.CodiceLoL3aDirectEventsDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.CodiceLoProcessor.process_l3a_direct_event_data_product")
+    @patch(f"{MODULE}.save_data")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_direct_events(
+        self,
+        mock_spiceypy,
+        mock_save_data,
+        mock_process_direct_event,
+        mock_fetch_dependencies,
+    ):
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1'), Path('path/to/parent_file_2')]
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor='lo-direct-events')
+        input_collection.get_file_paths.return_value = [
+            Path("path/to/parent_file_1"),
+            Path("path/to/parent_file_2"),
+        ]
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor="lo-direct-events",
+        )
         mock_spiceypy.ktotal.return_value = 0
 
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         product = processor.process()
 
         mock_fetch_dependencies.assert_called_once_with(processor.dependencies)
-        mock_process_direct_event.assert_called_once_with(mock_fetch_dependencies.return_value)
+        mock_process_direct_event.assert_called_once_with(
+            mock_fetch_dependencies.return_value
+        )
 
         mock_save_data.assert_called_once_with(mock_process_direct_event.return_value)
 
         self.assertEqual([mock_save_data.return_value], product)
 
     def test_raises_exception_on_non_l3_input_metadata(self):
-        input_metadata = InputMetadata('codice', "L2a", Mock(), Mock(), 'v02', "bad-descriptor")
+        input_metadata = InputMetadata(
+            "codice", "L2a", Mock(), Mock(), "v02", "bad-descriptor"
+        )
 
         processor = CodiceLoProcessor(Mock(), input_metadata)
         with self.assertRaises(NotImplementedError) as context:
             processor.process()
-        self.assertEqual("Unknown data level and descriptor for CoDICE: L2a, bad-descriptor", str(context.exception))
+        self.assertEqual(
+            "Unknown data level and descriptor for CoDICE: L2a, bad-descriptor",
+            str(context.exception),
+        )
 
-    @patch(f'{MODULE}.calculate_partial_densities')
+    @patch(f"{MODULE}.calculate_partial_densities")
     def test_process_l3a_partial_densities(self, mock_calculate_partial_densities):
         input_collection = ProcessingInputCollection()
-        input_metadata = InputMetadata('codice', "l3a", Mock(spec=datetime), Mock(spec=datetime), 'v02',
-                                       descriptor='lo-partial-densities')
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        input_metadata = InputMetadata(
+            "codice",
+            "l3a",
+            Mock(spec=datetime),
+            Mock(spec=datetime),
+            "v02",
+            descriptor="lo-partial-densities",
+        )
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
 
-        epochs = np.array([datetime(2025, 1, 1), datetime(2025, 1, 2), datetime(2025, 1, 3)])
+        epochs = np.array(
+            [datetime(2025, 1, 1), datetime(2025, 1, 2), datetime(2025, 1, 3)]
+        )
         num_species = 14
 
         codice_lo_l2_data = CodiceLoL2SWSpeciesData(
             *[Mock() for _ in range(len(fields(CodiceLoL2SWSpeciesData)))],
         )
 
-        mass_per_charge_lookup = MassPerChargeLookup(*[i for i in range(len(fields(MassPerChargeLookup)))])
+        mass_per_charge_lookup = MassPerChargeLookup(
+            *[i for i in range(len(fields(MassPerChargeLookup)))]
+        )
 
         codice_lo_l2_data.epoch = epochs
 
@@ -480,31 +687,46 @@ def test_process_l3a_partial_densities(self, mock_calculate_partial_densities):
         self.assertIsInstance(result_data, CodiceLoPartialDensityData)
 
         np.testing.assert_array_equal(result_data.epoch, codice_lo_l2_data.epoch)
-        np.testing.assert_array_equal(result_data.epoch_delta, codice_lo_l2_data.epoch_delta_plus)
-        self.assertEqual(sentinel.hplus_partial_density, result_data.hplus_partial_density),
-        self.assertEqual(sentinel.heplusplus_partial_density, result_data.heplusplus_partial_density),
-        self.assertEqual(cplus4_partial_density, result_data.cplus4_partial_density),
-        self.assertEqual(cplus5_partial_density, result_data.cplus5_partial_density),
-        self.assertEqual(cplus6_partial_density, result_data.cplus6_partial_density),
-        self.assertEqual(oplus5_partial_density, result_data.oplus5_partial_density),
-        self.assertEqual(oplus6_partial_density, result_data.oplus6_partial_density),
-        self.assertEqual(oplus7_partial_density, result_data.oplus7_partial_density),
-        self.assertEqual(oplus8_partial_density, result_data.oplus8_partial_density),
-        self.assertEqual(sentinel.ne_partial_density, result_data.ne_partial_density),
-        self.assertEqual(mg_partial_density, result_data.mg_partial_density),
-        self.assertEqual(sentinel.si_partial_density, result_data.si_partial_density),
-        self.assertEqual(fe_loq_partial_density, result_data.fe_loq_partial_density),
-        self.assertEqual(fe_hiq_partial_density, result_data.fe_hiq_partial_density),
-
-    @patch(f'{MODULE}.SpinAngleLookup')
-    @patch(f'{MODULE}.lookup_normalization_per_event', autospec=True)
-    @patch(f'{MODULE}.calculate_normalization_factor', autospec=True)
-    @patch(f'{MODULE}.calculate_mass_per_charge', autospec=True)
-    @patch(f'{MODULE}.calculate_mass', autospec=True)
-    def test_process_l3a_direct_events(self, mock_calculate_mass, mock_calculate_mass_per_charge,
-                                       mock_calculate_normalization_factor,
-                                       mock_lookup_normalization_per_event,
-                                       mock_spin_angle_lookup_class):
+        np.testing.assert_array_equal(
+            result_data.epoch_delta, codice_lo_l2_data.epoch_delta_plus
+        )
+        (
+            self.assertEqual(
+                sentinel.hplus_partial_density, result_data.hplus_partial_density
+            ),
+        )
+        (
+            self.assertEqual(
+                sentinel.heplusplus_partial_density,
+                result_data.heplusplus_partial_density,
+            ),
+        )
+        (self.assertEqual(cplus4_partial_density, result_data.cplus4_partial_density),)
+        (self.assertEqual(cplus5_partial_density, result_data.cplus5_partial_density),)
+        (self.assertEqual(cplus6_partial_density, result_data.cplus6_partial_density),)
+        (self.assertEqual(oplus5_partial_density, result_data.oplus5_partial_density),)
+        (self.assertEqual(oplus6_partial_density, result_data.oplus6_partial_density),)
+        (self.assertEqual(oplus7_partial_density, result_data.oplus7_partial_density),)
+        (self.assertEqual(oplus8_partial_density, result_data.oplus8_partial_density),)
+        (self.assertEqual(sentinel.ne_partial_density, result_data.ne_partial_density),)
+        (self.assertEqual(mg_partial_density, result_data.mg_partial_density),)
+        (self.assertEqual(sentinel.si_partial_density, result_data.si_partial_density),)
+        (self.assertEqual(fe_loq_partial_density, result_data.fe_loq_partial_density),)
+        (self.assertEqual(fe_hiq_partial_density, result_data.fe_hiq_partial_density),)
+
+    @patch(f"{MODULE}.SpinAngleLookup")
+    @patch(f"{MODULE}.lookup_normalization_per_event", autospec=True)
+    @patch(f"{MODULE}.calculate_normalization_factor", autospec=True)
+    @patch(f"{MODULE}.calculate_mass_per_charge", autospec=True)
+    @patch(f"{MODULE}.calculate_mass", autospec=True)
+    def test_process_l3a_direct_events(
+        self,
+        mock_calculate_mass,
+        mock_calculate_mass_per_charge,
+        mock_calculate_normalization_factor,
+        mock_lookup_normalization_per_event,
+        mock_spin_angle_lookup_class,
+    ):
         rng = np.random.default_rng()
 
         num_spin_angle_bins = 24
@@ -519,7 +741,11 @@ def test_process_l3a_direct_events(self, mock_calculate_mass, mock_calculate_mas
         epochs = np.array([datetime.now(), datetime.now() + timedelta(hours=1)])
         num_epochs = len(epochs)
 
-        priority_counts_variable_shape = (num_epochs, num_energy_bins, num_spin_angle_bins // 2)
+        priority_counts_variable_shape = (
+            num_epochs,
+            num_energy_bins,
+            num_spin_angle_bins // 2,
+        )
         sw_priority_rates = create_dataclass_mock(CodiceLoL1aSWPriorityRates)
         sw_priority_rates.epoch = epochs
         sw_priority_rates.p0_tcrs = rng.random(priority_counts_variable_shape)
@@ -527,7 +753,9 @@ def test_process_l3a_direct_events(self, mock_calculate_mass, mock_calculate_mas
         sw_priority_rates.p2_heplusplus = rng.random(priority_counts_variable_shape)
         sw_priority_rates.p3_heavies = rng.random(priority_counts_variable_shape)
         sw_priority_rates.p4_dcrs = rng.random(priority_counts_variable_shape)
-        sw_priority_rates.half_spin_per_esa_step = rng.random((num_epochs, num_energy_bins))
+        sw_priority_rates.half_spin_per_esa_step = rng.random(
+            (num_epochs, num_energy_bins)
+        )
         sw_priority_rates.rgfo_spin_sector = rng.random(num_epochs)
         sw_priority_rates.rgfo_esa_step = rng.random(num_epochs)
         sw_priority_rates.nso_spin_sector = rng.random(num_epochs)
@@ -535,25 +763,35 @@ def test_process_l3a_direct_events(self, mock_calculate_mass, mock_calculate_mas
         sw_priority_rates.esa_step = np.arange(128)
         sw_priority_rates.rgfo_half_spin = rng.random(num_epochs)
         sw_priority_rates.nso_half_spin = rng.random(num_epochs)
-        sw_priority_rates.acquisition_time_per_esa_step = rng.random((num_epochs, num_energy_bins))
+        sw_priority_rates.acquisition_time_per_esa_step = rng.random(
+            (num_epochs, num_energy_bins)
+        )
 
         nsw_priority_rates = create_dataclass_mock(CodiceLoL1aNSWPriorityRates)
         nsw_priority_rates.epoch = epochs
         nsw_priority_rates.p5_heavies = rng.random(priority_counts_variable_shape)
-        nsw_priority_rates.p6_hplus_heplusplus = rng.random(priority_counts_variable_shape)
+        nsw_priority_rates.p6_hplus_heplusplus = rng.random(
+            priority_counts_variable_shape
+        )
 
-        mass_per_charge = rng.random((num_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass_per_charge = rng.random(
+            (num_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass_per_charge.return_value = mass_per_charge
         mass = rng.random((num_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
         mock_calculate_mass.return_value = mass
 
-        codice_l2_variables = {f.name: rng.random((num_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)) for f
-                               in fields(CodiceLoL2DirectEventData)}
+        codice_l2_variables = {
+            f.name: rng.random(
+                (num_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+            )
+            for f in fields(CodiceLoL2DirectEventData)
+        }
 
         codice_l2_variables["epoch"] = epochs
         codice_l2_variables["spin_angle"] *= 360
-        codice_l2_variables["data_quality"] = rng.random((num_epochs))
-        codice_l2_variables["num_events"] = rng.random((num_epochs))
+        codice_l2_variables["data_quality"] = rng.random(num_epochs)
+        codice_l2_variables["num_events"] = rng.random(num_epochs)
 
         expected_energy_per_charge = codice_l2_variables["energy_per_charge"]
         expected_apd_energy = codice_l2_variables["apd_energy"]
@@ -580,109 +818,232 @@ def test_process_l3a_direct_events(self, mock_calculate_mass, mock_calculate_mas
         mock_energy_lookup.bin_centers = np.geomspace(14100, 88.082825, 128)
         mock_energy_lookup.num_bins = num_energy_bins
 
-        expected_normalization = rng.random((num_epochs, num_priorities, num_energy_bins, num_spin_angle_bins))
+        expected_normalization = rng.random(
+            (num_epochs, num_priorities, num_energy_bins, num_spin_angle_bins)
+        )
         mock_calculate_normalization_factor.return_value = expected_normalization
 
-        dependencies = CodiceLoL3aDirectEventsDependencies(sw_priority_rates, nsw_priority_rates, direct_events, Mock(),
-                                                           mock_energy_lookup)
+        dependencies = CodiceLoL3aDirectEventsDependencies(
+            sw_priority_rates,
+            nsw_priority_rates,
+            direct_events,
+            Mock(),
+            mock_energy_lookup,
+        )
 
         input_collection = ProcessingInputCollection()
-        input_metadata = InputMetadata('codice', "l3a", Mock(spec=datetime), Mock(spec=datetime), 'v02')
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
-        l3a_direct_event_data_product = processor.process_l3a_direct_event_data_product(dependencies)
+        input_metadata = InputMetadata(
+            "codice", "l3a", Mock(spec=datetime), Mock(spec=datetime), "v02"
+        )
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
+        l3a_direct_event_data_product = processor.process_l3a_direct_event_data_product(
+            dependencies
+        )
 
         self.assertEqual(1, mock_calculate_mass.call_count)
-        np.testing.assert_equal(mock_calculate_mass.call_args.args[0], expected_apd_energy)
+        np.testing.assert_equal(
+            mock_calculate_mass.call_args.args[0], expected_apd_energy
+        )
         np.testing.assert_equal(mock_calculate_mass.call_args.args[1], expected_tof)
-        np.testing.assert_equal(mock_calculate_mass.call_args.args[2], dependencies.mass_coefficient_lookup)
+        np.testing.assert_equal(
+            mock_calculate_mass.call_args.args[2], dependencies.mass_coefficient_lookup
+        )
 
         self.assertEqual(1, mock_calculate_mass_per_charge.call_count)
-        np.testing.assert_equal(mock_calculate_mass_per_charge.call_args.args[0], expected_energy_per_charge)
-        np.testing.assert_equal(mock_calculate_mass_per_charge.call_args.args[1], expected_tof)
+        np.testing.assert_equal(
+            mock_calculate_mass_per_charge.call_args.args[0], expected_energy_per_charge
+        )
+        np.testing.assert_equal(
+            mock_calculate_mass_per_charge.call_args.args[1], expected_tof
+        )
 
         self.assertEqual(1, mock_calculate_normalization_factor.call_count)
-        actual_stacked_priorities = mock_calculate_normalization_factor.call_args.args[0]
+        actual_stacked_priorities = mock_calculate_normalization_factor.call_args.args[
+            0
+        ]
 
-        np.testing.assert_equal(actual_stacked_priorities[:, 0], sw_priority_rates.p0_tcrs)
-        np.testing.assert_equal(actual_stacked_priorities[:, 1], sw_priority_rates.p1_hplus)
-        np.testing.assert_equal(actual_stacked_priorities[:, 2], sw_priority_rates.p2_heplusplus)
-        np.testing.assert_equal(actual_stacked_priorities[:, 3], sw_priority_rates.p3_heavies)
-        np.testing.assert_equal(actual_stacked_priorities[:, 4], sw_priority_rates.p4_dcrs)
-        np.testing.assert_equal(actual_stacked_priorities[:, 5], nsw_priority_rates.p5_heavies)
-        np.testing.assert_equal(actual_stacked_priorities[:, 6], nsw_priority_rates.p6_hplus_heplusplus)
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 0], sw_priority_rates.p0_tcrs
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 1], sw_priority_rates.p1_hplus
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 2], sw_priority_rates.p2_heplusplus
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 3], sw_priority_rates.p3_heavies
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 4], sw_priority_rates.p4_dcrs
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 5], nsw_priority_rates.p5_heavies
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[:, 6], nsw_priority_rates.p6_hplus_heplusplus
+        )
 
-        np.testing.assert_equal(mock_calculate_normalization_factor.call_args.args[1], expected_num_events)
-        np.testing.assert_equal(mock_calculate_normalization_factor.call_args.args[2], expected_energy_step)
-        np.testing.assert_equal(mock_calculate_normalization_factor.call_args.args[3], expected_spin_sector)
+        np.testing.assert_equal(
+            mock_calculate_normalization_factor.call_args.args[1], expected_num_events
+        )
+        np.testing.assert_equal(
+            mock_calculate_normalization_factor.call_args.args[2], expected_energy_step
+        )
+        np.testing.assert_equal(
+            mock_calculate_normalization_factor.call_args.args[3], expected_spin_sector
+        )
 
         self.assertEqual(1, mock_lookup_normalization_per_event.call_count)
-        np.testing.assert_equal(mock_lookup_normalization_per_event.call_args.args[0], expected_normalization)
-        np.testing.assert_equal(mock_lookup_normalization_per_event.call_args.args[1], expected_num_events)
-        np.testing.assert_equal(mock_lookup_normalization_per_event.call_args.args[2], expected_energy_step)
-        np.testing.assert_equal(mock_lookup_normalization_per_event.call_args.args[3], expected_spin_sector)
+        np.testing.assert_equal(
+            mock_lookup_normalization_per_event.call_args.args[0],
+            expected_normalization,
+        )
+        np.testing.assert_equal(
+            mock_lookup_normalization_per_event.call_args.args[1], expected_num_events
+        )
+        np.testing.assert_equal(
+            mock_lookup_normalization_per_event.call_args.args[2], expected_energy_step
+        )
+        np.testing.assert_equal(
+            mock_lookup_normalization_per_event.call_args.args[3], expected_spin_sector
+        )
 
-        self.assertIsInstance(l3a_direct_event_data_product, CodiceLoL3aDirectEventDataProduct)
+        self.assertIsInstance(
+            l3a_direct_event_data_product, CodiceLoL3aDirectEventDataProduct
+        )
         self.assertEqual(input_metadata, l3a_direct_event_data_product.input_metadata)
 
         np.testing.assert_array_equal(l3a_direct_event_data_product.epoch, epochs)
-        np.testing.assert_array_equal(l3a_direct_event_data_product.epoch_delta, direct_events.epoch_delta_plus)
+        np.testing.assert_array_equal(
+            l3a_direct_event_data_product.epoch_delta, direct_events.epoch_delta_plus
+        )
 
         np.testing.assert_array_equal(
-            np.arange(CODICE_LO_L2_NUM_PRIORITIES), l3a_direct_event_data_product.priority_index
+            np.arange(CODICE_LO_L2_NUM_PRIORITIES),
+            l3a_direct_event_data_product.priority_index,
+        )
+        np.testing.assert_array_equal(
+            expected_mass_per_charge, l3a_direct_event_data_product.mass_per_charge
         )
-        np.testing.assert_array_equal(expected_mass_per_charge, l3a_direct_event_data_product.mass_per_charge)
         np.testing.assert_array_equal(expected_mass, l3a_direct_event_data_product.mass)
 
         np.testing.assert_array_equal(
-            l3a_direct_event_data_product.normalization, np.flip(expected_normalization, axis=2)
-        )
-        self.assertEqual(l3a_direct_event_data_product.normalization_per_event,
-                         mock_lookup_normalization_per_event.return_value)
-
-        np.testing.assert_array_equal(expected_apd_energy, l3a_direct_event_data_product.apd_energy)
-        np.testing.assert_array_equal(expected_apd_gain, l3a_direct_event_data_product.gain)
-        np.testing.assert_array_equal(expected_apd_id, l3a_direct_event_data_product.apd_id)
-        np.testing.assert_array_equal(expected_data_quality, l3a_direct_event_data_product.data_quality)
-        np.testing.assert_array_equal(expected_elevation, l3a_direct_event_data_product.elevation)
-        np.testing.assert_array_equal(expected_energy_per_charge, l3a_direct_event_data_product.energy_per_charge)
-        np.testing.assert_array_equal(expected_energy_step, l3a_direct_event_data_product.energy_step)
-        np.testing.assert_array_equal(expected_multi_flag, l3a_direct_event_data_product.multi_flag)
-        np.testing.assert_array_equal(expected_num_events, l3a_direct_event_data_product.num_events)
-        np.testing.assert_array_equal(expected_position, l3a_direct_event_data_product.position)
-        np.testing.assert_array_equal(expected_spin_angle, l3a_direct_event_data_product.spin_angle)
-        np.testing.assert_array_equal(expected_spin_sector, l3a_direct_event_data_product.spin_sector)
+            l3a_direct_event_data_product.normalization,
+            np.flip(expected_normalization, axis=2),
+        )
+        self.assertEqual(
+            l3a_direct_event_data_product.normalization_per_event,
+            mock_lookup_normalization_per_event.return_value,
+        )
+
+        np.testing.assert_array_equal(
+            expected_apd_energy, l3a_direct_event_data_product.apd_energy
+        )
+        np.testing.assert_array_equal(
+            expected_apd_gain, l3a_direct_event_data_product.gain
+        )
+        np.testing.assert_array_equal(
+            expected_apd_id, l3a_direct_event_data_product.apd_id
+        )
+        np.testing.assert_array_equal(
+            expected_data_quality, l3a_direct_event_data_product.data_quality
+        )
+        np.testing.assert_array_equal(
+            expected_elevation, l3a_direct_event_data_product.elevation
+        )
+        np.testing.assert_array_equal(
+            expected_energy_per_charge, l3a_direct_event_data_product.energy_per_charge
+        )
+        np.testing.assert_array_equal(
+            expected_energy_step, l3a_direct_event_data_product.energy_step
+        )
+        np.testing.assert_array_equal(
+            expected_multi_flag, l3a_direct_event_data_product.multi_flag
+        )
+        np.testing.assert_array_equal(
+            expected_num_events, l3a_direct_event_data_product.num_events
+        )
+        np.testing.assert_array_equal(
+            expected_position, l3a_direct_event_data_product.position
+        )
+        np.testing.assert_array_equal(
+            expected_spin_angle, l3a_direct_event_data_product.spin_angle
+        )
+        np.testing.assert_array_equal(
+            expected_spin_sector, l3a_direct_event_data_product.spin_sector
+        )
         np.testing.assert_array_equal(expected_tof, l3a_direct_event_data_product.tof)
         np.testing.assert_array_equal(expected_type, l3a_direct_event_data_product.type)
 
-        np.testing.assert_array_equal(np.flip(mock_energy_lookup.bin_centers), l3a_direct_event_data_product.energy_bin)
-        np.testing.assert_array_equal(np.flip(mock_energy_lookup.delta_plus),
-                                      l3a_direct_event_data_product.energy_bin_delta_plus)
-        np.testing.assert_array_equal(np.flip(mock_energy_lookup.delta_minus),
-                                      l3a_direct_event_data_product.energy_bin_delta_minus)
-        self.assertEqual(mock_spin_angle_lookup.bin_centers, l3a_direct_event_data_product.spin_angle_bin)
-        self.assertEqual(mock_spin_angle_lookup.bin_deltas, l3a_direct_event_data_product.spin_angle_bin_delta)
-        np.testing.assert_array_equal(sw_priority_rates.half_spin_per_esa_step,
-                                      l3a_direct_event_data_product.half_spin_per_esa_step)
-        np.testing.assert_array_equal(sw_priority_rates.rgfo_spin_sector,
-                                      l3a_direct_event_data_product.rgfo_spin_sector)
-        np.testing.assert_array_equal(sw_priority_rates.rgfo_esa_step, l3a_direct_event_data_product.rgfo_esa_step)
-        np.testing.assert_array_equal(sw_priority_rates.nso_spin_sector, l3a_direct_event_data_product.nso_spin_sector)
-        np.testing.assert_array_equal(sw_priority_rates.nso_esa_step, l3a_direct_event_data_product.nso_esa_step)
-        np.testing.assert_array_equal(sw_priority_rates.esa_step, l3a_direct_event_data_product.esa_step)
-        np.testing.assert_array_equal(sw_priority_rates.rgfo_half_spin, l3a_direct_event_data_product.rgfo_half_spin)
-        np.testing.assert_array_equal(sw_priority_rates.nso_half_spin, l3a_direct_event_data_product.nso_half_spin)
-        np.testing.assert_array_equal(sw_priority_rates.acquisition_time_per_esa_step,
-                                      l3a_direct_event_data_product.acquisition_time_per_esa_step)
-
-    @patch(f'{MODULE}.SpinAngleLookup')
-    @patch(f'{MODULE}.lookup_normalization_per_event', autospec=True)
-    @patch(f'{MODULE}.calculate_normalization_factor', autospec=True)
-    @patch(f'{MODULE}.calculate_mass_per_charge', autospec=True)
-    @patch(f'{MODULE}.calculate_mass', autospec=True)
+        np.testing.assert_array_equal(
+            np.flip(mock_energy_lookup.bin_centers),
+            l3a_direct_event_data_product.energy_bin,
+        )
+        np.testing.assert_array_equal(
+            np.flip(mock_energy_lookup.delta_plus),
+            l3a_direct_event_data_product.energy_bin_delta_plus,
+        )
+        np.testing.assert_array_equal(
+            np.flip(mock_energy_lookup.delta_minus),
+            l3a_direct_event_data_product.energy_bin_delta_minus,
+        )
+        self.assertEqual(
+            mock_spin_angle_lookup.bin_centers,
+            l3a_direct_event_data_product.spin_angle_bin,
+        )
+        self.assertEqual(
+            mock_spin_angle_lookup.bin_deltas,
+            l3a_direct_event_data_product.spin_angle_bin_delta,
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.half_spin_per_esa_step,
+            l3a_direct_event_data_product.half_spin_per_esa_step,
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.rgfo_spin_sector,
+            l3a_direct_event_data_product.rgfo_spin_sector,
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.rgfo_esa_step, l3a_direct_event_data_product.rgfo_esa_step
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.nso_spin_sector,
+            l3a_direct_event_data_product.nso_spin_sector,
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.nso_esa_step, l3a_direct_event_data_product.nso_esa_step
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.esa_step, l3a_direct_event_data_product.esa_step
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.rgfo_half_spin,
+            l3a_direct_event_data_product.rgfo_half_spin,
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.nso_half_spin, l3a_direct_event_data_product.nso_half_spin
+        )
+        np.testing.assert_array_equal(
+            sw_priority_rates.acquisition_time_per_esa_step,
+            l3a_direct_event_data_product.acquisition_time_per_esa_step,
+        )
+
+    @patch(f"{MODULE}.SpinAngleLookup")
+    @patch(f"{MODULE}.lookup_normalization_per_event", autospec=True)
+    @patch(f"{MODULE}.calculate_normalization_factor", autospec=True)
+    @patch(f"{MODULE}.calculate_mass_per_charge", autospec=True)
+    @patch(f"{MODULE}.calculate_mass", autospec=True)
     def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_missing(
-            self, mock_calculate_mass, mock_calculate_mass_per_charge,
-            mock_calculate_normalization_factor, mock_lookup_normalization_per_event,
-            mock_spin_angle_lookup_class):
+        self,
+        mock_calculate_mass,
+        mock_calculate_mass_per_charge,
+        mock_calculate_normalization_factor,
+        mock_lookup_normalization_per_event,
+        mock_spin_angle_lookup_class,
+    ):
         rng = np.random.default_rng(seed=42)
 
         num_spin_angle_bins = 4
@@ -708,8 +1069,16 @@ def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_m
         num_sw_epochs = len(sw_priority_epochs)
         num_nsw_epochs = len(nsw_priority_epochs)
 
-        sw_priority_counts_shape = (num_sw_epochs, num_energy_bins, priority_l1_spin_sectors)
-        nsw_priority_counts_shape = (num_nsw_epochs, num_energy_bins, priority_l1_spin_sectors)
+        sw_priority_counts_shape = (
+            num_sw_epochs,
+            num_energy_bins,
+            priority_l1_spin_sectors,
+        )
+        nsw_priority_counts_shape = (
+            num_nsw_epochs,
+            num_energy_bins,
+            priority_l1_spin_sectors,
+        )
 
         sw_priority_rates = create_dataclass_mock(CodiceLoL1aSWPriorityRates)
         sw_priority_rates.epoch = sw_priority_epochs
@@ -718,7 +1087,9 @@ def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_m
         sw_priority_rates.p2_heplusplus = rng.random(sw_priority_counts_shape)
         sw_priority_rates.p3_heavies = rng.random(sw_priority_counts_shape)
         sw_priority_rates.p4_dcrs = rng.random(sw_priority_counts_shape)
-        sw_priority_rates.half_spin_per_esa_step = rng.random((num_sw_epochs, num_energy_bins))
+        sw_priority_rates.half_spin_per_esa_step = rng.random(
+            (num_sw_epochs, num_energy_bins)
+        )
         sw_priority_rates.rgfo_spin_sector = rng.random(num_sw_epochs)
         sw_priority_rates.rgfo_esa_step = rng.random(num_sw_epochs)
         sw_priority_rates.nso_spin_sector = rng.random(num_sw_epochs)
@@ -726,20 +1097,32 @@ def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_m
         sw_priority_rates.esa_step = np.arange(num_energy_bins)
         sw_priority_rates.rgfo_half_spin = rng.random(num_sw_epochs)
         sw_priority_rates.nso_half_spin = rng.random(num_sw_epochs)
-        sw_priority_rates.acquisition_time_per_esa_step = rng.random((num_sw_epochs, num_energy_bins))
+        sw_priority_rates.acquisition_time_per_esa_step = rng.random(
+            (num_sw_epochs, num_energy_bins)
+        )
 
         nsw_priority_rates = create_dataclass_mock(CodiceLoL1aNSWPriorityRates)
         nsw_priority_rates.epoch = nsw_priority_epochs
         nsw_priority_rates.p5_heavies = rng.random(nsw_priority_counts_shape)
         nsw_priority_rates.p6_hplus_heplusplus = rng.random(nsw_priority_counts_shape)
 
-        mass_per_charge = rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass_per_charge = rng.random(
+            (num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass_per_charge.return_value = mass_per_charge
-        mass = rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass = rng.random(
+            (num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass.return_value = mass
 
         codice_l2_variables = {
-            f.name: rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+            f.name: rng.random(
+                (
+                    num_direct_event_epochs,
+                    CODICE_LO_L2_NUM_PRIORITIES,
+                    event_buffer_size,
+                )
+            )
             for f in fields(CodiceLoL2DirectEventData)
         }
         codice_l2_variables["epoch"] = direct_event_epochs
@@ -756,7 +1139,12 @@ def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_m
         mock_energy_lookup.num_bins = num_energy_bins
 
         expected_normalization = rng.random(
-            (num_direct_event_epochs, num_priorities, num_energy_bins, num_spin_angle_bins)
+            (
+                num_direct_event_epochs,
+                num_priorities,
+                num_energy_bins,
+                num_spin_angle_bins,
+            )
         )
         mock_calculate_normalization_factor.return_value = expected_normalization
 
@@ -767,52 +1155,120 @@ def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_m
         )
 
         dependencies = CodiceLoL3aDirectEventsDependencies(
-            sw_priority_rates, nsw_priority_rates, direct_events, Mock(), mock_energy_lookup
+            sw_priority_rates,
+            nsw_priority_rates,
+            direct_events,
+            Mock(),
+            mock_energy_lookup,
         )
 
         input_collection = ProcessingInputCollection()
-        input_metadata = InputMetadata('codice', "l3a", Mock(spec=datetime), Mock(spec=datetime), 'v02')
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        input_metadata = InputMetadata(
+            "codice", "l3a", Mock(spec=datetime), Mock(spec=datetime), "v02"
+        )
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         result = processor.process_l3a_direct_event_data_product(dependencies)
 
         self.assertEqual(1, mock_calculate_normalization_factor.call_count)
-        actual_stacked_priorities = mock_calculate_normalization_factor.call_args.args[0]
+        actual_stacked_priorities = mock_calculate_normalization_factor.call_args.args[
+            0
+        ]
         self.assertEqual(
-            (num_direct_event_epochs, num_priorities, num_energy_bins, priority_l1_spin_sectors),
+            (
+                num_direct_event_epochs,
+                num_priorities,
+                num_energy_bins,
+                priority_l1_spin_sectors,
+            ),
             actual_stacked_priorities.shape,
         )
 
-        np.testing.assert_equal(actual_stacked_priorities[0, 0], sw_priority_rates.p0_tcrs[0])
-        np.testing.assert_equal(actual_stacked_priorities[0, 1], sw_priority_rates.p1_hplus[0])
-        np.testing.assert_equal(actual_stacked_priorities[0, 2], sw_priority_rates.p2_heplusplus[0])
-        np.testing.assert_equal(actual_stacked_priorities[0, 3], sw_priority_rates.p3_heavies[0])
-        np.testing.assert_equal(actual_stacked_priorities[0, 4], sw_priority_rates.p4_dcrs[0])
-        np.testing.assert_equal(actual_stacked_priorities[0, 5], nsw_priority_rates.p5_heavies[0])
-        np.testing.assert_equal(actual_stacked_priorities[0, 6], nsw_priority_rates.p6_hplus_heplusplus[0])
-
-        expected_sw_nan_block = np.full((5, num_energy_bins, priority_l1_spin_sectors), np.nan)
-        np.testing.assert_equal(actual_stacked_priorities[1, 0:5], expected_sw_nan_block)
-        np.testing.assert_equal(actual_stacked_priorities[1, 5], nsw_priority_rates.p5_heavies[1])
-        np.testing.assert_equal(actual_stacked_priorities[1, 6], nsw_priority_rates.p6_hplus_heplusplus[1])
-
-        np.testing.assert_equal(actual_stacked_priorities[2, 0], sw_priority_rates.p0_tcrs[1])
-        np.testing.assert_equal(actual_stacked_priorities[2, 1], sw_priority_rates.p1_hplus[1])
-        np.testing.assert_equal(actual_stacked_priorities[2, 2], sw_priority_rates.p2_heplusplus[1])
-        np.testing.assert_equal(actual_stacked_priorities[2, 3], sw_priority_rates.p3_heavies[1])
-        np.testing.assert_equal(actual_stacked_priorities[2, 4], sw_priority_rates.p4_dcrs[1])
-        expected_nsw_nan_block = np.full((2, num_energy_bins, priority_l1_spin_sectors), np.nan)
-        np.testing.assert_equal(actual_stacked_priorities[2, 5:7], expected_nsw_nan_block)
-
-        np.testing.assert_equal(actual_stacked_priorities[3, 0], sw_priority_rates.p0_tcrs[2])
-        np.testing.assert_equal(actual_stacked_priorities[3, 1], sw_priority_rates.p1_hplus[2])
-        np.testing.assert_equal(actual_stacked_priorities[3, 2], sw_priority_rates.p2_heplusplus[2])
-        np.testing.assert_equal(actual_stacked_priorities[3, 3], sw_priority_rates.p3_heavies[2])
-        np.testing.assert_equal(actual_stacked_priorities[3, 4], sw_priority_rates.p4_dcrs[2])
-        np.testing.assert_equal(actual_stacked_priorities[3, 5], nsw_priority_rates.p5_heavies[2])
-        np.testing.assert_equal(actual_stacked_priorities[3, 6], nsw_priority_rates.p6_hplus_heplusplus[2])
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 0], sw_priority_rates.p0_tcrs[0]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 1], sw_priority_rates.p1_hplus[0]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 2], sw_priority_rates.p2_heplusplus[0]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 3], sw_priority_rates.p3_heavies[0]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 4], sw_priority_rates.p4_dcrs[0]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 5], nsw_priority_rates.p5_heavies[0]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, 6], nsw_priority_rates.p6_hplus_heplusplus[0]
+        )
+
+        expected_sw_nan_block = np.full(
+            (5, num_energy_bins, priority_l1_spin_sectors), np.nan
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[1, 0:5], expected_sw_nan_block
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[1, 5], nsw_priority_rates.p5_heavies[1]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[1, 6], nsw_priority_rates.p6_hplus_heplusplus[1]
+        )
+
+        np.testing.assert_equal(
+            actual_stacked_priorities[2, 0], sw_priority_rates.p0_tcrs[1]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[2, 1], sw_priority_rates.p1_hplus[1]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[2, 2], sw_priority_rates.p2_heplusplus[1]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[2, 3], sw_priority_rates.p3_heavies[1]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[2, 4], sw_priority_rates.p4_dcrs[1]
+        )
+        expected_nsw_nan_block = np.full(
+            (2, num_energy_bins, priority_l1_spin_sectors), np.nan
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[2, 5:7], expected_nsw_nan_block
+        )
+
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 0], sw_priority_rates.p0_tcrs[2]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 1], sw_priority_rates.p1_hplus[2]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 2], sw_priority_rates.p2_heplusplus[2]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 3], sw_priority_rates.p3_heavies[2]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 4], sw_priority_rates.p4_dcrs[2]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 5], nsw_priority_rates.p5_heavies[2]
+        )
+        np.testing.assert_equal(
+            actual_stacked_priorities[3, 6], nsw_priority_rates.p6_hplus_heplusplus[2]
+        )
 
         self.assertEqual(1, mock_lookup_normalization_per_event.call_count)
-        np.testing.assert_equal(mock_lookup_normalization_per_event.call_args.args[0], expected_normalization)
+        np.testing.assert_equal(
+            mock_lookup_normalization_per_event.call_args.args[0],
+            expected_normalization,
+        )
 
         actual_normalization_per_event = result.normalization_per_event
 
@@ -823,34 +1279,62 @@ def test_process_l3a_direct_events_aligns_priority_to_direct_event_epochs_when_m
             actual_normalization_per_event[2, 5:7, :], np.zeros((2, event_buffer_size))
         )
 
-        sentinel_row = np.full(event_buffer_size, sentinel_normalization_per_event_value)
+        sentinel_row = np.full(
+            event_buffer_size, sentinel_normalization_per_event_value
+        )
         np.testing.assert_array_equal(
             actual_normalization_per_event[0],
-            np.full((num_priorities, event_buffer_size), sentinel_normalization_per_event_value),
+            np.full(
+                (num_priorities, event_buffer_size),
+                sentinel_normalization_per_event_value,
+            ),
         )
         np.testing.assert_array_equal(
             actual_normalization_per_event[3],
-            np.full((num_priorities, event_buffer_size), sentinel_normalization_per_event_value),
-        )
-        np.testing.assert_array_equal(actual_normalization_per_event[1, 5], sentinel_row)
-        np.testing.assert_array_equal(actual_normalization_per_event[1, 6], sentinel_row)
-        np.testing.assert_array_equal(actual_normalization_per_event[2, 0], sentinel_row)
-        np.testing.assert_array_equal(actual_normalization_per_event[2, 1], sentinel_row)
-        np.testing.assert_array_equal(actual_normalization_per_event[2, 2], sentinel_row)
-        np.testing.assert_array_equal(actual_normalization_per_event[2, 3], sentinel_row)
-        np.testing.assert_array_equal(actual_normalization_per_event[2, 4], sentinel_row)
-
-        np.testing.assert_array_equal(result.normalization, np.flip(expected_normalization, axis=2))
-
-    @patch(f'{MODULE}.SpinAngleLookup')
-    @patch(f'{MODULE}.lookup_normalization_per_event', autospec=True)
-    @patch(f'{MODULE}.calculate_normalization_factor', autospec=True)
-    @patch(f'{MODULE}.calculate_mass_per_charge', autospec=True)
-    @patch(f'{MODULE}.calculate_mass', autospec=True)
+            np.full(
+                (num_priorities, event_buffer_size),
+                sentinel_normalization_per_event_value,
+            ),
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[1, 5], sentinel_row
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[1, 6], sentinel_row
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[2, 0], sentinel_row
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[2, 1], sentinel_row
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[2, 2], sentinel_row
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[2, 3], sentinel_row
+        )
+        np.testing.assert_array_equal(
+            actual_normalization_per_event[2, 4], sentinel_row
+        )
+
+        np.testing.assert_array_equal(
+            result.normalization, np.flip(expected_normalization, axis=2)
+        )
+
+    @patch(f"{MODULE}.SpinAngleLookup")
+    @patch(f"{MODULE}.lookup_normalization_per_event", autospec=True)
+    @patch(f"{MODULE}.calculate_normalization_factor", autospec=True)
+    @patch(f"{MODULE}.calculate_mass_per_charge", autospec=True)
+    @patch(f"{MODULE}.calculate_mass", autospec=True)
     def test_process_l3a_direct_events_handles_masked_priority_counts(
-            self, mock_calculate_mass, mock_calculate_mass_per_charge,
-            mock_calculate_normalization_factor, mock_lookup_normalization_per_event,
-            mock_spin_angle_lookup_class):
+        self,
+        mock_calculate_mass,
+        mock_calculate_mass_per_charge,
+        mock_calculate_normalization_factor,
+        mock_lookup_normalization_per_event,
+        mock_spin_angle_lookup_class,
+    ):
         rng = np.random.default_rng(seed=42)
 
         num_spin_angle_bins = 4
@@ -876,18 +1360,38 @@ def test_process_l3a_direct_events_handles_masked_priority_counts(
         num_sw_epochs = len(sw_priority_epochs)
         num_nsw_epochs = len(nsw_priority_epochs)
 
-        sw_priority_counts_shape = (num_sw_epochs, num_energy_bins, priority_l1_spin_sectors)
-        nsw_priority_counts_shape = (num_nsw_epochs, num_energy_bins, priority_l1_spin_sectors)
+        sw_priority_counts_shape = (
+            num_sw_epochs,
+            num_energy_bins,
+            priority_l1_spin_sectors,
+        )
+        nsw_priority_counts_shape = (
+            num_nsw_epochs,
+            num_energy_bins,
+            priority_l1_spin_sectors,
+        )
         sw_mask = np.full(sw_priority_counts_shape, False)
         sw_mask[0, 0, 0] = True
         sw_priority_rates = create_dataclass_mock(CodiceLoL1aSWPriorityRates)
         sw_priority_rates.epoch = sw_priority_epochs
-        sw_priority_rates.p0_tcrs = np.ma.array(rng.random(sw_priority_counts_shape), mask=sw_mask)
-        sw_priority_rates.p1_hplus = np.ma.array(rng.random(sw_priority_counts_shape), mask=sw_mask)
-        sw_priority_rates.p2_heplusplus = np.ma.array(rng.random(sw_priority_counts_shape), mask=sw_mask)
-        sw_priority_rates.p3_heavies = np.ma.array(rng.random(sw_priority_counts_shape), mask=sw_mask)
-        sw_priority_rates.p4_dcrs = np.ma.array(rng.random(sw_priority_counts_shape), mask=sw_mask)
-        sw_priority_rates.half_spin_per_esa_step = rng.random((num_sw_epochs, num_energy_bins))
+        sw_priority_rates.p0_tcrs = np.ma.array(
+            rng.random(sw_priority_counts_shape), mask=sw_mask
+        )
+        sw_priority_rates.p1_hplus = np.ma.array(
+            rng.random(sw_priority_counts_shape), mask=sw_mask
+        )
+        sw_priority_rates.p2_heplusplus = np.ma.array(
+            rng.random(sw_priority_counts_shape), mask=sw_mask
+        )
+        sw_priority_rates.p3_heavies = np.ma.array(
+            rng.random(sw_priority_counts_shape), mask=sw_mask
+        )
+        sw_priority_rates.p4_dcrs = np.ma.array(
+            rng.random(sw_priority_counts_shape), mask=sw_mask
+        )
+        sw_priority_rates.half_spin_per_esa_step = rng.random(
+            (num_sw_epochs, num_energy_bins)
+        )
         sw_priority_rates.rgfo_spin_sector = rng.random(num_sw_epochs)
         sw_priority_rates.rgfo_esa_step = rng.random(num_sw_epochs)
         sw_priority_rates.nso_spin_sector = rng.random(num_sw_epochs)
@@ -895,22 +1399,38 @@ def test_process_l3a_direct_events_handles_masked_priority_counts(
         sw_priority_rates.esa_step = np.arange(num_energy_bins)
         sw_priority_rates.rgfo_half_spin = rng.integers(0, 12, num_sw_epochs, np.uint8)
         sw_priority_rates.nso_half_spin = rng.integers(0, 12, num_sw_epochs, np.uint8)
-        sw_priority_rates.acquisition_time_per_esa_step = rng.random((num_sw_epochs, num_energy_bins))
+        sw_priority_rates.acquisition_time_per_esa_step = rng.random(
+            (num_sw_epochs, num_energy_bins)
+        )
 
         nsw_mask = np.full(nsw_priority_counts_shape, False)
         nsw_mask[0, 0, 0] = True
         nsw_priority_rates = create_dataclass_mock(CodiceLoL1aNSWPriorityRates)
         nsw_priority_rates.epoch = nsw_priority_epochs
-        nsw_priority_rates.p5_heavies = np.ma.array(rng.random(nsw_priority_counts_shape), mask=nsw_mask)
-        nsw_priority_rates.p6_hplus_heplusplus = np.ma.array(rng.random(nsw_priority_counts_shape), mask=nsw_mask)
+        nsw_priority_rates.p5_heavies = np.ma.array(
+            rng.random(nsw_priority_counts_shape), mask=nsw_mask
+        )
+        nsw_priority_rates.p6_hplus_heplusplus = np.ma.array(
+            rng.random(nsw_priority_counts_shape), mask=nsw_mask
+        )
 
-        mass_per_charge = rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass_per_charge = rng.random(
+            (num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass_per_charge.return_value = mass_per_charge
-        mass = rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass = rng.random(
+            (num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass.return_value = mass
 
         codice_l2_variables = {
-            f.name: rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+            f.name: rng.random(
+                (
+                    num_direct_event_epochs,
+                    CODICE_LO_L2_NUM_PRIORITIES,
+                    event_buffer_size,
+                )
+            )
             for f in fields(CodiceLoL2DirectEventData)
         }
         codice_l2_variables["epoch"] = direct_event_epochs
@@ -927,7 +1447,12 @@ def test_process_l3a_direct_events_handles_masked_priority_counts(
         mock_energy_lookup.num_bins = num_energy_bins
 
         expected_normalization = rng.random(
-            (num_direct_event_epochs, num_priorities, num_energy_bins, num_spin_angle_bins)
+            (
+                num_direct_event_epochs,
+                num_priorities,
+                num_energy_bins,
+                num_spin_angle_bins,
+            )
         )
         mock_calculate_normalization_factor.return_value = expected_normalization
 
@@ -938,32 +1463,53 @@ def test_process_l3a_direct_events_handles_masked_priority_counts(
         )
 
         dependencies = CodiceLoL3aDirectEventsDependencies(
-            sw_priority_rates, nsw_priority_rates, direct_events, Mock(), mock_energy_lookup
+            sw_priority_rates,
+            nsw_priority_rates,
+            direct_events,
+            Mock(),
+            mock_energy_lookup,
         )
 
         input_collection = ProcessingInputCollection()
-        input_metadata = InputMetadata('codice', "l3a", Mock(spec=datetime), Mock(spec=datetime), 'v02')
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        input_metadata = InputMetadata(
+            "codice", "l3a", Mock(spec=datetime), Mock(spec=datetime), "v02"
+        )
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         result = processor.process_l3a_direct_event_data_product(dependencies)
 
         self.assertEqual(1, mock_calculate_normalization_factor.call_count)
-        actual_stacked_priorities = mock_calculate_normalization_factor.call_args.args[0]
+        actual_stacked_priorities = mock_calculate_normalization_factor.call_args.args[
+            0
+        ]
         self.assertEqual(
-            (num_direct_event_epochs, num_priorities, num_energy_bins, priority_l1_spin_sectors),
+            (
+                num_direct_event_epochs,
+                num_priorities,
+                num_energy_bins,
+                priority_l1_spin_sectors,
+            ),
             actual_stacked_priorities.shape,
         )
 
-        np.testing.assert_equal(actual_stacked_priorities[0, :, 0, 0], np.full(7, np.nan))
+        np.testing.assert_equal(
+            actual_stacked_priorities[0, :, 0, 0], np.full(7, np.nan)
+        )
 
-    @patch(f'{MODULE}.SpinAngleLookup')
-    @patch(f'{MODULE}.lookup_normalization_per_event', autospec=True)
-    @patch(f'{MODULE}.calculate_normalization_factor', autospec=True)
-    @patch(f'{MODULE}.calculate_mass_per_charge', autospec=True)
-    @patch(f'{MODULE}.calculate_mass', autospec=True)
+    @patch(f"{MODULE}.SpinAngleLookup")
+    @patch(f"{MODULE}.lookup_normalization_per_event", autospec=True)
+    @patch(f"{MODULE}.calculate_normalization_factor", autospec=True)
+    @patch(f"{MODULE}.calculate_mass_per_charge", autospec=True)
+    @patch(f"{MODULE}.calculate_mass", autospec=True)
     def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epochs_when_missing(
-            self, mock_calculate_mass, mock_calculate_mass_per_charge,
-            mock_calculate_normalization_factor, mock_lookup_normalization_per_event,
-            mock_spin_angle_lookup_class):
+        self,
+        mock_calculate_mass,
+        mock_calculate_mass_per_charge,
+        mock_calculate_normalization_factor,
+        mock_lookup_normalization_per_event,
+        mock_spin_angle_lookup_class,
+    ):
         rng = np.random.default_rng(seed=42)
 
         num_spin_angle_bins = 4
@@ -989,8 +1535,16 @@ def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epoch
         num_sw_epochs = len(sw_priority_epochs)
         num_nsw_epochs = len(nsw_priority_epochs)
 
-        sw_priority_counts_shape = (num_sw_epochs, num_energy_bins, priority_l1_spin_sectors)
-        nsw_priority_counts_shape = (num_nsw_epochs, num_energy_bins, priority_l1_spin_sectors)
+        sw_priority_counts_shape = (
+            num_sw_epochs,
+            num_energy_bins,
+            priority_l1_spin_sectors,
+        )
+        nsw_priority_counts_shape = (
+            num_nsw_epochs,
+            num_energy_bins,
+            priority_l1_spin_sectors,
+        )
 
         sw_priority_rates = create_dataclass_mock(CodiceLoL1aSWPriorityRates)
         sw_priority_rates.epoch = sw_priority_epochs
@@ -999,7 +1553,9 @@ def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epoch
         sw_priority_rates.p2_heplusplus = rng.random(sw_priority_counts_shape)
         sw_priority_rates.p3_heavies = rng.random(sw_priority_counts_shape)
         sw_priority_rates.p4_dcrs = rng.random(sw_priority_counts_shape)
-        sw_priority_rates.half_spin_per_esa_step = rng.random((num_sw_epochs, num_energy_bins))
+        sw_priority_rates.half_spin_per_esa_step = rng.random(
+            (num_sw_epochs, num_energy_bins)
+        )
         sw_priority_rates.rgfo_spin_sector = rng.random(num_sw_epochs)
         sw_priority_rates.rgfo_esa_step = rng.random(num_sw_epochs)
         sw_priority_rates.nso_spin_sector = rng.random(num_sw_epochs)
@@ -1007,20 +1563,32 @@ def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epoch
         sw_priority_rates.esa_step = np.arange(num_energy_bins)
         sw_priority_rates.rgfo_half_spin = rng.integers(0, 12, num_sw_epochs, np.uint8)
         sw_priority_rates.nso_half_spin = rng.integers(0, 12, num_sw_epochs, np.uint8)
-        sw_priority_rates.acquisition_time_per_esa_step = rng.random((num_sw_epochs, num_energy_bins))
+        sw_priority_rates.acquisition_time_per_esa_step = rng.random(
+            (num_sw_epochs, num_energy_bins)
+        )
 
         nsw_priority_rates = create_dataclass_mock(CodiceLoL1aNSWPriorityRates)
         nsw_priority_rates.epoch = nsw_priority_epochs
         nsw_priority_rates.p5_heavies = rng.random(nsw_priority_counts_shape)
         nsw_priority_rates.p6_hplus_heplusplus = rng.random(nsw_priority_counts_shape)
 
-        mass_per_charge = rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass_per_charge = rng.random(
+            (num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass_per_charge.return_value = mass_per_charge
-        mass = rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+        mass = rng.random(
+            (num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size)
+        )
         mock_calculate_mass.return_value = mass
 
         codice_l2_variables = {
-            f.name: rng.random((num_direct_event_epochs, CODICE_LO_L2_NUM_PRIORITIES, event_buffer_size))
+            f.name: rng.random(
+                (
+                    num_direct_event_epochs,
+                    CODICE_LO_L2_NUM_PRIORITIES,
+                    event_buffer_size,
+                )
+            )
             for f in fields(CodiceLoL2DirectEventData)
         }
         codice_l2_variables["epoch"] = direct_event_epochs
@@ -1037,7 +1605,12 @@ def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epoch
         mock_energy_lookup.num_bins = num_energy_bins
 
         expected_normalization = rng.random(
-            (num_direct_event_epochs, num_priorities, num_energy_bins, num_spin_angle_bins)
+            (
+                num_direct_event_epochs,
+                num_priorities,
+                num_energy_bins,
+                num_spin_angle_bins,
+            )
         )
         mock_calculate_normalization_factor.return_value = expected_normalization
 
@@ -1048,16 +1621,27 @@ def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epoch
         )
 
         dependencies = CodiceLoL3aDirectEventsDependencies(
-            sw_priority_rates, nsw_priority_rates, direct_events, Mock(), mock_energy_lookup
+            sw_priority_rates,
+            nsw_priority_rates,
+            direct_events,
+            Mock(),
+            mock_energy_lookup,
         )
 
         input_collection = ProcessingInputCollection()
-        input_metadata = InputMetadata('codice', "l3a", Mock(spec=datetime), Mock(spec=datetime), 'v02')
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        input_metadata = InputMetadata(
+            "codice", "l3a", Mock(spec=datetime), Mock(spec=datetime), "v02"
+        )
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         result = processor.process_l3a_direct_event_data_product(dependencies)
 
         variables_to_compare = [
-            (sw_priority_rates.acquisition_time_per_esa_step, result.acquisition_time_per_esa_step),
+            (
+                sw_priority_rates.acquisition_time_per_esa_step,
+                result.acquisition_time_per_esa_step,
+            ),
             (sw_priority_rates.rgfo_half_spin, result.rgfo_half_spin),
             (sw_priority_rates.rgfo_spin_sector, result.rgfo_spin_sector),
             (sw_priority_rates.rgfo_esa_step, result.rgfo_esa_step),
@@ -1085,18 +1669,24 @@ def test_process_l3a_direct_events_aligns_passthrough_vars_to_direct_event_epoch
             self.assertTrue(np.all(result_var.mask[2] == False))
             self.assertTrue(np.all(result_var.mask[3] == False))
 
-    @patch(f'{MODULE}.rebin_3d_distribution_azimuth_to_elevation')
-    @patch(f'{MODULE}.combine_priorities_for_species_and_calculate_count_uncertainty')
-    @patch(f'{MODULE}.PositionToElevationLookup')
-    @patch(f'{MODULE}.rebin_to_counts_by_species_elevation_and_spin_sector')
-    @patch(f'{MODULE}.convert_counts_to_intensity')
-    def test_process_l3b_3d_distributions(self, mock_convert_counts_to_intensity, mock_rebin,
-                                          mock_elevation_angle_lookup_class,
-                                          mock_combine_priorities_for_species_and_calculate_uncertainty,
-                                          mock_rebin_3d_distribution_azimuth_to_elevation):
+    @patch(f"{MODULE}.rebin_3d_distribution_azimuth_to_elevation")
+    @patch(f"{MODULE}.combine_priorities_for_species_and_calculate_count_uncertainty")
+    @patch(f"{MODULE}.PositionToElevationLookup")
+    @patch(f"{MODULE}.rebin_to_counts_by_species_elevation_and_spin_sector")
+    @patch(f"{MODULE}.convert_counts_to_intensity")
+    def test_process_l3b_3d_distributions(
+        self,
+        mock_convert_counts_to_intensity,
+        mock_rebin,
+        mock_elevation_angle_lookup_class,
+        mock_combine_priorities_for_species_and_calculate_uncertainty,
+        mock_rebin_3d_distribution_azimuth_to_elevation,
+    ):
         mock_elevation_lookup = mock_elevation_angle_lookup_class.return_value
 
-        input_metadata = InputMetadata('codice', "l3a", datetime(2026, 5, 13), Mock(spec=datetime), 'v02')
+        input_metadata = InputMetadata(
+            "codice", "l3a", datetime(2026, 5, 13), Mock(spec=datetime), "v02"
+        )
 
         l3a_de_half_spin_per_esa_step = np.array([[3, 2, 1]])
         mock_l3a_direct_event_data = Mock(
@@ -1123,32 +1713,51 @@ def test_process_l3b_3d_distributions(self, mock_convert_counts_to_intensity, mo
             geometric_factors_lookup=mock_geometric_factor_lut,
             efficiency_factors_lut=mock_efficiency_lut,
             energy_per_charge_lut=mock_energy_lookup,
-            species=sentinel.species
+            species=sentinel.species,
         )
 
         counts = np.array([])
         count_uncerts = np.array([])
-        mock_combine_priorities_for_species_and_calculate_uncertainty.return_value = (counts, count_uncerts)
+        mock_combine_priorities_for_species_and_calculate_uncertainty.return_value = (
+            counts,
+            count_uncerts,
+        )
         mock_mass_bin_lookup.get_species_index.return_value = sentinel.species_index
-        mock_convert_counts_to_intensity.side_effect = [sentinel.intensity, sentinel.intensity_uncert]
+        mock_convert_counts_to_intensity.side_effect = [
+            sentinel.intensity,
+            sentinel.intensity_uncert,
+        ]
         mock_rebinned_intensity = np.arange(25).reshape(5, 5)
         mock_rebinned_uncert = np.arange(25, 50).reshape(5, 5)
-        mock_rebin_3d_distribution_azimuth_to_elevation.side_effect = [mock_rebinned_intensity, mock_rebinned_uncert]
+        mock_rebin_3d_distribution_azimuth_to_elevation.side_effect = [
+            mock_rebinned_intensity,
+            mock_rebinned_uncert,
+        ]
 
         counts_3d_distribution = mock_rebin.return_value
-        counts_3d_distribution.__getitem__.return_value = sentinel.counts_for_species_by_priority
+        counts_3d_distribution.__getitem__.return_value = (
+            sentinel.counts_for_species_by_priority
+        )
 
-        processor = CodiceLoProcessor(dependencies=Mock(), input_metadata=input_metadata)
-        l3b_3d_distribution_data_product = processor.process_l3b_3d_distribution_product(dependencies)
+        processor = CodiceLoProcessor(
+            dependencies=Mock(), input_metadata=input_metadata
+        )
+        l3b_3d_distribution_data_product = (
+            processor.process_l3b_3d_distribution_product(dependencies)
+        )
 
         mock_elevation_angle_lookup_class.assert_called_once()
-        counts_3d_distribution.__getitem__.assert_called_once_with(sentinel.species_index)
+        counts_3d_distribution.__getitem__.assert_called_once_with(
+            sentinel.species_index
+        )
         mock_rebin.assert_called_once_with(
             direct_event_data=mock_l3a_direct_event_data,
             mass_species_bin_lookup=dependencies.mass_species_bin_lookup,
         )
 
-        mock_combine_priorities_for_species_and_calculate_uncertainty.assert_called_once_with(sentinel.counts_for_species_by_priority)
+        mock_combine_priorities_for_species_and_calculate_uncertainty.assert_called_once_with(
+            sentinel.counts_for_species_by_priority
+        )
 
         mock_compute_geometric_factors = mock_geometric_factor_lut.get_geometric_factors
         mock_compute_geometric_factors.assert_called_once_with(
@@ -1156,50 +1765,121 @@ def test_process_l3b_3d_distributions(self, mock_convert_counts_to_intensity, mo
             sentinel.rgfo_spin_sector,
             sentinel.rgfo_esa_step,
             NumpyArrayMatcher(l3a_de_half_spin_per_esa_step),
-            date(2026, 5, 13)
+            date(2026, 5, 13),
+        )
+        mock_convert_counts_to_intensity.assert_has_calls(
+            [
+                call(
+                    counts,
+                    sentinel.acquisition_time,
+                    mock_energy_lookup,
+                    mock_efficiency_lut,
+                    mock_compute_geometric_factors.return_value,
+                ),
+                call(
+                    count_uncerts,
+                    sentinel.acquisition_time,
+                    mock_energy_lookup,
+                    mock_efficiency_lut,
+                    mock_compute_geometric_factors.return_value,
+                ),
+            ]
         )
-        mock_convert_counts_to_intensity.assert_has_calls([
-            call(counts, sentinel.acquisition_time, mock_energy_lookup, mock_efficiency_lut, mock_compute_geometric_factors.return_value),
-            call(count_uncerts, sentinel.acquisition_time, mock_energy_lookup, mock_efficiency_lut, mock_compute_geometric_factors.return_value),
-        ])
 
-        mock_rebin_3d_distribution_azimuth_to_elevation.assert_has_calls([
-            call(sentinel.intensity, NumpyArrayMatcher(np.arange(1, 25)), mock_elevation_lookup, NumpyArrayMatcher(l3a_de_half_spin_per_esa_step)),
-            call(sentinel.intensity_uncert, NumpyArrayMatcher(np.arange(1, 25)), mock_elevation_lookup, NumpyArrayMatcher(l3a_de_half_spin_per_esa_step))]
+        mock_rebin_3d_distribution_azimuth_to_elevation.assert_has_calls(
+            [
+                call(
+                    sentinel.intensity,
+                    NumpyArrayMatcher(np.arange(1, 25)),
+                    mock_elevation_lookup,
+                    NumpyArrayMatcher(l3a_de_half_spin_per_esa_step),
+                ),
+                call(
+                    sentinel.intensity_uncert,
+                    NumpyArrayMatcher(np.arange(1, 25)),
+                    mock_elevation_lookup,
+                    NumpyArrayMatcher(l3a_de_half_spin_per_esa_step),
+                ),
+            ]
         )
 
         expected_offset = 45.9086
-        expected_spin_angles = np.array([
-            30 + expected_offset,
-            90 + expected_offset,
-            170 + expected_offset,
-            350 + expected_offset - 360
-        ])
-
-        self.assertIsInstance(l3b_3d_distribution_data_product, CodiceLoL3b3dDistributionDataProduct)
-        self.assertEqual(processor.input_metadata, l3b_3d_distribution_data_product.input_metadata)
-        self.assertEqual(mock_l3a_direct_event_data.epoch, l3b_3d_distribution_data_product.epoch)
-        self.assertEqual(mock_l3a_direct_event_data.epoch_delta, l3b_3d_distribution_data_product.epoch_delta)
-        self.assertEqual(mock_l3a_direct_event_data.rgfo_esa_step, l3b_3d_distribution_data_product.rgfo_esa_step)
-        self.assertEqual(mock_l3a_direct_event_data.rgfo_spin_sector, l3b_3d_distribution_data_product.rgfo_spin_sector)
-        self.assertEqual(mock_l3a_direct_event_data.rgfo_half_spin, l3b_3d_distribution_data_product.rgfo_half_spin)
-        np.testing.assert_array_equal(l3b_3d_distribution_data_product.half_spin_per_esa_step,
-                                      np.flip(mock_l3a_direct_event_data.half_spin_per_esa_step, axis=1))
-
-        self.assertEqual(mock_elevation_lookup.bin_centers, l3b_3d_distribution_data_product.elevation)
-        self.assertEqual(mock_elevation_lookup.bin_deltas, l3b_3d_distribution_data_product.elevation_delta)
-        np.testing.assert_array_equal(l3b_3d_distribution_data_product.spin_angle, expected_spin_angles)
-        self.assertEqual(sentinel.spin_angle_bin_delta, l3b_3d_distribution_data_product.spin_angle_delta)
-
-        np.testing.assert_array_equal(np.flip(mock_energy_lookup.bin_centers), l3b_3d_distribution_data_product.energy)
-        np.testing.assert_array_equal(np.flip(mock_energy_lookup.delta_plus),
-                                      l3b_3d_distribution_data_product.energy_delta_plus)
-        np.testing.assert_array_equal(np.flip(mock_energy_lookup.delta_minus),
-                                      l3b_3d_distribution_data_product.energy_delta_minus)
-
-        np.testing.assert_array_equal(np.flip(mock_rebinned_intensity, axis=1),
-                                      l3b_3d_distribution_data_product.species_data)
-        np.testing.assert_array_equal(np.flip(mock_rebinned_uncert, axis=1), l3b_3d_distribution_data_product.species_data_stat_uncert)
+        expected_spin_angles = np.array(
+            [
+                30 + expected_offset,
+                90 + expected_offset,
+                170 + expected_offset,
+                350 + expected_offset - 360,
+            ]
+        )
+
+        self.assertIsInstance(
+            l3b_3d_distribution_data_product, CodiceLoL3b3dDistributionDataProduct
+        )
+        self.assertEqual(
+            processor.input_metadata, l3b_3d_distribution_data_product.input_metadata
+        )
+        self.assertEqual(
+            mock_l3a_direct_event_data.epoch, l3b_3d_distribution_data_product.epoch
+        )
+        self.assertEqual(
+            mock_l3a_direct_event_data.epoch_delta,
+            l3b_3d_distribution_data_product.epoch_delta,
+        )
+        self.assertEqual(
+            mock_l3a_direct_event_data.rgfo_esa_step,
+            l3b_3d_distribution_data_product.rgfo_esa_step,
+        )
+        self.assertEqual(
+            mock_l3a_direct_event_data.rgfo_spin_sector,
+            l3b_3d_distribution_data_product.rgfo_spin_sector,
+        )
+        self.assertEqual(
+            mock_l3a_direct_event_data.rgfo_half_spin,
+            l3b_3d_distribution_data_product.rgfo_half_spin,
+        )
+        np.testing.assert_array_equal(
+            l3b_3d_distribution_data_product.half_spin_per_esa_step,
+            np.flip(mock_l3a_direct_event_data.half_spin_per_esa_step, axis=1),
+        )
+
+        self.assertEqual(
+            mock_elevation_lookup.bin_centers,
+            l3b_3d_distribution_data_product.elevation,
+        )
+        self.assertEqual(
+            mock_elevation_lookup.bin_deltas,
+            l3b_3d_distribution_data_product.elevation_delta,
+        )
+        np.testing.assert_array_equal(
+            l3b_3d_distribution_data_product.spin_angle, expected_spin_angles
+        )
+        self.assertEqual(
+            sentinel.spin_angle_bin_delta,
+            l3b_3d_distribution_data_product.spin_angle_delta,
+        )
+
+        np.testing.assert_array_equal(
+            np.flip(mock_energy_lookup.bin_centers),
+            l3b_3d_distribution_data_product.energy,
+        )
+        np.testing.assert_array_equal(
+            np.flip(mock_energy_lookup.delta_plus),
+            l3b_3d_distribution_data_product.energy_delta_plus,
+        )
+        np.testing.assert_array_equal(
+            np.flip(mock_energy_lookup.delta_minus),
+            l3b_3d_distribution_data_product.energy_delta_minus,
+        )
+
+        np.testing.assert_array_equal(
+            np.flip(mock_rebinned_intensity, axis=1),
+            l3b_3d_distribution_data_product.species_data,
+        )
+        np.testing.assert_array_equal(
+            np.flip(mock_rebinned_uncert, axis=1),
+            l3b_3d_distribution_data_product.species_data_stat_uncert,
+        )
         self.assertEqual(sentinel.species, l3b_3d_distribution_data_product.species)
 
     def test_process_3d_distributions_save_for_each_species(self):
@@ -1208,59 +1888,89 @@ def test_process_3d_distributions_save_for_each_species(self):
             with self.subTest(species=species):
                 self._test_process_3d_distributions_save(species)
 
-    @patch(f'{MODULE}.CodiceLoL3b3dDistributionsDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.CodiceLoProcessor.process_l3b_3d_distribution_product')
-    @patch(f'{MODULE}.save_data')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def _test_process_3d_distributions_save(self, species, mock_spiceypy, mock_save_data,
-                                            mock_process_l3b_3d_distribution_product,
-                                            mock_fetch_dependencies):
+    @patch(f"{MODULE}.CodiceLoL3b3dDistributionsDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.CodiceLoProcessor.process_l3b_3d_distribution_product")
+    @patch(f"{MODULE}.save_data")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def _test_process_3d_distributions_save(
+        self,
+        species,
+        mock_spiceypy,
+        mock_save_data,
+        mock_process_l3b_3d_distribution_product,
+        mock_fetch_dependencies,
+    ):
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1'), Path('path/to/parent_file_2')]
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor=f'lo-{species}-3d-distribution')
+        input_collection.get_file_paths.return_value = [
+            Path("path/to/parent_file_1"),
+            Path("path/to/parent_file_2"),
+        ]
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor=f"lo-{species}-3d-distribution",
+        )
         mock_spiceypy.ktotal.return_value = 0
 
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
         product = processor.process()
 
         mock_fetch_dependencies.assert_called_once_with(processor.dependencies, species)
-        mock_process_l3b_3d_distribution_product.assert_called_once_with(mock_fetch_dependencies.return_value)
+        mock_process_l3b_3d_distribution_product.assert_called_once_with(
+            mock_fetch_dependencies.return_value
+        )
 
-        mock_save_data.assert_called_once_with(mock_process_l3b_3d_distribution_product.return_value)
+        mock_save_data.assert_called_once_with(
+            mock_process_l3b_3d_distribution_product.return_value
+        )
 
-        self.assertEqual(['parent_file_1', 'parent_file_2'],
-                         mock_process_l3b_3d_distribution_product.return_value.parent_file_names)
+        self.assertEqual(
+            ["parent_file_1", "parent_file_2"],
+            mock_process_l3b_3d_distribution_product.return_value.parent_file_names,
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
     def test_process_l3a_direct_events_all_fill_integration(self):
         input_collection = MagicMock()
-        input_collection.get_file_paths.return_value = [Path('path/to/parent_file_1'), Path('path/to/parent_file_2')]
-        input_metadata = InputMetadata(instrument='codice',
-                                       data_level="l3a",
-                                       start_date=Mock(spec=datetime),
-                                       end_date=Mock(spec=datetime),
-                                       version='v02',
-                                       descriptor='lo-3d-instrument-frame')
+        input_collection.get_file_paths.return_value = [
+            Path("path/to/parent_file_1"),
+            Path("path/to/parent_file_2"),
+        ]
+        input_metadata = InputMetadata(
+            instrument="codice",
+            data_level="l3a",
+            start_date=Mock(spec=datetime),
+            end_date=Mock(spec=datetime),
+            version="v02",
+            descriptor="lo-3d-instrument-frame",
+        )
 
         dependencies = CodiceLoL3aDirectEventsDependencies.from_file_paths(
             sw_priority_rates_cdf=get_test_data_path(
-                "codice/imap_codice_l1a_lo-sw-priority_20260307_v003-all-fill.cdf"),
+                "codice/imap_codice_l1a_lo-sw-priority_20260307_v003-all-fill.cdf"
+            ),
             nsw_priority_rates_cdf=get_test_data_path(
-                "codice/imap_codice_l1a_lo-nsw-priority_20260307_v003-all-fill.cdf"),
-            direct_event_path=get_test_data_path("codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf"),
+                "codice/imap_codice_l1a_lo-nsw-priority_20260307_v003-all-fill.cdf"
+            ),
+            direct_event_path=get_test_data_path(
+                "codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf"
+            ),
             mass_coefficients_file_path=get_test_data_path(
-                "codice/imap_codice_mass-coefficient-lookup_20241110_v003.csv"),
+                "codice/imap_codice_mass-coefficient-lookup_20241110_v003.csv"
+            ),
             esa_to_energy_per_charge_file_path=get_test_data_path(
                 "codice/imap_codice_lo-energy-per-charge_20241110_v002.csv"
-            )
+            ),
         )
 
-        processor = CodiceLoProcessor(dependencies=input_collection, input_metadata=input_metadata)
+        processor = CodiceLoProcessor(
+            dependencies=input_collection, input_metadata=input_metadata
+        )
 
         try:
             processor.process_l3a_direct_event_data_product(dependencies)
@@ -1268,5 +1978,5 @@ def test_process_l3a_direct_events_all_fill_integration(self):
             self.fail(e)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/codice/l3/lo/test_models.py b/tests/codice/l3/lo/test_models.py
index a53a8cfae..b79af4211 100644
--- a/tests/codice/l3/lo/test_models.py
+++ b/tests/codice/l3/lo/test_models.py
@@ -9,28 +9,34 @@
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.codice.l3.lo.models import (
-    CodiceLoL2SWSpeciesData,
-    CodiceLoL3aPartialDensityDataProduct,
-    CodiceLoL2DirectEventData,
-    CodiceLoL3aDirectEventDataProduct,
-    CodiceLoL1aSWPriorityRates,
-    CodiceLoL1aNSWPriorityRates,
     CODICE_LO_L2_NUM_PRIORITIES,
-    CodiceLoL3aRatiosDataProduct,
-    CodiceLoPartialDensityData,
-    CodiceLoL3ChargeStateDistributionsDataProduct,
-    CodiceLoDirectEventData,
-    EPOCH_VAR_NAME,
-    EPOCH_DELTA_VAR_NAME,
+    ELEVATION_ANGLE_LABEL_VAR_NAME,
+    ELEVATION_DELTA_VAR_NAME,
     ELEVATION_VAR_NAME,
+    ENERGY_DELTA_MINUS_VAR_NAME,
+    ENERGY_DELTA_PLUS_VAR_NAME,
+    ENERGY_LABEL_VAR_NAME,
     ENERGY_VAR_NAME,
-    ELEVATION_DELTA_VAR_NAME,
+    EPOCH_DELTA_VAR_NAME,
+    EPOCH_VAR_NAME,
+    HALF_SPIN_PER_ESA_STEP_VAR_NAME,
+    RGFO_ESA_STEP_VAR_NAME,
+    RGFO_HALF_SPIN_VAR_NAME,
+    RGFO_SPIN_SECTOR_VAR_NAME,
+    SPIN_ANGLE_DELTA_VAR_NAME,
+    SPIN_ANGLE_LABEL_VAR_NAME,
+    SPIN_ANGLE_VAR_NAME,
+    CodiceLoDirectEventData,
+    CodiceLoL1aNSWPriorityRates,
+    CodiceLoL1aSWPriorityRates,
+    CodiceLoL2DirectEventData,
+    CodiceLoL2SWSpeciesData,
+    CodiceLoL3aDirectEventDataProduct,
+    CodiceLoL3aPartialDensityDataProduct,
+    CodiceLoL3aRatiosDataProduct,
     CodiceLoL3b3dDistributionDataProduct,
-    ENERGY_DELTA_PLUS_VAR_NAME,
-    ENERGY_DELTA_MINUS_VAR_NAME,
-    ELEVATION_ANGLE_LABEL_VAR_NAME,
-    ENERGY_LABEL_VAR_NAME, SPIN_ANGLE_DELTA_VAR_NAME, SPIN_ANGLE_VAR_NAME, SPIN_ANGLE_LABEL_VAR_NAME,
-    RGFO_ESA_STEP_VAR_NAME, RGFO_SPIN_SECTOR_VAR_NAME, RGFO_HALF_SPIN_VAR_NAME, HALF_SPIN_PER_ESA_STEP_VAR_NAME,
+    CodiceLoL3ChargeStateDistributionsDataProduct,
+    CodiceLoPartialDensityData,
 )
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.utils import save_data
@@ -38,22 +44,22 @@
 from tests.test_helpers import get_test_data_path
 
 LO_L2_SW_SPECIES_DATA_VARS = [
-    'hplus',
-    'heplusplus',
-    'heplus',
-    'ne',
-    'cplus4',
-    'cplus5',
-    'cplus6',
-    'oplus5',
-    'oplus6',
-    'oplus7',
-    'oplus8',
-    'cnoplus',
-    'mg',
-    'si',
-    'fe_loq',
-    'fe_hiq',
+    "hplus",
+    "heplusplus",
+    "heplus",
+    "ne",
+    "cplus4",
+    "cplus5",
+    "cplus6",
+    "oplus5",
+    "oplus6",
+    "oplus7",
+    "oplus8",
+    "cnoplus",
+    "mg",
+    "si",
+    "fe_loq",
+    "fe_hiq",
 ]
 
 
@@ -68,51 +74,77 @@ def test_lo_l2_sw_species_read_from_cdf(self):
                 epoch_delta_plus = rng.random(len(epoch))
                 energy_per_charge = np.geomspace(2, 1000)
                 spin_sector = np.linspace(0, 360, 24)
-                hplus = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                heplus = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                heplusplus = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
+                hplus = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                heplus = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                heplusplus = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
                 ne = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                cplus4 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                cplus5 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                cplus6 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                oplus5 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                oplus6 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                oplus7 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                oplus8 = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
+                cplus4 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                cplus5 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                cplus6 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                oplus5 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                oplus6 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                oplus7 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                oplus8 = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
                 mg = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
                 si = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
                 cnoplus = rng.random((len(epoch), len(energy_per_charge)))
-                fe_loq = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
-                fe_hiq = rng.random((len(epoch), len(energy_per_charge), len(spin_sector)))
+                fe_loq = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
+                fe_hiq = rng.random(
+                    (len(epoch), len(energy_per_charge), len(spin_sector))
+                )
                 data_quality = rng.random(len(epoch))
 
-                cdf_file['epoch'] = epoch
-                cdf_file['epoch_delta_minus'] = epoch_delta_minus
-                cdf_file['epoch_delta_plus'] = epoch_delta_plus
-                cdf_file['energy_per_charge'] = energy_per_charge
-                cdf_file['spin_sector'] = spin_sector
-                cdf_file['hplus'] = hplus
-                cdf_file['heplusplus'] = heplusplus
-                cdf_file['heplus'] = heplus
-                cdf_file['ne'] = ne
-                cdf_file['cplus4'] = cplus4
-                cdf_file['cplus5'] = cplus5
-                cdf_file['cplus6'] = cplus6
-                cdf_file['oplus5'] = oplus5
-                cdf_file['oplus6'] = oplus6
-                cdf_file['oplus7'] = oplus7
-                cdf_file['oplus8'] = oplus8
-                cdf_file['cnoplus'] = cnoplus
-                cdf_file['mg'] = mg
-                cdf_file['si'] = si
-                cdf_file['fe_loq'] = fe_loq
-                cdf_file['fe_hiq'] = fe_hiq
-                cdf_file['data_quality'] = data_quality
+                cdf_file["epoch"] = epoch
+                cdf_file["epoch_delta_minus"] = epoch_delta_minus
+                cdf_file["epoch_delta_plus"] = epoch_delta_plus
+                cdf_file["energy_per_charge"] = energy_per_charge
+                cdf_file["spin_sector"] = spin_sector
+                cdf_file["hplus"] = hplus
+                cdf_file["heplusplus"] = heplusplus
+                cdf_file["heplus"] = heplus
+                cdf_file["ne"] = ne
+                cdf_file["cplus4"] = cplus4
+                cdf_file["cplus5"] = cplus5
+                cdf_file["cplus6"] = cplus6
+                cdf_file["oplus5"] = oplus5
+                cdf_file["oplus6"] = oplus6
+                cdf_file["oplus7"] = oplus7
+                cdf_file["oplus8"] = oplus8
+                cdf_file["cnoplus"] = cnoplus
+                cdf_file["mg"] = mg
+                cdf_file["si"] = si
+                cdf_file["fe_loq"] = fe_loq
+                cdf_file["fe_hiq"] = fe_hiq
+                cdf_file["data_quality"] = data_quality
 
                 for var in LO_L2_SW_SPECIES_DATA_VARS:
                     cdf_file[var].attrs["FILLVAL"] = -1e31
 
-            result: CodiceLoL2SWSpeciesData = CodiceLoL2SWSpeciesData.read_from_cdf(cdf_file_path)
+            result: CodiceLoL2SWSpeciesData = CodiceLoL2SWSpeciesData.read_from_cdf(
+                cdf_file_path
+            )
             np.testing.assert_array_equal(result.epoch, epoch)
             np.testing.assert_array_equal(result.epoch_delta_minus, epoch_delta_minus)
             np.testing.assert_array_equal(result.epoch_delta_plus, epoch_delta_plus)
@@ -145,24 +177,30 @@ def test_lo_l2_sw_species_read_from_cdf_handles_fillval(self):
                 epoch_delta_plus = rng.random(len(epoch))
                 energy_per_charge = np.geomspace(2, 1000)
                 spin_sector = np.linspace(0, 360, 24)
-                fills = np.full((len(epoch), len(energy_per_charge), len(spin_sector)), -1e31)
+                fills = np.full(
+                    (len(epoch), len(energy_per_charge), len(spin_sector)), -1e31
+                )
 
                 data_quality = rng.random(len(epoch))
 
-                cdf_file['epoch'] = epoch
-                cdf_file['epoch_delta_minus'] = epoch_delta_minus
-                cdf_file['epoch_delta_plus'] = epoch_delta_plus
-                cdf_file['energy_per_charge'] = energy_per_charge
-                cdf_file['spin_sector'] = spin_sector
-                cdf_file['data_quality'] = data_quality
+                cdf_file["epoch"] = epoch
+                cdf_file["epoch_delta_minus"] = epoch_delta_minus
+                cdf_file["epoch_delta_plus"] = epoch_delta_plus
+                cdf_file["energy_per_charge"] = energy_per_charge
+                cdf_file["spin_sector"] = spin_sector
+                cdf_file["data_quality"] = data_quality
 
                 for var in LO_L2_SW_SPECIES_DATA_VARS:
                     cdf_file[var] = fills
                     cdf_file[var].attrs["FILLVAL"] = -1e31
 
-            nans = np.full((len(epoch), len(energy_per_charge), len(spin_sector)), np.nan)
+            nans = np.full(
+                (len(epoch), len(energy_per_charge), len(spin_sector)), np.nan
+            )
 
-            result: CodiceLoL2SWSpeciesData = CodiceLoL2SWSpeciesData.read_from_cdf(cdf_file_path)
+            result: CodiceLoL2SWSpeciesData = CodiceLoL2SWSpeciesData.read_from_cdf(
+                cdf_file_path
+            )
             np.testing.assert_array_equal(result.epoch, epoch)
             np.testing.assert_array_equal(result.epoch_delta_minus, epoch_delta_minus)
             np.testing.assert_array_equal(result.epoch_delta_plus, epoch_delta_plus)
@@ -214,11 +252,15 @@ def test_codice_lo_l3a_partial_density_to_data_product(self):
 
         self.assertEqual(16, len(actual_data_product_variables))
 
-        for input_variable, actual_data_product_variable in zip(input_data_product_kwargs.items(),
-                                                                actual_data_product_variables):
+        for input_variable, actual_data_product_variable in zip(
+            input_data_product_kwargs.items(), actual_data_product_variables
+        ):
             input_name, expected_value = input_variable
 
-            np.testing.assert_array_equal(actual_data_product_variable.value, getattr(data_product.data, input_name))
+            np.testing.assert_array_equal(
+                actual_data_product_variable.value,
+                getattr(data_product.data, input_name),
+            )
             self.assertEqual(input_name, actual_data_product_variable.name)
 
     def test_codice_lo_l3a_ratios_to_data_product(self):
@@ -236,52 +278,69 @@ def test_codice_lo_l3a_ratios_to_data_product(self):
             "felo_to_fehi_ratio": np.array([8]),
         }
 
-        data_product = CodiceLoL3aRatiosDataProduct(
-            Mock(), **input_data_product_kwargs
-        )
+        data_product = CodiceLoL3aRatiosDataProduct(Mock(), **input_data_product_kwargs)
         actual_data_product_variables = data_product.to_data_product_variables()
 
         self.assertEqual(9, len(actual_data_product_variables))
 
-        for input_variable, actual_data_product_variable in zip(input_data_product_kwargs.items(),
-                                                                actual_data_product_variables):
+        for input_variable, actual_data_product_variable in zip(
+            input_data_product_kwargs.items(), actual_data_product_variables
+        ):
             input_name, expected_value = input_variable
 
-            np.testing.assert_array_equal(actual_data_product_variable.value, getattr(data_product, input_name))
+            np.testing.assert_array_equal(
+                actual_data_product_variable.value, getattr(data_product, input_name)
+            )
             self.assertEqual(input_name, actual_data_product_variable.name)
 
     def test_codice_lo_l3a_partial_density_read_from_cdf(self):
         rng = np.random.default_rng()
         epoch = [datetime.now(), datetime.now() + timedelta(days=1)]
-        density_data = CodiceLoPartialDensityData(epoch=np.array(epoch), epoch_delta=np.array([2, 2]),
-                                                  hplus_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  heplusplus_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  cplus4_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  cplus5_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  cplus6_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  oplus5_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  oplus6_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  oplus7_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  oplus8_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  ne_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  mg_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  si_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  fe_loq_partial_density=rng.random(len(epoch), dtype='float32'),
-                                                  fe_hiq_partial_density=rng.random(len(epoch), dtype='float32'), )
-
-        input_metadata = InputMetadata("codice", "l3a", datetime.now(), datetime.now(), VersionMap({"lo-partial-densities": Version(None, 1)}), "lo-partial-densities")
-
-        codice_lo_l3a_partial_densities = CodiceLoL3aPartialDensityDataProduct(input_metadata, density_data)
+        density_data = CodiceLoPartialDensityData(
+            epoch=np.array(epoch),
+            epoch_delta=np.array([2, 2]),
+            hplus_partial_density=rng.random(len(epoch), dtype="float32"),
+            heplusplus_partial_density=rng.random(len(epoch), dtype="float32"),
+            cplus4_partial_density=rng.random(len(epoch), dtype="float32"),
+            cplus5_partial_density=rng.random(len(epoch), dtype="float32"),
+            cplus6_partial_density=rng.random(len(epoch), dtype="float32"),
+            oplus5_partial_density=rng.random(len(epoch), dtype="float32"),
+            oplus6_partial_density=rng.random(len(epoch), dtype="float32"),
+            oplus7_partial_density=rng.random(len(epoch), dtype="float32"),
+            oplus8_partial_density=rng.random(len(epoch), dtype="float32"),
+            ne_partial_density=rng.random(len(epoch), dtype="float32"),
+            mg_partial_density=rng.random(len(epoch), dtype="float32"),
+            si_partial_density=rng.random(len(epoch), dtype="float32"),
+            fe_loq_partial_density=rng.random(len(epoch), dtype="float32"),
+            fe_hiq_partial_density=rng.random(len(epoch), dtype="float32"),
+        )
+
+        input_metadata = InputMetadata(
+            "codice",
+            "l3a",
+            datetime.now(),
+            datetime.now(),
+            VersionMap({"lo-partial-densities": Version(None, 1)}),
+            "lo-partial-densities",
+        )
+
+        codice_lo_l3a_partial_densities = CodiceLoL3aPartialDensityDataProduct(
+            input_metadata, density_data
+        )
 
         with tempfile.TemporaryDirectory() as tmpdir:
-            cdf_path = save_data(codice_lo_l3a_partial_densities, folder_path=Path(tmpdir))
+            cdf_path = save_data(
+                codice_lo_l3a_partial_densities, folder_path=Path(tmpdir)
+            )
 
             read_in_density_data = CodiceLoPartialDensityData.read_from_cdf(cdf_path)
 
         for field in fields(CodiceLoPartialDensityData):
-            np.testing.assert_array_equal(getattr(read_in_density_data, field.name),
-                                          getattr(density_data, field.name),
-                                          strict=True)
+            np.testing.assert_array_equal(
+                getattr(read_in_density_data, field.name),
+                getattr(density_data, field.name),
+                strict=True,
+            )
 
     def test_codice_lo_l3a_charge_state_distributions_to_data_product(self):
         rng = np.random.default_rng()
@@ -293,32 +352,46 @@ def test_codice_lo_l3a_charge_state_distributions_to_data_product(self):
             "carbon_charge_state_distribution": rng.random((len(epoch_data), 3)),
         }
 
-        data_product = CodiceLoL3ChargeStateDistributionsDataProduct(Mock(), **input_data_product_kwargs)
+        data_product = CodiceLoL3ChargeStateDistributionsDataProduct(
+            Mock(), **input_data_product_kwargs
+        )
         actual_data_product_variables = data_product.to_data_product_variables()
         self.assertEqual(6, len(actual_data_product_variables))
-        for input_variable, actual_data_product_variable in zip(input_data_product_kwargs.items(),
-                                                                actual_data_product_variables[:4]):
+        for input_variable, actual_data_product_variable in zip(
+            input_data_product_kwargs.items(), actual_data_product_variables[:4]
+        ):
             input_name, expected_value = input_variable
 
-            np.testing.assert_array_equal(actual_data_product_variable.value, getattr(data_product, input_name))
+            np.testing.assert_array_equal(
+                actual_data_product_variable.value, getattr(data_product, input_name)
+            )
             self.assertEqual(input_name, actual_data_product_variable.name)
 
-        oxygen_charge_states, carbon_charge_states, = actual_data_product_variables[4:]
+        (
+            oxygen_charge_states,
+            carbon_charge_states,
+        ) = actual_data_product_variables[4:]
         np.testing.assert_array_equal(oxygen_charge_states.value, [5, 6, 7, 8])
         self.assertEqual("oxygen_charge_state", oxygen_charge_states.name)
 
         np.testing.assert_array_equal(carbon_charge_states.value, [4, 5, 6])
         self.assertEqual("carbon_charge_state", carbon_charge_states.name)
 
-    def test_codice_lo_l3a_direct_event_read_from_cdf_reads_where_no_variables_have_fill_value(self):
-        no_fill_l3a_cdf_path = get_test_data_path("codice/imap_codice_l3a_lo-direct-events_20260504_v003.cdf")
+    def test_codice_lo_l3a_direct_event_read_from_cdf_reads_where_no_variables_have_fill_value(
+        self,
+    ):
+        no_fill_l3a_cdf_path = get_test_data_path(
+            "codice/imap_codice_l3a_lo-direct-events_20260504_v003.cdf"
+        )
 
         rng = np.random.default_rng()
         with tempfile.TemporaryDirectory() as tmpdir:
             tmpdir = Path(tmpdir)
 
             test_l3a_direct_event = tmpdir / "test_l3a_cdf.cdf"
-            with CDF(str(test_l3a_direct_event), masterpath=str(no_fill_l3a_cdf_path)) as cdf:
+            with CDF(
+                str(test_l3a_direct_event), masterpath=str(no_fill_l3a_cdf_path)
+            ) as cdf:
                 cdf["apd_energy"] = rng.random(cdf["apd_energy"].shape)
                 cdf["elevation"] = rng.random(cdf["elevation"].shape)
                 cdf["energy_per_charge"] = rng.random(cdf["energy_per_charge"].shape)
@@ -328,61 +401,146 @@ def test_codice_lo_l3a_direct_event_read_from_cdf_reads_where_no_variables_have_
                 cdf["normalization"] = rng.random(cdf["normalization"].shape)
                 cdf["spin_angle"] = rng.random(cdf["spin_angle"].shape)
                 cdf["spin_angle_bin"] = rng.random(cdf["spin_angle_bin"].shape)
-                cdf["spin_angle_bin_delta"] = rng.random(cdf["spin_angle_bin_delta"].shape)
+                cdf["spin_angle_bin_delta"] = rng.random(
+                    cdf["spin_angle_bin_delta"].shape
+                )
                 cdf["type"] = rng.integers(0, 5, cdf["type"].shape)
-                cdf["normalization_per_event"] = rng.random(cdf["normalization_per_event"].shape)
+                cdf["normalization_per_event"] = rng.random(
+                    cdf["normalization_per_event"].shape
+                )
                 cdf["position"] = rng.random(cdf["position"].shape)
                 cdf["spin_sector"] = rng.random(cdf["spin_sector"].shape)
-                cdf["acquisition_time_per_esa_step"] = rng.random(cdf["acquisition_time_per_esa_step"].shape)
+                cdf["acquisition_time_per_esa_step"] = rng.random(
+                    cdf["acquisition_time_per_esa_step"].shape
+                )
 
-                cdf["rgfo_spin_sector"] = rng.integers(0, 24, cdf["rgfo_spin_sector"].shape)
+                cdf["rgfo_spin_sector"] = rng.integers(
+                    0, 24, cdf["rgfo_spin_sector"].shape
+                )
                 cdf["rgfo_half_spin"] = rng.integers(0, 32, cdf["rgfo_half_spin"].shape)
                 cdf["rgfo_esa_step"] = rng.integers(0, 128, cdf["rgfo_esa_step"].shape)
-                cdf["half_spin_per_esa_step"] = rng.integers(0, 32, cdf["half_spin_per_esa_step"].shape)
+                cdf["half_spin_per_esa_step"] = rng.integers(
+                    0, 32, cdf["half_spin_per_esa_step"].shape
+                )
 
-            actual_event_data = CodiceLoDirectEventData.read_from_cdf(test_l3a_direct_event)
+            actual_event_data = CodiceLoDirectEventData.read_from_cdf(
+                test_l3a_direct_event
+            )
 
             with CDF(str(test_l3a_direct_event)) as cdf:
-                np.testing.assert_array_equal(actual_event_data.normalization_per_event, cdf["normalization_per_event"])
-                np.testing.assert_array_equal(actual_event_data.position, cdf["position"])
-                np.testing.assert_array_equal(actual_event_data.energy_step, cdf["energy_step"])
-                np.testing.assert_array_equal(actual_event_data.spin_sector, cdf["spin_sector"])
-                np.testing.assert_array_equal(actual_event_data.acquisition_time_per_esa_step,
-                                              cdf["acquisition_time_per_esa_step"])
-                np.testing.assert_array_equal(actual_event_data.apd_energy, cdf["apd_energy"])
-                np.testing.assert_array_equal(actual_event_data.elevation, cdf["elevation"])
-                np.testing.assert_array_equal(actual_event_data.energy_per_charge, cdf["energy_per_charge"])
-                np.testing.assert_array_equal(actual_event_data.energy_step, cdf["energy_step"])
+                np.testing.assert_array_equal(
+                    actual_event_data.normalization_per_event,
+                    cdf["normalization_per_event"],
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.position, cdf["position"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.energy_step, cdf["energy_step"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.spin_sector, cdf["spin_sector"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.acquisition_time_per_esa_step,
+                    cdf["acquisition_time_per_esa_step"],
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.apd_energy, cdf["apd_energy"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.elevation, cdf["elevation"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.energy_per_charge, cdf["energy_per_charge"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.energy_step, cdf["energy_step"]
+                )
                 np.testing.assert_array_equal(actual_event_data.mass, cdf["mass"])
-                np.testing.assert_array_equal(actual_event_data.mass_per_charge, cdf["mass_per_charge"])
-                np.testing.assert_array_equal(actual_event_data.normalization, cdf["normalization"])
-                np.testing.assert_array_equal(actual_event_data.spin_angle, cdf["spin_angle"])
-                np.testing.assert_array_equal(actual_event_data.spin_angle_bin, cdf["spin_angle_bin"])
-                np.testing.assert_array_equal(actual_event_data.spin_angle_bin_delta, cdf["spin_angle_bin_delta"])
+                np.testing.assert_array_equal(
+                    actual_event_data.mass_per_charge, cdf["mass_per_charge"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.normalization, cdf["normalization"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.spin_angle, cdf["spin_angle"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.spin_angle_bin, cdf["spin_angle_bin"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.spin_angle_bin_delta, cdf["spin_angle_bin_delta"]
+                )
                 np.testing.assert_array_equal(actual_event_data.type, cdf["type"])
-                np.testing.assert_array_equal(actual_event_data.rgfo_spin_sector, cdf["rgfo_spin_sector"])
-                np.testing.assert_array_equal(actual_event_data.rgfo_half_spin, cdf["rgfo_half_spin"])
-                np.testing.assert_array_equal(actual_event_data.rgfo_esa_step, cdf["rgfo_esa_step"])
-                np.testing.assert_array_equal(actual_event_data.half_spin_per_esa_step, cdf["half_spin_per_esa_step"])
+                np.testing.assert_array_equal(
+                    actual_event_data.rgfo_spin_sector, cdf["rgfo_spin_sector"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.rgfo_half_spin, cdf["rgfo_half_spin"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.rgfo_esa_step, cdf["rgfo_esa_step"]
+                )
+                np.testing.assert_array_equal(
+                    actual_event_data.half_spin_per_esa_step,
+                    cdf["half_spin_per_esa_step"],
+                )
 
     def test_codice_lo_l3a_direct_event_read_from_cdf_handles_fill_value(self):
-        all_fill_l3a_cdf_path = get_test_data_path("codice/imap_codice_l3a_lo-direct-events_20260504_v003-all-fill.cdf")
+        all_fill_l3a_cdf_path = get_test_data_path(
+            "codice/imap_codice_l3a_lo-direct-events_20260504_v003-all-fill.cdf"
+        )
         actual_event_data = CodiceLoDirectEventData.read_from_cdf(all_fill_l3a_cdf_path)
 
         with CDF(str(all_fill_l3a_cdf_path)) as cdf:
             # @formatter:off
-            np.testing.assert_array_equal(actual_event_data.apd_energy, np.full_like(cdf["apd_energy"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.data_quality, cdf["data_quality"])
-            np.testing.assert_array_equal(actual_event_data.elevation, np.full_like(cdf["elevation"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.energy_per_charge, np.full_like(cdf["energy_per_charge"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.mass, np.full_like(cdf["mass"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.mass_per_charge, np.full_like(cdf["mass_per_charge"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.normalization, np.full_like(cdf["normalization"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.normalization_per_event, np.full_like(cdf["normalization_per_event"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.acquisition_time_per_esa_step, np.full_like(cdf["acquisition_time_per_esa_step"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.spin_angle, np.full_like(cdf["spin_angle"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.spin_angle_bin_delta, np.full_like(cdf["spin_angle_bin_delta"][...], np.nan))
-            np.testing.assert_array_equal(actual_event_data.rgfo_spin_sector, np.full_like(cdf["rgfo_spin_sector"][...], np.nan))
+            np.testing.assert_array_equal(
+                actual_event_data.apd_energy,
+                np.full_like(cdf["apd_energy"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.data_quality, cdf["data_quality"]
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.elevation, np.full_like(cdf["elevation"][...], np.nan)
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.energy_per_charge,
+                np.full_like(cdf["energy_per_charge"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.mass, np.full_like(cdf["mass"][...], np.nan)
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.mass_per_charge,
+                np.full_like(cdf["mass_per_charge"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.normalization,
+                np.full_like(cdf["normalization"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.normalization_per_event,
+                np.full_like(cdf["normalization_per_event"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.acquisition_time_per_esa_step,
+                np.full_like(cdf["acquisition_time_per_esa_step"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.spin_angle,
+                np.full_like(cdf["spin_angle"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.spin_angle_bin_delta,
+                np.full_like(cdf["spin_angle_bin_delta"][...], np.nan),
+            )
+            np.testing.assert_array_equal(
+                actual_event_data.rgfo_spin_sector,
+                np.full_like(cdf["rgfo_spin_sector"][...], np.nan),
+            )
             # @formatter:on
 
             masked_variables = [
@@ -424,7 +582,9 @@ def test_codice_lo_l3a_direct_event_to_data_product(self):
             acquisition_time_per_esa_step=rng.random((len(epoch), len(energy_step))),
             epoch=epoch,
             epoch_delta=epoch_delta,
-            normalization=rng.random((len(epoch), len(priority), len(energy_step), len(spin_angle))),
+            normalization=rng.random(
+                (len(epoch), len(priority), len(energy_step), len(spin_angle))
+            ),
             mass_per_charge=rng.random((len(epoch), len(priority), len(event_num))),
             mass=rng.random((len(epoch), len(priority), len(event_num))),
             apd_energy=rng.random((len(epoch), len(priority), len(event_num))),
@@ -451,84 +611,187 @@ def test_codice_lo_l3a_direct_event_to_data_product(self):
             nso_half_spin=rng.random(len(epoch)),
             nso_spin_sector=rng.random(len(epoch)),
             nso_esa_step=rng.random(len(epoch)),
-            normalization_per_event=rng.random((len(epoch), len(priority), len(energy_step), len(spin_angle))),
+            normalization_per_event=rng.random(
+                (len(epoch), len(priority), len(energy_step), len(spin_angle))
+            ),
             esa_step=esa_step,
             energy_per_charge=rng.random((len(epoch), len(priority), len(event_num))),
             type=rng.integers(0, 5, (len(epoch), len(priority), len(event_num))),
         )
 
-        np.testing.assert_array_equal(direct_event.event_index, np.arange(len(event_num)))
-        np.testing.assert_array_equal(direct_event.priority_index_label, np.array(["0", "1", "2", "3", "4", "5", "6"]))
-        np.testing.assert_array_equal(direct_event.event_index_label, np.array([str(i) for i in range(len(event_num))]))
-        np.testing.assert_array_equal(direct_event.energy_bin_label,
-                                      np.array([str(e) for e in direct_event.energy_bin]))
-        np.testing.assert_array_equal(direct_event.spin_angle_bin_label,
-                                      np.array([str(spin_angle) for spin_angle in direct_event.spin_angle_bin]))
+        np.testing.assert_array_equal(
+            direct_event.event_index, np.arange(len(event_num))
+        )
+        np.testing.assert_array_equal(
+            direct_event.priority_index_label,
+            np.array(["0", "1", "2", "3", "4", "5", "6"]),
+        )
+        np.testing.assert_array_equal(
+            direct_event.event_index_label,
+            np.array([str(i) for i in range(len(event_num))]),
+        )
+        np.testing.assert_array_equal(
+            direct_event.energy_bin_label,
+            np.array([str(e) for e in direct_event.energy_bin]),
+        )
+        np.testing.assert_array_equal(
+            direct_event.spin_angle_bin_label,
+            np.array([str(spin_angle) for spin_angle in direct_event.spin_angle_bin]),
+        )
 
         data_products = direct_event.to_data_product_variables()
 
-        non_parent_fields = [f for f in fields(CodiceLoL3aDirectEventDataProduct)
-                             if f.name in CodiceLoL3aDirectEventDataProduct.__annotations__ or
-                             f.name in CodiceLoDirectEventData.__annotations__]
+        non_parent_fields = [
+            f
+            for f in fields(CodiceLoL3aDirectEventDataProduct)
+            if f.name in CodiceLoL3aDirectEventDataProduct.__annotations__
+            or f.name in CodiceLoDirectEventData.__annotations__
+        ]
 
         self.assertEqual(len(non_parent_fields), len(data_products))
         for data_product in data_products:
-            np.testing.assert_array_equal(getattr(direct_event, data_product.name), data_product.value)
+            np.testing.assert_array_equal(
+                getattr(direct_event, data_product.name), data_product.value
+            )
 
     def test_codice_lo_l1a_sw_priority_read_from_cdf(self):
-        cdf_path = get_test_data_path("codice/imap_codice_l1a_lo-sw-priority_20260307_v004.cdf")
-        actual_l1a_sw_priority_rates = CodiceLoL1aSWPriorityRates.read_from_cdf(cdf_path)
+        cdf_path = get_test_data_path(
+            "codice/imap_codice_l1a_lo-sw-priority_20260307_v004.cdf"
+        )
+        actual_l1a_sw_priority_rates = CodiceLoL1aSWPriorityRates.read_from_cdf(
+            cdf_path
+        )
 
         with CDF(str(cdf_path)) as cdf:
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.epoch, cdf["epoch"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.epoch_delta_plus, cdf["epoch_delta_plus"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.epoch_delta_minus, cdf["epoch_delta_minus"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.acquisition_time_per_esa_step,
-                                          cdf["acquisition_time_per_esa_step"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.spin_sector_index, cdf["spin_sector"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.rgfo_half_spin, cdf["rgfo_half_spin"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.nso_half_spin, cdf["nso_half_spin"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.sw_bias_gain_mode, cdf["sw_bias_gain_mode"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.st_bias_gain_mode, cdf["st_bias_gain_mode"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.data_quality, cdf["data_quality"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.spin_period, cdf["spin_period"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.p0_tcrs, cdf["p0_tcrs"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.p1_hplus, cdf["p1_hplus"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.p2_heplusplus, cdf["p2_heplusplus"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.p3_heavies, cdf["p3_heavies"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.p4_dcrs, cdf["p4_dcrs"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.half_spin_per_esa_step,
-                                          cdf["half_spin_per_esa_step"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.rgfo_spin_sector, cdf["rgfo_spin_sector"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.rgfo_esa_step, cdf["rgfo_esa_step"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.nso_spin_sector, cdf["nso_spin_sector"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.nso_esa_step, cdf["nso_esa_step"][...])
-            np.testing.assert_array_equal(actual_l1a_sw_priority_rates.esa_step, cdf["esa_step"][...])
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.epoch, cdf["epoch"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.epoch_delta_plus,
+                cdf["epoch_delta_plus"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.epoch_delta_minus,
+                cdf["epoch_delta_minus"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.acquisition_time_per_esa_step,
+                cdf["acquisition_time_per_esa_step"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.spin_sector_index, cdf["spin_sector"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.rgfo_half_spin, cdf["rgfo_half_spin"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.nso_half_spin, cdf["nso_half_spin"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.sw_bias_gain_mode,
+                cdf["sw_bias_gain_mode"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.st_bias_gain_mode,
+                cdf["st_bias_gain_mode"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.data_quality, cdf["data_quality"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.spin_period, cdf["spin_period"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.p0_tcrs, cdf["p0_tcrs"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.p1_hplus, cdf["p1_hplus"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.p2_heplusplus, cdf["p2_heplusplus"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.p3_heavies, cdf["p3_heavies"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.p4_dcrs, cdf["p4_dcrs"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.half_spin_per_esa_step,
+                cdf["half_spin_per_esa_step"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.rgfo_spin_sector,
+                cdf["rgfo_spin_sector"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.rgfo_esa_step, cdf["rgfo_esa_step"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.nso_spin_sector,
+                cdf["nso_spin_sector"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.nso_esa_step, cdf["nso_esa_step"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_sw_priority_rates.esa_step, cdf["esa_step"][...]
+            )
 
     def test_codice_lo_l1a_nsw_priority_read_from_cdf(self):
-        cdf_path = get_test_data_path("codice/imap_codice_l1a_lo-nsw-priority_20260307_v004.cdf")
-        actual_l1a_nsw_priority_rates = CodiceLoL1aNSWPriorityRates.read_from_cdf(cdf_path)
+        cdf_path = get_test_data_path(
+            "codice/imap_codice_l1a_lo-nsw-priority_20260307_v004.cdf"
+        )
+        actual_l1a_nsw_priority_rates = CodiceLoL1aNSWPriorityRates.read_from_cdf(
+            cdf_path
+        )
 
         with CDF(str(cdf_path)) as cdf:
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.epoch, cdf["epoch"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.epoch_delta_plus, cdf["epoch_delta_plus"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.epoch_delta_minus,
-                                          cdf["epoch_delta_minus"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.acquisition_time_per_esa_step,
-                                          cdf["acquisition_time_per_esa_step"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.spin_sector_index,
-                                          cdf["spin_sector"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.rgfo_half_spin, cdf["rgfo_half_spin"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.data_quality, cdf["data_quality"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.p5_heavies, cdf["p5_heavies"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.p6_hplus_heplusplus,
-                                          cdf["p6_hplus_heplusplus"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.nso_half_spin, cdf["nso_half_spin"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.sw_bias_gain_mode,
-                                          cdf["sw_bias_gain_mode"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.st_bias_gain_mode,
-                                          cdf["st_bias_gain_mode"][...])
-            np.testing.assert_array_equal(actual_l1a_nsw_priority_rates.spin_period, cdf["spin_period"][...])
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.epoch, cdf["epoch"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.epoch_delta_plus,
+                cdf["epoch_delta_plus"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.epoch_delta_minus,
+                cdf["epoch_delta_minus"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.acquisition_time_per_esa_step,
+                cdf["acquisition_time_per_esa_step"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.spin_sector_index, cdf["spin_sector"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.rgfo_half_spin, cdf["rgfo_half_spin"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.data_quality, cdf["data_quality"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.p5_heavies, cdf["p5_heavies"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.p6_hplus_heplusplus,
+                cdf["p6_hplus_heplusplus"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.nso_half_spin, cdf["nso_half_spin"][...]
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.sw_bias_gain_mode,
+                cdf["sw_bias_gain_mode"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.st_bias_gain_mode,
+                cdf["st_bias_gain_mode"][...],
+            )
+            np.testing.assert_array_equal(
+                actual_l1a_nsw_priority_rates.spin_period, cdf["spin_period"][...]
+            )
 
     def test_codice_lo_3d_distributions_data_product(self):
         species = "hplus"
@@ -562,29 +825,71 @@ def test_codice_lo_3d_distributions_data_product(self):
         self.assertEqual(18, len(actual_data_product_variables))
         actual_variables = iter(actual_data_product_variables)
 
-        self.assert_variable_attributes(next(actual_variables), sentinel.epoch, EPOCH_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.epoch_delta, EPOCH_DELTA_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), elevation, ELEVATION_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.elevation_delta, ELEVATION_DELTA_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), spin_angle, SPIN_ANGLE_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.spin_angle_delta, SPIN_ANGLE_DELTA_VAR_NAME)
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.epoch, EPOCH_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.epoch_delta, EPOCH_DELTA_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), elevation, ELEVATION_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.elevation_delta, ELEVATION_DELTA_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), spin_angle, SPIN_ANGLE_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.spin_angle_delta, SPIN_ANGLE_DELTA_VAR_NAME
+        )
         self.assert_variable_attributes(next(actual_variables), energy, ENERGY_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.energy_delta_plus, ENERGY_DELTA_PLUS_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.energy_delta_minus,
-                                        ENERGY_DELTA_MINUS_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.species_data,
-                                        species)
-        self.assert_variable_attributes(next(actual_variables), energy.astype(str), ENERGY_LABEL_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), spin_angle.astype(str), SPIN_ANGLE_LABEL_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), elevation.astype(str), ELEVATION_ANGLE_LABEL_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.uncertainties, species_uncertainty)
-        self.assert_variable_attributes(next(actual_variables), sentinel.rgfo_esa_step, RGFO_ESA_STEP_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.rgfo_spin_sector, RGFO_SPIN_SECTOR_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.rgfo_half_spin, RGFO_HALF_SPIN_VAR_NAME)
-        self.assert_variable_attributes(next(actual_variables), sentinel.half_spin_per_esa_step, HALF_SPIN_PER_ESA_STEP_VAR_NAME)
+        self.assert_variable_attributes(
+            next(actual_variables),
+            sentinel.energy_delta_plus,
+            ENERGY_DELTA_PLUS_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(actual_variables),
+            sentinel.energy_delta_minus,
+            ENERGY_DELTA_MINUS_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.species_data, species
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), energy.astype(str), ENERGY_LABEL_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), spin_angle.astype(str), SPIN_ANGLE_LABEL_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables),
+            elevation.astype(str),
+            ELEVATION_ANGLE_LABEL_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.uncertainties, species_uncertainty
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.rgfo_esa_step, RGFO_ESA_STEP_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.rgfo_spin_sector, RGFO_SPIN_SECTOR_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables), sentinel.rgfo_half_spin, RGFO_HALF_SPIN_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(actual_variables),
+            sentinel.half_spin_per_esa_step,
+            HALF_SPIN_PER_ESA_STEP_VAR_NAME,
+        )
 
     def test_codice_lo_l2_direct_events_reads_from_correct_float_data(self):
-        all_fill_l2_cdf_path = get_test_data_path('codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf')
+        all_fill_l2_cdf_path = get_test_data_path(
+            "codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf"
+        )
 
         rng = np.random.default_rng()
         with tempfile.TemporaryDirectory() as tmpdir:
@@ -604,16 +909,33 @@ def test_codice_lo_l2_direct_events_reads_from_correct_float_data(self):
             l2_direct_event = CodiceLoL2DirectEventData.read_from_cdf(test_cdf_path)
 
             with CDF(str(test_cdf_path)) as cdf:
-                np.testing.assert_array_equal(l2_direct_event.apd_energy, cdf["apd_energy"][:, :7, ...])
-                np.testing.assert_array_equal(l2_direct_event.elevation_angle, cdf["elevation_angle"][:, :7, ...])
-                np.testing.assert_array_equal(l2_direct_event.energy_step, cdf["energy_step"][:, :7, ...])
-                np.testing.assert_array_equal(l2_direct_event.energy_per_charge, cdf["energy_per_charge"][:, :7, ...])
-                np.testing.assert_array_equal(l2_direct_event.spin_sector, cdf["spin_sector"][:, :7, ...])
-                np.testing.assert_array_equal(l2_direct_event.spin_angle, cdf["spin_angle"][:, :7, ...])
-                np.testing.assert_array_equal(l2_direct_event.type, cdf["type"][:, :7, ...])
+                np.testing.assert_array_equal(
+                    l2_direct_event.apd_energy, cdf["apd_energy"][:, :7, ...]
+                )
+                np.testing.assert_array_equal(
+                    l2_direct_event.elevation_angle, cdf["elevation_angle"][:, :7, ...]
+                )
+                np.testing.assert_array_equal(
+                    l2_direct_event.energy_step, cdf["energy_step"][:, :7, ...]
+                )
+                np.testing.assert_array_equal(
+                    l2_direct_event.energy_per_charge,
+                    cdf["energy_per_charge"][:, :7, ...],
+                )
+                np.testing.assert_array_equal(
+                    l2_direct_event.spin_sector, cdf["spin_sector"][:, :7, ...]
+                )
+                np.testing.assert_array_equal(
+                    l2_direct_event.spin_angle, cdf["spin_angle"][:, :7, ...]
+                )
+                np.testing.assert_array_equal(
+                    l2_direct_event.type, cdf["type"][:, :7, ...]
+                )
 
     def test_workaround_to_convert_float_spin_sector_to_int(self):
-        all_fill_l2_cdf_path = get_test_data_path('codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf')
+        all_fill_l2_cdf_path = get_test_data_path(
+            "codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf"
+        )
 
         rng = np.random.default_rng()
         with tempfile.TemporaryDirectory() as tmpdir:
@@ -621,59 +943,99 @@ def test_workaround_to_convert_float_spin_sector_to_int(self):
 
             test_cdf_path = tmpdir / "test_cdf.cdf"
             with CDF(str(test_cdf_path), masterpath=str(all_fill_l2_cdf_path)) as cdf:
-                expected_spin_sector = rng.integers(0, 24, cdf["spin_sector"].shape, dtype=int)
+                expected_spin_sector = rng.integers(
+                    0, 24, cdf["spin_sector"].shape, dtype=int
+                )
                 cdf["spin_sector"] = expected_spin_sector.astype(float)
 
             l2_direct_event = CodiceLoL2DirectEventData.read_from_cdf(test_cdf_path)
 
-            np.testing.assert_array_equal(l2_direct_event.spin_sector, expected_spin_sector[:, :7, ...], strict=True)
+            np.testing.assert_array_equal(
+                l2_direct_event.spin_sector,
+                expected_spin_sector[:, :7, ...],
+                strict=True,
+            )
 
     def test_codice_lo_l2_direct_events_read_from_cdf_handles_fill_value(self):
-        all_fill_l2_cdf_path = get_test_data_path('codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf')
+        all_fill_l2_cdf_path = get_test_data_path(
+            "codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf"
+        )
         l2_direct_event = CodiceLoL2DirectEventData.read_from_cdf(all_fill_l2_cdf_path)
 
         with CDF(str(all_fill_l2_cdf_path)) as cdf:
             np.testing.assert_array_equal(cdf["epoch"], l2_direct_event.epoch)
-            np.testing.assert_array_equal(cdf["epoch_delta_plus"], l2_direct_event.epoch_delta_plus)
-            np.testing.assert_array_equal(cdf["epoch_delta_minus"], l2_direct_event.epoch_delta_minus)
-
-            np.testing.assert_array_equal(l2_direct_event.apd_energy, np.full((2, 7, 10000), np.nan))
-            np.testing.assert_array_equal(l2_direct_event.energy_step, np.full((2, 7, 10000), np.nan))
-            np.testing.assert_array_equal(l2_direct_event.energy_per_charge, np.full((2, 7, 10000), np.nan))
-            np.testing.assert_array_equal(l2_direct_event.spin_angle, np.full((2, 7, 10000), np.nan))
-            np.testing.assert_array_equal(l2_direct_event.spin_sector, np.full((2, 7, 10000), np.nan))
-            np.testing.assert_array_equal(l2_direct_event.elevation_angle, np.full((2, 7, 10000), np.nan))
+            np.testing.assert_array_equal(
+                cdf["epoch_delta_plus"], l2_direct_event.epoch_delta_plus
+            )
+            np.testing.assert_array_equal(
+                cdf["epoch_delta_minus"], l2_direct_event.epoch_delta_minus
+            )
+
+            np.testing.assert_array_equal(
+                l2_direct_event.apd_energy, np.full((2, 7, 10000), np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_direct_event.energy_step, np.full((2, 7, 10000), np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_direct_event.energy_per_charge, np.full((2, 7, 10000), np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_direct_event.spin_angle, np.full((2, 7, 10000), np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_direct_event.spin_sector, np.full((2, 7, 10000), np.nan)
+            )
+            np.testing.assert_array_equal(
+                l2_direct_event.elevation_angle, np.full((2, 7, 10000), np.nan)
+            )
 
             self.assertIsInstance(l2_direct_event.position, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.position.data, cdf["position"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.position.data, cdf["position"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.position.mask))
 
             self.assertIsInstance(l2_direct_event.gain, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.gain.data, cdf["gain"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.gain.data, cdf["gain"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.gain.mask))
 
             self.assertIsInstance(l2_direct_event.apd_id, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.apd_id.data, cdf["apd_id"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.apd_id.data, cdf["apd_id"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.apd_id.mask))
 
             self.assertIsInstance(l2_direct_event.data_quality, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.data_quality.data, cdf["data_quality"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.data_quality.data, cdf["data_quality"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.data_quality.mask))
 
             self.assertIsInstance(l2_direct_event.multi_flag, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.multi_flag.data, cdf["multi_flag"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.multi_flag.data, cdf["multi_flag"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.multi_flag.mask))
 
             self.assertIsInstance(l2_direct_event.num_events, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.num_events.data, cdf["num_events"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.num_events.data, cdf["num_events"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.num_events.mask))
 
             self.assertIsInstance(l2_direct_event.tof, np.ma.masked_array)
-            np.testing.assert_array_equal(l2_direct_event.tof.data, cdf["tof"][:, :7, ...])
+            np.testing.assert_array_equal(
+                l2_direct_event.tof.data, cdf["tof"][:, :7, ...]
+            )
             self.assertTrue(np.all(l2_direct_event.tof.mask))
 
     def test_codice_lo_l2_direct_events_read_from_cdf_masks_negative_tof_values(self):
-        all_fill_l2_cdf_path = get_test_data_path('codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf')
+        all_fill_l2_cdf_path = get_test_data_path(
+            "codice/imap_codice_l2_lo-direct-events_20260307_v003-all-fill.cdf"
+        )
 
         rng = np.random.default_rng()
         with tempfile.TemporaryDirectory() as tmpdir:
@@ -690,15 +1052,21 @@ def test_codice_lo_l2_direct_events_read_from_cdf_masks_negative_tof_values(self
             expected_truncated_tof = tof[:, :CODICE_LO_L2_NUM_PRIORITIES, ...]
             expected_mask = expected_truncated_tof < 0
 
-            np.testing.assert_array_equal(l2_direct_event.tof.data, expected_truncated_tof)
+            np.testing.assert_array_equal(
+                l2_direct_event.tof.data, expected_truncated_tof
+            )
             np.testing.assert_array_equal(l2_direct_event.tof.mask, expected_mask)
 
     def test_codice_lo_l1a_sw_priority_read_from_cdf_handles_fill_value(self):
-        l1a_sw_all_fill_path = get_test_data_path("codice/imap_codice_l1a_lo-sw-priority_20260307_v003-all-fill.cdf")
+        l1a_sw_all_fill_path = get_test_data_path(
+            "codice/imap_codice_l1a_lo-sw-priority_20260307_v003-all-fill.cdf"
+        )
         l1a_sw = CodiceLoL1aSWPriorityRates.read_from_cdf(l1a_sw_all_fill_path)
 
         with CDF(str(l1a_sw_all_fill_path)) as cdf:
-            np.testing.assert_array_equal(l1a_sw.spin_period, np.full_like(cdf['spin_period'], np.nan))
+            np.testing.assert_array_equal(
+                l1a_sw.spin_period, np.full_like(cdf["spin_period"], np.nan)
+            )
 
             integer_variable_names = [
                 "nso_half_spin",
@@ -724,37 +1092,55 @@ def test_codice_lo_l1a_sw_priority_read_from_cdf_handles_fill_value(self):
                 np.testing.assert_equal(var_data.mask, np.full_like(cdf[var], True))
 
     def test_codice_lo_l1a_nsw_priority_read_from_cdf_handles_fill_value(self):
-        l1a_nsw_all_fill_path = get_test_data_path("codice/imap_codice_l1a_lo-nsw-priority_20260307_v003-all-fill.cdf")
+        l1a_nsw_all_fill_path = get_test_data_path(
+            "codice/imap_codice_l1a_lo-nsw-priority_20260307_v003-all-fill.cdf"
+        )
 
         l1a_nsw_model = CodiceLoL1aNSWPriorityRates.read_from_cdf(l1a_nsw_all_fill_path)
 
         with CDF(str(l1a_nsw_all_fill_path)) as cdf:
-            np.testing.assert_array_equal(l1a_nsw_model.spin_period, np.full_like(cdf['spin_period'], np.nan))
+            np.testing.assert_array_equal(
+                l1a_nsw_model.spin_period, np.full_like(cdf["spin_period"], np.nan)
+            )
 
             self.assertIsInstance(l1a_nsw_model.rgfo_half_spin, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.rgfo_half_spin.data, cdf["rgfo_half_spin"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.rgfo_half_spin.data, cdf["rgfo_half_spin"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.rgfo_half_spin.mask))
 
             self.assertIsInstance(l1a_nsw_model.data_quality, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.data_quality.data, cdf["data_quality"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.data_quality.data, cdf["data_quality"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.data_quality.mask))
 
             self.assertIsInstance(l1a_nsw_model.p5_heavies, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.p5_heavies.data, cdf["p5_heavies"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.p5_heavies.data, cdf["p5_heavies"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.p5_heavies.mask))
 
             self.assertIsInstance(l1a_nsw_model.p6_hplus_heplusplus, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.p6_hplus_heplusplus.data, cdf["p6_hplus_heplusplus"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.p6_hplus_heplusplus.data, cdf["p6_hplus_heplusplus"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.p6_hplus_heplusplus.mask))
 
             self.assertIsInstance(l1a_nsw_model.nso_half_spin, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.nso_half_spin.data, cdf["nso_half_spin"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.nso_half_spin.data, cdf["nso_half_spin"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.nso_half_spin.mask))
 
             self.assertIsInstance(l1a_nsw_model.sw_bias_gain_mode, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.sw_bias_gain_mode.data, cdf["sw_bias_gain_mode"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.sw_bias_gain_mode.data, cdf["sw_bias_gain_mode"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.sw_bias_gain_mode.mask))
 
             self.assertIsInstance(l1a_nsw_model.st_bias_gain_mode, np.ma.masked_array)
-            np.testing.assert_array_equal(l1a_nsw_model.st_bias_gain_mode.data, cdf["st_bias_gain_mode"])
+            np.testing.assert_array_equal(
+                l1a_nsw_model.st_bias_gain_mode.data, cdf["st_bias_gain_mode"]
+            )
             self.assertTrue(np.all(l1a_nsw_model.st_bias_gain_mode.mask))
diff --git a/tests/debug_utils/profile_wrappers.py b/tests/debug_utils/profile_wrappers.py
index 64ee9015a..b5a9591b1 100644
--- a/tests/debug_utils/profile_wrappers.py
+++ b/tests/debug_utils/profile_wrappers.py
@@ -9,7 +9,7 @@ def wrapper_cprofile(*args, **kwargs):
         with cProfile.Profile() as p:
             result = function_to_be_decorated(*args, **kwargs)
             print(f"========\n{function_to_be_decorated}")
-            p.dump_stats(f"profile_{datetime.now().strftime("%Y%m%dT%H%M%S")}.prof")
+            p.dump_stats(f"profile_{datetime.now().strftime('%Y%m%dT%H%M%S')}.prof")
         return result
 
     return wrapper_cprofile
diff --git a/tests/glows/l3a/science/test_bad_angle_flag_configuration.py b/tests/glows/l3a/science/test_bad_angle_flag_configuration.py
index b23113ae8..5ca4d6a14 100644
--- a/tests/glows/l3a/science/test_bad_angle_flag_configuration.py
+++ b/tests/glows/l3a/science/test_bad_angle_flag_configuration.py
@@ -6,7 +6,9 @@
 
 import numpy as np
 
-from imap_l3_processing.glows.l3a.science.bad_angle_flag_configuration import BadAngleFlagConfiguration
+from imap_l3_processing.glows.l3a.science.bad_angle_flag_configuration import (
+    BadAngleFlagConfiguration,
+)
 from tests.test_helpers import get_test_data_path
 
 
@@ -16,21 +18,27 @@ def test_masks_data_when_flags_match_configuration(self):
         excluded_region = [True, False, True, False, False]
         excluded_by_instr_team = [False, False, False, True, False]
         suspected_transient = [False, True, True, False, False]
-        test_data = np.array([
-            close_to_uv_source,
-            excluded_region,
-            excluded_by_instr_team,
-            suspected_transient,
-        ])
-
-        test_cases = [([True, True, False, True], [True, True, True, True, False]),
-                      ([True, False, False, False], close_to_uv_source),
-                      ([False, True, False, False], excluded_region),
-                      ([False, False, True, False], excluded_by_instr_team),
-                      ([False, False, False, True], suspected_transient),
-                      ([False, True, False, True], [a or b for a, b in zip(excluded_region, suspected_transient)]),
-                      ([False, False, False, False], [False, False, False, False, False]),
-                      ]
+        test_data = np.array(
+            [
+                close_to_uv_source,
+                excluded_region,
+                excluded_by_instr_team,
+                suspected_transient,
+            ]
+        )
+
+        test_cases = [
+            ([True, True, False, True], [True, True, True, True, False]),
+            ([True, False, False, False], close_to_uv_source),
+            ([False, True, False, False], excluded_region),
+            ([False, False, True, False], excluded_by_instr_team),
+            ([False, False, False, True], suspected_transient),
+            (
+                [False, True, False, True],
+                [a or b for a, b in zip(excluded_region, suspected_transient)],
+            ),
+            ([False, False, False, False], [False, False, False, False, False]),
+        ]
 
         for flag_configuration, expected_mask in test_cases:
             with self.subTest(f"Flag configuration: {flag_configuration}"):
@@ -40,7 +48,8 @@ def test_masks_data_when_flags_match_configuration(self):
 
     def test_load_from_file(self):
         path = get_test_data_path(
-            "glows/imap_glows_l2_histogram-bad-angle-flags-configuration-json-not-cdf_20250701_v001.cdf")
+            "glows/imap_glows_l2_histogram-bad-angle-flags-configuration-json-not-cdf_20250701_v001.cdf"
+        )
         expected = BadAngleFlagConfiguration(
             mask_close_to_uv_source=True,
             mask_inside_excluded_region=True,
@@ -58,6 +67,6 @@ def test_load_from_file_v2(self):
                 mask_excluded_by_instr_team=True,
                 mask_suspected_transient=False,
             )
-            with open(temp_file, 'w') as f:
+            with open(temp_file, "w") as f:
                 json.dump(dataclasses.asdict(expected), f)
             self.assertEqual(expected, BadAngleFlagConfiguration.from_file(temp_file))
diff --git a/tests/glows/l3a/science/test_calculate_daily_lightcurve.py b/tests/glows/l3a/science/test_calculate_daily_lightcurve.py
index 5948da19d..d00d822a2 100644
--- a/tests/glows/l3a/science/test_calculate_daily_lightcurve.py
+++ b/tests/glows/l3a/science/test_calculate_daily_lightcurve.py
@@ -2,22 +2,32 @@
 from unittest.mock import Mock
 
 import numpy as np
-from uncertainties.unumpy import uarray, nominal_values, std_devs
+from uncertainties.unumpy import nominal_values, std_devs, uarray
 
-from imap_l3_processing.glows.l3a.science.bad_angle_flag_configuration import BadAngleFlagConfiguration
-from imap_l3_processing.glows.l3a.science.calculate_daily_lightcurve import rebin_lightcurve, calculate_spin_angles
-from imap_l3_processing.glows.l3a.science.time_independent_background_lookup_table import \
-    TimeIndependentBackgroundLookupTable
+from imap_l3_processing.glows.l3a.science.bad_angle_flag_configuration import (
+    BadAngleFlagConfiguration,
+)
+from imap_l3_processing.glows.l3a.science.calculate_daily_lightcurve import (
+    calculate_spin_angles,
+    rebin_lightcurve,
+)
+from imap_l3_processing.glows.l3a.science.time_independent_background_lookup_table import (
+    TimeIndependentBackgroundLookupTable,
+)
 
 
 class TestCalculateDailyLightcurve(unittest.TestCase):
     def setUp(self):
-        self.all_zero_time_independent_background = TimeIndependentBackgroundLookupTable(
-            latitudes=np.array([-90, 90]),
-            longitudes=np.array([0]),
-            background_values=np.zeros(
-                shape=(2, 1)))
-        self.enable_all_flags_configuration = BadAngleFlagConfiguration(True, True, True, True)
+        self.all_zero_time_independent_background = (
+            TimeIndependentBackgroundLookupTable(
+                latitudes=np.array([-90, 90]),
+                longitudes=np.array([0]),
+                background_values=np.zeros(shape=(2, 1)),
+            )
+        )
+        self.enable_all_flags_configuration = BadAngleFlagConfiguration(
+            True, True, True, True
+        )
 
     def test_rebin_with_no_flags_and_equal_exposure_times(self):
         photon_flux = np.full(3600, 1.0)
@@ -30,10 +40,17 @@ def test_rebin_with_no_flags_and_equal_exposure_times(self):
         exposure_times = np.full(3600, 24.0)
         output_size = 90
         background = np.zeros(90)
-        result, rebinned_exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                                     self.enable_all_flags_configuration,
-                                                     photon_flux, latitudes, longitudes, flags, exposure_times,
-                                                     output_size, background)
+        result, rebinned_exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            output_size,
+            background,
+        )
 
         expected_output = np.full(90, 1.0)
         expected_output[45:] = 5.0
@@ -51,10 +68,17 @@ def test_rebin_with_different_output_size(self):
         latitudes = np.linspace(start=0, stop=0, num=2400)
         longitudes = np.linspace(start=0, stop=360, num=2400)
 
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, 80, background)
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            80,
+            background,
+        )
         expected_output = np.full(80, 1.0)
         expected_exposure = np.full(80, 300.0)
         np.testing.assert_equal(result, expected_output, strict=True)
@@ -77,10 +101,17 @@ def test_rebin_masking_flagged_bins(self):
         latitudes = np.linspace(start=0, stop=0, num=3600)
         longitudes = np.linspace(start=0, stop=360, num=3600)
 
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, output_size, background)
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            output_size,
+            background,
+        )
 
         expected_output = np.full(90, 1.0)
         expected_output[45:] = 5.0
@@ -113,14 +144,23 @@ def test_uses_configuration_to_flag_bins(self):
         latitudes = np.linspace(start=0, stop=0, num=3600)
         longitudes = np.linspace(start=0, stop=360, num=3600)
 
-        flag_configuration = BadAngleFlagConfiguration(mask_close_to_uv_source=True,
-                                                       mask_inside_excluded_region=True,
-                                                       mask_excluded_by_instr_team=False,
-                                                       mask_suspected_transient=True, )
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background, flag_configuration, photon_flux,
-                                            latitudes,
-                                            longitudes, flags,
-                                            exposure_times, output_size, background)
+        flag_configuration = BadAngleFlagConfiguration(
+            mask_close_to_uv_source=True,
+            mask_inside_excluded_region=True,
+            mask_excluded_by_instr_team=False,
+            mask_suspected_transient=True,
+        )
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            flag_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            output_size,
+            background,
+        )
 
         expected_output = np.full(90, 1.0)
         expected_output[45:] = 5.0
@@ -136,110 +176,156 @@ def test_uses_configuration_to_flag_bins(self):
         self.assertFalse(np.ma.isMaskedArray(exposure))
 
     def test_rebin_uses_exposure_times(self):
-        photon_flux = np.array([10., 20., 30., 40.])
+        photon_flux = np.array([10.0, 20.0, 30.0, 40.0])
         flags = np.zeros((4, 4))
-        exposure_times = np.array([2., 1., 0., 1.])
+        exposure_times = np.array([2.0, 1.0, 0.0, 1.0])
         background = np.zeros(1)
         latitudes = np.linspace(start=0, stop=0, num=4)
         longitudes = np.linspace(start=0, stop=360, num=4)
 
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, 1, background)
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            1,
+            background,
+        )
         np.testing.assert_equal(result, [20.0], strict=True)
         np.testing.assert_equal(exposure, [4.0], strict=True)
 
     def test_rebin_uses_exposure_times_all_zero_bin(self):
-        photon_flux = np.array([10., 20., 30., 40.])
+        photon_flux = np.array([10.0, 20.0, 30.0, 40.0])
         flags = np.zeros((4, 4))
-        exposure_times = np.array([0., 0., 0., 0.])
+        exposure_times = np.array([0.0, 0.0, 0.0, 0.0])
         background = np.zeros(1)
         latitudes = np.linspace(start=0, stop=0, num=4)
         longitudes = np.linspace(start=0, stop=360, num=4)
 
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, 1, background)
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            1,
+            background,
+        )
         np.testing.assert_equal(result, [np.nan], strict=True)
         np.testing.assert_equal(exposure, [0.0], strict=True)
 
     def test_rebin_uses_exposure_times_and_background(self):
-        photon_flux = np.array([5., 20., 30., 40.])
+        photon_flux = np.array([5.0, 20.0, 30.0, 40.0])
         flags = np.zeros((4, 4))
-        exposure_times = np.array([2., 1., 0., 1.])
+        exposure_times = np.array([2.0, 1.0, 0.0, 1.0])
         background = np.array([5, 4])
         latitudes = np.linspace(start=0, stop=0, num=4)
         longitudes = np.linspace(start=0, stop=360, num=4)
 
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, 2, background)
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            2,
+            background,
+        )
         np.testing.assert_equal(result, [5.0, 36.0], strict=True)
         np.testing.assert_equal(exposure, [3.0, 1.0], strict=True)
 
     def test_rebin_uses_uncertain_photon_flux_and_background(self):
         photon_flux = np.array([5, 20, 30, 40])
-        photon_flux_uncertainty = np.array([0.1, 2.0, 3.0, .5])
+        photon_flux_uncertainty = np.array([0.1, 2.0, 3.0, 0.5])
         photon_flux = uarray(photon_flux, photon_flux_uncertainty)
         latitudes = np.linspace(start=0, stop=0, num=4)
         longitudes = np.linspace(start=0, stop=360, num=4)
 
         flags = np.zeros((4, 4))
-        exposure_times = np.array([2., 1., 0., 1.])
+        exposure_times = np.array([2.0, 1.0, 0.0, 1.0])
         background = np.array([5, 4])
         background_uncertainty = np.array([0.02, 0.08])
         background = uarray(background, background_uncertainty)
 
-        result, exposure = rebin_lightcurve(self.all_zero_time_independent_background,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, 2, background)
+        result, exposure = rebin_lightcurve(
+            self.all_zero_time_independent_background,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            2,
+            background,
+        )
         np.testing.assert_equal(nominal_values(result), [5.0, 36.0], strict=True)
-        np.testing.assert_equal(std_devs(result), [0.6702901527613909, 0.5063595560468865], strict=True)
+        np.testing.assert_equal(
+            std_devs(result), [0.6702901527613909, 0.5063595560468865], strict=True
+        )
         np.testing.assert_equal(exposure, [3.0, 1.0], strict=True)
 
     def test_rebin_subtracts_time_independent_background(self):
         time_independent_background = np.array([20, 2, 1, 0.2])
 
         photon_flux = np.array([5, 20, 30, 40]) + time_independent_background
-        photon_flux_uncertainty = np.array([0.1, 2.0, 3.0, .5])
+        photon_flux_uncertainty = np.array([0.1, 2.0, 3.0, 0.5])
         photon_flux = uarray(photon_flux, photon_flux_uncertainty)
         latitudes = np.linspace(start=0, stop=0, num=4)
         longitudes = np.linspace(start=0, stop=360, num=4)
 
         flags = np.zeros((4, 4))
-        exposure_times = np.array([2., 1., 0., 1.])
+        exposure_times = np.array([2.0, 1.0, 0.0, 1.0])
         background = np.array([5, 4])
         background_uncertainty = np.array([0.02, 0.08])
         background = uarray(background, background_uncertainty)
 
         mock_time_independent_background_lookup_table = Mock()
-        mock_time_independent_background_lookup_table.lookup.return_value = time_independent_background
-
-        result, exposure = rebin_lightcurve(mock_time_independent_background_lookup_table,
-                                            self.enable_all_flags_configuration, photon_flux, latitudes,
-                                            longitudes, flags,
-                                            exposure_times, 2, background)
-
-        mock_time_independent_background_lookup_table.lookup.assert_called_once_with(lat=latitudes,
-                                                                                     lon=longitudes)
+        mock_time_independent_background_lookup_table.lookup.return_value = (
+            time_independent_background
+        )
+
+        result, exposure = rebin_lightcurve(
+            mock_time_independent_background_lookup_table,
+            self.enable_all_flags_configuration,
+            photon_flux,
+            latitudes,
+            longitudes,
+            flags,
+            exposure_times,
+            2,
+            background,
+        )
+
+        mock_time_independent_background_lookup_table.lookup.assert_called_once_with(
+            lat=latitudes, lon=longitudes
+        )
 
         np.testing.assert_equal(nominal_values(result), [5.0, 36.0], strict=True)
-        np.testing.assert_equal(std_devs(result), [0.6702901527613909, 0.5063595560468865], strict=True)
+        np.testing.assert_equal(
+            std_devs(result), [0.6702901527613909, 0.5063595560468865], strict=True
+        )
         np.testing.assert_equal(exposure, [3.0, 1.0], strict=True)
 
     def test_calculate_spin_angles(self):
         spin_angles = np.array([70, 130, 190, 250, 310, 10, 70, 130, 190, 350, 10, 30])
 
-        np.testing.assert_array_equal([130, 310, 130, 10], calculate_spin_angles(4, spin_angles))
+        np.testing.assert_array_equal(
+            [130, 310, 130, 10], calculate_spin_angles(4, spin_angles)
+        )
 
         realistic_angles = np.linspace(0, 360, 3600, endpoint=False) + 0.05
         expected_4_degree_bins = np.linspace(0, 360, 90, endpoint=False) + 2
-        np.testing.assert_allclose(expected_4_degree_bins, calculate_spin_angles(90, realistic_angles))
+        np.testing.assert_allclose(
+            expected_4_degree_bins, calculate_spin_angles(90, realistic_angles)
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/glows/l3a/science/test_time_independent_background_lookup_table.py b/tests/glows/l3a/science/test_time_independent_background_lookup_table.py
index 008bc9d0b..fcd650acb 100644
--- a/tests/glows/l3a/science/test_time_independent_background_lookup_table.py
+++ b/tests/glows/l3a/science/test_time_independent_background_lookup_table.py
@@ -2,8 +2,9 @@
 
 import numpy as np
 
-from imap_l3_processing.glows.l3a.science.time_independent_background_lookup_table import \
-    TimeIndependentBackgroundLookupTable
+from imap_l3_processing.glows.l3a.science.time_independent_background_lookup_table import (
+    TimeIndependentBackgroundLookupTable,
+)
 from tests.test_helpers import get_test_data_path
 
 
@@ -11,11 +12,13 @@ class TestTimeIndependentBackgroundLookupTable(unittest.TestCase):
     def test_interpolates_to_find_background(self):
         latitudes = np.array([90, 0, -90])
         longitudes = np.array([0, 90, 180, 270])
-        bg_map = np.array([
-            [4, 4, 4, 4],
-            [5, 6, 7, 8],
-            [9, 9, 9, 9],
-        ])
+        bg_map = np.array(
+            [
+                [4, 4, 4, 4],
+                [5, 6, 7, 8],
+                [9, 9, 9, 9],
+            ]
+        )
         table = TimeIndependentBackgroundLookupTable(longitudes, latitudes, bg_map)
 
         self.assertEqual(5, table.lookup(lat=0, lon=0))
@@ -28,16 +31,23 @@ def test_interpolates_to_find_background(self):
 
         self.assertEqual(4.75, table.lookup(lat=45, lon=45))
 
-        np.testing.assert_equal([5, 5.5, 4.75], table.lookup(lat=np.array([0, 0, 45]), lon=np.array([0, 45, 45])))
+        np.testing.assert_equal(
+            [5, 5.5, 4.75],
+            table.lookup(lat=np.array([0, 0, 45]), lon=np.array([0, 45, 45])),
+        )
 
     def test_load_from_file(self):
         table = TimeIndependentBackgroundLookupTable.from_file(
             get_test_data_path(
-                "glows/imap_glows_l2_histogram-time-independent-background-map-text-not-cdf_20250701_v001.cdf"))
+                "glows/imap_glows_l2_histogram-time-independent-background-map-text-not-cdf_20250701_v001.cdf"
+            )
+        )
 
         expected_latitudes = np.array([90, 0, -90])
         expected_longitudes = np.array([0, 90, 180, 270])
 
         np.testing.assert_equal(expected_latitudes, table.latitudes)
         np.testing.assert_equal(expected_longitudes, table.longitudes)
-        np.testing.assert_array_equal([[0, 4, 3, 1], [1, 1, 3, 2], [5, 3, 2, 0]], table.background_values)
+        np.testing.assert_array_equal(
+            [[0, 4, 3, 1], [1, 1, 3, 2], [5, 3, 2, 0]], table.background_values
+        )
diff --git a/tests/glows/l3a/test_glows_l3a_dependencies.py b/tests/glows/l3a/test_glows_l3a_dependencies.py
index ca6a198ce..f39efdd71 100644
--- a/tests/glows/l3a/test_glows_l3a_dependencies.py
+++ b/tests/glows/l3a/test_glows_l3a_dependencies.py
@@ -1,22 +1,31 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
 import imap_data_access
-from imap_data_access.processing_input import ScienceInput, AncillaryInput, ProcessingInputCollection
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
-from imap_l3_processing.glows.descriptors import GLOWS_L2_DESCRIPTOR, GLOWS_CALIBRATION_DATA_DESCRIPTOR, \
-    GLOWS_PIPELINE_SETTINGS_DESCRIPTOR, GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR, \
-    GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_CALIBRATION_DATA_DESCRIPTOR,
+    GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR,
+    GLOWS_L2_DESCRIPTOR,
+    GLOWS_PIPELINE_SETTINGS_DESCRIPTOR,
+    GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3a.glows_l3a_dependencies import GlowsL3ADependencies
 
 
 class TestGlowsL3aDependencies(unittest.TestCase):
-
-    @patch('imap_l3_processing.glows.l3a.glows_l3a_dependencies.CDF')
-    @patch('imap_l3_processing.glows.l3a.glows_l3a_dependencies.download')
-    @patch('imap_l3_processing.glows.l3a.glows_l3a_dependencies.read_l2_glows_data')
-    def test_fetch_dependencies(self, mock_read_l2_glows_data, mock_download, mock_cdf_constructor):
+    @patch("imap_l3_processing.glows.l3a.glows_l3a_dependencies.CDF")
+    @patch("imap_l3_processing.glows.l3a.glows_l3a_dependencies.download")
+    @patch("imap_l3_processing.glows.l3a.glows_l3a_dependencies.read_l2_glows_data")
+    def test_fetch_dependencies(
+        self, mock_read_l2_glows_data, mock_download, mock_cdf_constructor
+    ):
         mission = "imap"
         instrument = "glows"
         level = "l2a"
@@ -24,12 +33,14 @@ def test_fetch_dependencies(self, mock_read_l2_glows_data, mock_download, mock_c
         version = "v001"
         repointing = "repoint00002"
 
-        ignored_dependency_filename = f'{mission}_{instrument}_{level}_ignored-data_{start_date}_{version}.cdf'
-        cdf_dependency_filename = f'{mission}_{instrument}_{level}_{GLOWS_L2_DESCRIPTOR}_{start_date}-{repointing}_{version}.cdf'
-        calibration_data_dependency_filename = f'{mission}_{instrument}_{GLOWS_CALIBRATION_DATA_DESCRIPTOR}_{start_date}_{version}.dat'
-        settings_dependency_filename = f'{mission}_{instrument}_{GLOWS_PIPELINE_SETTINGS_DESCRIPTOR}_{start_date}_{version}.json'
-        extra_heliospheric_background_dependency_filename = f'{mission}_{instrument}_{GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR}_{start_date}_{version}.dat'
-        time_dependent_background_dependency_filename = f'{mission}_{instrument}_{GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR}_{start_date}_{version}.dat'
+        ignored_dependency_filename = (
+            f"{mission}_{instrument}_{level}_ignored-data_{start_date}_{version}.cdf"
+        )
+        cdf_dependency_filename = f"{mission}_{instrument}_{level}_{GLOWS_L2_DESCRIPTOR}_{start_date}-{repointing}_{version}.cdf"
+        calibration_data_dependency_filename = f"{mission}_{instrument}_{GLOWS_CALIBRATION_DATA_DESCRIPTOR}_{start_date}_{version}.dat"
+        settings_dependency_filename = f"{mission}_{instrument}_{GLOWS_PIPELINE_SETTINGS_DESCRIPTOR}_{start_date}_{version}.json"
+        extra_heliospheric_background_dependency_filename = f"{mission}_{instrument}_{GLOWS_EXTRA_HELIOSPHERIC_BACKGROUND_DESCRIPTOR}_{start_date}_{version}.dat"
+        time_dependent_background_dependency_filename = f"{mission}_{instrument}_{GLOWS_TIME_DEPENDENT_BACKGROUND_DESCRIPTOR}_{start_date}_{version}.dat"
 
         processing_input_collection = ProcessingInputCollection(
             ScienceInput(ignored_dependency_filename),
@@ -39,8 +50,17 @@ def test_fetch_dependencies(self, mock_read_l2_glows_data, mock_download, mock_c
             AncillaryInput(extra_heliospheric_background_dependency_filename),
             AncillaryInput(time_dependent_background_dependency_filename),
         )
-        science_data_dir = imap_data_access.config['DATA_DIR'] / 'imap' / 'glows' / 'l2a' / '2023' / '01'
-        ancillary_data_dir = imap_data_access.config['DATA_DIR'] / 'imap' / 'ancillary' / 'glows'
+        science_data_dir = (
+            imap_data_access.config["DATA_DIR"]
+            / "imap"
+            / "glows"
+            / "l2a"
+            / "2023"
+            / "01"
+        )
+        ancillary_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "ancillary" / "glows"
+        )
 
         cdf_path_str = "some_cdf.cdf"
         cdf_path_name = Path(cdf_path_str)
@@ -66,15 +86,27 @@ def test_fetch_dependencies(self, mock_read_l2_glows_data, mock_download, mock_c
 
         self.assertEqual(calibration_data, result.ancillary_files["calibration_data"])
         self.assertEqual(settings, result.ancillary_files["settings"])
-        self.assertEqual(time_dependent_background_path,
-                         result.ancillary_files["time_dependent_bckgrd"])
-        self.assertEqual(extra_heliospheric_background,
-                         result.ancillary_files["extra_heliospheric_bckgrd"])
+        self.assertEqual(
+            time_dependent_background_path,
+            result.ancillary_files["time_dependent_bckgrd"],
+        )
+        self.assertEqual(
+            extra_heliospheric_background,
+            result.ancillary_files["extra_heliospheric_bckgrd"],
+        )
 
-        self.assertEqual([
-            call(science_data_dir / cdf_dependency_filename),
-            call(ancillary_data_dir / calibration_data_dependency_filename),
-            call(ancillary_data_dir / extra_heliospheric_background_dependency_filename),
-            call(ancillary_data_dir / time_dependent_background_dependency_filename),
-            call(ancillary_data_dir / settings_dependency_filename),
-        ], mock_download.call_args_list)
+        self.assertEqual(
+            [
+                call(science_data_dir / cdf_dependency_filename),
+                call(ancillary_data_dir / calibration_data_dependency_filename),
+                call(
+                    ancillary_data_dir
+                    / extra_heliospheric_background_dependency_filename
+                ),
+                call(
+                    ancillary_data_dir / time_dependent_background_dependency_filename
+                ),
+                call(ancillary_data_dir / settings_dependency_filename),
+            ],
+            mock_download.call_args_list,
+        )
diff --git a/tests/glows/l3a/test_models.py b/tests/glows/l3a/test_models.py
index 0538e6c3a..d48576b65 100644
--- a/tests/glows/l3a/test_models.py
+++ b/tests/glows/l3a/test_models.py
@@ -5,27 +5,50 @@
 import numpy as np
 from numpy import ndarray
 
-from imap_l3_processing.glows.l3a.models import GlowsL3LightCurve, PHOTON_FLUX_CDF_VAR_NAME, \
-    EXPOSURE_TIMES_CDF_VAR_NAME, \
-    NUM_OF_BINS_CDF_VAR_NAME, EPOCH_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME, \
-    PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME, SPIN_ANGLE_CDF_VAR_NAME, LATITUDE_CDF_VAR_NAME, LONGITUDE_CDF_VAR_NAME, \
-    EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME, TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME, \
-    FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME, FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME, HV_VOLTAGE_AVERAGE_CDF_VAR_NAME, \
-    HV_VOLTAGE_STD_DEV_CDF_VAR_NAME, SPIN_PERIOD_AVERAGE_CDF_VAR_NAME, SPIN_PERIOD_STD_DEV_CDF_VAR_NAME, \
-    SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME, SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME, PULSE_LENGTH_AVERAGE_CDF_VAR_NAME, \
-    PULSE_LENGTH_STD_DEV_CDF_VAR_NAME, POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME, \
-    POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME, SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME, \
-    SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME, SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME, \
-    SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME, SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME, \
-    SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME, RAW_HISTOGRAM_CDF_VAR_NAME, SPIN_ANGLE_DELTA_CDF_VAR_NAME, \
-    START_TIME_CDF_VAR_NAME, END_TIME_CDF_VAR_NAME, REPOINTING_CDF_VAR_NAME
+from imap_l3_processing.glows.l3a.models import (
+    END_TIME_CDF_VAR_NAME,
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+    EXPOSURE_TIMES_CDF_VAR_NAME,
+    EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME,
+    FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME,
+    FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME,
+    HV_VOLTAGE_AVERAGE_CDF_VAR_NAME,
+    HV_VOLTAGE_STD_DEV_CDF_VAR_NAME,
+    LATITUDE_CDF_VAR_NAME,
+    LONGITUDE_CDF_VAR_NAME,
+    NUM_OF_BINS_CDF_VAR_NAME,
+    PHOTON_FLUX_CDF_VAR_NAME,
+    PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME,
+    POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME,
+    POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME,
+    PULSE_LENGTH_AVERAGE_CDF_VAR_NAME,
+    PULSE_LENGTH_STD_DEV_CDF_VAR_NAME,
+    RAW_HISTOGRAM_CDF_VAR_NAME,
+    REPOINTING_CDF_VAR_NAME,
+    SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME,
+    SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME,
+    SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME,
+    SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME,
+    SPIN_ANGLE_CDF_VAR_NAME,
+    SPIN_ANGLE_DELTA_CDF_VAR_NAME,
+    SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME,
+    SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME,
+    SPIN_PERIOD_AVERAGE_CDF_VAR_NAME,
+    SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME,
+    SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME,
+    SPIN_PERIOD_STD_DEV_CDF_VAR_NAME,
+    START_TIME_CDF_VAR_NAME,
+    TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME,
+    GlowsL3LightCurve,
+)
 from tests.swapi.cdf_model_test_case import CdfModelTestCase
 
 
 class TestModels(CdfModelTestCase):
     def test_data_to_product_variables(self):
         photon_flux: ndarray = (np.arange(360) + 50).reshape(1, -1)
-        photon_flux_uncertainty = photon_flux / 100.
+        photon_flux_uncertainty = photon_flux / 100.0
         raw_histogram = Mock()
         exposure_times: ndarray = (np.arange(360) + 100).reshape(1, -1)
         epoch: ndarray = np.array(datetime(2024, 11, 18, 12))
@@ -61,100 +84,190 @@ def test_data_to_product_variables(self):
         spacecraft_velocity_std_dev = Mock()
 
         repointing = 11
-        data = GlowsL3LightCurve(input_metadata=Mock(),
-                                 identifier=repointing,
-                                 parent_file_names=Mock(),
-                                 exposure_times=exposure_times,
-                                 raw_histogram=raw_histogram,
-                                 number_of_bins=number_of_bins,
-                                 photon_flux=photon_flux,
-                                 epoch=epoch, epoch_delta=epoch_delta,
-                                 start_time=start_time, end_time=end_time,
-                                 spin_angle=spin_angle, spin_angle_delta=spin_angle_delta,
-                                 photon_flux_uncertainty=photon_flux_uncertainty,
-                                 latitude=latitudes,
-                                 longitude=longitudes,
-                                 extra_heliospheric_background=extra_heliospheric_background,
-                                 time_dependent_background=time_dependent_background,
-                                 filter_temperature_average=filter_temperature_average,
-                                 filter_temperature_std_dev=filter_temperature_std_dev,
-                                 hv_voltage_average=hv_voltage_average,
-                                 hv_voltage_std_dev=hv_voltage_std_dev,
-                                 spin_period_average=spin_period_average,
-                                 spin_period_std_dev=spin_period_std_dev,
-                                 spin_period_ground_average=spin_period_ground_average,
-                                 spin_period_ground_std_dev=spin_period_ground_std_dev,
-                                 pulse_length_average=pulse_length_average,
-                                 pulse_length_std_dev=pulse_length_std_dev,
-                                 position_angle_offset_average=position_angle_offset_average,
-                                 position_angle_offset_std_dev=position_angle_offset_std_dev,
-                                 spin_axis_orientation_average=spin_axis_orientation_average,
-                                 spin_axis_orientation_std_dev=spin_axis_orientation_std_dev,
-                                 spacecraft_location_average=spacecraft_location_average,
-                                 spacecraft_location_std_dev=spacecraft_location_std_dev,
-                                 spacecraft_velocity_average=spacecraft_velocity_average,
-                                 spacecraft_velocity_std_dev=spacecraft_velocity_std_dev,
-                                 )
+        data = GlowsL3LightCurve(
+            input_metadata=Mock(),
+            identifier=repointing,
+            parent_file_names=Mock(),
+            exposure_times=exposure_times,
+            raw_histogram=raw_histogram,
+            number_of_bins=number_of_bins,
+            photon_flux=photon_flux,
+            epoch=epoch,
+            epoch_delta=epoch_delta,
+            start_time=start_time,
+            end_time=end_time,
+            spin_angle=spin_angle,
+            spin_angle_delta=spin_angle_delta,
+            photon_flux_uncertainty=photon_flux_uncertainty,
+            latitude=latitudes,
+            longitude=longitudes,
+            extra_heliospheric_background=extra_heliospheric_background,
+            time_dependent_background=time_dependent_background,
+            filter_temperature_average=filter_temperature_average,
+            filter_temperature_std_dev=filter_temperature_std_dev,
+            hv_voltage_average=hv_voltage_average,
+            hv_voltage_std_dev=hv_voltage_std_dev,
+            spin_period_average=spin_period_average,
+            spin_period_std_dev=spin_period_std_dev,
+            spin_period_ground_average=spin_period_ground_average,
+            spin_period_ground_std_dev=spin_period_ground_std_dev,
+            pulse_length_average=pulse_length_average,
+            pulse_length_std_dev=pulse_length_std_dev,
+            position_angle_offset_average=position_angle_offset_average,
+            position_angle_offset_std_dev=position_angle_offset_std_dev,
+            spin_axis_orientation_average=spin_axis_orientation_average,
+            spin_axis_orientation_std_dev=spin_axis_orientation_std_dev,
+            spacecraft_location_average=spacecraft_location_average,
+            spacecraft_location_std_dev=spacecraft_location_std_dev,
+            spacecraft_velocity_average=spacecraft_velocity_average,
+            spacecraft_velocity_std_dev=spacecraft_velocity_std_dev,
+        )
 
         variables = data.to_data_product_variables()
         self.assertEqual(38, len(variables))
 
         variables = iter(variables)
         # @formatter:off
-        self.assert_variable_attributes(next(variables), [repointing], REPOINTING_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), photon_flux, PHOTON_FLUX_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), photon_flux_uncertainty, PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), raw_histogram, RAW_HISTOGRAM_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), exposure_times, EXPOSURE_TIMES_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), number_of_bins, NUM_OF_BINS_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(variables), [repointing], REPOINTING_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), photon_flux, PHOTON_FLUX_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            photon_flux_uncertainty,
+            PHOTON_FLUX_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), raw_histogram, RAW_HISTOGRAM_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), exposure_times, EXPOSURE_TIMES_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), number_of_bins, NUM_OF_BINS_CDF_VAR_NAME
+        )
         self.assert_variable_attributes(next(variables), epoch, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [start_time], START_TIME_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [end_time], END_TIME_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_angle, SPIN_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_angle_delta, SPIN_ANGLE_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), latitudes, LATITUDE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), longitudes, LONGITUDE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), extra_heliospheric_background,
-                                        EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), time_dependent_background,
-                                        TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), filter_temperature_average,
-                                        FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), filter_temperature_std_dev,
-                                        FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), hv_voltage_average, HV_VOLTAGE_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), hv_voltage_std_dev, HV_VOLTAGE_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_period_average, SPIN_PERIOD_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_period_std_dev, SPIN_PERIOD_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_period_ground_average,
-                                        SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_period_ground_std_dev,
-                                        SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), pulse_length_average, PULSE_LENGTH_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), pulse_length_std_dev, PULSE_LENGTH_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), position_angle_offset_average,
-                                        POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), position_angle_offset_std_dev,
-                                        POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_axis_orientation_average,
-                                        SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spin_axis_orientation_std_dev,
-                                        SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spacecraft_location_average,
-                                        SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spacecraft_location_std_dev,
-                                        SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spacecraft_velocity_average,
-                                        SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), spacecraft_velocity_std_dev,
-                                        SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(variables), epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), [start_time], START_TIME_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), [end_time], END_TIME_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), spin_angle, SPIN_ANGLE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), spin_angle_delta, SPIN_ANGLE_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), latitudes, LATITUDE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), longitudes, LONGITUDE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            extra_heliospheric_background,
+            EXTRA_HELIOSPHERIC_BACKGROUND_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            time_dependent_background,
+            TIME_DEPENDENT_BACKGROUND_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            filter_temperature_average,
+            FILTER_TEMPERATURE_AVERAGE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            filter_temperature_std_dev,
+            FILTER_TEMPERATURE_STD_DEV_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), hv_voltage_average, HV_VOLTAGE_AVERAGE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), hv_voltage_std_dev, HV_VOLTAGE_STD_DEV_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), spin_period_average, SPIN_PERIOD_AVERAGE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), spin_period_std_dev, SPIN_PERIOD_STD_DEV_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spin_period_ground_average,
+            SPIN_PERIOD_GROUND_AVERAGE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spin_period_ground_std_dev,
+            SPIN_PERIOD_GROUND_STD_DEV_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), pulse_length_average, PULSE_LENGTH_AVERAGE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), pulse_length_std_dev, PULSE_LENGTH_STD_DEV_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            position_angle_offset_average,
+            POSITION_ANGLE_OFFSET_AVERAGE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            position_angle_offset_std_dev,
+            POSITION_ANGLE_OFFSET_STD_DEV_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spin_axis_orientation_average,
+            SPIN_AXIS_ORIENTATION_AVERAGE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spin_axis_orientation_std_dev,
+            SPIN_AXIS_ORIENTATION_STD_DEV_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spacecraft_location_average,
+            SPACECRAFT_LOCATION_AVERAGE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spacecraft_location_std_dev,
+            SPACECRAFT_LOCATION_STD_DEV_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spacecraft_velocity_average,
+            SPACECRAFT_VELOCITY_AVERAGE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            spacecraft_velocity_std_dev,
+            SPACECRAFT_VELOCITY_STD_DEV_CDF_VAR_NAME,
+        )
 
         self.assert_variable_attributes(next(variables), [0, 1], "lon_lat")
-        self.assert_variable_attributes(next(variables), ["lon", "lat"], "lon_lat_labels")
+        self.assert_variable_attributes(
+            next(variables), ["lon", "lat"], "lon_lat_labels"
+        )
         self.assert_variable_attributes(next(variables), [0, 1, 2], "x_y_z")
-        self.assert_variable_attributes(next(variables), ["X", "Y", "Z"], "x_y_z_labels")
+        self.assert_variable_attributes(
+            next(variables), ["X", "Y", "Z"], "x_y_z_labels"
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/glows/l3a/test_utils.py b/tests/glows/l3a/test_utils.py
index 5828a5e7e..e7d80e144 100644
--- a/tests/glows/l3a/test_utils.py
+++ b/tests/glows/l3a/test_utils.py
@@ -7,65 +7,140 @@
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.glows.l3a.models import GlowsL2Data, GlowsL2Header
-from imap_l3_processing.glows.l3a.utils import read_l2_glows_data, create_glows_l3a_from_dictionary, \
-    create_glows_l3a_dictionary_from_cdf
+from imap_l3_processing.glows.l3a.utils import (
+    create_glows_l3a_dictionary_from_cdf,
+    create_glows_l3a_from_dictionary,
+    read_l2_glows_data,
+)
 from imap_l3_processing.models import InputMetadata
 from tests.test_helpers import get_test_data_path
 
 
 class TestUtils(unittest.TestCase):
     def test_reading_l2_glows_data_into_models(self):
-        cdf = CDF(str(get_test_data_path('glows/imap_glows_l2_hist_20251224-repoint00088_v001.cdf')))
+        cdf = CDF(
+            str(
+                get_test_data_path(
+                    "glows/imap_glows_l2_hist_20251224-repoint00088_v001.cdf"
+                )
+            )
+        )
 
         glows_l2_data: GlowsL2Data = read_l2_glows_data(cdf)
 
         self.assertEqual("2025-12-24T11:36:21.588935256", glows_l2_data["start_time"])
         self.assertEqual("2025-12-25T09:39:47.969074488", glows_l2_data["end_time"])
 
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["photon_flux"].shape)
-        self.assertEqual(887.6062785692615, glows_l2_data["daily_lightcurve"]["photon_flux"][0])
-        self.assertEqual(872.260576384695, glows_l2_data["daily_lightcurve"]["photon_flux"][-1])
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["photon_flux"].shape
+        )
+        self.assertEqual(
+            887.6062785692615, glows_l2_data["daily_lightcurve"]["photon_flux"][0]
+        )
+        self.assertEqual(
+            872.260576384695, glows_l2_data["daily_lightcurve"]["photon_flux"][-1]
+        )
 
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["flux_uncertainties"].shape)
-        self.assertEqual(6.338751356172933, glows_l2_data["daily_lightcurve"]["flux_uncertainties"][0])
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["flux_uncertainties"].shape
+        )
+        self.assertEqual(
+            6.338751356172933,
+            glows_l2_data["daily_lightcurve"]["flux_uncertainties"][0],
+        )
 
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["exposure_times"].shape)
-        self.assertEqual(22.090875733333327, glows_l2_data["daily_lightcurve"]["exposure_times"][0])
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["exposure_times"].shape
+        )
+        self.assertEqual(
+            22.090875733333327, glows_l2_data["daily_lightcurve"]["exposure_times"][0]
+        )
 
         self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["spin_angle"].shape)
-        self.assertEqual(0.08840215463658296, glows_l2_data["daily_lightcurve"]["spin_angle"][0])
+        self.assertEqual(
+            0.08840215463658296, glows_l2_data["daily_lightcurve"]["spin_angle"][0]
+        )
 
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["histogram_flag_array"].shape)
-        self.assertEqual(np.int64, glows_l2_data["daily_lightcurve"]["histogram_flag_array"].dtype)
-        self.assertEqual(np.uint8(0), glows_l2_data["daily_lightcurve"]["histogram_flag_array"][84])
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["histogram_flag_array"].shape
+        )
+        self.assertEqual(
+            np.int64, glows_l2_data["daily_lightcurve"]["histogram_flag_array"].dtype
+        )
+        self.assertEqual(
+            np.uint8(0), glows_l2_data["daily_lightcurve"]["histogram_flag_array"][84]
+        )
 
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["ecliptic_lat"].shape)
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["ecliptic_lon"].shape)
-        self.assertEqual(75.04420784333895, glows_l2_data["daily_lightcurve"]["ecliptic_lat"][0])
-        self.assertEqual(268.7416390634532, glows_l2_data["daily_lightcurve"]["ecliptic_lon"][0])
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["ecliptic_lat"].shape
+        )
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["ecliptic_lon"].shape
+        )
+        self.assertEqual(
+            75.04420784333895, glows_l2_data["daily_lightcurve"]["ecliptic_lat"][0]
+        )
+        self.assertEqual(
+            268.7416390634532, glows_l2_data["daily_lightcurve"]["ecliptic_lon"][0]
+        )
 
-        self.assertEqual((3600,), glows_l2_data["daily_lightcurve"]["raw_histogram"].shape)
+        self.assertEqual(
+            (3600,), glows_l2_data["daily_lightcurve"]["raw_histogram"].shape
+        )
         self.assertEqual(19608, glows_l2_data["daily_lightcurve"]["raw_histogram"][0])
 
-        self.assertEqual({
-            "lon": -2.9634262485046428,
-            "lat": 0.06861815065029926
-        }, glows_l2_data["spin_axis_orientation_average"])
-
-        self.assertEqual(cdf['identifier'][0], glows_l2_data['identifier'])
-        self.assertEqual(cdf['filter_temperature_average'][0], glows_l2_data['filter_temperature_average'])
-        self.assertEqual(cdf['filter_temperature_std_dev'][0], glows_l2_data['filter_temperature_std_dev'])
-        self.assertEqual(cdf['hv_voltage_average'][0], glows_l2_data['hv_voltage_average'])
-        self.assertEqual(cdf['hv_voltage_std_dev'][0], glows_l2_data['hv_voltage_std_dev'])
-        self.assertEqual(cdf['spin_period_average'][0], glows_l2_data['spin_period_average'])
-        self.assertEqual(cdf['spin_period_std_dev'][0], glows_l2_data['spin_period_std_dev'])
-        self.assertEqual(cdf['spin_period_ground_average'][0], glows_l2_data['spin_period_ground_average'])
-        self.assertEqual(cdf['spin_period_ground_std_dev'][0], glows_l2_data['spin_period_ground_std_dev'])
-        self.assertEqual(cdf['pulse_length_average'][0], glows_l2_data['pulse_length_average'])
-        self.assertEqual(cdf['pulse_length_std_dev'][0], glows_l2_data['pulse_length_std_dev'])
-        self.assertEqual(cdf['position_angle_offset_average'][0], glows_l2_data['position_angle_offset_average'])
-        self.assertEqual(cdf['position_angle_offset_std_dev'][0], glows_l2_data['position_angle_offset_std_dev'])
-        self.assertEqual("imap_glows_l2_hist_20251224-repoint00088_v001.cdf", glows_l2_data['l2_file_name'])
+        self.assertEqual(
+            {"lon": -2.9634262485046428, "lat": 0.06861815065029926},
+            glows_l2_data["spin_axis_orientation_average"],
+        )
+
+        self.assertEqual(cdf["identifier"][0], glows_l2_data["identifier"])
+        self.assertEqual(
+            cdf["filter_temperature_average"][0],
+            glows_l2_data["filter_temperature_average"],
+        )
+        self.assertEqual(
+            cdf["filter_temperature_std_dev"][0],
+            glows_l2_data["filter_temperature_std_dev"],
+        )
+        self.assertEqual(
+            cdf["hv_voltage_average"][0], glows_l2_data["hv_voltage_average"]
+        )
+        self.assertEqual(
+            cdf["hv_voltage_std_dev"][0], glows_l2_data["hv_voltage_std_dev"]
+        )
+        self.assertEqual(
+            cdf["spin_period_average"][0], glows_l2_data["spin_period_average"]
+        )
+        self.assertEqual(
+            cdf["spin_period_std_dev"][0], glows_l2_data["spin_period_std_dev"]
+        )
+        self.assertEqual(
+            cdf["spin_period_ground_average"][0],
+            glows_l2_data["spin_period_ground_average"],
+        )
+        self.assertEqual(
+            cdf["spin_period_ground_std_dev"][0],
+            glows_l2_data["spin_period_ground_std_dev"],
+        )
+        self.assertEqual(
+            cdf["pulse_length_average"][0], glows_l2_data["pulse_length_average"]
+        )
+        self.assertEqual(
+            cdf["pulse_length_std_dev"][0], glows_l2_data["pulse_length_std_dev"]
+        )
+        self.assertEqual(
+            cdf["position_angle_offset_average"][0],
+            glows_l2_data["position_angle_offset_average"],
+        )
+        self.assertEqual(
+            cdf["position_angle_offset_std_dev"][0],
+            glows_l2_data["position_angle_offset_std_dev"],
+        )
+        self.assertEqual(
+            "imap_glows_l2_hist_20251224-repoint00088_v001.cdf",
+            glows_l2_data["l2_file_name"],
+        )
 
         self.assert_equal_xyz(cdf, "spacecraft_location_average", glows_l2_data)
         self.assert_equal_xyz(cdf, "spacecraft_location_std_dev", glows_l2_data)
@@ -73,39 +148,54 @@ def test_reading_l2_glows_data_into_models(self):
         self.assert_equal_xyz(cdf, "spacecraft_velocity_std_dev", glows_l2_data)
 
         expected_header = GlowsL2Header(
-            flight_software_version='123456',
+            flight_software_version="123456",
             pkts_file_name="data_l0/repointings/imap_glows_l0_raw_20251224-repoint00088_v000.pkts",
             ancillary_data_files=[
                 "imap_glows_pipeline-settings_20251112_v001.json",
-                "imap_glows_l2-calibration_20251112_v002.dat"
+                "imap_glows_l2-calibration_20251112_v002.dat",
             ],
         )
         self.assertEqual(expected_header, glows_l2_data["header"])
 
     def assert_equal_xyz(self, cdf, variable_name, actual):
         expected_spacecraft_location_average = {
-            'x': cdf[variable_name][0, 0],
-            'y': cdf[variable_name][0, 1],
-            'z': cdf[variable_name][0, 2],
+            "x": cdf[variable_name][0, 0],
+            "y": cdf[variable_name][0, 1],
+            "z": cdf[variable_name][0, 2],
         }
         self.assertEqual(expected_spacecraft_location_average, actual[variable_name])
 
     def test_fails_if_cdf_has_more_than_one_histogram(self):
-        cdf = CDF(str(get_test_data_path('glows/glows_l2_with_too_many_histograms.cdf')))
+        cdf = CDF(
+            str(get_test_data_path("glows/glows_l2_with_too_many_histograms.cdf"))
+        )
 
         with self.assertRaises(AssertionError) as cm:
             glows_l2_data: GlowsL2Data = read_l2_glows_data(cdf)
-        self.assertEqual(("Level 2 file should have only one histogram",), cm.exception.args)
+        self.assertEqual(
+            ("Level 2 file should have only one histogram",), cm.exception.args
+        )
 
     def test_create_glows_l3a_from_dictionary(self):
-        with open(get_test_data_path("glows/imap_glows_l3a_20130908085214_orbX_modX_p_v00.json")) as f:
+        with open(
+            get_test_data_path(
+                "glows/imap_glows_l3a_20130908085214_orbX_modX_p_v00.json"
+            )
+        ) as f:
             data = json.load(f)
             expected_spin_delta = 3
-            data["daily_lightcurve"]['spin_angle_delta'] = np.full_like(data['daily_lightcurve']['spin_angle'],
-                                                                        expected_spin_delta)
-            input_metadata = InputMetadata(instrument='glows', data_level='l3a',
-                                           descriptor='hist', start_date=datetime(2013, 9, 8),
-                                           end_date=datetime(2013, 9, 9), version="v003", repointing=sentinel.repoint)
+            data["daily_lightcurve"]["spin_angle_delta"] = np.full_like(
+                data["daily_lightcurve"]["spin_angle"], expected_spin_delta
+            )
+            input_metadata = InputMetadata(
+                instrument="glows",
+                data_level="l3a",
+                descriptor="hist",
+                start_date=datetime(2013, 9, 8),
+                end_date=datetime(2013, 9, 9),
+                version="v003",
+                repointing=sentinel.repoint,
+            )
 
             result = create_glows_l3a_from_dictionary(data, input_metadata)
 
@@ -130,7 +220,7 @@ def test_create_glows_l3a_from_dictionary(self):
             self.assertEqual(498484, result.raw_histogram[0, 0])
 
             self.assertEqual((1, 90), result.exposure_times.shape)
-            self.assertEqual(8.028e+02, result.exposure_times[0, 0])
+            self.assertEqual(8.028e02, result.exposure_times[0, 0])
 
             self.assertEqual(1, len(result.epoch_delta))
             self.assertEqual(72360 / 2 * 1e9, result.epoch_delta[0])
@@ -188,11 +278,11 @@ def test_create_glows_l3a_from_dictionary(self):
             self.assertEqual(result.epoch.shape, result.number_of_bins.shape)
 
             self.assertEqual(-27.84, result.filter_temperature_average[0])
-            self.assertEqual(0.000e+00, result.filter_temperature_std_dev[0])
+            self.assertEqual(0.000e00, result.filter_temperature_std_dev[0])
             self.assertEqual(1527.1, result.hv_voltage_average[0])
-            self.assertEqual(8.795e+01, result.hv_voltage_std_dev[0])
+            self.assertEqual(8.795e01, result.hv_voltage_std_dev[0])
             self.assertEqual(15.0000000, result.spin_period_average[0])
-            self.assertEqual(0.000e+00, result.spin_period_std_dev[0])
+            self.assertEqual(0.000e00, result.spin_period_std_dev[0])
             self.assertEqual(15.2366823, result.spin_period_ground_average[0])
             self.assertEqual(1.498e-03, result.spin_period_ground_std_dev[0])
             self.assertEqual(2.990e-01, result.pulse_length_average[0])
@@ -201,77 +291,177 @@ def test_create_glows_l3a_from_dictionary(self):
             self.assertEqual(9.991e-03, result.position_angle_offset_std_dev[0])
             np.testing.assert_array_equal(65, result.number_of_bins[0])
 
-            np.testing.assert_array_equal([162.092, 0.000], result.spin_axis_orientation_average[0])
-            np.testing.assert_array_equal([2.345e-01, 0.000e+00], result.spin_axis_orientation_std_dev[0])
-            np.testing.assert_array_equal([146231106.1, -36212054.4, 1049.3], result.spacecraft_location_average[0])
-            np.testing.assert_array_equal([1.421e+05, 6.001e+05, 4.082e+01], result.spacecraft_location_std_dev[0])
-            np.testing.assert_array_equal([6.669, 28.805, -0.002], result.spacecraft_velocity_average[0])
-            np.testing.assert_array_equal([1.188e-01, 2.961e-02, 4.337e-19], result.spacecraft_velocity_std_dev[0])
+            np.testing.assert_array_equal(
+                [162.092, 0.000], result.spin_axis_orientation_average[0]
+            )
+            np.testing.assert_array_equal(
+                [2.345e-01, 0.000e00], result.spin_axis_orientation_std_dev[0]
+            )
+            np.testing.assert_array_equal(
+                [146231106.1, -36212054.4, 1049.3],
+                result.spacecraft_location_average[0],
+            )
+            np.testing.assert_array_equal(
+                [1.421e05, 6.001e05, 4.082e01], result.spacecraft_location_std_dev[0]
+            )
+            np.testing.assert_array_equal(
+                [6.669, 28.805, -0.002], result.spacecraft_velocity_average[0]
+            )
+            np.testing.assert_array_equal(
+                [1.188e-01, 2.961e-02, 4.337e-19], result.spacecraft_velocity_std_dev[0]
+            )
 
     def test_create_glows_l3a_dictionary_from_cdf(self):
         cdf_path = get_test_data_path("glows") / "imap_glows_l3a_hist_20100101_v001.cdf"
         actual_dictionary = create_glows_l3a_dictionary_from_cdf(cdf_path)
 
-        self.assertEqual("imap_glows_l3a_hist_20100101_v001.cdf", actual_dictionary["filename"])
-        self.assertEqual(datetime(2013, 9, 8, 8, 52, 14).strftime("%Y-%m-%d %H:%M:%S"),
-                         actual_dictionary['start_time'])
-        self.assertEqual(datetime(2013, 9, 9, 4, 58, 14).strftime("%Y-%m-%d %H:%M:%S"), actual_dictionary['end_time'])
-
-        self.assertEqual(74.87028793594668, actual_dictionary['daily_lightcurve']['ecliptic_lat'][0])
-        self.assertEqual(74.87028793594668, actual_dictionary['daily_lightcurve']['ecliptic_lat'][-1])
-        self.assertEqual(154.67118387992855, actual_dictionary['daily_lightcurve']['ecliptic_lon'][0])
-        self.assertEqual(169.51280659858702, actual_dictionary['daily_lightcurve']['ecliptic_lon'][-1])
-        self.assertEqual(802.8, actual_dictionary['daily_lightcurve']['exposure_times'][0])
-        self.assertEqual(80.28, actual_dictionary['daily_lightcurve']['exposure_times'][2])
-        self.assertAlmostEqual(620.93173891, actual_dictionary['daily_lightcurve']['photon_flux'][0])
-        self.assertAlmostEqual(635.46586946, actual_dictionary['daily_lightcurve']['photon_flux'][-1])
-        self.assertAlmostEqual(0.87946437, actual_dictionary['daily_lightcurve']['flux_uncertainties'][0])
-        self.assertAlmostEqual(0.88969763, actual_dictionary['daily_lightcurve']['flux_uncertainties'][-1])
-        self.assertAlmostEqual(7.20014416e-04, actual_dictionary['daily_lightcurve']['extra_heliospheric_bckgrd'][0])
-        self.assertAlmostEqual(3.66514655e-04, actual_dictionary['daily_lightcurve']['extra_heliospheric_bckgrd'][-1])
-        self.assertEqual(0, actual_dictionary['daily_lightcurve']['time_dependent_bckgrd'][0])
-        self.assertEqual(1.0000e-02, actual_dictionary['daily_lightcurve']['time_dependent_bckgrd'][10])
-        self.assertEqual(2, actual_dictionary['daily_lightcurve']['spin_angle'][0])
-        self.assertEqual(358, actual_dictionary['daily_lightcurve']['spin_angle'][-1])
-        self.assertEqual(498484, actual_dictionary['daily_lightcurve']['raw_histogram'][0])
-        self.assertEqual(510152, actual_dictionary['daily_lightcurve']['raw_histogram'][-1])
-        self.assertEqual(65, actual_dictionary['daily_lightcurve']['number_of_bins'])
-
-        self.assertEqual(-27.84000015258789, actual_dictionary['filter_temperature_average'])
-        self.assertEqual(0.0, actual_dictionary['filter_temperature_std_dev'])
-        self.assertEqual(1527.0999755859375, actual_dictionary['hv_voltage_average'])
-        self.assertEqual(87.94999694824219, actual_dictionary['hv_voltage_std_dev'])
-        self.assertEqual(15.0, actual_dictionary['spin_period_average'])
-        self.assertEqual(0.0, actual_dictionary['spin_period_std_dev'])
-        self.assertEqual(15.236681938171387, actual_dictionary['spin_period_ground_average'])
-        self.assertEqual(0.0014979999978095293, actual_dictionary['spin_period_ground_std_dev'])
-        self.assertEqual(0.29899999499320984, actual_dictionary['pulse_length_average'])
-        self.assertEqual(0.017260000109672546, actual_dictionary['pulse_length_std_dev'])
-
-        self.assertEqual(91.5780029296875, actual_dictionary['position_angle_offset_average'])
-        self.assertEqual(0.009991000406444073, actual_dictionary['position_angle_offset_std_dev'])
-
-        self.assertAlmostEqual(162.09199524, actual_dictionary['spin_axis_orientation_average']['lon'])
-        self.assertAlmostEqual(0.0, actual_dictionary['spin_axis_orientation_average']['lat'])
-        self.assertAlmostEqual(0.23450001, actual_dictionary['spin_axis_orientation_std_dev']['lon'])
-        self.assertAlmostEqual(0.0, actual_dictionary['spin_axis_orientation_std_dev']['lat'])
-
-        self.assertAlmostEqual(1.46231104e+08, actual_dictionary['spacecraft_location_average']['x'])
-        self.assertAlmostEqual(-3.62120560e+07, actual_dictionary['spacecraft_location_average']['y'])
-        self.assertAlmostEqual(1049.300048828125, actual_dictionary['spacecraft_location_average']['z'])
-
-        self.assertAlmostEqual(1.42100000e+05, actual_dictionary['spacecraft_location_std_dev']['x'])
-        self.assertAlmostEqual(6.00100000e+05, actual_dictionary['spacecraft_location_std_dev']['y'])
-        self.assertAlmostEqual(4.08199997e+01, actual_dictionary['spacecraft_location_std_dev']['z'])
-
-        self.assertAlmostEqual(6.66900015e+00, actual_dictionary['spacecraft_velocity_average']['x'])
-        self.assertAlmostEqual(2.88050003e+01, actual_dictionary['spacecraft_velocity_average']['y'])
-        self.assertAlmostEqual(-2.00000009e-03, actual_dictionary['spacecraft_velocity_average']['z'])
-
-        self.assertAlmostEqual(1.18799999e-01, actual_dictionary['spacecraft_velocity_std_dev']['x'])
-        self.assertAlmostEqual(2.96100006e-02, actual_dictionary['spacecraft_velocity_std_dev']['y'])
-        self.assertAlmostEqual(4.33699998e-19, actual_dictionary['spacecraft_velocity_std_dev']['z'])
-
-
-if __name__ == '__main__':
+        self.assertEqual(
+            "imap_glows_l3a_hist_20100101_v001.cdf", actual_dictionary["filename"]
+        )
+        self.assertEqual(
+            datetime(2013, 9, 8, 8, 52, 14).strftime("%Y-%m-%d %H:%M:%S"),
+            actual_dictionary["start_time"],
+        )
+        self.assertEqual(
+            datetime(2013, 9, 9, 4, 58, 14).strftime("%Y-%m-%d %H:%M:%S"),
+            actual_dictionary["end_time"],
+        )
+
+        self.assertEqual(
+            74.87028793594668, actual_dictionary["daily_lightcurve"]["ecliptic_lat"][0]
+        )
+        self.assertEqual(
+            74.87028793594668, actual_dictionary["daily_lightcurve"]["ecliptic_lat"][-1]
+        )
+        self.assertEqual(
+            154.67118387992855, actual_dictionary["daily_lightcurve"]["ecliptic_lon"][0]
+        )
+        self.assertEqual(
+            169.51280659858702,
+            actual_dictionary["daily_lightcurve"]["ecliptic_lon"][-1],
+        )
+        self.assertEqual(
+            802.8, actual_dictionary["daily_lightcurve"]["exposure_times"][0]
+        )
+        self.assertEqual(
+            80.28, actual_dictionary["daily_lightcurve"]["exposure_times"][2]
+        )
+        self.assertAlmostEqual(
+            620.93173891, actual_dictionary["daily_lightcurve"]["photon_flux"][0]
+        )
+        self.assertAlmostEqual(
+            635.46586946, actual_dictionary["daily_lightcurve"]["photon_flux"][-1]
+        )
+        self.assertAlmostEqual(
+            0.87946437, actual_dictionary["daily_lightcurve"]["flux_uncertainties"][0]
+        )
+        self.assertAlmostEqual(
+            0.88969763, actual_dictionary["daily_lightcurve"]["flux_uncertainties"][-1]
+        )
+        self.assertAlmostEqual(
+            7.20014416e-04,
+            actual_dictionary["daily_lightcurve"]["extra_heliospheric_bckgrd"][0],
+        )
+        self.assertAlmostEqual(
+            3.66514655e-04,
+            actual_dictionary["daily_lightcurve"]["extra_heliospheric_bckgrd"][-1],
+        )
+        self.assertEqual(
+            0, actual_dictionary["daily_lightcurve"]["time_dependent_bckgrd"][0]
+        )
+        self.assertEqual(
+            1.0000e-02,
+            actual_dictionary["daily_lightcurve"]["time_dependent_bckgrd"][10],
+        )
+        self.assertEqual(2, actual_dictionary["daily_lightcurve"]["spin_angle"][0])
+        self.assertEqual(358, actual_dictionary["daily_lightcurve"]["spin_angle"][-1])
+        self.assertEqual(
+            498484, actual_dictionary["daily_lightcurve"]["raw_histogram"][0]
+        )
+        self.assertEqual(
+            510152, actual_dictionary["daily_lightcurve"]["raw_histogram"][-1]
+        )
+        self.assertEqual(65, actual_dictionary["daily_lightcurve"]["number_of_bins"])
+
+        self.assertEqual(
+            -27.84000015258789, actual_dictionary["filter_temperature_average"]
+        )
+        self.assertEqual(0.0, actual_dictionary["filter_temperature_std_dev"])
+        self.assertEqual(1527.0999755859375, actual_dictionary["hv_voltage_average"])
+        self.assertEqual(87.94999694824219, actual_dictionary["hv_voltage_std_dev"])
+        self.assertEqual(15.0, actual_dictionary["spin_period_average"])
+        self.assertEqual(0.0, actual_dictionary["spin_period_std_dev"])
+        self.assertEqual(
+            15.236681938171387, actual_dictionary["spin_period_ground_average"]
+        )
+        self.assertEqual(
+            0.0014979999978095293, actual_dictionary["spin_period_ground_std_dev"]
+        )
+        self.assertEqual(0.29899999499320984, actual_dictionary["pulse_length_average"])
+        self.assertEqual(
+            0.017260000109672546, actual_dictionary["pulse_length_std_dev"]
+        )
+
+        self.assertEqual(
+            91.5780029296875, actual_dictionary["position_angle_offset_average"]
+        )
+        self.assertEqual(
+            0.009991000406444073, actual_dictionary["position_angle_offset_std_dev"]
+        )
+
+        self.assertAlmostEqual(
+            162.09199524, actual_dictionary["spin_axis_orientation_average"]["lon"]
+        )
+        self.assertAlmostEqual(
+            0.0, actual_dictionary["spin_axis_orientation_average"]["lat"]
+        )
+        self.assertAlmostEqual(
+            0.23450001, actual_dictionary["spin_axis_orientation_std_dev"]["lon"]
+        )
+        self.assertAlmostEqual(
+            0.0, actual_dictionary["spin_axis_orientation_std_dev"]["lat"]
+        )
+
+        self.assertAlmostEqual(
+            1.46231104e08, actual_dictionary["spacecraft_location_average"]["x"]
+        )
+        self.assertAlmostEqual(
+            -3.62120560e07, actual_dictionary["spacecraft_location_average"]["y"]
+        )
+        self.assertAlmostEqual(
+            1049.300048828125, actual_dictionary["spacecraft_location_average"]["z"]
+        )
+
+        self.assertAlmostEqual(
+            1.42100000e05, actual_dictionary["spacecraft_location_std_dev"]["x"]
+        )
+        self.assertAlmostEqual(
+            6.00100000e05, actual_dictionary["spacecraft_location_std_dev"]["y"]
+        )
+        self.assertAlmostEqual(
+            4.08199997e01, actual_dictionary["spacecraft_location_std_dev"]["z"]
+        )
+
+        self.assertAlmostEqual(
+            6.66900015e00, actual_dictionary["spacecraft_velocity_average"]["x"]
+        )
+        self.assertAlmostEqual(
+            2.88050003e01, actual_dictionary["spacecraft_velocity_average"]["y"]
+        )
+        self.assertAlmostEqual(
+            -2.00000009e-03, actual_dictionary["spacecraft_velocity_average"]["z"]
+        )
+
+        self.assertAlmostEqual(
+            1.18799999e-01, actual_dictionary["spacecraft_velocity_std_dev"]["x"]
+        )
+        self.assertAlmostEqual(
+            2.96100006e-02, actual_dictionary["spacecraft_velocity_std_dev"]["y"]
+        )
+        self.assertAlmostEqual(
+            4.33699998e-19, actual_dictionary["spacecraft_velocity_std_dev"]["z"]
+        )
+
+
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/glows/l3bc/l3bc_toolkit/test_funcs.py b/tests/glows/l3bc/l3bc_toolkit/test_funcs.py
index 77229ac19..4486cf8da 100644
--- a/tests/glows/l3bc/l3bc_toolkit/test_funcs.py
+++ b/tests/glows/l3bc/l3bc_toolkit/test_funcs.py
@@ -5,12 +5,11 @@
 from imap_l3_processing.glows.l3bc.l3bc_toolkit.funcs import process_omni_param
 from imap_l3_processing.glows.quality_flags import NOMINAL_ALPHA_PROTON_RATIO_VALUE
 
-
 ALPHA_PARAM_SETTINGS = {
     "column_numbers": (0, 1, 2, 6, 9),
     "gap_marker": 9.999,
     "scale": False,
-    "const_if_empty": True
+    "const_if_empty": True,
 }
 
 
@@ -38,7 +37,9 @@ def test_returns_zero_substitution_for_all_valid_alpha(self):
         np.testing.assert_array_equal(used_nominal_per_cr, np.array([False, False]))
         np.testing.assert_array_almost_equal(averaged, np.array([0.05, 0.06]))
 
-    def test_filters_invalid_samples_and_does_not_flag_cr_when_some_samples_remain(self):
+    def test_filters_invalid_samples_and_does_not_flag_cr_when_some_samples_remain(
+        self,
+    ):
         omni_raw = np.array(
             [
                 _row(2000, 1, 0, 0.04),
@@ -79,7 +80,12 @@ def test_fills_trailing_missing_crs_with_nominal_value(self):
         np.testing.assert_array_almost_equal(
             averaged,
             np.array(
-                [0.05, 0.06, (NOMINAL_ALPHA_PROTON_RATIO_VALUE + 0.06) / 2, NOMINAL_ALPHA_PROTON_RATIO_VALUE]
+                [
+                    0.05,
+                    0.06,
+                    (NOMINAL_ALPHA_PROTON_RATIO_VALUE + 0.06) / 2,
+                    NOMINAL_ALPHA_PROTON_RATIO_VALUE,
+                ]
             ),
         )
 
@@ -118,7 +124,9 @@ def test_smooths_interior_cr_gap_via_interpolation_and_does_not_flag(self):
             omni_raw, cr_grid, ALPHA_PARAM_SETTINGS
         )
 
-        np.testing.assert_array_equal(used_nominal_per_cr, np.array([False, False, False]))
+        np.testing.assert_array_equal(
+            used_nominal_per_cr, np.array([False, False, False])
+        )
         np.testing.assert_array_almost_equal(averaged, np.array([0.05, 0.06, 0.07]))
 
 
diff --git a/tests/glows/l3bc/science/test_filter_out_bad_days.py b/tests/glows/l3bc/science/test_filter_out_bad_days.py
index 2f012866d..e588aa477 100644
--- a/tests/glows/l3bc/science/test_filter_out_bad_days.py
+++ b/tests/glows/l3bc/science/test_filter_out_bad_days.py
@@ -16,7 +16,7 @@ def test_filter_l3a_files(self):
             create_l3a_dict("2010-01-19 00:00:00", "2010-01-20 00:00:00"),
             create_l3a_dict("2010-01-30 00:00:00", "2010-01-30 07:28:00"),
             create_l3a_dict("2010-01-30 12:00:00", "2010-01-30 21:00:00"),
-            create_l3a_dict("2010-01-30 19:00:00", "2010-01-30 23:59:00")
+            create_l3a_dict("2010-01-30 19:00:00", "2010-01-30 23:59:00"),
         ]
 
         expected_filtered_list = [
@@ -28,13 +28,17 @@ def test_filter_l3a_files(self):
             create_l3a_dict("2010-01-30 12:00:00", "2010-01-30 21:00:00"),
         ]
 
-        filtered_list = filter_l3a_files(l3a_data=l3a_data, bad_day_list_path=get_test_data_path(
-            "glows") / "imap_glows_bad-days-list_v001.dat", cr=2092)
+        filtered_list = filter_l3a_files(
+            l3a_data=l3a_data,
+            bad_day_list_path=get_test_data_path("glows")
+            / "imap_glows_bad-days-list_v001.dat",
+            cr=2092,
+        )
         self.assertEqual(expected_filtered_list, filtered_list)
 
 
 def create_l3a_dict(start_date: str, end_date: str) -> dict:
     return {
-        'start_time': start_date,
-        'end_time': end_date,
+        "start_time": start_date,
+        "end_time": end_date,
     }
diff --git a/tests/glows/l3bc/science/test_generate_l3bc.py b/tests/glows/l3bc/science/test_generate_l3bc.py
index e77d3f18a..f6201a0f1 100644
--- a/tests/glows/l3bc/science/test_generate_l3bc.py
+++ b/tests/glows/l3bc/science/test_generate_l3bc.py
@@ -6,32 +6,53 @@
 from imap_data_access.file_validation import Version
 
 from imap_l3_processing.glows.l3a.utils import create_glows_l3a_dictionary_from_cdf
-from imap_l3_processing.glows.l3bc.cannot_process_carrington_rotation_error import CannotProcessCarringtonRotationError
+from imap_l3_processing.glows.l3bc.cannot_process_carrington_rotation_error import (
+    CannotProcessCarringtonRotationError,
+)
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
 from imap_l3_processing.glows.l3bc.science.generate_l3bc import generate_l3bc
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
-from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path, assert_dict_close
+from tests.test_helpers import (
+    assert_dict_close,
+    get_test_data_path,
+    get_test_instrument_team_data_path,
+)
 
 
 class TestGenerateL3BC(TestCase):
     def test_generate_l3bc_integration(self):
         cr = 2091
         external_files = {
-            'f107_raw_data': get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-            'omni_raw_data': get_test_instrument_team_data_path('glows/omni2_all_years.dat')
+            "f107_raw_data": get_test_instrument_team_data_path(
+                "glows/f107_fluxtable.txt"
+            ),
+            "omni_raw_data": get_test_instrument_team_data_path(
+                "glows/omni2_all_years.dat"
+            ),
         }
         ancillary_files = {
-            'uv_anisotropy': get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20250514_v002.json'),
-            'WawHelioIonMP_parameters': get_test_data_path('glows/imap_glows_WawHelioIonMP_v002.json'),
-            'bad_days_list': get_test_data_path('glows/imap_glows_bad-days-list_v001.dat'),
-            'pipeline_settings': get_test_data_path(
-                "glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json")
+            "uv_anisotropy": get_test_data_path(
+                "glows/imap_glows_uv-anisotropy-1CR_20250514_v002.json"
+            ),
+            "WawHelioIonMP_parameters": get_test_data_path(
+                "glows/imap_glows_WawHelioIonMP_v002.json"
+            ),
+            "bad_days_list": get_test_data_path(
+                "glows/imap_glows_bad-days-list_v001.dat"
+            ),
+            "pipeline_settings": get_test_data_path(
+                "glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+            ),
         }
-        l3a_data_folder_path = get_test_data_path('glows/l3a_products')
+        l3a_data_folder_path = get_test_data_path("glows/l3a_products")
         l3a_data = []
-        l3a_file_names = [f"imap_glows_l3a_hist_2010010{x}-repoint0000{x}_v001.cdf" for x in (1, 2, 3)]
+        l3a_file_names = [
+            f"imap_glows_l3a_hist_2010010{x}-repoint0000{x}_v001.cdf" for x in (1, 2, 3)
+        ]
         for name in l3a_file_names:
-            l3a_data.append(create_glows_l3a_dictionary_from_cdf(l3a_data_folder_path / name))
+            l3a_data.append(
+                create_glows_l3a_dictionary_from_cdf(l3a_data_folder_path / name)
+            )
 
         dependencies = GlowsL3BCDependencies(
             l3a_data=l3a_data,
@@ -40,16 +61,20 @@ def test_generate_l3bc_integration(self):
             carrington_rotation_number=cr,
             start_date=datetime.now(),
             end_date=datetime.now() + timedelta(days=1),
-            version=Version(1,1),
-            repointing_file_path=sentinel.repointing_file_path
+            version=Version(1, 1),
+            repointing_file_path=sentinel.repointing_file_path,
         )
 
         actual_l3b, actual_l3c = generate_l3bc(dependencies)
 
-        with open(get_test_instrument_team_data_path("glows/imap_glows_l3b_cr_2091_v00.json")) as f:
+        with open(
+            get_test_instrument_team_data_path("glows/imap_glows_l3b_cr_2091_v00.json")
+        ) as f:
             expected_l3b = json.load(f)
 
-        with open(get_test_instrument_team_data_path("glows/imap_glows_l3c_cr_2091_v00.json")) as f:
+        with open(
+            get_test_instrument_team_data_path("glows/imap_glows_l3c_cr_2091_v00.json")
+        ) as f:
             expected_l3c = json.load(f)
 
         self.assertEqual(
@@ -71,20 +96,34 @@ def test_generate_l3bc_integration(self):
     def test_generate_l3bc_integration_all_data_in_bad_season(self):
         cr = 2093
         external_files = {
-            'f107_raw_data': get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-            'omni_raw_data': get_test_instrument_team_data_path('glows/omni2_all_years.dat')
+            "f107_raw_data": get_test_instrument_team_data_path(
+                "glows/f107_fluxtable.txt"
+            ),
+            "omni_raw_data": get_test_instrument_team_data_path(
+                "glows/omni2_all_years.dat"
+            ),
         }
         ancillary_files = {
-            'uv_anisotropy': get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-            'WawHelioIonMP_parameters': get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-            'bad_days_list': get_test_data_path('glows/imap_glows_bad-days-list_v001.dat'),
-            'pipeline_settings': get_test_instrument_team_data_path(
-                'glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json')
+            "uv_anisotropy": get_test_data_path(
+                "glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"
+            ),
+            "WawHelioIonMP_parameters": get_test_data_path(
+                "glows/imap_glows_WawHelioIonMP_20100101_v002.json"
+            ),
+            "bad_days_list": get_test_data_path(
+                "glows/imap_glows_bad-days-list_v001.dat"
+            ),
+            "pipeline_settings": get_test_instrument_team_data_path(
+                "glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json"
+            ),
         }
-        l3a_data_folder_path = get_test_data_path('glows/l3a_products')
+        l3a_data_folder_path = get_test_data_path("glows/l3a_products")
         l3a_data = [
             create_glows_l3a_dictionary_from_cdf(
-                l3a_data_folder_path / 'imap_glows_l3a_hist_20100201-repoint00032_v001.cdf')]
+                l3a_data_folder_path
+                / "imap_glows_l3a_hist_20100201-repoint00032_v001.cdf"
+            )
+        ]
 
         dependencies = GlowsL3BCDependencies(
             l3a_data=l3a_data,
@@ -93,25 +132,40 @@ def test_generate_l3bc_integration_all_data_in_bad_season(self):
             carrington_rotation_number=cr,
             start_date=datetime.now(),
             end_date=datetime.now() + timedelta(days=1),
-            version=Version(1,1),
-            repointing_file_path=sentinel.repointing_file_path
+            version=Version(1, 1),
+            repointing_file_path=sentinel.repointing_file_path,
         )
 
         with self.assertRaises(CannotProcessCarringtonRotationError) as context:
             generate_l3bc(dependencies)
-        self.assertTrue("All days for Carrington Rotation are in a bad season." in str(context.exception))
+        self.assertTrue(
+            "All days for Carrington Rotation are in a bad season."
+            in str(context.exception)
+        )
 
     def test_generate_l3bc_appends_used_l3a_files_to_header(self):
         cr = 2096
         external_files = {
-            'f107_raw_data': get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-            'omni_raw_data': get_test_instrument_team_data_path('glows/omni2_all_years.dat')
+            "f107_raw_data": get_test_instrument_team_data_path(
+                "glows/f107_fluxtable.txt"
+            ),
+            "omni_raw_data": get_test_instrument_team_data_path(
+                "glows/omni2_all_years.dat"
+            ),
         }
         ancillary_files = {
-            'uv_anisotropy': get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-            'WawHelioIonMP_parameters': get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-            'bad_days_list': get_test_data_path('glows/imap_glows_bad-days-list_v001.dat'),
-            'pipeline_settings': get_test_data_path('glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json')
+            "uv_anisotropy": get_test_data_path(
+                "glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"
+            ),
+            "WawHelioIonMP_parameters": get_test_data_path(
+                "glows/imap_glows_WawHelioIonMP_20100101_v002.json"
+            ),
+            "bad_days_list": get_test_data_path(
+                "glows/imap_glows_bad-days-list_v001.dat"
+            ),
+            "pipeline_settings": get_test_data_path(
+                "glows/imap_glows_pipeline-settings-l3bcde_20250423_v001.json"
+            ),
         }
         l3a_files = [
             "imap_glows_l3a_hist_20100422-repoint00112_v012.cdf",
@@ -143,8 +197,11 @@ def test_generate_l3bc_appends_used_l3a_files_to_header(self):
             "imap_glows_l3a_hist_20100518-repoint00138_v012.cdf",
         ]
 
-        l3a_folder_path = get_test_data_path('glows/l3a_products')
-        l3a_data = [create_glows_l3a_dictionary_from_cdf(l3a_folder_path / file) for file in l3a_files]
+        l3a_folder_path = get_test_data_path("glows/l3a_products")
+        l3a_data = [
+            create_glows_l3a_dictionary_from_cdf(l3a_folder_path / file)
+            for file in l3a_files
+        ]
 
         dependencies = GlowsL3BCDependencies(
             l3a_data=l3a_data,
@@ -153,8 +210,8 @@ def test_generate_l3bc_appends_used_l3a_files_to_header(self):
             carrington_rotation_number=cr,
             start_date=datetime.now(),
             end_date=datetime.now() + timedelta(days=1),
-            version=Version(1,1),
-            repointing_file_path=sentinel.repointing_file_path
+            version=Version(1, 1),
+            repointing_file_path=sentinel.repointing_file_path,
         )
         l3b, l3c = generate_l3bc(dependencies)
 
@@ -169,4 +226,6 @@ def test_generate_l3bc_appends_used_l3a_files_to_header(self):
             "imap_glows_l3a_hist_20100518-repoint00138_v012.cdf",
         ]
 
-        self.assertEqual(expected_l3a_parent_files, l3b['header']['l3a_input_files_name'])
+        self.assertEqual(
+            expected_l3a_parent_files, l3b["header"]["l3a_input_files_name"]
+        )
diff --git a/tests/glows/l3bc/science/test_time_parsing.py b/tests/glows/l3bc/science/test_time_parsing.py
index 8d3803f51..8050ba64b 100644
--- a/tests/glows/l3bc/science/test_time_parsing.py
+++ b/tests/glows/l3bc/science/test_time_parsing.py
@@ -3,36 +3,54 @@
 import numpy as np
 from astropy.time import Time, conf
 
-from imap_l3_processing.glows.l3bc.l3bc_toolkit.funcs import time_from_yday as time_from_yday_l3bc
-from imap_l3_processing.glows.l3d.science.toolkit.funcs import time_from_yday as time_from_yday_l3d
+from imap_l3_processing.glows.l3bc.l3bc_toolkit.funcs import (
+    time_from_yday as time_from_yday_l3bc,
+)
+from imap_l3_processing.glows.l3d.science.toolkit.funcs import (
+    time_from_yday as time_from_yday_l3d,
+)
 
 
 class TestTimeParsing(unittest.TestCase):
     def test_works_with_fast_parser(self):
-        time_rows = np.array([
-            [2000.0, 5.0,1.0],
-            [2000.0, 51.0, 12.0],
-            [2000.0, 365.0, 23.0],
-        ])
+        time_rows = np.array(
+            [
+                [2000.0, 5.0, 1.0],
+                [2000.0, 51.0, 12.0],
+                [2000.0, 365.0, 23.0],
+            ]
+        )
         with conf.set_temp("use_fast_parser", "force"):
             try:
                 result_l3bc = time_from_yday_l3bc(time_rows)
                 result_l3d = time_from_yday_l3d(time_rows)
-            except ValueError as e:
-                self.fail('Fast parser should not crash, check formatting of date string.')
-
-        np.testing.assert_array_equal(result_l3bc, Time([
-            "2000-01-05T01:00",
-            "2000-02-20T12:00",
-            "2000-12-30T23:00",
-        ]))
-
-        np.testing.assert_array_equal(result_l3d, Time([
-            "2000-01-05T01:00",
-            "2000-02-20T12:00",
-            "2000-12-30T23:00",
-        ]))
-
-
-if __name__ == '__main__':
+            except ValueError:
+                self.fail(
+                    "Fast parser should not crash, check formatting of date string."
+                )
+
+        np.testing.assert_array_equal(
+            result_l3bc,
+            Time(
+                [
+                    "2000-01-05T01:00",
+                    "2000-02-20T12:00",
+                    "2000-12-30T23:00",
+                ]
+            ),
+        )
+
+        np.testing.assert_array_equal(
+            result_l3d,
+            Time(
+                [
+                    "2000-01-05T01:00",
+                    "2000-02-20T12:00",
+                    "2000-12-30T23:00",
+                ]
+            ),
+        )
+
+
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/glows/l3bc/test_dependency_validator.py b/tests/glows/l3bc/test_dependency_validator.py
index d6c3cf2db..ccc3e3ed8 100644
--- a/tests/glows/l3bc/test_dependency_validator.py
+++ b/tests/glows/l3bc/test_dependency_validator.py
@@ -2,17 +2,21 @@
 from datetime import datetime, timedelta
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from unittest.mock import patch, Mock
-
-from astropy.time import Time, TimeDelta
-
-from imap_l3_processing.glows.l3bc.dependency_validator import validate_omni2_dependency, \
-    validate_dependencies, validate_f107_fluxtable_dependency, validate_lyman_alpha_dependency
+from unittest.mock import Mock, patch
+
+from imap_l3_processing.glows.l3bc.dependency_validator import (
+    validate_dependencies,
+    validate_f107_fluxtable_dependency,
+    validate_lyman_alpha_dependency,
+    validate_omni2_dependency,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestDependencyValidator(unittest.TestCase):
-    def test_validate_omni2_dependency_returns_true_when_alpha_fill_but_density_and_speed_valid(self):
+    def test_validate_omni2_dependency_returns_true_when_alpha_fill_but_density_and_speed_valid(
+        self,
+    ):
         with TemporaryDirectory() as tmp:
             omni_path = Path(tmp) / "omni2_slice.txt"
             row = ["0"] * 35
@@ -52,22 +56,33 @@ def test_validate_omni2_dependency(self):
 
         for name, cr_start_date, cr_end_date_exclusive, expected in test_cases:
             with self.subTest(name):
-                actual = validate_omni2_dependency(file_path=file_path,
-                                                   cr_start_date=cr_start_date,
-                                                   cr_end_date_exclusive=cr_end_date_exclusive)
+                actual = validate_omni2_dependency(
+                    file_path=file_path,
+                    cr_start_date=cr_start_date,
+                    cr_end_date_exclusive=cr_end_date_exclusive,
+                )
                 self.assertEqual(expected, actual)
 
-    @patch("imap_l3_processing.glows.l3bc.dependency_validator.validate_lyman_alpha_dependency")
-    @patch("imap_l3_processing.glows.l3bc.dependency_validator.validate_f107_fluxtable_dependency")
-    @patch("imap_l3_processing.glows.l3bc.dependency_validator.validate_omni2_dependency")
-    def test_validate_dependencies(self, mock_validate_omni2_dependency: Mock,
-                                   mock_validate_f107_fluxtable_dependency: Mock,
-                                   mock_validate_lyman_alpha_dependency: Mock):
+    @patch(
+        "imap_l3_processing.glows.l3bc.dependency_validator.validate_lyman_alpha_dependency"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.dependency_validator.validate_f107_fluxtable_dependency"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.dependency_validator.validate_omni2_dependency"
+    )
+    def test_validate_dependencies(
+        self,
+        mock_validate_omni2_dependency: Mock,
+        mock_validate_f107_fluxtable_dependency: Mock,
+        mock_validate_lyman_alpha_dependency: Mock,
+    ):
         test_cases = [
             ("happy case", True, True, True, True),
             ("omni validation fails", False, True, True, False),
             ("flux validation fails", True, False, True, False),
-            ("lyman validation fails", True, True, False, False)
+            ("lyman validation fails", True, True, False, False),
         ]
         cr_start_date = datetime(1994, 7, 10)
         cr_end_date = datetime(1994, 7, 17)
@@ -76,24 +91,41 @@ def test_validate_dependencies(self, mock_validate_omni2_dependency: Mock,
         fluxtable_file_path = Path("fluxtable path")
         lyman_alpha_file_path = Path("fluxtable path")
 
-        for case_name, omni2_validation, flux_validation, lyman_alpha_validation, expected in test_cases:
+        for (
+            case_name,
+            omni2_validation,
+            flux_validation,
+            lyman_alpha_validation,
+            expected,
+        ) in test_cases:
             with self.subTest(case_name):
                 mock_validate_omni2_dependency.return_value = omni2_validation
                 mock_validate_f107_fluxtable_dependency.return_value = flux_validation
-                mock_validate_lyman_alpha_dependency.return_value = lyman_alpha_validation
+                mock_validate_lyman_alpha_dependency.return_value = (
+                    lyman_alpha_validation
+                )
 
-                actual = validate_dependencies(cr_start_date, cr_end_date, buffer, omni_file_path,
-                                               fluxtable_file_path, lyman_alpha_file_path)
+                actual = validate_dependencies(
+                    cr_start_date,
+                    cr_end_date,
+                    buffer,
+                    omni_file_path,
+                    fluxtable_file_path,
+                    lyman_alpha_file_path,
+                )
                 self.assertEqual(expected, actual)
 
-                mock_validate_omni2_dependency.assert_called_once_with(cr_start_date, cr_end_date,
-                                                                       omni_file_path)
+                mock_validate_omni2_dependency.assert_called_once_with(
+                    cr_start_date, cr_end_date, omni_file_path
+                )
 
-                mock_validate_f107_fluxtable_dependency.assert_called_once_with(cr_end_date, buffer,
-                                                                                fluxtable_file_path)
+                mock_validate_f107_fluxtable_dependency.assert_called_once_with(
+                    cr_end_date, buffer, fluxtable_file_path
+                )
 
-                mock_validate_lyman_alpha_dependency.assert_called_once_with(cr_end_date, buffer,
-                                                                             lyman_alpha_file_path)
+                mock_validate_lyman_alpha_dependency.assert_called_once_with(
+                    cr_end_date, buffer, lyman_alpha_file_path
+                )
                 mock_validate_omni2_dependency.reset_mock()
                 mock_validate_f107_fluxtable_dependency.reset_mock()
                 mock_validate_lyman_alpha_dependency.reset_mock()
@@ -106,19 +138,21 @@ def test_validate_f107_dependency(self):
         ]
         for name, end_date, buffer, expected in test_cases:
             with self.subTest(name):
-                is_validated = validate_f107_fluxtable_dependency(file_path=file_path,
-                                                                  end_date=end_date,
-                                                                  buffer=buffer)
+                is_validated = validate_f107_fluxtable_dependency(
+                    file_path=file_path, end_date=end_date, buffer=buffer
+                )
 
                 self.assertEqual(expected, is_validated)
 
     def test_validate_lyman_alpha_dependency(self):
         file_path = get_test_data_path("glows/lyman_alpha_composite.nc")
         test_cases = [
-            ("happy case", datetime(2025,3,16), timedelta(4), True),
-            ("not enough data end", datetime(2025,3,18), timedelta(4), False)
+            ("happy case", datetime(2025, 3, 16), timedelta(4), True),
+            ("not enough data end", datetime(2025, 3, 18), timedelta(4), False),
         ]
         for name, end_date, buffer, expected in test_cases:
             with self.subTest(name):
-                is_validated = validate_lyman_alpha_dependency(end_date=end_date, buffer=buffer, file_path=file_path)
+                is_validated = validate_lyman_alpha_dependency(
+                    end_date=end_date, buffer=buffer, file_path=file_path
+                )
                 self.assertEqual(expected, is_validated)
diff --git a/tests/glows/l3bc/test_glows_l3bc_dependencies.py b/tests/glows/l3bc/test_glows_l3bc_dependencies.py
index 533557bb6..a323bd749 100644
--- a/tests/glows/l3bc/test_glows_l3bc_dependencies.py
+++ b/tests/glows/l3bc/test_glows_l3bc_dependencies.py
@@ -1,7 +1,7 @@
 import unittest
 from datetime import datetime
 from pathlib import Path
-from unittest.mock import patch, sentinel, call
+from unittest.mock import call, patch, sentinel
 
 from imap_l3_processing.constants import TEMP_CDF_FOLDER_PATH
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
@@ -9,10 +9,15 @@
 
 
 class TestGlowsL3BCDependencies(unittest.TestCase):
-
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_dependencies.create_glows_l3a_dictionary_from_cdf')
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_dependencies.imap_data_access.download')
-    def test_download_from_cr_to_process(self, mock_download, mock_create_dictionary_from_cdf):
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_dependencies.create_glows_l3a_dictionary_from_cdf"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_dependencies.imap_data_access.download"
+    )
+    def test_download_from_cr_to_process(
+        self, mock_download, mock_create_dictionary_from_cdf
+    ):
         mock_download.side_effect = [
             sentinel.uv_anisotropy_downloaded_path,
             sentinel.waw_downloaded_path,
@@ -40,35 +45,64 @@ def test_download_from_cr_to_process(self, mock_download, mock_create_dictionary
         )
 
         external_dependencies = ExternalDependencies(
-            f107_index_file_path=TEMP_CDF_FOLDER_PATH / 'f107_fluxtable.txt',
-            omni2_data_path=TEMP_CDF_FOLDER_PATH / 'omni2_all_years.dat',
+            f107_index_file_path=TEMP_CDF_FOLDER_PATH / "f107_fluxtable.txt",
+            omni2_data_path=TEMP_CDF_FOLDER_PATH / "omni2_all_years.dat",
             lyman_alpha_path=Path("not used"),
         )
 
-        dependency: GlowsL3BCDependencies = GlowsL3BCDependencies.download_from_cr_to_process(cr_to_process,
-                                                                                              sentinel.version,
-                                                                                              external_dependencies,
-                                                                                              repointing_file_path)
+        dependency: GlowsL3BCDependencies = (
+            GlowsL3BCDependencies.download_from_cr_to_process(
+                cr_to_process,
+                sentinel.version,
+                external_dependencies,
+                repointing_file_path,
+            )
+        )
 
-        self.assertEqual([call(sentinel.l3a_downloaded_path_1), call(sentinel.l3a_downloaded_path_2)],
-                         mock_create_dictionary_from_cdf.call_args_list)
+        self.assertEqual(
+            [
+                call(sentinel.l3a_downloaded_path_1),
+                call(sentinel.l3a_downloaded_path_2),
+            ],
+            mock_create_dictionary_from_cdf.call_args_list,
+        )
 
-        mock_download.assert_has_calls([
-            call("uv_anisotropy_path"),
-            call("waw_path"),
-            call("bad_days_list_path"),
-            call("pipeline_settings_path"),
-            call("l3a_path_1"),
-            call("l3a_path_2")
-        ])
+        mock_download.assert_has_calls(
+            [
+                call("uv_anisotropy_path"),
+                call("waw_path"),
+                call("bad_days_list_path"),
+                call("pipeline_settings_path"),
+                call("l3a_path_1"),
+                call("l3a_path_2"),
+            ]
+        )
 
-        self.assertEqual(TEMP_CDF_FOLDER_PATH / 'f107_fluxtable.txt', dependency.external_files['f107_raw_data'])
-        self.assertEqual(TEMP_CDF_FOLDER_PATH / 'omni2_all_years.dat', dependency.external_files['omni_raw_data'])
+        self.assertEqual(
+            TEMP_CDF_FOLDER_PATH / "f107_fluxtable.txt",
+            dependency.external_files["f107_raw_data"],
+        )
+        self.assertEqual(
+            TEMP_CDF_FOLDER_PATH / "omni2_all_years.dat",
+            dependency.external_files["omni_raw_data"],
+        )
 
-        self.assertEqual(sentinel.uv_anisotropy_downloaded_path, dependency.ancillary_files['uv_anisotropy'])
-        self.assertEqual(sentinel.waw_downloaded_path, dependency.ancillary_files['WawHelioIonMP_parameters'])
-        self.assertEqual(sentinel.bad_day_list_downloaded_path, dependency.ancillary_files['bad_days_list'])
-        self.assertEqual(sentinel.settings_downloaded_path, dependency.ancillary_files['pipeline_settings'])
+        self.assertEqual(
+            sentinel.uv_anisotropy_downloaded_path,
+            dependency.ancillary_files["uv_anisotropy"],
+        )
+        self.assertEqual(
+            sentinel.waw_downloaded_path,
+            dependency.ancillary_files["WawHelioIonMP_parameters"],
+        )
+        self.assertEqual(
+            sentinel.bad_day_list_downloaded_path,
+            dependency.ancillary_files["bad_days_list"],
+        )
+        self.assertEqual(
+            sentinel.settings_downloaded_path,
+            dependency.ancillary_files["pipeline_settings"],
+        )
 
         self.assertEqual(sentinel.l3a_dictionary_1, dependency.l3a_data[0])
         self.assertEqual(sentinel.l3a_dictionary_2, dependency.l3a_data[1])
diff --git a/tests/glows/l3bc/test_glows_l3bc_initializer.py b/tests/glows/l3bc/test_glows_l3bc_initializer.py
index a132de81c..e944fbdc3 100644
--- a/tests/glows/l3bc/test_glows_l3bc_initializer.py
+++ b/tests/glows/l3bc/test_glows_l3bc_initializer.py
@@ -2,12 +2,15 @@
 import unittest
 from datetime import datetime
 from pathlib import Path
-from unittest.mock import Mock, patch, call, sentinel
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access import ProcessingInputCollection, RepointInput
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.glows.l3bc.glows_l3bc_initializer import GlowsL3BCInitializer, GlowsL3BCInitializerData
+from imap_l3_processing.glows.l3bc.glows_l3bc_initializer import (
+    GlowsL3BCInitializer,
+    GlowsL3BCInitializerData,
+)
 from imap_l3_processing.glows.l3bc.models import CRToProcess, ExternalDependencies
 from tests.test_helpers import create_mock_query_results, get_test_data_path
 
@@ -15,15 +18,20 @@
 class TestGlowsL3BCInitializer(unittest.TestCase):
     def setUp(self):
         self.external_dependencies_fetch_patcher = patch(
-            "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.ExternalDependencies.fetch_dependencies")
-        self.mock_external_dependencies_fetch = self.external_dependencies_fetch_patcher.start()
+            "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.ExternalDependencies.fetch_dependencies"
+        )
+        self.mock_external_dependencies_fetch = (
+            self.external_dependencies_fetch_patcher.start()
+        )
 
         self.download_patcher = patch(
-            "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download")
+            "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download"
+        )
         self.download_patcher.start()
 
         self.set_global_repoint_table_paths_patcher = patch(
-            'imap_l3_processing.glows.l3bc.glows_l3bc_initializer.set_global_repoint_table_paths')
+            "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.set_global_repoint_table_paths"
+        )
         self.set_global_repoint_table_paths_patcher.start()
 
     def tearDown(self):
@@ -31,41 +39,70 @@ def tearDown(self):
         self.download_patcher.stop()
         self.set_global_repoint_table_paths_patcher.stop()
 
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_initializer.set_global_repoint_table_paths')
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCDependencies.download_from_cr_to_process')
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.ExternalDependencies.fetch_dependencies")
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.set_global_repoint_table_paths"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCDependencies.download_from_cr_to_process"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.ExternalDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.get_date_range_of_cr")
     @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.get_best_ancillary")
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.query")
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.group_l3a_by_cr")
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.should_process_cr_candidate")
-    def test_get_crs_to_process(self, mock_should_process_cr_candidate, mock_group_l3a_by_cr, mock_query,
-                                mock_get_best_ancillary, mock_get_date_range_of_cr, mock_fetch_external_deps,
-                                mock_l3bc_deps_from_cr, mock_set_global_repoint_table_paths, mock_download):
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.query"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.group_l3a_by_cr"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.should_process_cr_candidate"
+    )
+    def test_get_crs_to_process(
+        self,
+        mock_should_process_cr_candidate,
+        mock_group_l3a_by_cr,
+        mock_query,
+        mock_get_best_ancillary,
+        mock_get_date_range_of_cr,
+        mock_fetch_external_deps,
+        mock_l3bc_deps_from_cr,
+        mock_set_global_repoint_table_paths,
+        mock_download,
+    ):
 
         mock_query.side_effect = [
-            create_mock_query_results([
-                "imap_glows_l3a_hist_20100101_v001.cdf",
-                "imap_glows_l3a_hist_20100201_v001.cdf"
-            ]),
-            create_mock_query_results([
-                "imap_glows_l3b_ion-rate-profile_20100101-cr02091_v001.cdf",
-                "imap_glows_l3b_ion-rate-profile_20100201-cr02092_v001.cdf"
-            ]),
-            create_mock_query_results([
-                "imap_glows_l3c_sw-profile_20100101-cr02091_v001.cdf",
-                "imap_glows_l3c_sw-profile_20100201-cr02092_v001.cdf"
-            ]),
+            create_mock_query_results(
+                [
+                    "imap_glows_l3a_hist_20100101_v001.cdf",
+                    "imap_glows_l3a_hist_20100201_v001.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    "imap_glows_l3b_ion-rate-profile_20100101-cr02091_v001.cdf",
+                    "imap_glows_l3b_ion-rate-profile_20100201-cr02092_v001.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    "imap_glows_l3c_sw-profile_20100101-cr02091_v001.cdf",
+                    "imap_glows_l3c_sw-profile_20100201-cr02092_v001.cdf",
+                ]
+            ),
             sentinel.uv_anisotropy_query_result,
             sentinel.waw_helio_ion_query_result,
             sentinel.bad_days_list_query_result,
-            sentinel.pipeline_settings_query_result
+            sentinel.pipeline_settings_query_result,
         ]
 
         mock_group_l3a_by_cr.return_value = {
             2091: {"imap_glows_l3a_hist_20100101_v001.cdf"},
-            2092: {"imap_glows_l3a_hist_20100201_v001.cdf"}
+            2092: {"imap_glows_l3a_hist_20100201_v001.cdf"},
         }
 
         mock_get_date_range_of_cr.side_effect = [
@@ -88,51 +125,114 @@ def test_get_crs_to_process(self, mock_should_process_cr_candidate, mock_group_l
 
         expected_l3bs_by_cr = {
             2091: "imap_glows_l3b_ion-rate-profile_20100101-cr02091_v001.cdf",
-            2092: "imap_glows_l3b_ion-rate-profile_20100201-cr02092_v001.cdf"
+            2092: "imap_glows_l3b_ion-rate-profile_20100201-cr02092_v001.cdf",
         }
 
         expected_l3cs_by_cr = {
             2091: "imap_glows_l3c_sw-profile_20100101-cr02091_v001.cdf",
-            2092: "imap_glows_l3c_sw-profile_20100201-cr02092_v001.cdf"
+            2092: "imap_glows_l3c_sw-profile_20100201-cr02092_v001.cdf",
         }
 
         major_version_to_process = 3
 
         l3bc_initializer_data = GlowsL3BCInitializer.get_crs_to_process(
             ProcessingInputCollection(RepointInput("imap_2026_269_05.repoint.csv")),
-            major_version_to_process
+            major_version_to_process,
         )
 
         mock_download.assert_called_once()
-        mock_set_global_repoint_table_paths.assert_called_with([mock_download.return_value])
-
-        mock_query.assert_has_calls([
-            call(instrument="glows", data_level="l3a", descriptor="hist", version="latest"),
-            call(instrument="glows", data_level="l3b", descriptor='ion-rate-profile', version="latest"),
-            call(instrument="glows", data_level="l3c", descriptor='sw-profile', version="latest"),
-            call(table="ancillary", instrument="glows", descriptor="uv-anisotropy-1CR"),
-            call(table="ancillary", instrument="glows", descriptor="WawHelioIonMP"),
-            call(table="ancillary", instrument="glows", descriptor="bad-days-list"),
-            call(table="ancillary", instrument="glows", descriptor="pipeline-settings-l3bcde"),
-        ])
-
-        mock_group_l3a_by_cr.assert_called_once_with([
-            "imap_glows_l3a_hist_20100101_v001.cdf",
-            "imap_glows_l3a_hist_20100201_v001.cdf",
-        ])
+        mock_set_global_repoint_table_paths.assert_called_with(
+            [mock_download.return_value]
+        )
+
+        mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3a",
+                    descriptor="hist",
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3b",
+                    descriptor="ion-rate-profile",
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3c",
+                    descriptor="sw-profile",
+                    version="latest",
+                ),
+                call(
+                    table="ancillary",
+                    instrument="glows",
+                    descriptor="uv-anisotropy-1CR",
+                ),
+                call(table="ancillary", instrument="glows", descriptor="WawHelioIonMP"),
+                call(table="ancillary", instrument="glows", descriptor="bad-days-list"),
+                call(
+                    table="ancillary",
+                    instrument="glows",
+                    descriptor="pipeline-settings-l3bcde",
+                ),
+            ]
+        )
+
+        mock_group_l3a_by_cr.assert_called_once_with(
+            [
+                "imap_glows_l3a_hist_20100101_v001.cdf",
+                "imap_glows_l3a_hist_20100201_v001.cdf",
+            ]
+        )
 
         mock_fetch_external_deps.assert_called_once()
 
-        mock_get_best_ancillary.assert_has_calls([
-            call(datetime(2010, 1, 1), datetime(2010, 1, 31), sentinel.uv_anisotropy_query_result),
-            call(datetime(2010, 1, 1), datetime(2010, 1, 31), sentinel.waw_helio_ion_query_result),
-            call(datetime(2010, 1, 1), datetime(2010, 1, 31), sentinel.bad_days_list_query_result),
-            call(datetime(2010, 1, 1), datetime(2010, 1, 31), sentinel.pipeline_settings_query_result),
-            call(datetime(2010, 2, 1), datetime(2010, 2, 28), sentinel.uv_anisotropy_query_result),
-            call(datetime(2010, 2, 1), datetime(2010, 2, 28), sentinel.waw_helio_ion_query_result),
-            call(datetime(2010, 2, 1), datetime(2010, 2, 28), sentinel.bad_days_list_query_result),
-            call(datetime(2010, 2, 1), datetime(2010, 2, 28), sentinel.pipeline_settings_query_result),
-        ])
+        mock_get_best_ancillary.assert_has_calls(
+            [
+                call(
+                    datetime(2010, 1, 1),
+                    datetime(2010, 1, 31),
+                    sentinel.uv_anisotropy_query_result,
+                ),
+                call(
+                    datetime(2010, 1, 1),
+                    datetime(2010, 1, 31),
+                    sentinel.waw_helio_ion_query_result,
+                ),
+                call(
+                    datetime(2010, 1, 1),
+                    datetime(2010, 1, 31),
+                    sentinel.bad_days_list_query_result,
+                ),
+                call(
+                    datetime(2010, 1, 1),
+                    datetime(2010, 1, 31),
+                    sentinel.pipeline_settings_query_result,
+                ),
+                call(
+                    datetime(2010, 2, 1),
+                    datetime(2010, 2, 28),
+                    sentinel.uv_anisotropy_query_result,
+                ),
+                call(
+                    datetime(2010, 2, 1),
+                    datetime(2010, 2, 28),
+                    sentinel.waw_helio_ion_query_result,
+                ),
+                call(
+                    datetime(2010, 2, 1),
+                    datetime(2010, 2, 28),
+                    sentinel.bad_days_list_query_result,
+                ),
+                call(
+                    datetime(2010, 2, 1),
+                    datetime(2010, 2, 28),
+                    sentinel.pipeline_settings_query_result,
+                ),
+            ]
+        )
 
         expected_cr_candidate_1 = CRToProcess(
             l3a_file_names={"imap_glows_l3a_hist_20100101_v001.cdf"},
@@ -140,7 +240,8 @@ def test_get_crs_to_process(self, mock_should_process_cr_candidate, mock_group_l
             waw_helio_ion_mp_file_name="waw_helio_ion_best_ancillary_1",
             bad_days_list_file_name="bad_days_list_best_ancillary_1",
             pipeline_settings_file_name="pipeline_settings_best_ancillary_1",
-            cr_start_date=datetime(2010, 1, 1), cr_end_date=datetime(2010, 1, 31),
+            cr_start_date=datetime(2010, 1, 1),
+            cr_end_date=datetime(2010, 1, 31),
             cr_rotation_number=2091,
         )
 
@@ -150,18 +251,35 @@ def test_get_crs_to_process(self, mock_should_process_cr_candidate, mock_group_l
             waw_helio_ion_mp_file_name="waw_helio_ion_best_ancillary_2",
             bad_days_list_file_name="bad_days_list_best_ancillary_2",
             pipeline_settings_file_name="pipeline_settings_best_ancillary_2",
-            cr_start_date=datetime(2010, 2, 1), cr_end_date=datetime(2010, 2, 28),
+            cr_start_date=datetime(2010, 2, 1),
+            cr_end_date=datetime(2010, 2, 28),
             cr_rotation_number=2092,
         )
 
-        mock_should_process_cr_candidate.assert_has_calls([
-            call(expected_cr_candidate_1, expected_l3bs_by_cr, mock_fetch_external_deps.return_value, major_version_to_process),
-            call(expected_cr_candidate_2, expected_l3bs_by_cr, mock_fetch_external_deps.return_value, major_version_to_process)
-        ], any_order=False)
+        mock_should_process_cr_candidate.assert_has_calls(
+            [
+                call(
+                    expected_cr_candidate_1,
+                    expected_l3bs_by_cr,
+                    mock_fetch_external_deps.return_value,
+                    major_version_to_process,
+                ),
+                call(
+                    expected_cr_candidate_2,
+                    expected_l3bs_by_cr,
+                    mock_fetch_external_deps.return_value,
+                    major_version_to_process,
+                ),
+            ],
+            any_order=False,
+        )
 
-        mock_l3bc_deps_from_cr.assert_called_once_with(expected_cr_candidate_1, 2,
-                                                       mock_fetch_external_deps.return_value,
-                                                       mock_download.return_value)
+        mock_l3bc_deps_from_cr.assert_called_once_with(
+            expected_cr_candidate_1,
+            2,
+            mock_fetch_external_deps.return_value,
+            mock_download.return_value,
+        )
 
         expected_initializer_data = GlowsL3BCInitializerData(
             external_dependencies=mock_fetch_external_deps.return_value,
@@ -173,25 +291,42 @@ def test_get_crs_to_process(self, mock_should_process_cr_candidate, mock_group_l
 
         self.assertEqual(expected_initializer_data, l3bc_initializer_data)
 
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download')
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.group_l3a_by_cr")
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.query")
-    def test_get_crs_to_process_return_no_crs_when_missing_ancillaries(self, mock_query, mock_group_l3a_by_cr,
-                                                                       mock_download):
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.group_l3a_by_cr"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.query"
+    )
+    def test_get_crs_to_process_return_no_crs_when_missing_ancillaries(
+        self, mock_query, mock_group_l3a_by_cr, mock_download
+    ):
 
         test_cases = [
             "uv-anisotropy-1CR",
             "WawHelioIonMP",
             "bad-days-list",
-            "pipeline-settings-l3bcde"
+            "pipeline-settings-l3bcde",
         ]
 
         for missing_ancillary in test_cases:
             with self.subTest(missing_ancillary):
+
                 def query_that_returns_empty_list_for_missing_ancillary(**kwargs):
-                    if kwargs.get("table") == "ancillary" and kwargs["descriptor"] != missing_ancillary:
-                        return [{"start_date": "20100101", "end_date": None, "ingestion_date": "20100101 00:00:00",
-                                 "file_path": f"some/ancillary/{missing_ancillary}"}]
+                    if (
+                        kwargs.get("table") == "ancillary"
+                        and kwargs["descriptor"] != missing_ancillary
+                    ):
+                        return [
+                            {
+                                "start_date": "20100101",
+                                "end_date": None,
+                                "ingestion_date": "20100101 00:00:00",
+                                "file_path": f"some/ancillary/{missing_ancillary}",
+                            }
+                        ]
                     else:
                         return []
 
@@ -199,42 +334,96 @@ def query_that_returns_empty_list_for_missing_ancillary(**kwargs):
                     2091: {"glows_l3a_hist_20100101_v001.cdf"}
                 }
 
-                mock_query.side_effect = query_that_returns_empty_list_for_missing_ancillary
+                mock_query.side_effect = (
+                    query_that_returns_empty_list_for_missing_ancillary
+                )
 
                 actual_crs_to_process = GlowsL3BCInitializer.get_crs_to_process(
-                    ProcessingInputCollection(RepointInput("imap_2026_269_05.repoint.csv")), sentinel.major_version)
-
-                mock_query.assert_has_calls([
-                    call(instrument="glows", data_level="l3a", descriptor="hist", version="latest"),
-                    call(instrument="glows", data_level="l3b", descriptor="ion-rate-profile", version="latest"),
-                    call(instrument="glows", data_level="l3c", descriptor="sw-profile", version="latest"),
-                    call(table="ancillary", instrument="glows", descriptor="uv-anisotropy-1CR"),
-                    call(table="ancillary", instrument="glows", descriptor="WawHelioIonMP"),
-                    call(table="ancillary", instrument="glows", descriptor="bad-days-list"),
-                    call(table="ancillary", instrument="glows", descriptor="pipeline-settings-l3bcde")
-                ])
+                    ProcessingInputCollection(
+                        RepointInput("imap_2026_269_05.repoint.csv")
+                    ),
+                    sentinel.major_version,
+                )
+
+                mock_query.assert_has_calls(
+                    [
+                        call(
+                            instrument="glows",
+                            data_level="l3a",
+                            descriptor="hist",
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3b",
+                            descriptor="ion-rate-profile",
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3c",
+                            descriptor="sw-profile",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="uv-anisotropy-1CR",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="WawHelioIonMP",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="bad-days-list",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="pipeline-settings-l3bcde",
+                        ),
+                    ]
+                )
 
                 expected_initializer_result = GlowsL3BCInitializerData(
                     external_dependencies=self.mock_external_dependencies_fetch.return_value,
                     l3bc_dependencies=[],
                     l3bs_by_cr={},
                     l3cs_by_cr={},
-                    repoint_file_path=mock_download.return_value
+                    repoint_file_path=mock_download.return_value,
                 )
 
                 self.assertEqual(expected_initializer_result, actual_crs_to_process)
 
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.should_process_cr_candidate",
-           return_value=True)
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.should_process_cr_candidate",
+        return_value=True,
+    )
     @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.get_best_ancillary")
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download')
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.query")
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.ExternalDependencies.fetch_dependencies")
-    @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.group_l3a_by_cr")
-    def test_get_crs_to_process_returns_no_crs_when_missing_external_deps(self, mock_group_l3a_by_cr,
-                                                                          mock_fetch_ext_dependencies, mock_query,
-                                                                          mock_download, ___,
-                                                                          ____):
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.query"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.ExternalDependencies.fetch_dependencies"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.GlowsL3BCInitializer.group_l3a_by_cr"
+    )
+    def test_get_crs_to_process_returns_no_crs_when_missing_external_deps(
+        self,
+        mock_group_l3a_by_cr,
+        mock_fetch_ext_dependencies,
+        mock_query,
+        mock_download,
+        ___,
+        ____,
+    ):
         test_cases = [
             ExternalDependencies(None, Path("some_lyman"), Path("some_omni")),
             ExternalDependencies(Path("some_f107"), None, Path("some_omni")),
@@ -246,37 +435,94 @@ def test_get_crs_to_process_returns_no_crs_when_missing_external_deps(self, mock
                 mock_query.reset_mock()
 
                 mock_fetch_ext_dependencies.return_value = external_deps
-                mock_group_l3a_by_cr.return_value = {2091: {"glows_l3a_hist_20100101_v001.cdf"}}
+                mock_group_l3a_by_cr.return_value = {
+                    2091: {"glows_l3a_hist_20100101_v001.cdf"}
+                }
 
                 mock_query.side_effect = [
-                    create_mock_query_results(["imap_glows_l3a_hist_20100101_v001.cdf"]),
-                    create_mock_query_results(["imap_glows_l3b_ion-rate-profile_20100101-cr00001_v001.cdf"]),
-                    create_mock_query_results(["imap_glows_l3c_sw-profile_20100101-cr00001_v001.cdf"]),
-                    create_mock_query_results(["imap_glows_uv-anisotropy-1CR_20100101_v001.dat"]),
-                    create_mock_query_results(["imap_glows_WawHelioIonMP_20100101_v001.dat"]),
-                    create_mock_query_results(["imap_glows_bad-days-list_20100101_v001.dat"]),
-                    create_mock_query_results(["imap_glows_pipeline-settings-l3bcde_20100101_v003.json"])
+                    create_mock_query_results(
+                        ["imap_glows_l3a_hist_20100101_v001.cdf"]
+                    ),
+                    create_mock_query_results(
+                        ["imap_glows_l3b_ion-rate-profile_20100101-cr00001_v001.cdf"]
+                    ),
+                    create_mock_query_results(
+                        ["imap_glows_l3c_sw-profile_20100101-cr00001_v001.cdf"]
+                    ),
+                    create_mock_query_results(
+                        ["imap_glows_uv-anisotropy-1CR_20100101_v001.dat"]
+                    ),
+                    create_mock_query_results(
+                        ["imap_glows_WawHelioIonMP_20100101_v001.dat"]
+                    ),
+                    create_mock_query_results(
+                        ["imap_glows_bad-days-list_20100101_v001.dat"]
+                    ),
+                    create_mock_query_results(
+                        ["imap_glows_pipeline-settings-l3bcde_20100101_v003.json"]
+                    ),
                 ]
 
                 actual_crs_to_process = GlowsL3BCInitializer.get_crs_to_process(
-                    ProcessingInputCollection(RepointInput("imap_2026_269_05.repoint.csv")), sentinel.major_version)
-
-                mock_query.assert_has_calls([
-                    call(instrument="glows", data_level="l3a", descriptor="hist", version="latest"),
-                    call(instrument="glows", data_level="l3b", descriptor="ion-rate-profile", version="latest"),
-                    call(instrument="glows", data_level="l3c", descriptor="sw-profile", version="latest"),
-                    call(table="ancillary", instrument="glows", descriptor="uv-anisotropy-1CR"),
-                    call(table="ancillary", instrument="glows", descriptor="WawHelioIonMP"),
-                    call(table="ancillary", instrument="glows", descriptor="bad-days-list"),
-                    call(table="ancillary", instrument="glows", descriptor="pipeline-settings-l3bcde"),
-                ])
+                    ProcessingInputCollection(
+                        RepointInput("imap_2026_269_05.repoint.csv")
+                    ),
+                    sentinel.major_version,
+                )
+
+                mock_query.assert_has_calls(
+                    [
+                        call(
+                            instrument="glows",
+                            data_level="l3a",
+                            descriptor="hist",
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3b",
+                            descriptor="ion-rate-profile",
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3c",
+                            descriptor="sw-profile",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="uv-anisotropy-1CR",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="WawHelioIonMP",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="bad-days-list",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="pipeline-settings-l3bcde",
+                        ),
+                    ]
+                )
 
                 expected_initializer_result = GlowsL3BCInitializerData(
                     external_dependencies=external_deps,
                     l3bc_dependencies=[],
-                    l3bs_by_cr={1: "imap_glows_l3b_ion-rate-profile_20100101-cr00001_v001.cdf"},
-                    l3cs_by_cr={1: "imap_glows_l3c_sw-profile_20100101-cr00001_v001.cdf"},
-                    repoint_file_path=mock_download.return_value
+                    l3bs_by_cr={
+                        1: "imap_glows_l3b_ion-rate-profile_20100101-cr00001_v001.cdf"
+                    },
+                    l3cs_by_cr={
+                        1: "imap_glows_l3c_sw-profile_20100101-cr00001_v001.cdf"
+                    },
+                    repoint_file_path=mock_download.return_value,
                 )
 
                 self.assertEqual(expected_initializer_result, actual_crs_to_process)
@@ -291,7 +537,6 @@ def test_should_process_cr_excludes_crs_within_buffer_period(self):
             cr_start_date=datetime(2012, 2, 6, 3, 36, 31, 680000),
             cr_end_date=datetime(2012, 3, 4, 10, 12, 57, 600000),
             cr_rotation_number=2120,
-
         )
         cr_candidate.buffer_time_has_elapsed_since_cr = Mock(return_value=False)
         cr_candidate.has_valid_external_dependencies = Mock(return_value=True)
@@ -302,7 +547,11 @@ def test_should_process_cr_excludes_crs_within_buffer_period(self):
             omni2_data_path=Path("omni2_data_path"),
         )
 
-        self.assertIsNone(GlowsL3BCInitializer.should_process_cr_candidate(cr_candidate, {}, external_dependencies, 1))
+        self.assertIsNone(
+            GlowsL3BCInitializer.should_process_cr_candidate(
+                cr_candidate, {}, external_dependencies, 1
+            )
+        )
 
     def test_should_process_cr_no_existing_l3b(self):
 
@@ -314,7 +563,7 @@ def test_should_process_cr_no_existing_l3b(self):
             pipeline_settings_file_name="pipeline_settings.json",
             cr_start_date=datetime(2010, 1, 1),
             cr_end_date=datetime(2010, 2, 1),
-            cr_rotation_number=2091
+            cr_rotation_number=2091,
         )
         cr_candidate.buffer_time_has_elapsed_since_cr = Mock(return_value=True)
         cr_candidate.has_valid_external_dependencies = Mock(return_value=True)
@@ -325,7 +574,9 @@ def test_should_process_cr_no_existing_l3b(self):
             omni2_data_path=Path("omni2_data_path"),
         )
 
-        actual_version = GlowsL3BCInitializer.should_process_cr_candidate(cr_candidate, {}, external_dependencies, major_version=2)
+        actual_version = GlowsL3BCInitializer.should_process_cr_candidate(
+            cr_candidate, {}, external_dependencies, major_version=2
+        )
 
         self.assertEqual(Version(2, 1), actual_version)
 
@@ -335,7 +586,7 @@ def test_should_process_cr_existing_cr_should_process(self, mock_read_cdf_parent
         new_l3a_version = {
             "l3a_file_names": {
                 "imap_glows_l3a_hist_20100101-repoint000001_v001.cdf",
-                "imap_glows_l3a_hist_20100102-repoint000002_v002.cdf"
+                "imap_glows_l3a_hist_20100102-repoint000002_v002.cdf",
             }
         }
 
@@ -343,19 +594,17 @@ def test_should_process_cr_existing_cr_should_process(self, mock_read_cdf_parent
             "l3a_file_names": {
                 "imap_glows_l3a_hist_20100101-repoint000001_v001.cdf",
                 "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf"
-                "imap_glows_l3a_hist_20100103-repoint000002_v001.cdf"
+                "imap_glows_l3a_hist_20100103-repoint000002_v001.cdf",
             }
         }
 
-        new_ancillary = {
-            "uv_anisotropy_file_name": "new_uv_anisotropy.dat"
-        }
+        new_ancillary = {"uv_anisotropy_file_name": "new_uv_anisotropy.dat"}
 
         test_cases = [
             ("new l3a file version", new_l3a_version, 2),
             ("new l3a files arrived", new_l3a_file, 2),
             ("new ancillary file arrived", new_ancillary, 2),
-            ("new major version", {}, 3)
+            ("new major version", {}, 3),
         ]
 
         for name, new_server_data, new_major_version in test_cases:
@@ -368,7 +617,7 @@ def test_should_process_cr_existing_cr_should_process(self, mock_read_cdf_parent
                 already_processed_cr = CRToProcess(
                     l3a_file_names={
                         "imap_glows_l3a_hist_20100101-repoint000001_v001.cdf",
-                        "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf"
+                        "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf",
                     },
                     uv_anisotropy_file_name="uv_anisotropy.dat",
                     waw_helio_ion_mp_file_name="waw_helio_ion.dat",
@@ -385,7 +634,9 @@ def test_should_process_cr_existing_cr_should_process(self, mock_read_cdf_parent
                     omni2_data_path=Path("omni2_data_path"),
                 )
 
-                cr_candidate = dataclasses.replace(already_processed_cr, **new_server_data)
+                cr_candidate = dataclasses.replace(
+                    already_processed_cr, **new_server_data
+                )
                 cr_candidate.buffer_time_has_elapsed_since_cr = Mock(return_value=True)
                 cr_candidate.has_valid_external_dependencies = Mock(return_value=True)
 
@@ -396,26 +647,36 @@ def test_should_process_cr_existing_cr_should_process(self, mock_read_cdf_parent
                     "waw_helio_ion.dat",
                     "bad_days_list.dat",
                     "pipeline_settings.json",
-                    "some_zip_file.zip"
+                    "some_zip_file.zip",
                 }
 
-                existing_file_version = Version(existing_file_major_version, existing_file_minor_version)
-                l3bs = {2091: f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_file_version}.cdf"}
-                actual_version = GlowsL3BCInitializer.should_process_cr_candidate(cr_candidate, l3bs,
-                                                                                  external_dependencies, new_major_version)
-                expected_version = Version(new_major_version, existing_file_minor_version + 1)
+                existing_file_version = Version(
+                    existing_file_major_version, existing_file_minor_version
+                )
+                l3bs = {
+                    2091: f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_file_version}.cdf"
+                }
+                actual_version = GlowsL3BCInitializer.should_process_cr_candidate(
+                    cr_candidate, l3bs, external_dependencies, new_major_version
+                )
+                expected_version = Version(
+                    new_major_version, existing_file_minor_version + 1
+                )
 
                 mock_read_cdf_parents.assert_called_once_with(
-                    f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_file_version}.cdf")
+                    f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_file_version}.cdf"
+                )
 
                 self.assertEqual(expected_version, actual_version)
 
     @patch("imap_l3_processing.glows.l3bc.glows_l3bc_initializer.read_cdf_parents")
-    def test_should_process_cr_existing_cr_should_not_reprocess(self, mock_read_cdf_parents):
+    def test_should_process_cr_existing_cr_should_not_reprocess(
+        self, mock_read_cdf_parents
+    ):
         cr_candidate = CRToProcess(
             l3a_file_names={
                 "imap_glows_l3a_hist_20100101-repoint000001_v001.cdf",
-                "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf"
+                "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf",
             },
             uv_anisotropy_file_name="uv_anisotropy.dat",
             waw_helio_ion_mp_file_name="waw_helio_ion.dat",
@@ -428,7 +689,9 @@ def test_should_process_cr_existing_cr_should_not_reprocess(self, mock_read_cdf_
         cr_candidate.buffer_time_has_elapsed_since_cr = Mock(return_value=True)
         cr_candidate.has_valid_external_dependencies = Mock(return_value=True)
         existing_version = Version(2, 1)
-        l3bs = {2091: f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_version}.cdf"}
+        l3bs = {
+            2091: f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_version}.cdf"
+        }
 
         external_dependencies = ExternalDependencies(
             f107_index_file_path=Path("f107_index_file_path"),
@@ -443,12 +706,18 @@ def test_should_process_cr_existing_cr_should_not_reprocess(self, mock_read_cdf_
             "waw_helio_ion.dat",
             "bad_days_list.dat",
             "pipeline_settings.json",
-            "some_zip_file.zip"
+            "some_zip_file.zip",
         }
 
-        self.assertIsNone(GlowsL3BCInitializer.should_process_cr_candidate(cr_candidate, l3bs, external_dependencies, existing_version.major))
+        self.assertIsNone(
+            GlowsL3BCInitializer.should_process_cr_candidate(
+                cr_candidate, l3bs, external_dependencies, existing_version.major
+            )
+        )
 
-        mock_read_cdf_parents.assert_called_once_with(f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_version}.cdf")
+        mock_read_cdf_parents.assert_called_once_with(
+            f"imap_glows_l3b_ion-rate-profile_20100101-cr02091_{existing_version}.cdf"
+        )
 
     def test_should_process_cr_with_invalid_dependencies(self):
         cr_candidate = CRToProcess(
@@ -471,35 +740,42 @@ def test_should_process_cr_with_invalid_dependencies(self):
             omni2_data_path=Path("omni2_data_path"),
         )
 
-        self.assertIsNone(GlowsL3BCInitializer.should_process_cr_candidate(cr_candidate, {}, external_dependencies, 1))
+        self.assertIsNone(
+            GlowsL3BCInitializer.should_process_cr_candidate(
+                cr_candidate, {}, external_dependencies, 1
+            )
+        )
 
-        cr_candidate.has_valid_external_dependencies.assert_called_once_with(external_dependencies)
+        cr_candidate.has_valid_external_dependencies.assert_called_once_with(
+            external_dependencies
+        )
 
-    @patch('imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download')
+    @patch(
+        "imap_l3_processing.glows.l3bc.glows_l3bc_initializer.imap_data_access.download"
+    )
     def test_group_l3a_by_cr(self, mock_download):
-        l3a_1 = 'imap_glows_l3a_hist_20100101-repoint00001_v001.cdf'
-        l3a_2 = 'imap_glows_l3a_hist_20100102-repoint00002_v001.cdf'
-        l3a_3 = 'imap_glows_l3a_hist_20100103-repoint00003_v001.cdf'
-        l3a_4 = 'imap_glows_l3a_hist_20100104-repoint00004_v001.cdf'
+        l3a_1 = "imap_glows_l3a_hist_20100101-repoint00001_v001.cdf"
+        l3a_2 = "imap_glows_l3a_hist_20100102-repoint00002_v001.cdf"
+        l3a_3 = "imap_glows_l3a_hist_20100103-repoint00003_v001.cdf"
+        l3a_4 = "imap_glows_l3a_hist_20100104-repoint00004_v001.cdf"
 
         glows_l3a_files = [
-            get_test_data_path('glows/l3a_products') / l3a_1,
-            get_test_data_path('glows/l3a_products') / l3a_2,
-            get_test_data_path('glows/l3a_products') / l3a_3,
-            get_test_data_path('glows/l3a_products') / l3a_4,
+            get_test_data_path("glows/l3a_products") / l3a_1,
+            get_test_data_path("glows/l3a_products") / l3a_2,
+            get_test_data_path("glows/l3a_products") / l3a_3,
+            get_test_data_path("glows/l3a_products") / l3a_4,
         ]
 
         mock_download.side_effect = glows_l3a_files
 
-        expected = {
-            2091: {l3a_1, l3a_2, l3a_3},
-            2092: {l3a_4}
-        }
+        expected = {2091: {l3a_1, l3a_2, l3a_3}, 2092: {l3a_4}}
 
-        grouped_l3a_files = GlowsL3BCInitializer.group_l3a_by_cr([l3a_1, l3a_2, l3a_3, l3a_4])
+        grouped_l3a_files = GlowsL3BCInitializer.group_l3a_by_cr(
+            [l3a_1, l3a_2, l3a_3, l3a_4]
+        )
 
-        mock_download.assert_has_calls([
-            call(l3a_1), call(l3a_2), call(l3a_3), call(l3a_4)
-        ])
+        mock_download.assert_has_calls(
+            [call(l3a_1), call(l3a_2), call(l3a_3), call(l3a_4)]
+        )
 
         self.assertEqual(expected, grouped_l3a_files)
diff --git a/tests/glows/l3bc/test_models.py b/tests/glows/l3bc/test_models.py
index 4d408d576..dc2bfc8e1 100644
--- a/tests/glows/l3bc/test_models.py
+++ b/tests/glows/l3bc/test_models.py
@@ -4,23 +4,34 @@
 from datetime import datetime, timedelta
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from unittest.mock import sentinel, patch, call
+from unittest.mock import call, patch, sentinel
 
 import numpy as np
 from spacepy import pycdf
 
-from imap_l3_processing.constants import TEMP_CDF_FOLDER_PATH, CARRINGTON_ROTATION_IN_NANOSECONDS
-from imap_l3_processing.glows.l3bc.models import GlowsL3BIonizationRate, GlowsL3CSolarWind, CRToProcess, \
-    ExternalDependencies, F107_FLUX_TABLE_URL, LYMAN_ALPHA_COMPOSITE_INDEX_URL, OMNI2_URL
+from imap_l3_processing.constants import (
+    CARRINGTON_ROTATION_IN_NANOSECONDS,
+    TEMP_CDF_FOLDER_PATH,
+)
+from imap_l3_processing.glows.l3bc.models import (
+    F107_FLUX_TABLE_URL,
+    LYMAN_ALPHA_COMPOSITE_INDEX_URL,
+    OMNI2_URL,
+    CRToProcess,
+    ExternalDependencies,
+    GlowsL3BIonizationRate,
+    GlowsL3CSolarWind,
+)
 from tests.swapi.cdf_model_test_case import CdfModelTestCase
 from tests.test_helpers import get_test_instrument_team_data_path
 
 
 class TestModels(CdfModelTestCase):
-
     @patch("imap_l3_processing.glows.l3bc.models.ExternalDependencies._comment_headers")
     @patch("imap_l3_processing.glows.l3bc.models.download_external_dependency")
-    def test_external_dependencies_fetch_dependencies(self, mock_download_external_dependencies, mock_comment_header):
+    def test_external_dependencies_fetch_dependencies(
+        self, mock_download_external_dependencies, mock_comment_header
+    ):
         mock_download_external_dependencies.side_effect = [
             Path("external/file/f107_path"),
             Path("external/file/lya_path"),
@@ -29,11 +40,16 @@ def test_external_dependencies_fetch_dependencies(self, mock_download_external_d
 
         external_dependencies = ExternalDependencies.fetch_dependencies()
 
-        mock_download_external_dependencies.assert_has_calls([
-            call(F107_FLUX_TABLE_URL, TEMP_CDF_FOLDER_PATH / 'f107_fluxtable.txt'),
-            call(LYMAN_ALPHA_COMPOSITE_INDEX_URL, TEMP_CDF_FOLDER_PATH / 'lyman_alpha.txt'),
-            call(OMNI2_URL, TEMP_CDF_FOLDER_PATH / 'omni2.txt')
-        ])
+        mock_download_external_dependencies.assert_has_calls(
+            [
+                call(F107_FLUX_TABLE_URL, TEMP_CDF_FOLDER_PATH / "f107_fluxtable.txt"),
+                call(
+                    LYMAN_ALPHA_COMPOSITE_INDEX_URL,
+                    TEMP_CDF_FOLDER_PATH / "lyman_alpha.txt",
+                ),
+                call(OMNI2_URL, TEMP_CDF_FOLDER_PATH / "omni2.txt"),
+            ]
+        )
 
         mock_comment_header.assert_called_once_with(Path("external/file/f107_path"))
 
@@ -45,7 +61,9 @@ def test_external_dependencies_fetch_dependencies(self, mock_download_external_d
         self.assertEqual(expected_external_deps, external_dependencies)
 
     @patch("imap_l3_processing.glows.l3bc.models.download_external_dependency")
-    def test_external_dependencies_files_not_found(self, mock_download_external_dependencies):
+    def test_external_dependencies_files_not_found(
+        self, mock_download_external_dependencies
+    ):
         mock_download_external_dependencies.return_value = None
 
         external_dependencies = ExternalDependencies.fetch_dependencies()
@@ -53,21 +71,22 @@ def test_external_dependencies_files_not_found(self, mock_download_external_depe
         self.assertEqual(ExternalDependencies(None, None, None), external_dependencies)
 
     @patch("imap_l3_processing.glows.l3bc.models.download_external_dependency")
-    def test_external_dependencies_comment_headers(self, mock_download_external_dependencies):
+    def test_external_dependencies_comment_headers(
+        self, mock_download_external_dependencies
+    ):
         with tempfile.TemporaryDirectory() as tmpdir:
             file_name = Path(tmpdir) / "flux_table.txt"
             with open(file_name, "w") as fp:
-                fp.writelines([
-                    "fluxdate    fluxtime    fluxjulian    fluxcarrington  fluxobsflux  fluxadjflux  fluxursi\n",
-                    "----------  ----------  ------------  --------------  -----------  -----------  ----------\n",
-                    "20041028    170000      02453307.229  002022.605      000132.7     000130.9     000117.8\n",
-                    "20041028    200000      02453307.354  002022.610      000135.8     000134.0     000120.6\n"])
-
-            mock_download_external_dependencies.side_effect = [
-                file_name,
-                None,
-                None
-            ]
+                fp.writelines(
+                    [
+                        "fluxdate    fluxtime    fluxjulian    fluxcarrington  fluxobsflux  fluxadjflux  fluxursi\n",
+                        "----------  ----------  ------------  --------------  -----------  -----------  ----------\n",
+                        "20041028    170000      02453307.229  002022.605      000132.7     000130.9     000117.8\n",
+                        "20041028    200000      02453307.354  002022.610      000135.8     000134.0     000120.6\n",
+                    ]
+                )
+
+            mock_download_external_dependencies.side_effect = [file_name, None, None]
 
             ExternalDependencies.fetch_dependencies()
 
@@ -78,7 +97,9 @@ def test_external_dependencies_comment_headers(self, mock_download_external_depe
 
     @patch("imap_l3_processing.glows.l3bc.models.datetime")
     @patch("imap_l3_processing.glows.l3bc.models.imap_data_access.download")
-    def test_cr_to_process_buffer_time_has_elapsed_since_cr(self, mock_download, mock_datetime):
+    def test_cr_to_process_buffer_time_has_elapsed_since_cr(
+        self, mock_download, mock_datetime
+    ):
         with TemporaryDirectory() as temp_dir:
             temp_dir = Path(temp_dir)
             fake_pipeline_settings = temp_dir / "fake_pipeline_settings.json"
@@ -99,10 +120,7 @@ def test_cr_to_process_buffer_time_has_elapsed_since_cr(self, mock_download, moc
                 cr_rotation_number=2091,
             )
 
-            test_cases = [
-                (datetime(2010, 2, 28), False),
-                (datetime(2010, 3, 10), True)
-            ]
+            test_cases = [(datetime(2010, 2, 28), False), (datetime(2010, 3, 10), True)]
 
             for current_time, expected_buffer_time_has_elapsed_since_cr in test_cases:
                 with self.subTest(current_time):
@@ -118,7 +136,9 @@ def test_cr_to_process_buffer_time_has_elapsed_since_cr(self, mock_download, moc
 
     @patch("imap_l3_processing.glows.l3bc.models.validate_dependencies")
     @patch("imap_l3_processing.glows.l3bc.models.imap_data_access.download")
-    def test_cr_to_process_has_valid_external_dependencies(self, mock_download, mock_validate_dependencies):
+    def test_cr_to_process_has_valid_external_dependencies(
+        self, mock_download, mock_validate_dependencies
+    ):
         with TemporaryDirectory() as temp_dir:
             temp_dir = Path(temp_dir)
             fake_pipeline_settings = temp_dir / "fake_pipeline_settings.json"
@@ -136,16 +156,18 @@ def test_cr_to_process_has_valid_external_dependencies(self, mock_download, mock
                 pipeline_settings_file_name="pipeline_settings.json",
                 cr_start_date=datetime(2010, 1, 1),
                 cr_end_date=datetime(2010, 2, 1),
-                cr_rotation_number=2091
+                cr_rotation_number=2091,
             )
 
             external_dependencies = ExternalDependencies(
                 f107_index_file_path=Path("f107_index_file_path"),
                 lyman_alpha_path=Path("lyman_alpha_path"),
-                omni2_data_path=Path("omni2_data_path")
+                omni2_data_path=Path("omni2_data_path"),
             )
 
-            actual_has_valid_ext_deps = cr_to_process.has_valid_external_dependencies(external_dependencies)
+            actual_has_valid_ext_deps = cr_to_process.has_valid_external_dependencies(
+                external_dependencies
+            )
 
             mock_validate_dependencies.assert_called_once_with(
                 datetime(2010, 1, 1),
@@ -155,191 +177,372 @@ def test_cr_to_process_has_valid_external_dependencies(self, mock_download, mock
                 Path("f107_index_file_path"),
                 Path("lyman_alpha_path"),
             )
-            self.assertEqual(mock_validate_dependencies.return_value, actual_has_valid_ext_deps)
+            self.assertEqual(
+                mock_validate_dependencies.return_value, actual_has_valid_ext_deps
+            )
 
     def test_l3b_to_data_product_variables(self):
-        data = GlowsL3BIonizationRate(input_metadata=sentinel.input_metadata,
-                                      epoch=sentinel.epoch,
-                                      epoch_delta=sentinel.epoch_delta,
-                                      mean_time=sentinel.mean_time,
-                                      cr=sentinel.cr,
-                                      uv_anisotropy_factor=sentinel.uv_anisotropy_factor,
-                                      lat_grid=sentinel.lat_grid,
-                                      sum_rate=sentinel.sum_rate,
-                                      ph_rate=sentinel.ph_rate,
-                                      cx_rate=sentinel.cx_rate,
-                                      sum_uncert=sentinel.sum_uncert,
-                                      ph_uncert=sentinel.ph_uncert,
-                                      cx_uncert=sentinel.cx_uncert,
-                                      lat_grid_label=sentinel.lat_grid_label,
-                                      uv_anisotropy_flag=sentinel.uv_anisotropy_flag,
-                                      used_l3a=sentinel.used_l3a,
-                                      glows_flags=sentinel.glows_flags,
-                                      )
+        data = GlowsL3BIonizationRate(
+            input_metadata=sentinel.input_metadata,
+            epoch=sentinel.epoch,
+            epoch_delta=sentinel.epoch_delta,
+            mean_time=sentinel.mean_time,
+            cr=sentinel.cr,
+            uv_anisotropy_factor=sentinel.uv_anisotropy_factor,
+            lat_grid=sentinel.lat_grid,
+            sum_rate=sentinel.sum_rate,
+            ph_rate=sentinel.ph_rate,
+            cx_rate=sentinel.cx_rate,
+            sum_uncert=sentinel.sum_uncert,
+            ph_uncert=sentinel.ph_uncert,
+            cx_uncert=sentinel.cx_uncert,
+            lat_grid_label=sentinel.lat_grid_label,
+            uv_anisotropy_flag=sentinel.uv_anisotropy_flag,
+            used_l3a=sentinel.used_l3a,
+            glows_flags=sentinel.glows_flags,
+        )
 
         variables = data.to_data_product_variables()
 
         variables = iter(variables)
         self.assert_variable_attributes(next(variables), sentinel.epoch, "epoch")
-        self.assert_variable_attributes(next(variables), sentinel.epoch_delta, "epoch_delta")
-        self.assert_variable_attributes(next(variables), sentinel.mean_time, "mean_time")
+        self.assert_variable_attributes(
+            next(variables), sentinel.epoch_delta, "epoch_delta"
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.mean_time, "mean_time"
+        )
         self.assert_variable_attributes(next(variables), sentinel.cr, "cr")
-        self.assert_variable_attributes(next(variables), sentinel.uv_anisotropy_factor, "uv_anisotropy_factor")
+        self.assert_variable_attributes(
+            next(variables), sentinel.uv_anisotropy_factor, "uv_anisotropy_factor"
+        )
         self.assert_variable_attributes(next(variables), sentinel.lat_grid, "lat_grid")
         self.assert_variable_attributes(next(variables), sentinel.sum_rate, "sum_rate")
         self.assert_variable_attributes(next(variables), sentinel.ph_rate, "ph_rate")
         self.assert_variable_attributes(next(variables), sentinel.cx_rate, "cx_rate")
-        self.assert_variable_attributes(next(variables), sentinel.sum_uncert, "sum_uncert")
-        self.assert_variable_attributes(next(variables), sentinel.ph_uncert, "ph_uncert")
-        self.assert_variable_attributes(next(variables), sentinel.cx_uncert, "cx_uncert")
-        self.assert_variable_attributes(next(variables), sentinel.lat_grid_label, "lat_grid_label")
-        self.assert_variable_attributes(next(variables), sentinel.uv_anisotropy_flag, "uv_anisotropy_flag")
+        self.assert_variable_attributes(
+            next(variables), sentinel.sum_uncert, "sum_uncert"
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.ph_uncert, "ph_uncert"
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.cx_uncert, "cx_uncert"
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.lat_grid_label, "lat_grid_label"
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.uv_anisotropy_flag, "uv_anisotropy_flag"
+        )
         self.assert_variable_attributes(next(variables), sentinel.used_l3a, "used_l3a")
-        self.assert_variable_attributes(next(variables), sentinel.glows_flags, "glows_flags")
+        self.assert_variable_attributes(
+            next(variables), sentinel.glows_flags, "glows_flags"
+        )
 
         self.assertIsNone(next(variables, None))
 
     def test_l3b_from_instrument_team_dictionary(self):
-        glows_instrument_team_data_path = get_test_instrument_team_data_path('glows')
-        with open(glows_instrument_team_data_path / 'imap_glows_l3b_cr_2091_v00.json') as f:
+        glows_instrument_team_data_path = get_test_instrument_team_data_path("glows")
+        with open(
+            glows_instrument_team_data_path / "imap_glows_l3b_cr_2091_v00.json"
+        ) as f:
             instrument_team_l3b_dict = json.load(f)
 
-        instrument_team_l3b_dict['header']['ancillary_data_files'][
-            'pipeline_settings'] = glows_instrument_team_data_path / 'imap_glows_pipeline-settings-l3bcde_20250514_v004.json'
+        instrument_team_l3b_dict["header"]["ancillary_data_files"][
+            "pipeline_settings"
+        ] = (
+            glows_instrument_team_data_path
+            / "imap_glows_pipeline-settings-l3bcde_20250514_v004.json"
+        )
 
-        result = GlowsL3BIonizationRate.from_instrument_team_dictionary(instrument_team_l3b_dict,
-                                                                        sentinel.input_metadata)
+        result = GlowsL3BIonizationRate.from_instrument_team_dictionary(
+            instrument_team_l3b_dict, sentinel.input_metadata
+        )
 
-        latitude_grid = np.array(instrument_team_l3b_dict["ion_rate_profile"]["lat_grid"])
+        latitude_grid = np.array(
+            instrument_team_l3b_dict["ion_rate_profile"]["lat_grid"]
+        )
         self.assertIsInstance(result, GlowsL3BIonizationRate)
 
         self.assertEqual(sentinel.input_metadata, result.input_metadata)
 
-        np.testing.assert_array_equal([datetime.fromisoformat("2009-12-20 20:14:51.360")], result.epoch)
-        np.testing.assert_equal([CARRINGTON_ROTATION_IN_NANOSECONDS / 2], result.epoch_delta)
-        np.testing.assert_equal([datetime.fromisoformat("2010-01-02 03:43:28.667")], result.mean_time)
+        np.testing.assert_array_equal(
+            [datetime.fromisoformat("2009-12-20 20:14:51.360")], result.epoch
+        )
+        np.testing.assert_equal(
+            [CARRINGTON_ROTATION_IN_NANOSECONDS / 2], result.epoch_delta
+        )
+        np.testing.assert_equal(
+            [datetime.fromisoformat("2010-01-02 03:43:28.667")], result.mean_time
+        )
         self.assertEqual([10000], result.uv_anisotropy_flag)
         self.assertEqual([2091], result.cr)
-        np.testing.assert_equal([instrument_team_l3b_dict["uv_anisotropy_factor"]], result.uv_anisotropy_factor)
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["uv_anisotropy_factor"]],
+            result.uv_anisotropy_factor,
+        )
         np.testing.assert_equal(latitude_grid, result.lat_grid)
-        np.testing.assert_equal([instrument_team_l3b_dict["ion_rate_profile"]["sum_rate"]], result.sum_rate)
-        np.testing.assert_equal([instrument_team_l3b_dict["ion_rate_profile"]["ph_rate"]], result.ph_rate)
-        np.testing.assert_equal([instrument_team_l3b_dict["ion_rate_profile"]["cx_rate"]], result.cx_rate)
-        np.testing.assert_equal([instrument_team_l3b_dict["ion_rate_profile"]["sum_uncert"]], result.sum_uncert)
-        np.testing.assert_equal([instrument_team_l3b_dict["ion_rate_profile"]["ph_uncert"]], result.ph_uncert)
-        np.testing.assert_equal([instrument_team_l3b_dict["ion_rate_profile"]["cx_uncert"]], result.cx_uncert)
         np.testing.assert_equal(
-            ['-90°', '-80°', '-70°', '-60°', '-50°', '-40°', '-30°', '-20°', '-10°', '0°', '10°', '20°', '30°', '40°',
-             '50°', '60°', '70°', '80°', '90°'], result.lat_grid_label)
-        self.assertEqual([
-            'imap_glows_WawHelioIonMP_v002.json',
-            'imap_glows_bad-days-list_v001.dat',
-            'imap_glows_pipeline-settings-l3bcde_20250514_v004.json',
-            'imap_glows_uv-anisotropy-1CR_v002.json',
-            'imap_glows_plasma-speed-2010a_v003.dat',
-            'imap_glows_proton-density-2010a_v003.dat',
-            'imap_glows_uv-anisotropy-2010a_v003.dat',
-            'imap_glows_photoion-2010a_v003.dat',
-            'imap_glows_lya-2010a_v003.dat',
-            'imap_glows_electron-density-2010a_v003.dat',
-            'f107_fluxtable.txt'
-        ], result.parent_file_names)
-        np.testing.assert_equal(result.used_l3a[0, :3], np.array([
-            "imap_glows_l3a_20100101000000_orbX_modX_p_v00.json",
-            "imap_glows_l3a_20100102000000_orbX_modX_p_v00.json",
-            "imap_glows_l3a_20100103000000_orbX_modX_p_v00.json"]))
+            [instrument_team_l3b_dict["ion_rate_profile"]["sum_rate"]], result.sum_rate
+        )
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["ion_rate_profile"]["ph_rate"]], result.ph_rate
+        )
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["ion_rate_profile"]["cx_rate"]], result.cx_rate
+        )
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["ion_rate_profile"]["sum_uncert"]],
+            result.sum_uncert,
+        )
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["ion_rate_profile"]["ph_uncert"]],
+            result.ph_uncert,
+        )
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["ion_rate_profile"]["cx_uncert"]],
+            result.cx_uncert,
+        )
+        np.testing.assert_equal(
+            [
+                "-90°",
+                "-80°",
+                "-70°",
+                "-60°",
+                "-50°",
+                "-40°",
+                "-30°",
+                "-20°",
+                "-10°",
+                "0°",
+                "10°",
+                "20°",
+                "30°",
+                "40°",
+                "50°",
+                "60°",
+                "70°",
+                "80°",
+                "90°",
+            ],
+            result.lat_grid_label,
+        )
+        self.assertEqual(
+            [
+                "imap_glows_WawHelioIonMP_v002.json",
+                "imap_glows_bad-days-list_v001.dat",
+                "imap_glows_pipeline-settings-l3bcde_20250514_v004.json",
+                "imap_glows_uv-anisotropy-1CR_v002.json",
+                "imap_glows_plasma-speed-2010a_v003.dat",
+                "imap_glows_proton-density-2010a_v003.dat",
+                "imap_glows_uv-anisotropy-2010a_v003.dat",
+                "imap_glows_photoion-2010a_v003.dat",
+                "imap_glows_lya-2010a_v003.dat",
+                "imap_glows_electron-density-2010a_v003.dat",
+                "f107_fluxtable.txt",
+            ],
+            result.parent_file_names,
+        )
+        np.testing.assert_equal(
+            result.used_l3a[0, :3],
+            np.array(
+                [
+                    "imap_glows_l3a_20100101000000_orbX_modX_p_v00.json",
+                    "imap_glows_l3a_20100102000000_orbX_modX_p_v00.json",
+                    "imap_glows_l3a_20100103000000_orbX_modX_p_v00.json",
+                ]
+            ),
+        )
         self.assertEqual((1, 50), result.used_l3a.shape)
         np.testing.assert_array_equal(result.used_l3a[0, 3:], "")
-        np.testing.assert_equal([instrument_team_l3b_dict["glows_flags"]], result.glows_flags)
+        np.testing.assert_equal(
+            [instrument_team_l3b_dict["glows_flags"]], result.glows_flags
+        )
 
     def test_l3c_to_data_product_variables(self):
-        data = GlowsL3CSolarWind(input_metadata=sentinel.input_metadata,
-                                 epoch=sentinel.epoch,
-                                 epoch_delta=sentinel.epoch_delta,
-                                 mean_time=sentinel.mean_time,
-                                 cr=sentinel.cr,
-                                 lat_grid=sentinel.lat_grid,
-                                 lat_grid_label=sentinel.lat_grid_label,
-                                 plasma_speed_ecliptic=sentinel.plasma_speed_ecliptic,
-                                 proton_density_ecliptic=sentinel.proton_density_ecliptic,
-                                 alpha_abundance_ecliptic=sentinel.alpha_abundance_ecliptic,
-                                 plasma_speed_profile=sentinel.plasma_speed_profile,
-                                 proton_density_profile=sentinel.proton_density_profile,
-                                 glows_flags=sentinel.glows_flags,
-                                 )
+        data = GlowsL3CSolarWind(
+            input_metadata=sentinel.input_metadata,
+            epoch=sentinel.epoch,
+            epoch_delta=sentinel.epoch_delta,
+            mean_time=sentinel.mean_time,
+            cr=sentinel.cr,
+            lat_grid=sentinel.lat_grid,
+            lat_grid_label=sentinel.lat_grid_label,
+            plasma_speed_ecliptic=sentinel.plasma_speed_ecliptic,
+            proton_density_ecliptic=sentinel.proton_density_ecliptic,
+            alpha_abundance_ecliptic=sentinel.alpha_abundance_ecliptic,
+            plasma_speed_profile=sentinel.plasma_speed_profile,
+            proton_density_profile=sentinel.proton_density_profile,
+            glows_flags=sentinel.glows_flags,
+        )
 
         variables = data.to_data_product_variables()
 
         variables = iter(variables)
-        self.assert_variable_attributes(next(variables), sentinel.epoch, "epoch",
-                                        expected_data_type=pycdf.const.CDF_TIME_TT2000)
-        self.assert_variable_attributes(next(variables), sentinel.epoch_delta, "epoch_delta",
-                                        expected_data_type=pycdf.const.CDF_INT8)
-        self.assert_variable_attributes(next(variables), sentinel.mean_time, "mean_time",
-                                        expected_data_type=pycdf.const.CDF_TIME_TT2000)
-        self.assert_variable_attributes(next(variables), sentinel.cr, "cr", expected_data_type=pycdf.const.CDF_INT2)
-        self.assert_variable_attributes(next(variables), sentinel.lat_grid, "lat_grid",
-                                        expected_data_type=pycdf.const.CDF_FLOAT, expected_record_varying=False)
-        self.assert_variable_attributes(next(variables), sentinel.lat_grid_label, "lat_grid_label",
-                                        expected_data_type=pycdf.const.CDF_CHAR, expected_record_varying=False)
-        self.assert_variable_attributes(next(variables), sentinel.plasma_speed_ecliptic, "plasma_speed_ecliptic",
-                                        expected_data_type=pycdf.const.CDF_FLOAT)
-        self.assert_variable_attributes(next(variables), sentinel.proton_density_ecliptic, "proton_density_ecliptic",
-                                        expected_data_type=pycdf.const.CDF_FLOAT)
-        self.assert_variable_attributes(next(variables), sentinel.alpha_abundance_ecliptic, "alpha_abundance_ecliptic",
-                                        expected_data_type=pycdf.const.CDF_FLOAT)
-        self.assert_variable_attributes(next(variables), sentinel.plasma_speed_profile, "plasma_speed_profile",
-                                        expected_data_type=pycdf.const.CDF_FLOAT)
-        self.assert_variable_attributes(next(variables), sentinel.proton_density_profile, "proton_density_profile",
-                                        expected_data_type=pycdf.const.CDF_FLOAT)
-        self.assert_variable_attributes(next(variables), sentinel.glows_flags, "glows_flags")
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.epoch,
+            "epoch",
+            expected_data_type=pycdf.const.CDF_TIME_TT2000,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.epoch_delta,
+            "epoch_delta",
+            expected_data_type=pycdf.const.CDF_INT8,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.mean_time,
+            "mean_time",
+            expected_data_type=pycdf.const.CDF_TIME_TT2000,
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.cr, "cr", expected_data_type=pycdf.const.CDF_INT2
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.lat_grid,
+            "lat_grid",
+            expected_data_type=pycdf.const.CDF_FLOAT,
+            expected_record_varying=False,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.lat_grid_label,
+            "lat_grid_label",
+            expected_data_type=pycdf.const.CDF_CHAR,
+            expected_record_varying=False,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.plasma_speed_ecliptic,
+            "plasma_speed_ecliptic",
+            expected_data_type=pycdf.const.CDF_FLOAT,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.proton_density_ecliptic,
+            "proton_density_ecliptic",
+            expected_data_type=pycdf.const.CDF_FLOAT,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.alpha_abundance_ecliptic,
+            "alpha_abundance_ecliptic",
+            expected_data_type=pycdf.const.CDF_FLOAT,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.plasma_speed_profile,
+            "plasma_speed_profile",
+            expected_data_type=pycdf.const.CDF_FLOAT,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.proton_density_profile,
+            "proton_density_profile",
+            expected_data_type=pycdf.const.CDF_FLOAT,
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.glows_flags, "glows_flags"
+        )
 
         self.assertIsNone(next(variables, None))
 
     def test_l3c_from_instrument_team_dictionary(self):
-        glows_instrument_team_data_path = get_test_instrument_team_data_path('glows')
-        with open(glows_instrument_team_data_path / 'imap_glows_l3c_cr_2091_v00.json') as f:
+        glows_instrument_team_data_path = get_test_instrument_team_data_path("glows")
+        with open(
+            glows_instrument_team_data_path / "imap_glows_l3c_cr_2091_v00.json"
+        ) as f:
             model = json.load(f)
-        model['header']['ancillary_data_files'][
-            'pipeline_settings'] = glows_instrument_team_data_path / 'imap_glows_pipeline-settings-L3bc_20250707_v002.json'
+        model["header"]["ancillary_data_files"]["pipeline_settings"] = (
+            glows_instrument_team_data_path
+            / "imap_glows_pipeline-settings-L3bc_20250707_v002.json"
+        )
         latitude_grid = model["solar_wind_profile"]["lat_grid"]
         result = GlowsL3CSolarWind.from_instrument_team_dictionary(
-            model, sentinel.input_metadata)
+            model, sentinel.input_metadata
+        )
 
         self.assertIsInstance(result, GlowsL3CSolarWind)
 
         self.assertEqual(sentinel.input_metadata, result.input_metadata)
-        np.testing.assert_array_equal([datetime.fromisoformat("2009-12-20 20:14:51.360")], result.epoch)
-        np.testing.assert_equal([CARRINGTON_ROTATION_IN_NANOSECONDS / 2], result.epoch_delta)
-        np.testing.assert_array_equal([datetime.fromisoformat("2010-01-02 03:43:28.667")], result.mean_time)
+        np.testing.assert_array_equal(
+            [datetime.fromisoformat("2009-12-20 20:14:51.360")], result.epoch
+        )
+        np.testing.assert_equal(
+            [CARRINGTON_ROTATION_IN_NANOSECONDS / 2], result.epoch_delta
+        )
+        np.testing.assert_array_equal(
+            [datetime.fromisoformat("2010-01-02 03:43:28.667")], result.mean_time
+        )
 
         self.assertEqual([2091], result.cr)
         np.testing.assert_equal(latitude_grid, result.lat_grid)
         self.assertEqual(
-            ['-90°', '-80°', '-70°', '-60°', '-50°', '-40°', '-30°', '-20°', '-10°', '0°', '10°', '20°', '30°', '40°',
-             '50°', '60°', '70°', '80°', '90°'], result.lat_grid_label)
-        np.testing.assert_equal([model["solar_wind_profile"]["plasma_speed"]], result.plasma_speed_profile)
-        np.testing.assert_equal([model["solar_wind_profile"]["proton_density"]], result.proton_density_profile)
-
-        self.assertEqual([model["solar_wind_ecliptic"]["plasma_speed"]], result.plasma_speed_ecliptic)
-        self.assertEqual([model["solar_wind_ecliptic"]["proton_density"]], result.proton_density_ecliptic)
-        self.assertEqual([model["solar_wind_ecliptic"]["alpha_abundance"]], result.alpha_abundance_ecliptic)
+            [
+                "-90°",
+                "-80°",
+                "-70°",
+                "-60°",
+                "-50°",
+                "-40°",
+                "-30°",
+                "-20°",
+                "-10°",
+                "0°",
+                "10°",
+                "20°",
+                "30°",
+                "40°",
+                "50°",
+                "60°",
+                "70°",
+                "80°",
+                "90°",
+            ],
+            result.lat_grid_label,
+        )
+        np.testing.assert_equal(
+            [model["solar_wind_profile"]["plasma_speed"]], result.plasma_speed_profile
+        )
+        np.testing.assert_equal(
+            [model["solar_wind_profile"]["proton_density"]],
+            result.proton_density_profile,
+        )
+
+        self.assertEqual(
+            [model["solar_wind_ecliptic"]["plasma_speed"]], result.plasma_speed_ecliptic
+        )
+        self.assertEqual(
+            [model["solar_wind_ecliptic"]["proton_density"]],
+            result.proton_density_ecliptic,
+        )
+        self.assertEqual(
+            [model["solar_wind_ecliptic"]["alpha_abundance"]],
+            result.alpha_abundance_ecliptic,
+        )
         np.testing.assert_equal([model["glows_flags"]], result.glows_flags)
 
-        self.assertEqual([
-            "imap_glows_WawHelioIonMP_v002.json",
-            "imap_glows_bad-days-list_v001.dat",
-            "imap_glows_pipeline-settings-L3bc_20250707_v002.json",
-            "imap_glows_uv-anisotropy-1CR_v002.json",
-            "imap_glows_plasma-speed-2010a_v003.dat",
-            "imap_glows_proton-density-2010a_v003.dat",
-            "imap_glows_uv-anisotropy-2010a_v003.dat",
-            "imap_glows_photoion-2010a_v003.dat",
-            "imap_glows_lya-2010a_v003.dat",
-            "imap_glows_electron-density-2010a_v003.dat",
-            "omni2_all_years.dat"
-        ], result.parent_file_names)
-
-
-if __name__ == '__main__':
+        self.assertEqual(
+            [
+                "imap_glows_WawHelioIonMP_v002.json",
+                "imap_glows_bad-days-list_v001.dat",
+                "imap_glows_pipeline-settings-L3bc_20250707_v002.json",
+                "imap_glows_uv-anisotropy-1CR_v002.json",
+                "imap_glows_plasma-speed-2010a_v003.dat",
+                "imap_glows_proton-density-2010a_v003.dat",
+                "imap_glows_uv-anisotropy-2010a_v003.dat",
+                "imap_glows_photoion-2010a_v003.dat",
+                "imap_glows_lya-2010a_v003.dat",
+                "imap_glows_electron-density-2010a_v003.dat",
+                "omni2_all_years.dat",
+            ],
+            result.parent_file_names,
+        )
+
+
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/glows/l3bc/test_utils.py b/tests/glows/l3bc/test_utils.py
index 306a3745f..c51de9ea1 100644
--- a/tests/glows/l3bc/test_utils.py
+++ b/tests/glows/l3bc/test_utils.py
@@ -1,11 +1,13 @@
 import unittest
 from datetime import datetime
-from typing import Optional
 
 import numpy as np
 from imap_processing.spice.repoint import set_global_repoint_table_paths
 
-from imap_l3_processing.glows.l3bc.utils import get_pointing_date_range, get_best_ancillary
+from imap_l3_processing.glows.l3bc.utils import (
+    get_best_ancillary,
+    get_pointing_date_range,
+)
 from tests.test_helpers import get_test_data_path
 
 
@@ -20,45 +22,103 @@ def test_determine_crs_to_process_based_on_ancillary_files(self):
         end_date = datetime(2010, 1, 2)
 
         case_1 = [
-            {"file_path": "some/server/path/" + "older_ancillary.dat", "start_date": "20100101", "end_date": None,
-             "ingestion_date": "20100101 00:00:00"},
-            {"file_path": "some/server/path/" + "newer_ancillary.dat", "start_date": "20100101", "end_date": None,
-             "ingestion_date": "20100102 00:00:00"}
+            {
+                "file_path": "some/server/path/" + "older_ancillary.dat",
+                "start_date": "20100101",
+                "end_date": None,
+                "ingestion_date": "20100101 00:00:00",
+            },
+            {
+                "file_path": "some/server/path/" + "newer_ancillary.dat",
+                "start_date": "20100101",
+                "end_date": None,
+                "ingestion_date": "20100102 00:00:00",
+            },
         ]
 
         case_2 = [
-            {"file_path": "some/server/path/" + "older_ancillary.dat", "start_date": "20100101", "end_date": None,
-             "ingestion_date": "20100101 00:00:00"},
-            {"file_path": "some/server/path/" + "newer_ancillary.dat", "start_date": "20100104", "end_date": None,
-             "ingestion_date": "20100102 00:00:00"}
+            {
+                "file_path": "some/server/path/" + "older_ancillary.dat",
+                "start_date": "20100101",
+                "end_date": None,
+                "ingestion_date": "20100101 00:00:00",
+            },
+            {
+                "file_path": "some/server/path/" + "newer_ancillary.dat",
+                "start_date": "20100104",
+                "end_date": None,
+                "ingestion_date": "20100102 00:00:00",
+            },
         ]
 
         case_3 = [
-            {"file_path": "some/server/path/" + "older_ancillary.dat", "start_date": "20100101", "end_date": None,
-             "ingestion_date": "20100101 00:00:00"},
-            {"file_path": "some/server/path/" + "newer_ancillary.dat", "start_date": "20091205", "end_date": "20091206",
-             "ingestion_date": "20100102 00:00:00"}
+            {
+                "file_path": "some/server/path/" + "older_ancillary.dat",
+                "start_date": "20100101",
+                "end_date": None,
+                "ingestion_date": "20100101 00:00:00",
+            },
+            {
+                "file_path": "some/server/path/" + "newer_ancillary.dat",
+                "start_date": "20091205",
+                "end_date": "20091206",
+                "ingestion_date": "20100102 00:00:00",
+            },
         ]
 
         test_cases = [
             ("picks latest ingestion date", case_1, "newer_ancillary.dat"),
-            ("ignores ancillary with start date after cr", case_2, "older_ancillary.dat"),
-            ("ignores ancillary with end date before cr", case_3, "older_ancillary.dat")
+            (
+                "ignores ancillary with start date after cr",
+                case_2,
+                "older_ancillary.dat",
+            ),
+            (
+                "ignores ancillary with end date before cr",
+                case_3,
+                "older_ancillary.dat",
+            ),
         ]
 
-        for name, available_ancillaries, expected_best_ancillary_file_name in test_cases:
+        for (
+            name,
+            available_ancillaries,
+            expected_best_ancillary_file_name,
+        ) in test_cases:
             with self.subTest(name):
-                actual_ancillary_name = get_best_ancillary(start_date, end_date, available_ancillaries)
+                actual_ancillary_name = get_best_ancillary(
+                    start_date, end_date, available_ancillaries
+                )
 
-                self.assertEqual(expected_best_ancillary_file_name, actual_ancillary_name)
+                self.assertEqual(
+                    expected_best_ancillary_file_name, actual_ancillary_name
+                )
 
     def test_get_pointing_date_range(self):
         repointing_number = 13
         actual_start, actual_end = get_pointing_date_range(repointing_number)
         expected_start = np.datetime64(
-            datetime(year=2000, month=1, day=14, hour=12, minute=13, second=55, microsecond=816000).isoformat())
+            datetime(
+                year=2000,
+                month=1,
+                day=14,
+                hour=12,
+                minute=13,
+                second=55,
+                microsecond=816000,
+            ).isoformat()
+        )
         expected_end = np.datetime64(
-            datetime(year=2000, month=1, day=15, hour=11, minute=58, second=55, microsecond=816000).isoformat())
+            datetime(
+                year=2000,
+                month=1,
+                day=15,
+                hour=11,
+                minute=58,
+                second=55,
+                microsecond=816000,
+            ).isoformat()
+        )
 
         np.testing.assert_array_equal(actual_start, expected_start)
         np.testing.assert_array_equal(actual_end, expected_end)
@@ -67,16 +127,29 @@ def test_get_repoint_date_range_handles_no_pointing(self):
         repointing_number = 5998
         with self.assertRaises(ValueError) as err:
             _, _ = get_pointing_date_range(repointing_number)
-        self.assertEqual(str(err.exception), f"No pointing found for pointing: 5998")
-
-
-def create_imap_data_access_json(file_path: str, data_level: str, start_date: str,
-                                 descriptor: str = "hist", version: str = "v001",
-                                 repointing: Optional[int] = None) -> dict:
-    return {'file_path': file_path, 'instrument': 'glows', 'data_level': data_level, 'descriptor': descriptor,
-            'start_date': start_date, 'repointing': repointing, 'version': version, 'extension': 'pkts',
-            'ingestion_date': '2024-10-11 15:28:32'}
+        self.assertEqual(str(err.exception), "No pointing found for pointing: 5998")
+
+
+def create_imap_data_access_json(
+    file_path: str,
+    data_level: str,
+    start_date: str,
+    descriptor: str = "hist",
+    version: str = "v001",
+    repointing: int | None = None,
+) -> dict:
+    return {
+        "file_path": file_path,
+        "instrument": "glows",
+        "data_level": data_level,
+        "descriptor": descriptor,
+        "start_date": start_date,
+        "repointing": repointing,
+        "version": version,
+        "extension": "pkts",
+        "ingestion_date": "2024-10-11 15:28:32",
+    }
 
 
 def create_l3a_path_by_date(file_date: str, repointing: int) -> str:
-    return f'imap/glows/l3a/2000/01/imap_glows_l3a_hist_{file_date}-repoint{str(repointing).zfill(5)}_v001.pkts'
+    return f"imap/glows/l3a/2000/01/imap_glows_l3a_hist_{file_date}-repoint{str(repointing).zfill(5)}_v001.pkts"
diff --git a/tests/glows/l3d/test_glows_l3d_dependencies.py b/tests/glows/l3d/test_glows_l3d_dependencies.py
index 49164a85a..1a8800f9e 100644
--- a/tests/glows/l3d/test_glows_l3d_dependencies.py
+++ b/tests/glows/l3d/test_glows_l3d_dependencies.py
@@ -1,6 +1,6 @@
 import unittest
 from pathlib import Path
-from unittest.mock import Mock, sentinel, patch, call
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access.processing_input import ProcessingInputCollection
 
@@ -11,15 +11,31 @@
 
 class TestGlowsL3DDependencies(unittest.TestCase):
     @patch("imap_l3_processing.glows.l3d.glows_l3d_dependencies.ScienceFilePath")
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_dependencies.imap_data_access.download')
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_dependencies.imap_data_access.download"
+    )
     def test_fetch_dependencies(self, mock_download, mock_science_file_path):
-        waw_helio_ion_mp_speed = get_test_data_path("glows/imap_glows_plasma-speed-2026d_20251113_v003.dat")
-        waw_helio_ion_mp_p_dens = get_test_data_path("glows/imap_glows_proton-density-2026d_20251113_v003.dat")
-        waw_helio_ion_mp_uv_anis = get_test_data_path("glows/imap_glows_uv-anisotropy-2026d_20251113_v003.dat")
-        waw_helio_ion_mp_phion = get_test_data_path("glows/imap_glows_photoion-2026d_20251113_v003.dat")
-        waw_helio_ion_mp_lya = get_test_data_path("glows/imap_glows_lya-2026d_20251113_v003.dat")
-        waw_helio_ion_mp_e_dens = get_test_data_path("glows/imap_glows_electron-density-2026d_20251113_v003.dat")
-        pipeline_settings = get_test_data_path("glows/imap_glows_pipeline-settings-l3bcde_20251113_v005.json")
+        waw_helio_ion_mp_speed = get_test_data_path(
+            "glows/imap_glows_plasma-speed-2026d_20251113_v003.dat"
+        )
+        waw_helio_ion_mp_p_dens = get_test_data_path(
+            "glows/imap_glows_proton-density-2026d_20251113_v003.dat"
+        )
+        waw_helio_ion_mp_uv_anis = get_test_data_path(
+            "glows/imap_glows_uv-anisotropy-2026d_20251113_v003.dat"
+        )
+        waw_helio_ion_mp_phion = get_test_data_path(
+            "glows/imap_glows_photoion-2026d_20251113_v003.dat"
+        )
+        waw_helio_ion_mp_lya = get_test_data_path(
+            "glows/imap_glows_lya-2026d_20251113_v003.dat"
+        )
+        waw_helio_ion_mp_e_dens = get_test_data_path(
+            "glows/imap_glows_electron-density-2026d_20251113_v003.dat"
+        )
+        pipeline_settings = get_test_data_path(
+            "glows/imap_glows_pipeline-settings-l3bcde_20251113_v005.json"
+        )
 
         mock_processing_input_collection = Mock(spec=ProcessingInputCollection)
 
@@ -55,66 +71,82 @@ def test_fetch_dependencies(self, mock_download, mock_science_file_path):
         ]
 
         mock_science_file_path.side_effect = [
-            Mock(cr = 2000.5),
-            Mock(cr = 4000.5),
-            Mock(cr = 2000.5),
-            Mock(cr = 3000.5),
+            Mock(cr=2000.5),
+            Mock(cr=4000.5),
+            Mock(cr=2000.5),
+            Mock(cr=3000.5),
         ]
 
         external_dependencies = ExternalDependencies(
             lyman_alpha_path=sentinel.lyman_alpha_path,
             f107_index_file_path=None,
-            omni2_data_path=None
+            omni2_data_path=None,
         )
 
-        actual_dependencies: GlowsL3DDependencies = GlowsL3DDependencies.fetch_dependencies(
-            mock_processing_input_collection,
-            external_dependencies
+        actual_dependencies: GlowsL3DDependencies = (
+            GlowsL3DDependencies.fetch_dependencies(
+                mock_processing_input_collection, external_dependencies
+            )
         )
 
-        mock_processing_input_collection.get_file_paths.assert_has_calls([
-            call(source='glows', descriptor="plasma-speed-2026d"),
-            call(source='glows', descriptor="proton-density-2026d"),
-            call(source='glows', descriptor="uv-anisotropy-2026d"),
-            call(source='glows', descriptor="photoion-2026d"),
-            call(source='glows', descriptor="lya-2026d"),
-            call(source='glows', descriptor="electron-density-2026d"),
-            call(source='glows', descriptor="pipeline-settings-l3bcde"),
-            call(source='glows', descriptor="ion-rate-profile"),
-            call(source='glows', descriptor="sw-profile"),
-        ])
-
-        mock_download.assert_has_calls([
-            call(str(sentinel.waw_helio_ion_mp_speed)),
-            call(str(sentinel.waw_helio_ion_mp_p_dens)),
-            call(str(sentinel.waw_helio_ion_mp_uv_anis)),
-            call(str(sentinel.waw_helio_ion_mp_phion)),
-            call(str(sentinel.waw_helio_ion_mp_lya)),
-            call(str(sentinel.waw_helio_ion_mp_e_dens)),
-            call(str(sentinel.pipeline_settings)),
-            call(l3b_file_1),
-            call(l3b_file_2),
-            call(l3c_file_1),
-            call(l3c_file_2),
-        ])
-
-        self.assertEqual({
-            'pipeline_settings': pipeline_settings,
-            'WawHelioIon': {
-                'speed': waw_helio_ion_mp_speed,
-                'p-dens': waw_helio_ion_mp_p_dens,
-                'uv-anis': waw_helio_ion_mp_uv_anis,
-                'phion': waw_helio_ion_mp_phion,
-                'lya': waw_helio_ion_mp_lya,
-                'e-dens': waw_helio_ion_mp_e_dens
-            }}, actual_dependencies.ancillary_files)
-
-        self.assertEqual([sentinel.l3b_downloaded_path_1, sentinel.l3b_downloaded_path_2],
-                         actual_dependencies.l3b_file_paths)
-
-        self.assertEqual([sentinel.l3c_downloaded_path_1, sentinel.l3c_downloaded_path_2],
-                         actual_dependencies.l3c_file_paths)
+        mock_processing_input_collection.get_file_paths.assert_has_calls(
+            [
+                call(source="glows", descriptor="plasma-speed-2026d"),
+                call(source="glows", descriptor="proton-density-2026d"),
+                call(source="glows", descriptor="uv-anisotropy-2026d"),
+                call(source="glows", descriptor="photoion-2026d"),
+                call(source="glows", descriptor="lya-2026d"),
+                call(source="glows", descriptor="electron-density-2026d"),
+                call(source="glows", descriptor="pipeline-settings-l3bcde"),
+                call(source="glows", descriptor="ion-rate-profile"),
+                call(source="glows", descriptor="sw-profile"),
+            ]
+        )
+
+        mock_download.assert_has_calls(
+            [
+                call(str(sentinel.waw_helio_ion_mp_speed)),
+                call(str(sentinel.waw_helio_ion_mp_p_dens)),
+                call(str(sentinel.waw_helio_ion_mp_uv_anis)),
+                call(str(sentinel.waw_helio_ion_mp_phion)),
+                call(str(sentinel.waw_helio_ion_mp_lya)),
+                call(str(sentinel.waw_helio_ion_mp_e_dens)),
+                call(str(sentinel.pipeline_settings)),
+                call(l3b_file_1),
+                call(l3b_file_2),
+                call(l3c_file_1),
+                call(l3c_file_2),
+            ]
+        )
+
+        self.assertEqual(
+            {
+                "pipeline_settings": pipeline_settings,
+                "WawHelioIon": {
+                    "speed": waw_helio_ion_mp_speed,
+                    "p-dens": waw_helio_ion_mp_p_dens,
+                    "uv-anis": waw_helio_ion_mp_uv_anis,
+                    "phion": waw_helio_ion_mp_phion,
+                    "lya": waw_helio_ion_mp_lya,
+                    "e-dens": waw_helio_ion_mp_e_dens,
+                },
+            },
+            actual_dependencies.ancillary_files,
+        )
+
+        self.assertEqual(
+            [sentinel.l3b_downloaded_path_1, sentinel.l3b_downloaded_path_2],
+            actual_dependencies.l3b_file_paths,
+        )
+
+        self.assertEqual(
+            [sentinel.l3c_downloaded_path_1, sentinel.l3c_downloaded_path_2],
+            actual_dependencies.l3c_file_paths,
+        )
 
         self.assertEqual(actual_dependencies.end_cr, 3001.5)
 
-        self.assertEqual({'lya_raw_data': sentinel.lyman_alpha_path}, actual_dependencies.external_files)
+        self.assertEqual(
+            {"lya_raw_data": sentinel.lyman_alpha_path},
+            actual_dependencies.external_files,
+        )
diff --git a/tests/glows/l3d/test_glows_l3d_initializer.py b/tests/glows/l3d/test_glows_l3d_initializer.py
index 232d52c04..9182204c5 100644
--- a/tests/glows/l3d/test_glows_l3d_initializer.py
+++ b/tests/glows/l3d/test_glows_l3d_initializer.py
@@ -1,256 +1,379 @@
 import unittest
-from itertools import product
-from math import prod
-from unittest.mock import patch, call, Mock, sentinel
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.glows.descriptors import GLOWS_L3E_ULTRA_HF_DESCRIPTOR, GLOWS_L3E_HI_45_DESCRIPTOR, \
-    GLOWS_L3E_HI_90_DESCRIPTOR, GLOWS_L3E_LO_DESCRIPTOR, GLOWS_L3E_ULTRA_SF_DESCRIPTOR, GLOWS_L3B_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3B_DESCRIPTOR,
+    GLOWS_L3E_HI_45_DESCRIPTOR,
+    GLOWS_L3E_HI_90_DESCRIPTOR,
+    GLOWS_L3E_LO_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+)
 from imap_l3_processing.glows.l3d.glows_l3d_initializer import GlowsL3DInitializer
 from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo, ReprocessTargets
 from tests.test_helpers import create_mock_query_results
 
 
 class TestGlowsL3DInitializer(unittest.TestCase):
-
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer(self, mock_fetch_l3d_dependencies, mock_download, mock_query, mock_read_pipeline_settings,
-                             mock_read_cdf_parents):
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer(
+        self,
+        mock_fetch_l3d_dependencies,
+        mock_download,
+        mock_query,
+        mock_read_pipeline_settings,
+        mock_read_cdf_parents,
+    ):
         l3bs = [
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200201-cr00002_v001.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf',
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200201-cr00002_v001.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf",
         ]
         l3cs = [
-            'imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200201-cr00002_v001.cdf',
-            'imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf',
+            "imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200201-cr00002_v001.cdf",
+            "imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf",
         ]
 
         external_dependencies = Mock()
 
         mock_read_cdf_parents.return_value = {
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200201-cr00002_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200201-cr00002_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf',
-            'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-            'imap_glows_proton-density-2026d_19470301_v000.dat',
-            'imap_glows_lya-2026d_19470301_v000.dat',
-            'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-            'imap_glows_photoion-2026d_19470301_v000.dat',
-            'imap_glows_electron-density-2026d_19470301_v000.dat'
-            'lyman-alpha-composite.nc'
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200201-cr00002_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200201-cr00002_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf",
+            "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+            "imap_glows_proton-density-2026d_19470301_v000.dat",
+            "imap_glows_lya-2026d_19470301_v000.dat",
+            "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+            "imap_glows_photoion-2026d_19470301_v000.dat",
+            "imap_glows_electron-density-2026d_19470301_v000.dat"
+            "lyman-alpha-composite.nc",
         }
 
         mock_read_pipeline_settings.return_value = {"start_cr": 1}
 
         test_cases = {
-            'matching versions with legacy': (None, None),
-            'matching versions with new version format': (1, 1),
-            'upgraded major version': (1, 2),
+            "matching versions with legacy": (None, None),
+            "matching versions with new version format": (1, 1),
+            "upgraded major version": (1, 2),
         }
-        for name, (existing_major_version, requested_major_version) in test_cases.items():
+        for name, (
+            existing_major_version,
+            requested_major_version,
+        ) in test_cases.items():
             with self.subTest(name=name):
-                most_recent_l3d = f'imap_glows_l3d_solar-hist_19470301-cr00002_{Version(existing_major_version, 1)}.cdf'
-                mock_query.side_effect = self._create_ancillary_query_results([
-                    'imap_glows_l3d_solar-hist_19470301-cr00001_v000.cdf',
-                    'imap_glows_l3d_solar-hist_19470301-cr00001_v001.cdf',
-                    'imap_glows_l3d_solar-hist_19470301-cr00001_v002.cdf',
-                    'imap_glows_l3d_solar-hist_19470301-cr00002_v000.cdf',
-                    most_recent_l3d,
-                ])
+                most_recent_l3d = f"imap_glows_l3d_solar-hist_19470301-cr00002_{Version(existing_major_version, 1)}.cdf"
+                mock_query.side_effect = self._create_ancillary_query_results(
+                    [
+                        "imap_glows_l3d_solar-hist_19470301-cr00001_v000.cdf",
+                        "imap_glows_l3d_solar-hist_19470301-cr00001_v001.cdf",
+                        "imap_glows_l3d_solar-hist_19470301-cr00001_v002.cdf",
+                        "imap_glows_l3d_solar-hist_19470301-cr00002_v000.cdf",
+                        most_recent_l3d,
+                    ]
+                )
                 mock_read_cdf_parents.reset_mock()
                 mock_download.reset_mock()
                 mock_read_pipeline_settings.reset_mock()
                 mock_fetch_l3d_dependencies.reset_mock()
-                actual_version, actual_l3d_deps, actual_old_l3d = GlowsL3DInitializer.should_process_l3d(external_dependencies,
-                                                                                                         l3bs, l3cs, ReprocessInfo([]), requested_major_version)
+                actual_version, actual_l3d_deps, actual_old_l3d = (
+                    GlowsL3DInitializer.should_process_l3d(
+                        external_dependencies,
+                        l3bs,
+                        l3cs,
+                        ReprocessInfo([]),
+                        requested_major_version,
+                    )
+                )
 
                 mock_read_cdf_parents.assert_called_once_with(most_recent_l3d)
 
-                mock_query.assert_has_calls([
-                    call(instrument='glows', data_level="l3d", descriptor="solar-hist"),
-                    call(table='ancillary', instrument='glows', descriptor='plasma-speed-2026d', version='latest'),
-                    call(table='ancillary', instrument='glows', descriptor='proton-density-2026d', version='latest'),
-                    call(table='ancillary', instrument='glows', descriptor='uv-anisotropy-2026d', version='latest'),
-                    call(table='ancillary', instrument='glows', descriptor='photoion-2026d', version='latest'),
-                    call(table='ancillary', instrument='glows', descriptor='lya-2026d', version='latest'),
-                    call(table='ancillary', instrument='glows', descriptor='electron-density-2026d', version='latest'),
-                    call(table='ancillary', instrument='glows', descriptor='pipeline-settings-l3bcde', version='latest'),
-                ])
-
-                mock_download.assert_called_once_with("imap_glows_pipeline-settings-l3bcde_19470301_v000.json")
-                mock_read_pipeline_settings.assert_called_once_with(mock_download.return_value)
+                mock_query.assert_has_calls(
+                    [
+                        call(
+                            instrument="glows",
+                            data_level="l3d",
+                            descriptor="solar-hist",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="plasma-speed-2026d",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="proton-density-2026d",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="uv-anisotropy-2026d",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="photoion-2026d",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="lya-2026d",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="electron-density-2026d",
+                            version="latest",
+                        ),
+                        call(
+                            table="ancillary",
+                            instrument="glows",
+                            descriptor="pipeline-settings-l3bcde",
+                            version="latest",
+                        ),
+                    ]
+                )
+
+                mock_download.assert_called_once_with(
+                    "imap_glows_pipeline-settings-l3bcde_19470301_v000.json"
+                )
+                mock_read_pipeline_settings.assert_called_once_with(
+                    mock_download.return_value
+                )
 
                 [fetch_dependencies_call] = mock_fetch_l3d_dependencies.call_args_list
 
                 [actual_l3d_inputs, actual_external_deps] = fetch_dependencies_call.args
 
-                pipeline_l3d_input_paths = actual_l3d_inputs.get_file_paths(source="glows")
-                pipeline_l3d_input_filenames = [p.name for p in pipeline_l3d_input_paths]
-                self.assertEqual([
-                    'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-                    'imap_glows_l3b_ion-rate-profile_20200201-cr00002_v001.cdf',
-                    'imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf',
-                    'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-                    'imap_glows_l3c_sw-profile_20200201-cr00002_v001.cdf',
-                    'imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf',
-                    'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-                    'imap_glows_proton-density-2026d_19470301_v000.dat',
-                    'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-                    'imap_glows_photoion-2026d_19470301_v000.dat',
-                    'imap_glows_lya-2026d_19470301_v000.dat',
-                    'imap_glows_electron-density-2026d_19470301_v000.dat',
-                    'imap_glows_pipeline-settings-l3bcde_19470301_v000.json'
-                ], pipeline_l3d_input_filenames)
+                pipeline_l3d_input_paths = actual_l3d_inputs.get_file_paths(
+                    source="glows"
+                )
+                pipeline_l3d_input_filenames = [
+                    p.name for p in pipeline_l3d_input_paths
+                ]
+                self.assertEqual(
+                    [
+                        "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+                        "imap_glows_l3b_ion-rate-profile_20200201-cr00002_v001.cdf",
+                        "imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf",
+                        "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+                        "imap_glows_l3c_sw-profile_20200201-cr00002_v001.cdf",
+                        "imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf",
+                        "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+                        "imap_glows_proton-density-2026d_19470301_v000.dat",
+                        "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+                        "imap_glows_photoion-2026d_19470301_v000.dat",
+                        "imap_glows_lya-2026d_19470301_v000.dat",
+                        "imap_glows_electron-density-2026d_19470301_v000.dat",
+                        "imap_glows_pipeline-settings-l3bcde_19470301_v000.json",
+                    ],
+                    pipeline_l3d_input_filenames,
+                )
 
                 self.assertEqual(external_dependencies, actual_external_deps)
 
                 self.assertEqual(Version(requested_major_version, 2), actual_version)
-                self.assertEqual(mock_fetch_l3d_dependencies.return_value, actual_l3d_deps)
+                self.assertEqual(
+                    mock_fetch_l3d_dependencies.return_value, actual_l3d_deps
+                )
                 self.assertEqual(most_recent_l3d, actual_old_l3d)
 
-
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer_should_process_l3d_when_major_version_changes(self, mock_fetch_l3d_dependencies, mock_query, _, mock_read_pipeline_settings, mock_read_cdf_parents):
-        most_recent_l3d = 'imap_glows_l3d_solar-hist_19470301-cr00002_v001.cdf'
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer_should_process_l3d_when_major_version_changes(
+        self,
+        mock_fetch_l3d_dependencies,
+        mock_query,
+        _,
+        mock_read_pipeline_settings,
+        mock_read_cdf_parents,
+    ):
+        most_recent_l3d = "imap_glows_l3d_solar-hist_19470301-cr00002_v001.cdf"
         external_dependencies = Mock()
         l3bs = [
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200201-cr00002_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf',
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200201-cr00002_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf",
         ]
         l3cs = [
-            'imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200201-cr00002_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf',
+            "imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200201-cr00002_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf",
         ]
 
         mock_read_cdf_parents.return_value = {
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200201-cr00002_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200201-cr00002_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf',
-            'lyman-alpha-composite.nc',
-            'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-            'imap_glows_proton-density-2026d_19470301_v000.dat',
-            'imap_glows_lya-2026d_19470301_v000.dat',
-            'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-            'imap_glows_photoion-2026d_19470301_v000.dat',
-            'imap_glows_electron-density-2026d_19470301_v000.dat'
-
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200201-cr00002_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200301-cr00003_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200201-cr00002_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200301-cr00003_v000.cdf",
+            "lyman-alpha-composite.nc",
+            "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+            "imap_glows_proton-density-2026d_19470301_v000.dat",
+            "imap_glows_lya-2026d_19470301_v000.dat",
+            "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+            "imap_glows_photoion-2026d_19470301_v000.dat",
+            "imap_glows_electron-density-2026d_19470301_v000.dat",
         }
 
         mock_read_pipeline_settings.return_value = {"start_cr": 1}
 
-        mock_query.side_effect = self._create_ancillary_query_results([
-            most_recent_l3d
-        ])
+        mock_query.side_effect = self._create_ancillary_query_results([most_recent_l3d])
         new_major_version = 1
-        actual_version, actual_l3d_deps, actual_old_l3d = GlowsL3DInitializer.should_process_l3d(external_dependencies,
-                                                                                                 l3bs, l3cs,
-                                                                                                 ReprocessInfo([]),
-                                                                                                 new_major_version)
+        actual_version, actual_l3d_deps, actual_old_l3d = (
+            GlowsL3DInitializer.should_process_l3d(
+                external_dependencies, l3bs, l3cs, ReprocessInfo([]), new_major_version
+            )
+        )
 
         self.assertEqual(Version(new_major_version, 2), actual_version)
         self.assertEqual(mock_fetch_l3d_dependencies.return_value, actual_l3d_deps)
         self.assertEqual(most_recent_l3d, actual_old_l3d)
 
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings', return_value={"start_cr": 0})
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer_returns_no_old_cdf_if_none_found(self, mock_fetch_l3d_deps, mock_query,
-                                                              mock_read_cdf_parents, _, __):
-        l3bs = ['imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf']
-        l3cs = ['imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf']
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings",
+        return_value={"start_cr": 0},
+    )
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer_returns_no_old_cdf_if_none_found(
+        self, mock_fetch_l3d_deps, mock_query, mock_read_cdf_parents, _, __
+    ):
+        l3bs = ["imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf"]
+        l3cs = ["imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf"]
 
         external_deps = Mock()
 
         mock_query.side_effect = self._create_ancillary_query_results([])
 
-        _, __, old_l3d = GlowsL3DInitializer.should_process_l3d(external_deps, l3bs, l3cs, ReprocessInfo([]),None)
+        _, __, old_l3d = GlowsL3DInitializer.should_process_l3d(
+            external_deps, l3bs, l3cs, ReprocessInfo([]), None
+        )
         mock_read_cdf_parents.assert_not_called()
         mock_fetch_l3d_deps.assert_called_once()
         self.assertIsNone(old_l3d)
 
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer_should_not_process_if_same_major_version(self, mock_fetch_l3d_deps, mock_query,
-                                                mock_read_cdf_parents, mock_read_pipeline_settings, _):
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer_should_not_process_if_same_major_version(
+        self,
+        mock_fetch_l3d_deps,
+        mock_query,
+        mock_read_cdf_parents,
+        mock_read_pipeline_settings,
+        _,
+    ):
         l3bs = [
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf'
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
         ]
         l3cs = [
-            'imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf'
+            "imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
         ]
 
         external_dependencies = Mock()
 
         mock_query.side_effect = self._create_ancillary_query_results(
-            ['imap_glows_l3d_solar-hist_19470301-cr00002_v001.cdf'])
+            ["imap_glows_l3d_solar-hist_19470301-cr00002_v001.cdf"]
+        )
 
         mock_read_cdf_parents.return_value = {
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-            'imap_glows_proton-density-2026d_19470301_v000.dat',
-            'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-            'imap_glows_photoion-2026d_19470301_v000.dat',
-            'imap_glows_lya-2026d_19470301_v000.dat',
-            'imap_glows_electron-density-2026d_19470301_v000.dat',
-            'lyman-alpha-composite.nc'
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+            "imap_glows_proton-density-2026d_19470301_v000.dat",
+            "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+            "imap_glows_photoion-2026d_19470301_v000.dat",
+            "imap_glows_lya-2026d_19470301_v000.dat",
+            "imap_glows_electron-density-2026d_19470301_v000.dat",
+            "lyman-alpha-composite.nc",
         }
 
         mock_read_pipeline_settings.return_value = {"start_cr": 1}
 
-        actual_l3d_deps = GlowsL3DInitializer.should_process_l3d(external_dependencies, l3bs, l3cs, ReprocessInfo({}),None)
+        actual_l3d_deps = GlowsL3DInitializer.should_process_l3d(
+            external_dependencies, l3bs, l3cs, ReprocessInfo({}), None
+        )
         mock_fetch_l3d_deps.assert_not_called()
         self.assertIsNone(actual_l3d_deps)
 
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
     def test_l3d_initializer_should_not_process_when_no_l3bs(self, mock_fetch_l3d_deps):
         l3bs = []
         l3cs = []
 
         external_dependencies = Mock()
 
-        actual_l3d_deps = GlowsL3DInitializer.should_process_l3d(external_dependencies, l3bs, l3cs, ReprocessInfo([]),None)
+        actual_l3d_deps = GlowsL3DInitializer.should_process_l3d(
+            external_dependencies, l3bs, l3cs, ReprocessInfo([]), None
+        )
         mock_fetch_l3d_deps.assert_not_called()
         self.assertIsNone(actual_l3d_deps)
 
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer_does_not_process_when_no_l3bs_after_start_cr(self, mock_fetch_l3d_deps,
-                                                                          mock_read_pipeline_settings, mock_query, __):
-        l3bs = ['imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf', ]
-        l3cs = ['imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf', ]
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer_does_not_process_when_no_l3bs_after_start_cr(
+        self, mock_fetch_l3d_deps, mock_read_pipeline_settings, mock_query, __
+    ):
+        l3bs = [
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf",
+        ]
+        l3cs = [
+            "imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf",
+        ]
 
         external_dependencies = Mock()
 
@@ -258,56 +381,86 @@ def test_l3d_initializer_does_not_process_when_no_l3bs_after_start_cr(self, mock
 
         mock_read_pipeline_settings.return_value = {"start_cr": 1}
 
-        actual_l3d_deps = GlowsL3DInitializer.should_process_l3d(external_dependencies, l3bs, l3cs, ReprocessInfo([]),None)
+        actual_l3d_deps = GlowsL3DInitializer.should_process_l3d(
+            external_dependencies, l3bs, l3cs, ReprocessInfo([]), None
+        )
         mock_fetch_l3d_deps.assert_not_called()
         self.assertIsNone(actual_l3d_deps)
 
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer_forces_reprocess_if_reprocess_ancillary_specifies_l3e(self, mock_fetch_dependencies, mock_read_pipeline_settings, mock_query, mock_read_cdf_parents, _):
-        most_recent_l3d = 'imap_glows_l3d_solar-hist_19470301-cr00002_v001.0002.cdf'
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer_forces_reprocess_if_reprocess_ancillary_specifies_l3e(
+        self,
+        mock_fetch_dependencies,
+        mock_read_pipeline_settings,
+        mock_query,
+        mock_read_cdf_parents,
+        _,
+    ):
+        most_recent_l3d = "imap_glows_l3d_solar-hist_19470301-cr00002_v001.0002.cdf"
 
         mock_fetch_dependencies.return_value = sentinel.l3d_deps
-        mock_query.side_effect = self._create_ancillary_query_results([most_recent_l3d]) * 6
+        mock_query.side_effect = (
+            self._create_ancillary_query_results([most_recent_l3d]) * 6
+        )
         mock_read_pipeline_settings.return_value = {"start_cr": 1}
 
         l3bs = [
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00002_v000.cdf',
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00002_v000.cdf",
         ]
         l3cs = [
-            'imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00002_v000.cdf',
+            "imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00002_v000.cdf",
         ]
 
         external_dependencies = Mock()
 
         mock_read_cdf_parents.return_value = {
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00002_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00002_v000.cdf',
-            'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-            'imap_glows_proton-density-2026d_19470301_v000.dat',
-            'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-            'imap_glows_photoion-2026d_19470301_v000.dat',
-            'imap_glows_lya-2026d_19470301_v000.dat',
-            'imap_glows_electron-density-2026d_19470301_v000.dat'
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00002_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00000_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00002_v000.cdf",
+            "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+            "imap_glows_proton-density-2026d_19470301_v000.dat",
+            "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+            "imap_glows_photoion-2026d_19470301_v000.dat",
+            "imap_glows_lya-2026d_19470301_v000.dat",
+            "imap_glows_electron-density-2026d_19470301_v000.dat",
         }
 
         cases = [
-            (GLOWS_L3E_HI_45_DESCRIPTOR, {GLOWS_L3E_HI_45_DESCRIPTOR: ReprocessTargets([], [])}),
-            (GLOWS_L3E_HI_90_DESCRIPTOR, {GLOWS_L3E_HI_90_DESCRIPTOR: ReprocessTargets([], [])}),
-            (GLOWS_L3E_LO_DESCRIPTOR, {GLOWS_L3E_LO_DESCRIPTOR: ReprocessTargets([], [])}),
-            (GLOWS_L3E_ULTRA_SF_DESCRIPTOR, {GLOWS_L3E_ULTRA_SF_DESCRIPTOR: ReprocessTargets([], [])}),
-            (GLOWS_L3E_ULTRA_HF_DESCRIPTOR, {GLOWS_L3E_ULTRA_HF_DESCRIPTOR: ReprocessTargets([], [])}),
+            (
+                GLOWS_L3E_HI_45_DESCRIPTOR,
+                {GLOWS_L3E_HI_45_DESCRIPTOR: ReprocessTargets([], [])},
+            ),
+            (
+                GLOWS_L3E_HI_90_DESCRIPTOR,
+                {GLOWS_L3E_HI_90_DESCRIPTOR: ReprocessTargets([], [])},
+            ),
+            (
+                GLOWS_L3E_LO_DESCRIPTOR,
+                {GLOWS_L3E_LO_DESCRIPTOR: ReprocessTargets([], [])},
+            ),
+            (
+                GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                {GLOWS_L3E_ULTRA_SF_DESCRIPTOR: ReprocessTargets([], [])},
+            ),
+            (
+                GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                {GLOWS_L3E_ULTRA_HF_DESCRIPTOR: ReprocessTargets([], [])},
+            ),
         ]
 
         for case, products_to_reprocess in cases:
@@ -316,7 +469,9 @@ def test_l3d_initializer_forces_reprocess_if_reprocess_ancillary_specifies_l3e(s
 
                 reprocess_info = ReprocessInfo(products_to_reprocess)
 
-                version, l3d_deps, old_l3d = GlowsL3DInitializer.should_process_l3d(external_dependencies, l3bs, l3cs, reprocess_info, 1)
+                version, l3d_deps, old_l3d = GlowsL3DInitializer.should_process_l3d(
+                    external_dependencies, l3bs, l3cs, reprocess_info, 1
+                )
 
                 self.assertEqual(Version(1, 3), version)
                 self.assertIs(sentinel.l3d_deps, l3d_deps)
@@ -326,73 +481,109 @@ def test_l3d_initializer_forces_reprocess_if_reprocess_ancillary_specifies_l3e(s
 
                 [actual_l3d_inputs, actual_external_deps] = fetch_dependencies_call.args
 
-                pipeline_l3d_input_paths = actual_l3d_inputs.get_file_paths(source="glows")
-                pipeline_l3d_input_filenames = [p.name for p in pipeline_l3d_input_paths]
-                self.assertEqual([
-                    'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-                    'imap_glows_l3b_ion-rate-profile_20200101-cr00002_v000.cdf',
-                    'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-                    'imap_glows_l3c_sw-profile_20200101-cr00002_v000.cdf',
-                    'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-                    'imap_glows_proton-density-2026d_19470301_v000.dat',
-                    'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-                    'imap_glows_photoion-2026d_19470301_v000.dat',
-                    'imap_glows_lya-2026d_19470301_v000.dat',
-                    'imap_glows_electron-density-2026d_19470301_v000.dat',
-                    'imap_glows_pipeline-settings-l3bcde_19470301_v000.json'
-                ], pipeline_l3d_input_filenames)
+                pipeline_l3d_input_paths = actual_l3d_inputs.get_file_paths(
+                    source="glows"
+                )
+                pipeline_l3d_input_filenames = [
+                    p.name for p in pipeline_l3d_input_paths
+                ]
+                self.assertEqual(
+                    [
+                        "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+                        "imap_glows_l3b_ion-rate-profile_20200101-cr00002_v000.cdf",
+                        "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+                        "imap_glows_l3c_sw-profile_20200101-cr00002_v000.cdf",
+                        "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+                        "imap_glows_proton-density-2026d_19470301_v000.dat",
+                        "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+                        "imap_glows_photoion-2026d_19470301_v000.dat",
+                        "imap_glows_lya-2026d_19470301_v000.dat",
+                        "imap_glows_electron-density-2026d_19470301_v000.dat",
+                        "imap_glows_pipeline-settings-l3bcde_19470301_v000.json",
+                    ],
+                    pipeline_l3d_input_filenames,
+                )
 
                 self.assertEqual(external_dependencies, actual_external_deps)
 
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings')
-    @patch('imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies')
-    def test_l3d_initializer_does_not_force_reprocess_if_no_l3e_products(self, mock_fetch_dependencies, mock_read_pipeline_settings, mock_query, mock_read_cdf_parents, _):
-        most_recent_l3d = 'imap_glows_l3d_solar-hist_19470301-cr00002_v001.0002.cdf'
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.download"
+    )
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_cdf_parents")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.imap_data_access.query")
+    @patch("imap_l3_processing.glows.l3d.glows_l3d_initializer.read_pipeline_settings")
+    @patch(
+        "imap_l3_processing.glows.l3d.glows_l3d_initializer.GlowsL3DDependencies.fetch_dependencies"
+    )
+    def test_l3d_initializer_does_not_force_reprocess_if_no_l3e_products(
+        self,
+        mock_fetch_dependencies,
+        mock_read_pipeline_settings,
+        mock_query,
+        mock_read_cdf_parents,
+        _,
+    ):
+        most_recent_l3d = "imap_glows_l3d_solar-hist_19470301-cr00002_v001.0002.cdf"
 
         mock_fetch_dependencies.return_value = sentinel.l3d_deps
-        mock_query.side_effect = self._create_ancillary_query_results([most_recent_l3d]) * 2
+        mock_query.side_effect = (
+            self._create_ancillary_query_results([most_recent_l3d]) * 2
+        )
         mock_read_pipeline_settings.return_value = {"start_cr": 1}
 
-        l3bs = ['imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf']
-        l3cs = ['imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',]
+        l3bs = ["imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf"]
+        l3cs = [
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+        ]
 
         external_dependencies = Mock()
 
         mock_read_cdf_parents.return_value = {
-            'imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf',
-            'imap_glows_plasma-speed-2026d_19470301_v001.dat',
-            'imap_glows_proton-density-2026d_19470301_v000.dat',
-            'imap_glows_uv-anisotropy-2026d_19470301_v000.dat',
-            'imap_glows_photoion-2026d_19470301_v000.dat',
-            'imap_glows_lya-2026d_19470301_v000.dat',
-            'imap_glows_electron-density-2026d_19470301_v000.dat'
+            "imap_glows_l3b_ion-rate-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_l3c_sw-profile_20200101-cr00001_v000.cdf",
+            "imap_glows_plasma-speed-2026d_19470301_v001.dat",
+            "imap_glows_proton-density-2026d_19470301_v000.dat",
+            "imap_glows_uv-anisotropy-2026d_19470301_v000.dat",
+            "imap_glows_photoion-2026d_19470301_v000.dat",
+            "imap_glows_lya-2026d_19470301_v000.dat",
+            "imap_glows_electron-density-2026d_19470301_v000.dat",
         }
 
         cases = [
-            ("no l3e products to reprocess", {GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}),
-            ("no products", {})
+            (
+                "no l3e products to reprocess",
+                {GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])},
+            ),
+            ("no products", {}),
         ]
 
         for case, products_to_reprocess in cases:
             reprocess_info = ReprocessInfo(products_to_reprocess)
 
-            self.assertIsNone(GlowsL3DInitializer.should_process_l3d(external_dependencies, l3bs, l3cs,
-                                                                            reprocess_info, 1))
-
-
+            self.assertIsNone(
+                GlowsL3DInitializer.should_process_l3d(
+                    external_dependencies, l3bs, l3cs, reprocess_info, 1
+                )
+            )
 
     def _create_ancillary_query_results(self, l3d_query_result):
         return [
             create_mock_query_results(l3d_query_result),
-            create_mock_query_results(['imap_glows_plasma-speed-2026d_19470301_v001.dat']),
-            create_mock_query_results(['imap_glows_proton-density-2026d_19470301_v000.dat']),
-            create_mock_query_results(['imap_glows_uv-anisotropy-2026d_19470301_v000.dat']),
-            create_mock_query_results(['imap_glows_photoion-2026d_19470301_v000.dat']),
-            create_mock_query_results(['imap_glows_lya-2026d_19470301_v000.dat']),
-            create_mock_query_results(['imap_glows_electron-density-2026d_19470301_v000.dat']),
-            create_mock_query_results(['imap_glows_pipeline-settings-l3bcde_19470301_v000.json']),
+            create_mock_query_results(
+                ["imap_glows_plasma-speed-2026d_19470301_v001.dat"]
+            ),
+            create_mock_query_results(
+                ["imap_glows_proton-density-2026d_19470301_v000.dat"]
+            ),
+            create_mock_query_results(
+                ["imap_glows_uv-anisotropy-2026d_19470301_v000.dat"]
+            ),
+            create_mock_query_results(["imap_glows_photoion-2026d_19470301_v000.dat"]),
+            create_mock_query_results(["imap_glows_lya-2026d_19470301_v000.dat"]),
+            create_mock_query_results(
+                ["imap_glows_electron-density-2026d_19470301_v000.dat"]
+            ),
+            create_mock_query_results(
+                ["imap_glows_pipeline-settings-l3bcde_19470301_v000.json"]
+            ),
         ]
diff --git a/tests/glows/l3d/test_l3d_utils.py b/tests/glows/l3d/test_l3d_utils.py
index 059fe3c06..c17a25974 100644
--- a/tests/glows/l3d/test_l3d_utils.py
+++ b/tests/glows/l3d/test_l3d_utils.py
@@ -2,41 +2,85 @@
 import unittest
 from datetime import datetime
 from pathlib import Path
-from unittest.mock import patch, mock_open, MagicMock, sentinel, Mock, call
+from unittest.mock import MagicMock, Mock, call, mock_open, patch, sentinel
 
 import numpy as np
 
 import imap_l3_processing
 from imap_l3_processing.glows.l3d.models import GlowsL3DSolarParamsHistory
-from imap_l3_processing.glows.l3d.utils import create_glows_l3b_json_file_from_cdf, convert_json_to_l3d_data_product, \
-    create_glows_l3c_json_file_from_cdf, get_parent_file_names_from_l3d_json, rename_l3d_text_outputs, \
-    get_most_recently_uploaded_ancillary
+from imap_l3_processing.glows.l3d.utils import (
+    convert_json_to_l3d_data_product,
+    create_glows_l3b_json_file_from_cdf,
+    create_glows_l3c_json_file_from_cdf,
+    get_most_recently_uploaded_ancillary,
+    get_parent_file_names_from_l3d_json,
+    rename_l3d_text_outputs,
+)
 from imap_l3_processing.models import InputMetadata
-from tests.test_helpers import get_test_data_path, create_mock_query_results
+from tests.test_helpers import create_mock_query_results, get_test_data_path
 
 
 class TestL3dUtils(unittest.TestCase):
-
-    @patch('imap_l3_processing.glows.l3d.utils.os')
-    @patch('imap_l3_processing.glows.l3d.utils.json')
-    @patch('builtins.open', new_callable=mock_open, create=False)
-    def test_create_glows_l3c_json_file_from_cdf(self, mock_open_file, mock_json, mock_os):
-        l3c_path = get_test_data_path('glows/imap_glows_l3c_sw-profile_20101030_v008.cdf')
+    @patch("imap_l3_processing.glows.l3d.utils.os")
+    @patch("imap_l3_processing.glows.l3d.utils.json")
+    @patch("builtins.open", new_callable=mock_open, create=False)
+    def test_create_glows_l3c_json_file_from_cdf(
+        self, mock_open_file, mock_json, mock_os
+    ):
+        l3c_path = get_test_data_path(
+            "glows/imap_glows_l3c_sw-profile_20101030_v008.cdf"
+        )
 
         expected: dict = {
-            'solar_wind_profile': {
-                'proton_density': [2.3197076, 2.2874057, 2.1938286, 2.5905547, 3.4460852, 4.4701824,
-                                   5.5983787, 6.1791005, 7.7572236, 9.174307, 7.96888, 6.582614,
-                                   5.348592, 4.2680264, 3.3404691, 2.4287271, 2.0275042, 1.9867367,
-                                   1.9867367],
-                'plasma_speed': [522., 526., 538., 491., 415., 351., 300., 279., 234., 204., 229.,
-                                 266., 310., 362., 423., 509., 561., 567., 567.]
+            "solar_wind_profile": {
+                "proton_density": [
+                    2.3197076,
+                    2.2874057,
+                    2.1938286,
+                    2.5905547,
+                    3.4460852,
+                    4.4701824,
+                    5.5983787,
+                    6.1791005,
+                    7.7572236,
+                    9.174307,
+                    7.96888,
+                    6.582614,
+                    5.348592,
+                    4.2680264,
+                    3.3404691,
+                    2.4287271,
+                    2.0275042,
+                    1.9867367,
+                    1.9867367,
+                ],
+                "plasma_speed": [
+                    522.0,
+                    526.0,
+                    538.0,
+                    491.0,
+                    415.0,
+                    351.0,
+                    300.0,
+                    279.0,
+                    234.0,
+                    204.0,
+                    229.0,
+                    266.0,
+                    310.0,
+                    362.0,
+                    423.0,
+                    509.0,
+                    561.0,
+                    567.0,
+                    567.0,
+                ],
             },
-            'solar_wind_ecliptic': {
-                'proton_density': 6.015008449554443,
-                'alpha_abundance': 0.02850634977221489,
+            "solar_wind_ecliptic": {
+                "proton_density": 6.015008449554443,
+                "alpha_abundance": 0.02850634977221489,
             },
-            'CR': 2103
+            "CR": 2103,
         }
 
         json_file = MagicMock()
@@ -44,57 +88,134 @@ def test_create_glows_l3c_json_file_from_cdf(self, mock_open_file, mock_json, mo
 
         create_glows_l3c_json_file_from_cdf(l3c_path)
 
-        data_path = Path(imap_l3_processing.__file__).parent / 'glows' / 'l3d' / 'science' / 'data_l3c'
+        data_path = (
+            Path(imap_l3_processing.__file__).parent
+            / "glows"
+            / "l3d"
+            / "science"
+            / "data_l3c"
+        )
 
         mock_os.makedirs.assert_called_once_with(data_path, exist_ok=True)
 
-        mock_open_file.assert_called_once_with(data_path / 'imap_glows_l3c_cr_2103_v008.json', 'w')
+        mock_open_file.assert_called_once_with(
+            data_path / "imap_glows_l3c_cr_2103_v008.json", "w"
+        )
 
         mock_json.dump.assert_called_once()
         actual = mock_json.dump.call_args.args[0]
         self.assertEqual(json_file, mock_json.dump.call_args.args[1])
-        self.assertEqual('imap_glows_l3c_sw-profile_20101030_v008.cdf', actual['header']['filename'])
-
-        self.assertIsInstance(actual['solar_wind_profile']['proton_density'], list)
-        np.testing.assert_allclose(actual['solar_wind_profile']['proton_density'],
-                                   expected['solar_wind_profile']['proton_density'])
+        self.assertEqual(
+            "imap_glows_l3c_sw-profile_20101030_v008.cdf", actual["header"]["filename"]
+        )
 
-        self.assertIsInstance(actual['solar_wind_profile']['plasma_speed'], list)
-        np.testing.assert_array_equal(actual['solar_wind_profile']['plasma_speed'],
-                                      expected['solar_wind_profile']['plasma_speed'])
+        self.assertIsInstance(actual["solar_wind_profile"]["proton_density"], list)
+        np.testing.assert_allclose(
+            actual["solar_wind_profile"]["proton_density"],
+            expected["solar_wind_profile"]["proton_density"],
+        )
 
-        self.assertIsInstance(actual['solar_wind_ecliptic']['proton_density'], float)
-        self.assertEqual(expected['solar_wind_ecliptic']['proton_density'],
-                         actual['solar_wind_ecliptic']['proton_density'])
+        self.assertIsInstance(actual["solar_wind_profile"]["plasma_speed"], list)
+        np.testing.assert_array_equal(
+            actual["solar_wind_profile"]["plasma_speed"],
+            expected["solar_wind_profile"]["plasma_speed"],
+        )
 
-        self.assertIsInstance(actual['solar_wind_ecliptic']['alpha_abundance'], float)
-        self.assertEqual(expected['solar_wind_ecliptic']['alpha_abundance'],
-                         actual['solar_wind_ecliptic']['alpha_abundance'])
+        self.assertIsInstance(actual["solar_wind_ecliptic"]["proton_density"], float)
+        self.assertEqual(
+            expected["solar_wind_ecliptic"]["proton_density"],
+            actual["solar_wind_ecliptic"]["proton_density"],
+        )
 
-        self.assertIsInstance(actual['CR'], float)
-        self.assertEqual(expected['CR'], actual['CR'])
+        self.assertIsInstance(actual["solar_wind_ecliptic"]["alpha_abundance"], float)
+        self.assertEqual(
+            expected["solar_wind_ecliptic"]["alpha_abundance"],
+            actual["solar_wind_ecliptic"]["alpha_abundance"],
+        )
 
-    @patch('imap_l3_processing.glows.l3d.utils.os')
-    @patch('imap_l3_processing.glows.l3d.utils.json')
-    @patch('builtins.open', new_callable=mock_open, create=False)
-    def test_create_glows_l3b_json_file_from_cdf(self, mock_open_file, mock_json, mock_os):
-        l3b_path = get_test_data_path("glows/imap_glows_l3b_ion-rate-profile_20100422_v011.cdf")
+        self.assertIsInstance(actual["CR"], float)
+        self.assertEqual(expected["CR"], actual["CR"])
+
+    @patch("imap_l3_processing.glows.l3d.utils.os")
+    @patch("imap_l3_processing.glows.l3d.utils.json")
+    @patch("builtins.open", new_callable=mock_open, create=False)
+    def test_create_glows_l3b_json_file_from_cdf(
+        self, mock_open_file, mock_json, mock_os
+    ):
+        l3b_path = get_test_data_path(
+            "glows/imap_glows_l3b_ion-rate-profile_20100422_v011.cdf"
+        )
 
         expected: dict = {
-            'header': {
-                'filename': 'imap_glows_l3b_ion-rate-profile_20100422_v011.cdf',
+            "header": {
+                "filename": "imap_glows_l3b_ion-rate-profile_20100422_v011.cdf",
             },
-            'CR': 2096,
-            'date': '2010-05-06T05:17:00.959996',
-            'uv_anisotropy_factor': [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
-            'ion_rate_profile': {
-                'lat_grid': [-90., -80., -70., -60., -50., -40., -30., -20., -10., 0., 10., 20., 30., 40., 50., 60.,
-                             70., 80., 90.],
-                'ph_rate': [1.0475524e-07, 1.0475524e-07, 1.0475524e-07, 1.0475524e-07,
-                            1.0475524e-07, 1.0475524e-07, 1.0475524e-07, 1.0475524e-07,
-                            1.0475524e-07, 1.0475524e-07, 1.0475524e-07, 1.0475524e-07,
-                            1.0475524e-07, 1.0475524e-07, 1.0475524e-07, 1.0475524e-07,
-                            1.0475524e-07, 1.0475524e-07, 1.0475524e-07]
+            "CR": 2096,
+            "date": "2010-05-06T05:17:00.959996",
+            "uv_anisotropy_factor": [
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            "ion_rate_profile": {
+                "lat_grid": [
+                    -90.0,
+                    -80.0,
+                    -70.0,
+                    -60.0,
+                    -50.0,
+                    -40.0,
+                    -30.0,
+                    -20.0,
+                    -10.0,
+                    0.0,
+                    10.0,
+                    20.0,
+                    30.0,
+                    40.0,
+                    50.0,
+                    60.0,
+                    70.0,
+                    80.0,
+                    90.0,
+                ],
+                "ph_rate": [
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                    1.0475524e-07,
+                ],
             },
             "uv_anisotropy_flag": 1000,
             "glows_flags": 4,
@@ -105,7 +226,13 @@ def test_create_glows_l3b_json_file_from_cdf(self, mock_open_file, mock_json, mo
 
         create_glows_l3b_json_file_from_cdf(l3b_path)
 
-        data_path = Path(imap_l3_processing.__file__).parent / 'glows' / 'l3d' / 'science' / 'data_l3b'
+        data_path = (
+            Path(imap_l3_processing.__file__).parent
+            / "glows"
+            / "l3d"
+            / "science"
+            / "data_l3b"
+        )
 
         mock_os.makedirs.assert_called_once_with(data_path, exist_ok=True)
 
@@ -113,27 +240,35 @@ def test_create_glows_l3b_json_file_from_cdf(self, mock_open_file, mock_json, mo
         actual = mock_json.dump.call_args.args[0]
         self.assertEqual(json_file, mock_json.dump.call_args.args[1])
 
-        mock_open_file.assert_called_once_with(data_path / 'imap_glows_l3b_cr_2096_v011.json', 'w')
+        mock_open_file.assert_called_once_with(
+            data_path / "imap_glows_l3b_cr_2096_v011.json", "w"
+        )
 
-        self.assertIsInstance(actual['CR'], int)
-        self.assertEqual(expected['CR'], actual['CR'])
+        self.assertIsInstance(actual["CR"], int)
+        self.assertEqual(expected["CR"], actual["CR"])
 
-        self.assertIsInstance(actual['date'], str)
-        self.assertEqual(actual['date'], expected['date'])
+        self.assertIsInstance(actual["date"], str)
+        self.assertEqual(actual["date"], expected["date"])
 
-        np.testing.assert_array_equal(expected["header"]['filename'],
-                                      actual["header"]['filename'])
+        np.testing.assert_array_equal(
+            expected["header"]["filename"], actual["header"]["filename"]
+        )
 
-        self.assertIsInstance(actual['uv_anisotropy_factor'], list)
-        np.testing.assert_array_equal(expected["uv_anisotropy_factor"],
-                                      actual["uv_anisotropy_factor"])
+        self.assertIsInstance(actual["uv_anisotropy_factor"], list)
+        np.testing.assert_array_equal(
+            expected["uv_anisotropy_factor"], actual["uv_anisotropy_factor"]
+        )
 
-        np.testing.assert_array_equal(expected["ion_rate_profile"]['lat_grid'],
-                                      actual["ion_rate_profile"]['lat_grid'])
+        np.testing.assert_array_equal(
+            expected["ion_rate_profile"]["lat_grid"],
+            actual["ion_rate_profile"]["lat_grid"],
+        )
         self.assertIsInstance(actual["ion_rate_profile"]["lat_grid"], list)
 
-        np.testing.assert_array_almost_equal(expected["ion_rate_profile"]['ph_rate'],
-                                             actual["ion_rate_profile"]['ph_rate'])
+        np.testing.assert_array_almost_equal(
+            expected["ion_rate_profile"]["ph_rate"],
+            actual["ion_rate_profile"]["ph_rate"],
+        )
         self.assertIsInstance(actual["ion_rate_profile"]["ph_rate"], list)
 
         self.assertIsInstance(actual["uv_anisotropy_flag"], int)
@@ -146,34 +281,88 @@ def test_convert_json_l3d_to_data_product(self):
         input_metadata = Mock(spec=InputMetadata)
         input_metadata.start_date = datetime.fromisoformat("2025-05-12")
         l3d_data_product: GlowsL3DSolarParamsHistory = convert_json_to_l3d_data_product(
-            get_test_data_path('glows/imap_glows_l3d_solar-params-history_19470303-cr02095_v00.json'),
+            get_test_data_path(
+                "glows/imap_glows_l3d_solar-params-history_19470303-cr02095_v00.json"
+            ),
             input_metadata,
             sentinel.parent_file_names,
         )
 
         self.assertEqual(input_metadata, l3d_data_product.input_metadata)
-        self.assertEqual(input_metadata.start_date, datetime.fromisoformat("1947-03-03").date())
+        self.assertEqual(
+            input_metadata.start_date, datetime.fromisoformat("1947-03-03").date()
+        )
 
         self.assertEqual(sentinel.parent_file_names, l3d_data_product.parent_file_names)
 
         np.testing.assert_array_equal(
-            [-90, -80, -70, -60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90],
-            l3d_data_product.latitude)
+            [
+                -90,
+                -80,
+                -70,
+                -60,
+                -50,
+                -40,
+                -30,
+                -20,
+                -10,
+                0,
+                10,
+                20,
+                30,
+                40,
+                50,
+                60,
+                70,
+                80,
+                90,
+            ],
+            l3d_data_product.latitude,
+        )
 
         self.assertEqual(846, len(l3d_data_product.cr))
         self.assertEqual(1250.5, l3d_data_product.cr[0])
         self.assertEqual(2095.5, l3d_data_product.cr[-1])
 
         self.assertEqual(846, len(l3d_data_product.epoch))
-        self.assertEqual(datetime.fromisoformat("1947-03-03 07:35:32.640"), l3d_data_product.epoch[0])
-        self.assertEqual(datetime.fromisoformat("2010-04-08 22:40:35.040"), l3d_data_product.epoch[-1])
+        self.assertEqual(
+            datetime.fromisoformat("1947-03-03 07:35:32.640"), l3d_data_product.epoch[0]
+        )
+        self.assertEqual(
+            datetime.fromisoformat("2010-04-08 22:40:35.040"),
+            l3d_data_product.epoch[-1],
+        )
 
-        np.testing.assert_array_equal(np.arange(-90, 100, step=10), l3d_data_product.latitude)
+        np.testing.assert_array_equal(
+            np.arange(-90, 100, step=10), l3d_data_product.latitude
+        )
 
         self.assertEqual(846, len(l3d_data_product.plasma_speed))
         np.testing.assert_allclose(
-            [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0,
-             -1.0, -1.0, -1.0], l3d_data_product.plasma_speed[0], rtol=1e-6)
+            [
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+            ],
+            l3d_data_product.plasma_speed[0],
+            rtol=1e-6,
+        )
         np.testing.assert_allclose(
             [
                 468.1725019310069,
@@ -194,15 +383,38 @@ def test_convert_json_l3d_to_data_product(self):
                 460.79164287941717,
                 503.2472821431676,
                 508.93503849297326,
-                509.10575708733097
+                509.10575708733097,
             ],
             l3d_data_product.plasma_speed[-1],
-            rtol=1e-6)
+            rtol=1e-6,
+        )
 
         self.assertEqual(846, len(l3d_data_product.proton_density))
         np.testing.assert_allclose(
-            [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0,
-             -1.0, -1.0, -1.0], l3d_data_product.proton_density[0], rtol=1e-6)
+            [
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+            ],
+            l3d_data_product.proton_density[0],
+            rtol=1e-6,
+        )
         np.testing.assert_allclose(
             [
                 3.134819994443553,
@@ -223,40 +435,103 @@ def test_convert_json_l3d_to_data_product(self):
                 3.1651607050304924,
                 2.68129704271522,
                 2.6272758631787716,
-                2.625943370301117
-            ], l3d_data_product.proton_density[-1], rtol=1e-6)
+                2.625943370301117,
+            ],
+            l3d_data_product.proton_density[-1],
+            rtol=1e-6,
+        )
 
         self.assertEqual(846, len(l3d_data_product.ultraviolet_anisotropy))
         np.testing.assert_allclose(
-            [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0,
-             -1.0, -1.0, -1.0], l3d_data_product.ultraviolet_anisotropy[0], rtol=1e-6)
+            [
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+                -1.0,
+            ],
+            l3d_data_product.ultraviolet_anisotropy[0],
+            rtol=1e-6,
+        )
         np.testing.assert_allclose(
-            [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
-             ], l3d_data_product.ultraviolet_anisotropy[-1], rtol=1e-6)
+            [
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            l3d_data_product.ultraviolet_anisotropy[-1],
+            rtol=1e-6,
+        )
 
         self.assertEqual(846, len(l3d_data_product.phion))
-        np.testing.assert_allclose(1.830438692701064e-07, l3d_data_product.phion[0], rtol=1e-6)
-        np.testing.assert_allclose(1.0538921234648196e-07, l3d_data_product.phion[-1], rtol=1e-6)
+        np.testing.assert_allclose(
+            1.830438692701064e-07, l3d_data_product.phion[0], rtol=1e-6
+        )
+        np.testing.assert_allclose(
+            1.0538921234648196e-07, l3d_data_product.phion[-1], rtol=1e-6
+        )
 
         self.assertEqual(846, len(l3d_data_product.lyman_alpha))
-        np.testing.assert_allclose(619975763789.9209, l3d_data_product.lyman_alpha[0], rtol=1e-6)
-        np.testing.assert_allclose(396312787467.8571, l3d_data_product.lyman_alpha[-1], rtol=1e-6)
+        np.testing.assert_allclose(
+            619975763789.9209, l3d_data_product.lyman_alpha[0], rtol=1e-6
+        )
+        np.testing.assert_allclose(
+            396312787467.8571, l3d_data_product.lyman_alpha[-1], rtol=1e-6
+        )
 
         self.assertEqual(846, len(l3d_data_product.electron_density))
         self.assertEqual(-1.0, l3d_data_product.electron_density[0])
-        np.testing.assert_allclose(5.978193763000784, l3d_data_product.electron_density[-1], rtol=1e-6)
+        np.testing.assert_allclose(
+            5.978193763000784, l3d_data_product.electron_density[-1], rtol=1e-6
+        )
 
         self.assertEqual(846, len(l3d_data_product.plasma_speed_flag))
         self.assertEqual(-1.0, l3d_data_product.plasma_speed_flag[0])
-        np.testing.assert_allclose(20001.0, l3d_data_product.plasma_speed_flag[-1], rtol=1e-6)
+        np.testing.assert_allclose(
+            20001.0, l3d_data_product.plasma_speed_flag[-1], rtol=1e-6
+        )
 
         self.assertEqual(846, len(l3d_data_product.uv_anisotropy_flag))
         self.assertEqual(-1.0, l3d_data_product.uv_anisotropy_flag[0])
-        np.testing.assert_allclose(30001.0, l3d_data_product.uv_anisotropy_flag[-1], rtol=1e-6)
+        np.testing.assert_allclose(
+            30001.0, l3d_data_product.uv_anisotropy_flag[-1], rtol=1e-6
+        )
 
         self.assertEqual(846, len(l3d_data_product.proton_density_flag))
         self.assertEqual(-1.0, l3d_data_product.proton_density_flag[0])
-        np.testing.assert_allclose(10001.0, l3d_data_product.proton_density_flag[-1], rtol=1e-6)
+        np.testing.assert_allclose(
+            10001.0, l3d_data_product.proton_density_flag[-1], rtol=1e-6
+        )
 
         self.assertEqual(846, len(l3d_data_product.glows_flags))
         self.assertEqual(0, int(l3d_data_product.glows_flags[0]))
@@ -278,7 +553,7 @@ def test_get_parent_file_names_from_l3d_json(self):
             "imap_glows_l3b_ion-rate-profile_20100422_v011.cdf",
             "imap_glows_l3b_ion-rate-profile_20100519_v011.cdf",
             "imap_glows_l3c_sw-profile_20100422_v011.cdf",
-            "imap_glows_l3c_sw-profile_20100519_v011.cdf"
+            "imap_glows_l3c_sw-profile_20100519_v011.cdf",
         ]
 
         self.assertCountEqual(expected, l3bc_filenames)
@@ -288,7 +563,9 @@ def test_rename_l3d_text_outputs(self, mock_get_midpoint_of_cr):
         with tempfile.TemporaryDirectory() as tmpdir:
             tmpdir = Path(tmpdir)
             original_lya_path = tmpdir / "imap_glows_l3d_lya_19470303-cr02092_v00.dat"
-            original_p_dens_path = tmpdir / "imap_glows_l3d_p-dens_19470303-cr02092_v00.dat"
+            original_p_dens_path = (
+                tmpdir / "imap_glows_l3d_p-dens_19470303-cr02092_v00.dat"
+            )
 
             original_lya_path.write_text("lya")
             original_p_dens_path.write_text("p_dens")
@@ -296,14 +573,20 @@ def test_rename_l3d_text_outputs(self, mock_get_midpoint_of_cr):
             mock_get_midpoint_of_cr.return_value = datetime(2010, 2, 1)
 
             version = "v012"
-            actual_new_paths = rename_l3d_text_outputs([original_lya_path, original_p_dens_path], version)
+            actual_new_paths = rename_l3d_text_outputs(
+                [original_lya_path, original_p_dens_path], version
+            )
 
             mock_get_midpoint_of_cr.assert_has_calls([call(2092), call(2092)])
 
             expected_lya_output = tmpdir / "imap_glows_lya_19470303_20100201_v012.dat"
-            expected_p_dens_output = tmpdir / "imap_glows_p-dens_19470303_20100201_v012.dat"
+            expected_p_dens_output = (
+                tmpdir / "imap_glows_p-dens_19470303_20100201_v012.dat"
+            )
 
-            self.assertEqual([expected_lya_output, expected_p_dens_output], actual_new_paths)
+            self.assertEqual(
+                [expected_lya_output, expected_p_dens_output], actual_new_paths
+            )
 
             self.assertEqual("lya", expected_lya_output.read_text())
             self.assertEqual("p_dens", expected_p_dens_output.read_text())
@@ -312,15 +595,23 @@ def test_rename_l3d_text_outputs(self, mock_get_midpoint_of_cr):
             self.assertFalse(original_p_dens_path.exists())
 
     def test_get_most_recently_uploaded_ancillary(self):
-        query_result = create_mock_query_results([
-            "imap_glows_l3d_solar-hist_20100101-cr02091_v001.cdf",
-            "imap_glows_l3d_solar-hist_20100201-cr02092_v002.cdf",
-            "imap_glows_l3d_solar-hist_20100301-cr02093_v001.cdf"
-
-        ], ingestion_dates=[datetime(2010, 1, 2), datetime(2010, 5, 2), datetime(2010, 3, 2)])
+        query_result = create_mock_query_results(
+            [
+                "imap_glows_l3d_solar-hist_20100101-cr02091_v001.cdf",
+                "imap_glows_l3d_solar-hist_20100201-cr02092_v002.cdf",
+                "imap_glows_l3d_solar-hist_20100301-cr02093_v001.cdf",
+            ],
+            ingestion_dates=[
+                datetime(2010, 1, 2),
+                datetime(2010, 5, 2),
+                datetime(2010, 3, 2),
+            ],
+        )
 
-        [expected] = create_mock_query_results(["imap_glows_l3d_solar-hist_20100201-cr02092_v002.cdf"],
-                                               ingestion_dates=[datetime(2010, 5, 2)])
+        [expected] = create_mock_query_results(
+            ["imap_glows_l3d_solar-hist_20100201-cr02092_v002.cdf"],
+            ingestion_dates=[datetime(2010, 5, 2)],
+        )
 
         self.assertEqual(expected, get_most_recently_uploaded_ancillary(query_result))
 
diff --git a/tests/glows/l3d/test_models.py b/tests/glows/l3d/test_models.py
index 47bfecfef..6cea5ebf2 100644
--- a/tests/glows/l3d/test_models.py
+++ b/tests/glows/l3d/test_models.py
@@ -3,11 +3,24 @@
 import numpy as np
 
 from imap_l3_processing.constants import CARRINGTON_ROTATION_IN_NANOSECONDS
-from imap_l3_processing.glows.l3d.models import GlowsL3DSolarParamsHistory, SPEED_CDF_VAR_NAME, PHION_CDF_VAR_NAME, \
-    EPOCH_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME, LATITUDE_CDF_CDF_VAR_NAME, \
-    LATITUDE_LABEL_CDF_VAR_NAME, CR_CDF_VAR_NAME, PROTON_DENSITY_CDF_VAR_NAME, UV_ANISOTROPY_CDF_VAR_NAME, \
-    LYMAN_ALPHA_CDF_VAR_NAME, ELECTRON_DENSITY_CDF_VAR_NAME, PLASMA_SPEED_FLAG_CDF_VAR_NAME, \
-    UV_ANISOTROPY_FLAG_CDF_VAR_NAME, PROTON_DENSITY_FLAG_CDF_VAR_NAME, GLOWS_FLAGS_CDF_VAR_NAME
+from imap_l3_processing.glows.l3d.models import (
+    CR_CDF_VAR_NAME,
+    ELECTRON_DENSITY_CDF_VAR_NAME,
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+    GLOWS_FLAGS_CDF_VAR_NAME,
+    LATITUDE_CDF_CDF_VAR_NAME,
+    LATITUDE_LABEL_CDF_VAR_NAME,
+    LYMAN_ALPHA_CDF_VAR_NAME,
+    PHION_CDF_VAR_NAME,
+    PLASMA_SPEED_FLAG_CDF_VAR_NAME,
+    PROTON_DENSITY_CDF_VAR_NAME,
+    PROTON_DENSITY_FLAG_CDF_VAR_NAME,
+    SPEED_CDF_VAR_NAME,
+    UV_ANISOTROPY_CDF_VAR_NAME,
+    UV_ANISOTROPY_FLAG_CDF_VAR_NAME,
+    GlowsL3DSolarParamsHistory,
+)
 from tests.swapi.cdf_model_test_case import CdfModelTestCase
 
 
@@ -49,23 +62,49 @@ def test_glows_l3d_solar_params_history_to_data_product(self):
 
         variables = data_product.to_data_product_variables()
 
-        expected_epoch_delta = np.full_like(epoch, CARRINGTON_ROTATION_IN_NANOSECONDS / 2)
-        expected_latitude_label = [f'{deg:.1f} degrees' for deg in latitude]
+        expected_epoch_delta = np.full_like(
+            epoch, CARRINGTON_ROTATION_IN_NANOSECONDS / 2
+        )
+        expected_latitude_label = [f"{deg:.1f} degrees" for deg in latitude]
 
         self.assertEqual(15, len(variables))
         variables = iter(variables)
         self.assert_variable_attributes(next(variables), epoch, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), expected_epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), latitude, LATITUDE_CDF_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), expected_latitude_label, LATITUDE_LABEL_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(variables), expected_epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), latitude, LATITUDE_CDF_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), expected_latitude_label, LATITUDE_LABEL_CDF_VAR_NAME
+        )
         self.assert_variable_attributes(next(variables), cr, CR_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), plasma_speed, SPEED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), proton_density, PROTON_DENSITY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), ultraviolet_anisotropy, UV_ANISOTROPY_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(variables), plasma_speed, SPEED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), proton_density, PROTON_DENSITY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), ultraviolet_anisotropy, UV_ANISOTROPY_CDF_VAR_NAME
+        )
         self.assert_variable_attributes(next(variables), phion, PHION_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), lyman_alpha, LYMAN_ALPHA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), electron_density, ELECTRON_DENSITY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), plasma_speed_flag, PLASMA_SPEED_FLAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), uv_anisotropy_flag, UV_ANISOTROPY_FLAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), proton_density_flag, PROTON_DENSITY_FLAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), glows_flags, GLOWS_FLAGS_CDF_VAR_NAME)
\ No newline at end of file
+        self.assert_variable_attributes(
+            next(variables), lyman_alpha, LYMAN_ALPHA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), electron_density, ELECTRON_DENSITY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), plasma_speed_flag, PLASMA_SPEED_FLAG_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), uv_anisotropy_flag, UV_ANISOTROPY_FLAG_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), proton_density_flag, PROTON_DENSITY_FLAG_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), glows_flags, GLOWS_FLAGS_CDF_VAR_NAME
+        )
diff --git a/tests/glows/l3e/test_glows_l3e_call_arguments.py b/tests/glows/l3e/test_glows_l3e_call_arguments.py
index edcaa3f50..22a4c65e8 100644
--- a/tests/glows/l3e/test_glows_l3e_call_arguments.py
+++ b/tests/glows/l3e/test_glows_l3e_call_arguments.py
@@ -2,11 +2,13 @@
 
 import numpy as np
 
-from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eCallArguments, GlowsL3eSpacecraftInfo
+from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
+    GlowsL3eCallArguments,
+    GlowsL3eSpacecraftInfo,
+)
 
 
 class TestGlowsL3eCallArguments(unittest.TestCase):
-
     def test_to_argument_list(self):
         glows_l3e_call_arguments = GlowsL3eCallArguments(
             elongation=80.0001,
@@ -14,14 +16,14 @@ def test_to_argument_list(self):
             formatted_date="20240101",
             spacecraft_info=GlowsL3eSpacecraftInfo(
                 spacecraft_radius=np.float32(987),
-                spin_axis_latitude=np.float32(.01234),
+                spin_axis_latitude=np.float32(0.01234),
                 spin_axis_longitude=np.float32(87.11213),
                 spacecraft_latitude=np.float32(1.134141),
                 spacecraft_longitude=np.float32(84.12314),
                 spacecraft_velocity_x=np.float32(1.23143),
                 spacecraft_velocity_y=np.float32(12.34141),
                 spacecraft_velocity_z=np.float32(123),
-            )
+            ),
         )
 
         call_args_list = glows_l3e_call_arguments.to_argument_list()
diff --git a/tests/glows/l3e/test_glows_l3e_dependencies.py b/tests/glows/l3e/test_glows_l3e_dependencies.py
index 534d1e2e3..accc0c6e3 100644
--- a/tests/glows/l3e/test_glows_l3e_dependencies.py
+++ b/tests/glows/l3e/test_glows_l3e_dependencies.py
@@ -1,32 +1,34 @@
 from pathlib import Path
 from unittest import TestCase
-from unittest.mock import patch, Mock, call, sentinel, mock_open
+from unittest.mock import Mock, call, mock_open, patch, sentinel
 
 from imap_data_access import RepointInput
 
 from imap_l3_processing.glows.l3e.glows_l3e_dependencies import GlowsL3EDependencies
-from imap_l3_processing.utils import SpiceKernelTypes, FurnishMetakernelOutput
+from imap_l3_processing.utils import FurnishMetakernelOutput, SpiceKernelTypes
 from tests.test_helpers import get_test_data_path
 
 
 class TestGlowsL3EDependencies(TestCase):
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_dependencies.imap_data_access.download')
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_dependencies.imap_data_access.download"
+    )
     def test_fetch_dependencies(self, mock_download):
         mock_processing_input_collection = Mock()
 
-        mock_energy_grid_lo = Path('energy_grid_lo_path')
-        mock_energy_grid_hi = Path('energy_grid_hi_sdc_path')
-        mock_energy_grid_ultra = Path('energy_grid_ultra_sdc_path')
-        mock_tess_xyz_8 = Path('tess_xyz_8_sdc_path')
-        mock_lya_series = Path('lya_series_sdc_path')
-        mock_solar_uv_anisotropy = Path('solar_uv_anisotropy_sdc_path')
-        mock_speed_3d_sw = Path('speed_3d_sw_sdc_path')
-        mock_density_3d_sw = Path('density_3d_sw_sdc_path')
-        mock_phion_hydrogen = Path('phion_hydrogen_sdc_path')
-        mock_sw_eqtr_electrons = Path('sw_eqtr_electrons_sdc_path')
-        mock_pipeline_settings = Path('l3bcde_pipeline_settings.json')
-        mock_tess_ang_16 = Path('tess_ang_16_path')
-        mock_repoint_file = Path('repoint.csv')
+        mock_energy_grid_lo = Path("energy_grid_lo_path")
+        mock_energy_grid_hi = Path("energy_grid_hi_sdc_path")
+        mock_energy_grid_ultra = Path("energy_grid_ultra_sdc_path")
+        mock_tess_xyz_8 = Path("tess_xyz_8_sdc_path")
+        mock_lya_series = Path("lya_series_sdc_path")
+        mock_solar_uv_anisotropy = Path("solar_uv_anisotropy_sdc_path")
+        mock_speed_3d_sw = Path("speed_3d_sw_sdc_path")
+        mock_density_3d_sw = Path("density_3d_sw_sdc_path")
+        mock_phion_hydrogen = Path("phion_hydrogen_sdc_path")
+        mock_sw_eqtr_electrons = Path("sw_eqtr_electrons_sdc_path")
+        mock_pipeline_settings = Path("l3bcde_pipeline_settings.json")
+        mock_tess_ang_16 = Path("tess_ang_16_path")
+        mock_repoint_file = Path("repoint.csv")
 
         mock_processing_input_collection.get_file_paths.side_effect = [
             [mock_lya_series],
@@ -41,7 +43,7 @@ def test_fetch_dependencies(self, mock_download):
             [mock_energy_grid_lo],
             [mock_energy_grid_hi],
             [mock_energy_grid_ultra],
-            [mock_repoint_file]
+            [mock_repoint_file],
         ]
 
         mock_lya_series_path = Mock()
@@ -50,7 +52,9 @@ def test_fetch_dependencies(self, mock_download):
         mock_density_3d_sw_path = Mock()
         mock_phion_hydrogen_path = Mock()
         mock_sw_eqtr_electrons_path = Mock()
-        pipeline_settings_path = get_test_data_path("glows/imap_glows_pipeline-settings-l3bcde_20251113_v004.json")
+        pipeline_settings_path = get_test_data_path(
+            "glows/imap_glows_pipeline-settings-l3bcde_20251113_v004.json"
+        )
         mock_energy_grid_lo_path = Mock()
         mock_tess_xyz_8_path = Mock()
         mock_energy_grid_hi_path = Mock()
@@ -74,62 +78,79 @@ def test_fetch_dependencies(self, mock_download):
             mock_downloaded_repoint_file,
         ]
 
-        actual_dependencies = GlowsL3EDependencies.fetch_dependencies(mock_processing_input_collection)
+        actual_dependencies = GlowsL3EDependencies.fetch_dependencies(
+            mock_processing_input_collection
+        )
 
         self.assertEqual(13, mock_processing_input_collection.get_file_paths.call_count)
 
-        mock_processing_input_collection.get_file_paths.assert_has_calls([
-            call(source="glows", descriptor="lya"),
-            call(source="glows", descriptor="uv-anis"),
-            call(source="glows", descriptor="speed"),
-            call(source="glows", descriptor="p-dens"),
-            call(source="glows", descriptor="phion"),
-            call(source="glows", descriptor="e-dens"),
-            call(source="glows", descriptor="pipeline-settings-l3bcde"),
-            call(source="glows", descriptor="tess-xyz-8"),
-            call(source="glows", descriptor="tess-ang-16"),
-            call(source="glows", descriptor="energy-grid-lo"),
-            call(source="glows", descriptor="energy-grid-hi"),
-            call(source="glows", descriptor="energy-grid-ultra"),
-            call(data_type=RepointInput.data_type),
-        ])
-
-        mock_download.assert_has_calls([
-            call(mock_lya_series),
-            call(mock_solar_uv_anisotropy),
-            call(mock_speed_3d_sw),
-            call(mock_density_3d_sw),
-            call(mock_phion_hydrogen),
-            call(mock_sw_eqtr_electrons),
-            call(mock_pipeline_settings),
-            call(mock_tess_xyz_8),
-            call(mock_tess_ang_16),
-            call(mock_energy_grid_lo),
-            call(mock_energy_grid_hi),
-            call(mock_energy_grid_ultra),
-            call(mock_repoint_file),
-        ], any_order=False)
-
-        expected_pipeline_settings = {"executable_dependency_paths": {
-            "energy-grid-lo": "EnGridLo.dat",
-            "energy-grid-hi": "EnGridHi.dat",
-            "energy-grid-ultra": "EnGridUltra.dat",
-            "tess-xyz-8": "tessXYZ8.dat",
-            "tess-ang-16": "tessAng16.dat",
-            "ionization-files": "ionization.files.dat",
-        }}
+        mock_processing_input_collection.get_file_paths.assert_has_calls(
+            [
+                call(source="glows", descriptor="lya"),
+                call(source="glows", descriptor="uv-anis"),
+                call(source="glows", descriptor="speed"),
+                call(source="glows", descriptor="p-dens"),
+                call(source="glows", descriptor="phion"),
+                call(source="glows", descriptor="e-dens"),
+                call(source="glows", descriptor="pipeline-settings-l3bcde"),
+                call(source="glows", descriptor="tess-xyz-8"),
+                call(source="glows", descriptor="tess-ang-16"),
+                call(source="glows", descriptor="energy-grid-lo"),
+                call(source="glows", descriptor="energy-grid-hi"),
+                call(source="glows", descriptor="energy-grid-ultra"),
+                call(data_type=RepointInput.data_type),
+            ]
+        )
+
+        mock_download.assert_has_calls(
+            [
+                call(mock_lya_series),
+                call(mock_solar_uv_anisotropy),
+                call(mock_speed_3d_sw),
+                call(mock_density_3d_sw),
+                call(mock_phion_hydrogen),
+                call(mock_sw_eqtr_electrons),
+                call(mock_pipeline_settings),
+                call(mock_tess_xyz_8),
+                call(mock_tess_ang_16),
+                call(mock_energy_grid_lo),
+                call(mock_energy_grid_hi),
+                call(mock_energy_grid_ultra),
+                call(mock_repoint_file),
+            ],
+            any_order=False,
+        )
+
+        expected_pipeline_settings = {
+            "executable_dependency_paths": {
+                "energy-grid-lo": "EnGridLo.dat",
+                "energy-grid-hi": "EnGridHi.dat",
+                "energy-grid-ultra": "EnGridUltra.dat",
+                "tess-xyz-8": "tessXYZ8.dat",
+                "tess-ang-16": "tessAng16.dat",
+                "ionization-files": "ionization.files.dat",
+            }
+        }
 
         self.assertEqual(mock_lya_series_path, actual_dependencies.lya_series)
-        self.assertEqual(mock_solar_uv_anisotropy_path, actual_dependencies.solar_uv_anisotropy)
+        self.assertEqual(
+            mock_solar_uv_anisotropy_path, actual_dependencies.solar_uv_anisotropy
+        )
         self.assertEqual(mock_speed_3d_sw_path, actual_dependencies.speed_3d_sw)
         self.assertEqual(mock_density_3d_sw_path, actual_dependencies.density_3d_sw)
         self.assertEqual(mock_phion_hydrogen_path, actual_dependencies.phion_hydrogen)
-        self.assertEqual(mock_sw_eqtr_electrons_path, actual_dependencies.sw_eqtr_electrons)
-        self.assertEqual(expected_pipeline_settings['executable_dependency_paths'],
-                         actual_dependencies.pipeline_settings['executable_dependency_paths'])
+        self.assertEqual(
+            mock_sw_eqtr_electrons_path, actual_dependencies.sw_eqtr_electrons
+        )
+        self.assertEqual(
+            expected_pipeline_settings["executable_dependency_paths"],
+            actual_dependencies.pipeline_settings["executable_dependency_paths"],
+        )
         self.assertEqual(mock_energy_grid_lo_path, actual_dependencies.energy_grid_lo)
         self.assertEqual(mock_energy_grid_hi_path, actual_dependencies.energy_grid_hi)
-        self.assertEqual(mock_energy_grid_ultra_path, actual_dependencies.energy_grid_ultra)
+        self.assertEqual(
+            mock_energy_grid_ultra_path, actual_dependencies.energy_grid_ultra
+        )
         self.assertEqual(mock_tess_xyz_8_path, actual_dependencies.tess_xyz_8)
         self.assertEqual(mock_tess_ang_16_path, actual_dependencies.tess_ang16)
         self.assertEqual(
@@ -140,7 +161,9 @@ def test_fetch_dependencies(self, mock_download):
             mock_downloaded_repoint_file, actual_dependencies.repointing_file
         )
 
-    @patch("imap_l3_processing.glows.l3e.glows_l3e_dependencies.furnish_spice_metakernel")
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_dependencies.furnish_spice_metakernel"
+    )
     def test_collect_spice_dependencies(self, mock_furnish_spice_metakernel):
         expected_kernel_types_with_predicted = [
             SpiceKernelTypes.ScienceFrames,
@@ -150,47 +173,65 @@ def test_collect_spice_dependencies(self, mock_furnish_spice_metakernel):
             SpiceKernelTypes.PointingAttitude,
             SpiceKernelTypes.PlanetaryEphemeris,
             SpiceKernelTypes.Leapseconds,
-            SpiceKernelTypes.SpacecraftClock
+            SpiceKernelTypes.SpacecraftClock,
         ]
         expected_kernel_types_without_predicted = [
-            x for x in expected_kernel_types_with_predicted if x != SpiceKernelTypes.EphemerisPredicted
+            x
+            for x in expected_kernel_types_with_predicted
+            if x != SpiceKernelTypes.EphemerisPredicted
         ]
 
         metakernel_with_predict_path = Path("metakernel_with_predict_ephem.txt")
-        kernels_with_predict = [Path("some_kernel_with_imap_data"), Path("predicted_ephem"), Path("some_kernel_with_planet_data")]
+        kernels_with_predict = [
+            Path("some_kernel_with_imap_data"),
+            Path("predicted_ephem"),
+            Path("some_kernel_with_planet_data"),
+        ]
         metakernel_without_predict_path = Path("metakernel_without_predict_ephem.txt")
-        kernels_without_predict = [Path("some_kernel_with_imap_data"), Path("some_kernel_with_planet_data")]
-        metakernel_output_with_predict = FurnishMetakernelOutput(metakernel_path=metakernel_with_predict_path,
-                                         spice_kernel_paths=kernels_with_predict, )
-        metakernel_output_without_predict = FurnishMetakernelOutput(metakernel_path=metakernel_without_predict_path,
-                                         spice_kernel_paths=kernels_without_predict)
+        kernels_without_predict = [
+            Path("some_kernel_with_imap_data"),
+            Path("some_kernel_with_planet_data"),
+        ]
+        metakernel_output_with_predict = FurnishMetakernelOutput(
+            metakernel_path=metakernel_with_predict_path,
+            spice_kernel_paths=kernels_with_predict,
+        )
+        metakernel_output_without_predict = FurnishMetakernelOutput(
+            metakernel_path=metakernel_without_predict_path,
+            spice_kernel_paths=kernels_without_predict,
+        )
         mock_furnish_spice_metakernel.side_effect = [
             metakernel_output_with_predict,
-            metakernel_output_without_predict
+            metakernel_output_without_predict,
         ]
 
-        actual_metakernel_with_predict, actual_metakernel_without_predict = GlowsL3EDependencies.collect_spice_dependencies(sentinel.start_date, sentinel.end_date)
-
-        mock_furnish_spice_metakernel.assert_has_calls([
-            call(
-                start_date=sentinel.start_date,
-                end_date=sentinel.end_date,
-                kernel_types=expected_kernel_types_with_predicted,
-                metakernel_file_name="metakernel_with_predict_ephem.txt",
-            ),
-            call(
-                start_date=sentinel.start_date,
-                end_date=sentinel.end_date,
-                kernel_types=expected_kernel_types_without_predicted,
-                metakernel_file_name="metakernel_without_predict_ephem.txt",
+        actual_metakernel_with_predict, actual_metakernel_without_predict = (
+            GlowsL3EDependencies.collect_spice_dependencies(
+                sentinel.start_date, sentinel.end_date
+            )
+        )
 
-            ),
-        ])
+        mock_furnish_spice_metakernel.assert_has_calls(
+            [
+                call(
+                    start_date=sentinel.start_date,
+                    end_date=sentinel.end_date,
+                    kernel_types=expected_kernel_types_with_predicted,
+                    metakernel_file_name="metakernel_with_predict_ephem.txt",
+                ),
+                call(
+                    start_date=sentinel.start_date,
+                    end_date=sentinel.end_date,
+                    kernel_types=expected_kernel_types_without_predicted,
+                    metakernel_file_name="metakernel_without_predict_ephem.txt",
+                ),
+            ]
+        )
 
         self.assertEqual(metakernel_output_with_predict, actual_metakernel_with_predict)
-        self.assertEqual(metakernel_output_without_predict, actual_metakernel_without_predict)
-
-
+        self.assertEqual(
+            metakernel_output_without_predict, actual_metakernel_without_predict
+        )
 
     def test_get_hi_parents(self):
         dependencies = self._create_l3e_dependencies()
@@ -245,21 +286,21 @@ def test_get_ul_parents(self):
 
         self.assertEqual(expected_parent_file_names, dependencies.get_ul_parents())
 
-    @patch('builtins.open', new_callable=mock_open)
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_dependencies.shutil')
+    @patch("builtins.open", new_callable=mock_open)
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_dependencies.shutil")
     def test_copy_dependencies(self, mock_shutil, mock_file):
         glows_l3e_dependencies = GlowsL3EDependencies(
-            Path('2025/05/03/imap_glows_energy_grid_lo'),
-            Path('2025/05/03/imap_glows_energy_grid_hi'),
-            Path('2025/05/03/imap_glows_energy_grid_ultra'),
-            Path('2025/05/03/imap_glows_tess_xyz_8'),
-            Path('2025/05/03/imap_glows_tess_ang16'),
-            Path('2025/05/03/imap_glows_lya_series'),
-            Path('2025/05/03/imap_glows_solar_uv_anisotropy'),
-            Path('2025/05/03/imap_glows_speed_3d_sw'),
-            Path('2025/05/03/imap_glows_density_3d_sw'),
-            Path('2025/05/03/imap_glows_phion_hydrogen'),
-            Path('2025/05/03/imap_glows_sw_eqtr_electrons'),
+            Path("2025/05/03/imap_glows_energy_grid_lo"),
+            Path("2025/05/03/imap_glows_energy_grid_hi"),
+            Path("2025/05/03/imap_glows_energy_grid_ultra"),
+            Path("2025/05/03/imap_glows_tess_xyz_8"),
+            Path("2025/05/03/imap_glows_tess_ang16"),
+            Path("2025/05/03/imap_glows_lya_series"),
+            Path("2025/05/03/imap_glows_solar_uv_anisotropy"),
+            Path("2025/05/03/imap_glows_speed_3d_sw"),
+            Path("2025/05/03/imap_glows_density_3d_sw"),
+            Path("2025/05/03/imap_glows_phion_hydrogen"),
+            Path("2025/05/03/imap_glows_sw_eqtr_electrons"),
             {
                 "executable_dependency_paths": {
                     "energy-grid-lo": "EnGridLo.dat",
@@ -271,40 +312,62 @@ def test_copy_dependencies(self, mock_shutil, mock_file):
                 }
             },
             Path("path/to/some/pipeline_settings_file.csv"),
-            Path("repoint.csv")
+            Path("repoint.csv"),
         )
 
-        expected_energy_grid_lo = 'EnGridLo.dat'
-        expected_energy_grid_hi = 'EnGridHi.dat'
-        expected_energy_grid_ultra = 'EnGridUltra.dat'
-        expected_tess_xyz_8 = 'tessXYZ8.dat'
-        expected_tess_ang16 = 'tessAng16.dat'
+        expected_energy_grid_lo = "EnGridLo.dat"
+        expected_energy_grid_hi = "EnGridHi.dat"
+        expected_energy_grid_ultra = "EnGridUltra.dat"
+        expected_tess_xyz_8 = "tessXYZ8.dat"
+        expected_tess_ang16 = "tessAng16.dat"
 
         glows_l3e_dependencies.copy_dependencies()
 
-        mock_shutil.copy.assert_has_calls([
-            call(glows_l3e_dependencies.energy_grid_lo, expected_energy_grid_lo),
-            call(glows_l3e_dependencies.energy_grid_hi, expected_energy_grid_hi),
-            call(glows_l3e_dependencies.energy_grid_ultra, expected_energy_grid_ultra),
-            call(glows_l3e_dependencies.tess_xyz_8, expected_tess_xyz_8),
-            call(glows_l3e_dependencies.tess_ang16, expected_tess_ang16),
-            call(glows_l3e_dependencies.lya_series, glows_l3e_dependencies.lya_series.name),
-            call(glows_l3e_dependencies.solar_uv_anisotropy, glows_l3e_dependencies.solar_uv_anisotropy.name),
-            call(glows_l3e_dependencies.speed_3d_sw, glows_l3e_dependencies.speed_3d_sw.name),
-            call(glows_l3e_dependencies.density_3d_sw, glows_l3e_dependencies.density_3d_sw.name),
-            call(glows_l3e_dependencies.phion_hydrogen, glows_l3e_dependencies.phion_hydrogen.name),
-            call(glows_l3e_dependencies.sw_eqtr_electrons, glows_l3e_dependencies.sw_eqtr_electrons.name),
-        ])
+        mock_shutil.copy.assert_has_calls(
+            [
+                call(glows_l3e_dependencies.energy_grid_lo, expected_energy_grid_lo),
+                call(glows_l3e_dependencies.energy_grid_hi, expected_energy_grid_hi),
+                call(
+                    glows_l3e_dependencies.energy_grid_ultra, expected_energy_grid_ultra
+                ),
+                call(glows_l3e_dependencies.tess_xyz_8, expected_tess_xyz_8),
+                call(glows_l3e_dependencies.tess_ang16, expected_tess_ang16),
+                call(
+                    glows_l3e_dependencies.lya_series,
+                    glows_l3e_dependencies.lya_series.name,
+                ),
+                call(
+                    glows_l3e_dependencies.solar_uv_anisotropy,
+                    glows_l3e_dependencies.solar_uv_anisotropy.name,
+                ),
+                call(
+                    glows_l3e_dependencies.speed_3d_sw,
+                    glows_l3e_dependencies.speed_3d_sw.name,
+                ),
+                call(
+                    glows_l3e_dependencies.density_3d_sw,
+                    glows_l3e_dependencies.density_3d_sw.name,
+                ),
+                call(
+                    glows_l3e_dependencies.phion_hydrogen,
+                    glows_l3e_dependencies.phion_hydrogen.name,
+                ),
+                call(
+                    glows_l3e_dependencies.sw_eqtr_electrons,
+                    glows_l3e_dependencies.sw_eqtr_electrons.name,
+                ),
+            ]
+        )
 
         mock_file.assert_called_once_with("ionization.files.dat", "w")
-        mock_file.return_value.write.assert_called_once_with((
+        mock_file.return_value.write.assert_called_once_with(
             f"{glows_l3e_dependencies.lya_series.name}\n"
             f"{glows_l3e_dependencies.solar_uv_anisotropy.name}\n"
             f"{glows_l3e_dependencies.speed_3d_sw.name}\n"
             f"{glows_l3e_dependencies.density_3d_sw.name}\n"
             f"{glows_l3e_dependencies.phion_hydrogen.name}\n"
             f"{glows_l3e_dependencies.sw_eqtr_electrons.name}\n"
-        ))
+        )
 
     def _create_l3e_dependencies(self) -> GlowsL3EDependencies:
         return GlowsL3EDependencies(
diff --git a/tests/glows/l3e/test_glows_l3e_hi_model.py b/tests/glows/l3e/test_glows_l3e_hi_model.py
index 06a04b3f1..88f0c9cad 100644
--- a/tests/glows/l3e/test_glows_l3e_hi_model.py
+++ b/tests/glows/l3e/test_glows_l3e_hi_model.py
@@ -1,10 +1,13 @@
 import unittest
 from datetime import datetime, timedelta
-from unittest.mock import sentinel, Mock, MagicMock, patch
+from unittest.mock import MagicMock, Mock, patch, sentinel
 
 import numpy as np
 
-from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eCallArguments, GlowsL3eSpacecraftInfo
+from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
+    GlowsL3eCallArguments,
+    GlowsL3eSpacecraftInfo,
+)
 from imap_l3_processing.glows.l3e.glows_l3e_hi_model import GlowsL3EHiData
 from imap_l3_processing.models import DataProductVariable
 from tests.test_helpers import get_test_instrument_team_data_path
@@ -14,21 +17,26 @@ class TestL3eHiModel(unittest.TestCase):
     def test_l3e_hi_model_to_data_product_variables(self):
 
         test_cases = {
-            'case 1':(
-                [1.2234,2,4.51,66.7666],
-                [1,41650233.0,4.22,9.5],
-                ['1.22','2.00','4.51','66.77'],
-                ['1','41650233','4','10']
+            "case 1": (
+                [1.2234, 2, 4.51, 66.7666],
+                [1, 41650233.0, 4.22, 9.5],
+                ["1.22", "2.00", "4.51", "66.77"],
+                ["1", "41650233", "4", "10"],
+            ),
+            "case 2": (
+                [4.536, 48.193, 4253.1],
+                [13, 14.0, 34.2, 19.5],
+                ["4.54", "48.19", "4253.10"],
+                ["13", "14", "34", "20"],
             ),
-            'case 2':(
-                [4.536,48.193,4253.1],
-                [13,14.0,34.2,19.5],
-                ['4.54','48.19','4253.10'],
-                ['13','14','34','20']
-            )
         }
 
-        for name, (energy_array, spin_angle_array, expected_energy_labels, expected_spin_angle_labels) in test_cases.items():
+        for name, (
+            energy_array,
+            spin_angle_array,
+            expected_energy_labels,
+            expected_spin_angle_labels,
+        ) in test_cases.items():
             with self.subTest(name):
                 l3e_hi: GlowsL3EHiData = GlowsL3EHiData(
                     Mock(),
@@ -58,134 +66,806 @@ def test_l3e_hi_model_to_data_product_variables(self):
                     DataProductVariable("epoch", sentinel.epoch),
                     DataProductVariable("epoch_delta", sentinel.epoch_delta),
                     DataProductVariable("energy_grid", energy_array),
-                    DataProductVariable("energy_delta_plus", sentinel.energy_delta_plus),
-                    DataProductVariable("energy_delta_minus", sentinel.energy_delta_minus),
+                    DataProductVariable(
+                        "energy_delta_plus", sentinel.energy_delta_plus
+                    ),
+                    DataProductVariable(
+                        "energy_delta_minus", sentinel.energy_delta_minus
+                    ),
                     DataProductVariable("spin_angle", spin_angle_array),
                     DataProductVariable("surv_prob", sentinel.probability_of_survival),
                     DataProductVariable("energy_label", expected_energy_labels),
                     DataProductVariable("spin_angle_label", expected_spin_angle_labels),
-                    DataProductVariable("spin_axis_latitude", sentinel.spin_axis_latitude),
-                    DataProductVariable("spin_axis_longitude", sentinel.spin_axis_longitude),
+                    DataProductVariable(
+                        "spin_axis_latitude", sentinel.spin_axis_latitude
+                    ),
+                    DataProductVariable(
+                        "spin_axis_longitude", sentinel.spin_axis_longitude
+                    ),
                     DataProductVariable("program_version", sentinel.program_version),
-                    DataProductVariable("spacecraft_radius", sentinel.spacecraft_radius),
-                    DataProductVariable("spacecraft_latitude", sentinel.spacecraft_latitude),
-                    DataProductVariable("spacecraft_longitude", sentinel.spacecraft_longitude),
-                    DataProductVariable("spacecraft_velocity_x", sentinel.spacecraft_velocity_x),
-                    DataProductVariable("spacecraft_velocity_y", sentinel.spacecraft_velocity_y),
-                    DataProductVariable("spacecraft_velocity_z", sentinel.spacecraft_velocity_z),
+                    DataProductVariable(
+                        "spacecraft_radius", sentinel.spacecraft_radius
+                    ),
+                    DataProductVariable(
+                        "spacecraft_latitude", sentinel.spacecraft_latitude
+                    ),
+                    DataProductVariable(
+                        "spacecraft_longitude", sentinel.spacecraft_longitude
+                    ),
+                    DataProductVariable(
+                        "spacecraft_velocity_x", sentinel.spacecraft_velocity_x
+                    ),
+                    DataProductVariable(
+                        "spacecraft_velocity_y", sentinel.spacecraft_velocity_y
+                    ),
+                    DataProductVariable(
+                        "spacecraft_velocity_z", sentinel.spacecraft_velocity_z
+                    ),
                     DataProductVariable("elongation", sentinel.elongation),
                     DataProductVariable("glows_flags", sentinel.glows_flags),
                 ]
 
                 self.assertEqual(expected_data_products, data_products)
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_hi_model.calculate_energy_deltas')
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_hi_model.calculate_energy_deltas")
     def test_convert_dat_to_glows_l3e_hi_product(self, mock_calculate_energy_deltas):
-        hi_file_path = get_test_instrument_team_data_path("glows/probSur.Imap.Hi_2009.000_90.00.txt")
+        hi_file_path = get_test_instrument_team_data_path(
+            "glows/probSur.Imap.Hi_2009.000_90.00.txt"
+        )
         expected_epoch = datetime(year=2009, month=8, day=16)
         epoch_delta = timedelta(hours=8)
-        expected_epoch_delta_in_nanoseconds = 8*3600*1e9
-        expected_energy = [0.3749896542976582, 0.4881381387720609, 0.6354277772546533, 0.8271602401781013,
-                           1.076745599456691, 1.401640250140305, 1.824567838312671, 2.375108588863466,
-                           3.091768193234094, 4.024670958420553, 5.239065580337116, 6.819888740878518,
-                           8.877705713882047, 11.55644347537280, 15.04345718406192, 19.58263409767632]
+        expected_epoch_delta_in_nanoseconds = 8 * 3600 * 1e9
+        expected_energy = [
+            0.3749896542976582,
+            0.4881381387720609,
+            0.6354277772546533,
+            0.8271602401781013,
+            1.076745599456691,
+            1.401640250140305,
+            1.824567838312671,
+            2.375108588863466,
+            3.091768193234094,
+            4.024670958420553,
+            5.239065580337116,
+            6.819888740878518,
+            8.877705713882047,
+            11.55644347537280,
+            15.04345718406192,
+            19.58263409767632,
+        ]
 
-        mock_calculate_energy_deltas.return_value = (sentinel.expected_energy_delta_plus, sentinel.expected_energy_delta_minus)
+        mock_calculate_energy_deltas.return_value = (
+            sentinel.expected_energy_delta_plus,
+            sentinel.expected_energy_delta_minus,
+        )
 
         expected_spin_angle = np.array(
-            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
-                               25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
-                               47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
-                               69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
-                               91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-                               110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127,
-                               128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,
-                               146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-                               164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
-                               182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199,
-                               200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217,
-                               218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235,
-                               236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253,
-                               254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
-                               272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-                               290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,
-                               308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,
-                               326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343,
-             344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360], dtype=np.float64)
+            [
+                1,
+                2,
+                3,
+                4,
+                5,
+                6,
+                7,
+                8,
+                9,
+                10,
+                11,
+                12,
+                13,
+                14,
+                15,
+                16,
+                17,
+                18,
+                19,
+                20,
+                21,
+                22,
+                23,
+                24,
+                25,
+                26,
+                27,
+                28,
+                29,
+                30,
+                31,
+                32,
+                33,
+                34,
+                35,
+                36,
+                37,
+                38,
+                39,
+                40,
+                41,
+                42,
+                43,
+                44,
+                45,
+                46,
+                47,
+                48,
+                49,
+                50,
+                51,
+                52,
+                53,
+                54,
+                55,
+                56,
+                57,
+                58,
+                59,
+                60,
+                61,
+                62,
+                63,
+                64,
+                65,
+                66,
+                67,
+                68,
+                69,
+                70,
+                71,
+                72,
+                73,
+                74,
+                75,
+                76,
+                77,
+                78,
+                79,
+                80,
+                81,
+                82,
+                83,
+                84,
+                85,
+                86,
+                87,
+                88,
+                89,
+                90,
+                91,
+                92,
+                93,
+                94,
+                95,
+                96,
+                97,
+                98,
+                99,
+                100,
+                101,
+                102,
+                103,
+                104,
+                105,
+                106,
+                107,
+                108,
+                109,
+                110,
+                111,
+                112,
+                113,
+                114,
+                115,
+                116,
+                117,
+                118,
+                119,
+                120,
+                121,
+                122,
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+            dtype=np.float64,
+        )
 
         expected_survival_probability_index_8 = [
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+            0.8700869953326894,
+            0.8697240184707551,
+            0.8693228177240478,
+            0.8689058188918802,
+            0.8684725481212315,
+            0.8679864074740811,
+            0.8674846479170634,
+            0.8669565386658245,
+            0.8663987931741293,
+            0.8658292976477142,
+            0.8652400888199558,
+            0.8646159344170304,
+            0.8639645675796087,
+            0.8632894095541491,
+            0.8625775450692091,
+            0.8618508940390988,
+            0.8610662587207843,
+            0.8602420947979763,
+            0.8593876584994568,
+            0.8585135191949332,
+            0.8576052938566121,
+            0.8566647318199154,
+            0.8557040506174665,
+            0.8546950723577439,
+            0.8536288094574640,
+            0.8525140899403636,
+            0.8513132917711719,
+            0.8500547007246933,
+            0.8487031312551817,
+            0.8473557396340265,
+            0.8459512894425343,
+            0.8445566831823717,
+            0.8431045795291137,
+            0.8416159404817778,
+            0.8400524806016000,
+            0.8384689895980637,
+            0.8367684445784317,
+            0.8350331558210311,
+            0.8332845367962914,
+            0.8315062693986432,
+            0.8297374174385194,
+            0.8279583012690772,
+            0.8261865084237222,
+            0.8243942252989166,
+            0.8225734619575098,
+            0.8207549333753910,
+            0.8189216746886322,
+            0.8170647071344279,
+            0.8152032410605699,
+            0.8133635963914283,
+            0.8115323792261186,
+            0.8097108939706253,
+            0.8079113822965517,
+            0.8061213846923494,
+            0.8043408988724510,
+            0.8025426088683201,
+            0.8007814521258226,
+            0.7990987049136656,
+            0.7976307399916195,
+            0.7964515215938459,
+            0.7955921535640210,
+            0.7949442384447885,
+            0.7944722659231427,
+            0.7940835322381860,
+            0.7937574117930607,
+            0.7935319301733967,
+            0.7935410410847828,
+            0.7938430647730597,
+            0.7944549685472722,
+            0.7953201271372370,
+            0.7963353399742404,
+            0.7974666717653967,
+            0.7986284092599631,
+            0.7998758861147037,
+            0.8011160184065348,
+            0.8023966457837027,
+            0.8037516415367600,
+            0.8050684075928743,
+            0.8064302119915984,
+            0.8078016416648872,
+            0.8091911716432389,
+            0.8105695706967822,
+            0.8120156466367995,
+            0.8134179619097438,
+            0.8148718795368090,
+            0.8163047327580164,
+            0.8177597708329662,
+            0.8192293148111823,
+            0.8207074925156855,
+            0.8222059167731761,
+            0.8236269008644003,
+            0.8250637062532796,
+            0.8264471756096386,
+            0.8278214795110156,
+            0.8291718933569523,
+            0.8305216689363951,
+            0.8318736481229983,
+            0.8331525651945888,
+            0.8344452634309963,
+            0.8356818249509860,
+            0.8368599136784342,
+            0.8379673523433857,
+            0.8390344990656603,
+            0.8400886238827425,
+            0.8411180665764300,
+            0.8421142442355391,
+            0.8431022785867203,
+            0.8440249657050137,
+            0.8449432095397274,
+            0.8458194063508257,
+            0.8466739192575265,
+            0.8474814859179764,
+            0.8482437338193161,
+            0.8489939765878994,
+            0.8497227079261302,
+            0.8503990404728226,
+            0.8510604321333799,
+            0.8516904214587106,
+            0.8522976961031688,
+            0.8528845012461620,
+            0.8534308899160006,
+            0.8539713965861790,
+            0.8544590509167357,
+            0.8549124471041260,
+            0.8553675524645131,
+            0.8557728466303356,
+            0.8561569562069312,
+            0.8565541426261458,
+            0.8568977169319288,
+            0.8572347416962208,
+            0.8575440542473425,
+            0.8578267049055189,
+            0.8580766194405365,
+            0.8583205882562980,
+            0.8585270608521915,
+            0.8587229325896764,
+            0.8589085011920993,
+            0.8590676557001768,
+            0.8592175194277411,
+            0.8593425048092776,
+            0.8594494587601790,
+            0.8595467856580804,
+            0.8596372067196015,
+            0.8596791152243492,
+            0.8597382969327882,
+            0.8597823055545957,
+            0.8597949778619707,
+            0.8598094662503897,
+            0.8598097898645332,
+            0.8597880908859125,
+            0.8597611602425049,
+            0.8597126790735709,
+            0.8596589797562888,
+            0.8595919584381491,
+            0.8595034812547143,
+            0.8594095601838275,
+            0.8593104306912213,
+            0.8591896577901775,
+            0.8590583978048111,
         ]
         expected_survival_probability_shape = (1, 16, 360)
 
@@ -199,7 +879,7 @@ def test_convert_dat_to_glows_l3e_hi_product(self, mock_calculate_energy_deltas)
         spacecraft_info = MagicMock(spec=GlowsL3eSpacecraftInfo)
         spacecraft_info.spin_axis_latitude = spin_axis_lat
         spacecraft_info.spin_axis_longitude = spin_axis_lon
-        spacecraft_info.spacecraft_radius = .5
+        spacecraft_info.spacecraft_radius = 0.5
         spacecraft_info.spacecraft_longitude = 85.4
         spacecraft_info.spacecraft_latitude = 45.1
 
@@ -211,36 +891,75 @@ def test_convert_dat_to_glows_l3e_hi_product(self, mock_calculate_energy_deltas)
 
         args.elongation = 135.0
 
-        expected_program_version = 'Hi.v00.01'
+        expected_program_version = "Hi.v00.01"
 
-        l3e_hi_product: GlowsL3EHiData = GlowsL3EHiData.convert_dat_to_glows_l3e_hi_product(mock_metadata, hi_file_path,
-                                                                                            expected_epoch,
-                                                                                            epoch_delta,
-                                                                                            args)
+        l3e_hi_product: GlowsL3EHiData = (
+            GlowsL3EHiData.convert_dat_to_glows_l3e_hi_product(
+                mock_metadata, hi_file_path, expected_epoch, epoch_delta, args
+            )
+        )
 
-        np.testing.assert_equal(np.array([expected_epoch]), l3e_hi_product.epoch, strict=True)
-        np.testing.assert_equal(np.array([expected_epoch_delta_in_nanoseconds]), l3e_hi_product.epoch_delta, strict=True)
+        np.testing.assert_equal(
+            np.array([expected_epoch]), l3e_hi_product.epoch, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([expected_epoch_delta_in_nanoseconds]),
+            l3e_hi_product.epoch_delta,
+            strict=True,
+        )
         np.testing.assert_equal(l3e_hi_product.energy, expected_energy, strict=True)
-        np.testing.assert_equal(l3e_hi_product.spin_angle, expected_spin_angle, strict=True)
-        np.testing.assert_equal(l3e_hi_product.probability_of_survival.shape, expected_survival_probability_shape,
-                                strict=True)
-        np.testing.assert_equal(l3e_hi_product.probability_of_survival[0][7],
-                                expected_survival_probability_index_8, strict=True)
+        np.testing.assert_equal(
+            l3e_hi_product.spin_angle, expected_spin_angle, strict=True
+        )
+        np.testing.assert_equal(
+            l3e_hi_product.probability_of_survival.shape,
+            expected_survival_probability_shape,
+            strict=True,
+        )
+        np.testing.assert_equal(
+            l3e_hi_product.probability_of_survival[0][7],
+            expected_survival_probability_index_8,
+            strict=True,
+        )
 
-        np.testing.assert_equal(np.array([spin_axis_lat]), l3e_hi_product.spin_axis_lat, strict=True)
-        np.testing.assert_equal(np.array([spin_axis_lon]), l3e_hi_product.spin_axis_lon, strict=True)
-        np.testing.assert_equal(np.array([expected_program_version]), l3e_hi_product.program_version, strict=True)
+        np.testing.assert_equal(
+            np.array([spin_axis_lat]), l3e_hi_product.spin_axis_lat, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([spin_axis_lon]), l3e_hi_product.spin_axis_lon, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([expected_program_version]),
+            l3e_hi_product.program_version,
+            strict=True,
+        )
 
-        np.testing.assert_equal(np.array([.5]), l3e_hi_product.spacecraft_radius, strict=True)
-        np.testing.assert_equal(np.array([85.4]), l3e_hi_product.spacecraft_longitude, strict=True)
-        np.testing.assert_equal(np.array([45.1]), l3e_hi_product.spacecraft_latitude, strict=True)
+        np.testing.assert_equal(
+            np.array([0.5]), l3e_hi_product.spacecraft_radius, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([85.4]), l3e_hi_product.spacecraft_longitude, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([45.1]), l3e_hi_product.spacecraft_latitude, strict=True
+        )
 
-        np.testing.assert_equal(np.array([2.1]), l3e_hi_product.spacecraft_velocity_x, strict=True)
-        np.testing.assert_equal(np.array([2.2]), l3e_hi_product.spacecraft_velocity_y, strict=True)
-        np.testing.assert_equal(np.array([2.3]), l3e_hi_product.spacecraft_velocity_z, strict=True)
+        np.testing.assert_equal(
+            np.array([2.1]), l3e_hi_product.spacecraft_velocity_x, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([2.2]), l3e_hi_product.spacecraft_velocity_y, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([2.3]), l3e_hi_product.spacecraft_velocity_z, strict=True
+        )
 
         np.testing.assert_equal([135.0], l3e_hi_product.elongation)
-        np.testing.assert_equal(sentinel.expected_energy_delta_plus, l3e_hi_product.energy_delta_plus)
-        np.testing.assert_equal(sentinel.expected_energy_delta_minus, l3e_hi_product.energy_delta_minus)
+        np.testing.assert_equal(
+            sentinel.expected_energy_delta_plus, l3e_hi_product.energy_delta_plus
+        )
+        np.testing.assert_equal(
+            sentinel.expected_energy_delta_minus, l3e_hi_product.energy_delta_minus
+        )
 
         mock_calculate_energy_deltas.assert_called_once_with(l3e_hi_product.energy)
diff --git a/tests/glows/l3e/test_glows_l3e_initializer.py b/tests/glows/l3e/test_glows_l3e_initializer.py
index b3e6ca806..b6e9b2157 100644
--- a/tests/glows/l3e/test_glows_l3e_initializer.py
+++ b/tests/glows/l3e/test_glows_l3e_initializer.py
@@ -1,63 +1,95 @@
 import unittest
 from datetime import datetime
 from pathlib import Path
-from unittest.mock import patch, call, sentinel, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access import RepointInput
 from imap_data_access.file_validation import Version
 
 from imap_l3_processing.glows.descriptors import GLOWS_L3BCDE_DESCRIPTORS
 from imap_l3_processing.glows.l3d.models import GlowsL3DProcessorOutput
-from imap_l3_processing.glows.l3e.glows_l3e_initializer import GlowsL3EInitializer, GlowsL3EInitializerOutput
+from imap_l3_processing.glows.l3e.glows_l3e_initializer import (
+    GlowsL3EInitializer,
+    GlowsL3EInitializerOutput,
+)
 from imap_l3_processing.glows.l3e.glows_l3e_utils import GlowsL3eVersionsForRepointings
 from imap_l3_processing.models import VersionMap
 from tests.test_helpers import create_mock_query_results
 
-MODULE = 'imap_l3_processing.glows.l3e.glows_l3e_initializer'
+MODULE = "imap_l3_processing.glows.l3e.glows_l3e_initializer"
+
 
 class TestGlowsL3EInitializer(unittest.TestCase):
-    @patch(f'{MODULE}.get_pointing_date_range')
-    @patch(f'{MODULE}.GlowsL3EDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.identify_versions_for_l3e_output_files')
-    @patch(f'{MODULE}.find_first_updated_cr')
-    @patch(f'{MODULE}.get_most_recently_uploaded_ancillary')
-    @patch(f'{MODULE}.imap_data_access.query')
-    @patch(f'{MODULE}.GlowsL3EDependencies.collect_spice_dependencies')
-    def test_get_repointings_to_process(self, mock_collect_spice_dependencies, mock_query, mock_get_most_recently_uploaded_ancillary,
-                                        mock_find_first_updated_cr, mock_identify_versions_for_l3e_output_files,
-                                        mock_fetch_dependencies, mock_get_pointing_date_range):
-        mock_query.side_effect = create_mock_query_results([
-            'imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf',
-            'imap_glows_energy-grid-lo_20200101_v000.cdf',
-            'imap_glows_tess-xyz-8_20200101_v000.cdf',
-            'imap_glows_energy-grid-hi_20200101_v000.cdf',
-            'imap_glows_energy-grid-ultra_20200101_v000.cdf',
-            'imap_glows_tess-ang-16_20200101_v000.cdf',
-        ])
+    @patch(f"{MODULE}.get_pointing_date_range")
+    @patch(f"{MODULE}.GlowsL3EDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.identify_versions_for_l3e_output_files")
+    @patch(f"{MODULE}.find_first_updated_cr")
+    @patch(f"{MODULE}.get_most_recently_uploaded_ancillary")
+    @patch(f"{MODULE}.imap_data_access.query")
+    @patch(f"{MODULE}.GlowsL3EDependencies.collect_spice_dependencies")
+    def test_get_repointings_to_process(
+        self,
+        mock_collect_spice_dependencies,
+        mock_query,
+        mock_get_most_recently_uploaded_ancillary,
+        mock_find_first_updated_cr,
+        mock_identify_versions_for_l3e_output_files,
+        mock_fetch_dependencies,
+        mock_get_pointing_date_range,
+    ):
+        mock_query.side_effect = create_mock_query_results(
+            [
+                "imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf",
+                "imap_glows_energy-grid-lo_20200101_v000.cdf",
+                "imap_glows_tess-xyz-8_20200101_v000.cdf",
+                "imap_glows_energy-grid-hi_20200101_v000.cdf",
+                "imap_glows_energy-grid-ultra_20200101_v000.cdf",
+                "imap_glows_tess-ang-16_20200101_v000.cdf",
+            ]
+        )
 
         mock_get_most_recently_uploaded_ancillary.side_effect = [
-            create_mock_query_results(['imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-lo_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_tess-xyz-8_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-hi_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-ultra_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_tess-ang-16_20200101_v000.cdf'])[0],
+            create_mock_query_results(
+                ["imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf"]
+            )[0],
+            create_mock_query_results(["imap_glows_energy-grid-lo_20200101_v000.cdf"])[
+                0
+            ],
+            create_mock_query_results(["imap_glows_tess-xyz-8_20200101_v000.cdf"])[0],
+            create_mock_query_results(["imap_glows_energy-grid-hi_20200101_v000.cdf"])[
+                0
+            ],
+            create_mock_query_results(
+                ["imap_glows_energy-grid-ultra_20200101_v000.cdf"]
+            )[0],
+            create_mock_query_results(["imap_glows_tess-ang-16_20200101_v000.cdf"])[0],
         ]
 
         mock_spice_with_predict, mock_spice_without_predict = Mock(), Mock()
 
-        mock_collect_spice_dependencies.return_value = (mock_spice_with_predict, mock_spice_without_predict)
+        mock_collect_spice_dependencies.return_value = (
+            mock_spice_with_predict,
+            mock_spice_without_predict,
+        )
 
-        updated_l3d = Path('path/to/imap_glows_l3d_solar-hist_19470303-cr02091_v000.cdf')
-        updated_l3d_text_file_path = Path("imap_glows_e-dens_19470303_20100101_v000.dat")
-        glows_l3d_processor_output = GlowsL3DProcessorOutput(updated_l3d, [updated_l3d_text_file_path], sentinel.last_processed_cr)
-        previous_l3d = 'imap_glows_l3d_solar-hist_19470303-cr02090_v000.cdf'
+        updated_l3d = Path(
+            "path/to/imap_glows_l3d_solar-hist_19470303-cr02091_v000.cdf"
+        )
+        updated_l3d_text_file_path = Path(
+            "imap_glows_e-dens_19470303_20100101_v000.dat"
+        )
+        glows_l3d_processor_output = GlowsL3DProcessorOutput(
+            updated_l3d, [updated_l3d_text_file_path], sentinel.last_processed_cr
+        )
+        previous_l3d = "imap_glows_l3d_solar-hist_19470303-cr02090_v000.cdf"
 
         mock_find_first_updated_cr.return_value = 2091
 
         mock_l3e_dependencies = mock_fetch_dependencies.return_value
-        mock_l3e_dependencies.pipeline_settings = {"start_cr": sentinel.start_of_mission_cr}
-        mock_l3e_dependencies.repointing_file = Path('path/to/repointing_file')
+        mock_l3e_dependencies.pipeline_settings = {
+            "start_cr": sentinel.start_of_mission_cr
+        }
+        mock_l3e_dependencies.repointing_file = Path("path/to/repointing_file")
 
         expected_hi_45 = {1234: Version(None, 1), 2468: Version(None, 1)}
         expected_hi_90 = {1234: Version(None, 2), 2468: Version(None, 2)}
@@ -103,17 +135,37 @@ def test_get_repointings_to_process(self, mock_collect_spice_dependencies, mock_
 
         mock_find_first_updated_cr.assert_called_once_with(updated_l3d, previous_l3d)
 
-        mock_identify_versions_for_l3e_output_files.assert_called_once_with(sentinel.start_of_mission_cr, sentinel.last_processed_cr, 2090, repointing_file_path,
-                                                                            input_major_version, sentinel.reprocess_info)
+        mock_identify_versions_for_l3e_output_files.assert_called_once_with(
+            sentinel.start_of_mission_cr,
+            sentinel.last_processed_cr,
+            2090,
+            repointing_file_path,
+            input_major_version,
+            sentinel.reprocess_info,
+        )
 
-        mock_query.assert_has_calls([
-            call(table="ancillary", instrument='glows', descriptor='pipeline-settings-l3bcde'),
-            call(table="ancillary", instrument='glows', descriptor='energy-grid-lo'),
-            call(table="ancillary", instrument='glows', descriptor='tess-xyz-8'),
-            call(table="ancillary", instrument='glows', descriptor='energy-grid-hi'),
-            call(table="ancillary", instrument='glows', descriptor='energy-grid-ultra'),
-            call(table="ancillary", instrument='glows', descriptor='tess-ang-16'),
-        ])
+        mock_query.assert_has_calls(
+            [
+                call(
+                    table="ancillary",
+                    instrument="glows",
+                    descriptor="pipeline-settings-l3bcde",
+                ),
+                call(
+                    table="ancillary", instrument="glows", descriptor="energy-grid-lo"
+                ),
+                call(table="ancillary", instrument="glows", descriptor="tess-xyz-8"),
+                call(
+                    table="ancillary", instrument="glows", descriptor="energy-grid-hi"
+                ),
+                call(
+                    table="ancillary",
+                    instrument="glows",
+                    descriptor="energy-grid-ultra",
+                ),
+                call(table="ancillary", instrument="glows", descriptor="tess-ang-16"),
+            ]
+        )
 
         mock_l3e_dependencies.copy_dependencies.assert_called_once()
 
@@ -124,37 +176,39 @@ def test_get_repointings_to_process(self, mock_collect_spice_dependencies, mock_
         pipeline_l3e_input_paths = actual_l3e_inputs.get_file_paths(source="glows")
         pipeline_l3e_input_filenames = [p.name for p in pipeline_l3e_input_paths]
 
-        self.assertEqual([
-            updated_l3d.name,
-            "imap_glows_e-dens_19470303_20100101_v000.dat",
-            'imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf',
-            'imap_glows_energy-grid-lo_20200101_v000.cdf',
-            'imap_glows_tess-xyz-8_20200101_v000.cdf',
-            'imap_glows_energy-grid-hi_20200101_v000.cdf',
-            'imap_glows_energy-grid-ultra_20200101_v000.cdf',
-            'imap_glows_tess-ang-16_20200101_v000.cdf',
-        ], pipeline_l3e_input_filenames)
-
-        [repoint_input] = actual_l3e_inputs.get_file_paths(data_type=RepointInput.data_type)
+        self.assertEqual(
+            [
+                updated_l3d.name,
+                "imap_glows_e-dens_19470303_20100101_v000.dat",
+                "imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf",
+                "imap_glows_energy-grid-lo_20200101_v000.cdf",
+                "imap_glows_tess-xyz-8_20200101_v000.cdf",
+                "imap_glows_energy-grid-hi_20200101_v000.cdf",
+                "imap_glows_energy-grid-ultra_20200101_v000.cdf",
+                "imap_glows_tess-ang-16_20200101_v000.cdf",
+            ],
+            pipeline_l3e_input_filenames,
+        )
+
+        [repoint_input] = actual_l3e_inputs.get_file_paths(
+            data_type=RepointInput.data_type
+        )
         self.assertEqual("imap_2026_105_01.repoint.csv", repoint_input.name)
 
         self.assertEqual(actual_initializer_output, expected_initializer_data)
 
-        mock_get_pointing_date_range.assert_has_calls([
-            call(1234),
-            call(2468)
-        ])
+        mock_get_pointing_date_range.assert_has_calls([call(1234), call(2468)])
 
         mock_collect_spice_dependencies.assert_called_once_with(
             start_date=datetime(2010, 1, 1),
             end_date=datetime(2011, 2, 2),
         )
 
-    @patch(f'{MODULE}.get_most_recently_uploaded_ancillary')
-    @patch(f'{MODULE}.imap_data_access')
-    @patch(f'{MODULE}.GlowsL3EDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.identify_versions_for_l3e_output_files')
-    @patch(f'{MODULE}.find_first_updated_cr')
+    @patch(f"{MODULE}.get_most_recently_uploaded_ancillary")
+    @patch(f"{MODULE}.imap_data_access")
+    @patch(f"{MODULE}.GlowsL3EDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.identify_versions_for_l3e_output_files")
+    @patch(f"{MODULE}.find_first_updated_cr")
     def test_get_repointings_to_process_identical_l3d_files(
         self,
         mock_find_first_updated_cr,
@@ -180,21 +234,31 @@ def test_get_repointings_to_process_identical_l3d_files(
             "start_cr": sentinel.start_of_mission_cr
         }
         mock_find_first_updated_cr.return_value = None
-        mock_identify_versions_for_l3e_output_files.return_value = GlowsL3eVersionsForRepointings(
-            repointing_numbers=[],
-            hi_90_repointings={},
-            hi_45_repointings={},
-            lo_repointings={},
-            ultra_sf_repointings={},
-            ultra_hf_repointings={},
+        mock_identify_versions_for_l3e_output_files.return_value = (
+            GlowsL3eVersionsForRepointings(
+                repointing_numbers=[],
+                hi_90_repointings={},
+                hi_45_repointings={},
+                lo_repointings={},
+                ultra_sf_repointings={},
+                ultra_hf_repointings={},
+            )
         )
         mock_get_most_recently_uploaded_ancillary.side_effect = [
-            create_mock_query_results(['imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-lo_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_tess-xyz-8_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-hi_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-ultra_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_tess-ang-16_20200101_v000.cdf'])[0],
+            create_mock_query_results(
+                ["imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf"]
+            )[0],
+            create_mock_query_results(["imap_glows_energy-grid-lo_20200101_v000.cdf"])[
+                0
+            ],
+            create_mock_query_results(["imap_glows_tess-xyz-8_20200101_v000.cdf"])[0],
+            create_mock_query_results(["imap_glows_energy-grid-hi_20200101_v000.cdf"])[
+                0
+            ],
+            create_mock_query_results(
+                ["imap_glows_energy-grid-ultra_20200101_v000.cdf"]
+            )[0],
+            create_mock_query_results(["imap_glows_tess-ang-16_20200101_v000.cdf"])[0],
         ]
 
         repointing_file_path = Path("imap_2026_105_01.repoint.csv")
@@ -205,7 +269,9 @@ def test_get_repointings_to_process_identical_l3d_files(
             sentinel.version_map,
             sentinel.reprocess_info,
         )
-        mock_find_first_updated_cr.assert_called_once_with(glows_l3d_processor_output.l3d_cdf_file_path, previous_l3d)
+        mock_find_first_updated_cr.assert_called_once_with(
+            glows_l3d_processor_output.l3d_cdf_file_path, previous_l3d
+        )
         mock_identify_versions_for_l3e_output_files.assert_called_once_with(
             sentinel.start_of_mission_cr,
             expected_last_cr,
@@ -216,43 +282,64 @@ def test_get_repointings_to_process_identical_l3d_files(
         )
         self.assertIsNone(actual_initializer_output)
 
-    @patch(f'{MODULE}.imap_data_access.query')
-    @patch(f'{MODULE}.get_most_recently_uploaded_ancillary')
-    @patch(f'{MODULE}.GlowsL3EDependencies.fetch_dependencies')
-    @patch(f'{MODULE}.identify_versions_for_l3e_output_files')
-    @patch(f'{MODULE}.find_first_updated_cr')
-    def test_get_repointings_to_process_uses_mission_start_cr_when_no_previous_l3d(self, mock_find_first_updated_cr,
-                                                                                   mock_identify_versions_for_l3e_output_files,
-                                                                                   mock_fetch_dependencies,
-                                                                                   mock_get_most_recently_uploaded_ancillary,
-                                                                                   _, ):
+    @patch(f"{MODULE}.imap_data_access.query")
+    @patch(f"{MODULE}.get_most_recently_uploaded_ancillary")
+    @patch(f"{MODULE}.GlowsL3EDependencies.fetch_dependencies")
+    @patch(f"{MODULE}.identify_versions_for_l3e_output_files")
+    @patch(f"{MODULE}.find_first_updated_cr")
+    def test_get_repointings_to_process_uses_mission_start_cr_when_no_previous_l3d(
+        self,
+        mock_find_first_updated_cr,
+        mock_identify_versions_for_l3e_output_files,
+        mock_fetch_dependencies,
+        mock_get_most_recently_uploaded_ancillary,
+        _,
+    ):
         mock_get_most_recently_uploaded_ancillary.side_effect = [
-            create_mock_query_results(['imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-lo_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_tess-xyz-8_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-hi_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_energy-grid-ultra_20200101_v000.cdf'])[0],
-            create_mock_query_results(['imap_glows_tess-ang-16_20200101_v000.cdf'])[0],
+            create_mock_query_results(
+                ["imap_glows_pipeline-settings-l3bcde_20200101_v000.cdf"]
+            )[0],
+            create_mock_query_results(["imap_glows_energy-grid-lo_20200101_v000.cdf"])[
+                0
+            ],
+            create_mock_query_results(["imap_glows_tess-xyz-8_20200101_v000.cdf"])[0],
+            create_mock_query_results(["imap_glows_energy-grid-hi_20200101_v000.cdf"])[
+                0
+            ],
+            create_mock_query_results(
+                ["imap_glows_energy-grid-ultra_20200101_v000.cdf"]
+            )[0],
+            create_mock_query_results(["imap_glows_tess-ang-16_20200101_v000.cdf"])[0],
         ]
-        mock_identify_versions_for_l3e_output_files.return_value = GlowsL3eVersionsForRepointings(
-            repointing_numbers=[],
-            ultra_sf_repointings={},
-            ultra_hf_repointings={},
-            lo_repointings={},
-            hi_45_repointings={},
-            hi_90_repointings={}
+        mock_identify_versions_for_l3e_output_files.return_value = (
+            GlowsL3eVersionsForRepointings(
+                repointing_numbers=[],
+                ultra_sf_repointings={},
+                ultra_hf_repointings={},
+                lo_repointings={},
+                hi_45_repointings={},
+                hi_90_repointings={},
+            )
         )
 
-        updated_l3d = Path('path/to/imap_glows_l3d_solar-hist_19470303-cr02091_v012.0001.cdf')
-        glows_l3d_processor_output = GlowsL3DProcessorOutput(updated_l3d, [], sentinel.last_processed_cr)
+        updated_l3d = Path(
+            "path/to/imap_glows_l3d_solar-hist_19470303-cr02091_v012.0001.cdf"
+        )
+        glows_l3d_processor_output = GlowsL3DProcessorOutput(
+            updated_l3d, [], sentinel.last_processed_cr
+        )
         previous_l3d = None
 
-        mock_fetch_dependencies.return_value.pipeline_settings = {"start_cr": sentinel.start_of_mission_cr}
+        mock_fetch_dependencies.return_value.pipeline_settings = {
+            "start_cr": sentinel.start_of_mission_cr
+        }
 
         repointing_file_path = Path("imap_2026_105_01.repoint.csv")
         _ = GlowsL3EInitializer.get_repointings_to_process(
-            glows_l3d_processor_output, previous_l3d,
-            repointing_file_path, sentinel.version_map,
+            glows_l3d_processor_output,
+            previous_l3d,
+            repointing_file_path,
+            sentinel.version_map,
             sentinel.reprocess_info,
         )
 
diff --git a/tests/glows/l3e/test_glows_l3e_lo_model.py b/tests/glows/l3e/test_glows_l3e_lo_model.py
index e73c086fa..8eeaa47cc 100644
--- a/tests/glows/l3e/test_glows_l3e_lo_model.py
+++ b/tests/glows/l3e/test_glows_l3e_lo_model.py
@@ -1,159 +1,536 @@
 import unittest
 from datetime import datetime, timedelta
-from unittest.mock import sentinel, Mock, MagicMock, patch
+from unittest.mock import MagicMock, Mock, patch, sentinel
 
 import numpy as np
 
-from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eCallArguments, GlowsL3eSpacecraftInfo
-from imap_l3_processing.glows.l3e.glows_l3e_lo_model import GlowsL3ELoData, SPIN_AXIS_LONGITUDE_VAR_NAME, \
-    SPIN_AXIS_LATITUDE_VAR_NAME, EPOCH_CDF_VAR_NAME, ENERGY_VAR_NAME, SPIN_ANGLE_VAR_NAME, \
-    PROBABILITY_OF_SURVIVAL_VAR_NAME, ENERGY_LABEL_VAR_NAME, SPIN_ANGLE_LABEL_VAR_NAME, ELONGATION_VAR_NAME, \
-    PROGRAM_VERSION_VAR_NAME, SPACECRAFT_RADIUS_VAR_NAME, SPACECRAFT_LONGITUDE_VAR_NAME, SPACECRAFT_LATITUDE_VAR_NAME, \
-    SPACECRAFT_VELOCITY_X_VAR_NAME, SPACECRAFT_VELOCITY_Y_VAR_NAME, SPACECRAFT_VELOCITY_Z_VAR_NAME, \
-    GLOWS_FLAGS_VAR_NAME, ENERGY_DELTA_PLUS_VAR_NAME, ENERGY_DELTA_MINUS_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME
+from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
+    GlowsL3eCallArguments,
+    GlowsL3eSpacecraftInfo,
+)
+from imap_l3_processing.glows.l3e.glows_l3e_lo_model import (
+    ELONGATION_VAR_NAME,
+    ENERGY_DELTA_MINUS_VAR_NAME,
+    ENERGY_DELTA_PLUS_VAR_NAME,
+    ENERGY_LABEL_VAR_NAME,
+    ENERGY_VAR_NAME,
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+    GLOWS_FLAGS_VAR_NAME,
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+    PROGRAM_VERSION_VAR_NAME,
+    SPACECRAFT_LATITUDE_VAR_NAME,
+    SPACECRAFT_LONGITUDE_VAR_NAME,
+    SPACECRAFT_RADIUS_VAR_NAME,
+    SPACECRAFT_VELOCITY_X_VAR_NAME,
+    SPACECRAFT_VELOCITY_Y_VAR_NAME,
+    SPACECRAFT_VELOCITY_Z_VAR_NAME,
+    SPIN_ANGLE_LABEL_VAR_NAME,
+    SPIN_ANGLE_VAR_NAME,
+    SPIN_AXIS_LATITUDE_VAR_NAME,
+    SPIN_AXIS_LONGITUDE_VAR_NAME,
+    GlowsL3ELoData,
+)
 from imap_l3_processing.models import DataProductVariable
-from tests.test_helpers import get_test_instrument_team_data_path, NumpyArrayMatcher
+from tests.test_helpers import NumpyArrayMatcher, get_test_instrument_team_data_path
 
 
 class TestL3eLoModel(unittest.TestCase):
     def test_l3e_lo_model_to_data_product_variables(self):
         test_cases = {
-            'case 1':(
-                [1.2234,2,4.51,66.7666],
-                [1,41650233.0,4.22,9.5],
-                ['1.22','2.00','4.51','66.77'],
-                ['1','41650233','4','10']
+            "case 1": (
+                [1.2234, 2, 4.51, 66.7666],
+                [1, 41650233.0, 4.22, 9.5],
+                ["1.22", "2.00", "4.51", "66.77"],
+                ["1", "41650233", "4", "10"],
+            ),
+            "case 2": (
+                [4.536, 48.193, 4253.1],
+                [13, 14.0, 34.2, 19.5],
+                ["4.54", "48.19", "4253.10"],
+                ["13", "14", "34", "20"],
             ),
-            'case 2':(
-                [4.536,48.193,4253.1],
-                [13,14.0,34.2,19.5],
-                ['4.54','48.19','4253.10'],
-                ['13','14','34','20']
-            )
         }
 
-        for name, (energy_array, spin_angle_array, expected_energy_labels, expected_spin_angle_labels) in test_cases.items():
+        for name, (
+            energy_array,
+            spin_angle_array,
+            expected_energy_labels,
+            expected_spin_angle_labels,
+        ) in test_cases.items():
             with self.subTest(name):
                 l3e_lo: GlowsL3ELoData = GlowsL3ELoData(
-                        Mock(),
-                        sentinel.epoch,
-                        sentinel.epoch_delta,
-                        energy_array,
-                        sentinel.energy_delta_plus,
-                        sentinel.energy_delta_minus,
-                        spin_angle_array,
-                        sentinel.probability_of_survival,
-                        sentinel.elongation,
-                        sentinel.spin_axis_latitude,
-                        sentinel.spin_axis_longitude,
-                        sentinel.program_version,
-                        sentinel.spacecraft_radius,
-                        sentinel.spacecraft_latitude,
-                        sentinel.spacecraft_longitude,
-                        sentinel.spacecraft_velocity_x,
-                        sentinel.spacecraft_velocity_y,
-                        sentinel.spacecraft_velocity_z,
-                        sentinel.glows_flags,
-                    )
+                    Mock(),
+                    sentinel.epoch,
+                    sentinel.epoch_delta,
+                    energy_array,
+                    sentinel.energy_delta_plus,
+                    sentinel.energy_delta_minus,
+                    spin_angle_array,
+                    sentinel.probability_of_survival,
+                    sentinel.elongation,
+                    sentinel.spin_axis_latitude,
+                    sentinel.spin_axis_longitude,
+                    sentinel.program_version,
+                    sentinel.spacecraft_radius,
+                    sentinel.spacecraft_latitude,
+                    sentinel.spacecraft_longitude,
+                    sentinel.spacecraft_velocity_x,
+                    sentinel.spacecraft_velocity_y,
+                    sentinel.spacecraft_velocity_z,
+                    sentinel.glows_flags,
+                )
                 data_products = l3e_lo.to_data_product_variables()
 
-
                 expected_data_products = [
-                        DataProductVariable(EPOCH_CDF_VAR_NAME, sentinel.epoch),
-                        DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, sentinel.epoch_delta),
-                        DataProductVariable(ENERGY_VAR_NAME, energy_array),
-                        DataProductVariable(ENERGY_DELTA_PLUS_VAR_NAME, sentinel.energy_delta_plus),
-                        DataProductVariable(ENERGY_DELTA_MINUS_VAR_NAME, sentinel.energy_delta_minus),
-                        DataProductVariable(SPIN_ANGLE_VAR_NAME, spin_angle_array),
-                        DataProductVariable(PROBABILITY_OF_SURVIVAL_VAR_NAME, sentinel.probability_of_survival),
-                        DataProductVariable(ENERGY_LABEL_VAR_NAME, expected_energy_labels),
-                        DataProductVariable(SPIN_ANGLE_LABEL_VAR_NAME, expected_spin_angle_labels),
-                        DataProductVariable(ELONGATION_VAR_NAME, sentinel.elongation),
-                        DataProductVariable(SPIN_AXIS_LATITUDE_VAR_NAME, np.array([sentinel.spin_axis_latitude])),
-                        DataProductVariable(SPIN_AXIS_LONGITUDE_VAR_NAME, np.array([sentinel.spin_axis_longitude])),
-                        DataProductVariable(PROGRAM_VERSION_VAR_NAME, np.array([sentinel.program_version])),
-                        DataProductVariable(SPACECRAFT_RADIUS_VAR_NAME, np.array([sentinel.spacecraft_radius])),
-                        DataProductVariable(SPACECRAFT_LATITUDE_VAR_NAME, np.array([sentinel.spacecraft_latitude])),
-                        DataProductVariable(SPACECRAFT_LONGITUDE_VAR_NAME, np.array([sentinel.spacecraft_longitude])),
-                        DataProductVariable(SPACECRAFT_VELOCITY_X_VAR_NAME, np.array([sentinel.spacecraft_velocity_x])),
-                        DataProductVariable(SPACECRAFT_VELOCITY_Y_VAR_NAME, np.array([sentinel.spacecraft_velocity_y])),
-                        DataProductVariable(SPACECRAFT_VELOCITY_Z_VAR_NAME, np.array([sentinel.spacecraft_velocity_z])),
-                        DataProductVariable(GLOWS_FLAGS_VAR_NAME, sentinel.glows_flags),
+                    DataProductVariable(EPOCH_CDF_VAR_NAME, sentinel.epoch),
+                    DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, sentinel.epoch_delta),
+                    DataProductVariable(ENERGY_VAR_NAME, energy_array),
+                    DataProductVariable(
+                        ENERGY_DELTA_PLUS_VAR_NAME, sentinel.energy_delta_plus
+                    ),
+                    DataProductVariable(
+                        ENERGY_DELTA_MINUS_VAR_NAME, sentinel.energy_delta_minus
+                    ),
+                    DataProductVariable(SPIN_ANGLE_VAR_NAME, spin_angle_array),
+                    DataProductVariable(
+                        PROBABILITY_OF_SURVIVAL_VAR_NAME,
+                        sentinel.probability_of_survival,
+                    ),
+                    DataProductVariable(ENERGY_LABEL_VAR_NAME, expected_energy_labels),
+                    DataProductVariable(
+                        SPIN_ANGLE_LABEL_VAR_NAME, expected_spin_angle_labels
+                    ),
+                    DataProductVariable(ELONGATION_VAR_NAME, sentinel.elongation),
+                    DataProductVariable(
+                        SPIN_AXIS_LATITUDE_VAR_NAME,
+                        np.array([sentinel.spin_axis_latitude]),
+                    ),
+                    DataProductVariable(
+                        SPIN_AXIS_LONGITUDE_VAR_NAME,
+                        np.array([sentinel.spin_axis_longitude]),
+                    ),
+                    DataProductVariable(
+                        PROGRAM_VERSION_VAR_NAME, np.array([sentinel.program_version])
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_RADIUS_VAR_NAME,
+                        np.array([sentinel.spacecraft_radius]),
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_LATITUDE_VAR_NAME,
+                        np.array([sentinel.spacecraft_latitude]),
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_LONGITUDE_VAR_NAME,
+                        np.array([sentinel.spacecraft_longitude]),
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_VELOCITY_X_VAR_NAME,
+                        np.array([sentinel.spacecraft_velocity_x]),
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_VELOCITY_Y_VAR_NAME,
+                        np.array([sentinel.spacecraft_velocity_y]),
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_VELOCITY_Z_VAR_NAME,
+                        np.array([sentinel.spacecraft_velocity_z]),
+                    ),
+                    DataProductVariable(GLOWS_FLAGS_VAR_NAME, sentinel.glows_flags),
                 ]
                 self.assertEqual(expected_data_products, data_products)
 
     @patch("imap_l3_processing.glows.l3e.glows_l3e_lo_model.calculate_energy_deltas")
     def test_convert_dat_to_glows_l3e_lo_product(self, mock_calculate_energy_deltas):
-        lo_file_path = get_test_instrument_team_data_path("glows/probSur.Imap.Lo_20090101_010101_2009.000_60.00.txt")
+        lo_file_path = get_test_instrument_team_data_path(
+            "glows/probSur.Imap.Lo_20090101_010101_2009.000_60.00.txt"
+        )
         epoch = datetime(year=2009, month=1, day=1)
         epoch_delta = timedelta(hours=3)
-        expected_epoch_delta_in_nanoseconds = 3*3600*1e9
-        expected_energy = [0.1700000, 0.2212954, 0.2880685, 0.3749897, 0.4881381, 0.6354278, 0.8271602, 1.0767456,
-                           1.4016403, 1.8245678, 2.3751086, 3.0917682, 4.0246710]
+        expected_epoch_delta_in_nanoseconds = 3 * 3600 * 1e9
+        expected_energy = [
+            0.1700000,
+            0.2212954,
+            0.2880685,
+            0.3749897,
+            0.4881381,
+            0.6354278,
+            0.8271602,
+            1.0767456,
+            1.4016403,
+            1.8245678,
+            2.3751086,
+            3.0917682,
+            4.0246710,
+        ]
 
-        mock_calculate_energy_deltas.return_value = sentinel.energy_delta_plus, sentinel.energy_delta_minus
+        mock_calculate_energy_deltas.return_value = (
+            sentinel.energy_delta_plus,
+            sentinel.energy_delta_minus,
+        )
 
         expected_prob_of_survival_first_col_1 = [
-            0.48543928E+00, 0.48415770E+00, 0.48286930E+00, 0.48155365E+00, 0.48022886E+00, 0.47885535E+00,
-            0.47745419E+00, 0.47602527E+00, 0.47456880E+00, 0.47308723E+00, 0.47161934E+00, 0.47007995E+00,
-            0.46855039E+00, 0.46696817E+00, 0.46535270E+00, 0.46368022E+00, 0.46201160E+00, 0.46026144E+00,
-            0.45852743E+00, 0.45670696E+00, 0.45486823E+00, 0.45294031E+00, 0.45103086E+00, 0.44905075E+00,
-            0.44699322E+00, 0.44493658E+00, 0.44274056E+00, 0.44049151E+00, 0.43813752E+00, 0.43578356E+00,
-            0.43334287E+00, 0.43079981E+00, 0.42816069E+00, 0.42550222E+00, 0.42269926E+00, 0.41984889E+00,
-            0.41688223E+00, 0.41387416E+00, 0.41075224E+00, 0.40761023E+00, 0.40432881E+00, 0.40096472E+00,
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+            0.50566424e00,
+            0.50580062e00,
+            0.50590890e00,
+            0.50589201e00,
+            0.50588645e00,
+            0.50578122e00,
+            0.50561852e00,
+            0.50539304e00,
+            0.50510737e00,
+            0.50474719e00,
+            0.50435977e00,
+            0.50384726e00,
+            0.50336489e00,
+            0.50279180e00,
+            0.50216504e00,
+            0.50148265e00,
+            0.50078978e00,
+            0.50000804e00,
+            0.49918459e00,
+            0.49836367e00,
+            0.49744147e00,
+            0.49651714e00,
+            0.49557195e00,
+            0.49456744e00,
+            0.49353034e00,
+            0.49245761e00,
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+            0.49023631e00,
+            0.48909069e00,
+            0.48791635e00,
+            0.48669295e00,
         ]
 
         expected_spin_angle = np.arange(1, 361, 1, dtype=np.float64)
@@ -166,12 +543,12 @@ def test_convert_dat_to_glows_l3e_lo_product(self, mock_calculate_energy_deltas)
         spin_axis_lon = 90.0
 
         args = MagicMock(spec=GlowsL3eCallArguments)
-        expected_program_version = 'Lo.v00.01'
+        expected_program_version = "Lo.v00.01"
 
         spacecraft_info = MagicMock(spec=GlowsL3eSpacecraftInfo)
         spacecraft_info.spin_axis_latitude = spin_axis_lat
         spacecraft_info.spin_axis_longitude = spin_axis_lon
-        spacecraft_info.spacecraft_radius = .5
+        spacecraft_info.spacecraft_radius = 0.5
         spacecraft_info.spacecraft_longitude = 85.4
         spacecraft_info.spacecraft_latitude = 45.1
 
@@ -181,37 +558,75 @@ def test_convert_dat_to_glows_l3e_lo_product(self, mock_calculate_energy_deltas)
 
         args.spacecraft_info = spacecraft_info
 
-        l3e_lo_product: GlowsL3ELoData = GlowsL3ELoData.convert_dat_to_glows_l3e_lo_product(mock_metadata, lo_file_path,
-                                                                                            epoch,
-                                                                                            epoch_delta,
-                                                                                            elongation_value,
-                                                                                            args)
+        l3e_lo_product: GlowsL3ELoData = (
+            GlowsL3ELoData.convert_dat_to_glows_l3e_lo_product(
+                mock_metadata, lo_file_path, epoch, epoch_delta, elongation_value, args
+            )
+        )
 
-        mock_calculate_energy_deltas.assert_called_once_with(NumpyArrayMatcher(l3e_lo_product.energy))
+        mock_calculate_energy_deltas.assert_called_once_with(
+            NumpyArrayMatcher(l3e_lo_product.energy)
+        )
 
         np.testing.assert_equal([epoch], l3e_lo_product.epoch, strict=True)
-        np.testing.assert_equal([expected_epoch_delta_in_nanoseconds], l3e_lo_product.epoch_delta, strict=True)
+        np.testing.assert_equal(
+            [expected_epoch_delta_in_nanoseconds],
+            l3e_lo_product.epoch_delta,
+            strict=True,
+        )
         np.testing.assert_equal(l3e_lo_product.energy, expected_energy, strict=True)
-        np.testing.assert_equal(l3e_lo_product.energy_delta_plus, sentinel.energy_delta_plus)
-        np.testing.assert_equal(l3e_lo_product.energy_delta_minus, sentinel.energy_delta_minus,)
-
-        np.testing.assert_equal(l3e_lo_product.spin_angle, expected_spin_angle, strict=True)
-        np.testing.assert_equal(l3e_lo_product.probability_of_survival.shape, expected_survival_probability_shape,
-                                strict=True)
-        np.testing.assert_equal(l3e_lo_product.probability_of_survival[0][0],
-                                expected_prob_of_survival_first_col_1, strict=True)
-        np.testing.assert_equal(l3e_lo_product.elongation, np.array([elongation_value]), strict=True)
+        np.testing.assert_equal(
+            l3e_lo_product.energy_delta_plus, sentinel.energy_delta_plus
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.energy_delta_minus,
+            sentinel.energy_delta_minus,
+        )
 
-        np.testing.assert_equal(np.array([spin_axis_lat]), l3e_lo_product.spin_axis_lat, strict=True)
-        np.testing.assert_equal(np.array([spin_axis_lon]), l3e_lo_product.spin_axis_lon, strict=True)
+        np.testing.assert_equal(
+            l3e_lo_product.spin_angle, expected_spin_angle, strict=True
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.probability_of_survival.shape,
+            expected_survival_probability_shape,
+            strict=True,
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.probability_of_survival[0][0],
+            expected_prob_of_survival_first_col_1,
+            strict=True,
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.elongation, np.array([elongation_value]), strict=True
+        )
 
-        np.testing.assert_equal([expected_program_version], l3e_lo_product.program_version, strict=True)
+        np.testing.assert_equal(
+            np.array([spin_axis_lat]), l3e_lo_product.spin_axis_lat, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([spin_axis_lon]), l3e_lo_product.spin_axis_lon, strict=True
+        )
 
-        np.testing.assert_equal(l3e_lo_product.spacecraft_radius, np.array([.5]), strict=True)
-        np.testing.assert_equal(l3e_lo_product.spacecraft_longitude, np.array([85.4]), strict=True)
-        np.testing.assert_equal(l3e_lo_product.spacecraft_latitude, np.array([45.1]), strict=True)
+        np.testing.assert_equal(
+            [expected_program_version], l3e_lo_product.program_version, strict=True
+        )
 
-        np.testing.assert_equal(l3e_lo_product.spacecraft_velocity_x, np.array([2.1]), strict=True)
-        np.testing.assert_equal(l3e_lo_product.spacecraft_velocity_y, np.array([2.2]), strict=True)
-        np.testing.assert_equal(l3e_lo_product.spacecraft_velocity_z, np.array([2.3]), strict=True)
+        np.testing.assert_equal(
+            l3e_lo_product.spacecraft_radius, np.array([0.5]), strict=True
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.spacecraft_longitude, np.array([85.4]), strict=True
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.spacecraft_latitude, np.array([45.1]), strict=True
+        )
 
+        np.testing.assert_equal(
+            l3e_lo_product.spacecraft_velocity_x, np.array([2.1]), strict=True
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.spacecraft_velocity_y, np.array([2.2]), strict=True
+        )
+        np.testing.assert_equal(
+            l3e_lo_product.spacecraft_velocity_z, np.array([2.3]), strict=True
+        )
diff --git a/tests/glows/l3e/test_glows_l3e_ultra_model.py b/tests/glows/l3e/test_glows_l3e_ultra_model.py
index 82a18e779..f9bacdc42 100644
--- a/tests/glows/l3e/test_glows_l3e_ultra_model.py
+++ b/tests/glows/l3e/test_glows_l3e_ultra_model.py
@@ -1,40 +1,65 @@
 import unittest
 from datetime import datetime, timedelta
-from unittest.mock import sentinel, Mock, MagicMock, patch
+from unittest.mock import MagicMock, Mock, patch, sentinel
 
 import numpy as np
 
-from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eCallArguments, GlowsL3eSpacecraftInfo
-from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import GlowsL3EUltraData, EPOCH_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME, ENERGY_VAR_NAME, \
-    PROBABILITY_OF_SURVIVAL_VAR_NAME, HEALPIX_INDEX_VAR_NAME, ENERGY_LABEL_VAR_NAME, PIXEL_INDEX_LABEL_VAR_NAME, \
-    SPIN_AXIS_LATITUDE_VAR_NAME, SPIN_AXIS_LONGITUDE_VAR_NAME, PROGRAM_VERSION_VAR_NAME, SPACECRAFT_RADIUS_VAR_NAME, \
-    SPACECRAFT_LONGITUDE_VAR_NAME, SPACECRAFT_LATITUDE_VAR_NAME, SPACECRAFT_VELOCITY_X_VAR_NAME, \
-    SPACECRAFT_VELOCITY_Y_VAR_NAME, SPACECRAFT_VELOCITY_Z_VAR_NAME, ELONGATION_EXCLUDED_VAR_NAME, \
-    PIXEL_LATITUDE_VAR_NAME, PIXEL_LONGITUDE_VAR_NAME, GLOWS_FLAGS_VAR_NAME, ENERGY_DELTA_PLUS_VAR_NAME, \
-    ENERGY_DELTA_MINUS_VAR_NAME
+from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
+    GlowsL3eCallArguments,
+    GlowsL3eSpacecraftInfo,
+)
+from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import (
+    ELONGATION_EXCLUDED_VAR_NAME,
+    ENERGY_DELTA_MINUS_VAR_NAME,
+    ENERGY_DELTA_PLUS_VAR_NAME,
+    ENERGY_LABEL_VAR_NAME,
+    ENERGY_VAR_NAME,
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+    GLOWS_FLAGS_VAR_NAME,
+    HEALPIX_INDEX_VAR_NAME,
+    PIXEL_INDEX_LABEL_VAR_NAME,
+    PIXEL_LATITUDE_VAR_NAME,
+    PIXEL_LONGITUDE_VAR_NAME,
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+    PROGRAM_VERSION_VAR_NAME,
+    SPACECRAFT_LATITUDE_VAR_NAME,
+    SPACECRAFT_LONGITUDE_VAR_NAME,
+    SPACECRAFT_RADIUS_VAR_NAME,
+    SPACECRAFT_VELOCITY_X_VAR_NAME,
+    SPACECRAFT_VELOCITY_Y_VAR_NAME,
+    SPACECRAFT_VELOCITY_Z_VAR_NAME,
+    SPIN_AXIS_LATITUDE_VAR_NAME,
+    SPIN_AXIS_LONGITUDE_VAR_NAME,
+    GlowsL3EUltraData,
+)
 from imap_l3_processing.models import DataProductVariable
-from tests.test_helpers import get_test_instrument_team_data_path, NumpyArrayMatcher
+from tests.test_helpers import NumpyArrayMatcher, get_test_instrument_team_data_path
 
 
 class TestL3eUltraModel(unittest.TestCase):
     def test_l3e_ultra_model_to_data_product_variables(self):
         test_cases = {
-            'case 1': (
+            "case 1": (
                 [1.2234, 2, 4.51, 66.7666],
                 [1, 41650233.0, 4.22, 9.5],
-                ['1.22', '2.00', '4.51', '66.77'],
-                ['1', '41650233', '4', '10']
+                ["1.22", "2.00", "4.51", "66.77"],
+                ["1", "41650233", "4", "10"],
             ),
-            'case 2': (
+            "case 2": (
                 [4.536, 48.193, 4253.1],
                 [13, 14.0, 34.2, 19.5],
-                ['4.54', '48.19', '4253.10'],
-                ['13', '14', '34', '20']
-            )
+                ["4.54", "48.19", "4253.10"],
+                ["13", "14", "34", "20"],
+            ),
         }
 
-        for name, (energy_array, healpix_index_array, expected_energy_labels,
-                   expected_healpix_labels) in test_cases.items():
+        for name, (
+            energy_array,
+            healpix_index_array,
+            expected_energy_labels,
+            expected_healpix_labels,
+        ) in test_cases.items():
             with self.subTest(name):
                 l3e_ultra: GlowsL3EUltraData = GlowsL3EUltraData(
                     Mock(),
@@ -65,24 +90,57 @@ def test_l3e_ultra_model_to_data_product_variables(self):
                     DataProductVariable(EPOCH_CDF_VAR_NAME, sentinel.epoch),
                     DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, sentinel.epoch_delta),
                     DataProductVariable(ENERGY_VAR_NAME, energy_array),
-                    DataProductVariable(ENERGY_DELTA_PLUS_VAR_NAME, sentinel.energy_delta_plus),
-                    DataProductVariable(ENERGY_DELTA_MINUS_VAR_NAME, sentinel.energy_delta_minus),
+                    DataProductVariable(
+                        ENERGY_DELTA_PLUS_VAR_NAME, sentinel.energy_delta_plus
+                    ),
+                    DataProductVariable(
+                        ENERGY_DELTA_MINUS_VAR_NAME, sentinel.energy_delta_minus
+                    ),
                     DataProductVariable(HEALPIX_INDEX_VAR_NAME, healpix_index_array),
-                    DataProductVariable(PROBABILITY_OF_SURVIVAL_VAR_NAME, sentinel.probability_of_survival),
+                    DataProductVariable(
+                        PROBABILITY_OF_SURVIVAL_VAR_NAME,
+                        sentinel.probability_of_survival,
+                    ),
                     DataProductVariable(ENERGY_LABEL_VAR_NAME, expected_energy_labels),
-                    DataProductVariable(PIXEL_INDEX_LABEL_VAR_NAME, expected_healpix_labels),
-                    DataProductVariable(SPIN_AXIS_LATITUDE_VAR_NAME, sentinel.spin_axis_latitude),
-                    DataProductVariable(SPIN_AXIS_LONGITUDE_VAR_NAME, sentinel.spin_axis_longitude),
-                    DataProductVariable(PROGRAM_VERSION_VAR_NAME, sentinel.program_version),
-                    DataProductVariable(SPACECRAFT_RADIUS_VAR_NAME, sentinel.spacecraft_radius),
-                    DataProductVariable(SPACECRAFT_LATITUDE_VAR_NAME, sentinel.spacecraft_latitude),
-                    DataProductVariable(SPACECRAFT_LONGITUDE_VAR_NAME, sentinel.spacecraft_longitude),
-                    DataProductVariable(SPACECRAFT_VELOCITY_X_VAR_NAME, sentinel.spacecraft_velocity_x),
-                    DataProductVariable(SPACECRAFT_VELOCITY_Y_VAR_NAME, sentinel.spacecraft_velocity_y),
-                    DataProductVariable(SPACECRAFT_VELOCITY_Z_VAR_NAME, sentinel.spacecraft_velocity_z),
-                    DataProductVariable(ELONGATION_EXCLUDED_VAR_NAME, sentinel.elongation_excluded),
-                    DataProductVariable(PIXEL_LATITUDE_VAR_NAME, sentinel.pixel_latitude),
-                    DataProductVariable(PIXEL_LONGITUDE_VAR_NAME, sentinel.pixel_longitude),
+                    DataProductVariable(
+                        PIXEL_INDEX_LABEL_VAR_NAME, expected_healpix_labels
+                    ),
+                    DataProductVariable(
+                        SPIN_AXIS_LATITUDE_VAR_NAME, sentinel.spin_axis_latitude
+                    ),
+                    DataProductVariable(
+                        SPIN_AXIS_LONGITUDE_VAR_NAME, sentinel.spin_axis_longitude
+                    ),
+                    DataProductVariable(
+                        PROGRAM_VERSION_VAR_NAME, sentinel.program_version
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_RADIUS_VAR_NAME, sentinel.spacecraft_radius
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_LATITUDE_VAR_NAME, sentinel.spacecraft_latitude
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_LONGITUDE_VAR_NAME, sentinel.spacecraft_longitude
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_VELOCITY_X_VAR_NAME, sentinel.spacecraft_velocity_x
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_VELOCITY_Y_VAR_NAME, sentinel.spacecraft_velocity_y
+                    ),
+                    DataProductVariable(
+                        SPACECRAFT_VELOCITY_Z_VAR_NAME, sentinel.spacecraft_velocity_z
+                    ),
+                    DataProductVariable(
+                        ELONGATION_EXCLUDED_VAR_NAME, sentinel.elongation_excluded
+                    ),
+                    DataProductVariable(
+                        PIXEL_LATITUDE_VAR_NAME, sentinel.pixel_latitude
+                    ),
+                    DataProductVariable(
+                        PIXEL_LONGITUDE_VAR_NAME, sentinel.pixel_longitude
+                    ),
                     DataProductVariable(GLOWS_FLAGS_VAR_NAME, sentinel.glows_flags),
                 ]
 
@@ -90,33 +148,116 @@ def test_l3e_ultra_model_to_data_product_variables(self):
 
     @patch("imap_l3_processing.glows.l3e.glows_l3e_ultra_model.calculate_energy_deltas")
     def test_convert_dat_to_glows_l3e_ul_product(self, mock_calculate_energy_deltas):
-        ul_file_path = get_test_instrument_team_data_path("glows/probSur.Imap.Ul_20250420_000000_2025.300.txt")
+        ul_file_path = get_test_instrument_team_data_path(
+            "glows/probSur.Imap.Ul_20250420_000000_2025.300.txt"
+        )
         expected_epoch = datetime(year=2009, month=1, day=1)
         epoch_delta = timedelta(hours=10)
-        expected_epoch_delta_in_nanoseconds = 10*3600*1e9
+        expected_epoch_delta_in_nanoseconds = 10 * 3600 * 1e9
 
-        mock_calculate_energy_deltas.return_value = sentinel.energy_delta_plus, sentinel.energy_delta_minus
+        mock_calculate_energy_deltas.return_value = (
+            sentinel.energy_delta_plus,
+            sentinel.energy_delta_minus,
+        )
         expected_energy = np.array(
-            [2.3751086, 3.0917682, 4.0246710, 5.2390656, 6.8198887, 8.8777057, 11.5564435, 15.0434572, 19.5826341,
-             25.4914514, 33.1831812, 43.1957952, 56.2295915, 73.1961745, 95.2822137, 124.0324417, 161.4576950,
-             210.1755551, 273.5934261, 356.1468544])
+            [
+                2.3751086,
+                3.0917682,
+                4.0246710,
+                5.2390656,
+                6.8198887,
+                8.8777057,
+                11.5564435,
+                15.0434572,
+                19.5826341,
+                25.4914514,
+                33.1831812,
+                43.1957952,
+                56.2295915,
+                73.1961745,
+                95.2822137,
+                124.0324417,
+                161.4576950,
+                210.1755551,
+                273.5934261,
+                356.1468544,
+            ]
+        )
 
         row_0_probability_of_survival = np.array(
-            [0.84827693E+00, 0.86517360E+00, 0.88086645E+00, 0.89551288E+00, 0.90925039E+00, 0.92211605E+00,
-             0.93428862E+00, 0.94576000E+00, 0.95660359E+00, 0.96660933E+00, 0.97533752E+00, 0.98226689E+00,
-             0.98714107E+00, 0.99025210E+00, 0.99218849E+00, 0.99346478E+00, 0.99439230E+00, 0.99512632E+00,
-             0.99574163E+00, 0.99627086E+00])
-
-        row_804_probability_of_survival = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
-                                                    np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
-                                                    np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
-                                                    np.nan, np.nan])
+            [
+                0.84827693e00,
+                0.86517360e00,
+                0.88086645e00,
+                0.89551288e00,
+                0.90925039e00,
+                0.92211605e00,
+                0.93428862e00,
+                0.94576000e00,
+                0.95660359e00,
+                0.96660933e00,
+                0.97533752e00,
+                0.98226689e00,
+                0.98714107e00,
+                0.99025210e00,
+                0.99218849e00,
+                0.99346478e00,
+                0.99439230e00,
+                0.99512632e00,
+                0.99574163e00,
+                0.99627086e00,
+            ]
+        )
+
+        row_804_probability_of_survival = np.array(
+            [
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+                np.nan,
+            ]
+        )
 
         row_3071_expected_probability_of_survival = np.array(
-            [0.84498413E+00, 0.86231633E+00, 0.87840630E+00, 0.89344499E+00, 0.90753390E+00, 0.92079246E+00,
-             0.93326641E+00, 0.94509283E+00, 0.95623858E+00, 0.96649540E+00, 0.97543873E+00, 0.98248159E+00,
-             0.98738469E+00, 0.99048237E+00, 0.99239223E+00, 0.99364490E+00, 0.99455000E+00, 0.99526291E+00,
-             0.99585178E+00, 0.99635733E+00])
+            [
+                0.84498413e00,
+                0.86231633e00,
+                0.87840630e00,
+                0.89344499e00,
+                0.90753390e00,
+                0.92079246e00,
+                0.93326641e00,
+                0.94509283e00,
+                0.95623858e00,
+                0.96649540e00,
+                0.97543873e00,
+                0.98248159e00,
+                0.98738469e00,
+                0.99048237e00,
+                0.99239223e00,
+                0.99364490e00,
+                0.99455000e00,
+                0.99526291e00,
+                0.99585178e00,
+                0.99635733e00,
+            ]
+        )
 
         expected_survival_probability_shape = (1, 20, 3072)
 
@@ -127,12 +268,12 @@ def test_convert_dat_to_glows_l3e_ul_product(self, mock_calculate_energy_deltas)
         spin_axis_lon = 90.0
 
         args = MagicMock(spec=GlowsL3eCallArguments)
-        expected_program_version = 'Ultra.v00.01'
+        expected_program_version = "Ultra.v00.01"
 
         spacecraft_info = MagicMock(spec=GlowsL3eSpacecraftInfo)
         spacecraft_info.spin_axis_latitude = spin_axis_lat
         spacecraft_info.spin_axis_longitude = spin_axis_lon
-        spacecraft_info.spacecraft_radius = .5
+        spacecraft_info.spacecraft_radius = 0.5
         spacecraft_info.spacecraft_longitude = 85.4
         spacecraft_info.spacecraft_latitude = 45.1
 
@@ -144,51 +285,100 @@ def test_convert_dat_to_glows_l3e_ul_product(self, mock_calculate_energy_deltas)
 
         args.elongation = 30.0
 
-        l3e_ul_product: GlowsL3EUltraData = GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(mock_metadata,
-                                                                                                  ul_file_path,
-                                                                                                  expected_epoch,
-                                                                                                  epoch_delta,
-                                                                                                  args)
+        l3e_ul_product: GlowsL3EUltraData = (
+            GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product(
+                mock_metadata, ul_file_path, expected_epoch, epoch_delta, args
+            )
+        )
 
-        mock_calculate_energy_deltas.assert_called_once_with(NumpyArrayMatcher(l3e_ul_product.energy),)
+        mock_calculate_energy_deltas.assert_called_once_with(
+            NumpyArrayMatcher(l3e_ul_product.energy),
+        )
 
         np.testing.assert_equal([expected_epoch], l3e_ul_product.epoch, strict=True)
-        np.testing.assert_equal([expected_epoch_delta_in_nanoseconds], l3e_ul_product.epoch_delta, strict=True)
+        np.testing.assert_equal(
+            [expected_epoch_delta_in_nanoseconds],
+            l3e_ul_product.epoch_delta,
+            strict=True,
+        )
 
         np.testing.assert_equal(l3e_ul_product.energy, expected_energy, strict=True)
-        np.testing.assert_equal(l3e_ul_product.energy_delta_plus, sentinel.energy_delta_plus, strict=True)
-        np.testing.assert_equal(l3e_ul_product.energy_delta_minus, sentinel.energy_delta_minus, strict=True)
-
-        np.testing.assert_equal(expected_heal_pix, l3e_ul_product.healpix_index, strict=True)
-        np.testing.assert_equal(expected_survival_probability_shape, l3e_ul_product.probability_of_survival.shape,
-                                strict=True)
-        np.testing.assert_equal(l3e_ul_product.probability_of_survival[0].T[0, :], row_0_probability_of_survival,
-                                strict=True)
-        np.testing.assert_equal(l3e_ul_product.probability_of_survival[0].T[804, :],
-                                row_804_probability_of_survival, strict=True)
-        np.testing.assert_equal(l3e_ul_product.probability_of_survival[0].T[3071, :],
-                                row_3071_expected_probability_of_survival, strict=True)
-
-        np.testing.assert_equal(np.array([spin_axis_lat]), l3e_ul_product.spin_axis_lat, strict=True)
-        np.testing.assert_equal(np.array([spin_axis_lon]), l3e_ul_product.spin_axis_lon, strict=True)
-
-        np.testing.assert_equal(np.array([expected_program_version]), l3e_ul_product.program_version, strict=True)
-
-        np.testing.assert_equal(np.array([.5]), l3e_ul_product.spacecraft_radius, strict=True)
-        np.testing.assert_equal(np.array([85.4]), l3e_ul_product.spacecraft_longitude, strict=True)
-        np.testing.assert_equal(np.array([45.1]), l3e_ul_product.spacecraft_latitude, strict=True)
-
-        np.testing.assert_equal(np.array([2.1]), l3e_ul_product.spacecraft_velocity_x, strict=True)
-        np.testing.assert_equal(np.array([2.2]), l3e_ul_product.spacecraft_velocity_y, strict=True)
-        np.testing.assert_equal(np.array([2.3]), l3e_ul_product.spacecraft_velocity_z, strict=True)
-
-        np.testing.assert_equal(np.array([30.0]), l3e_ul_product.elongation_excluded, strict=True)
-
-        np.testing.assert_equal((1, 3072), l3e_ul_product.pixel_latitude.shape, strict=True)
-        np.testing.assert_equal((1, 3072), l3e_ul_product.pixel_longitude.shape, strict=True)
+        np.testing.assert_equal(
+            l3e_ul_product.energy_delta_plus, sentinel.energy_delta_plus, strict=True
+        )
+        np.testing.assert_equal(
+            l3e_ul_product.energy_delta_minus, sentinel.energy_delta_minus, strict=True
+        )
+
+        np.testing.assert_equal(
+            expected_heal_pix, l3e_ul_product.healpix_index, strict=True
+        )
+        np.testing.assert_equal(
+            expected_survival_probability_shape,
+            l3e_ul_product.probability_of_survival.shape,
+            strict=True,
+        )
+        np.testing.assert_equal(
+            l3e_ul_product.probability_of_survival[0].T[0, :],
+            row_0_probability_of_survival,
+            strict=True,
+        )
+        np.testing.assert_equal(
+            l3e_ul_product.probability_of_survival[0].T[804, :],
+            row_804_probability_of_survival,
+            strict=True,
+        )
+        np.testing.assert_equal(
+            l3e_ul_product.probability_of_survival[0].T[3071, :],
+            row_3071_expected_probability_of_survival,
+            strict=True,
+        )
+
+        np.testing.assert_equal(
+            np.array([spin_axis_lat]), l3e_ul_product.spin_axis_lat, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([spin_axis_lon]), l3e_ul_product.spin_axis_lon, strict=True
+        )
+
+        np.testing.assert_equal(
+            np.array([expected_program_version]),
+            l3e_ul_product.program_version,
+            strict=True,
+        )
+
+        np.testing.assert_equal(
+            np.array([0.5]), l3e_ul_product.spacecraft_radius, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([85.4]), l3e_ul_product.spacecraft_longitude, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([45.1]), l3e_ul_product.spacecraft_latitude, strict=True
+        )
+
+        np.testing.assert_equal(
+            np.array([2.1]), l3e_ul_product.spacecraft_velocity_x, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([2.2]), l3e_ul_product.spacecraft_velocity_y, strict=True
+        )
+        np.testing.assert_equal(
+            np.array([2.3]), l3e_ul_product.spacecraft_velocity_z, strict=True
+        )
+
+        np.testing.assert_equal(
+            np.array([30.0]), l3e_ul_product.elongation_excluded, strict=True
+        )
+
+        np.testing.assert_equal(
+            (1, 3072), l3e_ul_product.pixel_latitude.shape, strict=True
+        )
+        np.testing.assert_equal(
+            (1, 3072), l3e_ul_product.pixel_longitude.shape, strict=True
+        )
         np.testing.assert_equal(87.07582, l3e_ul_product.pixel_latitude[0, 0])
         np.testing.assert_equal(45.00000, l3e_ul_product.pixel_longitude[0, 0])
 
         np.testing.assert_equal(np.nan, l3e_ul_product.pixel_latitude[0, 804])
         np.testing.assert_equal(np.nan, l3e_ul_product.pixel_longitude[0, 804])
-
diff --git a/tests/glows/l3e/test_glows_l3e_utils.py b/tests/glows/l3e/test_glows_l3e_utils.py
index 8db7dbbfd..f91525c74 100644
--- a/tests/glows/l3e/test_glows_l3e_utils.py
+++ b/tests/glows/l3e/test_glows_l3e_utils.py
@@ -2,63 +2,78 @@
 import unittest
 from datetime import datetime, timedelta
 from pathlib import Path
-from unittest.mock import patch, Mock, call, sentinel, create_autospec
+from unittest.mock import Mock, call, create_autospec, patch, sentinel
 
 import imap_data_access
 import numpy as np
 from imap_data_access import SPICEFilePath
 from imap_data_access.file_validation import Version
-from imap_processing.spice.repoint import get_repoint_data, set_global_repoint_table_paths
+from imap_processing.spice.repoint import (
+    get_repoint_data,
+    set_global_repoint_table_paths,
+)
 from spacepy.pycdf import CDF, const
 
-from imap_l3_processing.constants import ONE_SECOND_IN_NANOSECONDS
 from imap_l3_processing.glows.descriptors import (
     GLOWS_L3E_DESCRIPTORS,
     GLOWS_L3E_HI_45_DESCRIPTOR,
     GLOWS_L3E_HI_90_DESCRIPTOR,
     GLOWS_L3E_LO_DESCRIPTOR,
-    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
     GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
 )
 from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
     GlowsL3eCallArguments,
     GlowsL3eSpacecraftInfo,
 )
 from imap_l3_processing.glows.l3e.glows_l3e_utils import (
+    LoPivotAngle,
+    calculate_energy_deltas,
+    compute_glows_flags_for_repoint,
     determine_call_args_for_l3e_executable,
-    identify_versions_for_l3e_output_files,
+    determine_spacecraft_info_for_l3e_executable,
+    determine_spacecraft_info_using_predict_if_needed,
     find_first_updated_cr,
-    get_lo_pivot_angles,
     get_lo_pivot_angle_from_l1b_file,
-    LoPivotAngle,
-    compute_glows_flags_for_repoint,
+    get_lo_pivot_angles,
     get_repoint_numbers_within_cr_window,
-    determine_spacecraft_info_for_l3e_executable,
-    determine_spacecraft_info_using_predict_if_needed, calculate_energy_deltas,
+    identify_versions_for_l3e_output_files,
 )
-from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo, ReprocessTargets
+from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.models import VersionMap
 from imap_l3_processing.utils import FurnishMetakernelOutput
-from tests.integration.integration_test_helpers import mock_imap_data_access, create_metakernel
-from tests.test_helpers import get_test_data_path, create_mock_query_results, get_integration_test_data_path
+from tests.integration.integration_test_helpers import (
+    create_metakernel,
+    mock_imap_data_access,
+)
+from tests.test_helpers import (
+    create_mock_query_results,
+    get_integration_test_data_path,
+    get_test_data_path,
+)
 
 
 class TestGlowsL3EUtils(unittest.TestCase):
-
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.datetime2et")
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.spkezr")
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.reclat")
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.pxform")
-    def test_determine_call_args_for_l3e_executable(self, mock_pxform: Mock, mock_reclat: Mock, mock_spkezr: Mock,
-                                                    mock_date_time_2et: Mock):
+    def test_determine_call_args_for_l3e_executable(
+        self,
+        mock_pxform: Mock,
+        mock_reclat: Mock,
+        mock_spkezr: Mock,
+        mock_date_time_2et: Mock,
+    ):
         start_time = datetime.fromisoformat("2025-05-01 00:00:00")
         repointing_midpoint = datetime.fromisoformat("2025-05-01 12:00:00")
 
         elongation = 90
         spacecraft_info = Mock()
-        call_args: GlowsL3eCallArguments = determine_call_args_for_l3e_executable(start_time, repointing_midpoint,
-                                                                                  elongation, spacecraft_info)
+        call_args: GlowsL3eCallArguments = determine_call_args_for_l3e_executable(
+            start_time, repointing_midpoint, elongation, spacecraft_info
+        )
 
         self.assertEqual("20250501_000000", call_args.formatted_date)
         self.assertEqual("2025.33014", call_args.decimal_date)
@@ -69,34 +84,52 @@ def test_determine_call_args_for_l3e_executable(self, mock_pxform: Mock, mock_re
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.spkezr")
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.reclat")
     @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.spiceypy.pxform")
-    def test_determine_spacecraft_info_for_l3e_executable(self, mock_pxform: Mock, mock_reclat: Mock, mock_spkezr: Mock,
-                                                    mock_date_time_2et: Mock):
+    def test_determine_spacecraft_info_for_l3e_executable(
+        self,
+        mock_pxform: Mock,
+        mock_reclat: Mock,
+        mock_spkezr: Mock,
+        mock_date_time_2et: Mock,
+    ):
         repointing_midpoint = datetime.fromisoformat("2025-05-01 12:00:00")
 
         x, y, z, vx, vy, vz = 1.0, 2.0, 3.0, 4.0, 5.0, 6.0
         position_data = [x, y, z, vx, vy, vz]
         mock_spkezr.return_value = (position_data, Mock())
 
-        radius, longitude, latitude = 70000000, -8.0, -.9
+        radius, longitude, latitude = 70000000, -8.0, -0.9
 
-        rotation_matrix = np.array([[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]])
+        rotation_matrix = np.array(
+            [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]]
+        )
         mock_pxform.return_value = rotation_matrix
 
         spin_axis_long, spin_axis_lat = -1.4, 0.2
-        mock_reclat.side_effect = [(radius, longitude, latitude), (Mock(), spin_axis_long, spin_axis_lat)]
+        mock_reclat.side_effect = [
+            (radius, longitude, latitude),
+            (Mock(), spin_axis_long, spin_axis_lat),
+        ]
 
-        call_args: GlowsL3eSpacecraftInfo = determine_spacecraft_info_for_l3e_executable(repointing_midpoint)
+        call_args: GlowsL3eSpacecraftInfo = (
+            determine_spacecraft_info_for_l3e_executable(repointing_midpoint)
+        )
 
         self.assertIsInstance(call_args, GlowsL3eSpacecraftInfo)
 
         mock_date_time_2et.assert_called_once_with(repointing_midpoint)
 
-        mock_spkezr.assert_called_once_with("IMAP", mock_date_time_2et.return_value, "ECLIPJ2000", "NONE", "SUN")
+        mock_spkezr.assert_called_once_with(
+            "IMAP", mock_date_time_2et.return_value, "ECLIPJ2000", "NONE", "SUN"
+        )
 
         np.testing.assert_array_equal([x, y, z], mock_reclat.call_args_list[0][0][0])
 
-        mock_pxform.assert_called_once_with("IMAP_DPS", "ECLIPJ2000", mock_date_time_2et.return_value)
-        np.testing.assert_array_equal([12.0, 15.0, 18.0], mock_reclat.call_args_list[1][0][0])
+        mock_pxform.assert_called_once_with(
+            "IMAP_DPS", "ECLIPJ2000", mock_date_time_2et.return_value
+        )
+        np.testing.assert_array_equal(
+            [12.0, 15.0, 18.0], mock_reclat.call_args_list[1][0][0]
+        )
 
         self.assertEqual(0.4679210985587912, call_args.spacecraft_radius)
         self.assertEqual(261.6337638953414, call_args.spacecraft_longitude)
@@ -104,17 +137,24 @@ def test_determine_spacecraft_info_for_l3e_executable(self, mock_pxform: Mock, m
         self.assertEqual(vx, call_args.spacecraft_velocity_x)
         self.assertEqual(vy, call_args.spacecraft_velocity_y)
         self.assertEqual(vz, call_args.spacecraft_velocity_z)
-        self.assertEqual(np.rad2deg(spin_axis_long) % 360, call_args.spin_axis_longitude)
+        self.assertEqual(
+            np.rad2deg(spin_axis_long) % 360, call_args.spin_axis_longitude
+        )
         self.assertEqual(np.rad2deg(spin_axis_lat), call_args.spin_axis_latitude)
 
     def test_determine_spacecraft_info_using_predict_if_needed(self):
-        with (tempfile.TemporaryDirectory() as tmp_dir):
-
+        with tempfile.TemporaryDirectory() as tmp_dir:
             input_files = [
                 get_integration_test_data_path("spice/de440.bsp"),
-                get_integration_test_data_path("spice/imap_dps_2025_359_2026_058_002.ah.bc"),
-                get_integration_test_data_path("spice/imap_dps_2025_359_2026_131_002.ah.bc"),
-                get_integration_test_data_path("spice/imap_recon_20250925_20260520_v01.bsp"),
+                get_integration_test_data_path(
+                    "spice/imap_dps_2025_359_2026_058_002.ah.bc"
+                ),
+                get_integration_test_data_path(
+                    "spice/imap_dps_2025_359_2026_131_002.ah.bc"
+                ),
+                get_integration_test_data_path(
+                    "spice/imap_recon_20250925_20260520_v01.bsp"
+                ),
                 get_integration_test_data_path("spice/imap_science_120.tf"),
                 get_integration_test_data_path("spice/imap_sclk_0171.tsc"),
                 get_integration_test_data_path("spice/naif0012.tls"),
@@ -122,67 +162,109 @@ def test_determine_spacecraft_info_using_predict_if_needed(self):
 
             with mock_imap_data_access(Path(tmp_dir), input_files):
                 spice_paths_with_predict = [SPICEFilePath(p.name) for p in input_files]
-                spice_paths_without_predict = [p for p in spice_paths_with_predict if "imap_dps_2025_359_2026_131" not in p.filename.name]
+                spice_paths_without_predict = [
+                    p
+                    for p in spice_paths_with_predict
+                    if "imap_dps_2025_359_2026_131" not in p.filename.name
+                ]
 
                 spice_dir = imap_data_access.config["DATA_DIR"] / "imap/spice"
                 metakernel_with_predict = Path(tmp_dir, "metakernel_with_predict")
-                metakernel_with_predict.write_text(create_metakernel(spice_dir, spice_paths_with_predict))
+                metakernel_with_predict.write_text(
+                    create_metakernel(spice_dir, spice_paths_with_predict)
+                )
 
                 metakernel_without_predict = Path(tmp_dir, "metakernel_without_predict")
-                metakernel_without_predict.write_text(create_metakernel(spice_dir, spice_paths_without_predict))
-
-                input_files_with_predict = [Path("parent/kernel 1"), Path("kernel 2"), Path("kernel 3"), Path("kernel 4")]
-                input_files_without_predict = [Path("kernel 5"), Path("kernel 6"), Path("kernel 7"), Path("kernel 8")]
+                metakernel_without_predict.write_text(
+                    create_metakernel(spice_dir, spice_paths_without_predict)
+                )
 
-                spice_with_predict = FurnishMetakernelOutput(metakernel_with_predict, input_files_with_predict)
-                spice_without_predict = FurnishMetakernelOutput(metakernel_without_predict, input_files_without_predict)
+                input_files_with_predict = [
+                    Path("parent/kernel 1"),
+                    Path("kernel 2"),
+                    Path("kernel 3"),
+                    Path("kernel 4"),
+                ]
+                input_files_without_predict = [
+                    Path("kernel 5"),
+                    Path("kernel 6"),
+                    Path("kernel 7"),
+                    Path("kernel 8"),
+                ]
+
+                spice_with_predict = FurnishMetakernelOutput(
+                    metakernel_with_predict, input_files_with_predict
+                )
+                spice_without_predict = FurnishMetakernelOutput(
+                    metakernel_without_predict, input_files_without_predict
+                )
 
                 date_with_predict = datetime(2026, 3, 25, 12)
                 date_without_predict = datetime(2026, 1, 15, 12)
 
-                result_without_predict = determine_spacecraft_info_using_predict_if_needed(date_without_predict, spice_with_predict, spice_without_predict)
+                result_without_predict = (
+                    determine_spacecraft_info_using_predict_if_needed(
+                        date_without_predict, spice_with_predict, spice_without_predict
+                    )
+                )
                 spacecraft_info, glows_flags, kernel_names = result_without_predict
                 self.assertEqual(GlowsL3Flags.NONE, glows_flags)
-                self.assertEqual(["kernel 5", "kernel 6", "kernel 7", "kernel 8"], kernel_names)
-                self.assertEqual(GlowsL3eSpacecraftInfo(spacecraft_radius=0.974926812207828,
-                       spacecraft_longitude=np.float64(114.98987223111648),
-                       spacecraft_latitude=np.float64(-0.02732735672494243),
-                       spacecraft_velocity_x=np.float64(-27.105121226323945),
-                       spacecraft_velocity_y=np.float64(-12.572196725157356),
-                       spacecraft_velocity_z=np.float64(0.04038850296808949),
-                       spin_axis_longitude=np.float64(291.46869723545007),
-                       spin_axis_latitude=np.float64(0.08089846053031882)), spacecraft_info)
-
-                result_with_predict = determine_spacecraft_info_using_predict_if_needed(date_with_predict, spice_with_predict, spice_without_predict)
+                self.assertEqual(
+                    ["kernel 5", "kernel 6", "kernel 7", "kernel 8"], kernel_names
+                )
+                self.assertEqual(
+                    GlowsL3eSpacecraftInfo(
+                        spacecraft_radius=0.974926812207828,
+                        spacecraft_longitude=np.float64(114.98987223111648),
+                        spacecraft_latitude=np.float64(-0.02732735672494243),
+                        spacecraft_velocity_x=np.float64(-27.105121226323945),
+                        spacecraft_velocity_y=np.float64(-12.572196725157356),
+                        spacecraft_velocity_z=np.float64(0.04038850296808949),
+                        spin_axis_longitude=np.float64(291.46869723545007),
+                        spin_axis_latitude=np.float64(0.08089846053031882),
+                    ),
+                    spacecraft_info,
+                )
+
+                result_with_predict = determine_spacecraft_info_using_predict_if_needed(
+                    date_with_predict, spice_with_predict, spice_without_predict
+                )
                 spacecraft_info, glows_flags, kernel_names = result_with_predict
                 self.assertEqual(GlowsL3Flags.PREDICTIVE_EPHEMERIS, glows_flags)
-                self.assertEqual(["kernel 1", "kernel 2", "kernel 3", "kernel 4"], kernel_names)
-                self.assertEqual(GlowsL3eSpacecraftInfo(spacecraft_radius=0.9869148557889134,
-                       spacecraft_longitude=np.float64(184.44380384879602),
-                       spacecraft_latitude=np.float64(0.04367233154056944),
-                       spacecraft_velocity_x=np.float64(1.8304001531697336),
-                       spacecraft_velocity_y=np.float64(-29.587138738875474),
-                       spacecraft_velocity_z=np.float64(-0.009923833866874787),
-                       spin_axis_longitude=np.float64(0.9116206658792297),
-                       spin_axis_latitude=np.float64(0.0758987849137639)) ,spacecraft_info)
-
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access.download')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.CDF')
+                self.assertEqual(
+                    ["kernel 1", "kernel 2", "kernel 3", "kernel 4"], kernel_names
+                )
+                self.assertEqual(
+                    GlowsL3eSpacecraftInfo(
+                        spacecraft_radius=0.9869148557889134,
+                        spacecraft_longitude=np.float64(184.44380384879602),
+                        spacecraft_latitude=np.float64(0.04367233154056944),
+                        spacecraft_velocity_x=np.float64(1.8304001531697336),
+                        spacecraft_velocity_y=np.float64(-29.587138738875474),
+                        spacecraft_velocity_z=np.float64(-0.009923833866874787),
+                        spin_axis_longitude=np.float64(0.9116206658792297),
+                        spin_axis_latitude=np.float64(0.0758987849137639),
+                    ),
+                    spacecraft_info,
+                )
+
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access.download")
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.CDF")
     def test_find_first_updated_cr(self, mock_CDF, mock_download):
         num_crs = 10
         mock_download.return_value = sentinel.download_old_l3d
 
         old_l3d = {
-            'cr_grid': np.arange(num_crs) + 0.5,
-            'lyman_alpha': np.arange(num_crs),
-            'phion': np.arange(num_crs),
-            'plasma_speed': np.arange(0, 20).reshape((num_crs, 2)),
-            'plasma_speed_flag': np.arange(num_crs),
-            'proton_density': np.arange(0, 20).reshape((num_crs, 2)),
-            'proton_density_flag': np.arange(num_crs),
-            'uv_anisotropy': np.arange(0, 20).reshape((num_crs, 2)),
-            'uv_anisotropy_flag': np.arange(num_crs),
-            'glows_flags': np.arange(num_crs),
+            "cr_grid": np.arange(num_crs) + 0.5,
+            "lyman_alpha": np.arange(num_crs),
+            "phion": np.arange(num_crs),
+            "plasma_speed": np.arange(0, 20).reshape((num_crs, 2)),
+            "plasma_speed_flag": np.arange(num_crs),
+            "proton_density": np.arange(0, 20).reshape((num_crs, 2)),
+            "proton_density_flag": np.arange(num_crs),
+            "uv_anisotropy": np.arange(0, 20).reshape((num_crs, 2)),
+            "uv_anisotropy_flag": np.arange(num_crs),
+            "glows_flags": np.arange(num_crs),
         }
 
         new_lyman_alpha = np.arange(num_crs)
@@ -213,18 +295,17 @@ def test_find_first_updated_cr(self, mock_CDF, mock_download):
         new_glows_flags[9] = 10
 
         cases = [
-            ('cr_grid', np.append(old_l3d['cr_grid'], 10.5), 10),
-            ('lyman_alpha', new_lyman_alpha, 1),
-            ('phion', new_phion, 2),
-            ('plasma_speed_flag', new_plasma_speed_flag, 3),
-            ('proton_density_flag', new_proton_density_flag, 4),
-            ('uv_anisotropy_flag', new_uv_anisotropy_flag, 5),
-
-            ('plasma_speed', new_plasma_speed, 6),
-            ('proton_density', new_proton_density, 7),
-            ('uv_anisotropy', new_uv_anisotropy, 8),
-            ('glows_flags', new_glows_flags, 9),
-            ('no_change', None, None)
+            ("cr_grid", np.append(old_l3d["cr_grid"], 10.5), 10),
+            ("lyman_alpha", new_lyman_alpha, 1),
+            ("phion", new_phion, 2),
+            ("plasma_speed_flag", new_plasma_speed_flag, 3),
+            ("proton_density_flag", new_proton_density_flag, 4),
+            ("uv_anisotropy_flag", new_uv_anisotropy_flag, 5),
+            ("plasma_speed", new_plasma_speed, 6),
+            ("proton_density", new_proton_density, 7),
+            ("uv_anisotropy", new_uv_anisotropy, 8),
+            ("glows_flags", new_glows_flags, 9),
+            ("no_change", None, None),
         ]
 
         for case, change, expected in cases:
@@ -275,16 +356,38 @@ def test_get_lo_pivot_angle_from_l1b_file_scenarios(self):
             datetime(2026, 3, 20, 23, 0),
             datetime(2026, 3, 20, 23, 30),
         ]
-        epochs = first_thirty_minutes + thirty_minutes_to_22_hours_thirty_minutes + after_22_hours_thirty_minutes
+        epochs = (
+            first_thirty_minutes
+            + thirty_minutes_to_22_hours_thirty_minutes
+            + after_22_hours_thirty_minutes
+        )
         shifted_epochs = [e + timedelta(hours=10) for e in epochs]
         cases = [
-            ("realistic", epochs, [89.1, 89.9, 89.9, 89.9, 89.9, 89.9, 89.9, 89.9, 89.9], 90),
+            (
+                "realistic",
+                epochs,
+                [89.1, 89.9, 89.9, 89.9, 89.9, 89.9, 89.9, 89.9, 89.9],
+                90,
+            ),
             ("basic", epochs, [10, 20, 30, 40, 50, 60, 70, 80, 90], 50),
-            ("only uses data within 3-15 hours from first point", shifted_epochs,
-             [999, 999, 30, 40, 50, 60, 70, 999, 999], 50),
-            ("uses median and rounds", epochs, [999, 999, 120.2, 34.4, 86.8, 50.9, 77.7, 999, 999], 78),
-            ("fallback to 90 if no points in interval", first_thirty_minutes + after_22_hours_thirty_minutes,
-             [10, 10, 10, 10], 90),
+            (
+                "only uses data within 3-15 hours from first point",
+                shifted_epochs,
+                [999, 999, 30, 40, 50, 60, 70, 999, 999],
+                50,
+            ),
+            (
+                "uses median and rounds",
+                epochs,
+                [999, 999, 120.2, 34.4, 86.8, 50.9, 77.7, 999, 999],
+                78,
+            ),
+            (
+                "fallback to 90 if no points in interval",
+                first_thirty_minutes + after_22_hours_thirty_minutes,
+                [10, 10, 10, 10],
+                90,
+            ),
             ("fallback to 90 if no points at all", [], [], 90),
         ]
         for name, epochs, pivot_angles, expected in cases:
@@ -314,12 +417,34 @@ def test_compute_glows_flags_for_repoint(self):
         ]
 
         cases = [
-            ("between crs 1 and 2", datetime(2025, 5, 10), GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO),
+            (
+                "between crs 1 and 2",
+                datetime(2025, 5, 10),
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+            ),
             ("between crs 2 and 3", datetime(2025, 5, 25, 23), GlowsL3Flags.NONE),
-            ("between crs 3 and 4", datetime(2025, 5, 26, 1), GlowsL3Flags.PREDICTIVE_EPHEMERIS),
-            ("exactly on cr 4", datetime(2025, 6, 5), GlowsL3Flags.PREDICTIVE_EPHEMERIS),
-            ("between cr 4 and 5", datetime(2025, 6, 7), GlowsL3Flags.PREDICTIVE_EPHEMERIS | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO),
-            ("after last data point", datetime(2025, 6, 15, 1), GlowsL3Flags.PREDICTIVE_EPHEMERIS | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO)
+            (
+                "between crs 3 and 4",
+                datetime(2025, 5, 26, 1),
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+            ),
+            (
+                "exactly on cr 4",
+                datetime(2025, 6, 5),
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+            ),
+            (
+                "between cr 4 and 5",
+                datetime(2025, 6, 7),
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS
+                | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+            ),
+            (
+                "after last data point",
+                datetime(2025, 6, 15, 1),
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS
+                | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+            ),
         ]
 
         for name, repoint_midpoint, expected in cases:
@@ -328,22 +453,33 @@ def test_compute_glows_flags_for_repoint(self):
                     cdf_path = Path(tmp_dir, "l3d.cdf")
                     with CDF(str(cdf_path), create=True) as cdf:
                         cdf["epoch"] = epochs
-                        cdf.new("glows_flags", data=flags, type=const.CDF_UINT2, recVary=True)
+                        cdf.new(
+                            "glows_flags",
+                            data=flags,
+                            type=const.CDF_UINT2,
+                            recVary=True,
+                        )
 
                     actual = compute_glows_flags_for_repoint(cdf_path, repoint_midpoint)
 
                     self.assertIsInstance(actual, int)
                     self.assertEqual(expected, actual)
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.get_lo_pivot_angle_from_l1b_file')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access')
-    def test_get_lo_pivot_angles(self, mock_imap_data_access, mock_get_pivot_angle_from_file):
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_utils.get_lo_pivot_angle_from_l1b_file"
+    )
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access")
+    def test_get_lo_pivot_angles(
+        self, mock_imap_data_access, mock_get_pivot_angle_from_file
+    ):
         available_repointings = [1, 2, 3, 4, 5, 6]
         mock_imap_data_access.query.return_value = [
-            {'file_path': f'file{i}.cdf', 'repointing': i}
+            {"file_path": f"file{i}.cdf", "repointing": i}
             for i in available_repointings
         ]
-        mock_imap_data_access.download.side_effect = lambda name: Path("local/path/to", name)
+        mock_imap_data_access.download.side_effect = lambda name: Path(
+            "local/path/to", name
+        )
         pivot_angles_by_file_path = {
             Path("local/path/to/file1.cdf"): 25,
             Path("local/path/to/file2.cdf"): 75,
@@ -366,17 +502,22 @@ def mock_read_from_cdf(path: Path):
             descriptor="nhk",
             version="latest",
         )
-        mock_imap_data_access.download.assert_has_calls([
-            call("file3.cdf"),
-            call("file4.cdf"),
-            call("file6.cdf"),
-        ])
-        self.assertEqual({
-            3: LoPivotAngle(parent_filename="file3.cdf", pivot_angle=105),
-            4: LoPivotAngle(parent_filename="file4.cdf", pivot_angle=90),
-            6: LoPivotAngle(parent_filename="file6.cdf", pivot_angle=84),
-            10: LoPivotAngle(parent_filename=None, pivot_angle=90),
-        }, result)
+        mock_imap_data_access.download.assert_has_calls(
+            [
+                call("file3.cdf"),
+                call("file4.cdf"),
+                call("file6.cdf"),
+            ]
+        )
+        self.assertEqual(
+            {
+                3: LoPivotAngle(parent_filename="file3.cdf", pivot_angle=105),
+                4: LoPivotAngle(parent_filename="file4.cdf", pivot_angle=90),
+                6: LoPivotAngle(parent_filename="file6.cdf", pivot_angle=84),
+                10: LoPivotAngle(parent_filename=None, pivot_angle=90),
+            },
+            result,
+        )
 
     def test_get_repoint_numbers_within_cr_window(self):
         start_cr = 2093
@@ -388,9 +529,12 @@ def test_get_repoint_numbers_within_cr_window(self):
         set_global_repoint_table_paths([repointing_path])
         repointing_data = get_repoint_data()
 
-        actual_repoint_numbers = get_repoint_numbers_within_cr_window(start_cr, end_cr, repointing_data)
+        actual_repoint_numbers = get_repoint_numbers_within_cr_window(
+            start_cr, end_cr, repointing_data
+        )
 
         np.testing.assert_array_equal(actual_repoint_numbers, expected_repoint_numbers)
+
     def test_get_repoint_numbers_within_cr_window_returns_empty_for_none_start(self):
         start_cr = None
         end_cr = 2094
@@ -400,20 +544,26 @@ def test_get_repoint_numbers_within_cr_window_returns_empty_for_none_start(self)
         set_global_repoint_table_paths([repointing_path])
         repointing_data = get_repoint_data()
 
-        actual_repoint_numbers = get_repoint_numbers_within_cr_window(start_cr, end_cr, repointing_data)
+        actual_repoint_numbers = get_repoint_numbers_within_cr_window(
+            start_cr, end_cr, repointing_data
+        )
 
         np.testing.assert_array_equal(actual_repoint_numbers, [])
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access')
-    def test_identify_versions_for_l3e_output_files_gives_minor_version_1_for_non_existing_l3e(self,
-                                                                                               mock_imap_data_access,
-                                                                                               mock_get_repoint_numbers_within_cr_window):
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window"
+    )
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access")
+    def test_identify_versions_for_l3e_output_files_gives_minor_version_1_for_non_existing_l3e(
+        self, mock_imap_data_access, mock_get_repoint_numbers_within_cr_window
+    ):
         start_cr_of_mission = 2093
         end_cr_of_mission = 2094
         first_cr_updated_in_l3d = None
         repointing_path = get_test_data_path("fake_1_day_repointing_file.csv")
-        version_map = VersionMap({desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)})
+        version_map = VersionMap(
+            {desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)}
+        )
 
         mock_imap_data_access.query.side_effect = [
             create_mock_query_results([]),
@@ -427,52 +577,99 @@ def test_identify_versions_for_l3e_output_files_gives_minor_version_1_for_non_ex
         updated_repointing_numbers = list()
         mock_get_repoint_numbers_within_cr_window.side_effect = [
             all_repointing_numbers,
-            updated_repointing_numbers
+            updated_repointing_numbers,
         ]
         reprocess_info = ReprocessInfo({})
 
-        result = identify_versions_for_l3e_output_files(start_cr_of_mission, end_cr_of_mission, first_cr_updated_in_l3d,
-                                                        repointing_path, version_map, reprocess_info)
-
-        mock_imap_data_access.query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_45_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_90_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_LO_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR)
-        ])
-
-        expected_versions_for_hi45_repoint_number = {repoint_number: Version(3, 1) for repoint_number in
-                                                     all_repointing_numbers}
-        expected_versions_for_hi90_repoint_number = {repoint_number: Version(4, 1) for repoint_number in
-                                                     all_repointing_numbers}
-        expected_versions_for_lo_repoint_number = {repoint_number: Version(5, 1) for repoint_number in
-                                                   all_repointing_numbers}
-        expected_versions_for_ultra_sf_repoint_number = {repoint_number: Version(6, 1) for repoint_number in
-                                                         all_repointing_numbers}
-        expected_versions_for_ultra_hf_repoint_number = {repoint_number: Version(7, 1) for repoint_number in
-                                                         all_repointing_numbers}
+        result = identify_versions_for_l3e_output_files(
+            start_cr_of_mission,
+            end_cr_of_mission,
+            first_cr_updated_in_l3d,
+            repointing_path,
+            version_map,
+            reprocess_info,
+        )
+
+        mock_imap_data_access.query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_45_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_90_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_LO_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                ),
+            ]
+        )
+
+        expected_versions_for_hi45_repoint_number = {
+            repoint_number: Version(3, 1) for repoint_number in all_repointing_numbers
+        }
+        expected_versions_for_hi90_repoint_number = {
+            repoint_number: Version(4, 1) for repoint_number in all_repointing_numbers
+        }
+        expected_versions_for_lo_repoint_number = {
+            repoint_number: Version(5, 1) for repoint_number in all_repointing_numbers
+        }
+        expected_versions_for_ultra_sf_repoint_number = {
+            repoint_number: Version(6, 1) for repoint_number in all_repointing_numbers
+        }
+        expected_versions_for_ultra_hf_repoint_number = {
+            repoint_number: Version(7, 1) for repoint_number in all_repointing_numbers
+        }
 
         self.assertCountEqual(all_repointing_numbers, result.repointing_numbers)
-        self.assertEqual(expected_versions_for_hi90_repoint_number, result.hi_90_repointings)
-        self.assertEqual(expected_versions_for_hi45_repoint_number, result.hi_45_repointings)
+        self.assertEqual(
+            expected_versions_for_hi90_repoint_number, result.hi_90_repointings
+        )
+        self.assertEqual(
+            expected_versions_for_hi45_repoint_number, result.hi_45_repointings
+        )
         self.assertEqual(expected_versions_for_lo_repoint_number, result.lo_repointings)
         self.assertEqual(
             expected_versions_for_ultra_sf_repoint_number, result.ultra_sf_repointings
         )
-        self.assertEqual(expected_versions_for_ultra_hf_repoint_number, result.ultra_hf_repointings)
-
+        self.assertEqual(
+            expected_versions_for_ultra_hf_repoint_number, result.ultra_hf_repointings
+        )
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access')
-    def test_identify_versions_for_l3e_output_files_increments_major_and_minor_when_given_higher_major_version(self,
-                                                                                                               mock_imap_data_access,
-                                                                                                               mock_get_repoint_numbers_within_cr_window):
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window"
+    )
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access")
+    def test_identify_versions_for_l3e_output_files_increments_major_and_minor_when_given_higher_major_version(
+        self, mock_imap_data_access, mock_get_repoint_numbers_within_cr_window
+    ):
         start_cr_of_mission = 2093
         end_cr_of_mission = 2094
         first_cr_updated_in_l3d = None
         repointing_path = get_test_data_path("fake_1_day_repointing_file.csv")
-        version_map = VersionMap({desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)})
+        version_map = VersionMap(
+            {desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)}
+        )
 
         all_repointing_numbers = list(range(3682, 3736))
         updated_repointing_numbers = list()
@@ -482,48 +679,90 @@ def test_identify_versions_for_l3e_output_files_increments_major_and_minor_when_
 
         mock_get_repoint_numbers_within_cr_window.side_effect = [
             all_repointing_numbers,
-            updated_repointing_numbers
+            updated_repointing_numbers,
         ]
 
         mock_imap_data_access.query.side_effect = [
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-hi-90_20250101-repoint03682_{Version(old_major_version, 1)}.cdf',
-                f'imap_glows_l3e_survival-probability-hi-90_20250101-repoint03683_{Version(3, 1)}.cdf',
-                f'imap_glows_l3e_survival-probability-hi-90_20250101-repoint03735_{Version(3, 1)}.cdf'
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-hi-45_20250101-repoint03683_{Version(old_major_version, 2)}.cdf',
-                f'imap_glows_l3e_survival-probability-hi-45_20250101-repoint03684_{Version(4, 2)}.cdf',
-                f'imap_glows_l3e_survival-probability-hi-45_20250101-repoint03735_{Version(4, 2)}.cdf'
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-lo_20250101-repoint03684_{Version(old_major_version, 3)}.cdf',
-                f'imap_glows_l3e_survival-probability-lo_20250101-repoint03685_{Version(5, 3)}.cdf',
-                f'imap_glows_l3e_survival-probability-lo_20250101-repoint03735_{Version(5, 3)}.cdf'
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-ul-sf_20250101-repoint03685_{Version(old_major_version, 4)}.cdf',
-                f'imap_glows_l3e_survival-probability-ul-sf_20250101-repoint03686_{Version(6, 4)}.cdf',
-                f'imap_glows_l3e_survival-probability-ul-sf_20250101-repoint03735_{Version(6, 4)}.cdf'
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-ul-hf_20250101-repoint03686_{Version(old_major_version, 5)}.cdf',
-                f'imap_glows_l3e_survival-probability-ul-hf_20250101-repoint03687_{Version(7, 5)}.cdf',
-                f'imap_glows_l3e_survival-probability-ul-hf_20250101-repoint03735_{Version(7, 5)}.cdf'
-            ])
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint03682_{Version(old_major_version, 1)}.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint03683_{Version(3, 1)}.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint03735_{Version(3, 1)}.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint03683_{Version(old_major_version, 2)}.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint03684_{Version(4, 2)}.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint03735_{Version(4, 2)}.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-lo_20250101-repoint03684_{Version(old_major_version, 3)}.cdf",
+                    f"imap_glows_l3e_survival-probability-lo_20250101-repoint03685_{Version(5, 3)}.cdf",
+                    f"imap_glows_l3e_survival-probability-lo_20250101-repoint03735_{Version(5, 3)}.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint03685_{Version(old_major_version, 4)}.cdf",
+                    f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint03686_{Version(6, 4)}.cdf",
+                    f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint03735_{Version(6, 4)}.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint03686_{Version(old_major_version, 5)}.cdf",
+                    f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint03687_{Version(7, 5)}.cdf",
+                    f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint03735_{Version(7, 5)}.cdf",
+                ]
+            ),
         ]
 
-        result = identify_versions_for_l3e_output_files(start_cr_of_mission, end_cr_of_mission,
-                                                        first_cr_updated_in_l3d, repointing_path,
-                                                        version_map, ReprocessInfo({}))
+        result = identify_versions_for_l3e_output_files(
+            start_cr_of_mission,
+            end_cr_of_mission,
+            first_cr_updated_in_l3d,
+            repointing_path,
+            version_map,
+            ReprocessInfo({}),
+        )
 
-        mock_imap_data_access.query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_45_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_90_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_LO_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR)
-        ])
+        mock_imap_data_access.query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_45_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_90_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_LO_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                ),
+            ]
+        )
 
         self.assertCountEqual(list(range(3682, 3735)), result.repointing_numbers)
 
@@ -539,88 +778,168 @@ def test_identify_versions_for_l3e_output_files_increments_major_and_minor_when_
         self.assertEqual(Version(6, 5), result.ultra_sf_repointings[3685])
         self.assertEqual(Version(7, 6), result.ultra_hf_repointings[3686])
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access')
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window"
+    )
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access")
     def test_identify_versions_for_l3e_output_files_increments_minor_when_same_major_and_updated_l3d_covers_pointing(
-            self, mock_imap_data_access, mock_get_repoint_numbers_within_cr_window
+        self, mock_imap_data_access, mock_get_repoint_numbers_within_cr_window
     ):
         start_cr_of_mission = 2093
         end_cr_of_mission = 2095
         first_cr_updated_in_l3d = None
         repointing_path = get_test_data_path("fake_1_day_repointing_file.csv")
-        version_map = VersionMap({desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)})
+        version_map = VersionMap(
+            {desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)}
+        )
 
         all_repointing_numbers = list(range(3682, 3763))
         updated_repointing_numbers = list(range(3709, 3763))
 
         mock_get_repoint_numbers_within_cr_window.side_effect = [
             all_repointing_numbers,
-            updated_repointing_numbers
+            updated_repointing_numbers,
         ]
 
         mock_imap_data_access.query.side_effect = [
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-hi-90_20250101-repoint{repoint:05d}_{Version(3, 1)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-hi-45_20250101-repoint{repoint:05d}_{Version(4, 2)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-lo_20250101-repoint{repoint:05d}_{Version(5, 3)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-ul-sf_20250101-repoint{repoint:05d}_{Version(6, 4)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-ul-hf_20250101-repoint{repoint:05d}_{Version(7, 5)}.cdf' for repoint in all_repointing_numbers
-            ])
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint{repoint:05d}_{Version(3, 1)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint{repoint:05d}_{Version(4, 2)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-lo_20250101-repoint{repoint:05d}_{Version(5, 3)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint{repoint:05d}_{Version(6, 4)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint{repoint:05d}_{Version(7, 5)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
         ]
 
-        result = identify_versions_for_l3e_output_files(start_cr_of_mission, end_cr_of_mission,
-                                                        first_cr_updated_in_l3d, repointing_path,
-                                                        version_map, ReprocessInfo({}))
-
-        mock_imap_data_access.query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_45_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_90_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_LO_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR)
-        ])
-
-        expected_versions_for_hi45_repoint_number = {repoint_number: Version(3, 2) for repoint_number in
-                                                     updated_repointing_numbers}
-        expected_versions_for_hi90_repoint_number = {repoint_number: Version(4, 3) for repoint_number in
-                                                     updated_repointing_numbers}
-        expected_versions_for_lo_repoint_number = {repoint_number: Version(5, 4) for repoint_number in
-                                                   updated_repointing_numbers}
-        expected_versions_for_ultra_sf_repoint_number = {repoint_number: Version(6, 5) for repoint_number in
-                                                         updated_repointing_numbers}
-        expected_versions_for_ultra_hf_repoint_number = {repoint_number: Version(7, 6) for repoint_number in
-                                                         updated_repointing_numbers}
+        result = identify_versions_for_l3e_output_files(
+            start_cr_of_mission,
+            end_cr_of_mission,
+            first_cr_updated_in_l3d,
+            repointing_path,
+            version_map,
+            ReprocessInfo({}),
+        )
+
+        mock_imap_data_access.query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_45_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_90_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_LO_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                ),
+            ]
+        )
+
+        expected_versions_for_hi45_repoint_number = {
+            repoint_number: Version(3, 2)
+            for repoint_number in updated_repointing_numbers
+        }
+        expected_versions_for_hi90_repoint_number = {
+            repoint_number: Version(4, 3)
+            for repoint_number in updated_repointing_numbers
+        }
+        expected_versions_for_lo_repoint_number = {
+            repoint_number: Version(5, 4)
+            for repoint_number in updated_repointing_numbers
+        }
+        expected_versions_for_ultra_sf_repoint_number = {
+            repoint_number: Version(6, 5)
+            for repoint_number in updated_repointing_numbers
+        }
+        expected_versions_for_ultra_hf_repoint_number = {
+            repoint_number: Version(7, 6)
+            for repoint_number in updated_repointing_numbers
+        }
 
         self.assertCountEqual(updated_repointing_numbers, result.repointing_numbers)
-        self.assertEqual(expected_versions_for_hi90_repoint_number, result.hi_90_repointings)
-        self.assertEqual(expected_versions_for_hi45_repoint_number, result.hi_45_repointings)
+        self.assertEqual(
+            expected_versions_for_hi90_repoint_number, result.hi_90_repointings
+        )
+        self.assertEqual(
+            expected_versions_for_hi45_repoint_number, result.hi_45_repointings
+        )
         self.assertEqual(expected_versions_for_lo_repoint_number, result.lo_repointings)
-        self.assertEqual(expected_versions_for_ultra_sf_repoint_number, result.ultra_sf_repointings)
-        self.assertEqual(expected_versions_for_ultra_hf_repoint_number, result.ultra_hf_repointings)
+        self.assertEqual(
+            expected_versions_for_ultra_sf_repoint_number, result.ultra_sf_repointings
+        )
+        self.assertEqual(
+            expected_versions_for_ultra_hf_repoint_number, result.ultra_hf_repointings
+        )
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_data')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window')
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access')
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_data")
+    @patch(
+        "imap_l3_processing.glows.l3e.glows_l3e_utils.get_repoint_numbers_within_cr_window"
+    )
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access")
     def test_identify_versions_for_l3e_output_files_uses_reprocess_info(
-            self, mock_imap_data_access, mock_get_repoint_numbers_within_cr_window, mock_get_repoint_data
+        self,
+        mock_imap_data_access,
+        mock_get_repoint_numbers_within_cr_window,
+        mock_get_repoint_data,
     ):
         start_cr_of_mission = 2093
         end_cr_of_mission = 2095
         first_cr_updated_in_l3d = None
         repointing_path = get_test_data_path("fake_1_day_repointing_file.csv")
-        version_map = VersionMap({desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)})
+        version_map = VersionMap(
+            {desc: Version(3 + i, 5) for i, desc in enumerate(GLOWS_L3E_DESCRIPTORS)}
+        )
         repoint_number = 3700
         mock_reprocess_info = create_autospec(ReprocessInfo, instance=True)
         mock_reprocess_info.get_repoints_for_descriptor.side_effect = [
-            [], [], [repoint_number], [], []
+            [],
+            [],
+            [repoint_number],
+            [],
+            [],
         ]
 
         all_repointing_numbers = list(range(3682, 3763))
@@ -628,25 +947,40 @@ def test_identify_versions_for_l3e_output_files_uses_reprocess_info(
 
         mock_get_repoint_numbers_within_cr_window.side_effect = [
             all_repointing_numbers,
-            updated_repointing_numbers
+            updated_repointing_numbers,
         ]
 
         mock_imap_data_access.query.side_effect = [
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-hi-90_20250101-repoint{repoint:05d}_{Version(3, 1)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-hi-45_20250101-repoint{repoint:05d}_{Version(4, 2)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-lo_20250101-repoint{repoint:05d}_{Version(5, 3)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-ul-sf_20250101-repoint{repoint:05d}_{Version(6, 4)}.cdf' for repoint in all_repointing_numbers
-            ]),
-            create_mock_query_results([
-                f'imap_glows_l3e_survival-probability-ul-hf_20250101-repoint{repoint:05d}_{Version(7, 5)}.cdf' for repoint in all_repointing_numbers
-            ])
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint{repoint:05d}_{Version(3, 1)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint{repoint:05d}_{Version(4, 2)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-lo_20250101-repoint{repoint:05d}_{Version(5, 3)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint{repoint:05d}_{Version(6, 4)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint{repoint:05d}_{Version(7, 5)}.cdf"
+                    for repoint in all_repointing_numbers
+                ]
+            ),
         ]
 
         result = identify_versions_for_l3e_output_files(
@@ -658,33 +992,67 @@ def test_identify_versions_for_l3e_output_files_uses_reprocess_info(
             mock_reprocess_info,
         )
 
-        mock_imap_data_access.query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_45_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_HI_90_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_LO_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR),
-            call(instrument='glows', data_level='l3e', version="latest", descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR)
-        ])
-        mock_reprocess_info.get_repoints_for_descriptor.assert_has_calls([
-            call(GLOWS_L3E_HI_45_DESCRIPTOR, mock_get_repoint_data.return_value),
-            call(GLOWS_L3E_HI_90_DESCRIPTOR, mock_get_repoint_data.return_value),
-            call(GLOWS_L3E_LO_DESCRIPTOR, mock_get_repoint_data.return_value),
-            call(GLOWS_L3E_ULTRA_SF_DESCRIPTOR, mock_get_repoint_data.return_value),
-            call(GLOWS_L3E_ULTRA_HF_DESCRIPTOR, mock_get_repoint_data.return_value),
-        ])
+        mock_imap_data_access.query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_45_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_HI_90_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_LO_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    version="latest",
+                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                ),
+            ]
+        )
+        mock_reprocess_info.get_repoints_for_descriptor.assert_has_calls(
+            [
+                call(GLOWS_L3E_HI_45_DESCRIPTOR, mock_get_repoint_data.return_value),
+                call(GLOWS_L3E_HI_90_DESCRIPTOR, mock_get_repoint_data.return_value),
+                call(GLOWS_L3E_LO_DESCRIPTOR, mock_get_repoint_data.return_value),
+                call(GLOWS_L3E_ULTRA_SF_DESCRIPTOR, mock_get_repoint_data.return_value),
+                call(GLOWS_L3E_ULTRA_HF_DESCRIPTOR, mock_get_repoint_data.return_value),
+            ]
+        )
 
         expected_versions_for_lo_repoint_number = {repoint_number: Version(5, 4)}
 
         self.assertEqual([repoint_number], result.repointing_numbers)
         self.assertEqual(expected_versions_for_lo_repoint_number, result.lo_repointings)
 
-
     def test_calculate_energy_deltas(self):
         centers = np.array([10, 1000, 100000])
         expected_energy_delta_minus = np.array([9, 900, 90000])
         expected_energy_delta_plus = np.array([90, 9000, 900000])
 
-        actual_energy_delta_plus, actual_energy_delta_minus = calculate_energy_deltas(centers)
+        actual_energy_delta_plus, actual_energy_delta_minus = calculate_energy_deltas(
+            centers
+        )
 
-        np.testing.assert_array_equal(actual_energy_delta_plus, expected_energy_delta_plus)
-        np.testing.assert_array_equal(actual_energy_delta_minus, expected_energy_delta_minus)
\ No newline at end of file
+        np.testing.assert_array_equal(
+            actual_energy_delta_plus, expected_energy_delta_plus
+        )
+        np.testing.assert_array_equal(
+            actual_energy_delta_minus, expected_energy_delta_minus
+        )
diff --git a/tests/glows/l3e/test_reprocess_info.py b/tests/glows/l3e/test_reprocess_info.py
index b129aed57..610b7e066 100644
--- a/tests/glows/l3e/test_reprocess_info.py
+++ b/tests/glows/l3e/test_reprocess_info.py
@@ -3,14 +3,32 @@
 from pathlib import Path
 from unittest.mock import patch
 
-from imap_data_access import ProcessingInputCollection, AncillaryInput, RepointInput
-from imap_processing.spice.repoint import set_global_repoint_table_paths, get_repoint_data
-
-from imap_l3_processing.glows.descriptors import GLOWS_L3E_HI_45_DESCRIPTOR, GLOWS_L3E_HI_90_DESCRIPTOR, \
-    GLOWS_L3E_LO_DESCRIPTOR, GLOWS_L3E_ULTRA_SF_DESCRIPTOR, GLOWS_L3E_ULTRA_HF_DESCRIPTOR, GLOWS_L3B_DESCRIPTOR, \
-    GLOWS_L3C_DESCRIPTOR, GLOWS_L3D_DESCRIPTOR, GLOWS_REPROCESSING_DESCRIPTOR
-from imap_l3_processing.glows.l3e.reprocess_info import ReprocessInfo, ReprocessTargets, fetch_reprocess_info
-from tests.test_helpers import get_test_data_folder, get_test_data_path, create_mock_query_results
+from imap_data_access import AncillaryInput, ProcessingInputCollection, RepointInput
+from imap_processing.spice.repoint import (
+    get_repoint_data,
+    set_global_repoint_table_paths,
+)
+
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3B_DESCRIPTOR,
+    GLOWS_L3C_DESCRIPTOR,
+    GLOWS_L3D_DESCRIPTOR,
+    GLOWS_L3E_HI_45_DESCRIPTOR,
+    GLOWS_L3E_HI_90_DESCRIPTOR,
+    GLOWS_L3E_LO_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+    GLOWS_REPROCESSING_DESCRIPTOR,
+)
+from imap_l3_processing.glows.l3e.reprocess_info import (
+    ReprocessInfo,
+    ReprocessTargets,
+    fetch_reprocess_info,
+)
+from tests.test_helpers import (
+    get_test_data_folder,
+    get_test_data_path,
+)
 
 
 class TestReprocessInfo(unittest.TestCase):
@@ -27,7 +45,9 @@ def test_parse_from_ancillary(self, mock_datetime):
         test_file: Path = (
             get_test_data_folder() / "glows" / "glows_reprocessing_ancillary_file.dat"
         )
-        mock_datetime.now.return_value = datetime(2026, 8, 11, 11, 40, tzinfo=timezone.utc)
+        mock_datetime.now.return_value = datetime(
+            2026, 8, 11, 11, 40, tzinfo=timezone.utc
+        )
         cases = [
             (datetime(2026, 8, 11, 11, 41, tzinfo=timezone.utc), products),
             (datetime(2026, 8, 11, 11, 41), products),
@@ -40,25 +60,69 @@ def test_parse_from_ancillary(self, mock_datetime):
 
                 reprocess_info = ReprocessInfo.parse_from_ancillary(test_file)
 
-                self.assertEqual(expected_products, reprocess_info.products_to_reprocess)
+                self.assertEqual(
+                    expected_products, reprocess_info.products_to_reprocess
+                )
                 mock_datetime.fromisoformat.assert_called_with("2226-08-11 11:40")
                 mock_datetime.now.assert_called_with(timezone.utc)
 
     def test_should_reprocess_l3d_if_l3d_or_l3e_products_are_specified(self):
         cases = [
-            ("should reprocess l3e hi45",
-             {GLOWS_L3E_HI_45_DESCRIPTOR: ReprocessTargets([], []), GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}, True),
-            ("should reprocess l3e hi90",
-             {GLOWS_L3E_HI_90_DESCRIPTOR: ReprocessTargets([], []), GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}, True),
-            ("should reprocess l3e lo",
-             {GLOWS_L3E_LO_DESCRIPTOR: ReprocessTargets([], []), GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}, True),
-            ("should reprocess l3e ultra sf",
-             {GLOWS_L3E_ULTRA_SF_DESCRIPTOR: ReprocessTargets([], []), GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}, True),
-            ("should reprocess l3e ultra hf",
-             {GLOWS_L3E_ULTRA_HF_DESCRIPTOR: ReprocessTargets([], []), GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}, True),
-            ("should not reprocess l3b alone", {GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])}, False),
-            ("should not reprocess l3c alone", {GLOWS_L3C_DESCRIPTOR: ReprocessTargets([], [])}, False),
-            ("should reprocess l3d alone", {GLOWS_L3D_DESCRIPTOR: ReprocessTargets([], [])}, True),
+            (
+                "should reprocess l3e hi45",
+                {
+                    GLOWS_L3E_HI_45_DESCRIPTOR: ReprocessTargets([], []),
+                    GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], []),
+                },
+                True,
+            ),
+            (
+                "should reprocess l3e hi90",
+                {
+                    GLOWS_L3E_HI_90_DESCRIPTOR: ReprocessTargets([], []),
+                    GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], []),
+                },
+                True,
+            ),
+            (
+                "should reprocess l3e lo",
+                {
+                    GLOWS_L3E_LO_DESCRIPTOR: ReprocessTargets([], []),
+                    GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], []),
+                },
+                True,
+            ),
+            (
+                "should reprocess l3e ultra sf",
+                {
+                    GLOWS_L3E_ULTRA_SF_DESCRIPTOR: ReprocessTargets([], []),
+                    GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], []),
+                },
+                True,
+            ),
+            (
+                "should reprocess l3e ultra hf",
+                {
+                    GLOWS_L3E_ULTRA_HF_DESCRIPTOR: ReprocessTargets([], []),
+                    GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], []),
+                },
+                True,
+            ),
+            (
+                "should not reprocess l3b alone",
+                {GLOWS_L3B_DESCRIPTOR: ReprocessTargets([], [])},
+                False,
+            ),
+            (
+                "should not reprocess l3c alone",
+                {GLOWS_L3C_DESCRIPTOR: ReprocessTargets([], [])},
+                False,
+            ),
+            (
+                "should reprocess l3d alone",
+                {GLOWS_L3D_DESCRIPTOR: ReprocessTargets([], [])},
+                True,
+            ),
         ]
 
         for case, products_to_reprocess, expected_result in cases:
@@ -94,25 +158,35 @@ def test_get_repoints(self):
         repoints_for_cr2093 = list(range(3682, 3696))
         repoints_for_cr2094 = list(range(3710, 3724))
 
-        expected_repoints_lo = [2045, 2046] + repoints_for_cr2093 + [3700] + repoints_for_cr2094
+        expected_repoints_lo = (
+            [2045, 2046] + repoints_for_cr2093 + [3700] + repoints_for_cr2094
+        )
         expected_repoints_hi = [2050]
         expected_repoints_ultra = []
         self.assertEqual(expected_repoints_lo, repoints_for_lo)
         self.assertEqual(expected_repoints_hi, repoints_for_hi)
         self.assertEqual(expected_repoints_ultra, repoints_for_ultra)
 
-    @patch('imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access.download')
-    def test_fetch_reprocess_info_downloads_and_parses_reprocess_info(self, mock_download):
+    @patch("imap_l3_processing.glows.l3e.glows_l3e_utils.imap_data_access.download")
+    def test_fetch_reprocess_info_downloads_and_parses_reprocess_info(
+        self, mock_download
+    ):
         processing_input = ProcessingInputCollection(
-            AncillaryInput(f"imap_glows_{GLOWS_REPROCESSING_DESCRIPTOR}_20250101_v000.dat"),
+            AncillaryInput(
+                f"imap_glows_{GLOWS_REPROCESSING_DESCRIPTOR}_20250101_v000.dat"
+            ),
             RepointInput("imap_2026_269_12.repoint.csv"),
         )
-        path_to_downloaded_ancillary = get_test_data_folder() / 'glows' / 'glows_reprocessing_ancillary_file.dat'
+        path_to_downloaded_ancillary = (
+            get_test_data_folder() / "glows" / "glows_reprocessing_ancillary_file.dat"
+        )
         mock_download.return_value = path_to_downloaded_ancillary
 
         reprocess_info = fetch_reprocess_info(processing_input)
 
-        mock_download.assert_called_with(f'imap_glows_{GLOWS_REPROCESSING_DESCRIPTOR}_20250101_v000.dat')
+        mock_download.assert_called_with(
+            f"imap_glows_{GLOWS_REPROCESSING_DESCRIPTOR}_20250101_v000.dat"
+        )
 
         expected_products_to_reprocess = {
             GLOWS_L3E_LO_DESCRIPTOR: ReprocessTargets([245, 246], [2310]),
@@ -122,4 +196,6 @@ def test_fetch_reprocess_info_downloads_and_parses_reprocess_info(self, mock_dow
             GLOWS_L3E_ULTRA_HF_DESCRIPTOR: ReprocessTargets([243], []),
             GLOWS_L3D_DESCRIPTOR: ReprocessTargets([], []),
         }
-        self.assertEqual(expected_products_to_reprocess, reprocess_info.products_to_reprocess)
+        self.assertEqual(
+            expected_products_to_reprocess, reprocess_info.products_to_reprocess
+        )
diff --git a/tests/glows/test_glows_processor.py b/tests/glows/test_glows_processor.py
index 62aafaa98..fe2c47d68 100644
--- a/tests/glows/test_glows_processor.py
+++ b/tests/glows/test_glows_processor.py
@@ -10,7 +10,7 @@
 from datetime import datetime, timedelta
 from pathlib import Path
 from subprocess import CalledProcessError, CompletedProcess
-from unittest.mock import patch, Mock, sentinel, call, MagicMock
+from unittest.mock import MagicMock, Mock, call, patch, sentinel
 from zipfile import ZIP_DEFLATED
 
 import imap_data_access
@@ -21,38 +21,73 @@
 
 from imap_l3_processing.constants import TEMP_CDF_FOLDER_PATH
 from imap_l3_processing.glows import l3d
-from imap_l3_processing.glows.descriptors import GLOWS_L3A_DESCRIPTOR, GLOWS_L3E_ULTRA_SF_DESCRIPTOR, \
-    GLOWS_L3E_ULTRA_HF_DESCRIPTOR, GLOWS_L3B_DESCRIPTOR, GLOWS_L3C_DESCRIPTOR, GLOWS_L3D_DESCRIPTOR, \
-    GLOWS_L3E_HI_45_DESCRIPTOR, GLOWS_L3E_HI_90_DESCRIPTOR, GLOWS_L3E_LO_DESCRIPTOR
-from imap_l3_processing.glows.glows_processor import GlowsProcessor, process_l3d, process_l3e, process_l3bc, \
-    process_l3e_ul_sf, process_l3e_hi, process_l3e_lo, process_l3e_ul_hf
-from imap_l3_processing.glows.l3a.utils import create_glows_l3a_dictionary_from_cdf, create_glows_l3a_from_dictionary
-from imap_l3_processing.glows.l3bc.cannot_process_carrington_rotation_error import CannotProcessCarringtonRotationError
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3A_DESCRIPTOR,
+    GLOWS_L3B_DESCRIPTOR,
+    GLOWS_L3C_DESCRIPTOR,
+    GLOWS_L3D_DESCRIPTOR,
+    GLOWS_L3E_HI_45_DESCRIPTOR,
+    GLOWS_L3E_HI_90_DESCRIPTOR,
+    GLOWS_L3E_LO_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+)
+from imap_l3_processing.glows.glows_processor import (
+    GlowsProcessor,
+    process_l3bc,
+    process_l3d,
+    process_l3e,
+    process_l3e_hi,
+    process_l3e_lo,
+    process_l3e_ul_hf,
+    process_l3e_ul_sf,
+)
+from imap_l3_processing.glows.l3a.utils import (
+    create_glows_l3a_dictionary_from_cdf,
+    create_glows_l3a_from_dictionary,
+)
+from imap_l3_processing.glows.l3bc.cannot_process_carrington_rotation_error import (
+    CannotProcessCarringtonRotationError,
+)
 from imap_l3_processing.glows.l3bc.glows_l3bc_dependencies import GlowsL3BCDependencies
-from imap_l3_processing.glows.l3bc.glows_l3bc_initializer import GlowsL3BCInitializerData
+from imap_l3_processing.glows.l3bc.glows_l3bc_initializer import (
+    GlowsL3BCInitializerData,
+)
 from imap_l3_processing.glows.l3bc.models import ExternalDependencies
 from imap_l3_processing.glows.l3d.glows_l3d_dependencies import GlowsL3DDependencies
 from imap_l3_processing.glows.l3d.models import GlowsL3DProcessorOutput
 from imap_l3_processing.glows.l3d.utils import PATH_TO_L3D_TOOLKIT
-from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import GlowsL3eCallArguments, GlowsL3eSpacecraftInfo
+from imap_l3_processing.glows.l3e.glows_l3e_call_arguments import (
+    GlowsL3eCallArguments,
+    GlowsL3eSpacecraftInfo,
+)
 from imap_l3_processing.glows.l3e.glows_l3e_dependencies import GlowsL3EDependencies
 from imap_l3_processing.glows.l3e.glows_l3e_hi_model import GlowsL3EHiData
 from imap_l3_processing.glows.l3e.glows_l3e_initializer import GlowsL3EInitializerOutput
 from imap_l3_processing.glows.l3e.glows_l3e_lo_model import GlowsL3ELoData
 from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import GlowsL3EUltraData
-from imap_l3_processing.glows.l3e.glows_l3e_utils import GlowsL3eVersionsForRepointings, LoPivotAngle
+from imap_l3_processing.glows.l3e.glows_l3e_utils import (
+    GlowsL3eVersionsForRepointings,
+    LoPivotAngle,
+)
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.utils import save_data
-from tests.test_helpers import get_test_instrument_team_data_path, get_test_data_path, get_test_data_folder, \
-    assert_dataclass_fields
+from tests.test_helpers import (
+    assert_dataclass_fields,
+    get_test_data_folder,
+    get_test_data_path,
+    get_test_instrument_team_data_path,
+)
 
-MODULE = 'imap_l3_processing.glows.glows_processor'
+MODULE = "imap_l3_processing.glows.glows_processor"
 
-class TestGlowsProcessor(unittest.TestCase):
 
+class TestGlowsProcessor(unittest.TestCase):
     def setUp(self):
-        self.l3bc_initializer_patcher = patch(f"{MODULE}.GlowsL3BCInitializer.get_crs_to_process")
+        self.l3bc_initializer_patcher = patch(
+            f"{MODULE}.GlowsL3BCInitializer.get_crs_to_process"
+        )
         self.mock_l3bc_initializer = self.l3bc_initializer_patcher.start()
 
         self.mock_external_deps = Mock()
@@ -70,19 +105,21 @@ def setUp(self):
 
         self.mock_l3e_initializer_patcher = patch(f"{MODULE}.GlowsL3EInitializer")
         self.mock_l3e_initializer = self.mock_l3e_initializer_patcher.start()
-        self.mock_l3e_initializer.get_repointings_to_process.return_value = GlowsL3EInitializerOutput(
-            dependencies=Mock(),
-            repointings=GlowsL3eVersionsForRepointings(
-                repointing_numbers=[],
-                hi_90_repointings={},
-                hi_45_repointings={},
-                lo_repointings={},
-                ultra_sf_repointings={},
-                ultra_hf_repointings={},
-            ),
-            l3d_cdf_path=Path("path/to/l3d.cdf"),
-            metakernel_with_predict_ephem=Mock(),
-            metakernel_without_predict_ephem=Mock(),
+        self.mock_l3e_initializer.get_repointings_to_process.return_value = (
+            GlowsL3EInitializerOutput(
+                dependencies=Mock(),
+                repointings=GlowsL3eVersionsForRepointings(
+                    repointing_numbers=[],
+                    hi_90_repointings={},
+                    hi_45_repointings={},
+                    lo_repointings={},
+                    ultra_sf_repointings={},
+                    ultra_hf_repointings={},
+                ),
+                l3d_cdf_path=Path("path/to/l3d.cdf"),
+                metakernel_with_predict_ephem=Mock(),
+                metakernel_without_predict_ephem=Mock(),
+            )
         )
 
         self.fetch_reprocess_info_patcher = patch(f"{MODULE}.fetch_reprocess_info")
@@ -95,39 +132,64 @@ def tearDown(self):
         self.mock_l3e_initializer_patcher.stop()
         self.fetch_reprocess_info_patcher.stop()
 
-        if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3b'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3b')
-        if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3c'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3c')
-        if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3d'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3d')
-        if os.path.exists(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt'): shutil.rmtree(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt')
+        if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3b"):
+            shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3b")
+        if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3c"):
+            shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3c")
+        if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3d"):
+            shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3d")
+        if os.path.exists(PATH_TO_L3D_TOOLKIT / "data_l3d_txt"):
+            shutil.rmtree(PATH_TO_L3D_TOOLKIT / "data_l3d_txt")
 
     @patch("imap_l3_processing.glows.glows_processor.GlowsL3BCInitializer")
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3ADependencies')
-    @patch('imap_l3_processing.glows.glows_processor.L3aData')
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch('imap_l3_processing.utils.spiceypy')
-    def test_processor_handles_l3a(self, mock_spiceypy, mock_save_data, mock_l3a_data,
-                                   mock_glows_dependencies_class, mock_glows_initializer):
+    @patch("imap_l3_processing.glows.glows_processor.GlowsL3ADependencies")
+    @patch("imap_l3_processing.glows.glows_processor.L3aData")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch("imap_l3_processing.utils.spiceypy")
+    def test_processor_handles_l3a(
+        self,
+        mock_spiceypy,
+        mock_save_data,
+        mock_l3a_data,
+        mock_glows_dependencies_class,
+        mock_glows_initializer,
+    ):
         mock_spiceypy.ktotal.return_value = 0
-        instrument = 'glows'
+        instrument = "glows"
         start_date = datetime(2024, 10, 7, 10, 00, 00)
         end_date = datetime(2024, 10, 8, 10, 00, 00)
         outgoing_data_level = "l3a"
-        outgoing_version = 'v002'
+        outgoing_version = "v002"
 
-        mock_fetched_dependencies = mock_glows_dependencies_class.fetch_dependencies.return_value
-        mock_fetched_dependencies.ancillary_files = {"settings": get_test_instrument_team_data_path(
-            "glows/imap_glows_pipeline-settings_20100101_v001.json")}
-        l3a_json_path = get_test_data_folder() / "glows" / "imap_glows_l3a_20130908085214_orbX_modX_p_v00.json"
+        mock_fetched_dependencies = (
+            mock_glows_dependencies_class.fetch_dependencies.return_value
+        )
+        mock_fetched_dependencies.ancillary_files = {
+            "settings": get_test_instrument_team_data_path(
+                "glows/imap_glows_pipeline-settings_20100101_v001.json"
+            )
+        }
+        l3a_json_path = (
+            get_test_data_folder()
+            / "glows"
+            / "imap_glows_l3a_20130908085214_orbX_modX_p_v00.json"
+        )
         with open(l3a_json_path) as f:
             mock_l3a_data.return_value.data = json.load(f)
         mock_cdf_path = mock_save_data.return_value
 
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date,
-                                       outgoing_version, repointing=5)
+        input_metadata = InputMetadata(
+            instrument,
+            outgoing_data_level,
+            start_date,
+            end_date,
+            outgoing_version,
+            repointing=5,
+        )
 
         mock_processing_input_collection = Mock()
         parent_file_path = Path("test/parent_path")
-        expected_parent_file_names = ['parent_path']
+        expected_parent_file_names = ["parent_path"]
         expected_global_metadata_attrs = {
             "flight_software_version": 131848,
             "ground_software_version": "0.3",
@@ -142,52 +204,76 @@ def test_processor_handles_l3a(self, mock_spiceypy, mock_save_data, mock_l3a_dat
                 "data_ancillary/imap_glows_suspected_transients_v002.dat",
                 "data_ancillary/imap_glows_map_of_extra_helio_bckgrd_v001.dat",
                 "data_ancillary/imap_glows_time_dep_bckgrd_v001.dat",
-                "data_ancillary/imap_l1_anc_sc_Merged_2010-2030_mockByGlowsTeam001.csv"
+                "data_ancillary/imap_l1_anc_sc_Merged_2010-2030_mockByGlowsTeam001.csv",
             ],
-            "l2_input_file_name": "data_l2_histograms/imap_glows_l2_20130908085214_orbX_modX_p_v00.json"
+            "l2_input_file_name": "data_l2_histograms/imap_glows_l2_20130908085214_orbX_modX_p_v00.json",
         }
-        mock_processing_input_collection.get_file_paths.return_value = [parent_file_path]
+        mock_processing_input_collection.get_file_paths.return_value = [
+            parent_file_path
+        ]
 
-        processor = GlowsProcessor(dependencies=mock_processing_input_collection, input_metadata=input_metadata)
+        processor = GlowsProcessor(
+            dependencies=mock_processing_input_collection, input_metadata=input_metadata
+        )
         products = processor.process()
 
         mock_glows_initializer.assert_not_called()
-        mock_glows_dependencies_class.fetch_dependencies.assert_called_with(mock_processing_input_collection)
+        mock_glows_dependencies_class.fetch_dependencies.assert_called_with(
+            mock_processing_input_collection
+        )
         expected_data_to_save = create_glows_l3a_from_dictionary(
-            mock_l3a_data.return_value.data, replace(input_metadata, descriptor=GLOWS_L3A_DESCRIPTOR))
+            mock_l3a_data.return_value.data,
+            replace(input_metadata, descriptor=GLOWS_L3A_DESCRIPTOR),
+        )
 
         expected_data_to_save.parent_file_names = expected_parent_file_names
         actual_data = mock_save_data.call_args.args[0]
         self.assertEqual(expected_parent_file_names, actual_data.parent_file_names)
-        self.assertEqual(expected_global_metadata_attrs, actual_data.global_metadata_attrs)
-        assert_dataclass_fields(expected_data_to_save, actual_data, omit=["global_metadata_attrs"])
+        self.assertEqual(
+            expected_global_metadata_attrs, actual_data.global_metadata_attrs
+        )
+        assert_dataclass_fields(
+            expected_data_to_save, actual_data, omit=["global_metadata_attrs"]
+        )
         self.assertEqual([mock_cdf_path], products)
 
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3ADependencies')
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
+    @patch("imap_l3_processing.glows.glows_processor.GlowsL3ADependencies")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
     def test_does_not_save_cdf_if_process_l3a_returns_none(self, mock_save_data, _):
-        instrument = 'glows'
+        instrument = "glows"
         start_date = datetime(2024, 10, 7, 10, 00, 00)
         end_date = datetime(2024, 10, 8, 10, 00, 00)
         outgoing_data_level = "l3a"
-        outgoing_version = 'v002'
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date,
-                                       outgoing_version, repointing=5)
+        outgoing_version = "v002"
+        input_metadata = InputMetadata(
+            instrument,
+            outgoing_data_level,
+            start_date,
+            end_date,
+            outgoing_version,
+            repointing=5,
+        )
 
         mock_processing_input_collection = Mock()
-        processor = GlowsProcessor(dependencies=mock_processing_input_collection, input_metadata=input_metadata)
+        processor = GlowsProcessor(
+            dependencies=mock_processing_input_collection, input_metadata=input_metadata
+        )
         processor.process_l3a = Mock(return_value=None)
         products = processor.process()
 
         mock_save_data.assert_not_called()
         self.assertEqual([], products)
 
-    @patch('imap_l3_processing.glows.glows_processor.create_glows_l3a_from_dictionary')
-    @patch('imap_l3_processing.glows.glows_processor.L3aData')
+    @patch("imap_l3_processing.glows.glows_processor.create_glows_l3a_from_dictionary")
+    @patch("imap_l3_processing.glows.glows_processor.L3aData")
     def test_process_l3a(self, l3a_data_constructor, create_glows_l3a_from_dictionary):
-        input_metadata = InputMetadata('glows', "l3a", datetime(2024, 10, 7, 10, 00, 00),
-                                       datetime(2024, 10, 8, 10, 00, 00),
-                                       'v02')
+        input_metadata = InputMetadata(
+            "glows",
+            "l3a",
+            datetime(2024, 10, 7, 10, 00, 00),
+            datetime(2024, 10, 8, 10, 00, 00),
+            "v02",
+        )
 
         processor = GlowsProcessor(dependencies=Mock(), input_metadata=input_metadata)
 
@@ -195,40 +281,44 @@ def test_process_l3a(self, l3a_data_constructor, create_glows_l3a_from_dictionar
         fetched_dependencies = Mock()
 
         mock_l3a_data = l3a_data_constructor.return_value
-        mock_l3a_data.data = {
-            "daily_lightcurve": {
-                "exposure_times": np.array([1])
-            }
-        }
+        mock_l3a_data.data = {"daily_lightcurve": {"exposure_times": np.array([1])}}
         result = processor.process_l3a(fetched_dependencies)
 
         self.assertIs(create_glows_l3a_from_dictionary.return_value, result)
-        l3a_data_constructor.assert_called_once_with(fetched_dependencies.ancillary_files)
-        mock_l3a_data.process_l2_data_file.assert_called_once_with(fetched_dependencies.data)
+        l3a_data_constructor.assert_called_once_with(
+            fetched_dependencies.ancillary_files
+        )
+        mock_l3a_data.process_l2_data_file.assert_called_once_with(
+            fetched_dependencies.data
+        )
         mock_l3a_data.generate_l3a_data.assert_called_once_with(
-            fetched_dependencies.ancillary_files)
-        processor.add_spin_angle_delta.assert_called_with(mock_l3a_data.data,
-                                                          fetched_dependencies.ancillary_files)
-        create_glows_l3a_from_dictionary.assert_called_once_with(processor.add_spin_angle_delta.return_value,
-                                                                 replace(input_metadata,
-                                                                         descriptor=GLOWS_L3A_DESCRIPTOR))
-
-    @patch('imap_l3_processing.glows.glows_processor.create_glows_l3a_from_dictionary')
-    @patch('imap_l3_processing.glows.glows_processor.L3aData')
-    def test_process_l3a_returns_none_when_no_input_histrogram_bins_are_valid(self, l3a_data_constructor,
-                                                                              create_glows_l3a_from_dictionary):
-        input_metadata = InputMetadata('glows', "l3a", datetime(2024, 10, 7, 10, 00, 00),
-                                       datetime(2024, 10, 8, 10, 00, 00),
-                                       'v02')
+            fetched_dependencies.ancillary_files
+        )
+        processor.add_spin_angle_delta.assert_called_with(
+            mock_l3a_data.data, fetched_dependencies.ancillary_files
+        )
+        create_glows_l3a_from_dictionary.assert_called_once_with(
+            processor.add_spin_angle_delta.return_value,
+            replace(input_metadata, descriptor=GLOWS_L3A_DESCRIPTOR),
+        )
+
+    @patch("imap_l3_processing.glows.glows_processor.create_glows_l3a_from_dictionary")
+    @patch("imap_l3_processing.glows.glows_processor.L3aData")
+    def test_process_l3a_returns_none_when_no_input_histrogram_bins_are_valid(
+        self, l3a_data_constructor, create_glows_l3a_from_dictionary
+    ):
+        input_metadata = InputMetadata(
+            "glows",
+            "l3a",
+            datetime(2024, 10, 7, 10, 00, 00),
+            datetime(2024, 10, 8, 10, 00, 00),
+            "v02",
+        )
 
         processor = GlowsProcessor(dependencies=Mock(), input_metadata=input_metadata)
 
         mock_l3a_data = l3a_data_constructor.return_value
-        mock_l3a_data.data = {
-            "daily_lightcurve": {
-                "exposure_times": np.array([])
-            }
-        }
+        mock_l3a_data.data = {"daily_lightcurve": {"exposure_times": np.array([])}}
 
         processor.add_spin_angle_delta = Mock()
         fetched_dependencies = Mock()
@@ -236,39 +326,46 @@ def test_process_l3a_returns_none_when_no_input_histrogram_bins_are_valid(self,
 
         self.assertIsNone(result)
 
-        mock_l3a_data.process_l2_data_file.assert_called_once_with(fetched_dependencies.data)
+        mock_l3a_data.process_l2_data_file.assert_called_once_with(
+            fetched_dependencies.data
+        )
         mock_l3a_data.generate_l3a_data.assert_called_once_with(
-            fetched_dependencies.ancillary_files)
+            fetched_dependencies.ancillary_files
+        )
         processor.add_spin_angle_delta.assert_not_called()
         create_glows_l3a_from_dictionary.assert_not_called()
 
     def test_add_spin_angle_delta(self):
-        cases = [
-            (60, 3),
-            (90, 2)
-        ]
+        cases = [(60, 3), (90, 2)]
 
         for bins, expected_delta in cases:
             with self.subTest(bin=bins, expected_delta=expected_delta):
                 ancillary_files = {}
-                with open(get_test_data_path("glows/imap_glows_l3a_20130908085214_orbX_modX_p_v00.json")) as f:
+                with open(
+                    get_test_data_path(
+                        "glows/imap_glows_l3a_20130908085214_orbX_modX_p_v00.json"
+                    )
+                ) as f:
                     example_data = json.load(f)
 
                 with tempfile.TemporaryDirectory() as tempdir:
                     temp_file_path = Path(tempdir) / "settings.json"
                     example_settings = get_test_instrument_team_data_path(
-                        "glows/imap_glows_pipeline-settings_20100101_v001.json")
+                        "glows/imap_glows_pipeline-settings_20100101_v001.json"
+                    )
 
                     with open(example_settings) as file:
                         loaded_file = json.load(file)
 
-                    loaded_file['l3a_nominal_number_of_bins'] = bins
+                    loaded_file["l3a_nominal_number_of_bins"] = bins
 
-                    with open(temp_file_path, 'w') as file:
+                    with open(temp_file_path, "w") as file:
                         json.dump(loaded_file, file)
-                    ancillary_files['settings'] = temp_file_path
+                    ancillary_files["settings"] = temp_file_path
 
-                    result = GlowsProcessor.add_spin_angle_delta(deepcopy(example_data), ancillary_files)
+                    result = GlowsProcessor.add_spin_angle_delta(
+                        deepcopy(example_data), ancillary_files
+                    )
                 for k, v in example_data.items():
                     if k != "daily_lightcurve":
                         self.assertEqual(v, result[k])
@@ -276,73 +373,104 @@ def test_add_spin_angle_delta(self):
                 for k2, v2 in example_data["daily_lightcurve"].items():
                     self.assertEqual(v2, result["daily_lightcurve"][k2])
                 spin_angle_delta = result["daily_lightcurve"]["spin_angle_delta"]
-                self.assertEqual(len(example_data["daily_lightcurve"]["spin_angle"]), len(spin_angle_delta))
+                self.assertEqual(
+                    len(example_data["daily_lightcurve"]["spin_angle"]),
+                    len(spin_angle_delta),
+                )
                 self.assertTrue(np.all(spin_angle_delta == expected_delta))
 
     @patch("imap_l3_processing.utils.spiceypy")
     @patch("imap_l3_processing.glows.glows_processor.save_data")
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3BIonizationRate')
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3CSolarWind')
-    @patch('imap_l3_processing.glows.glows_processor.filter_l3a_files')
-    @patch('imap_l3_processing.glows.glows_processor.generate_l3bc')
-    @patch('imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies')
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3BCInitializer')
-    def test_process_l3bc(self, mock_glows_l3bc_initializer, mock_archive_dependencies, mock_generate_l3bc,
-                          mock_filter_bad_days,
-                          mock_l3c_model_class, mock_l3b_model_class, mock_save_data,
-                          mock_spiceypy):
+    @patch("imap_l3_processing.glows.glows_processor.GlowsL3BIonizationRate")
+    @patch("imap_l3_processing.glows.glows_processor.GlowsL3CSolarWind")
+    @patch("imap_l3_processing.glows.glows_processor.filter_l3a_files")
+    @patch("imap_l3_processing.glows.glows_processor.generate_l3bc")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.GlowsL3BCInitializer")
+    def test_process_l3bc(
+        self,
+        mock_glows_l3bc_initializer,
+        mock_archive_dependencies,
+        mock_generate_l3bc,
+        mock_filter_bad_days,
+        mock_l3c_model_class,
+        mock_l3b_model_class,
+        mock_save_data,
+        mock_spiceypy,
+    ):
         mock_spiceypy.ktotal.return_value = 1
-        mock_spiceypy.kdata.return_value = ['kernel_1', 'type', 'source', 'handle']
+        mock_spiceypy.kdata.return_value = ["kernel_1", "type", "source", "handle"]
 
         first_cr_number_to_process = 2091
         second_cr_number_to_process = 2092
 
-        external_deps = ExternalDependencies(f107_index_file_path=sentinel.f107_index_file_path,
-                                             lyman_alpha_path=sentinel.lyman_alpha_path,
-                                             omni2_data_path=sentinel.omni2_data_path)
+        external_deps = ExternalDependencies(
+            f107_index_file_path=sentinel.f107_index_file_path,
+            lyman_alpha_path=sentinel.lyman_alpha_path,
+            omni2_data_path=sentinel.omni2_data_path,
+        )
 
         mock_archive_dependencies.side_effect = [Path("path1.zip"), Path("path2.zip")]
 
         l3a_data_1 = {"filename": "l3a_file_1"}
         first_dependency_version = Version(1, 1)
-        first_dependency = GlowsL3BCDependencies(l3a_data=[l3a_data_1],
-                                                 external_files=sentinel.external_files_1,
-                                                 ancillary_files={
-                                                     'bad_days_list': sentinel.bad_days_list_1,
-                                                 },
-                                                 carrington_rotation_number=first_cr_number_to_process,
-                                                 version=first_dependency_version,
-                                                 start_date=Mock(),
-                                                 end_date=Mock(),
-                                                 repointing_file_path=sentinel.repointing_file_path)
+        first_dependency = GlowsL3BCDependencies(
+            l3a_data=[l3a_data_1],
+            external_files=sentinel.external_files_1,
+            ancillary_files={
+                "bad_days_list": sentinel.bad_days_list_1,
+            },
+            carrington_rotation_number=first_cr_number_to_process,
+            version=first_dependency_version,
+            start_date=Mock(),
+            end_date=Mock(),
+            repointing_file_path=sentinel.repointing_file_path,
+        )
 
         l3a_data_2 = {"filename": "l3a_file_2"}
         second_dependency_version = Version(1, 2)
-        second_dependency = GlowsL3BCDependencies(l3a_data=[l3a_data_2],
-                                                  external_files=sentinel.external_files_2,
-                                                  ancillary_files={
-                                                      'bad_days_list': sentinel.bad_days_list_2,
-                                                  },
-                                                  carrington_rotation_number=second_cr_number_to_process,
-                                                  version=second_dependency_version,
-                                                  start_date=Mock(),
-                                                  end_date=Mock(),
-                                                  repointing_file_path=sentinel.repointing_file_path)
-
-        mock_generate_l3bc.side_effect = [(sentinel.l3b_data_1, sentinel.l3c_data_1),
-                                          (sentinel.l3b_data_2, sentinel.l3c_data_2)]
-        mock_filter_bad_days.side_effect = [sentinel.filtered_days_1, sentinel.filtered_days_2]
+        second_dependency = GlowsL3BCDependencies(
+            l3a_data=[l3a_data_2],
+            external_files=sentinel.external_files_2,
+            ancillary_files={
+                "bad_days_list": sentinel.bad_days_list_2,
+            },
+            carrington_rotation_number=second_cr_number_to_process,
+            version=second_dependency_version,
+            start_date=Mock(),
+            end_date=Mock(),
+            repointing_file_path=sentinel.repointing_file_path,
+        )
+
+        mock_generate_l3bc.side_effect = [
+            (sentinel.l3b_data_1, sentinel.l3c_data_1),
+            (sentinel.l3b_data_2, sentinel.l3c_data_2),
+        ]
+        mock_filter_bad_days.side_effect = [
+            sentinel.filtered_days_1,
+            sentinel.filtered_days_2,
+        ]
 
         l3b_model_1 = Mock(parent_file_names=["file1"])
         l3b_model_2 = Mock(parent_file_names=["file2"])
         l3c_model_1 = Mock(parent_file_names=["file3"])
         l3c_model_2 = Mock(parent_file_names=["file4"])
-        mock_l3b_model_class.from_instrument_team_dictionary.side_effect = [l3b_model_1, l3b_model_2]
-        mock_l3c_model_class.from_instrument_team_dictionary.side_effect = [l3c_model_1, l3c_model_2]
-        mock_save_data.side_effect = [Path("path/to/l3b_file_1.cdf"),
-                                      Path("path/to/l3c_file_1.cdf"),
-                                      Path("path/to/l3b_file_2.cdf"),
-                                      Path("path/to/l3c_file_2.cdf")]
+        mock_l3b_model_class.from_instrument_team_dictionary.side_effect = [
+            l3b_model_1,
+            l3b_model_2,
+        ]
+        mock_l3c_model_class.from_instrument_team_dictionary.side_effect = [
+            l3c_model_1,
+            l3c_model_2,
+        ]
+        mock_save_data.side_effect = [
+            Path("path/to/l3b_file_1.cdf"),
+            Path("path/to/l3c_file_1.cdf"),
+            Path("path/to/l3b_file_2.cdf"),
+            Path("path/to/l3c_file_2.cdf"),
+        ]
         input_major_version = 2
         input_version = VersionMap(
             {
@@ -356,119 +484,211 @@ def test_process_l3bc(self, mock_glows_l3bc_initializer, mock_archive_dependenci
                 GLOWS_L3E_ULTRA_HF_DESCRIPTOR: Version(5, 1),
             }
         )
-        input_metadata = InputMetadata('glows', "l3b", datetime(2024, 10, 7, 10, 00, 00),
-                                       datetime(2024, 10, 8, 10, 00, 00),
-                                       version=input_version)
+        input_metadata = InputMetadata(
+            "glows",
+            "l3b",
+            datetime(2024, 10, 7, 10, 00, 00),
+            datetime(2024, 10, 8, 10, 00, 00),
+            version=input_version,
+        )
 
-        mock_glows_l3bc_initializer.get_crs_to_process.return_value = GlowsL3BCInitializerData(
-            external_dependencies=external_deps,
-            l3bc_dependencies=[first_dependency, second_dependency],
-            l3bs_by_cr={},
-            l3cs_by_cr={},
-            repoint_file_path=sentinel.repoint_file_path
+        mock_glows_l3bc_initializer.get_crs_to_process.return_value = (
+            GlowsL3BCInitializerData(
+                external_dependencies=external_deps,
+                l3bc_dependencies=[first_dependency, second_dependency],
+                l3bs_by_cr={},
+                l3cs_by_cr={},
+                repoint_file_path=sentinel.repoint_file_path,
+            )
         )
 
-        processor = GlowsProcessor(dependencies=sentinel.dependencies, input_metadata=input_metadata)
+        processor = GlowsProcessor(
+            dependencies=sentinel.dependencies, input_metadata=input_metadata
+        )
         products = processor.process()
 
-        self.assertEqual([Path("path/to/l3b_file_1.cdf"),
-                          Path("path/to/l3c_file_1.cdf"),
-                          Path("path1.zip"),
-                          Path("path/to/l3b_file_2.cdf"),
-                          Path("path/to/l3c_file_2.cdf"),
-                          Path("path2.zip")
-                          ], products)
+        self.assertEqual(
+            [
+                Path("path/to/l3b_file_1.cdf"),
+                Path("path/to/l3c_file_1.cdf"),
+                Path("path1.zip"),
+                Path("path/to/l3b_file_2.cdf"),
+                Path("path/to/l3c_file_2.cdf"),
+                Path("path2.zip"),
+            ],
+            products,
+        )
 
-        dependencies_with_filtered_list_1 = replace(first_dependency, l3a_data=sentinel.filtered_days_1)
-        dependencies_with_filtered_list_2 = replace(second_dependency, l3a_data=sentinel.filtered_days_2)
+        dependencies_with_filtered_list_1 = replace(
+            first_dependency, l3a_data=sentinel.filtered_days_1
+        )
+        dependencies_with_filtered_list_2 = replace(
+            second_dependency, l3a_data=sentinel.filtered_days_2
+        )
 
         mock_filter_bad_days.assert_has_calls(
-            [call([l3a_data_1], sentinel.bad_days_list_1, first_cr_number_to_process),
-             call([l3a_data_2], sentinel.bad_days_list_2, second_cr_number_to_process)])
+            [
+                call(
+                    [l3a_data_1], sentinel.bad_days_list_1, first_cr_number_to_process
+                ),
+                call(
+                    [l3a_data_2], sentinel.bad_days_list_2, second_cr_number_to_process
+                ),
+            ]
+        )
 
         mock_generate_l3bc.assert_has_calls(
-            [call(dependencies_with_filtered_list_1), call(dependencies_with_filtered_list_2)])
-
-        mock_glows_l3bc_initializer.get_crs_to_process.assert_called_once_with(sentinel.dependencies,
-                                                                               input_major_version)
-
-        expected_l3b_metadata_1 = InputMetadata("glows", "l3b", first_dependency.start_date,
-                                                first_dependency.end_date,
-                                                VersionMap({GLOWS_L3B_DESCRIPTOR: first_dependency_version}),
-                                                "ion-rate-profile")
-        expected_l3b_metadata_2 = InputMetadata("glows", "l3b", second_dependency.start_date,
-                                                second_dependency.end_date,
-                                                VersionMap({GLOWS_L3B_DESCRIPTOR: second_dependency_version}),
-                                                "ion-rate-profile")
-        expected_l3c_metadata_1 = InputMetadata("glows", "l3c", first_dependency.start_date,
-                                                first_dependency.end_date,
-                                                VersionMap({GLOWS_L3C_DESCRIPTOR: first_dependency_version}),
-                                                "sw-profile")
-        expected_l3c_metadata_2 = InputMetadata("glows", "l3c", second_dependency.start_date,
-                                                second_dependency.end_date,
-                                                VersionMap({GLOWS_L3C_DESCRIPTOR: second_dependency_version}),
-                                                "sw-profile")
+            [
+                call(dependencies_with_filtered_list_1),
+                call(dependencies_with_filtered_list_2),
+            ]
+        )
+
+        mock_glows_l3bc_initializer.get_crs_to_process.assert_called_once_with(
+            sentinel.dependencies, input_major_version
+        )
+
+        expected_l3b_metadata_1 = InputMetadata(
+            "glows",
+            "l3b",
+            first_dependency.start_date,
+            first_dependency.end_date,
+            VersionMap({GLOWS_L3B_DESCRIPTOR: first_dependency_version}),
+            "ion-rate-profile",
+        )
+        expected_l3b_metadata_2 = InputMetadata(
+            "glows",
+            "l3b",
+            second_dependency.start_date,
+            second_dependency.end_date,
+            VersionMap({GLOWS_L3B_DESCRIPTOR: second_dependency_version}),
+            "ion-rate-profile",
+        )
+        expected_l3c_metadata_1 = InputMetadata(
+            "glows",
+            "l3c",
+            first_dependency.start_date,
+            first_dependency.end_date,
+            VersionMap({GLOWS_L3C_DESCRIPTOR: first_dependency_version}),
+            "sw-profile",
+        )
+        expected_l3c_metadata_2 = InputMetadata(
+            "glows",
+            "l3c",
+            second_dependency.start_date,
+            second_dependency.end_date,
+            VersionMap({GLOWS_L3C_DESCRIPTOR: second_dependency_version}),
+            "sw-profile",
+        )
         mock_l3b_model_class.from_instrument_team_dictionary.assert_has_calls(
-            [call(sentinel.l3b_data_1, expected_l3b_metadata_1),
-             call(sentinel.l3b_data_2, expected_l3b_metadata_2)])
+            [
+                call(sentinel.l3b_data_1, expected_l3b_metadata_1),
+                call(sentinel.l3b_data_2, expected_l3b_metadata_2),
+            ]
+        )
 
         mock_l3c_model_class.from_instrument_team_dictionary.assert_has_calls(
-            [call(sentinel.l3c_data_1, expected_l3c_metadata_1),
-             call(sentinel.l3c_data_2, expected_l3c_metadata_2)])
-
-        mock_save_data.assert_has_calls([
-            call(l3b_model_1, cr_number=first_cr_number_to_process),
-            call(l3c_model_1, cr_number=first_cr_number_to_process),
-            call(l3b_model_2, cr_number=second_cr_number_to_process),
-            call(l3c_model_2, cr_number=second_cr_number_to_process)
-        ])
-
-        mock_archive_dependencies.assert_has_calls([
-            call(l3bc_deps=first_dependency, external_dependencies=external_deps),
-            call(l3bc_deps=second_dependency, external_dependencies=external_deps),
-        ])
-
-        self.assertEqual(["file1", "l3a_file_1", "path1.zip", "kernel_1"], l3b_model_1.parent_file_names)
-        self.assertEqual(["file2", "l3a_file_2", "path2.zip", "kernel_1"], l3b_model_2.parent_file_names)
-        self.assertEqual(["file3", "path1.zip", "l3b_file_1.cdf", "kernel_1", ], l3c_model_1.parent_file_names)
-        self.assertEqual(["file4", "path2.zip", "l3b_file_2.cdf", "kernel_1", ], l3c_model_2.parent_file_names)
-
-    @patch('imap_l3_processing.glows.glows_processor.filter_l3a_files')
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3BIonizationRate.from_instrument_team_dictionary')
-    @patch('imap_l3_processing.glows.glows_processor.GlowsL3CSolarWind.from_instrument_team_dictionary')
-    @patch('imap_l3_processing.glows.glows_processor.generate_l3bc')
-    @patch('imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies')
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
+            [
+                call(sentinel.l3c_data_1, expected_l3c_metadata_1),
+                call(sentinel.l3c_data_2, expected_l3c_metadata_2),
+            ]
+        )
+
+        mock_save_data.assert_has_calls(
+            [
+                call(l3b_model_1, cr_number=first_cr_number_to_process),
+                call(l3c_model_1, cr_number=first_cr_number_to_process),
+                call(l3b_model_2, cr_number=second_cr_number_to_process),
+                call(l3c_model_2, cr_number=second_cr_number_to_process),
+            ]
+        )
+
+        mock_archive_dependencies.assert_has_calls(
+            [
+                call(l3bc_deps=first_dependency, external_dependencies=external_deps),
+                call(l3bc_deps=second_dependency, external_dependencies=external_deps),
+            ]
+        )
+
+        self.assertEqual(
+            ["file1", "l3a_file_1", "path1.zip", "kernel_1"],
+            l3b_model_1.parent_file_names,
+        )
+        self.assertEqual(
+            ["file2", "l3a_file_2", "path2.zip", "kernel_1"],
+            l3b_model_2.parent_file_names,
+        )
+        self.assertEqual(
+            [
+                "file3",
+                "path1.zip",
+                "l3b_file_1.cdf",
+                "kernel_1",
+            ],
+            l3c_model_1.parent_file_names,
+        )
+        self.assertEqual(
+            [
+                "file4",
+                "path2.zip",
+                "l3b_file_2.cdf",
+                "kernel_1",
+            ],
+            l3c_model_2.parent_file_names,
+        )
+
+    @patch("imap_l3_processing.glows.glows_processor.filter_l3a_files")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsL3BIonizationRate.from_instrument_team_dictionary"
+    )
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsL3CSolarWind.from_instrument_team_dictionary"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.generate_l3bc")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
     @patch("imap_l3_processing.glows.glows_processor.GlowsL3BCInitializer")
-    def test_process_l3bc_catches_no_data_error_and_continues(self, mock_glows_initializer_class, mock_save_data,
-                                                              mock_archive_dependencies, mock_generate_l3bc,
-                                                              mock_l3c_from_instrument_team_dictionary,
-                                                              mock_l3b_from_instrument_team_dictionary, _):
-        bc_dependencies_1 = GlowsL3BCDependencies(version=Version(1, 1),
-                                                  carrington_rotation_number=2096,
-                                                  start_date=datetime(year=2021, month=1, day=1),
-                                                  end_date=datetime(year=2021, month=1, day=1),
-                                                  l3a_data=[],
-                                                  external_files=defaultdict(Mock),
-                                                  ancillary_files=defaultdict(Mock),
-                                                  repointing_file_path=sentinel.repointing_file_path
-                                                  )
-        bc_dependencies_2 = GlowsL3BCDependencies(version=Version(1, 1),
-                                                  carrington_rotation_number=2096,
-                                                  start_date=datetime(year=2021, month=1, day=1),
-                                                  end_date=datetime(year=2021, month=1, day=1),
-                                                  l3a_data=[],
-                                                  external_files=defaultdict(Mock),
-                                                  ancillary_files=defaultdict(Mock),
-                                                  repointing_file_path=sentinel.repointing_file_path
-                                                  )
+    def test_process_l3bc_catches_no_data_error_and_continues(
+        self,
+        mock_glows_initializer_class,
+        mock_save_data,
+        mock_archive_dependencies,
+        mock_generate_l3bc,
+        mock_l3c_from_instrument_team_dictionary,
+        mock_l3b_from_instrument_team_dictionary,
+        _,
+    ):
+        bc_dependencies_1 = GlowsL3BCDependencies(
+            version=Version(1, 1),
+            carrington_rotation_number=2096,
+            start_date=datetime(year=2021, month=1, day=1),
+            end_date=datetime(year=2021, month=1, day=1),
+            l3a_data=[],
+            external_files=defaultdict(Mock),
+            ancillary_files=defaultdict(Mock),
+            repointing_file_path=sentinel.repointing_file_path,
+        )
+        bc_dependencies_2 = GlowsL3BCDependencies(
+            version=Version(1, 1),
+            carrington_rotation_number=2096,
+            start_date=datetime(year=2021, month=1, day=1),
+            end_date=datetime(year=2021, month=1, day=1),
+            l3a_data=[],
+            external_files=defaultdict(Mock),
+            ancillary_files=defaultdict(Mock),
+            repointing_file_path=sentinel.repointing_file_path,
+        )
         external_dependencies = ExternalDependencies(None, None, None)
-        mock_glows_initializer_class.get_crs_to_process.return_value = GlowsL3BCInitializerData(
-            external_dependencies=external_dependencies,
-            l3bc_dependencies=[bc_dependencies_1, bc_dependencies_2],
-            l3bs_by_cr={},
-            l3cs_by_cr={},
-            repoint_file_path=sentinel.repoint_file_path
+        mock_glows_initializer_class.get_crs_to_process.return_value = (
+            GlowsL3BCInitializerData(
+                external_dependencies=external_dependencies,
+                l3bc_dependencies=[bc_dependencies_1, bc_dependencies_2],
+                l3bs_by_cr={},
+                l3cs_by_cr={},
+                repoint_file_path=sentinel.repoint_file_path,
+            )
         )
 
         zip_file_1 = "imap_glows_archive_20210101_v001.zip"
@@ -480,26 +700,34 @@ def test_process_l3bc_catches_no_data_error_and_continues(self, mock_glows_initi
         l3c_path = Path("path_to/l3c")
         mock_save_data.side_effect = [l3b_path, l3c_path]
 
-        version_map = VersionMap({
-            GLOWS_L3B_DESCRIPTOR: Version(1, 1),
-            GLOWS_L3C_DESCRIPTOR: Version(1, 1),
-            GLOWS_L3D_DESCRIPTOR: Version(5, 1),
-            GLOWS_L3E_HI_45_DESCRIPTOR: Version(5, 1),
-            GLOWS_L3E_HI_90_DESCRIPTOR: Version(5, 1),
-            GLOWS_L3E_LO_DESCRIPTOR: Version(5, 1),
-            GLOWS_L3E_ULTRA_SF_DESCRIPTOR: Version(5, 1),
-            GLOWS_L3E_ULTRA_HF_DESCRIPTOR: Version(5, 1),
-        })
-
-        input_metadata = InputMetadata('glows', "l3b", datetime(2024, 10, 7, 10, 00, 00),
-                                       datetime(2024, 10, 8, 10, 00, 00),
-                                       version_map)
+        version_map = VersionMap(
+            {
+                GLOWS_L3B_DESCRIPTOR: Version(1, 1),
+                GLOWS_L3C_DESCRIPTOR: Version(1, 1),
+                GLOWS_L3D_DESCRIPTOR: Version(5, 1),
+                GLOWS_L3E_HI_45_DESCRIPTOR: Version(5, 1),
+                GLOWS_L3E_HI_90_DESCRIPTOR: Version(5, 1),
+                GLOWS_L3E_LO_DESCRIPTOR: Version(5, 1),
+                GLOWS_L3E_ULTRA_SF_DESCRIPTOR: Version(5, 1),
+                GLOWS_L3E_ULTRA_HF_DESCRIPTOR: Version(5, 1),
+            }
+        )
+
+        input_metadata = InputMetadata(
+            "glows",
+            "l3b",
+            datetime(2024, 10, 7, 10, 00, 00),
+            datetime(2024, 10, 8, 10, 00, 00),
+            version_map,
+        )
 
         processor = GlowsProcessor(Mock(), input_metadata)
 
         mock_generate_l3bc.side_effect = [
-            CannotProcessCarringtonRotationError("All days for Carrington Rotation are in a bad season."),
-            ({}, {})
+            CannotProcessCarringtonRotationError(
+                "All days for Carrington Rotation are in a bad season."
+            ),
+            ({}, {}),
         ]
 
         products = processor.process()
@@ -507,54 +735,88 @@ def test_process_l3bc_catches_no_data_error_and_continues(self, mock_glows_initi
         mock_l3b_from_instrument_team_dictionary.assert_called_once()
         mock_l3c_from_instrument_team_dictionary.assert_called_once()
 
-        mock_archive_dependencies.assert_has_calls([
-            call(l3bc_deps=bc_dependencies_1, external_dependencies=external_dependencies),
-            call(l3bc_deps=bc_dependencies_2, external_dependencies=external_dependencies)
-        ])
+        mock_archive_dependencies.assert_has_calls(
+            [
+                call(
+                    l3bc_deps=bc_dependencies_1,
+                    external_dependencies=external_dependencies,
+                ),
+                call(
+                    l3bc_deps=bc_dependencies_2,
+                    external_dependencies=external_dependencies,
+                ),
+            ]
+        )
 
         self.assertEqual(2, mock_save_data.call_count)
-        mock_save_data.assert_has_calls([
-            call(mock_l3b_from_instrument_team_dictionary.return_value, cr_number=2096),
-            call(mock_l3c_from_instrument_team_dictionary.return_value, cr_number=2096),
-        ])
+        mock_save_data.assert_has_calls(
+            [
+                call(
+                    mock_l3b_from_instrument_team_dictionary.return_value,
+                    cr_number=2096,
+                ),
+                call(
+                    mock_l3c_from_instrument_team_dictionary.return_value,
+                    cr_number=2096,
+                ),
+            ]
+        )
 
         self.assertEqual(3, len(products))
-        self.assertEqual([
-            l3b_path,
-            l3c_path,
-            zip_file_2
-        ], products)
-
-    @patch('imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies')
-    @patch('imap_l3_processing.glows.glows_processor.generate_l3bc')
-    def test_process_l3bc_catches_exceptions_from_science_code_and_continues(self, mock_generate_l3bc, _):
-        l3a_data_folder_path = get_test_data_path('glows/l3a_products')
+        self.assertEqual([l3b_path, l3c_path, zip_file_2], products)
+
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.generate_l3bc")
+    def test_process_l3bc_catches_exceptions_from_science_code_and_continues(
+        self, mock_generate_l3bc, _
+    ):
+        l3a_data_folder_path = get_test_data_path("glows/l3a_products")
         l3a_data = [
             create_glows_l3a_dictionary_from_cdf(
-                l3a_data_folder_path / 'imap_glows_l3a_hist_20100201-repoint00032_v001.cdf')]
+                l3a_data_folder_path
+                / "imap_glows_l3a_hist_20100201-repoint00032_v001.cdf"
+            )
+        ]
 
         external_dependencies = ExternalDependencies(
-            f107_index_file_path=get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-            omni2_data_path=get_test_instrument_team_data_path('glows/omni_2010.dat'),
-            lyman_alpha_path=None
+            f107_index_file_path=get_test_instrument_team_data_path(
+                "glows/f107_fluxtable.txt"
+            ),
+            omni2_data_path=get_test_instrument_team_data_path("glows/omni_2010.dat"),
+            lyman_alpha_path=None,
         )
 
         l3bc_deps = GlowsL3BCDependencies(
             l3a_data=l3a_data,
             ancillary_files={
-                'uv_anisotropy': get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-                'WawHelioIonMP_parameters': get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-                'bad_days_list': get_test_data_path('glows/imap_glows_bad-days-list_v001.dat'),
-                'pipeline_settings': get_test_instrument_team_data_path(
-                    'glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json')
+                "uv_anisotropy": get_test_data_path(
+                    "glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"
+                ),
+                "WawHelioIonMP_parameters": get_test_data_path(
+                    "glows/imap_glows_WawHelioIonMP_20100101_v002.json"
+                ),
+                "bad_days_list": get_test_data_path(
+                    "glows/imap_glows_bad-days-list_v001.dat"
+                ),
+                "pipeline_settings": get_test_instrument_team_data_path(
+                    "glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json"
+                ),
             },
             external_files={
-                'f107_raw_data': get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-                'omni_raw_data': get_test_instrument_team_data_path('glows/omni_2010.dat'),
+                "f107_raw_data": get_test_instrument_team_data_path(
+                    "glows/f107_fluxtable.txt"
+                ),
+                "omni_raw_data": get_test_instrument_team_data_path(
+                    "glows/omni_2010.dat"
+                ),
             },
             carrington_rotation_number=1,
-            start_date=Mock(), end_date=Mock(), version=Version(1, 1),
-            repointing_file_path=sentinel.repointing_file_path
+            start_date=Mock(),
+            end_date=Mock(),
+            version=Version(1, 1),
+            repointing_file_path=sentinel.repointing_file_path,
         )
 
         initializer_data = GlowsL3BCInitializerData(
@@ -562,43 +824,66 @@ def test_process_l3bc_catches_exceptions_from_science_code_and_continues(self, m
             l3bc_dependencies=[l3bc_deps],
             l3bs_by_cr={},
             l3cs_by_cr={},
-            repoint_file_path=sentinel.repoint_file_path
+            repoint_file_path=sentinel.repoint_file_path,
         )
 
         mock_generate_l3bc.side_effect = Exception(
-            'L3c not generated: observed cx rate is smaller than min of the cx_grid')
+            "L3c not generated: observed cx rate is smaller than min of the cx_grid"
+        )
 
         processor = GlowsProcessor(dependencies=Mock(), input_metadata=Mock())
         processor_output = process_l3bc(processor, initializer_data)
 
         self.assertEqual([], processor_output.data_products)
 
-    @patch('imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies')
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsProcessor.archive_dependencies"
+    )
     @patch("imap_l3_processing.glows.glows_processor.save_data")
-    def test_l3bc_uses_all_l3a_file_names_for_l3b_parents(self, mock_save_data, mock_archive_deps):
+    def test_l3bc_uses_all_l3a_file_names_for_l3b_parents(
+        self, mock_save_data, mock_archive_deps
+    ):
         mock_archive_deps.return_value = Path("some/path/to/archive.zip")
 
-        l3a_data_folder_path = get_test_data_path('glows/l3a_products')
+        l3a_data_folder_path = get_test_data_path("glows/l3a_products")
 
-        l3a_filenames = ['imap_glows_l3a_hist_20100104-repoint00004_v001.cdf',
-                         'imap_glows_l3a_hist_20100119-repoint00019_v001.cdf']
-        l3a_data = [create_glows_l3a_dictionary_from_cdf(l3a_data_folder_path / file) for file in l3a_filenames]
+        l3a_filenames = [
+            "imap_glows_l3a_hist_20100104-repoint00004_v001.cdf",
+            "imap_glows_l3a_hist_20100119-repoint00019_v001.cdf",
+        ]
+        l3a_data = [
+            create_glows_l3a_dictionary_from_cdf(l3a_data_folder_path / file)
+            for file in l3a_filenames
+        ]
 
         l3bc_deps = GlowsL3BCDependencies(
             l3a_data=l3a_data,
             ancillary_files={
-                'uv_anisotropy': get_test_data_path('glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json'),
-                'WawHelioIonMP_parameters': get_test_data_path('glows/imap_glows_WawHelioIonMP_20100101_v002.json'),
-                'bad_days_list': get_test_data_path('glows/imap_glows_bad-days-list_v001.dat'),
-                'pipeline_settings': get_test_instrument_team_data_path(
-                    'glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json')
+                "uv_anisotropy": get_test_data_path(
+                    "glows/imap_glows_uv-anisotropy-1CR_20100101_v001.json"
+                ),
+                "WawHelioIonMP_parameters": get_test_data_path(
+                    "glows/imap_glows_WawHelioIonMP_20100101_v002.json"
+                ),
+                "bad_days_list": get_test_data_path(
+                    "glows/imap_glows_bad-days-list_v001.dat"
+                ),
+                "pipeline_settings": get_test_instrument_team_data_path(
+                    "glows/imap_glows_pipeline-settings-L3bc_20250707_v002.json"
+                ),
             },
             external_files={
-                'f107_raw_data': get_test_instrument_team_data_path('glows/f107_fluxtable.txt'),
-                'omni_raw_data': get_test_instrument_team_data_path('glows/omni2_all_years.dat'),
+                "f107_raw_data": get_test_instrument_team_data_path(
+                    "glows/f107_fluxtable.txt"
+                ),
+                "omni_raw_data": get_test_instrument_team_data_path(
+                    "glows/omni2_all_years.dat"
+                ),
             },
             carrington_rotation_number=2092,
-            start_date=Mock(), end_date=Mock(), version=Version(1, 1),
+            start_date=Mock(),
+            end_date=Mock(),
+            version=Version(1, 1),
             repointing_file_path=sentinel.repointing_file_path,
         )
 
@@ -607,7 +892,7 @@ def test_l3bc_uses_all_l3a_file_names_for_l3b_parents(self, mock_save_data, mock
             l3bc_dependencies=[l3bc_deps],
             l3bs_by_cr={},
             l3cs_by_cr={},
-            repoint_file_path=sentinel.repoint_file_path
+            repoint_file_path=sentinel.repoint_file_path,
         )
 
         processor = GlowsProcessor(dependencies=Mock(), input_metadata=Mock())
@@ -618,34 +903,52 @@ def test_l3bc_uses_all_l3a_file_names_for_l3b_parents(self, mock_save_data, mock
 
         expected_parents = [
             *l3a_filenames,
-            'archive.zip',
-            'f107_fluxtable.txt',
-            'imap_glows_uv-anisotropy-1CR_20100101_v001.json',
-            'imap_glows_WawHelioIonMP_20100101_v002.json',
-            'imap_glows_bad-days-list_v001.dat',
-            'imap_glows_pipeline-settings-L3bc_20250707_v002.json',
+            "archive.zip",
+            "f107_fluxtable.txt",
+            "imap_glows_uv-anisotropy-1CR_20100101_v001.json",
+            "imap_glows_WawHelioIonMP_20100101_v002.json",
+            "imap_glows_bad-days-list_v001.dat",
+            "imap_glows_pipeline-settings-L3bc_20250707_v002.json",
         ]
 
         self.assertEqual(set(expected_parents), set(l3b_data_product.parent_file_names))
 
     @patch("imap_l3_processing.glows.glows_processor.process_l3e")
-    @patch("imap_l3_processing.glows.glows_processor.create_glows_l3b_json_file_from_cdf")
-    @patch("imap_l3_processing.glows.glows_processor.create_glows_l3c_json_file_from_cdf")
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch('imap_l3_processing.glows.glows_processor.rename_l3d_text_outputs')
-    @patch('imap_l3_processing.glows.glows_processor.get_parent_file_names_from_l3d_json')
-    @patch('imap_l3_processing.glows.glows_processor.convert_json_to_l3d_data_product')
-    @patch('imap_l3_processing.glows.glows_processor.run')
-    @patch('imap_l3_processing.glows.glows_processor.shutil')
+    @patch(
+        "imap_l3_processing.glows.glows_processor.create_glows_l3b_json_file_from_cdf"
+    )
+    @patch(
+        "imap_l3_processing.glows.glows_processor.create_glows_l3c_json_file_from_cdf"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch("imap_l3_processing.glows.glows_processor.rename_l3d_text_outputs")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.get_parent_file_names_from_l3d_json"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.convert_json_to_l3d_data_product")
+    @patch("imap_l3_processing.glows.glows_processor.run")
+    @patch("imap_l3_processing.glows.glows_processor.shutil")
     @patch("imap_l3_processing.glows.glows_processor.os")
     @patch("imap_l3_processing.glows.glows_processor.GlowsL3DInitializer")
     @patch("imap_l3_processing.glows.glows_processor.read_pipeline_settings")
-    def test_process_l3d(self, mock_read_pipeline_settings, mock_glows_l3d_initializer, mock_os, mock_shutil, mock_run,
-                         mock_convert_json_to_l3d_data_product, mock_get_parent_file_names_from_l3d_json,
-                         mock_rename_l3d, mock_save_data, mock_convert_l3c_to_json, mock_convert_l3b_to_json, _):
+    def test_process_l3d(
+        self,
+        mock_read_pipeline_settings,
+        mock_glows_l3d_initializer,
+        mock_os,
+        mock_shutil,
+        mock_run,
+        mock_convert_json_to_l3d_data_product,
+        mock_get_parent_file_names_from_l3d_json,
+        mock_rename_l3d,
+        mock_save_data,
+        mock_convert_l3c_to_json,
+        mock_convert_l3b_to_json,
+        _,
+    ):
 
         cr_number = 2092
-        mock_read_pipeline_settings.return_value = {'start_cr': cr_number}
+        mock_read_pipeline_settings.return_value = {"start_cr": cr_number}
 
         expected_end_cr = cr_number + 1
         glows_l3d_dependencies = GlowsL3DDependencies(
@@ -670,7 +973,7 @@ def test_process_l3d(self, mock_read_pipeline_settings, mock_glows_l3d_initializ
             end_cr=expected_end_cr,
         )
 
-        old_l3d = Path('imap_glows_l3d_solar-hist_19470303-cr02090_v001.cdf')
+        old_l3d = Path("imap_glows_l3d_solar-hist_19470303-cr02090_v001.cdf")
         input_major_version = 12
         l3d_output_version = Version(input_major_version, 5)
         mock_glows_l3d_initializer.should_process_l3d.return_value = (
@@ -679,13 +982,15 @@ def test_process_l3d(self, mock_read_pipeline_settings, mock_glows_l3d_initializ
             old_l3d,
         )
 
-        mock_run.return_value = CompletedProcess(args=[], returncode=0, stdout=f'Processed CR= {expected_end_cr}')
+        mock_run.return_value = CompletedProcess(
+            args=[], returncode=0, stdout=f"Processed CR= {expected_end_cr}"
+        )
 
         mock_os.listdir.return_value = [
-            f'{cr_number}_txt_file_1',
-            f'{cr_number}_txt_file_2',
-            f'{cr_number + 1}_txt_file_1',
-            f'{cr_number + 1}_txt_file_2',
+            f"{cr_number}_txt_file_1",
+            f"{cr_number}_txt_file_2",
+            f"{cr_number + 1}_txt_file_1",
+            f"{cr_number + 1}_txt_file_2",
         ]
 
         mock_convert_json_to_l3d_data_product.return_value = sentinel.l3d_data_product
@@ -709,135 +1014,217 @@ def test_process_l3d(self, mock_read_pipeline_settings, mock_glows_l3d_initializ
                 GLOWS_L3E_ULTRA_HF_DESCRIPTOR: Version(2, 1),
             }
         )
-        input_metadata = InputMetadata('glows', "l3b", datetime(2024, 10, 7), None, version=input_version_map)
+        input_metadata = InputMetadata(
+            "glows", "l3b", datetime(2024, 10, 7), None, version=input_version_map
+        )
 
         processing_input_collection = Mock()
         processor = GlowsProcessor(processing_input_collection, input_metadata)
         products = processor.process()
 
-        mock_convert_l3b_to_json.assert_has_calls([call(sentinel.l3b_file_1), call(sentinel.l3b_file_2)])
-        mock_convert_l3c_to_json.assert_has_calls([call(sentinel.l3c_file_1), call(sentinel.l3c_file_2)])
+        mock_convert_l3b_to_json.assert_has_calls(
+            [call(sentinel.l3b_file_1), call(sentinel.l3b_file_2)]
+        )
+        mock_convert_l3c_to_json.assert_has_calls(
+            [call(sentinel.l3c_file_1), call(sentinel.l3c_file_2)]
+        )
         mock_glows_l3d_initializer.should_process_l3d.assert_called_with(
-            self.mock_external_deps, [], [], self.mock_reprocess_info, input_major_version)
+            self.mock_external_deps,
+            [],
+            [],
+            self.mock_reprocess_info,
+            input_major_version,
+        )
         self.mock_fetch_reprocess_info.assert_called_with(processing_input_collection)
-        self.assertEqual([
-            Path("imap_glows_e-dens_19470303_20100101_v000.dat"),
-            Path("imap_glows_lya_19470303_20100101_v000.dat"),
-            Path("l3d_cdf.cdf")
-        ], products)
+        self.assertEqual(
+            [
+                Path("imap_glows_e-dens_19470303_20100101_v000.dat"),
+                Path("imap_glows_lya_19470303_20100101_v000.dat"),
+                Path("l3d_cdf.cdf"),
+            ],
+            products,
+        )
 
         self.assertEqual(2, mock_shutil.copy.call_count)
-        mock_shutil.copy.assert_has_calls([
-            call(Path("imap_glows_e-dens_19470303_20100101_v000.dat"),
-                 imap_data_access.config[
-                     "DATA_DIR"] / "imap/ancillary/glows/imap_glows_e-dens_19470303_20100101_v000.dat"),
-            call(Path("imap_glows_lya_19470303_20100101_v000.dat"),
-                 imap_data_access.config["DATA_DIR"] / "imap/ancillary/glows/imap_glows_lya_19470303_20100101_v000.dat")
-        ])
+        mock_shutil.copy.assert_has_calls(
+            [
+                call(
+                    Path("imap_glows_e-dens_19470303_20100101_v000.dat"),
+                    imap_data_access.config["DATA_DIR"]
+                    / "imap/ancillary/glows/imap_glows_e-dens_19470303_20100101_v000.dat",
+                ),
+                call(
+                    Path("imap_glows_lya_19470303_20100101_v000.dat"),
+                    imap_data_access.config["DATA_DIR"]
+                    / "imap/ancillary/glows/imap_glows_lya_19470303_20100101_v000.dat",
+                ),
+            ]
+        )
 
         self.assertEqual(2, mock_os.makedirs.call_count)
-        mock_os.makedirs.assert_has_calls([
-            call(PATH_TO_L3D_TOOLKIT / 'data_l3d', exist_ok=True),
-            call(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt', exist_ok=True),
-        ])
-
-        expected_dependencies = json.dumps({
-            'external_files': {
-                'lya_raw_data': str(Path('path/to/lya')),
-            },
-            'ancillary_files': {
-                'pipeline_settings':
-                    str(Path('glows/imap_glows_pipeline-settings-l3bcde_20250514_v004.json')),
-                'WawHelioIon': {
-                    'speed': str(Path('path/to/speed')),
-                    'p-dens': str(Path('path/to/p-dens')),
-                    'uv-anis': str(Path('path/to/uv-anis')),
-                    'phion': str(Path('path/to/phion')),
-                    'lya': str(Path('path/to/lya')),
-                    'e-dens': str(Path('path/to/e-dens'))
-                }
+        mock_os.makedirs.assert_has_calls(
+            [
+                call(PATH_TO_L3D_TOOLKIT / "data_l3d", exist_ok=True),
+                call(PATH_TO_L3D_TOOLKIT / "data_l3d_txt", exist_ok=True),
+            ]
+        )
+
+        expected_dependencies = json.dumps(
+            {
+                "external_files": {
+                    "lya_raw_data": str(Path("path/to/lya")),
+                },
+                "ancillary_files": {
+                    "pipeline_settings": str(
+                        Path(
+                            "glows/imap_glows_pipeline-settings-l3bcde_20250514_v004.json"
+                        )
+                    ),
+                    "WawHelioIon": {
+                        "speed": str(Path("path/to/speed")),
+                        "p-dens": str(Path("path/to/p-dens")),
+                        "uv-anis": str(Path("path/to/uv-anis")),
+                        "phion": str(Path("path/to/phion")),
+                        "lya": str(Path("path/to/lya")),
+                        "e-dens": str(Path("path/to/e-dens")),
+                    },
+                },
             }
-        })
+        )
 
-        expected_working_directory = Path(l3d.__file__).parent / 'science'
+        expected_working_directory = Path(l3d.__file__).parent / "science"
 
         self.assertEqual(1, mock_run.call_count)
-        mock_run.assert_has_calls([
-            call([sys.executable, './generate_l3d.py', f'{expected_end_cr}', expected_dependencies],
-                 cwd=str(expected_working_directory),
-                 check=True,
-                 capture_output=True, text=True),
-        ])
+        mock_run.assert_has_calls(
+            [
+                call(
+                    [
+                        sys.executable,
+                        "./generate_l3d.py",
+                        f"{expected_end_cr}",
+                        expected_dependencies,
+                    ],
+                    cwd=str(expected_working_directory),
+                    check=True,
+                    capture_output=True,
+                    text=True,
+                ),
+            ]
+        )
 
-        mock_os.listdir.assert_called_once_with(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt')
+        mock_os.listdir.assert_called_once_with(PATH_TO_L3D_TOOLKIT / "data_l3d_txt")
 
         expected_l3d_txt_paths = [
-            Path(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt' / f'{expected_end_cr}_txt_file_1', ),
-            Path(PATH_TO_L3D_TOOLKIT / 'data_l3d_txt' / f'{expected_end_cr}_txt_file_2', ),
+            Path(
+                PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / f"{expected_end_cr}_txt_file_1",
+            ),
+            Path(
+                PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / f"{expected_end_cr}_txt_file_2",
+            ),
         ]
-        mock_rename_l3d.assert_has_calls([call(expected_l3d_txt_paths, str(Version(None, l3d_output_version.minor)))])
+        mock_rename_l3d.assert_has_calls(
+            [call(expected_l3d_txt_paths, str(Version(None, l3d_output_version.minor)))]
+        )
 
-        mock_get_parent_file_names_from_l3d_json.assert_called_once_with(expected_working_directory / 'data_l3d')
+        mock_get_parent_file_names_from_l3d_json.assert_called_once_with(
+            expected_working_directory / "data_l3d"
+        )
 
-        expected_data_product_metadata = InputMetadata(instrument="glows", data_level="l3d", descriptor="solar-hist",
-                                                       start_date=datetime(1947, 3, 3),
-                                                       end_date=datetime(1947, 3, 3),
-                                                       version=VersionMap({GLOWS_L3D_DESCRIPTOR: l3d_output_version}))
+        expected_data_product_metadata = InputMetadata(
+            instrument="glows",
+            data_level="l3d",
+            descriptor="solar-hist",
+            start_date=datetime(1947, 3, 3),
+            end_date=datetime(1947, 3, 3),
+            version=VersionMap({GLOWS_L3D_DESCRIPTOR: l3d_output_version}),
+        )
 
         mock_convert_json_to_l3d_data_product.assert_called_once_with(
-            expected_working_directory / 'data_l3d' / f'imap_glows_l3d_solar-params-history_19470303-cr0{expected_end_cr}_v00.json',
+            expected_working_directory
+            / "data_l3d"
+            / f"imap_glows_l3d_solar-params-history_19470303-cr0{expected_end_cr}_v00.json",
             expected_data_product_metadata,
-            mock_get_parent_file_names_from_l3d_json.return_value)
+            mock_get_parent_file_names_from_l3d_json.return_value,
+        )
 
-        mock_save_data.assert_called_once_with(sentinel.l3d_data_product, cr_number=expected_end_cr)
+        mock_save_data.assert_called_once_with(
+            sentinel.l3d_data_product, cr_number=expected_end_cr
+        )
 
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch('imap_l3_processing.glows.glows_processor.rename_l3d_text_outputs')
-    @patch('imap_l3_processing.glows.glows_processor.PATH_TO_L3D_TOOLKIT', get_test_data_path('glows/science'))
-    @patch('imap_l3_processing.glows.glows_processor.run')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3d_adds_parent_file_names_to_output(self, mock_spicepy, mock_run, mock_rename_l3d_text_output,
-                                                          mock_save_data):
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch("imap_l3_processing.glows.glows_processor.rename_l3d_text_outputs")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.PATH_TO_L3D_TOOLKIT",
+        get_test_data_path("glows/science"),
+    )
+    @patch("imap_l3_processing.glows.glows_processor.run")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_l3d_adds_parent_file_names_to_output(
+        self, mock_spicepy, mock_run, mock_rename_l3d_text_output, mock_save_data
+    ):
         mock_spicepy.ktotal.return_value = 0
-        l3b_path_1 = get_test_data_path('glows/imap_glows_l3b_ion-rate-profile_20100422_v011.cdf')
-        l3b_path_2 = get_test_data_path('glows/imap_glows_l3b_ion-rate-profile_20100519_v011.cdf')
-        l3c_path_1 = get_test_data_path('glows/imap_glows_l3c_sw-profile_20100422_v011.cdf')
-        l3c_path_2 = get_test_data_path('glows/imap_glows_l3c_sw-profile_20100519_v011.cdf')
+        l3b_path_1 = get_test_data_path(
+            "glows/imap_glows_l3b_ion-rate-profile_20100422_v011.cdf"
+        )
+        l3b_path_2 = get_test_data_path(
+            "glows/imap_glows_l3b_ion-rate-profile_20100519_v011.cdf"
+        )
+        l3c_path_1 = get_test_data_path(
+            "glows/imap_glows_l3c_sw-profile_20100422_v011.cdf"
+        )
+        l3c_path_2 = get_test_data_path(
+            "glows/imap_glows_l3c_sw-profile_20100519_v011.cdf"
+        )
 
         pipeline_settings_path = get_test_data_path(
-            'glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json')
-        speed_path = get_test_data_path('glows/imap_glows_plasma-speed-Legendre-2010a_v001.dat')
-        p_dens_path = get_test_data_path('glows/imap_glows_proton-density-Legendre-2010a_v001.dat')
-        uv_anis_path = get_test_data_path('glows/imap_glows_uv-anisotropy-2010a_v001.dat')
-        phion_path = get_test_data_path('glows/imap_glows_photoion-2010a_v001.dat')
-        lya_path = get_test_data_path('glows/imap_glows_lya-2010a_v001.dat')
-        e_dens_path = get_test_data_path('glows/imap_glows_electron-density-2010a_v001.dat')
+            "glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+        )
+        speed_path = get_test_data_path(
+            "glows/imap_glows_plasma-speed-Legendre-2010a_v001.dat"
+        )
+        p_dens_path = get_test_data_path(
+            "glows/imap_glows_proton-density-Legendre-2010a_v001.dat"
+        )
+        uv_anis_path = get_test_data_path(
+            "glows/imap_glows_uv-anisotropy-2010a_v001.dat"
+        )
+        phion_path = get_test_data_path("glows/imap_glows_photoion-2010a_v001.dat")
+        lya_path = get_test_data_path("glows/imap_glows_lya-2010a_v001.dat")
+        e_dens_path = get_test_data_path(
+            "glows/imap_glows_electron-density-2010a_v001.dat"
+        )
 
-        lyman_alpha_composite_path = get_test_data_path('glows/lyman_alpha_composite.nc')
+        lyman_alpha_composite_path = get_test_data_path(
+            "glows/lyman_alpha_composite.nc"
+        )
 
         l3b_file_paths = [l3b_path_1, l3b_path_2]
         l3c_file_paths = [l3c_path_1, l3c_path_2]
         ancillary_inputs = {
-            'pipeline_settings': pipeline_settings_path,
-            'WawHelioIon': {
-                'speed': speed_path,
-                'p-dens': p_dens_path,
-                'uv-anis': uv_anis_path,
-                'phion': phion_path,
-                'lya': lya_path,
-                'e-dens': e_dens_path
-            }
-        }
-        external_inputs = {
-            'lya_raw_data': lyman_alpha_composite_path
+            "pipeline_settings": pipeline_settings_path,
+            "WawHelioIon": {
+                "speed": speed_path,
+                "p-dens": p_dens_path,
+                "uv-anis": uv_anis_path,
+                "phion": phion_path,
+                "lya": lya_path,
+                "e-dens": e_dens_path,
+            },
         }
+        external_inputs = {"lya_raw_data": lyman_alpha_composite_path}
 
         cr_number = 2095
-        mock_run.return_value = CompletedProcess(args=[], returncode=0, stdout=f'Processed CR= {cr_number}')
+        mock_run.return_value = CompletedProcess(
+            args=[], returncode=0, stdout=f"Processed CR= {cr_number}"
+        )
 
-        l3d_dependencies = GlowsL3DDependencies(l3b_file_paths=l3b_file_paths, l3c_file_paths=l3c_file_paths,
-                                                ancillary_files=ancillary_inputs, external_files=external_inputs,
-                                                end_cr=cr_number)
+        l3d_dependencies = GlowsL3DDependencies(
+            l3b_file_paths=l3b_file_paths,
+            l3c_file_paths=l3c_file_paths,
+            ancillary_files=ancillary_inputs,
+            external_files=external_inputs,
+            end_cr=cr_number,
+        )
 
         _ = process_l3d(l3d_dependencies, Version(1, 1))
 
@@ -855,7 +1242,7 @@ def test_process_l3d_adds_parent_file_names_to_output(self, mock_spicepy, mock_r
             "imap_glows_l3b_ion-rate-profile_20100422_v011.cdf",
             "imap_glows_l3b_ion-rate-profile_20100519_v011.cdf",
             "imap_glows_l3c_sw-profile_20100422_v011.cdf",
-            "imap_glows_l3c_sw-profile_20100519_v011.cdf"
+            "imap_glows_l3c_sw-profile_20100519_v011.cdf",
         ]
 
         expected_l3d_txt_file_paths = [
@@ -887,47 +1274,59 @@ def test_process_l3d_adds_parent_file_names_to_output(self, mock_spicepy, mock_r
         )
         self.assertEqual("v001", mock_rename_l3d_text_output.call_args[0][1])
 
-    @patch('imap_l3_processing.glows.glows_processor.rename_l3d_text_outputs')
-    @patch('imap_l3_processing.glows.glows_processor.get_parent_file_names_from_l3d_json')
-    @patch('imap_l3_processing.glows.glows_processor.os')
-    @patch('imap_l3_processing.glows.glows_processor.run')
-    @patch('imap_l3_processing.glows.glows_processor.convert_json_to_l3d_data_product')
-    @patch('imap_l3_processing.glows.glows_processor.read_pipeline_settings')
-    def test_process_l3d_handles_unexpected_exception_from_science(self, mock_read_pipeline_settings,
-                                                                   mock_convert_json_to_l3d,
-                                                                   mock_run, mock_os, _, __):
+    @patch("imap_l3_processing.glows.glows_processor.rename_l3d_text_outputs")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.get_parent_file_names_from_l3d_json"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.os")
+    @patch("imap_l3_processing.glows.glows_processor.run")
+    @patch("imap_l3_processing.glows.glows_processor.convert_json_to_l3d_data_product")
+    @patch("imap_l3_processing.glows.glows_processor.read_pipeline_settings")
+    def test_process_l3d_handles_unexpected_exception_from_science(
+        self,
+        mock_read_pipeline_settings,
+        mock_convert_json_to_l3d,
+        mock_run,
+        mock_os,
+        _,
+        __,
+    ):
         ancillary_files = {
-            'pipeline_settings': get_test_data_path(
-                "glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json"),
-            'WawHelioIon': {
-                'speed': Path('path/to/speed'),
-                'p-dens': Path('path/to/p-dens'),
-                'uv-anis': Path('path/to/uv-anis'),
-                'phion': Path('path/to/phion'),
-                'lya': Path('path/to/lya'),
-                'e-dens': Path('path/to/e-dens')
-            }
+            "pipeline_settings": get_test_data_path(
+                "glows/l3d_drift_test/imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+            ),
+            "WawHelioIon": {
+                "speed": Path("path/to/speed"),
+                "p-dens": Path("path/to/p-dens"),
+                "uv-anis": Path("path/to/uv-anis"),
+                "phion": Path("path/to/phion"),
+                "lya": Path("path/to/lya"),
+                "e-dens": Path("path/to/e-dens"),
+            },
         }
         external_files = {
-            'lya_raw_data': Path('path/to/lya'),
+            "lya_raw_data": Path("path/to/lya"),
         }
-        mock_read_pipeline_settings.return_value = {'start_cr': 2091}
+        mock_read_pipeline_settings.return_value = {"start_cr": 2091}
         l3b_file_paths = []
         l3c_file_paths = []
         expected_cr = 2096
-        l3d_dependencies = GlowsL3DDependencies(ancillary_files=ancillary_files,
-                                                external_files=external_files,
-                                                l3b_file_paths=l3b_file_paths,
-                                                l3c_file_paths=l3c_file_paths,
-                                                end_cr=expected_cr)
-
-        mock_os.listdir.return_value = ['2096_txt_file_1',
-                                        '2096_txt_file_2',
-                                        '2096_txt_file_3',
-                                        '2096_txt_file_4',
-                                        '2096_txt_file_5',
-                                        '2096_txt_file_6'
-                                        ]
+        l3d_dependencies = GlowsL3DDependencies(
+            ancillary_files=ancillary_files,
+            external_files=external_files,
+            l3b_file_paths=l3b_file_paths,
+            l3c_file_paths=l3c_file_paths,
+            end_cr=expected_cr,
+        )
+
+        mock_os.listdir.return_value = [
+            "2096_txt_file_1",
+            "2096_txt_file_2",
+            "2096_txt_file_3",
+            "2096_txt_file_4",
+            "2096_txt_file_5",
+            "2096_txt_file_6",
+        ]
 
         unexpected_exception = r"""Traceback (most recent call last):
           File "...\imap-L3-processing\imap_l3_processing\glows\l3d\science\generate_l3d.py", line 46, in <module>
@@ -945,7 +1344,9 @@ def test_process_l3d_handles_unexpected_exception_from_science(self, mock_read_p
                  ^^^^^^^^^^^^^
         FileNotFoundError: [Errno 2] No such file or directory: 'imap_glows_l3a_hist_20100511-repoint00131_v011.cdf'"""
 
-        mock_run.side_effect = [CalledProcessError(cmd="", returncode=1, stderr=unexpected_exception)]
+        mock_run.side_effect = [
+            CalledProcessError(cmd="", returncode=1, stderr=unexpected_exception)
+        ]
 
         with self.assertRaises(Exception) as context:
             process_l3d(l3d_dependencies, 1)
@@ -953,25 +1354,40 @@ def test_process_l3d_handles_unexpected_exception_from_science(self, mock_read_p
 
         mock_convert_json_to_l3d.assert_not_called()
 
-    @patch('imap_l3_processing.glows.glows_processor.shutil')
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
+    @patch("imap_l3_processing.glows.glows_processor.shutil")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
     def test_process_glows_l3d_drift(self, mock_save_data, _):
         l3d_drift_test_data = get_test_data_path("glows/l3d_drift_test")
         lyman_alpha_composite = l3d_drift_test_data / "lyman_alpha_composite.nc"
         expected_cr = 2098
 
-        l3b_1 = l3d_drift_test_data / "imap_glows_l3b_ion-rate-profile_20100422_v013.cdf"
-        l3b_2 = l3d_drift_test_data / "imap_glows_l3b_ion-rate-profile_20100519_v013.cdf"
+        l3b_1 = (
+            l3d_drift_test_data / "imap_glows_l3b_ion-rate-profile_20100422_v013.cdf"
+        )
+        l3b_2 = (
+            l3d_drift_test_data / "imap_glows_l3b_ion-rate-profile_20100519_v013.cdf"
+        )
         l3c_1 = l3d_drift_test_data / "imap_glows_l3c_sw-profile_20100422_v012.cdf"
         l3c_2 = l3d_drift_test_data / "imap_glows_l3c_sw-profile_20100519_v012.cdf"
 
-        plasma_speed_legendre = l3d_drift_test_data / "imap_glows_plasma-speed-2010a_20100101_v003.dat"
-        proton_density_legendre = l3d_drift_test_data / "imap_glows_proton-density-2010a_20100101_v003.dat"
-        uv_anisotropy = l3d_drift_test_data / "imap_glows_uv-anisotropy-2010a_20100101_v003.dat"
+        plasma_speed_legendre = (
+            l3d_drift_test_data / "imap_glows_plasma-speed-2010a_20100101_v003.dat"
+        )
+        proton_density_legendre = (
+            l3d_drift_test_data / "imap_glows_proton-density-2010a_20100101_v003.dat"
+        )
+        uv_anisotropy = (
+            l3d_drift_test_data / "imap_glows_uv-anisotropy-2010a_20100101_v003.dat"
+        )
         photoion = l3d_drift_test_data / "imap_glows_photoion-2010a_20100101_v003.dat"
         lya_2010a = l3d_drift_test_data / "imap_glows_lya-2010a_20100101_v003.dat"
-        electron_density = l3d_drift_test_data / "imap_glows_electron-density-2010a_20100101_v003.dat"
-        pipeline_settings = l3d_drift_test_data / "imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+        electron_density = (
+            l3d_drift_test_data / "imap_glows_electron-density-2010a_20100101_v003.dat"
+        )
+        pipeline_settings = (
+            l3d_drift_test_data
+            / "imap_glows_pipeline-settings-l3bcde_20100101_v006.json"
+        )
 
         l3d_dependencies = GlowsL3DDependencies(
             external_files={
@@ -995,14 +1411,19 @@ def test_process_glows_l3d_drift(self, mock_save_data, _):
 
         glows_l3d_output = process_l3d(l3d_dependencies, Version(1, 4))
 
-        expected_txt_filenames = ["imap_glows_e-dens_19470303_20100629_v004.dat",
-                                  "imap_glows_lya_19470303_20100629_v004.dat",
-                                  "imap_glows_p-dens_19470303_20100629_v004.dat",
-                                  "imap_glows_phion_19470303_20100629_v004.dat",
-                                  "imap_glows_speed_19470303_20100629_v004.dat",
-                                  "imap_glows_uv-anis_19470303_20100629_v004.dat"]
+        expected_txt_filenames = [
+            "imap_glows_e-dens_19470303_20100629_v004.dat",
+            "imap_glows_lya_19470303_20100629_v004.dat",
+            "imap_glows_p-dens_19470303_20100629_v004.dat",
+            "imap_glows_phion_19470303_20100629_v004.dat",
+            "imap_glows_speed_19470303_20100629_v004.dat",
+            "imap_glows_uv-anis_19470303_20100629_v004.dat",
+        ]
         for file in expected_txt_filenames:
-            self.assertIn(PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / file, glows_l3d_output.l3d_text_file_paths)
+            self.assertIn(
+                PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / file,
+                glows_l3d_output.l3d_text_file_paths,
+            )
 
         test_cases = [
             (
@@ -1016,20 +1437,65 @@ def test_process_glows_l3d_drift(self, mock_save_data, _):
                 expected_txt_filenames[1],
                 "lyman_alpha",
                 849,
-                [1947.167990000000, 6.199757637899209e+11, 1250.5],
-                [2010.49252, 391688271318.51849, 2098.50000, ],
+                [1947.167990000000, 6.199757637899209e11, 1250.5],
+                [
+                    2010.49252,
+                    391688271318.51849,
+                    2098.50000,
+                ],
             ),
             (
                 expected_txt_filenames[2],
                 "proton_density",
                 849,
-                [1.94716799e+03, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-                 1.25050000e+03, -1.00000000e+00],
-                [2010.49252260274, 3.533487319946289, 3.50542426109314, 3.409455299377441, 3.878353357315063,
-                 4.971815586090088, 6.224776268005371, 7.651707649230957, 8.510316848754883, 8.865212440490723,
-                 8.813090324401855, 9.078702926635742, 9.078702926635742, 7.368694305419922, 5.78929615020752,
-                 4.63243579864502, 3.519419431686401, 3.034561634063721, 3.000047445297241, 3.000047445297241,
-                 2098.5, 88888.0],
+                [
+                    1.94716799e03,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    1.25050000e03,
+                    -1.00000000e00,
+                ],
+                [
+                    2010.49252260274,
+                    3.533487319946289,
+                    3.50542426109314,
+                    3.409455299377441,
+                    3.878353357315063,
+                    4.971815586090088,
+                    6.224776268005371,
+                    7.651707649230957,
+                    8.510316848754883,
+                    8.865212440490723,
+                    8.813090324401855,
+                    9.078702926635742,
+                    9.078702926635742,
+                    7.368694305419922,
+                    5.78929615020752,
+                    4.63243579864502,
+                    3.519419431686401,
+                    3.034561634063721,
+                    3.000047445297241,
+                    3.000047445297241,
+                    2098.5,
+                    88888.0,
+                ],
             ),
             (
                 expected_txt_filenames[3],
@@ -1042,70 +1508,183 @@ def test_process_glows_l3d_drift(self, mock_save_data, _):
                 expected_txt_filenames[4],
                 "plasma_speed",
                 849,
-                [1.94716799e+03, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-                 1.25050000e+03, -1],
-                [2010.49252260274, 397.0, 399.0, 406.0, 374.0, 316.0, 268.0, 228.0, 209.0, 202.0, 203.0, 198.0,
-                 198.0, 235.0, 283.0, 332.0, 398.0, 436.0, 439.0, 439.0, 2098.5, 88888.0],
+                [
+                    1.94716799e03,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    1.25050000e03,
+                    -1,
+                ],
+                [
+                    2010.49252260274,
+                    397.0,
+                    399.0,
+                    406.0,
+                    374.0,
+                    316.0,
+                    268.0,
+                    228.0,
+                    209.0,
+                    202.0,
+                    203.0,
+                    198.0,
+                    198.0,
+                    235.0,
+                    283.0,
+                    332.0,
+                    398.0,
+                    436.0,
+                    439.0,
+                    439.0,
+                    2098.5,
+                    88888.0,
+                ],
             ),
             (
                 expected_txt_filenames[5],
                 "uv_anisotropy",
                 849,
-                [1.94716799e+03, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-                 1.25050000e+03, -1],
-                [2010.49252260274, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2098.5, 88888.0],
+                [
+                    1.94716799e03,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    1.25050000e03,
+                    -1,
+                ],
+                [
+                    2010.49252260274,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    2098.5,
+                    88888.0,
+                ],
             ),
         ]
 
         [save_data_call_args] = mock_save_data.call_args_list
         l3d_data_product = save_data_call_args.args[0]
 
-        expected_cdf_filename = "imap_glows_l3d_solar-hist_19470303-cr02098_v001.0004.cdf"
+        expected_cdf_filename = (
+            "imap_glows_l3d_solar-hist_19470303-cr02098_v001.0004.cdf"
+        )
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_dir = Path(tmp_dir)
             save_data(l3d_data_product, cr_number=expected_cr, folder_path=tmp_dir)
 
             with CDF(str(tmp_dir / expected_cdf_filename)) as actual_cdf:
-                for filename, cdf_var_name, length_of_data, first_line, last_line in test_cases:
+                for (
+                    filename,
+                    cdf_var_name,
+                    length_of_data,
+                    first_line,
+                    last_line,
+                ) in test_cases:
                     with self.subTest(msg=filename):
-                        actual = np.loadtxt(PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / filename)
+                        actual = np.loadtxt(
+                            PATH_TO_L3D_TOOLKIT / "data_l3d_txt" / filename
+                        )
 
                         self.assertEqual(length_of_data, len(actual))
-                        self.assertEqual(length_of_data, len(actual_cdf[cdf_var_name][...]))
+                        self.assertEqual(
+                            length_of_data, len(actual_cdf[cdf_var_name][...])
+                        )
 
                         np.testing.assert_allclose(actual[0], first_line)
                         np.testing.assert_allclose(actual[-1], last_line)
 
-    @patch('imap_l3_processing.glows.glows_processor.compute_glows_flags_for_repoint')
-    @patch('imap_l3_processing.glows.glows_processor.get_lo_pivot_angles')
-    @patch('imap_l3_processing.glows.glows_processor.get_pointing_date_range')
-    @patch('imap_l3_processing.glows.glows_processor.determine_spacecraft_info_using_predict_if_needed')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_hi')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_lo')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_ul_hf')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_ul_sf')
-    def test_process_l3e(self, mock_process_ultra, mock_process_ultra_hf, mock_process_lo, mock_process_hi,
-                         mock_determine_spacecraft_info,
-                         mock_get_pointing_date_range,
-                         mock_get_lo_pivot_angles,
-                         mock_compute_glows_flags_for_repoint,
-                         ):
+    @patch("imap_l3_processing.glows.glows_processor.compute_glows_flags_for_repoint")
+    @patch("imap_l3_processing.glows.glows_processor.get_lo_pivot_angles")
+    @patch("imap_l3_processing.glows.glows_processor.get_pointing_date_range")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_spacecraft_info_using_predict_if_needed"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_hi")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_lo")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_ul_hf")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_ul_sf")
+    def test_process_l3e(
+        self,
+        mock_process_ultra,
+        mock_process_ultra_hf,
+        mock_process_lo,
+        mock_process_hi,
+        mock_determine_spacecraft_info,
+        mock_get_pointing_date_range,
+        mock_get_lo_pivot_angles,
+        mock_compute_glows_flags_for_repoint,
+    ):
         mock_process_hi.side_effect = [
-            [Path('path/to/first_hi_l3e')],
-            [Path('path/to/second_hi_l3e')],
+            [Path("path/to/first_hi_l3e")],
+            [Path("path/to/second_hi_l3e")],
         ]
-        mock_process_lo.return_value = [Path('path/to/lo_l3e')]
-        mock_process_ultra.return_value = [Path('path/to/ultra_l3e')]
-        mock_process_ultra_hf.return_value = [Path('path/to/ultra_l3e_hf')]
+        mock_process_lo.return_value = [Path("path/to/lo_l3e")]
+        mock_process_ultra.return_value = [Path("path/to/ultra_l3e")]
+        mock_process_ultra_hf.return_value = [Path("path/to/ultra_l3e_hf")]
         mock_get_lo_pivot_angles.return_value = {25: LoPivotAngle("l1b_nhk.cdf", 75)}
         mock_compute_glows_flags_for_repoint.return_value = 4
-        mock_determine_spacecraft_info.return_value = sentinel.spacecraft_info, GlowsL3Flags.PREDICTIVE_EPHEMERIS, ["spice kernel"]
+        mock_determine_spacecraft_info.return_value = (
+            sentinel.spacecraft_info,
+            GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+            ["spice kernel"],
+        )
 
         expected_l3e_products = [
-            Path('path/to/lo_l3e'),
-            Path('path/to/first_hi_l3e'), Path('path/to/second_hi_l3e'),
-            Path('path/to/ultra_l3e'),
-            Path('path/to/ultra_l3e_hf')
+            Path("path/to/lo_l3e"),
+            Path("path/to/first_hi_l3e"),
+            Path("path/to/second_hi_l3e"),
+            Path("path/to/ultra_l3e"),
+            Path("path/to/ultra_l3e_hf"),
         ]
 
         start_epoch = datetime(2020, 1, 1)
@@ -1138,7 +1717,9 @@ def test_process_l3e(self, mock_process_ultra, mock_process_ultra_hf, mock_proce
 
         actual_l3e_products = process_l3e(initializer_data)
         mock_get_pointing_date_range.assert_called_once_with(25)
-        mock_compute_glows_flags_for_repoint.assert_called_once_with(l3d_cdf_path, repointing_midpoint)
+        mock_compute_glows_flags_for_repoint.assert_called_once_with(
+            l3d_cdf_path, repointing_midpoint
+        )
         mock_determine_spacecraft_info.assert_called_once_with(
             datetime(2020, 1, 1, 12),
             initializer_data.metakernel_with_predict_ephem,
@@ -1169,37 +1750,82 @@ def test_process_l3e(self, mock_process_ultra, mock_process_ultra_hf, mock_proce
                 ),
             ]
         )
-        mock_process_lo.assert_called_once_with(["lo_ancillary.dat", "spice kernel", "l1b_nhk.cdf"], 25, start_epoch, epoch_delta, 75,
-                                                Version(None, 3), expected_flags, sentinel.spacecraft_info)
-        mock_process_ultra.assert_called_once_with(["ul_ancillary.dat", "spice kernel"], 25, start_epoch, epoch_delta, Version(None, 4), expected_flags, sentinel.spacecraft_info)
-        mock_process_ultra_hf.assert_called_once_with(["ul_ancillary.dat", "spice kernel"], 25, start_epoch, epoch_delta, Version(None, 4), expected_flags, sentinel.spacecraft_info)
+        mock_process_lo.assert_called_once_with(
+            ["lo_ancillary.dat", "spice kernel", "l1b_nhk.cdf"],
+            25,
+            start_epoch,
+            epoch_delta,
+            75,
+            Version(None, 3),
+            expected_flags,
+            sentinel.spacecraft_info,
+        )
+        mock_process_ultra.assert_called_once_with(
+            ["ul_ancillary.dat", "spice kernel"],
+            25,
+            start_epoch,
+            epoch_delta,
+            Version(None, 4),
+            expected_flags,
+            sentinel.spacecraft_info,
+        )
+        mock_process_ultra_hf.assert_called_once_with(
+            ["ul_ancillary.dat", "spice kernel"],
+            25,
+            start_epoch,
+            epoch_delta,
+            Version(None, 4),
+            expected_flags,
+            sentinel.spacecraft_info,
+        )
 
         self.assertEqual(expected_l3e_products, actual_l3e_products)
         mock_get_lo_pivot_angles.assert_called_once_with([25])
 
-    @patch('imap_l3_processing.glows.glows_processor.determine_spacecraft_info_using_predict_if_needed')
-    @patch('imap_l3_processing.glows.glows_processor.get_lo_pivot_angles')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_ul_hf')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_ul_sf')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_hi')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_lo')
-    @patch('imap_l3_processing.glows.glows_processor.compute_glows_flags_for_repoint')
-    @patch('imap_l3_processing.glows.glows_processor.get_pointing_date_range')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3d')
-    def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock_get_pointing_date_range, mock_compute_flags,
-                                         mock_process_lo, mock_process_hi, mock_process_ul_sf, mock_process_ul_hf,
-                                         mock_get_lo_pivot_angles, mock_determine_spacecraft_info):
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_spacecraft_info_using_predict_if_needed"
+    )
+    @patch("imap_l3_processing.glows.glows_processor.get_lo_pivot_angles")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_ul_hf")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_ul_sf")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_hi")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_lo")
+    @patch("imap_l3_processing.glows.glows_processor.compute_glows_flags_for_repoint")
+    @patch("imap_l3_processing.glows.glows_processor.get_pointing_date_range")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3d")
+    def test_process_l3e_invoked_from_top_level_process(
+        self,
+        mock_process_l3d,
+        mock_get_pointing_date_range,
+        mock_compute_flags,
+        mock_process_lo,
+        mock_process_hi,
+        mock_process_ul_sf,
+        mock_process_ul_hf,
+        mock_get_lo_pivot_angles,
+        mock_determine_spacecraft_info,
+    ):
         mock_get_lo_pivot_angles.return_value = {
             2902: LoPivotAngle("l1b_2902", 90),
             2905: LoPivotAngle("l1b_2905", 90),
         }
         mock_compute_flags.return_value = 0
-        self.mock_l3d_initializer.should_process_l3d.return_value = (sentinel.l3d_version, sentinel.glows_l3d_dependency, sentinel.old_l3d)
-        process_l3d_result = GlowsL3DProcessorOutput(sentinel.l3d_cdf_file_path, [sentinel.l3d_text_file_paths],
-                                         sentinel.last_processed_cr)
+        self.mock_l3d_initializer.should_process_l3d.return_value = (
+            sentinel.l3d_version,
+            sentinel.glows_l3d_dependency,
+            sentinel.old_l3d,
+        )
+        process_l3d_result = GlowsL3DProcessorOutput(
+            sentinel.l3d_cdf_file_path,
+            [sentinel.l3d_text_file_paths],
+            sentinel.last_processed_cr,
+        )
         mock_process_l3d.return_value = process_l3d_result
-        mock_determine_spacecraft_info.return_value = sentinel.spacecraft_info, GlowsL3Flags.PREDICTIVE_EPHEMERIS, ["spice kernel"]
-
+        mock_determine_spacecraft_info.return_value = (
+            sentinel.spacecraft_info,
+            GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+            ["spice kernel"],
+        )
 
         input_major_version = 5
 
@@ -1215,17 +1841,22 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
                 GLOWS_L3E_ULTRA_HF_DESCRIPTOR: Version(input_major_version, 1),
             }
         )
-        input_metadata = InputMetadata('glows', "l3b", datetime(2024, 10, 7), None, version=input_version_map)
+        input_metadata = InputMetadata(
+            "glows", "l3b", datetime(2024, 10, 7), None, version=input_version_map
+        )
 
-        mock_get_pointing_date_range.return_value = datetime(2025, 1, 1), datetime(2025, 1, 2)
+        mock_get_pointing_date_range.return_value = (
+            datetime(2025, 1, 1),
+            datetime(2025, 1, 2),
+        )
 
         l3e_initialzer_repointings = GlowsL3eVersionsForRepointings(
             [2902, 2903, 2904, 2905, 2906, 2907],
-            {2902: Version(None, 1), 2903: Version(1,11)},
-            {2902: Version(None, 1), 2904: Version(2,12)},
-            {2902: Version(None, 1), 2905: Version(3,13)},
-            {2902: Version(None, 1), 2906: Version(4,14)},
-            {2902: Version(None, 1), 2907: Version(5,15)},
+            {2902: Version(None, 1), 2903: Version(1, 11)},
+            {2902: Version(None, 1), 2904: Version(2, 12)},
+            {2902: Version(None, 1), 2905: Version(3, 13)},
+            {2902: Version(None, 1), 2906: Version(4, 14)},
+            {2902: Version(None, 1), 2907: Version(5, 15)},
         )
 
         l3e_dependencies = GlowsL3EDependencies(
@@ -1253,37 +1884,45 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
             Path("path/to/some/pipeline_settings_file.csv"),
             Path("repoint.csv"),
         )
-        self.mock_l3e_initializer.get_repointings_to_process.return_value = GlowsL3EInitializerOutput(
-            l3e_dependencies,
-            l3e_initialzer_repointings,
-            sentinel.l3d_cdf_file_path,
-            metakernel_with_predict_ephem=Mock(),
-            metakernel_without_predict_ephem=Mock(),
+        self.mock_l3e_initializer.get_repointings_to_process.return_value = (
+            GlowsL3EInitializerOutput(
+                l3e_dependencies,
+                l3e_initialzer_repointings,
+                sentinel.l3d_cdf_file_path,
+                metakernel_with_predict_ephem=Mock(),
+                metakernel_without_predict_ephem=Mock(),
+            )
         )
 
         expected_lo_files = [
-            [f'imap_glows_l3e_survival-probability-lo_20250101-repoint02902_v001.cdf', sentinel.lo_dat_1],
-            [f'imap_glows_l3e_survival-probability-lo_20250101-repoint02905_v003.0013.cdf', sentinel.lo_dat_2],
+            [
+                "imap_glows_l3e_survival-probability-lo_20250101-repoint02902_v001.cdf",
+                sentinel.lo_dat_1,
+            ],
+            [
+                "imap_glows_l3e_survival-probability-lo_20250101-repoint02905_v003.0013.cdf",
+                sentinel.lo_dat_2,
+            ],
         ]
         mock_process_lo.side_effect = expected_lo_files
 
         expected_hi90_files = [
             [
-                f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint02902_v001.cdf",
+                "imap_glows_l3e_survival-probability-hi-90_20250101-repoint02902_v001.cdf",
                 sentinel.hi90_dat_1,
             ],
             [
-                f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint02903_v001.0011.cdf",
+                "imap_glows_l3e_survival-probability-hi-90_20250101-repoint02903_v001.0011.cdf",
                 sentinel.hi90_dat_2,
             ],
         ]
         expected_hi45_files = [
             [
-                f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint02902_v001.cdf",
+                "imap_glows_l3e_survival-probability-hi-45_20250101-repoint02902_v001.cdf",
                 sentinel.hi45_dat_1,
             ],
             [
-                f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint02904_v002.0012.cdf",
+                "imap_glows_l3e_survival-probability-hi-45_20250101-repoint02904_v002.0012.cdf",
                 sentinel.hi45_dat_2,
             ],
         ]
@@ -1296,11 +1935,11 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
 
         expected_ul_sf_files = [
             [
-                f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02902_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02902_v001.cdf",
                 sentinel.ul_sf_dat_1,
             ],
             [
-                f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02906_v004.0014.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02906_v004.0014.cdf",
                 sentinel.ul_sf_dat_2,
             ],
         ]
@@ -1308,11 +1947,11 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
 
         expected_ul_hf_files = [
             [
-                f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02902_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02902_v001.cdf",
                 sentinel.ul_hf_dat_1,
             ],
             [
-                f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02907_v005.0015.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02907_v005.0015.cdf",
                 sentinel.ul_hf_dat_2,
             ],
         ]
@@ -1321,25 +1960,25 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
         expected_products = [
             sentinel.l3d_text_file_paths,
             sentinel.l3d_cdf_file_path,
-            f"imap_glows_l3e_survival-probability-lo_20250101-repoint02902_v001.cdf",
+            "imap_glows_l3e_survival-probability-lo_20250101-repoint02902_v001.cdf",
             sentinel.lo_dat_1,
-            f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint02902_v001.cdf",
+            "imap_glows_l3e_survival-probability-hi-90_20250101-repoint02902_v001.cdf",
             sentinel.hi90_dat_1,
-            f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint02902_v001.cdf",
+            "imap_glows_l3e_survival-probability-hi-45_20250101-repoint02902_v001.cdf",
             sentinel.hi45_dat_1,
-            f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02902_v001.cdf",
+            "imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02902_v001.cdf",
             sentinel.ul_sf_dat_1,
-            f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02902_v001.cdf",
+            "imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02902_v001.cdf",
             sentinel.ul_hf_dat_1,
-            f"imap_glows_l3e_survival-probability-hi-90_20250101-repoint02903_v001.0011.cdf",
+            "imap_glows_l3e_survival-probability-hi-90_20250101-repoint02903_v001.0011.cdf",
             sentinel.hi90_dat_2,
-            f"imap_glows_l3e_survival-probability-hi-45_20250101-repoint02904_v002.0012.cdf",
+            "imap_glows_l3e_survival-probability-hi-45_20250101-repoint02904_v002.0012.cdf",
             sentinel.hi45_dat_2,
-            f"imap_glows_l3e_survival-probability-lo_20250101-repoint02905_v003.0013.cdf",
+            "imap_glows_l3e_survival-probability-lo_20250101-repoint02905_v003.0013.cdf",
             sentinel.lo_dat_2,
-            f"imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02906_v004.0014.cdf",
+            "imap_glows_l3e_survival-probability-ul-sf_20250101-repoint02906_v004.0014.cdf",
             sentinel.ul_sf_dat_2,
-            f"imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02907_v005.0015.cdf",
+            "imap_glows_l3e_survival-probability-ul-hf_20250101-repoint02907_v005.0015.cdf",
             sentinel.ul_hf_dat_2,
         ]
 
@@ -1347,7 +1986,6 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
         processor = GlowsProcessor(processing_input_collection, input_metadata)
         products = processor.process()
 
-
         self.mock_l3e_initializer.get_repointings_to_process.assert_called_once_with(
             process_l3d_result,
             sentinel.old_l3d,
@@ -1356,18 +1994,35 @@ def test_process_l3e_invoked_from_top_level_process(self, mock_process_l3d, mock
             self.mock_reprocess_info,
         )
         self.assertEqual(expected_products, products)
-        self.mock_fetch_reprocess_info.assert_called_once_with(processing_input_collection)
+        self.mock_fetch_reprocess_info.assert_called_once_with(
+            processing_input_collection
+        )
 
-    @patch('imap_l3_processing.glows.glows_processor.Processor.get_parent_file_names')
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch("imap_l3_processing.glows.glows_processor.GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product")
+    @patch("imap_l3_processing.glows.glows_processor.Processor.get_parent_file_names")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product"
+    )
     @patch("imap_l3_processing.glows.glows_processor.run")
-    @patch("imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable"
+    )
     @patch("imap_l3_processing.glows.glows_processor.shutil")
-    def test_process_l3e_ultra_sf(self, mock_shutil, mock_determine_call_args, mock_run,
-                                  mock_convert_dat_to_glows_l3e_ul_product,
-                                  mock_save_data, mock_get_parent_file_names):
-        mock_get_parent_file_names.return_value = ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"]
+    def test_process_l3e_ultra_sf(
+        self,
+        mock_shutil,
+        mock_determine_call_args,
+        mock_run,
+        mock_convert_dat_to_glows_l3e_ul_product,
+        mock_save_data,
+        mock_get_parent_file_names,
+    ):
+        mock_get_parent_file_names.return_value = [
+            "l3d_file",
+            "ancillary_1",
+            "ancillary_2",
+            "ancillary_3",
+        ]
 
         epoch_start_date = datetime(year=2024, month=10, day=7)
         epoch_end_date = datetime(year=2024, month=10, day=7, hour=23)
@@ -1375,9 +2030,15 @@ def test_process_l3e_ultra_sf(self, mock_shutil, mock_determine_call_args, mock_
         repointing = 20
         version = Version(1, 12)
 
-        expected_input_metadata = InputMetadata('glows', "l3e", start_date=epoch_start_date, end_date=epoch_end_date,
-                                                version=VersionMap({GLOWS_L3E_ULTRA_SF_DESCRIPTOR:version}), descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
-                                                repointing=repointing)
+        expected_input_metadata = InputMetadata(
+            "glows",
+            "l3e",
+            start_date=epoch_start_date,
+            end_date=epoch_end_date,
+            version=VersionMap({GLOWS_L3E_ULTRA_SF_DESCRIPTOR: version}),
+            descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+            repointing=repointing,
+        )
 
         ultra_args = ["20241007_000000", "date.001", "vx", "vy", "vz", "30.000"]
 
@@ -1392,42 +2053,89 @@ def test_process_l3e_ultra_sf(self, mock_shutil, mock_determine_call_args, mock_
         parent_file_names = ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"]
         glows_flags = 4
         spacecraft_info = Mock()
-        products = process_l3e_ul_sf(parent_file_names, repointing, epoch_start_date, epoch_delta, version, glows_flags, spacecraft_info)
+        products = process_l3e_ul_sf(
+            parent_file_names,
+            repointing,
+            epoch_start_date,
+            epoch_delta,
+            version,
+            glows_flags,
+            spacecraft_info,
+        )
 
         expected_repointing_midpoint = epoch_start_date + epoch_delta
-        mock_determine_call_args.assert_called_once_with(epoch_start_date, expected_repointing_midpoint, 30, spacecraft_info=spacecraft_info)
+        mock_determine_call_args.assert_called_once_with(
+            epoch_start_date,
+            expected_repointing_midpoint,
+            30,
+            spacecraft_info=spacecraft_info,
+        )
 
         mock_run.assert_called_once_with(["./survProbUltra"] + ultra_args)
 
         output_data_path = Path("probSur.Imap.Ul_20241007_000000_date.001.dat")
 
         mock_convert_dat_to_glows_l3e_ul_product.assert_called_once_with(
-            expected_input_metadata, output_data_path, expected_repointing_midpoint, epoch_delta, call_args_object)
+            expected_input_metadata,
+            output_data_path,
+            expected_repointing_midpoint,
+            epoch_delta,
+            call_args_object,
+        )
 
         expected_first_data_path = AncillaryFilePath(
-            "imap_glows_survival-probability-ul-sf-raw_20241007-repoint00020_v012.dat").construct_path()
+            "imap_glows_survival-probability-ul-sf-raw_20241007-repoint00020_v012.dat"
+        ).construct_path()
 
-        mock_shutil.move.assert_called_once_with(output_data_path, expected_first_data_path)
+        mock_shutil.move.assert_called_once_with(
+            output_data_path, expected_first_data_path
+        )
 
         mock_save_data.assert_called_once_with(sentinel.ultra_data_1)
-        survival_data_product: GlowsL3EUltraData = mock_save_data.call_args_list[0].args[0]
-        self.assertEqual(["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"],
-                         survival_data_product.parent_file_names)
-        np.testing.assert_array_equal(survival_data_product.glows_flags, np.array([glows_flags], dtype=np.uint16))
+        survival_data_product: GlowsL3EUltraData = mock_save_data.call_args_list[
+            0
+        ].args[0]
+        self.assertEqual(
+            ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"],
+            survival_data_product.parent_file_names,
+        )
+        np.testing.assert_array_equal(
+            survival_data_product.glows_flags, np.array([glows_flags], dtype=np.uint16)
+        )
 
-        self.assertEqual(products, ["imap_glows_l3e_survival-probability-ul-sf_20241007-repoint00020_v001.0012.cdf",
-                                    expected_first_data_path])
+        self.assertEqual(
+            products,
+            [
+                "imap_glows_l3e_survival-probability-ul-sf_20241007-repoint00020_v001.0012.cdf",
+                expected_first_data_path,
+            ],
+        )
 
-    @patch('imap_l3_processing.glows.glows_processor.Processor.get_parent_file_names')
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch("imap_l3_processing.glows.glows_processor.GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product")
+    @patch("imap_l3_processing.glows.glows_processor.Processor.get_parent_file_names")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsL3EUltraData.convert_dat_to_glows_l3e_ul_product"
+    )
     @patch("imap_l3_processing.glows.glows_processor.run")
-    @patch("imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable"
+    )
     @patch("imap_l3_processing.glows.glows_processor.shutil")
-    def test_process_l3e_ultra_hf(self, mock_shutil, mock_determine_call_args, mock_run,
-                                  mock_convert_dat_to_glows_l3e_ul_product,
-                                  mock_save_data, mock_get_parent_file_names):
-        mock_get_parent_file_names.return_value = ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"]
+    def test_process_l3e_ultra_hf(
+        self,
+        mock_shutil,
+        mock_determine_call_args,
+        mock_run,
+        mock_convert_dat_to_glows_l3e_ul_product,
+        mock_save_data,
+        mock_get_parent_file_names,
+    ):
+        mock_get_parent_file_names.return_value = [
+            "l3d_file",
+            "ancillary_1",
+            "ancillary_2",
+            "ancillary_3",
+        ]
 
         epoch_start_date = datetime(year=2024, month=10, day=7)
         epoch_end_date = datetime(year=2024, month=10, day=7, hour=23)
@@ -1435,9 +2143,15 @@ def test_process_l3e_ultra_hf(self, mock_shutil, mock_determine_call_args, mock_
         repointing = 20
         version = Version(1, 12)
 
-        input_metadata = InputMetadata('glows', "l3e", start_date=epoch_start_date, end_date=epoch_end_date,
-                                       version=VersionMap({GLOWS_L3E_ULTRA_HF_DESCRIPTOR: version}), descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
-                                       repointing=repointing)
+        input_metadata = InputMetadata(
+            "glows",
+            "l3e",
+            start_date=epoch_start_date,
+            end_date=epoch_end_date,
+            version=VersionMap({GLOWS_L3E_ULTRA_HF_DESCRIPTOR: version}),
+            descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+            repointing=repointing,
+        )
 
         spacecraft_frame_spacecraft_info = GlowsL3eSpacecraftInfo(
             spacecraft_radius=500,
@@ -1454,7 +2168,7 @@ def test_process_l3e_ultra_hf(self, mock_shutil, mock_determine_call_args, mock_
             formatted_date="20241007_000000",
             decimal_date="date.001",
             spacecraft_info=spacecraft_frame_spacecraft_info,
-            elongation=30
+            elongation=30,
         )
 
         expected_rest_frame_args = GlowsL3eCallArguments(
@@ -1470,7 +2184,7 @@ def test_process_l3e_ultra_hf(self, mock_shutil, mock_determine_call_args, mock_
                 spin_axis_longitude=240,
                 spin_axis_latitude=3,
             ),
-            elongation=30
+            elongation=30,
         )
 
         mock_convert_dat_to_glows_l3e_ul_product.return_value = sentinel.ultra_data_hf
@@ -1479,47 +2193,91 @@ def test_process_l3e_ultra_hf(self, mock_shutil, mock_determine_call_args, mock_
 
         parent_file_names = ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"]
         glows_flags = 8
-        products = process_l3e_ul_hf(parent_file_names, repointing, epoch_start_date, epoch_delta, version, glows_flags, spacecraft_info=spacecraft_frame_spacecraft_info)
+        products = process_l3e_ul_hf(
+            parent_file_names,
+            repointing,
+            epoch_start_date,
+            epoch_delta,
+            version,
+            glows_flags,
+            spacecraft_info=spacecraft_frame_spacecraft_info,
+        )
 
         expected_repointing_midpoint = epoch_start_date + epoch_delta
-        mock_determine_call_args.assert_called_once_with(epoch_start_date, expected_repointing_midpoint, 30, spacecraft_info=spacecraft_frame_spacecraft_info)
+        mock_determine_call_args.assert_called_once_with(
+            epoch_start_date,
+            expected_repointing_midpoint,
+            30,
+            spacecraft_info=spacecraft_frame_spacecraft_info,
+        )
 
-        mock_run.assert_called_once_with(["./survProbUltra"] + expected_rest_frame_args.to_argument_list())
+        mock_run.assert_called_once_with(
+            ["./survProbUltra"] + expected_rest_frame_args.to_argument_list()
+        )
 
         output_data_path = Path("probSur.Imap.Ul.V0_20241007_000000_date.001.dat")
 
         mock_convert_dat_to_glows_l3e_ul_product.assert_called_once_with(
-            input_metadata, output_data_path, expected_repointing_midpoint, epoch_delta, expected_rest_frame_args)
+            input_metadata,
+            output_data_path,
+            expected_repointing_midpoint,
+            epoch_delta,
+            expected_rest_frame_args,
+        )
 
         expected_first_data_path = AncillaryFilePath(
-            "imap_glows_survival-probability-ul-hf-raw_20241007-repoint00020_v012.dat").construct_path()
+            "imap_glows_survival-probability-ul-hf-raw_20241007-repoint00020_v012.dat"
+        ).construct_path()
 
-        mock_shutil.move.assert_called_once_with(output_data_path, expected_first_data_path)
+        mock_shutil.move.assert_called_once_with(
+            output_data_path, expected_first_data_path
+        )
 
         mock_save_data.assert_called_once_with(sentinel.ultra_data_hf)
-        survival_data_product: GlowsL3EUltraData = mock_save_data.call_args_list[0].args[0]
-        self.assertEqual(["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"],
-                         survival_data_product.parent_file_names)
-        np.testing.assert_array_equal(survival_data_product.glows_flags, np.array([glows_flags], dtype=np.uint16))
+        survival_data_product: GlowsL3EUltraData = mock_save_data.call_args_list[
+            0
+        ].args[0]
+        self.assertEqual(
+            ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"],
+            survival_data_product.parent_file_names,
+        )
+        np.testing.assert_array_equal(
+            survival_data_product.glows_flags, np.array([glows_flags], dtype=np.uint16)
+        )
 
-        self.assertEqual(products, ["imap_glows_l3e_survival-probability-ul-hf_20241007-repoint00020_v001.0012.cdf",
-                                    expected_first_data_path])
+        self.assertEqual(
+            products,
+            [
+                "imap_glows_l3e_survival-probability-ul-hf_20241007-repoint00020_v001.0012.cdf",
+                expected_first_data_path,
+            ],
+        )
 
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch("imap_l3_processing.glows.glows_processor.GlowsL3EHiData.convert_dat_to_glows_l3e_hi_product")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsL3EHiData.convert_dat_to_glows_l3e_hi_product"
+    )
     @patch("imap_l3_processing.glows.glows_processor.run")
-    @patch("imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable"
+    )
     @patch("imap_l3_processing.glows.glows_processor.shutil")
-    def test_process_l3e_hi(self, mock_shutil, mock_determine_call_args,
-                            mock_run, mock_convert_dat_to_glows_l3e_hi_product, mock_save_data):
+    def test_process_l3e_hi(
+        self,
+        mock_shutil,
+        mock_determine_call_args,
+        mock_run,
+        mock_convert_dat_to_glows_l3e_hi_product,
+        mock_save_data,
+    ):
 
         test_cases = [
             ("90", 90, GLOWS_L3E_HI_90_DESCRIPTOR),
-            ("45", 135, GLOWS_L3E_HI_45_DESCRIPTOR)
+            ("45", 135, GLOWS_L3E_HI_45_DESCRIPTOR),
         ]
 
         for descriptor_elongation, elongation, descriptor in test_cases:
-            with (self.subTest(elongation=elongation)):
+            with self.subTest(elongation=elongation):
                 mock_determine_call_args.reset_mock()
                 mock_run.reset_mock()
                 mock_convert_dat_to_glows_l3e_hi_product.reset_mock()
@@ -1533,80 +2291,127 @@ def test_process_l3e_hi(self, mock_shutil, mock_determine_call_args,
                 version = Version(1, 12)
 
                 expected_input_metadata = InputMetadata(
-                    instrument='glows', data_level="l3e", start_date=epoch_start_date, end_date=epoch_end_date,
-                    version=VersionMap({descriptor: version}), descriptor=f'survival-probability-hi-{descriptor_elongation}',
-                    repointing=repointing)
+                    instrument="glows",
+                    data_level="l3e",
+                    start_date=epoch_start_date,
+                    end_date=epoch_end_date,
+                    version=VersionMap({descriptor: version}),
+                    descriptor=f"survival-probability-hi-{descriptor_elongation}",
+                    repointing=repointing,
+                )
 
                 l3e_dependencies = MagicMock(spec=GlowsL3EDependencies)
-                l3e_dependencies.pipeline_settings = {'start_cr': 2090}
-                l3e_dependencies.repointing_file = get_test_data_path("fake_2_day_repointing_on_may18_file.csv")
+                l3e_dependencies.pipeline_settings = {"start_cr": 2090}
+                l3e_dependencies.repointing_file = get_test_data_path(
+                    "fake_2_day_repointing_on_may18_file.csv"
+                )
 
-                hi_args = ["20241007_000000", "date.001", "vx", "vy", "vz", descriptor_elongation]
+                hi_args = [
+                    "20241007_000000",
+                    "date.001",
+                    "vx",
+                    "vy",
+                    "vz",
+                    descriptor_elongation,
+                ]
 
                 mock_call_args_object = MagicMock(spec=GlowsL3eCallArguments)
                 mock_call_args_object.to_argument_list.return_value = hi_args
                 mock_determine_call_args.return_value = mock_call_args_object
-                mock_convert_dat_to_glows_l3e_hi_product.return_value = sentinel.hi_data_1
+                mock_convert_dat_to_glows_l3e_hi_product.return_value = (
+                    sentinel.hi_data_1
+                )
 
                 saved_cdf_path = Path(
-                    f"imap_glows_l3e_survival-probability-hi-{descriptor_elongation}_20241007-repoint00020_v001.0012.cdf")
+                    f"imap_glows_l3e_survival-probability-hi-{descriptor_elongation}_20241007-repoint00020_v001.0012.cdf"
+                )
 
                 mock_save_data.return_value = saved_cdf_path
 
-                parent_file_names = ["some_l3e_hi_parent.dat", "some_repointing_file.repoint.csv"]
+                parent_file_names = [
+                    "some_l3e_hi_parent.dat",
+                    "some_repointing_file.repoint.csv",
+                ]
                 glows_flags = 16
                 spacecraft_info = Mock()
-                products = process_l3e_hi(parent_file_names, repointing, epoch_start_date, epoch_delta, elongation,
-                                          version, glows_flags, spacecraft_info=spacecraft_info)
+                products = process_l3e_hi(
+                    parent_file_names,
+                    repointing,
+                    epoch_start_date,
+                    epoch_delta,
+                    elongation,
+                    version,
+                    glows_flags,
+                    spacecraft_info=spacecraft_info,
+                )
 
                 expected_repointing_midpoint = epoch_start_date + epoch_delta
-                mock_determine_call_args.assert_called_once_with(epoch_start_date, expected_repointing_midpoint,
-                                                                 float(elongation), spacecraft_info=spacecraft_info)
+                mock_determine_call_args.assert_called_once_with(
+                    epoch_start_date,
+                    expected_repointing_midpoint,
+                    float(elongation),
+                    spacecraft_info=spacecraft_info,
+                )
 
                 mock_run.assert_called_once_with(["./survProbHi"] + hi_args)
 
                 first_output_data_path = Path(
-                    f"probSur.Imap.Hi_20241007_000000_date.001_{descriptor_elongation[:5]}.dat")
+                    f"probSur.Imap.Hi_20241007_000000_date.001_{descriptor_elongation[:5]}.dat"
+                )
 
                 mock_convert_dat_to_glows_l3e_hi_product.assert_called_once_with(
                     expected_input_metadata,
                     first_output_data_path,
                     expected_repointing_midpoint,
                     epoch_delta,
-                    mock_call_args_object
+                    mock_call_args_object,
                 )
 
                 mock_save_data.assert_called_once_with(sentinel.hi_data_1)
 
-                survival_data_product: GlowsL3EHiData = mock_save_data.call_args_list[0].args[0]
-                self.assertEqual(parent_file_names, survival_data_product.parent_file_names)
-                np.testing.assert_array_equal(survival_data_product.glows_flags,
-                                              np.array([glows_flags], dtype=np.uint16))
+                survival_data_product: GlowsL3EHiData = mock_save_data.call_args_list[
+                    0
+                ].args[0]
+                self.assertEqual(
+                    parent_file_names, survival_data_product.parent_file_names
+                )
+                np.testing.assert_array_equal(
+                    survival_data_product.glows_flags,
+                    np.array([glows_flags], dtype=np.uint16),
+                )
 
                 expected_output_data_path = AncillaryFilePath(
                     f"imap_glows_survival-probability-hi-{descriptor_elongation}-raw_20241007-repoint00020_v012.dat"
                 ).construct_path()
 
-                mock_shutil.move.assert_called_once_with(first_output_data_path, expected_output_data_path)
+                mock_shutil.move.assert_called_once_with(
+                    first_output_data_path, expected_output_data_path
+                )
 
-                expected_products = [
-                    saved_cdf_path,
-                    expected_output_data_path
-                ]
+                expected_products = [saved_cdf_path, expected_output_data_path]
                 self.assertEqual(expected_products, products)
 
-    @patch('imap_l3_processing.glows.glows_processor.save_data')
-    @patch("imap_l3_processing.glows.glows_processor.GlowsL3ELoData.convert_dat_to_glows_l3e_lo_product")
+    @patch("imap_l3_processing.glows.glows_processor.save_data")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.GlowsL3ELoData.convert_dat_to_glows_l3e_lo_product"
+    )
     @patch("imap_l3_processing.glows.glows_processor.run")
-    @patch("imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_call_args_for_l3e_executable"
+    )
     @patch("imap_l3_processing.glows.glows_processor.shutil")
-    def test_process_l3e_lo(self, mock_shutil,
-                            mock_determine_call_args,
-                            mock_run, mock_convert_dat_to_glows_l3e_lo_product, mock_save_data):
+    def test_process_l3e_lo(
+        self,
+        mock_shutil,
+        mock_determine_call_args,
+        mock_run,
+        mock_convert_dat_to_glows_l3e_lo_product,
+        mock_save_data,
+    ):
 
         test_cases = [(75, "75.00"), (105, "105.0")]
         for elongation, elongation_filename in test_cases:
-            with (self.subTest(elongation)):
+            with self.subTest(elongation):
                 epoch_start_date = datetime(year=2024, month=10, day=7)
                 epoch_end_date = datetime(year=2024, month=10, day=7, hour=23)
                 epoch_delta = (epoch_end_date - epoch_start_date) / 2
@@ -1614,10 +2419,23 @@ def test_process_l3e_lo(self, mock_shutil,
                 version = Version(1, 12)
 
                 expected_input_metadata = InputMetadata(
-                    instrument='glows', data_level="l3e", start_date=epoch_start_date, end_date=epoch_end_date,
-                    version=VersionMap({GLOWS_L3E_LO_DESCRIPTOR: version}), descriptor=GLOWS_L3E_LO_DESCRIPTOR, repointing=repointing)
+                    instrument="glows",
+                    data_level="l3e",
+                    start_date=epoch_start_date,
+                    end_date=epoch_end_date,
+                    version=VersionMap({GLOWS_L3E_LO_DESCRIPTOR: version}),
+                    descriptor=GLOWS_L3E_LO_DESCRIPTOR,
+                    repointing=repointing,
+                )
 
-                lo_call_args = ["20241007_000000", "date.100", "vx", "vy", "vz", f"{elongation:.3f}"]
+                lo_call_args = [
+                    "20241007_000000",
+                    "date.100",
+                    "vx",
+                    "vy",
+                    "vz",
+                    f"{elongation:.3f}",
+                ]
 
                 spacecraft_info = GlowsL3eSpacecraftInfo(
                     spacecraft_radius=np.float32(100.0),
@@ -1642,45 +2460,77 @@ def test_process_l3e_lo(self, mock_shutil,
                 lo_data_1 = Mock()
                 mock_convert_dat_to_glows_l3e_lo_product.return_value = lo_data_1
 
-                output_cdf_path = Path("imap_glows_l3e_survival-probability-lo_20241007-repoint00020_v001.0012.cdf")
+                output_cdf_path = Path(
+                    "imap_glows_l3e_survival-probability-lo_20241007-repoint00020_v001.0012.cdf"
+                )
                 mock_save_data.return_value = output_cdf_path
 
-                parent_file_names = ["l3d_file", "ancillary_1", "ancillary_2", "ancillary_3"]
+                parent_file_names = [
+                    "l3d_file",
+                    "ancillary_1",
+                    "ancillary_2",
+                    "ancillary_3",
+                ]
 
                 glows_flags = 32
 
-                products = process_l3e_lo(parent_file_names, repointing, epoch_start_date, epoch_delta, elongation,
-                                          version, glows_flags, spacecraft_info=spacecraft_info)
+                products = process_l3e_lo(
+                    parent_file_names,
+                    repointing,
+                    epoch_start_date,
+                    epoch_delta,
+                    elongation,
+                    version,
+                    glows_flags,
+                    spacecraft_info=spacecraft_info,
+                )
 
                 expected_repointing_midpoint = epoch_start_date + epoch_delta
-                mock_determine_call_args.assert_called_once_with(epoch_start_date, expected_repointing_midpoint,
-                                                                 elongation, spacecraft_info=spacecraft_info)
+                mock_determine_call_args.assert_called_once_with(
+                    epoch_start_date,
+                    expected_repointing_midpoint,
+                    elongation,
+                    spacecraft_info=spacecraft_info,
+                )
 
                 mock_run.assert_called_once_with(["./survProbLo"] + lo_call_args)
 
-                first_output_file_path = Path(f"probSur.Imap.Lo_20241007_000000_date.100_{elongation_filename}.dat")
+                first_output_file_path = Path(
+                    f"probSur.Imap.Lo_20241007_000000_date.100_{elongation_filename}.dat"
+                )
 
-                mock_convert_dat_to_glows_l3e_lo_product.assert_called_once_with(expected_input_metadata,
-                                                                                 first_output_file_path,
-                                                                                 expected_repointing_midpoint,
-                                                                                 epoch_delta,
-                                                                                 elongation, l3e_args)
+                mock_convert_dat_to_glows_l3e_lo_product.assert_called_once_with(
+                    expected_input_metadata,
+                    first_output_file_path,
+                    expected_repointing_midpoint,
+                    epoch_delta,
+                    elongation,
+                    l3e_args,
+                )
 
                 expected_first_output_file_path = AncillaryFilePath(
                     "imap_glows_survival-probability-lo-raw_20241007-repoint00020_v012.dat"
                 ).construct_path()
 
-                mock_shutil.move.assert_called_once_with(first_output_file_path, expected_first_output_file_path)
+                mock_shutil.move.assert_called_once_with(
+                    first_output_file_path, expected_first_output_file_path
+                )
 
                 mock_save_data.assert_called_once_with(lo_data_1)
-                survival_data_product: GlowsL3ELoData = mock_save_data.call_args_list[0].args[0]
-                self.assertEqual(parent_file_names,
-                                 survival_data_product.parent_file_names)
-                np.testing.assert_array_equal(survival_data_product.glows_flags,
-                                              np.array([glows_flags], dtype=np.uint16))
+                survival_data_product: GlowsL3ELoData = mock_save_data.call_args_list[
+                    0
+                ].args[0]
+                self.assertEqual(
+                    parent_file_names, survival_data_product.parent_file_names
+                )
+                np.testing.assert_array_equal(
+                    survival_data_product.glows_flags,
+                    np.array([glows_flags], dtype=np.uint16),
+                )
 
-                self.assertEqual([output_cdf_path,
-                                  expected_first_output_file_path], products)
+                self.assertEqual(
+                    [output_cdf_path, expected_first_output_file_path], products
+                )
 
                 mock_shutil.reset_mock()
                 mock_determine_call_args.reset_mock()
@@ -1688,47 +2538,59 @@ def test_process_l3e_lo(self, mock_shutil,
                 mock_convert_dat_to_glows_l3e_lo_product.reset_mock()
                 mock_save_data.reset_mock()
 
-    @patch('imap_l3_processing.glows.glows_processor.compute_glows_flags_for_repoint')
-    @patch('imap_l3_processing.glows.glows_processor.get_lo_pivot_angles')
-    @patch('imap_l3_processing.glows.glows_processor.get_pointing_date_range')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_hi')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_lo')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_ul_hf')
-    @patch('imap_l3_processing.glows.glows_processor.process_l3e_ul_sf')
-    @patch('imap_l3_processing.glows.glows_processor.determine_spacecraft_info_using_predict_if_needed')
-    def test_process_l3e_skips_repointing_on_exception(self, mock_determine_spacecraft_info, mock_process_ultra_sf, mock_process_ultra_hf,
-                                                       mock_process_lo,
-                                                       mock_process_hi, mock_get_pointing_date_range,
-                                                       mock_get_lo_pivot_angles,
-                                                       mock_compute_glows_flags_for_repoint):
+    @patch("imap_l3_processing.glows.glows_processor.compute_glows_flags_for_repoint")
+    @patch("imap_l3_processing.glows.glows_processor.get_lo_pivot_angles")
+    @patch("imap_l3_processing.glows.glows_processor.get_pointing_date_range")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_hi")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_lo")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_ul_hf")
+    @patch("imap_l3_processing.glows.glows_processor.process_l3e_ul_sf")
+    @patch(
+        "imap_l3_processing.glows.glows_processor.determine_spacecraft_info_using_predict_if_needed"
+    )
+    def test_process_l3e_skips_repointing_on_exception(
+        self,
+        mock_determine_spacecraft_info,
+        mock_process_ultra_sf,
+        mock_process_ultra_hf,
+        mock_process_lo,
+        mock_process_hi,
+        mock_get_pointing_date_range,
+        mock_get_lo_pivot_angles,
+        mock_compute_glows_flags_for_repoint,
+    ):
         mock_compute_glows_flags_for_repoint.return_value = 0
 
         mock_process_hi.side_effect = [
-            ValueError("Failed to generate hi"), [Path('path/to/first_hi-45_l3e')],
-            [Path('path/to/second_hi-90_l3e')], [Path('path/to/second_hi-45_l3e')],
-            [Path('path/to/third_hi-90_l3e')], [Path('path/to/third_hi-45_l3e')],
-            [Path('path/to/fourth_hi-90_l3e')], [Path('path/to/fourth_hi-45_l3e')],
+            ValueError("Failed to generate hi"),
+            [Path("path/to/first_hi-45_l3e")],
+            [Path("path/to/second_hi-90_l3e")],
+            [Path("path/to/second_hi-45_l3e")],
+            [Path("path/to/third_hi-90_l3e")],
+            [Path("path/to/third_hi-45_l3e")],
+            [Path("path/to/fourth_hi-90_l3e")],
+            [Path("path/to/fourth_hi-45_l3e")],
         ]
 
         mock_process_lo.side_effect = [
-            [Path('path/to/first_lo_l3e')],
+            [Path("path/to/first_lo_l3e")],
             ValueError("Failed to generate lo"),
-            [Path('path/to/third_lo_l3e')],
-            [Path('path/to/fourth_lo_l3e')],
+            [Path("path/to/third_lo_l3e")],
+            [Path("path/to/fourth_lo_l3e")],
         ]
 
         mock_process_ultra_sf.side_effect = [
-            [Path('path/to/first_ultra_l3e')],
-            [Path('path/to/second_ultra_l3e')],
+            [Path("path/to/first_ultra_l3e")],
+            [Path("path/to/second_ultra_l3e")],
             ValueError("Failed to generate ultra!"),
-            [Path('path/to/fourth_ultra_l3e')]
+            [Path("path/to/fourth_ultra_l3e")],
         ]
 
         mock_process_ultra_hf.side_effect = [
-            [Path('path/to/first_ultra_l3e_hf')],
-            [Path('path/to/second_ultra_l3e_hf')],
-            [Path('path/to/third_ultra_l3e_hf')],
-            ValueError("Failed to generate ultra!")
+            [Path("path/to/first_ultra_l3e_hf")],
+            [Path("path/to/second_ultra_l3e_hf")],
+            [Path("path/to/third_ultra_l3e_hf")],
+            ValueError("Failed to generate ultra!"),
         ]
         mock_get_lo_pivot_angles.return_value = {
             24: LoPivotAngle(parent_filename="l1b_nhk_24", pivot_angle=124),
@@ -1738,22 +2600,22 @@ def test_process_l3e_skips_repointing_on_exception(self, mock_determine_spacecra
         }
 
         expected_l3e_products = [
-            Path('path/to/first_lo_l3e'),
-            Path('path/to/first_hi-45_l3e'),
-            Path('path/to/first_ultra_l3e'),
-            Path('path/to/first_ultra_l3e_hf'),
-            Path('path/to/second_hi-90_l3e'),
-            Path('path/to/second_hi-45_l3e'),
-            Path('path/to/second_ultra_l3e'),
-            Path('path/to/second_ultra_l3e_hf'),
-            Path('path/to/third_lo_l3e'),
-            Path('path/to/third_hi-90_l3e'),
-            Path('path/to/third_hi-45_l3e'),
-            Path('path/to/third_ultra_l3e_hf'),
-            Path('path/to/fourth_lo_l3e'),
-            Path('path/to/fourth_hi-90_l3e'),
-            Path('path/to/fourth_hi-45_l3e'),
-            Path('path/to/fourth_ultra_l3e'),
+            Path("path/to/first_lo_l3e"),
+            Path("path/to/first_hi-45_l3e"),
+            Path("path/to/first_ultra_l3e"),
+            Path("path/to/first_ultra_l3e_hf"),
+            Path("path/to/second_hi-90_l3e"),
+            Path("path/to/second_hi-45_l3e"),
+            Path("path/to/second_ultra_l3e"),
+            Path("path/to/second_ultra_l3e_hf"),
+            Path("path/to/third_lo_l3e"),
+            Path("path/to/third_hi-90_l3e"),
+            Path("path/to/third_hi-45_l3e"),
+            Path("path/to/third_ultra_l3e_hf"),
+            Path("path/to/fourth_lo_l3e"),
+            Path("path/to/fourth_hi-90_l3e"),
+            Path("path/to/fourth_hi-45_l3e"),
+            Path("path/to/fourth_ultra_l3e"),
         ]
 
         start_epoch_1 = datetime(2020, 1, 1)
@@ -1783,7 +2645,11 @@ def test_process_l3e_skips_repointing_on_exception(self, mock_determine_spacecra
             (sentinel.spacecraft_info24, GlowsL3Flags.NONE, []),
             (sentinel.spacecraft_info25, GlowsL3Flags.NONE, []),
             (sentinel.spacecraft_info26, GlowsL3Flags.NONE, []),
-            (sentinel.spacecraft_info27, GlowsL3Flags.PREDICTIVE_EPHEMERIS, ["predict"]),
+            (
+                sentinel.spacecraft_info27,
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                ["predict"],
+            ),
         ]
 
         mock_dependencies = Mock()
@@ -1801,11 +2667,36 @@ def test_process_l3e_skips_repointing_on_exception(self, mock_determine_spacecra
             dependencies=mock_dependencies,
             repointings=GlowsL3eVersionsForRepointings(
                 repointing_numbers=[24, 25, 26, 27],
-                hi_90_repointings={24: Version(None, 1), 25: Version(None, 1), 26: Version(None, 1), 27: Version(None, 1)},
-                hi_45_repointings={24: Version(None, 2), 25: Version(None, 2), 26: Version(None, 2), 27: Version(None, 2)},
-                lo_repointings={24: Version(None, 3), 25: Version(None, 3), 26: Version(None, 3), 27: Version(None, 3)},
-                ultra_hf_repointings={24: Version(None, 4), 25: Version(None, 4), 26: Version(None, 4), 27: Version(None, 4)},
-                ultra_sf_repointings={24: Version(None, 4), 25: Version(None, 4), 26: Version(None, 4), 27: Version(None, 4)},
+                hi_90_repointings={
+                    24: Version(None, 1),
+                    25: Version(None, 1),
+                    26: Version(None, 1),
+                    27: Version(None, 1),
+                },
+                hi_45_repointings={
+                    24: Version(None, 2),
+                    25: Version(None, 2),
+                    26: Version(None, 2),
+                    27: Version(None, 2),
+                },
+                lo_repointings={
+                    24: Version(None, 3),
+                    25: Version(None, 3),
+                    26: Version(None, 3),
+                    27: Version(None, 3),
+                },
+                ultra_hf_repointings={
+                    24: Version(None, 4),
+                    25: Version(None, 4),
+                    26: Version(None, 4),
+                    27: Version(None, 4),
+                },
+                ultra_sf_repointings={
+                    24: Version(None, 4),
+                    25: Version(None, 4),
+                    26: Version(None, 4),
+                    27: Version(None, 4),
+                },
             ),
             l3d_cdf_path=Path("path/to/l3d.cdf"),
             metakernel_with_predict_ephem=metakernel_with_predict_ephem,
@@ -1814,38 +2705,220 @@ def test_process_l3e_skips_repointing_on_exception(self, mock_determine_spacecra
 
         actual_l3e_products = process_l3e(initializer_data)
 
-        mock_get_pointing_date_range.assert_has_calls([call(24), call(25), call(26), call(27)])
-
-        mock_process_hi.assert_has_calls([
-            call(hi_parents, 24, start_epoch_1, epoch_delta_1, 90, Version(None, 1), 0, sentinel.spacecraft_info24),
-            call(hi_parents, 24, start_epoch_1, epoch_delta_1, 135, Version(None, 2), 0, sentinel.spacecraft_info24),
-            call(hi_parents, 25, start_epoch_2, epoch_delta_2, 90, Version(None, 1), 0, sentinel.spacecraft_info25),
-            call(hi_parents, 25, start_epoch_2, epoch_delta_2, 135, Version(None, 2), 0, sentinel.spacecraft_info25),
-            call(hi_parents, 26, start_epoch_3, epoch_delta_3, 90, Version(None, 1), 0, sentinel.spacecraft_info26),
-            call(hi_parents, 26, start_epoch_3, epoch_delta_3, 135, Version(None, 2), 0, sentinel.spacecraft_info26),
-            call(hi_parents + ["predict"], 27, start_epoch_4, epoch_delta_4, 90, Version(None, 1), GlowsL3Flags.PREDICTIVE_EPHEMERIS, sentinel.spacecraft_info27),
-            call(hi_parents + ["predict"], 27, start_epoch_4, epoch_delta_4, 135, Version(None, 2), GlowsL3Flags.PREDICTIVE_EPHEMERIS, sentinel.spacecraft_info27),
-        ])
-        mock_process_lo.assert_has_calls([
-            call(lo_parents + ["l1b_nhk_24"], 24, start_epoch_1, epoch_delta_1, 124, Version(None, 3), 0, sentinel.spacecraft_info24),
-            call(lo_parents + ["l1b_nhk_25"], 25, start_epoch_2, epoch_delta_2, 125, Version(None, 3), 0, sentinel.spacecraft_info25),
-            call(lo_parents + ["l1b_nhk_26"], 26, start_epoch_3, epoch_delta_3, 126, Version(None, 3), 0, sentinel.spacecraft_info26),
-            call(lo_parents + ["predict"], 27, start_epoch_4, epoch_delta_4, 90, Version(None, 3), GlowsL3Flags.PREDICTIVE_EPHEMERIS, sentinel.spacecraft_info27),
-        ])
-
-        mock_process_ultra_sf.assert_has_calls([
-            call(ultra_parents, 24, start_epoch_1, epoch_delta_1, Version(None, 4), 0, sentinel.spacecraft_info24),
-            call(ultra_parents, 25, start_epoch_2, epoch_delta_2, Version(None, 4), 0, sentinel.spacecraft_info25),
-            call(ultra_parents, 26, start_epoch_3, epoch_delta_3, Version(None, 4), 0, sentinel.spacecraft_info26),
-            call(ultra_parents + ["predict"], 27, start_epoch_4, epoch_delta_4, Version(None, 4), GlowsL3Flags.PREDICTIVE_EPHEMERIS, sentinel.spacecraft_info27),
-        ])
-
-        mock_process_ultra_hf.assert_has_calls([
-            call(ultra_parents, 24, start_epoch_1, epoch_delta_1, Version(None, 4), 0, sentinel.spacecraft_info24),
-            call(ultra_parents, 25, start_epoch_2, epoch_delta_2, Version(None, 4), 0, sentinel.spacecraft_info25),
-            call(ultra_parents, 26, start_epoch_3, epoch_delta_3, Version(None, 4), 0, sentinel.spacecraft_info26),
-            call(ultra_parents + ["predict"], 27, start_epoch_4, epoch_delta_4, Version(None, 4), GlowsL3Flags.PREDICTIVE_EPHEMERIS, sentinel.spacecraft_info27),
-        ])
+        mock_get_pointing_date_range.assert_has_calls(
+            [call(24), call(25), call(26), call(27)]
+        )
+
+        mock_process_hi.assert_has_calls(
+            [
+                call(
+                    hi_parents,
+                    24,
+                    start_epoch_1,
+                    epoch_delta_1,
+                    90,
+                    Version(None, 1),
+                    0,
+                    sentinel.spacecraft_info24,
+                ),
+                call(
+                    hi_parents,
+                    24,
+                    start_epoch_1,
+                    epoch_delta_1,
+                    135,
+                    Version(None, 2),
+                    0,
+                    sentinel.spacecraft_info24,
+                ),
+                call(
+                    hi_parents,
+                    25,
+                    start_epoch_2,
+                    epoch_delta_2,
+                    90,
+                    Version(None, 1),
+                    0,
+                    sentinel.spacecraft_info25,
+                ),
+                call(
+                    hi_parents,
+                    25,
+                    start_epoch_2,
+                    epoch_delta_2,
+                    135,
+                    Version(None, 2),
+                    0,
+                    sentinel.spacecraft_info25,
+                ),
+                call(
+                    hi_parents,
+                    26,
+                    start_epoch_3,
+                    epoch_delta_3,
+                    90,
+                    Version(None, 1),
+                    0,
+                    sentinel.spacecraft_info26,
+                ),
+                call(
+                    hi_parents,
+                    26,
+                    start_epoch_3,
+                    epoch_delta_3,
+                    135,
+                    Version(None, 2),
+                    0,
+                    sentinel.spacecraft_info26,
+                ),
+                call(
+                    hi_parents + ["predict"],
+                    27,
+                    start_epoch_4,
+                    epoch_delta_4,
+                    90,
+                    Version(None, 1),
+                    GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                    sentinel.spacecraft_info27,
+                ),
+                call(
+                    hi_parents + ["predict"],
+                    27,
+                    start_epoch_4,
+                    epoch_delta_4,
+                    135,
+                    Version(None, 2),
+                    GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                    sentinel.spacecraft_info27,
+                ),
+            ]
+        )
+        mock_process_lo.assert_has_calls(
+            [
+                call(
+                    lo_parents + ["l1b_nhk_24"],
+                    24,
+                    start_epoch_1,
+                    epoch_delta_1,
+                    124,
+                    Version(None, 3),
+                    0,
+                    sentinel.spacecraft_info24,
+                ),
+                call(
+                    lo_parents + ["l1b_nhk_25"],
+                    25,
+                    start_epoch_2,
+                    epoch_delta_2,
+                    125,
+                    Version(None, 3),
+                    0,
+                    sentinel.spacecraft_info25,
+                ),
+                call(
+                    lo_parents + ["l1b_nhk_26"],
+                    26,
+                    start_epoch_3,
+                    epoch_delta_3,
+                    126,
+                    Version(None, 3),
+                    0,
+                    sentinel.spacecraft_info26,
+                ),
+                call(
+                    lo_parents + ["predict"],
+                    27,
+                    start_epoch_4,
+                    epoch_delta_4,
+                    90,
+                    Version(None, 3),
+                    GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                    sentinel.spacecraft_info27,
+                ),
+            ]
+        )
+
+        mock_process_ultra_sf.assert_has_calls(
+            [
+                call(
+                    ultra_parents,
+                    24,
+                    start_epoch_1,
+                    epoch_delta_1,
+                    Version(None, 4),
+                    0,
+                    sentinel.spacecraft_info24,
+                ),
+                call(
+                    ultra_parents,
+                    25,
+                    start_epoch_2,
+                    epoch_delta_2,
+                    Version(None, 4),
+                    0,
+                    sentinel.spacecraft_info25,
+                ),
+                call(
+                    ultra_parents,
+                    26,
+                    start_epoch_3,
+                    epoch_delta_3,
+                    Version(None, 4),
+                    0,
+                    sentinel.spacecraft_info26,
+                ),
+                call(
+                    ultra_parents + ["predict"],
+                    27,
+                    start_epoch_4,
+                    epoch_delta_4,
+                    Version(None, 4),
+                    GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                    sentinel.spacecraft_info27,
+                ),
+            ]
+        )
+
+        mock_process_ultra_hf.assert_has_calls(
+            [
+                call(
+                    ultra_parents,
+                    24,
+                    start_epoch_1,
+                    epoch_delta_1,
+                    Version(None, 4),
+                    0,
+                    sentinel.spacecraft_info24,
+                ),
+                call(
+                    ultra_parents,
+                    25,
+                    start_epoch_2,
+                    epoch_delta_2,
+                    Version(None, 4),
+                    0,
+                    sentinel.spacecraft_info25,
+                ),
+                call(
+                    ultra_parents,
+                    26,
+                    start_epoch_3,
+                    epoch_delta_3,
+                    Version(None, 4),
+                    0,
+                    sentinel.spacecraft_info26,
+                ),
+                call(
+                    ultra_parents + ["predict"],
+                    27,
+                    start_epoch_4,
+                    epoch_delta_4,
+                    Version(None, 4),
+                    GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                    sentinel.spacecraft_info27,
+                ),
+            ]
+        )
 
         self.assertEqual(expected_l3e_products, actual_l3e_products)
         mock_get_lo_pivot_angles.assert_called_once_with([24, 25, 26, 27])
@@ -1854,7 +2927,9 @@ def test_process_l3e_skips_repointing_on_exception(self, mock_determine_spacecra
     @patch("imap_l3_processing.glows.glows_processor.ZipFile")
     def test_archive_dependencies(self, mock_zip, mock_json):
         version_number = Version(1, 1)
-        expected_filepath = TEMP_CDF_FOLDER_PATH / f"imap_glows_l3b-archive_20250314_v001.zip"
+        expected_filepath = (
+            TEMP_CDF_FOLDER_PATH / "imap_glows_l3b-archive_20250314_v001.zip"
+        )
         expected_json_filename = "cr_to_process.json"
 
         l3bc_dependencies = GlowsL3BCDependencies(
@@ -1862,16 +2937,18 @@ def test_archive_dependencies(self, mock_zip, mock_json):
             carrington_rotation_number=2095,
             start_date=datetime.fromisoformat("2025-03-14 12:34:56.789"),
             end_date=datetime.fromisoformat("2025-03-24 12:34:56.789"),
-            l3a_data=[{"filename": "imap_glows_l3a_hist_20250314-repoint000001_v001.cdf"},
-                      {"filename": "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf"}],
+            l3a_data=[
+                {"filename": "imap_glows_l3a_hist_20250314-repoint000001_v001.cdf"},
+                {"filename": "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf"},
+            ],
             external_files={},
             ancillary_files={
-                'uv_anisotropy': Path("uv_anisotropy.dat"),
-                'WawHelioIonMP_parameters': Path("waw_helio_ion.dat"),
-                'bad_days_list': Path("bad_days_list.dat"),
-                'pipeline_settings': Path("pipeline_settings.json"),
+                "uv_anisotropy": Path("uv_anisotropy.dat"),
+                "WawHelioIonMP_parameters": Path("waw_helio_ion.dat"),
+                "bad_days_list": Path("bad_days_list.dat"),
+                "pipeline_settings": Path("pipeline_settings.json"),
             },
-            repointing_file_path=Path("repointing.csv")
+            repointing_file_path=Path("repointing.csv"),
         )
 
         external_dependencies = ExternalDependencies(
@@ -1880,22 +2957,27 @@ def test_archive_dependencies(self, mock_zip, mock_json):
             omni2_data_path=Path("omni2_data_path"),
         )
 
-        expected_json_to_serialize = {"cr_rotation_number": 2095,
-                                      "l3a_paths": ["imap_glows_l3a_hist_20250314-repoint000001_v001.cdf",
-                                                    "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf"],
-                                      "cr_start_date": "2025-03-14 12:34:56.789000",
-                                      "cr_end_date": "2025-03-24 12:34:56.789000",
-                                      "bad_days_list": "bad_days_list.dat",
-                                      "pipeline_settings": "pipeline_settings.json",
-                                      "waw_helioion_mp": "waw_helio_ion.dat",
-                                      "uv_anisotropy": "uv_anisotropy.dat",
-                                      "repointing_file": "repointing.csv",
-                                      }
+        expected_json_to_serialize = {
+            "cr_rotation_number": 2095,
+            "l3a_paths": [
+                "imap_glows_l3a_hist_20250314-repoint000001_v001.cdf",
+                "imap_glows_l3a_hist_20100102-repoint000002_v001.cdf",
+            ],
+            "cr_start_date": "2025-03-14 12:34:56.789000",
+            "cr_end_date": "2025-03-24 12:34:56.789000",
+            "bad_days_list": "bad_days_list.dat",
+            "pipeline_settings": "pipeline_settings.json",
+            "waw_helioion_mp": "waw_helio_ion.dat",
+            "uv_anisotropy": "uv_anisotropy.dat",
+            "repointing_file": "repointing.csv",
+        }
 
         mock_zip_file = MagicMock()
         mock_zip.return_value.__enter__.return_value = mock_zip_file
 
-        actual_zip_file_name = GlowsProcessor.archive_dependencies(l3bc_dependencies, external_dependencies)
+        actual_zip_file_name = GlowsProcessor.archive_dependencies(
+            l3bc_dependencies, external_dependencies
+        )
 
         self.assertEqual(expected_filepath, actual_zip_file_name)
 
@@ -1903,14 +2985,17 @@ def test_archive_dependencies(self, mock_zip, mock_json):
 
         mock_json.dumps.assert_called_once_with(expected_json_to_serialize)
 
-        mock_zip_file.write.assert_has_calls([
-            call(Path("lyman_alpha_path"), "lyman_alpha_composite.nc"),
-            call(Path("omni2_data_path"), "omni2_all_years.dat"),
-            call(Path("f107_index_file_path"), "f107_fluxtable.txt"),
-        ])
-        mock_zip_file.writestr.assert_called_once_with(expected_json_filename, mock_json.dumps.return_value)
-
+        mock_zip_file.write.assert_has_calls(
+            [
+                call(Path("lyman_alpha_path"), "lyman_alpha_composite.nc"),
+                call(Path("omni2_data_path"), "omni2_all_years.dat"),
+                call(Path("f107_index_file_path"), "f107_fluxtable.txt"),
+            ]
+        )
+        mock_zip_file.writestr.assert_called_once_with(
+            expected_json_filename, mock_json.dumps.return_value
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/hi/test_hi_combined_dependencies.py b/tests/hi/test_hi_combined_dependencies.py
index f701d0d6e..43c2b87e2 100644
--- a/tests/hi/test_hi_combined_dependencies.py
+++ b/tests/hi/test_hi_combined_dependencies.py
@@ -1,14 +1,18 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, call, sentinel
+from unittest.mock import call, patch, sentinel
 
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.hi.hi_combined_sensor_dependencies import HiL3CombinedMapDependencies
+from imap_l3_processing.hi.hi_combined_sensor_dependencies import (
+    HiL3CombinedMapDependencies,
+)
 
 
 class TestHiCombinedDependencies(unittest.TestCase):
-    @patch("imap_l3_processing.hi.hi_combined_sensor_dependencies.RectangularIntensityMapData.read_from_path")
+    @patch(
+        "imap_l3_processing.hi.hi_combined_sensor_dependencies.RectangularIntensityMapData.read_from_path"
+    )
     def test_from_file_paths(self, read_cdf):
         hi_map_paths = [
             Path("imap_hi_l3_h45-ena-h-hf-sp-ram-hae-4deg-6mo_20251022_v001.cdf"),
@@ -21,15 +25,19 @@ def test_from_file_paths(self, read_cdf):
             Path("imap_hi_l3_h90-ena-h-hf-sp-anti-hae-4deg-6mo_20250422_v001.cdf"),
         ]
 
-        expected_45_return_maps = [sentinel.read_data45_1,
-                                   sentinel.read_data45_2,
-                                   sentinel.read_data45_3,
-                                   sentinel.read_data45_4]
+        expected_45_return_maps = [
+            sentinel.read_data45_1,
+            sentinel.read_data45_2,
+            sentinel.read_data45_3,
+            sentinel.read_data45_4,
+        ]
 
-        expected_90_return_maps = [sentinel.read_data90_1,
-                                   sentinel.read_data90_2,
-                                   sentinel.read_data90_3,
-                                   sentinel.read_data90_4]
+        expected_90_return_maps = [
+            sentinel.read_data90_1,
+            sentinel.read_data90_2,
+            sentinel.read_data90_3,
+            sentinel.read_data90_4,
+        ]
 
         read_cdf.side_effect = expected_45_return_maps + expected_90_return_maps
 
@@ -41,8 +49,12 @@ def test_from_file_paths(self, read_cdf):
         self.assertEqual(expected_45_return_maps, result.h45_maps)
         self.assertEqual(expected_90_return_maps, result.h90_maps)
 
-    @patch("imap_l3_processing.hi.hi_combined_sensor_dependencies.imap_data_access.download")
-    @patch("imap_l3_processing.hi.hi_combined_sensor_dependencies.RectangularIntensityMapData.read_from_path")
+    @patch(
+        "imap_l3_processing.hi.hi_combined_sensor_dependencies.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.hi.hi_combined_sensor_dependencies.RectangularIntensityMapData.read_from_path"
+    )
     def test_fetch_dependencies(self, read_cdf, mock_download):
         file_name1 = "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-4deg-6mo_20251022_v001.cdf"
         file_name2 = "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-4deg-6mo_20251022_v001.cdf"
@@ -53,7 +65,16 @@ def test_fetch_dependencies(self, read_cdf, mock_download):
         file_name7 = "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-4deg-6mo_20250422_v001.cdf"
         file_name8 = "imap_hi_l3_h90-ena-h-hf-sp-anti-hae-4deg-6mo_20250422_v001.cdf"
 
-        file_names = [file_name1, file_name2, file_name3, file_name4, file_name5, file_name6, file_name7, file_name8]
+        file_names = [
+            file_name1,
+            file_name2,
+            file_name3,
+            file_name4,
+            file_name5,
+            file_name6,
+            file_name7,
+            file_name8,
+        ]
         processing_input = ProcessingInputCollection(
             ScienceInput(file_name1),
             ScienceInput(file_name2),
@@ -97,7 +118,21 @@ def test_fetch_dependencies(self, read_cdf, mock_download):
         self.assertEqual(read_cdf.call_count, 8)
         self.assertEqual(mock_download.call_count, 8)
 
-        self.assertEqual([sentinel.read_data1, sentinel.read_data2, sentinel.read_data3, sentinel.read_data4],
-                         result.h45_maps)
-        self.assertEqual([sentinel.read_data5, sentinel.read_data6, sentinel.read_data7, sentinel.read_data8],
-                         result.h90_maps)
+        self.assertEqual(
+            [
+                sentinel.read_data1,
+                sentinel.read_data2,
+                sentinel.read_data3,
+                sentinel.read_data4,
+            ],
+            result.h45_maps,
+        )
+        self.assertEqual(
+            [
+                sentinel.read_data5,
+                sentinel.read_data6,
+                sentinel.read_data7,
+                sentinel.read_data8,
+            ],
+            result.h90_maps,
+        )
diff --git a/tests/hi/test_hi_combined_initializer.py b/tests/hi/test_hi_combined_initializer.py
index 970f5d0bd..5935ef924 100644
--- a/tests/hi/test_hi_combined_initializer.py
+++ b/tests/hi/test_hi_combined_initializer.py
@@ -1,6 +1,6 @@
 import unittest
 from datetime import datetime
-from unittest.mock import patch, Mock, call
+from unittest.mock import Mock, call, patch
 
 from imap_data_access.file_validation import Version
 
@@ -10,392 +10,451 @@
 from tests.test_helpers import create_mock_query_results
 
 l3_sp_input_maps = [
-    'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-    'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
-    'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-    'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
+    "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+    "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
+    "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+    "imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
 ]
 
 l2_nsp_input_maps = [
-    'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf',
-    'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf',
-    'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf',
-    'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf',
+    "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf",
+    "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf",
+    "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf",
+    "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf",
 ]
 
 
 class TestHiCombinedInitializer(unittest.TestCase):
     def setUp(self):
         self.hi_combined_initializer_query_patcher = patch(
-            'imap_l3_processing.hi.hi_combined_initializer.imap_data_access.query')
-        self.hi_combined_initializer_mock_query = self.hi_combined_initializer_query_patcher.start()
+            "imap_l3_processing.hi.hi_combined_initializer.imap_data_access.query"
+        )
+        self.hi_combined_initializer_mock_query = (
+            self.hi_combined_initializer_query_patcher.start()
+        )
 
     def teardown(self):
         self.hi_combined_initializer_mock_query.stop()
 
     def test_get_maps_that_should_be_produced_no_existing_combined_sp(self):
         for deg in ["6deg", "4deg"]:
-            descriptor = f'hic-ena-h-hf-sp-full-hae-{deg}-1yr'
+            descriptor = f"hic-ena-h-hf-sp-full-hae-{deg}-1yr"
 
             for input_major_version in [None, 2]:
-                expected_version_map = VersionMap({descriptor: Version(input_major_version, 1)})
+                expected_version_map = VersionMap(
+                    {descriptor: Version(input_major_version, 1)}
+                )
 
                 with self.subTest(deg=deg, major_version=input_major_version):
                     self.hi_combined_initializer_mock_query.side_effect = [
-                        create_mock_query_results([
-                            f'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                            f'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf',
-
-                            f'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                            f'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf',
-                        ]),
-                        create_mock_query_results([])
+                        create_mock_query_results(
+                            [
+                                f"imap_hi_l3_h45-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                                f"imap_hi_l3_h45-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf",
+                                f"imap_hi_l3_h90-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                                f"imap_hi_l3_h90-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf",
+                            ]
+                        ),
+                        create_mock_query_results([]),
                     ]
 
                     expected_maps_to_produce = [
                         PossibleMapToProduce(
                             input_files={
-                                f'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                                f'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf',
-
-                                f'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                                f'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf',
+                                f"imap_hi_l3_h45-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                                f"imap_hi_l3_h45-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf",
+                                f"imap_hi_l3_h90-ena-h-hf-sp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                                f"imap_hi_l3_h90-ena-h-hf-sp-anti-hae-{deg}-1yr_20250101_v001.cdf",
                             },
                             input_metadata=InputMetadata(
-                                instrument='hi',
-                                data_level='l3',
+                                instrument="hi",
+                                data_level="l3",
                                 start_date=datetime(2025, 1, 1),
                                 end_date=datetime(2026, 1, 1),
                                 version=expected_version_map,
-                                descriptor=descriptor
-                            )
+                                descriptor=descriptor,
+                            ),
                         )
                     ]
 
                     initializer = HiCombinedInitializer()
-                    actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
-                        descriptor, input_major_version)
+                    actual_maps_to_produce = (
+                        initializer.get_maps_that_should_be_produced(
+                            descriptor, input_major_version
+                        )
+                    )
 
-                    self.hi_combined_initializer_mock_query.assert_has_calls([
-                        call(instrument='hi', data_level='l3'),
-                        call(instrument='hi', data_level='l2')
-                    ])
+                    self.hi_combined_initializer_mock_query.assert_has_calls(
+                        [
+                            call(instrument="hi", data_level="l3"),
+                            call(instrument="hi", data_level="l2"),
+                        ]
+                    )
 
                     self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_new_major_version(self, mock_read_cdf_parents: Mock):
-        mock_read_cdf_parents.return_value = {
-            *l3_sp_input_maps
-        }
-
-        existing_file_without_major_number = f'imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.cdf'
-        existing_file_with_lower_major_number = f'imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.0001.cdf'
-        for existing_file in [existing_file_without_major_number, existing_file_with_lower_major_number]:
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_new_major_version(
+        self, mock_read_cdf_parents: Mock
+    ):
+        mock_read_cdf_parents.return_value = {*l3_sp_input_maps}
+
+        existing_file_without_major_number = (
+            "imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.cdf"
+        )
+        existing_file_with_lower_major_number = (
+            "imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.0001.cdf"
+        )
+        for existing_file in [
+            existing_file_without_major_number,
+            existing_file_with_lower_major_number,
+        ]:
             with self.subTest(existing_file=existing_file):
                 mock_read_cdf_parents.reset_mock()
                 self.hi_combined_initializer_mock_query.reset_mock()
 
                 self.hi_combined_initializer_mock_query.side_effect = [
-                    create_mock_query_results([
-                        f'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                        f'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
-                        f'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                        f'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
-                        existing_file,
-                    ]),
-                    create_mock_query_results([])
+                    create_mock_query_results(
+                        [
+                            "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
+                            existing_file,
+                        ]
+                    ),
+                    create_mock_query_results([]),
                 ]
-                descriptor = f'hic-ena-h-hf-sp-full-hae-6deg-1yr'
+                descriptor = "hic-ena-h-hf-sp-full-hae-6deg-1yr"
 
                 expected_maps_to_produce = [
                     PossibleMapToProduce(
                         input_files={
-                            f'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                            f'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
-
-                            f'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                            f'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
+                            "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
                         },
                         input_metadata=InputMetadata(
-                            instrument='hi',
-                            data_level='l3',
+                            instrument="hi",
+                            data_level="l3",
                             start_date=datetime(2025, 1, 1),
                             end_date=datetime(2026, 1, 1),
                             version=VersionMap({descriptor: Version(2, 2)}),
-                            descriptor=descriptor
-                        )
+                            descriptor=descriptor,
+                        ),
                     )
                 ]
 
                 initializer = HiCombinedInitializer()
                 actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
-                    f'hic-ena-h-hf-sp-full-hae-6deg-1yr', 2)
+                    "hic-ena-h-hf-sp-full-hae-6deg-1yr", 2
+                )
 
                 mock_read_cdf_parents.assert_called_once_with(existing_file)
-                self.hi_combined_initializer_mock_query.assert_has_calls([
-                    call(instrument='hi', data_level='l3'),
-                    call(instrument='hi', data_level='l2')
-                ])
+                self.hi_combined_initializer_mock_query.assert_has_calls(
+                    [
+                        call(instrument="hi", data_level="l3"),
+                        call(instrument="hi", data_level="l2"),
+                    ]
+                )
 
                 self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
     def test_get_maps_that_should_be_produced_no_existing_combined_nsp(self):
         for deg in ["6deg", "4deg"]:
-            descriptor = f'hic-ena-h-hf-nsp-full-hae-{deg}-1yr'
+            descriptor = f"hic-ena-h-hf-nsp-full-hae-{deg}-1yr"
             with self.subTest(msg=descriptor):
                 self.hi_combined_initializer_mock_query.side_effect = [
                     create_mock_query_results([]),
-                    create_mock_query_results([
-                        f'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                        f'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf',
-
-                        f'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                        f'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf',
-                    ])
+                    create_mock_query_results(
+                        [
+                            f"imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                            f"imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf",
+                            f"imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                            f"imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf",
+                        ]
+                    ),
                 ]
 
                 expected_maps_to_produce = [
                     PossibleMapToProduce(
                         input_files={
-                            f'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                            f'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf',
-
-                            f'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf',
-                            f'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf',
+                            f"imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                            f"imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf",
+                            f"imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-{deg}-1yr_20250101_v001.cdf",
+                            f"imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-{deg}-1yr_20250101_v001.cdf",
                         },
                         input_metadata=InputMetadata(
-                            instrument='hi',
-                            data_level='l3',
+                            instrument="hi",
+                            data_level="l3",
                             start_date=datetime(2025, 1, 1),
                             end_date=datetime(2026, 1, 1),
                             version=VersionMap({descriptor: Version(None, 1)}),
-                            descriptor=descriptor
-                        )
+                            descriptor=descriptor,
+                        ),
                     )
                 ]
 
                 initializer = HiCombinedInitializer()
                 actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
-                    descriptor, None)
+                    descriptor, None
+                )
 
-                self.hi_combined_initializer_mock_query.assert_has_calls([
-                    call(instrument='hi', data_level='l3'),
-                    call(instrument='hi', data_level='l2')
-                ])
+                self.hi_combined_initializer_mock_query.assert_has_calls(
+                    [
+                        call(instrument="hi", data_level="l3"),
+                        call(instrument="hi", data_level="l2"),
+                    ]
+                )
 
                 self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_waits_for_full_set_of_new_inputs(self,
-                                                                               mock_read_cdf_parents: Mock):
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_waits_for_full_set_of_new_inputs(
+        self, mock_read_cdf_parents: Mock
+    ):
         self.hi_combined_initializer_mock_query.side_effect = [
-            create_mock_query_results([
-                *l3_sp_input_maps,
-                'imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.cdf',
-
-                'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20260101_v002.cdf',
-                'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20260101_v002.cdf',
-                'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20260101_v002.cdf',
-            ]),
-            create_mock_query_results([])
+            create_mock_query_results(
+                [
+                    *l3_sp_input_maps,
+                    "imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.cdf",
+                    "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20260101_v002.cdf",
+                    "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20260101_v002.cdf",
+                    "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20260101_v002.cdf",
+                ]
+            ),
+            create_mock_query_results([]),
         ]
 
-        mock_read_cdf_parents.return_value = {
-            *l3_sp_input_maps
-        }
+        mock_read_cdf_parents.return_value = {*l3_sp_input_maps}
 
         expected_maps_to_produce = []
 
         initializer = HiCombinedInitializer()
-        actual_maps_to_produce = initializer.get_maps_that_should_be_produced('hic-ena-h-hf-sp-full-hae-6deg-1yr', None)
+        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+            "hic-ena-h-hf-sp-full-hae-6deg-1yr", None
+        )
 
-        self.hi_combined_initializer_mock_query.assert_has_calls([
-            call(instrument='hi', data_level='l3'),
-            call(instrument='hi', data_level='l2')
-        ])
+        self.hi_combined_initializer_mock_query.assert_has_calls(
+            [
+                call(instrument="hi", data_level="l3"),
+                call(instrument="hi", data_level="l2"),
+            ]
+        )
 
-        mock_read_cdf_parents.assert_called_once_with('imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.cdf')
+        mock_read_cdf_parents.assert_called_once_with(
+            "imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_v001.cdf"
+        )
 
         self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
     def test_get_maps_that_should_be_produced_filters_based_on_input_descriptor(self):
         self.hi_combined_initializer_mock_query.side_effect = [
-            create_mock_query_results([
-                *l3_sp_input_maps,
-            ]),
-            create_mock_query_results([])
+            create_mock_query_results(
+                [
+                    *l3_sp_input_maps,
+                ]
+            ),
+            create_mock_query_results([]),
         ]
 
         expected_maps_to_produce = []
 
         initializer = HiCombinedInitializer()
-        actual_maps_to_produce = initializer.get_maps_that_should_be_produced('hic-ena-h-hf-nsp-full-hae-6deg-1yr',
-                                                                              None)
+        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+            "hic-ena-h-hf-nsp-full-hae-6deg-1yr", None
+        )
 
-        self.hi_combined_initializer_mock_query.assert_has_calls([
-            call(instrument='hi', data_level='l3'),
-            call(instrument='hi', data_level='l2')
-        ])
+        self.hi_combined_initializer_mock_query.assert_has_calls(
+            [
+                call(instrument="hi", data_level="l3"),
+                call(instrument="hi", data_level="l2"),
+            ]
+        )
 
         self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_increments_versions_correctly(self, mock_read_cdf_parents):
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_increments_versions_correctly(
+        self, mock_read_cdf_parents
+    ):
         cases = [(None, "v001", 2), (2, "v002.0001", 2), (2, "v002.0002", 3)]
 
         for input_major_version, existing_file_version, expected_minor_version in cases:
-            with self.subTest(input_major_version=input_major_version, existing_file_version=existing_file_version, expected_minor_version=expected_minor_version):
+            with self.subTest(
+                input_major_version=input_major_version,
+                existing_file_version=existing_file_version,
+                expected_minor_version=expected_minor_version,
+            ):
                 self.hi_combined_initializer_mock_query.reset_mock()
                 mock_read_cdf_parents.reset_mock()
                 self.hi_combined_initializer_mock_query.reset_mock()
                 mock_read_cdf_parents.reset_mock()
 
                 self.hi_combined_initializer_mock_query.side_effect = [
-                    create_mock_query_results([
-                        f'imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_{existing_file_version}.cdf',
-
-                        'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v002.cdf',
-                        *l3_sp_input_maps,
-                    ]),
-                    create_mock_query_results([])
+                    create_mock_query_results(
+                        [
+                            f"imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_{existing_file_version}.cdf",
+                            "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v002.cdf",
+                            *l3_sp_input_maps,
+                        ]
+                    ),
+                    create_mock_query_results([]),
                 ]
 
-                descriptor = 'hic-ena-h-hf-sp-full-hae-6deg-1yr'
+                descriptor = "hic-ena-h-hf-sp-full-hae-6deg-1yr"
                 expected_maps_to_produce = [
                     PossibleMapToProduce(
                         input_files={
-                            'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                            'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                            'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf',
-
-                            'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v002.cdf',
-
+                            "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v001.cdf",
+                            "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250101_v002.cdf",
                         },
                         input_metadata=InputMetadata(
-                            instrument='hi',
-                            data_level='l3',
+                            instrument="hi",
+                            data_level="l3",
                             start_date=datetime(2025, 1, 1),
                             end_date=datetime(2026, 1, 1),
-                            version=VersionMap({descriptor: Version(input_major_version, expected_minor_version)}),
-                            descriptor=descriptor
-                        )
+                            version=VersionMap(
+                                {
+                                    descriptor: Version(
+                                        input_major_version, expected_minor_version
+                                    )
+                                }
+                            ),
+                            descriptor=descriptor,
+                        ),
                     )
                 ]
 
-                mock_read_cdf_parents.return_value = [
-                    *l3_sp_input_maps
-                ]
+                mock_read_cdf_parents.return_value = [*l3_sp_input_maps]
 
                 initializer = HiCombinedInitializer()
-                actual_maps_to_produce = initializer.get_maps_that_should_be_produced(descriptor, input_major_version)
-                mock_read_cdf_parents.assert_called_once_with(f'imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_{existing_file_version}.cdf')
+                actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+                    descriptor, input_major_version
+                )
+                mock_read_cdf_parents.assert_called_once_with(
+                    f"imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250101_{existing_file_version}.cdf"
+                )
 
-                self.hi_combined_initializer_mock_query.assert_has_calls([
-                    call(instrument='hi', data_level='l3'),
-                    call(instrument='hi', data_level='l2')
-                ])
+                self.hi_combined_initializer_mock_query.assert_has_calls(
+                    [
+                        call(instrument="hi", data_level="l3"),
+                        call(instrument="hi", data_level="l2"),
+                    ]
+                )
 
                 self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_can_produce_multiple_maps(self, mock_read_cdf_parents):
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_can_produce_multiple_maps(
+        self, mock_read_cdf_parents
+    ):
         self.hi_combined_initializer_mock_query.side_effect = [
-            create_mock_query_results([
-            ]),
-            create_mock_query_results([
-                *l2_nsp_input_maps,
-                'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
-            ]),
+            create_mock_query_results([]),
+            create_mock_query_results(
+                [
+                    *l2_nsp_input_maps,
+                    "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
+                ]
+            ),
         ]
 
-        descriptor = 'hic-ena-h-hf-nsp-full-hae-6deg-1yr'
+        descriptor = "hic-ena-h-hf-nsp-full-hae-6deg-1yr"
         expected_maps_to_produce = [
             PossibleMapToProduce(
                 input_files={
-                    'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                    'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf',
-
-                    'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf',
-                    'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf',
+                    "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                    "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20250101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20250101_v001.cdf",
                 },
                 input_metadata=InputMetadata(
-                    instrument='hi',
-                    data_level='l3',
+                    instrument="hi",
+                    data_level="l3",
                     start_date=datetime(2025, 1, 1),
                     end_date=datetime(2026, 1, 1),
                     version=VersionMap({descriptor: Version(None, 1)}),
-                    descriptor=descriptor
-                )
+                    descriptor=descriptor,
+                ),
             ),
             PossibleMapToProduce(
                 input_files={
-                    'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                    'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
-
-                    'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                    'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
+                    "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
                 },
                 input_metadata=InputMetadata(
-                    instrument='hi',
-                    data_level='l3',
+                    instrument="hi",
+                    data_level="l3",
                     start_date=datetime(2026, 1, 1),
                     end_date=datetime(2027, 1, 1),
                     version=VersionMap({descriptor: Version(None, 1)}),
-                    descriptor=descriptor
-                )
+                    descriptor=descriptor,
+                ),
             ),
         ]
 
         initializer = HiCombinedInitializer()
-        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(f'hic-ena-h-hf-nsp-full-hae-6deg-1yr',
-                                                                              None)
+        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+            "hic-ena-h-hf-nsp-full-hae-6deg-1yr", None
+        )
 
         mock_read_cdf_parents.assert_not_called()
 
-        self.hi_combined_initializer_mock_query.assert_has_calls([
-            call(instrument='hi', data_level='l3'),
-            call(instrument='hi', data_level='l2')
-        ])
+        self.hi_combined_initializer_mock_query.assert_has_calls(
+            [
+                call(instrument="hi", data_level="l3"),
+                call(instrument="hi", data_level="l2"),
+            ]
+        )
 
         self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
     def test_get_maps_that_can_be_produced(self, mock_read_cdf_parents):
         query_results_for_existing_l3 = create_mock_query_results([])
-        query_results_for_available_inputs = create_mock_query_results(['imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                                             'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
-                                             'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                                             'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf', ])
+        query_results_for_available_inputs = create_mock_query_results(
+            [
+                "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
+                "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
+            ]
+        )
         self.hi_combined_initializer_mock_query.side_effect = [
-            query_results_for_existing_l3, query_results_for_available_inputs,
+            query_results_for_existing_l3,
+            query_results_for_available_inputs,
         ]
 
         initializer = HiCombinedInitializer()
-        descriptor = 'hic-ena-h-hf-nsp-anti-hae-6deg-1yr'
+        descriptor = "hic-ena-h-hf-nsp-anti-hae-6deg-1yr"
 
         actual_possible_maps = initializer.get_maps_that_can_be_produced(descriptor)
 
         expected_possible_maps = [
             PossibleMapToProduce(
                 input_files={
-                    'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                    'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
-                    'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf',
-                    'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf',
+                    "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20260101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20260101_v001.cdf",
                 },
                 input_metadata=InputMetadata(
-                    instrument='hi',
-                    data_level='l3',
-                    start_date=datetime(2026,1,1),
-                    end_date=datetime(2027,1,1),
+                    instrument="hi",
+                    data_level="l3",
+                    start_date=datetime(2026, 1, 1),
+                    end_date=datetime(2027, 1, 1),
                     version=VersionMap({}, Version(None, 1)),
                     descriptor=descriptor,
-                )
+                ),
             )
         ]
 
-        self.assertEqual(expected_possible_maps, actual_possible_maps)
\ No newline at end of file
+        self.assertEqual(expected_possible_maps, actual_possible_maps)
diff --git a/tests/hi/test_hi_processor.py b/tests/hi/test_hi_processor.py
index 1e9704703..8e4648fd0 100644
--- a/tests/hi/test_hi_processor.py
+++ b/tests/hi/test_hi_processor.py
@@ -1,30 +1,43 @@
 import unittest
 from datetime import datetime, timedelta
 from pathlib import Path
-from unittest.mock import patch, call, sentinel, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 from imap_processing.spice.geometry import SpiceFrame
 
 from imap_l3_processing.hi.hi_processor import HiProcessor
-from imap_l3_processing.hi.hi_spectral_fit_dependencies import HiSpectralIndexDependencies
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiL3SingleSensorFullSpinDependencies, \
-    HiLoL3SurvivalDependencies
-from imap_l3_processing.maps.map_models import RectangularSpectralIndexDataProduct, RectangularIntensityDataProduct
+from imap_l3_processing.hi.hi_spectral_fit_dependencies import (
+    HiSpectralIndexDependencies,
+)
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiL3SingleSensorFullSpinDependencies,
+    HiLoL3SurvivalDependencies,
+)
+from imap_l3_processing.maps.map_models import (
+    RectangularIntensityDataProduct,
+    RectangularSpectralIndexDataProduct,
+)
 from imap_l3_processing.models import InputMetadata, Instrument
 from tests.maps.test_builders import create_rectangular_intensity_map_data
 from tests.test_helpers import get_test_data_path, run_periodically
 
 
 class TestHiProcessor(unittest.TestCase):
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch('imap_l3_processing.hi.hi_processor.HiSpectralIndexDependencies.fetch_dependencies')
-    @patch('imap_l3_processing.maps.spectral_fit.fit_arrays_to_power_law')
-    @patch('imap_l3_processing.hi.hi_processor.save_data')
-    def test_process_spectral_fit(self, mock_save_data, mock_spectral_fit,
-                                  mock_fetch_dependencies,
-                                  mock_get_parent_file_names):
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch(
+        "imap_l3_processing.hi.hi_processor.HiSpectralIndexDependencies.fetch_dependencies"
+    )
+    @patch("imap_l3_processing.maps.spectral_fit.fit_arrays_to_power_law")
+    @patch("imap_l3_processing.hi.hi_processor.save_data")
+    def test_process_spectral_fit(
+        self,
+        mock_save_data,
+        mock_spectral_fit,
+        mock_fetch_dependencies,
+        mock_get_parent_file_names,
+    ):
         mock_get_parent_file_names.return_value = ["l2_or_l3_map"]
         lat = np.array([0, 45])
         long = np.array([0, 45, 90])
@@ -34,9 +47,15 @@ def test_process_spectral_fit(self, mock_save_data, mock_spectral_fit,
         flux = np.full((1, 2, 3, 2), 1)
         intensity_stat_unc = 5
 
-        hi_l3_data = create_rectangular_intensity_map_data(lat=lat, lon=long, energy=energy, epoch=epoch, flux=flux,
-                                                           intensity_stat_uncert=intensity_stat_unc,
-                                                           energy_delta=energy_delta)
+        hi_l3_data = create_rectangular_intensity_map_data(
+            lat=lat,
+            lon=long,
+            energy=energy,
+            epoch=epoch,
+            flux=flux,
+            intensity_stat_uncert=intensity_stat_unc,
+            energy_delta=energy_delta,
+        )
         intensity_data = hi_l3_data.intensity_map_data
         intensity_data.exposure_factor = np.full_like(flux, 1)
         dependencies = HiSpectralIndexDependencies(map_data=hi_l3_data)
@@ -45,31 +64,42 @@ def test_process_spectral_fit(self, mock_save_data, mock_spectral_fit,
 
         mock_fetch_dependencies.return_value = dependencies
 
-        input_metadata = InputMetadata(instrument="hi",
-                                       data_level="",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor="h90-spx-h-hf-sp-full-hae-4deg-6mo",
-                                       )
+        input_metadata = InputMetadata(
+            instrument="hi",
+            data_level="",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor="h90-spx-h-hf-sp-full-hae-4deg-6mo",
+        )
 
         expected_index_unc = np.full((1, 1, 3, 2), 1.0)
         expected_index = np.full_like(expected_index_unc, 2.0)
         expected_scalar = np.full_like(expected_index_unc, 3.0)
         expected_scalar_unc = np.full_like(expected_index_unc, 4.0)
         expected_chisq = np.full_like(expected_index_unc, 5.0)
-        mock_spectral_fit.return_value = expected_scalar, expected_scalar_unc, expected_index, expected_index_unc, expected_chisq
+        mock_spectral_fit.return_value = (
+            expected_scalar,
+            expected_scalar_unc,
+            expected_index,
+            expected_index_unc,
+            expected_chisq,
+        )
         processor = HiProcessor(upstream_dependencies, input_metadata)
         product = processor.process()
 
         mock_fetch_dependencies.assert_called_with(upstream_dependencies)
-        mock_spectral_fit.assert_called_once_with(intensity_data.ena_intensity,
-                                                  intensity_data.ena_intensity_stat_uncert,
-                                                  intensity_data.energy)
+        mock_spectral_fit.assert_called_once_with(
+            intensity_data.ena_intensity,
+            intensity_data.ena_intensity_stat_uncert,
+            intensity_data.energy,
+        )
 
         mock_save_data.assert_called_once()
         actual_hi_data_product = mock_save_data.call_args_list[0].args[0]
-        self.assertIsInstance(actual_hi_data_product, RectangularSpectralIndexDataProduct)
+        self.assertIsInstance(
+            actual_hi_data_product, RectangularSpectralIndexDataProduct
+        )
         self.assertEqual(input_metadata, actual_hi_data_product.input_metadata)
         actual_data = actual_hi_data_product.data
         spectral_index_data = actual_data.spectral_index_map_data
@@ -78,18 +108,39 @@ def test_process_spectral_fit(self, mock_save_data, mock_spectral_fit,
         expected_energy_delta_plus = np.array([12])
         expected_energy = np.array([4])
         expected_exposure = np.full((1, 1, 3, 2), 2)
-        np.testing.assert_array_equal( spectral_index_data.energy_delta_minus,expected_energy_delta_minus)
-        np.testing.assert_array_equal(spectral_index_data.energy_delta_plus, expected_energy_delta_plus)
-        np.testing.assert_array_equal(spectral_index_data.ena_spectral_index,expected_index)
-        np.testing.assert_array_equal(spectral_index_data.ena_spectral_index_stat_uncert, expected_index_unc)
-        np.testing.assert_array_equal(spectral_index_data.ena_spectral_index_scalar_coefficient, expected_scalar)
-        np.testing.assert_array_equal(spectral_index_data.ena_spectral_index_scalar_coefficient_stat_uncert, expected_scalar_unc)
-        np.testing.assert_array_equal(spectral_index_data.ena_spectral_index_chisq, expected_chisq)
+        np.testing.assert_array_equal(
+            spectral_index_data.energy_delta_minus, expected_energy_delta_minus
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.energy_delta_plus, expected_energy_delta_plus
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.ena_spectral_index, expected_index
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.ena_spectral_index_stat_uncert, expected_index_unc
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.ena_spectral_index_scalar_coefficient, expected_scalar
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.ena_spectral_index_scalar_coefficient_stat_uncert,
+            expected_scalar_unc,
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.ena_spectral_index_chisq, expected_chisq
+        )
         np.testing.assert_array_equal(spectral_index_data.energy, expected_energy)
-        np.testing.assert_array_equal(spectral_index_data.latitude, intensity_data.latitude)
-        np.testing.assert_array_equal(spectral_index_data.longitude, intensity_data.longitude)
+        np.testing.assert_array_equal(
+            spectral_index_data.latitude, intensity_data.latitude
+        )
+        np.testing.assert_array_equal(
+            spectral_index_data.longitude, intensity_data.longitude
+        )
         np.testing.assert_array_equal(spectral_index_data.epoch, intensity_data.epoch)
-        np.testing.assert_array_equal(spectral_index_data.exposure_factor, expected_exposure)
+        np.testing.assert_array_equal(
+            spectral_index_data.exposure_factor, expected_exposure
+        )
         self.assertEqual(["l2_or_l3_map"], actual_hi_data_product.parent_file_names)
         self.assertEqual(hi_l3_data.coords, spectral_index_coords)
         self.assertEqual(SpiceFrame.ECLIPJ2000, actual_hi_data_product.spice_frame_name)
@@ -99,51 +150,80 @@ def test_process_spectral_fit(self, mock_save_data, mock_spectral_fit,
     @run_periodically(timedelta(days=3))
     def test_spectral_fit_against_validation_data(self):
         test_cases = [
-            ("hi45", "hi/fake_l2_maps/hi45-6months.cdf",
-             "hi/validation/spectral_index/IMAP-Hi45_6months_4.0x4.0_fit_gam.csv",
-             "hi/validation/spectral_index/IMAP-Hi45_6months_4.0x4.0_fit_gam_sig.csv"),
-            ("hi90", "hi/fake_l2_maps/hi90-6months.cdf",
-             "hi/validation/spectral_index/IMAP-Hi90_6months_4.0x4.0_fit_gam.csv",
-             "hi/validation/spectral_index/IMAP-Hi90_6months_4.0x4.0_fit_gam_sig.csv"),
-            ("hi45-zirnstein-mondel", "hi/fake_l2_maps/hi45-zirnstein-mondel-6months.cdf",
-             "hi/validation/spectral_index/IMAP-Hi45_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam.csv",
-             "hi/validation/spectral_index/IMAP-Hi45_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam_sig.csv"),
-            ("hi90-zirnstein-mondel", "hi/fake_l2_maps/hi90-zirnstein-mondel-6months.cdf",
-             "hi/validation/spectral_index/IMAP-Hi90_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam.csv",
-             "hi/validation/spectral_index/IMAP-Hi90_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam_sig.csv"),
+            (
+                "hi45",
+                "hi/fake_l2_maps/hi45-6months.cdf",
+                "hi/validation/spectral_index/IMAP-Hi45_6months_4.0x4.0_fit_gam.csv",
+                "hi/validation/spectral_index/IMAP-Hi45_6months_4.0x4.0_fit_gam_sig.csv",
+            ),
+            (
+                "hi90",
+                "hi/fake_l2_maps/hi90-6months.cdf",
+                "hi/validation/spectral_index/IMAP-Hi90_6months_4.0x4.0_fit_gam.csv",
+                "hi/validation/spectral_index/IMAP-Hi90_6months_4.0x4.0_fit_gam_sig.csv",
+            ),
+            (
+                "hi45-zirnstein-mondel",
+                "hi/fake_l2_maps/hi45-zirnstein-mondel-6months.cdf",
+                "hi/validation/spectral_index/IMAP-Hi45_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam.csv",
+                "hi/validation/spectral_index/IMAP-Hi45_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam_sig.csv",
+            ),
+            (
+                "hi90-zirnstein-mondel",
+                "hi/fake_l2_maps/hi90-zirnstein-mondel-6months.cdf",
+                "hi/validation/spectral_index/IMAP-Hi90_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam.csv",
+                "hi/validation/spectral_index/IMAP-Hi90_gdf_Zirnstein_model_6months_4.0x4.0_fit_gam_sig.csv",
+            ),
         ]
 
-        for name, input_file_path, expected_gamma_path, expected_sigma_path in test_cases:
+        for (
+            name,
+            input_file_path,
+            expected_gamma_path,
+            expected_sigma_path,
+        ) in test_cases:
             with self.subTest(name):
                 dependencies = HiSpectralIndexDependencies.from_file_paths(
                     get_test_data_path(input_file_path)
                 )
 
-                expected_gamma = np.loadtxt(get_test_data_path(expected_gamma_path), delimiter=",", dtype=str).T
+                expected_gamma = np.loadtxt(
+                    get_test_data_path(expected_gamma_path), delimiter=",", dtype=str
+                ).T
                 expected_gamma[expected_gamma == "NaN"] = "-1"
                 expected_gamma = expected_gamma.astype(np.float64)
                 expected_gamma[expected_gamma == -1] = np.nan
 
-                expected_gamma_sigma = np.loadtxt(get_test_data_path(expected_sigma_path), delimiter=",",
-                                                  dtype=str).T
+                expected_gamma_sigma = np.loadtxt(
+                    get_test_data_path(expected_sigma_path), delimiter=",", dtype=str
+                ).T
                 expected_gamma_sigma[expected_gamma_sigma == "NaN"] = "-1"
                 expected_gamma_sigma = expected_gamma_sigma.astype(np.float64)
                 expected_gamma_sigma[expected_gamma_sigma == -1] = np.nan
 
-                input_metadata = InputMetadata(instrument="hi",
-                                               data_level="l3",
-                                               start_date=datetime.now(),
-                                               end_date=datetime.now() + timedelta(days=1),
-                                               version="",
-                                               descriptor="spectral-fit-index",
-                                               )
+                input_metadata = InputMetadata(
+                    instrument="hi",
+                    data_level="l3",
+                    start_date=datetime.now(),
+                    end_date=datetime.now() + timedelta(days=1),
+                    version="",
+                    descriptor="spectral-fit-index",
+                )
                 processor = HiProcessor(None, input_metadata)
                 output_data = processor.process_spectral_fit_index(dependencies)
 
-                np.testing.assert_allclose(output_data.spectral_index_map_data.ena_spectral_index[0, 0],
-                                           expected_gamma, atol=1e-3)
-                np.testing.assert_allclose(output_data.spectral_index_map_data.ena_spectral_index_stat_uncert[0, 0],
-                                           expected_gamma_sigma, atol=1e-3)
+                np.testing.assert_allclose(
+                    output_data.spectral_index_map_data.ena_spectral_index[0, 0],
+                    expected_gamma,
+                    atol=1e-3,
+                )
+                np.testing.assert_allclose(
+                    output_data.spectral_index_map_data.ena_spectral_index_stat_uncert[
+                        0, 0
+                    ],
+                    expected_gamma_sigma,
+                    atol=1e-3,
+                )
 
     def test_spectral_fit_fields_other_than_fit_fields(self):
         input_energies = np.array([1, 10, 99]) + 0.5
@@ -151,8 +231,9 @@ def test_spectral_fit_fields_other_than_fit_fields(self):
         lat = np.arange(-90, 90, 45)
         lon = np.arange(0, 360, 45)
 
-        input_map = create_rectangular_intensity_map_data(energy=input_energies, energy_delta=input_deltas, lat=lat,
-                                                          lon=lon)
+        input_map = create_rectangular_intensity_map_data(
+            energy=input_energies, energy_delta=input_deltas, lat=lat, lon=lon
+        )
         intensity_data = input_map.intensity_map_data
         intensity_data.obs_date[0, 0] = datetime(2025, 1, 1)
         intensity_data.obs_date[0, 1] = datetime(2025, 1, 1)
@@ -174,16 +255,28 @@ def test_spectral_fit_fields_other_than_fit_fields(self):
 
         self.assertEqual(output.spectral_index_map_data.energy.shape[0], 1)
         self.assertEqual(output.spectral_index_map_data.energy[0], 10)
-        np.testing.assert_allclose(output.spectral_index_map_data.energy_delta_minus, np.array([9]))
-        np.testing.assert_allclose(output.spectral_index_map_data.energy_delta_plus, np.array([90]))
+        np.testing.assert_allclose(
+            output.spectral_index_map_data.energy_delta_minus, np.array([9])
+        )
+        np.testing.assert_allclose(
+            output.spectral_index_map_data.energy_delta_plus, np.array([90])
+        )
         self.assertEqual(output.spectral_index_map_data.energy_label.shape[0], 1)
         self.assertEqual("1.0 - 100.0", output.spectral_index_map_data.energy_label[0])
 
         expected_ena_shape = np.array([1, 1, len(lon), len(lat)])
-        np.testing.assert_array_equal(output.spectral_index_map_data.obs_date,
-                                      np.full(expected_ena_shape, datetime(2026, 1, 1)))
-        np.testing.assert_array_equal(output.spectral_index_map_data.obs_date_range, np.full(expected_ena_shape, 2))
-        np.testing.assert_array_equal(output.spectral_index_map_data.exposure_factor, np.full(expected_ena_shape, 6))
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.obs_date,
+            np.full(expected_ena_shape, datetime(2026, 1, 1)),
+        )
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.obs_date_range,
+            np.full(expected_ena_shape, 2),
+        )
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.exposure_factor,
+            np.full(expected_ena_shape, 6),
+        )
 
     def test_spectral_fit_other_fields_with_one_zero_exposure(self):
         input_energies = np.array([1, 10, 99]) + 0.5
@@ -191,8 +284,9 @@ def test_spectral_fit_other_fields_with_one_zero_exposure(self):
         lat = np.arange(-90, 90, 45)
         lon = np.arange(0, 360, 45)
 
-        input_map = create_rectangular_intensity_map_data(energy=input_energies, energy_delta=input_deltas, lat=lat,
-                                                          lon=lon)
+        input_map = create_rectangular_intensity_map_data(
+            energy=input_energies, energy_delta=input_deltas, lat=lat, lon=lon
+        )
         intensity_data = input_map.intensity_map_data
         intensity_data.obs_date[0, 0] = datetime(2025, 1, 1)
         intensity_data.obs_date[0, 1] = datetime(2025, 1, 1)
@@ -207,7 +301,9 @@ def test_spectral_fit_other_fields_with_one_zero_exposure(self):
         intensity_data.obs_date_range[0, 0] = 1
         intensity_data.obs_date_range[0, 1] = 1
         intensity_data.obs_date_range[0, 2] = 3
-        intensity_data.obs_date_range.mask = np.ma.getmaskarray(intensity_data.obs_date_range)
+        intensity_data.obs_date_range.mask = np.ma.getmaskarray(
+            intensity_data.obs_date_range
+        )
         intensity_data.obs_date_range.mask[0, 1] = True
 
         processor = HiProcessor(sentinel.input_metadata, sentinel.dependencies)
@@ -217,10 +313,16 @@ def test_spectral_fit_other_fields_with_one_zero_exposure(self):
         output = processor.process_spectral_fit_index(dependencies)
 
         expected_ena_shape = np.array([1, 1, len(lon), len(lat)])
-        np.testing.assert_array_equal(output.spectral_index_map_data.obs_date,
-                                      np.full(expected_ena_shape, datetime(2026, 1, 1)), strict=True)
-        np.testing.assert_array_equal(output.spectral_index_map_data.obs_date_range, np.full(expected_ena_shape, 2.0),
-                                      strict=True)
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.obs_date,
+            np.full(expected_ena_shape, datetime(2026, 1, 1)),
+            strict=True,
+        )
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.obs_date_range,
+            np.full(expected_ena_shape, 2.0),
+            strict=True,
+        )
 
     def test_spectral_fit_other_fields_with_all_zero_exposure(self):
         input_energies = np.array([1, 10, 99]) + 0.5
@@ -228,15 +330,18 @@ def test_spectral_fit_other_fields_with_all_zero_exposure(self):
         lat = np.arange(-90, 90, 45)
         lon = np.arange(0, 360, 45)
 
-        input_map = create_rectangular_intensity_map_data(energy=input_energies, energy_delta=input_deltas, lat=lat,
-                                                          lon=lon)
+        input_map = create_rectangular_intensity_map_data(
+            energy=input_energies, energy_delta=input_deltas, lat=lat, lon=lon
+        )
         intensity_data = input_map.intensity_map_data
         intensity_data.obs_date.mask = np.ma.getmaskarray(intensity_data.obs_date)
         intensity_data.obs_date.mask[:] = True
 
         intensity_data.exposure_factor[:] = 0.0
 
-        intensity_data.obs_date_range.mask = np.ma.getmaskarray(intensity_data.obs_date_range)
+        intensity_data.obs_date_range.mask = np.ma.getmaskarray(
+            intensity_data.obs_date_range
+        )
         intensity_data.obs_date_range.mask[:] = True
 
         processor = HiProcessor(sentinel.input_metadata, sentinel.dependencies)
@@ -247,25 +352,37 @@ def test_spectral_fit_other_fields_with_all_zero_exposure(self):
 
         expected_ena_shape = np.array([1, 1, len(lon), len(lat)])
 
-        np.testing.assert_array_equal(output.spectral_index_map_data.obs_date.mask, np.full(expected_ena_shape, True),
-                                      strict=True)
-        np.testing.assert_array_equal(output.spectral_index_map_data.obs_date_range.mask,
-                                      np.full(expected_ena_shape, True), strict=True)
-
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch("imap_l3_processing.hi.hi_processor.HiLoL3SurvivalDependencies.fetch_dependencies")
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.obs_date.mask,
+            np.full(expected_ena_shape, True),
+            strict=True,
+        )
+        np.testing.assert_array_equal(
+            output.spectral_index_map_data.obs_date_range.mask,
+            np.full(expected_ena_shape, True),
+            strict=True,
+        )
+
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch(
+        "imap_l3_processing.hi.hi_processor.HiLoL3SurvivalDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.hi.hi_processor.process_survival_probabilities")
-    @patch('imap_l3_processing.hi.hi_processor.save_data')
-    def test_process_ram_or_antiram_spin(self, mock_save_data,
-                                         mock_process_survival_prob,
-                                         mock_fetch_survival_dependencies, mock_get_parent_file_names):
+    @patch("imap_l3_processing.hi.hi_processor.save_data")
+    def test_process_ram_or_antiram_spin(
+        self,
+        mock_save_data,
+        mock_process_survival_prob,
+        mock_fetch_survival_dependencies,
+        mock_get_parent_file_names,
+    ):
 
         mock_get_parent_file_names.return_value = ["somewhere"]
 
         cases = [
             ("ram", "h90-ena-h-sf-sp-ram-hae-4deg-6mo", SpiceFrame.ECLIPJ2000),
             ("anti", "h90-ena-h-sf-sp-anti-hae-4deg-6mo", SpiceFrame.IMAP_GCS),
-            ("anti", "h90-ena-h-hf-sp-anti-hae-4deg-6mo", SpiceFrame.IMAP_HNU)
+            ("anti", "h90-ena-h-hf-sp-anti-hae-4deg-6mo", SpiceFrame.IMAP_HNU),
         ]
 
         for case, descriptor, spice_frame_name in cases:
@@ -274,60 +391,82 @@ def test_process_ram_or_antiram_spin(self, mock_save_data,
                 mock_fetch_survival_dependencies.reset_mock()
                 mock_process_survival_prob.reset_mock()
 
-                input_metadata = InputMetadata(instrument="hi",
-                                               data_level="l3",
-                                               start_date=datetime.now(),
-                                               end_date=datetime.now() + timedelta(days=1),
-                                               version="",
-                                               descriptor=descriptor,
-                                               )
+                input_metadata = InputMetadata(
+                    instrument="hi",
+                    data_level="l3",
+                    start_date=datetime.now(),
+                    end_date=datetime.now() + timedelta(days=1),
+                    version="",
+                    descriptor=descriptor,
+                )
 
-                dependencies: HiLoL3SurvivalDependencies = mock_fetch_survival_dependencies.return_value
+                dependencies: HiLoL3SurvivalDependencies = (
+                    mock_fetch_survival_dependencies.return_value
+                )
                 dependencies.dependency_file_paths = [
-                    Path("folder/map"), Path("folder/l1c")]
+                    Path("folder/map"),
+                    Path("folder/l1c"),
+                ]
 
-                mock_process_survival_prob.return_value = sentinel.survival_probabilities
+                mock_process_survival_prob.return_value = (
+                    sentinel.survival_probabilities
+                )
 
                 processor = HiProcessor(sentinel.input_dependencies, input_metadata)
                 product = processor.process(spice_frame_name=spice_frame_name)
 
-                mock_fetch_survival_dependencies.assert_called_once_with(sentinel.input_dependencies,
-                                                                         Instrument.IMAP_HI)
+                mock_fetch_survival_dependencies.assert_called_once_with(
+                    sentinel.input_dependencies, Instrument.IMAP_HI
+                )
 
-                mock_process_survival_prob.assert_called_once_with(dependencies, spice_frame_name)
+                mock_process_survival_prob.assert_called_once_with(
+                    dependencies, spice_frame_name
+                )
 
-                mock_save_data.assert_called_once_with(RectangularIntensityDataProduct(
-                    input_metadata=input_metadata,
-                    parent_file_names=["l1c", "map", "somewhere"],
-                    data=sentinel.survival_probabilities,
-                    spice_frame_name=spice_frame_name,
-                ))
+                mock_save_data.assert_called_once_with(
+                    RectangularIntensityDataProduct(
+                        input_metadata=input_metadata,
+                        parent_file_names=["l1c", "map", "somewhere"],
+                        data=sentinel.survival_probabilities,
+                        spice_frame_name=spice_frame_name,
+                    )
+                )
                 self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch("imap_l3_processing.hi.hi_processor.HiL3SingleSensorFullSpinDependencies.fetch_dependencies")
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch(
+        "imap_l3_processing.hi.hi_processor.HiL3SingleSensorFullSpinDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.hi.hi_processor.process_survival_probabilities")
-    @patch('imap_l3_processing.hi.hi_processor.save_data')
-    @patch('imap_l3_processing.hi.hi_processor.UnweightedCombination')
-    def test_process_full_spin_single_sensor_map(self, mock_unweighted_combination_class, mock_save_data,
-                                                 mock_process_survival_prob,
-                                                 mock_fetch_full_spin_single_sensor_dependencies,
-                                                 mock_get_parent_file_names):
+    @patch("imap_l3_processing.hi.hi_processor.save_data")
+    @patch("imap_l3_processing.hi.hi_processor.UnweightedCombination")
+    def test_process_full_spin_single_sensor_map(
+        self,
+        mock_unweighted_combination_class,
+        mock_save_data,
+        mock_process_survival_prob,
+        mock_fetch_full_spin_single_sensor_dependencies,
+        mock_get_parent_file_names,
+    ):
         mock_unweighted_combination = Mock()
         mock_unweighted_combination_class.return_value = mock_unweighted_combination
 
         mock_get_parent_file_names.return_value = ["ram_map", "antiram_map"]
-        input_metadata = InputMetadata(instrument="hi",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor="h90-ena-h-sf-sp-full-hae-4deg-6mo",
-                                       )
-
-        full_spin_dependencies: HiL3SingleSensorFullSpinDependencies = mock_fetch_full_spin_single_sensor_dependencies.return_value
+        input_metadata = InputMetadata(
+            instrument="hi",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor="h90-ena-h-sf-sp-full-hae-4deg-6mo",
+        )
+
+        full_spin_dependencies: HiL3SingleSensorFullSpinDependencies = (
+            mock_fetch_full_spin_single_sensor_dependencies.return_value
+        )
         full_spin_dependencies.dependency_file_paths = [
-            Path("folder/ram_map", Path("folder/antiram_map"), Path("folder/l1c"))]
+            Path("folder/ram_map", Path("folder/antiram_map"), Path("folder/l1c"))
+        ]
 
         mock_process_survival_prob.side_effect = [
             sentinel.survival_corrected_ram,
@@ -337,33 +476,46 @@ def test_process_full_spin_single_sensor_map(self, mock_unweighted_combination_c
         processor = HiProcessor(sentinel.dependencies, input_metadata)
         product = processor.process(spice_frame_name=sentinel.spice_frame)
 
-        mock_fetch_full_spin_single_sensor_dependencies.assert_called_once_with(sentinel.dependencies)
-
-        mock_process_survival_prob.assert_has_calls([
-            call(full_spin_dependencies.ram_dependencies, sentinel.spice_frame),
-            call(full_spin_dependencies.antiram_dependencies, sentinel.spice_frame)
-        ])
-
-        mock_unweighted_combination.combine_rectangular_intensity_map_data.assert_called_once_with([
-            sentinel.survival_corrected_ram, sentinel.survival_corrected_antiram])
-
-        mock_save_data.assert_called_once_with(RectangularIntensityDataProduct(
-            input_metadata=input_metadata,
-            parent_file_names=["antiram_map", "l1c", "ram_map"],
-            data=mock_unweighted_combination.combine_rectangular_intensity_map_data.return_value,
-            spice_frame_name=sentinel.spice_frame,
-        ))
+        mock_fetch_full_spin_single_sensor_dependencies.assert_called_once_with(
+            sentinel.dependencies
+        )
+
+        mock_process_survival_prob.assert_has_calls(
+            [
+                call(full_spin_dependencies.ram_dependencies, sentinel.spice_frame),
+                call(full_spin_dependencies.antiram_dependencies, sentinel.spice_frame),
+            ]
+        )
+
+        mock_unweighted_combination.combine_rectangular_intensity_map_data.assert_called_once_with(
+            [sentinel.survival_corrected_ram, sentinel.survival_corrected_antiram]
+        )
+
+        mock_save_data.assert_called_once_with(
+            RectangularIntensityDataProduct(
+                input_metadata=input_metadata,
+                parent_file_names=["antiram_map", "l1c", "ram_map"],
+                data=mock_unweighted_combination.combine_rectangular_intensity_map_data.return_value,
+                spice_frame_name=sentinel.spice_frame,
+            )
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch('imap_l3_processing.hi.hi_processor.save_data')
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch("imap_l3_processing.hi.hi_processor.save_data")
     @patch("imap_l3_processing.hi.hi_processor.process_survival_probabilities")
-    @patch("imap_l3_processing.hi.hi_processor.HiL3SingleSensorFullSpinDependencies.fetch_dependencies")
+    @patch(
+        "imap_l3_processing.hi.hi_processor.HiL3SingleSensorFullSpinDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.hi.hi_processor.UncertaintyWeightedCombination")
-    def test_process_cg_corrected_full_spin_map(self, mock_uncertainty_weighted_combination,
-                                                mock_fetch_dependencies,
-                                                mock_process_survival_probabilities, mock_save_data,
-                                                mock_get_parent_file_names):
+    def test_process_cg_corrected_full_spin_map(
+        self,
+        mock_uncertainty_weighted_combination,
+        mock_fetch_dependencies,
+        mock_process_survival_probabilities,
+        mock_save_data,
+        mock_get_parent_file_names,
+    ):
 
         sensors = ["h45", "h90"]
 
@@ -374,14 +526,21 @@ def test_process_cg_corrected_full_spin_map(self, mock_uncertainty_weighted_comb
                 mock_process_survival_probabilities.reset_mock()
                 mock_save_data.reset_mock()
                 mock_get_parent_file_names.reset_mock()
-                mock_get_parent_file_names.return_value = ["ram_map", "antiram_map", "l1c"]
+                mock_get_parent_file_names.return_value = [
+                    "ram_map",
+                    "antiram_map",
+                    "l1c",
+                ]
 
                 mock_combination = Mock()
                 mock_uncertainty_weighted_combination.return_value = mock_combination
 
                 full_spin_dependencies: HiL3SingleSensorFullSpinDependencies = Mock()
                 full_spin_dependencies.dependency_file_paths = [
-                    Path("folder/ram_map", Path("folder/antiram_map"), Path("folder/l1c"))]
+                    Path(
+                        "folder/ram_map", Path("folder/antiram_map"), Path("folder/l1c")
+                    )
+                ]
                 mock_fetch_dependencies.return_value = full_spin_dependencies
 
                 mock_process_survival_probabilities.side_effect = [
@@ -389,51 +548,80 @@ def test_process_cg_corrected_full_spin_map(self, mock_uncertainty_weighted_comb
                     sentinel.survival_corrected_antiram,
                 ]
 
-                input_metadata = InputMetadata(instrument="hi",
-                                               data_level="l3",
-                                               start_date=datetime.now(),
-                                               end_date=datetime.now() + timedelta(days=1),
-                                               version="",
-                                               descriptor=f"{sensor}-ena-h-hf-sp-full-hae-4deg-6mo")
+                input_metadata = InputMetadata(
+                    instrument="hi",
+                    data_level="l3",
+                    start_date=datetime.now(),
+                    end_date=datetime.now() + timedelta(days=1),
+                    version="",
+                    descriptor=f"{sensor}-ena-h-hf-sp-full-hae-4deg-6mo",
+                )
 
                 processor = HiProcessor(sentinel.dependencies, input_metadata)
                 product = processor.process(spice_frame_name=sentinel.spice_frame)
 
                 mock_fetch_dependencies.assert_called_once_with(sentinel.dependencies)
 
-                mock_process_survival_probabilities.assert_has_calls([
-                    call(full_spin_dependencies.ram_dependencies, sentinel.spice_frame),
-                    call(full_spin_dependencies.antiram_dependencies, sentinel.spice_frame)
-                ])
+                mock_process_survival_probabilities.assert_has_calls(
+                    [
+                        call(
+                            full_spin_dependencies.ram_dependencies,
+                            sentinel.spice_frame,
+                        ),
+                        call(
+                            full_spin_dependencies.antiram_dependencies,
+                            sentinel.spice_frame,
+                        ),
+                    ]
+                )
 
-                mock_combination.combine_rectangular_intensity_map_data.assert_called_once_with([
-                    sentinel.survival_corrected_ram, sentinel.survival_corrected_antiram])
+                mock_combination.combine_rectangular_intensity_map_data.assert_called_once_with(
+                    [
+                        sentinel.survival_corrected_ram,
+                        sentinel.survival_corrected_antiram,
+                    ]
+                )
 
-                mock_save_data.assert_called_once_with(RectangularIntensityDataProduct(
-                    input_metadata=input_metadata,
-                    parent_file_names=["antiram_map", "l1c", "ram_map"],
-                    data=mock_combination.combine_rectangular_intensity_map_data.return_value,
-                    spice_frame_name=sentinel.spice_frame,
-                ))
+                mock_save_data.assert_called_once_with(
+                    RectangularIntensityDataProduct(
+                        input_metadata=input_metadata,
+                        parent_file_names=["antiram_map", "l1c", "ram_map"],
+                        data=mock_combination.combine_rectangular_intensity_map_data.return_value,
+                        spice_frame_name=sentinel.spice_frame,
+                    )
+                )
                 self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch('imap_l3_processing.hi.hi_processor.save_data')
-    @patch("imap_l3_processing.hi.hi_processor.HiL3CombinedMapDependencies.fetch_dependencies")
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch("imap_l3_processing.hi.hi_processor.save_data")
+    @patch(
+        "imap_l3_processing.hi.hi_processor.HiL3CombinedMapDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.hi.hi_processor.ExposureWeightedCombination")
     @patch("imap_l3_processing.hi.hi_processor.UncertaintyWeightedCombination")
-    def test_process_combined_sensor_map(self, mock_uncertainty_weighted_combination_class,
-                                         mock_exposure_weighted_combination_class, mock_fetch_dependencies,
-                                         mock_save_data, mock_get_parent_file_names):
+    def test_process_combined_sensor_map(
+        self,
+        mock_uncertainty_weighted_combination_class,
+        mock_exposure_weighted_combination_class,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_get_parent_file_names,
+    ):
         mock_exposure_combination = Mock()
-        mock_exposure_weighted_combination_class.return_value = mock_exposure_combination
+        mock_exposure_weighted_combination_class.return_value = (
+            mock_exposure_combination
+        )
 
         mock_uncertainty_weighted_combination = Mock()
 
-        mock_uncertainty_weighted_combination_class.return_value = mock_uncertainty_weighted_combination
+        mock_uncertainty_weighted_combination_class.return_value = (
+            mock_uncertainty_weighted_combination
+        )
 
         mock_uncertainty_weighted_combination.combine_rectangular_intensity_map_data.side_effect = [
-            sentinel.h45_full_spin, sentinel.h90_full_spin]
+            sentinel.h45_full_spin,
+            sentinel.h90_full_spin,
+        ]
 
         parent_map_names = [
             "imap_hi_l2_h45-ena-h-hf-sp-full-hae-4deg-6mo_20250101_v000.cdf",
@@ -444,33 +632,40 @@ def test_process_combined_sensor_map(self, mock_uncertainty_weighted_combination
 
         mock_get_parent_file_names.return_value = parent_map_names
 
-        input_metadata = InputMetadata(instrument="hi",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor=f"hic-ena-h-hf-sp-full-hae-4deg-6mo")
-        dependencies = ProcessingInputCollection(*[ScienceInput(map_name) for map_name in parent_map_names])
+        input_metadata = InputMetadata(
+            instrument="hi",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor="hic-ena-h-hf-sp-full-hae-4deg-6mo",
+        )
+        dependencies = ProcessingInputCollection(
+            *[ScienceInput(map_name) for map_name in parent_map_names]
+        )
         data_product = HiProcessor(dependencies, input_metadata).process()
 
         mock_fetch_dependencies.assert_called_once_with(dependencies)
 
-        mock_uncertainty_weighted_combination.combine_rectangular_intensity_map_data.assert_has_calls([
-            call(mock_fetch_dependencies.return_value.h45_maps),
-            call(mock_fetch_dependencies.return_value.h90_maps)
-        ])
-
-        mock_exposure_combination.combine_rectangular_intensity_map_data.assert_called_once_with([
-            sentinel.h45_full_spin,
-            sentinel.h90_full_spin
-        ])
-
-        mock_save_data.assert_called_once_with(RectangularIntensityDataProduct(
-            input_metadata=input_metadata,
-            parent_file_names=parent_map_names,
-            data=mock_exposure_combination.combine_rectangular_intensity_map_data.return_value,
-            spice_frame_name=SpiceFrame.ECLIPJ2000,
-        ))
+        mock_uncertainty_weighted_combination.combine_rectangular_intensity_map_data.assert_has_calls(
+            [
+                call(mock_fetch_dependencies.return_value.h45_maps),
+                call(mock_fetch_dependencies.return_value.h90_maps),
+            ]
+        )
+
+        mock_exposure_combination.combine_rectangular_intensity_map_data.assert_called_once_with(
+            [sentinel.h45_full_spin, sentinel.h90_full_spin]
+        )
+
+        mock_save_data.assert_called_once_with(
+            RectangularIntensityDataProduct(
+                input_metadata=input_metadata,
+                parent_file_names=parent_map_names,
+                data=mock_exposure_combination.combine_rectangular_intensity_map_data.return_value,
+                spice_frame_name=SpiceFrame.ECLIPJ2000,
+            )
+        )
 
         self.assertEqual([mock_save_data.return_value], data_product)
 
@@ -480,37 +675,50 @@ def test_raises_error_for_currently_unimplemented_maps(self):
         ]
         for descriptor in cases:
             with self.subTest(descriptor):
-                input_metadata = InputMetadata(instrument="hi",
-                                               data_level="l3",
-                                               start_date=datetime.now(),
-                                               end_date=datetime.now() + timedelta(days=1),
-                                               version="",
-                                               descriptor=descriptor,
-                                               )
+                input_metadata = InputMetadata(
+                    instrument="hi",
+                    data_level="l3",
+                    start_date=datetime.now(),
+                    end_date=datetime.now() + timedelta(days=1),
+                    version="",
+                    descriptor=descriptor,
+                )
 
                 processor = HiProcessor(ProcessingInputCollection(), input_metadata)
                 with self.assertRaises(NotImplementedError):
                     product = processor.process()
 
-    @patch('imap_l3_processing.hi.hi_processor.HiSpectralIndexDependencies.fetch_dependencies')
-    @patch('imap_l3_processing.hi.hi_processor.fit_spectral_index_map')
-    @patch('imap_l3_processing.hi.hi_processor.slice_energy_range_by_bin')
-    @patch('imap_l3_processing.hi.hi_processor.save_data')
-    def test_spectral_fit_with_energy_range(self, _, mock_slice_energy_range_by_bin,
-                                            mock_fit_spectral_index_map, mock_fetch_dependencies):
+    @patch(
+        "imap_l3_processing.hi.hi_processor.HiSpectralIndexDependencies.fetch_dependencies"
+    )
+    @patch("imap_l3_processing.hi.hi_processor.fit_spectral_index_map")
+    @patch("imap_l3_processing.hi.hi_processor.slice_energy_range_by_bin")
+    @patch("imap_l3_processing.hi.hi_processor.save_data")
+    def test_spectral_fit_with_energy_range(
+        self,
+        _,
+        mock_slice_energy_range_by_bin,
+        mock_fit_spectral_index_map,
+        mock_fetch_dependencies,
+    ):
         upstream_dependencies = ProcessingInputCollection()
         dependencies = mock_fetch_dependencies.return_value
 
-        input_metadata = InputMetadata(instrument="hi",
-                                       data_level="",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor="h90-spx1020-h-hf-sp-full-hae-4deg-6mo",
-                                       )
+        input_metadata = InputMetadata(
+            instrument="hi",
+            data_level="",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor="h90-spx1020-h-hf-sp-full-hae-4deg-6mo",
+        )
 
         processor = HiProcessor(upstream_dependencies, input_metadata)
         processor.process()
 
-        mock_slice_energy_range_by_bin.assert_called_with(dependencies.map_data.intensity_map_data, 10, 20)
-        mock_fit_spectral_index_map.assert_called_with(mock_slice_energy_range_by_bin.return_value)
+        mock_slice_energy_range_by_bin.assert_called_with(
+            dependencies.map_data.intensity_map_data, 10, 20
+        )
+        mock_fit_spectral_index_map.assert_called_with(
+            mock_slice_energy_range_by_bin.return_value
+        )
diff --git a/tests/hi/test_hi_sp_initializer.py b/tests/hi/test_hi_sp_initializer.py
index 304d435ec..a33dcf61b 100644
--- a/tests/hi/test_hi_sp_initializer.py
+++ b/tests/hi/test_hi_sp_initializer.py
@@ -1,12 +1,15 @@
 import unittest
+from collections.abc import Callable
 from datetime import datetime
-from typing import Callable
-from unittest.mock import patch, call, Mock, sentinel
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.hi.hi_sp_initializer import HiSPInitializer, HI_SP_SPICE_KERNELS
-from imap_l3_processing.maps.map_descriptors import parse_map_descriptor, map_descriptor_parts_to_string
+from imap_l3_processing.hi.hi_sp_initializer import HI_SP_SPICE_KERNELS, HiSPInitializer
+from imap_l3_processing.maps.map_descriptors import (
+    map_descriptor_parts_to_string,
+    parse_map_descriptor,
+)
 from imap_l3_processing.maps.map_initializer import PossibleMapToProduce
 from imap_l3_processing.maps.sp_map_initializer import logger
 from imap_l3_processing.models import InputMetadata, VersionMap
@@ -15,56 +18,63 @@
 
 class TestHiSPInitializer(unittest.TestCase):
     def setUp(self):
-        self.query_patcher = patch('imap_l3_processing.hi.hi_sp_initializer.imap_data_access.query')
+        self.query_patcher = patch(
+            "imap_l3_processing.hi.hi_sp_initializer.imap_data_access.query"
+        )
         self.mock_query = self.query_patcher.start()
 
     def tearDown(self):
         self.query_patcher.stop()
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_can_be_produced_always_returns_possible_maps_with_default_version_map(self, mock_read_cdf_parents):
-        descriptor = 'h45-ena-h-sf-sp-anti-hae-4deg-3mo'
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_can_be_produced_always_returns_possible_maps_with_default_version_map(
+        self, mock_read_cdf_parents
+    ):
+        descriptor = "h45-ena-h-sf-sp-anti-hae-4deg-3mo"
         cases = [
             (None, VersionMap({}, Version(None, 1))),
-             (4, VersionMap({}, Version(None, 1))),
+            (4, VersionMap({}, Version(None, 1))),
         ]
         for major_version, expected_version in cases:
             with self.subTest(major_version=major_version):
-
                 self.mock_query.side_effect = [
-                    create_mock_query_results([
-                        'imap_glows_l3e_survival-probability-hi-45_20100101-repoint00001_v001.cdf',
-                        'imap_glows_l3e_survival-probability-hi-45_20100102-repoint00002_v001.cdf',
-                        'imap_glows_l3e_survival-probability-hi-45_20100103-repoint00003_v001.cdf',
-
-                        'imap_glows_l3e_survival-probability-hi-45_20100401-repoint00101_v001.cdf',
-                        'imap_glows_l3e_survival-probability-hi-45_20100402-repoint00102_v001.cdf',
-                        'imap_glows_l3e_survival-probability-hi-45_20100403-repoint00103_v001.cdf',
-
-                        'imap_glows_l3e_survival-probability-hi-45_20100702-repoint00202_v001.cdf',
-                    ]),
+                    create_mock_query_results(
+                        [
+                            "imap_glows_l3e_survival-probability-hi-45_20100101-repoint00001_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100102-repoint00002_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100103-repoint00003_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100401-repoint00101_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100402-repoint00102_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100403-repoint00103_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100702-repoint00202_v001.cdf",
+                        ]
+                    ),
+                    create_mock_query_results([]),
+                    create_mock_query_results(
+                        [
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf",
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf",
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf",
+                        ]
+                    ),
                     create_mock_query_results([]),
-                    create_mock_query_results([
-                        'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                        'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf',
-                        'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf',
-                        'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf'
-                    ]),
-                    create_mock_query_results([])
                 ]
 
-                mock_read_cdf_parents.side_effect = self.create_fake_read_cdf_parents("45")
+                mock_read_cdf_parents.side_effect = self.create_fake_read_cdf_parents(
+                    "45"
+                )
 
                 expected_possible_maps = [
                     PossibleMapToProduce(
                         input_files={
-                            'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100101-repoint00001_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100102-repoint00002_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100103-repoint00003_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100101-repoint00001_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100102-repoint00002_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100103-repoint00003_v001.cdf',
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100101-repoint00001_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100102-repoint00002_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100103-repoint00003_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100101-repoint00001_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100102-repoint00002_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100103-repoint00003_v001.cdf",
                         },
                         input_metadata=InputMetadata(
                             instrument="hi",
@@ -73,17 +83,17 @@ def test_get_maps_that_can_be_produced_always_returns_possible_maps_with_default
                             end_date=datetime(2010, 4, 2, 7, 30),
                             version=expected_version,
                             descriptor=descriptor,
-                        )
+                        ),
                     ),
                     PossibleMapToProduce(
                         input_files={
-                            'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100401-repoint00101_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100402-repoint00102_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100403-repoint00103_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100401-repoint00101_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100402-repoint00102_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100403-repoint00103_v001.cdf',
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100401-repoint00101_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100402-repoint00102_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100403-repoint00103_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100401-repoint00101_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100402-repoint00102_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100403-repoint00103_v001.cdf",
                         },
                         input_metadata=InputMetadata(
                             instrument="hi",
@@ -92,15 +102,15 @@ def test_get_maps_that_can_be_produced_always_returns_possible_maps_with_default
                             end_date=datetime(2010, 7, 1, 7, 30),
                             version=expected_version,
                             descriptor=descriptor,
-                        )
+                        ),
                     ),
                     PossibleMapToProduce(
                         input_files={
-                            'imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-45_20100702-repoint00202_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100401-repoint00201_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100402-repoint00202_v001.cdf',
-                            'imap_hi_l1c_45sensor-pset_20100403-repoint00203_v001.cdf',
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-45_20100702-repoint00202_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100401-repoint00201_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100402-repoint00202_v001.cdf",
+                            "imap_hi_l1c_45sensor-pset_20100403-repoint00203_v001.cdf",
                         },
                         input_metadata=InputMetadata(
                             instrument="hi",
@@ -109,333 +119,462 @@ def test_get_maps_that_can_be_produced_always_returns_possible_maps_with_default
                             end_date=datetime(2010, 9, 30, 7, 30),
                             version=expected_version,
                             descriptor=descriptor,
-                        )
+                        ),
                     ),
                 ]
 
                 initializer = HiSPInitializer()
 
-                self.mock_query.assert_has_calls([
-                    call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-45', version="latest"),
-                    call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-90', version="latest"),
-                    call(instrument='hi', data_level='l2'),
-                    call(instrument='hi', data_level='l3'),
-                ])
+                self.mock_query.assert_has_calls(
+                    [
+                        call(
+                            instrument="glows",
+                            data_level="l3e",
+                            descriptor="survival-probability-hi-45",
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3e",
+                            descriptor="survival-probability-hi-90",
+                            version="latest",
+                        ),
+                        call(instrument="hi", data_level="l2"),
+                        call(instrument="hi", data_level="l3"),
+                    ]
+                )
 
-                actual_possible_maps = initializer.get_maps_that_can_be_produced('h45-ena-h-sf-sp-anti-hae-4deg-3mo')
+                actual_possible_maps = initializer.get_maps_that_can_be_produced(
+                    "h45-ena-h-sf-sp-anti-hae-4deg-3mo"
+                )
 
-                mock_read_cdf_parents.assert_has_calls([
-                    call('imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf'),
-                    call('imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf'),
-                    call('imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf'),
-                    call('imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf'),
-                ], any_order=True)
+                mock_read_cdf_parents.assert_has_calls(
+                    [
+                        call(
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                        call(
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf"
+                        ),
+                        call(
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf"
+                        ),
+                        call(
+                            "imap_hi_l2_h45-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf"
+                        ),
+                    ],
+                    any_order=True,
+                )
 
                 self.assertEqual(expected_possible_maps, actual_possible_maps)
 
-
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_can_be_produced_full_spin_descriptor(self, mock_read_cdf_parents):
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_can_be_produced_full_spin_descriptor(
+        self, mock_read_cdf_parents
+    ):
         self.mock_query.side_effect = [
             create_mock_query_results([]),
-            create_mock_query_results([
-                'imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100104-repoint00004_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100703-repoint00203_v001.cdf',
-
-            ]),
-            create_mock_query_results([
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20090101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20090701_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf',
-            ]),
-            create_mock_query_results([])
+            create_mock_query_results(
+                [
+                    "imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100104-repoint00004_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100703-repoint00203_v001.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20090101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20090701_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf",
+                ]
+            ),
+            create_mock_query_results([]),
         ]
 
         mock_read_cdf_parents.side_effect = [
             [
-                'imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf',
-                'imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf',
+                "imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf",
+                "imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf",
             ],
             [
-                'imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf',
-                'imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf',
-            ]
+                "imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf",
+                "imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf",
+            ],
         ]
 
         initializer = HiSPInitializer()
-        descriptor = 'h90-ena-h-sf-sp-full-hae-4deg-6mo'
+        descriptor = "h90-ena-h-sf-sp-full-hae-4deg-6mo"
         actual_possible_maps = initializer.get_maps_that_can_be_produced(descriptor)
 
-        self.mock_query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-45', version="latest"),
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-90', version="latest"),
-            call(instrument='hi', data_level='l2'),
-            call(instrument='hi', data_level='l3'),
-        ])
+        self.mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-hi-45",
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-hi-90",
+                    version="latest",
+                ),
+                call(instrument="hi", data_level="l2"),
+                call(instrument="hi", data_level="l3"),
+            ]
+        )
 
-        mock_read_cdf_parents.assert_has_calls([
-            call('imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf'),
-            call('imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf'),
-        ])
+        mock_read_cdf_parents.assert_has_calls(
+            [
+                call("imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf"),
+                call("imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf"),
+            ]
+        )
 
         expected_possible_map_to_produce = PossibleMapToProduce(
             input_files={
-                'imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf',
-                'imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf',
-                'imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf',
+                "imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf",
+                "imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf",
+                "imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf",
+                "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf",
+                "imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf",
             },
             input_metadata=InputMetadata(
                 instrument="hi",
                 data_level="l3",
                 start_date=datetime(2010, 1, 1),
                 end_date=datetime(2010, 7, 2, 15),
-                version=VersionMap({},Version(None,1)),
+                version=VersionMap({}, Version(None, 1)),
                 descriptor=descriptor,
-            )
+            ),
         )
 
         self.assertEqual([expected_possible_map_to_produce], actual_possible_maps)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_can_be_produced_full_spin_with_mismatched_parents(self, mock_read_cdf_parents):
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_can_be_produced_full_spin_with_mismatched_parents(
+        self, mock_read_cdf_parents
+    ):
         self.mock_query.side_effect = [
             create_mock_query_results([]),
-            create_mock_query_results([
-                'imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100104-repoint00004_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100703-repoint00203_v001.cdf',
-            ]),
-            create_mock_query_results([
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf',
-            ]),
-            create_mock_query_results([])
+            create_mock_query_results(
+                [
+                    "imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100104-repoint00004_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100703-repoint00203_v001.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf",
+                ]
+            ),
+            create_mock_query_results([]),
         ]
 
         mock_read_cdf_parents.side_effect = [
             [
-                'imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf',
-                'imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf',
+                "imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf",
+                "imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf",
             ],
             [
-                'imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf',
-            ]
+                "imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf",
+            ],
         ]
 
         initializer = HiSPInitializer()
 
-        with self.assertLogs(logger=logger, level='WARNING') as log_context:
-            actual_possible_maps = initializer.get_maps_that_can_be_produced('h90-ena-h-sf-sp-full-hae-4deg-6mo')
-            expected_message = ('Expected all input maps to be created from the same pointing sets! l2_file_paths: '
-                                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf, '
-                                'imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf')
+        with self.assertLogs(logger=logger, level="WARNING") as log_context:
+            actual_possible_maps = initializer.get_maps_that_can_be_produced(
+                "h90-ena-h-sf-sp-full-hae-4deg-6mo"
+            )
+            expected_message = (
+                "Expected all input maps to be created from the same pointing sets! l2_file_paths: "
+                "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf, "
+                "imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf"
+            )
             self.assertIn(expected_message, log_context.output[0])
 
+        self.mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-hi-45",
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-hi-90",
+                    version="latest",
+                ),
+                call(instrument="hi", data_level="l2"),
+                call(instrument="hi", data_level="l3"),
+            ]
+        )
 
-        self.mock_query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-45', version="latest"),
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-90', version="latest"),
-            call(instrument='hi', data_level='l2'),
-            call(instrument='hi', data_level='l3'),
-        ])
-
-        mock_read_cdf_parents.assert_has_calls([
-            call('imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf'),
-            call('imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf'),
-        ])
+        mock_read_cdf_parents.assert_has_calls(
+            [
+                call("imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-6mo_20100101_v001.cdf"),
+                call("imap_hi_l2_h90-ena-h-sf-nsp-ram-hae-4deg-6mo_20100101_v001.cdf"),
+            ]
+        )
 
         self.assertEqual([], actual_possible_maps)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
     def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
         self.mock_query.side_effect = [
             create_mock_query_results([]),
-            create_mock_query_results([
-                'imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf',
-
-                'imap_glows_l3e_survival-probability-hi-90_20100401-repoint00101_v002.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100402-repoint00102_v002.cdf',
-                'imap_glows_l3e_survival-probability-hi-90_20100403-repoint00103_v002.cdf',
-
-                'imap_glows_l3e_survival-probability-hi-90_20100702-repoint00201_v001.cdf',
-            ]),
-            create_mock_query_results([
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v000.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf',
-                'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf'
-
-            ]),
-            create_mock_query_results([
-                'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v000.cdf',
-                'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100401_v001.cdf',
-            ]),
+            create_mock_query_results(
+                [
+                    "imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100401-repoint00101_v002.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100402-repoint00102_v002.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100403-repoint00103_v002.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100702-repoint00201_v001.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v000.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf",
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf",
+                ]
+            ),
+            create_mock_query_results(
+                [
+                    "imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                    "imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v000.cdf",
+                    "imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100401_v001.cdf",
+                ]
+            ),
         ]
 
         mock_read_cdf_parents.side_effect = self.create_fake_read_cdf_parents("90")
 
-        descriptor = 'h90-ena-h-sf-sp-anti-hae-4deg-3mo'
+        descriptor = "h90-ena-h-sf-sp-anti-hae-4deg-3mo"
         expected_possible_maps = [
             PossibleMapToProduce(
                 input_files={
-                    'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf',
-                    'imap_glows_l3e_survival-probability-hi-90_20100401-repoint00101_v002.cdf',
-                    'imap_glows_l3e_survival-probability-hi-90_20100402-repoint00102_v002.cdf',
-                    'imap_glows_l3e_survival-probability-hi-90_20100403-repoint00103_v002.cdf',
-                    'imap_hi_l1c_90sensor-pset_20100401-repoint00101_v001.cdf',
-                    'imap_hi_l1c_90sensor-pset_20100402-repoint00102_v001.cdf',
-                    'imap_hi_l1c_90sensor-pset_20100403-repoint00103_v001.cdf',
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100401-repoint00101_v002.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100402-repoint00102_v002.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100403-repoint00103_v002.cdf",
+                    "imap_hi_l1c_90sensor-pset_20100401-repoint00101_v001.cdf",
+                    "imap_hi_l1c_90sensor-pset_20100402-repoint00102_v001.cdf",
+                    "imap_hi_l1c_90sensor-pset_20100403-repoint00103_v001.cdf",
                 },
                 input_metadata=InputMetadata(
                     instrument="hi",
                     data_level="l3",
                     start_date=datetime(2010, 4, 1),
                     end_date=datetime(2010, 7, 1, 7, 30),
-                    version=VersionMap({descriptor:Version(None,2)}),
+                    version=VersionMap({descriptor: Version(None, 2)}),
                     descriptor=descriptor,
-                )
+                ),
             ),
             PossibleMapToProduce(
                 input_files={
-                    'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf',
-                    'imap_glows_l3e_survival-probability-hi-90_20100702-repoint00201_v001.cdf',
-                    'imap_hi_l1c_90sensor-pset_20100401-repoint00201_v001.cdf',
-                    'imap_hi_l1c_90sensor-pset_20100402-repoint00202_v001.cdf',
-                    'imap_hi_l1c_90sensor-pset_20100403-repoint00203_v001.cdf',
+                    "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf",
+                    "imap_glows_l3e_survival-probability-hi-90_20100702-repoint00201_v001.cdf",
+                    "imap_hi_l1c_90sensor-pset_20100401-repoint00201_v001.cdf",
+                    "imap_hi_l1c_90sensor-pset_20100402-repoint00202_v001.cdf",
+                    "imap_hi_l1c_90sensor-pset_20100403-repoint00203_v001.cdf",
                 },
                 input_metadata=InputMetadata(
                     instrument="hi",
                     data_level="l3",
                     start_date=datetime(2010, 7, 1),
                     end_date=datetime(2010, 9, 30, 7, 30),
-                    version=VersionMap({descriptor:Version(None,1)}),
+                    version=VersionMap({descriptor: Version(None, 1)}),
                     descriptor=descriptor,
-                )
+                ),
             ),
         ]
 
         initializer = HiSPInitializer()
 
-        self.mock_query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-45', version="latest"),
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-90', version="latest"),
-            call(instrument='hi', data_level='l2'),
-            call(instrument='hi', data_level='l3'),
-        ])
-
-        actual_possible_maps = initializer.get_maps_that_should_be_produced(descriptor, None)
+        self.mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-hi-45",
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-hi-90",
+                    version="latest",
+                ),
+                call(instrument="hi", data_level="l2"),
+                call(instrument="hi", data_level="l3"),
+            ]
+        )
 
-        mock_read_cdf_parents.assert_has_calls([
-            call(f'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf'),
-            call(f'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf'),
-            call(f'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf'),
+        actual_possible_maps = initializer.get_maps_that_should_be_produced(
+            descriptor, None
+        )
 
-            call(f'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100401_v001.cdf'),
-        ])
+        mock_read_cdf_parents.assert_has_calls(
+            [
+                call("imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf"),
+                call("imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf"),
+                call("imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf"),
+                call("imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf"),
+                call("imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf"),
+                call("imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100401_v001.cdf"),
+            ]
+        )
 
         self.assertEqual(expected_possible_maps, actual_possible_maps)
 
-
-
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_remakes_when_major_version_changes(self, mock_read_cdf_parents):
-        for major_version in [2,3]:
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_remakes_when_major_version_changes(
+        self, mock_read_cdf_parents
+    ):
+        for major_version in [2, 3]:
             with self.subTest(major_version=major_version):
                 self.mock_query.reset_mock()
                 mock_read_cdf_parents.reset_mock()
                 self.mock_query.side_effect = [
                     create_mock_query_results([]),
-                    create_mock_query_results([
-                        'imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf',
-                        'imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf',
-                        'imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf',
-                    ]),
-                    create_mock_query_results([
-                        'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                        'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v000.cdf',
-                    ]),
-                    create_mock_query_results([
-                        'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                        'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v000.cdf',
-                    ]),
+                    create_mock_query_results(
+                        [
+                            "imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf",
+                        ]
+                    ),
+                    create_mock_query_results(
+                        [
+                            "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                            "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v000.cdf",
+                        ]
+                    ),
+                    create_mock_query_results(
+                        [
+                            "imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                            "imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v000.cdf",
+                        ]
+                    ),
                 ]
 
-                mock_read_cdf_parents.side_effect = self.create_fake_read_cdf_parents("90")
+                mock_read_cdf_parents.side_effect = self.create_fake_read_cdf_parents(
+                    "90"
+                )
 
-                descriptor = 'h90-ena-h-sf-sp-anti-hae-4deg-3mo'
+                descriptor = "h90-ena-h-sf-sp-anti-hae-4deg-3mo"
                 expected_possible_maps = [
                     PossibleMapToProduce(
                         input_files={
-                            'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf',
-                            'imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf',
-                            'imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf',
-                            'imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf',
-                            'imap_hi_l1c_90sensor-pset_20100103-repoint00003_v001.cdf',
+                            "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-90_20100101-repoint00001_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-90_20100102-repoint00002_v001.cdf",
+                            "imap_glows_l3e_survival-probability-hi-90_20100103-repoint00003_v001.cdf",
+                            "imap_hi_l1c_90sensor-pset_20100101-repoint00001_v001.cdf",
+                            "imap_hi_l1c_90sensor-pset_20100102-repoint00002_v001.cdf",
+                            "imap_hi_l1c_90sensor-pset_20100103-repoint00003_v001.cdf",
                         },
                         input_metadata=InputMetadata(
                             instrument="hi",
                             data_level="l3",
                             start_date=datetime(2010, 1, 1),
                             end_date=datetime(2010, 4, 2, 7, 30),
-                            version=VersionMap({descriptor:Version(major_version,2)}),
+                            version=VersionMap({descriptor: Version(major_version, 2)}),
                             descriptor=descriptor,
-                        )
+                        ),
                     ),
                 ]
 
                 initializer = HiSPInitializer()
 
-                self.mock_query.assert_has_calls([
-                    call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-45', version="latest"),
-                    call(instrument='glows', data_level='l3e', descriptor='survival-probability-hi-90', version="latest"),
-                    call(instrument='hi', data_level='l2'),
-                    call(instrument='hi', data_level='l3'),
-                ])
-
-                actual_possible_maps = initializer.get_maps_that_should_be_produced(descriptor, major_version)
+                self.mock_query.assert_has_calls(
+                    [
+                        call(
+                            instrument="glows",
+                            data_level="l3e",
+                            descriptor="survival-probability-hi-45",
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3e",
+                            descriptor="survival-probability-hi-90",
+                            version="latest",
+                        ),
+                        call(instrument="hi", data_level="l2"),
+                        call(instrument="hi", data_level="l3"),
+                    ]
+                )
 
-                mock_read_cdf_parents.assert_has_calls([
-                    call(f'imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf'),
+                actual_possible_maps = initializer.get_maps_that_should_be_produced(
+                    descriptor, major_version
+                )
 
-                    call(f'imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf'),
-                ])
+                mock_read_cdf_parents.assert_has_calls(
+                    [
+                        call(
+                            "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                        call(
+                            "imap_hi_l3_h90-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                    ]
+                )
 
                 self.assertEqual(expected_possible_maps, actual_possible_maps)
 
     def test_get_dependencies(self):
         cases = [
-            ("h90-ena-h-sf-sp-anti-hae-4deg-3mo", ["h90-ena-h-sf-nsp-anti-hae-4deg-3mo"]),
+            (
+                "h90-ena-h-sf-sp-anti-hae-4deg-3mo",
+                ["h90-ena-h-sf-nsp-anti-hae-4deg-3mo"],
+            ),
             ("h45-ena-h-sf-sp-ram-hae-4deg-3mo", ["h45-ena-h-sf-nsp-ram-hae-4deg-3mo"]),
-            ("h45-ena-h-sf-sp-full-hae-4deg-3mo",
-             ["h45-ena-h-sf-nsp-anti-hae-4deg-3mo", "h45-ena-h-sf-nsp-ram-hae-4deg-3mo"]),
-            ("h90-ena-h-sf-sp-full-hae-4deg-3mo",
-             ["h90-ena-h-sf-nsp-anti-hae-4deg-3mo", "h90-ena-h-sf-nsp-ram-hae-4deg-3mo"]),
+            (
+                "h45-ena-h-sf-sp-full-hae-4deg-3mo",
+                [
+                    "h45-ena-h-sf-nsp-anti-hae-4deg-3mo",
+                    "h45-ena-h-sf-nsp-ram-hae-4deg-3mo",
+                ],
+            ),
+            (
+                "h90-ena-h-sf-sp-full-hae-4deg-3mo",
+                [
+                    "h90-ena-h-sf-nsp-anti-hae-4deg-3mo",
+                    "h90-ena-h-sf-nsp-ram-hae-4deg-3mo",
+                ],
+            ),
         ]
 
         for descriptor, dependencies in cases:
             with self.subTest(descriptor):
                 initializer = HiSPInitializer()
-                actual_dependencies = initializer._get_l2_dependencies(parse_map_descriptor(descriptor))
-                self.assertEqual(dependencies, [map_descriptor_parts_to_string(d) for d in actual_dependencies])
+                actual_dependencies = initializer._get_l2_dependencies(
+                    parse_map_descriptor(descriptor)
+                )
+                self.assertEqual(
+                    dependencies,
+                    [map_descriptor_parts_to_string(d) for d in actual_dependencies],
+                )
 
-    @patch('imap_l3_processing.hi.hi_sp_initializer.furnish_spice_metakernel')
+    @patch("imap_l3_processing.hi.hi_sp_initializer.furnish_spice_metakernel")
     def test_furnish_spice_dependencies(self, mock_furnish_metakernel):
         start_date = datetime(2025, 4, 15)
         end_date = datetime(2025, 7, 15)
@@ -453,13 +592,15 @@ def test_furnish_spice_dependencies(self, mock_furnish_metakernel):
         hi_initializer = HiSPInitializer()
         hi_initializer.furnish_spice_dependencies(map_to_produce)
 
-        mock_furnish_metakernel.assert_called_once_with(start_date=start_date, end_date=end_date,
-                                                        kernel_types=HI_SP_SPICE_KERNELS)
+        mock_furnish_metakernel.assert_called_once_with(
+            start_date=start_date, end_date=end_date, kernel_types=HI_SP_SPICE_KERNELS
+        )
 
     @patch("imap_l3_processing.maps.map_initializer.generate_imap_input")
     @patch("imap_l3_processing.maps.map_initializer.ProcessingInputCollection")
-    def test_possible_maps_to_produce_constructs_processing_input_collection(self, mock_collection,
-                                                                             mock_generate_imap_input):
+    def test_possible_maps_to_produce_constructs_processing_input_collection(
+        self, mock_collection, mock_generate_imap_input
+    ):
         path1 = "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v000.cdf"
         path2 = "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100102_v000.cdf"
         path3 = "imap_hi_l2_h90-ena-h-sf-nsp-anti-hae-4deg-3mo_20100103_v000.cdf"
@@ -470,59 +611,67 @@ def test_possible_maps_to_produce_constructs_processing_input_collection(self, m
         }
 
         def mock_generate_input(path):
-            return {path1: sentinel.file1, path2: sentinel.file2, path3: sentinel.file3}[path]
+            return {
+                path1: sentinel.file1,
+                path2: sentinel.file2,
+                path3: sentinel.file3,
+            }[path]
 
         mock_generate_imap_input.side_effect = mock_generate_input
 
-        processing_input = PossibleMapToProduce(input_files=files, input_metadata=Mock()).processing_input_collection
+        processing_input = PossibleMapToProduce(
+            input_files=files, input_metadata=Mock()
+        ).processing_input_collection
 
-        mock_collection.assert_called_once_with(sentinel.file1, sentinel.file2, sentinel.file3)
+        mock_collection.assert_called_once_with(
+            sentinel.file1, sentinel.file2, sentinel.file3
+        )
 
         self.assertEqual(mock_collection.return_value, processing_input)
 
     def create_fake_read_cdf_parents(self, sensor: str) -> Callable[[str], set[str]]:
         def fake_read_cdf_parents(file_name: str) -> set[str]:
             mapping = {
-                f'imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf': {
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100101-repoint00001_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100102-repoint00002_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100103-repoint00003_v001.cdf',
-                    f'imap_science_0001.tf',
+                f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf": {
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100101-repoint00001_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100102-repoint00002_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100103-repoint00003_v001.cdf",
+                    "imap_science_0001.tf",
                 },
-                f'imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf': {
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100401-repoint00101_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100402-repoint00102_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100403-repoint00103_v001.cdf',
+                f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf": {
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100401-repoint00101_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100402-repoint00102_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100403-repoint00103_v001.cdf",
                 },
-                f'imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf': {
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100401-repoint00201_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100402-repoint00202_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100403-repoint00203_v001.cdf',
+                f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100701_v001.cdf": {
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100401-repoint00201_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100402-repoint00202_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100403-repoint00203_v001.cdf",
                 },
-                f'imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf': {
-                    f'imap_hi_l1c_{sensor}sensor-pset_20101001-repoint00301_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20101002-repoint00302_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20101003-repoint00303_v001.cdf',
+                f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20101001_v001.cdf": {
+                    f"imap_hi_l1c_{sensor}sensor-pset_20101001-repoint00301_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20101002-repoint00302_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20101003-repoint00303_v001.cdf",
                 },
-                f'imap_hi_l3_h{sensor}-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf': {
-                    f'imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100101-repoint00001_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100102-repoint00002_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100103-repoint00003_v001.cdf',
-                    f'imap_glows_l3e_survival-probability-hi-{sensor}_20100101-repoint00001_v001.cdf',
-                    f'imap_glows_l3e_survival-probability-hi-{sensor}_20100102-repoint00002_v001.cdf',
-                    f'imap_glows_l3e_survival-probability-hi-{sensor}_20100103-repoint00003_v001.cdf',
-                    f'imap_science_0001.tf',
+                f"imap_hi_l3_h{sensor}-ena-h-sf-sp-anti-hae-4deg-3mo_20100101_v001.cdf": {
+                    f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100101_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100101-repoint00001_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100102-repoint00002_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100103-repoint00003_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-{sensor}_20100101-repoint00001_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-{sensor}_20100102-repoint00002_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-{sensor}_20100103-repoint00003_v001.cdf",
+                    "imap_science_0001.tf",
                 },
-                f'imap_hi_l3_h{sensor}-ena-h-sf-sp-anti-hae-4deg-3mo_20100401_v001.cdf': {
-                    f'imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100401-repoint00101_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100402-repoint00102_v001.cdf',
-                    f'imap_hi_l1c_{sensor}sensor-pset_20100403-repoint00103_v001.cdf',
-                    f'imap_glows_l3e_survival-probability-hi-{sensor}_20100401-repoint00101_v001.cdf',
-                    f'imap_glows_l3e_survival-probability-hi-{sensor}_20100402-repoint00102_v001.cdf',
-                    f'imap_glows_l3e_survival-probability-hi-{sensor}_20100403-repoint00103_v001.cdf',
-                    f'imap_science_0001.tf',
+                f"imap_hi_l3_h{sensor}-ena-h-sf-sp-anti-hae-4deg-3mo_20100401_v001.cdf": {
+                    f"imap_hi_l2_h{sensor}-ena-h-sf-nsp-anti-hae-4deg-3mo_20100401_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100401-repoint00101_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100402-repoint00102_v001.cdf",
+                    f"imap_hi_l1c_{sensor}sensor-pset_20100403-repoint00103_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-{sensor}_20100401-repoint00101_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-{sensor}_20100402-repoint00102_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-hi-{sensor}_20100403-repoint00103_v001.cdf",
+                    "imap_science_0001.tf",
                 },
             }
             return mapping[file_name]
diff --git a/tests/hi/test_hi_spectral_fit_dependencies.py b/tests/hi/test_hi_spectral_fit_dependencies.py
index fb3f12ede..b4de28140 100644
--- a/tests/hi/test_hi_spectral_fit_dependencies.py
+++ b/tests/hi/test_hi_spectral_fit_dependencies.py
@@ -1,19 +1,28 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, Mock
+from unittest.mock import Mock, patch
 
 import numpy as np
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, AncillaryInput
-
-from imap_l3_processing.hi.hi_spectral_fit_dependencies import HiSpectralIndexDependencies
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData, IntensityMapData
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
+
+from imap_l3_processing.hi.hi_spectral_fit_dependencies import (
+    HiSpectralIndexDependencies,
+)
+from imap_l3_processing.maps.map_models import (
+    IntensityMapData,
+    RectangularIntensityMapData,
+)
 from imap_l3_processing.maps.quality_flags import MapL3Flags
 
 
 class TestHiSpectralFitDependencies(unittest.TestCase):
-
     @patch(
-        "imap_l3_processing.hi.hi_spectral_fit_dependencies.RectangularIntensityMapData.read_from_path")
+        "imap_l3_processing.hi.hi_spectral_fit_dependencies.RectangularIntensityMapData.read_from_path"
+    )
     def test_from_file_paths(self, mock_read_from_path):
         hi_l3 = Path("test_hi_l3_cdf.cdf")
 
@@ -24,15 +33,17 @@ def test_from_file_paths(self, mock_read_from_path):
         self.assertEqual(mock_read_from_path.call_count, 1)
         self.assertEqual(mock_read_from_path.return_value, result.map_data)
 
-    @patch("imap_l3_processing.hi.hi_spectral_fit_dependencies.imap_data_access.download")
     @patch(
-        "imap_l3_processing.hi.hi_spectral_fit_dependencies.RectangularIntensityMapData.read_from_path")
+        "imap_l3_processing.hi.hi_spectral_fit_dependencies.imap_data_access.download"
+    )
+    @patch(
+        "imap_l3_processing.hi.hi_spectral_fit_dependencies.RectangularIntensityMapData.read_from_path"
+    )
     def test_fetch_dependencies(self, mock_read_from_path, mock_download):
         file_name = "imap_hi_l3_h90-spx-h-hf-sp-full-hae-4deg-6mo_20250422_v001.cdf"
         ancillary_file = "imap_hi_ancillary_20250422_v001.dat"
         processing_input = ProcessingInputCollection(
-            ScienceInput(file_name),
-            AncillaryInput(ancillary_file)
+            ScienceInput(file_name), AncillaryInput(ancillary_file)
         )
 
         dependency = HiSpectralIndexDependencies.fetch_dependencies(processing_input)
@@ -43,8 +54,7 @@ def test_fetch_dependencies(self, mock_read_from_path, mock_download):
 
     def test_raises_value_error_if_instrument_doesnt_match(self):
         file_name = "imap_lo_l3_h90-spx-h-hf-sp-full-hae-4deg-6mo_20250422_v001.cdf"
-        processing_input = ProcessingInputCollection(
-            ScienceInput(file_name))
+        processing_input = ProcessingInputCollection(ScienceInput(file_name))
 
         with self.assertRaises(ValueError) as error:
             _ = HiSpectralIndexDependencies.fetch_dependencies(processing_input)
@@ -56,8 +66,11 @@ def test_fit_energy_ranges_returns_full_energy_range(self):
         energy_delta_minus = np.full((1,), 0.1)
         energy_delta_plus = np.full((1,), 0.2)
 
-        map_data = _create_intensity_map_data(energy=energy, energy_delta_plus=energy_delta_plus,
-                                              energy_delta_minus=energy_delta_minus)
+        map_data = _create_intensity_map_data(
+            energy=energy,
+            energy_delta_plus=energy_delta_plus,
+            energy_delta_minus=energy_delta_minus,
+        )
         data = RectangularIntensityMapData(map_data, Mock())
         deps = HiSpectralIndexDependencies(data)
 
@@ -69,14 +82,20 @@ def _create_intensity_map_data(
 ):
     full_shape = (1, 1, 8, 15)
     return IntensityMapData(
-        ena_intensity=ena_intensity if ena_intensity is not None else np.full(full_shape, 1),
+        ena_intensity=ena_intensity
+        if ena_intensity is not None
+        else np.full(full_shape, 1),
         ena_intensity_stat_uncert=None,
         ena_intensity_sys_err=None,
         epoch=None,
         epoch_delta=None,
         energy=energy if energy is not None else np.full((1,), 1),
-        energy_delta_plus=energy_delta_plus if energy_delta_plus is not None else np.full((1,), 0.1),
-        energy_delta_minus=energy_delta_minus if energy_delta_minus is not None else np.full((1,), 0.1),
+        energy_delta_plus=energy_delta_plus
+        if energy_delta_plus is not None
+        else np.full((1,), 0.1),
+        energy_delta_minus=energy_delta_minus
+        if energy_delta_minus is not None
+        else np.full((1,), 0.1),
         energy_label=None,
         latitude=None,
         longitude=None,
diff --git a/tests/hi/test_utils.py b/tests/hi/test_utils.py
index e0dac7ab0..bcf2a24d7 100644
--- a/tests/hi/test_utils.py
+++ b/tests/hi/test_utils.py
@@ -2,45 +2,48 @@
 import unittest
 from datetime import datetime
 from pathlib import Path
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
 import numpy as np
 from spacepy import pycdf
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.glows.l3e.glows_l3e_hi_model import (
-    PROBABILITY_OF_SURVIVAL_VAR_NAME,
-    SPIN_ANGLE_VAR_NAME,
     ENERGY_VAR_NAME,
     EPOCH_CDF_VAR_NAME,
     GLOWS_FLAGS_VAR_NAME,
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
+    SPIN_ANGLE_VAR_NAME,
+)
+from imap_l3_processing.hi.utils import (
+    read_glows_l3e_data,
+    read_l1c_rectangular_pointing_set_data,
 )
-from imap_l3_processing.hi.utils import read_l1c_rectangular_pointing_set_data, read_glows_l3e_data
 from tests.test_helpers import get_test_data_folder, with_tempdir
 
 
 class TestUtils(unittest.TestCase):
     def setUp(self):
-        if os.path.exists('imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf'):
-            os.remove('imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf')
+        if os.path.exists("imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf"):
+            os.remove("imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf")
 
     def tearDown(self):
-        if os.path.exists('imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf'):
-            os.remove('imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf')
+        if os.path.exists("imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf"):
+            os.remove("imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf")
 
     def test_read_l1c_data(self):
         rng = np.random.default_rng()
         pathname = "imap_hi_l1c_pointing-set_20250101-repoint01111_v000.cdf"
 
-        cases = ['exposure_times', 'exposure_time']
+        cases = ["exposure_times", "exposure_time"]
 
         for exposure_time_var_name in cases:
-            with CDF(pathname, '') as cdf:
+            with CDF(pathname, "") as cdf:
                 epoch = np.array([datetime(2000, 1, 2)])
                 epoch_delta = np.array([1_000_000_000])
                 repointing = 1111
                 exposure_times = rng.random((1, 9, 3600))
-                energy_step = rng.random((9))
+                energy_step = rng.random(9)
                 hae_longitude = np.ones(3600).reshape((1, 3600))
                 hae_latitude = np.ones(3600).reshape((1, 3600)) * 2
 
@@ -48,17 +51,21 @@ def test_read_l1c_data(self):
                 cdf["epoch_delta"] = epoch_delta
                 cdf[exposure_time_var_name] = exposure_times
                 cdf["esa_energy_step"] = energy_step
-                cdf['hae_longitude'] = hae_longitude
-                cdf['hae_latitude'] = hae_latitude
+                cdf["hae_longitude"] = hae_longitude
+                cdf["hae_latitude"] = hae_latitude
 
                 for var in cdf:
-                    cdf[var].attrs['FILLVAL'] = 1000000
+                    cdf[var].attrs["FILLVAL"] = 1000000
 
             for path in [pathname, Path(pathname)]:
-                with self.subTest(path=path, exposure_time_var_name=exposure_time_var_name):
+                with self.subTest(
+                    path=path, exposure_time_var_name=exposure_time_var_name
+                ):
                     result = read_l1c_rectangular_pointing_set_data(path)
                     self.assertEqual(epoch[0], result.epoch)
-                    np.testing.assert_array_equal(result.epoch_delta, epoch_delta, strict=True)
+                    np.testing.assert_array_equal(
+                        result.epoch_delta, epoch_delta, strict=True
+                    )
                     self.assertEqual(repointing, result.repointing)
                     self.assertEqual([43264184000000], result.epoch_j2000)
                     np.testing.assert_array_equal(exposure_times, result.exposure_times)
@@ -69,53 +76,76 @@ def test_read_l1c_data(self):
             self.tearDown()
 
     def test_read_hi_l1c_fill_values_create_nan_data(self):
-        path = get_test_data_folder() / 'hi' / 'imap_hi_l1c_pset-with-fill-values_20250101-repoint00001_v000.cdf'
+        path = (
+            get_test_data_folder()
+            / "hi"
+            / "imap_hi_l1c_pset-with-fill-values_20250101-repoint00001_v000.cdf"
+        )
         result = read_l1c_rectangular_pointing_set_data(path)
 
         with CDF(str(path)) as cdf:
-            self.assertEqual(cdf['epoch'][0], result.epoch)
-            np.testing.assert_array_equal(result.epoch_delta, cdf['epoch_delta'], strict=True)
+            self.assertEqual(cdf["epoch"][0], result.epoch)
+            np.testing.assert_array_equal(
+                result.epoch_delta, cdf["epoch_delta"], strict=True
+            )
             self.assertEqual(1, result.repointing)
-            np.testing.assert_array_equal(result.esa_energy_step, cdf['esa_energy_step'])
-            np.testing.assert_array_equal(result.exposure_times, np.full_like(cdf['exposure_times'], 0))
+            np.testing.assert_array_equal(
+                result.esa_energy_step, cdf["esa_energy_step"]
+            )
+            np.testing.assert_array_equal(
+                result.exposure_times, np.full_like(cdf["exposure_times"], 0)
+            )
 
     @patch("imap_l3_processing.hi.utils.met_to_ttj2000ns")
     def test_read_lo_l1c(self, mock_met_to_ttj2000ns):
-        path = get_test_data_folder() / 'lo' / 'imap_lo_l1c_pset_20260101-repoint01261_v001.cdf'
+        path = (
+            get_test_data_folder()
+            / "lo"
+            / "imap_lo_l1c_pset_20260101-repoint01261_v001.cdf"
+        )
 
-        mock_met_to_ttj2000ns.side_effect = [
-            np.array([3]),
-            np.array([2])
-        ]
+        mock_met_to_ttj2000ns.side_effect = [np.array([3]), np.array([2])]
 
         result = read_l1c_rectangular_pointing_set_data(path)
 
         with CDF(str(path)) as cdf:
-            mock_met_to_ttj2000ns.assert_has_calls([
-                call(cdf['pointing_end_met']),
-                call(cdf['pointing_start_met'])
-            ])
+            mock_met_to_ttj2000ns.assert_has_calls(
+                [call(cdf["pointing_end_met"]), call(cdf["pointing_start_met"])]
+            )
 
-            self.assertEqual(cdf['epoch'][0], result.epoch)
+            self.assertEqual(cdf["epoch"][0], result.epoch)
             self.assertEqual((1,), result.epoch_delta.shape)
             np.testing.assert_array_almost_equal(result.epoch_delta, [1])
             self.assertEqual(1261, result.repointing)
-            np.testing.assert_array_equal(result.esa_energy_step, cdf['esa_energy_step'])
-            np.testing.assert_array_equal(result.exposure_times, cdf['exposure_time'][...])
-            self.assertEqual(cdf['pointing_start_met'][0], result.pointing_start_met)
-            self.assertEqual(cdf['pointing_end_met'][0], result.pointing_end_met)
+            np.testing.assert_array_equal(
+                result.esa_energy_step, cdf["esa_energy_step"]
+            )
+            np.testing.assert_array_equal(
+                result.exposure_times, cdf["exposure_time"][...]
+            )
+            self.assertEqual(cdf["pointing_start_met"][0], result.pointing_start_met)
+            self.assertEqual(cdf["pointing_end_met"][0], result.pointing_end_met)
 
     @with_tempdir
     def test_read_glows_l3e_data(self, temp_dir: Path):
         rng = np.random.default_rng()
 
-        pathname = str(temp_dir / "imap_glows_l3e_survival-probability-hi_20250415-repoint01000_v001.cdf")
+        pathname = str(
+            temp_dir
+            / "imap_glows_l3e_survival-probability-hi_20250415-repoint01000_v001.cdf"
+        )
 
-        with CDF(pathname, '') as cdf:
+        with CDF(pathname, "") as cdf:
             epoch = np.array([datetime(2000, 1, 2)])
             energy = rng.random((16,))
             spin_angle = rng.random(360)
-            probability_of_survival = rng.random((1, 16, 360,))
+            probability_of_survival = rng.random(
+                (
+                    1,
+                    16,
+                    360,
+                )
+            )
             flags = np.array([2**15 | 2**2], dtype=np.uint16)
 
             cdf[EPOCH_CDF_VAR_NAME] = epoch
@@ -124,7 +154,7 @@ def test_read_glows_l3e_data(self, temp_dir: Path):
             cdf[PROBABILITY_OF_SURVIVAL_VAR_NAME] = probability_of_survival
             cdf[GLOWS_FLAGS_VAR_NAME] = flags
             for var in cdf:
-                cdf[var].attrs['FILLVAL'] = 1000000
+                cdf[var].attrs["FILLVAL"] = 1000000
 
         for path in [pathname, Path(pathname)]:
             with self.subTest(path=path):
@@ -136,11 +166,17 @@ def test_read_glows_l3e_data(self, temp_dir: Path):
                 self.assertEqual(1000, result.repointing)
                 np.testing.assert_array_equal(energy, result.energy)
                 np.testing.assert_array_equal(spin_angle, result.spin_angle)
-                np.testing.assert_array_equal(probability_of_survival, result.probability_of_survival)
+                np.testing.assert_array_equal(
+                    probability_of_survival, result.probability_of_survival
+                )
                 np.testing.assert_array_equal(flags, result.flags)
 
     def test_read_glows_l3e_fill_values_create_nan_data(self):
-        path = get_test_data_folder() / 'hi' / 'imap_glows_l3e_survival-probabilities-with-fill-values_20250101-repoint01111_v000.cdf'
+        path = (
+            get_test_data_folder()
+            / "hi"
+            / "imap_glows_l3e_survival-probabilities-with-fill-values_20250101-repoint01111_v000.cdf"
+        )
         result = read_glows_l3e_data(path)
 
         with CDF(str(path)) as cdf:
@@ -148,8 +184,14 @@ def test_read_glows_l3e_fill_values_create_nan_data(self):
             self.assertEqual(cdf.raw_var(EPOCH_CDF_VAR_NAME)[...], result.epoch_j2000)
 
             self.assertEqual(1111, result.repointing)
-            np.testing.assert_array_equal(result.energy, np.full_like(cdf[ENERGY_VAR_NAME], np.nan))
-            np.testing.assert_array_equal(result.spin_angle, np.full_like(cdf[SPIN_ANGLE_VAR_NAME], np.nan))
-            np.testing.assert_array_equal(result.probability_of_survival,
-                                          np.full_like(cdf[PROBABILITY_OF_SURVIVAL_VAR_NAME], np.nan))
-            np.testing.assert_array_equal(result.flags, [0])
\ No newline at end of file
+            np.testing.assert_array_equal(
+                result.energy, np.full_like(cdf[ENERGY_VAR_NAME], np.nan)
+            )
+            np.testing.assert_array_equal(
+                result.spin_angle, np.full_like(cdf[SPIN_ANGLE_VAR_NAME], np.nan)
+            )
+            np.testing.assert_array_equal(
+                result.probability_of_survival,
+                np.full_like(cdf[PROBABILITY_OF_SURVIVAL_VAR_NAME], np.nan),
+            )
+            np.testing.assert_array_equal(result.flags, [0])
diff --git a/tests/hit/l3/hit_test_builders.py b/tests/hit/l3/hit_test_builders.py
index a530a2ce7..67ea4a04e 100644
--- a/tests/hit/l3/hit_test_builders.py
+++ b/tests/hit/l3/hit_test_builders.py
@@ -1,19 +1,51 @@
-from imap_l3_processing.hit.l3.pha.pha_event_reader import RawPHAEvent, PHAWord, Detector
+from imap_l3_processing.hit.l3.pha.pha_event_reader import (
+    Detector,
+    PHAWord,
+    RawPHAEvent,
+)
 
 
-def create_raw_pha_event(particle_id=0, priority_buffer_num=0, stim_tag=False, haz_tag=False, time_tag=False,
-                         a_b_side=False, has_unread=False, long_event_flag=False, culling_flag=False, spare=False,
-                         pha_words=None) -> RawPHAEvent:
+def create_raw_pha_event(
+    particle_id=0,
+    priority_buffer_num=0,
+    stim_tag=False,
+    haz_tag=False,
+    time_tag=False,
+    a_b_side=False,
+    has_unread=False,
+    long_event_flag=False,
+    culling_flag=False,
+    spare=False,
+    pha_words=None,
+) -> RawPHAEvent:
     if pha_words is None:
         pha_words = [
-            PHAWord(adc_value=0, adc_overflow=False, detector=Detector.from_address(1), is_low_gain=False,
-                    is_last_pha=False),
-            PHAWord(adc_value=10, adc_overflow=False, detector=Detector.from_address(8), is_low_gain=True,
-                    is_last_pha=True)
+            PHAWord(
+                adc_value=0,
+                adc_overflow=False,
+                detector=Detector.from_address(1),
+                is_low_gain=False,
+                is_last_pha=False,
+            ),
+            PHAWord(
+                adc_value=10,
+                adc_overflow=False,
+                detector=Detector.from_address(8),
+                is_low_gain=True,
+                is_last_pha=True,
+            ),
         ]
 
-    return RawPHAEvent(particle_id=particle_id, priority_buffer_num=priority_buffer_num, haz_tag=haz_tag,
-                       stim_tag=stim_tag,
-                       time_tag=time_tag, long_event_flag=long_event_flag, a_b_side_flag=a_b_side,
-                       has_unread_adcs=has_unread,
-                       culling_flag=culling_flag, spare=spare, pha_words=pha_words)
+    return RawPHAEvent(
+        particle_id=particle_id,
+        priority_buffer_num=priority_buffer_num,
+        haz_tag=haz_tag,
+        stim_tag=stim_tag,
+        time_tag=time_tag,
+        long_event_flag=long_event_flag,
+        a_b_side_flag=a_b_side,
+        has_unread_adcs=has_unread,
+        culling_flag=culling_flag,
+        spare=spare,
+        pha_words=pha_words,
+    )
diff --git a/tests/hit/l3/pha/science/test_calculate_pha.py b/tests/hit/l3/pha/science/test_calculate_pha.py
index 9f2394291..d1f6d649b 100644
--- a/tests/hit/l3/pha/science/test_calculate_pha.py
+++ b/tests/hit/l3/pha/science/test_calculate_pha.py
@@ -1,15 +1,23 @@
 import math
 import unittest
-from collections import defaultdict
-from unittest.mock import patch, Mock, sentinel
+from unittest.mock import Mock, patch, sentinel
 
 import numpy as np
 
-from imap_l3_processing.hit.l3.pha.pha_event_reader import PHAWord, Detector
-from imap_l3_processing.hit.l3.pha.science.calculate_pha import EventAnalysis, analyze_event, calculate_mev, \
-    process_pha_event, EventOutput, compute_charge
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, DetectorRange, \
-    DetectorSide
+from imap_l3_processing.hit.l3.pha.pha_event_reader import Detector, PHAWord
+from imap_l3_processing.hit.l3.pha.science.calculate_pha import (
+    EventAnalysis,
+    EventOutput,
+    analyze_event,
+    calculate_mev,
+    compute_charge,
+    process_pha_event,
+)
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    DetectedRange,
+    DetectorRange,
+    DetectorSide,
+)
 from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import DetectorGain, Gain
 from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import Rule
 from imap_l3_processing.hit.l3.pha.science.range_fit_lookup import RangeFitLookup
@@ -31,35 +39,60 @@ def test_calculate_mev(self):
             DetectorGain.LOW: {10: Gain(a=low_gain_a_value, b=low_gain_b_value)},
             DetectorGain.HIGH: {22: Gain(a=high_gain_a_value, b=high_gain_b_value)},
         }
-        low_gain_word = PHAWord(adc_value=low_gain_adc_value, is_low_gain=True, detector=Detector.from_address(10),
-                                is_last_pha=False, adc_overflow=False)
+        low_gain_word = PHAWord(
+            adc_value=low_gain_adc_value,
+            is_low_gain=True,
+            detector=Detector.from_address(10),
+            is_last_pha=False,
+            adc_overflow=False,
+        )
         mev = calculate_mev(low_gain_word, gain_lookup_table)
         self.assertEqual(low_gain_a_value * low_gain_adc_value + low_gain_b_value, mev)
 
-        low_gain_word = PHAWord(adc_value=high_gain_adc_value, is_low_gain=False, detector=Detector.from_address(22),
-                                is_last_pha=False, adc_overflow=False)
+        low_gain_word = PHAWord(
+            adc_value=high_gain_adc_value,
+            is_low_gain=False,
+            detector=Detector.from_address(22),
+            is_last_pha=False,
+            adc_overflow=False,
+        )
         mev = calculate_mev(low_gain_word, gain_lookup_table)
-        self.assertEqual(high_gain_a_value * high_gain_adc_value + high_gain_b_value, mev)
+        self.assertEqual(
+            high_gain_a_value * high_gain_adc_value + high_gain_b_value, mev
+        )
 
     def test_analyze_range(self):
-        detector_1_in_group_1 = Detector(layer=1, side="A", segment="b", address=10, group="L1A14")
-        detector_2_in_group_1 = Detector(layer=1, side="A", segment="c", address=11, group="L1A14")
-        detector_1_in_group_2 = Detector(layer=2, side="A", segment="c", address=15, group="L2A")
-        detector_1_in_group_3 = Detector(layer=3, side="A", segment="a", address=20, group="L3A")
+        detector_1_in_group_1 = Detector(
+            layer=1, side="A", segment="b", address=10, group="L1A14"
+        )
+        detector_2_in_group_1 = Detector(
+            layer=1, side="A", segment="c", address=11, group="L1A14"
+        )
+        detector_1_in_group_2 = Detector(
+            layer=2, side="A", segment="c", address=15, group="L2A"
+        )
+        detector_1_in_group_3 = Detector(
+            layer=3, side="A", segment="a", address=20, group="L3A"
+        )
 
-        detector_to_pha_value = [(detector_1_in_group_1, 10),
-                                 (detector_2_in_group_1, 20),
-                                 (detector_1_in_group_2, 20),
-                                 (detector_1_in_group_3, 10)]
+        detector_to_pha_value = [
+            (detector_1_in_group_1, 10),
+            (detector_2_in_group_1, 20),
+            (detector_1_in_group_2, 20),
+            (detector_1_in_group_3, 10),
+        ]
 
-        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(detector_to_pha_value)
+        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(
+            detector_to_pha_value
+        )
         words = [word for _, word in detector_to_word_tuples.items()]
         raw_pha_event = create_raw_pha_event(pha_words=words)
 
-        rule_stub = Rule(range=DetectedRange(DetectorRange.R2, DetectorSide.A),
-                         included_detector_groups=["L1A14", "L2A"],
-                         excluded_detector_groups=["L2B"]
-                         )
+        rule_stub = Rule(
+            range=DetectedRange(DetectorRange.R2, DetectorSide.A),
+            included_detector_groups=["L1A14", "L2A"],
+            excluded_detector_groups=["L2B"],
+        )
         range_lookup_table = Mock()
         range_lookup_table.lookup_range.return_value = rule_stub
 
@@ -67,36 +100,61 @@ def test_analyze_range(self):
 
         range_lookup_table.lookup_range.assert_called_with({"L1A14", "L2A", "L3A"})
 
-        expected_highest_words = [detector_to_word_tuples[str(detector_2_in_group_1)],
-                                  detector_to_word_tuples[str(detector_1_in_group_2)]]
-        expected_event_analysis = EventAnalysis(range=rule_stub.range,
-                                                l1_detector=detector_2_in_group_1,
-                                                l2_detector=detector_1_in_group_2,
-                                                e_delta_word=detector_to_word_tuples[str(detector_2_in_group_1)],
-                                                e_prime_word=detector_to_word_tuples[str(detector_1_in_group_2)],
-                                                words_with_highest_energy=expected_highest_words)
+        expected_highest_words = [
+            detector_to_word_tuples[str(detector_2_in_group_1)],
+            detector_to_word_tuples[str(detector_1_in_group_2)],
+        ]
+        expected_event_analysis = EventAnalysis(
+            range=rule_stub.range,
+            l1_detector=detector_2_in_group_1,
+            l2_detector=detector_1_in_group_2,
+            e_delta_word=detector_to_word_tuples[str(detector_2_in_group_1)],
+            e_prime_word=detector_to_word_tuples[str(detector_1_in_group_2)],
+            words_with_highest_energy=expected_highest_words,
+        )
 
         self.assertEqual(expected_event_analysis, event_analysis)
 
     def test_analyze_range_mixed_sides(self):
-        detector_2_in_L2A_group = Detector(layer=2, side="A", segment="c", address=11, group="L2A")
-        detector_1_in_L2A_group = Detector(layer=2, side="A", segment="b", address=10, group="L2A")
-        detector_1_in_L2B_group = Detector(layer=2, side="B", segment="c", address=15, group="L2B")
-        detector_1_in_L1B14_group = Detector(layer=1, side="B", segment="a", address=20, group="L1B14")
+        detector_2_in_L2A_group = Detector(
+            layer=2, side="A", segment="c", address=11, group="L2A"
+        )
+        detector_1_in_L2A_group = Detector(
+            layer=2, side="A", segment="b", address=10, group="L2A"
+        )
+        detector_1_in_L2B_group = Detector(
+            layer=2, side="B", segment="c", address=15, group="L2B"
+        )
+        detector_1_in_L1B14_group = Detector(
+            layer=1, side="B", segment="a", address=20, group="L1B14"
+        )
 
-        detector_to_pha_value = [(detector_1_in_L2A_group, 10),
-                                 (detector_2_in_L2A_group, 20),
-                                 (detector_1_in_L2B_group, 20),
-                                 (detector_1_in_L1B14_group, 10)]
+        detector_to_pha_value = [
+            (detector_1_in_L2A_group, 10),
+            (detector_2_in_L2A_group, 20),
+            (detector_1_in_L2B_group, 20),
+            (detector_1_in_L1B14_group, 10),
+        ]
 
-        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(detector_to_pha_value)
+        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(
+            detector_to_pha_value
+        )
         words = [word for _, word in detector_to_word_tuples.items()]
         raw_pha_event = create_raw_pha_event(pha_words=words)
 
-        rule_stub = Rule(range=DetectedRange(DetectorRange.R2, DetectorSide.B),
-                         included_detector_groups=["L2B", "L2A", "L1B14"],
-                         excluded_detector_groups=["L1A14", "L1A0", "L3B", "L1B0", "L4iA", "L4iB", "L4oB"]
-                         )
+        rule_stub = Rule(
+            range=DetectedRange(DetectorRange.R2, DetectorSide.B),
+            included_detector_groups=["L2B", "L2A", "L1B14"],
+            excluded_detector_groups=[
+                "L1A14",
+                "L1A0",
+                "L3B",
+                "L1B0",
+                "L4iA",
+                "L4iB",
+                "L4oB",
+            ],
+        )
         range_lookup_table = Mock()
         range_lookup_table.lookup_range.return_value = rule_stub
 
@@ -110,132 +168,264 @@ def test_analyze_range_mixed_sides(self):
             detector_to_word_tuples[str(detector_1_in_L1B14_group)],
         ]
 
-        expected_event_analysis = EventAnalysis(range=rule_stub.range,
-                                                l1_detector=detector_1_in_L1B14_group,
-                                                l2_detector=detector_1_in_L2B_group,
-                                                e_delta_word=detector_to_word_tuples[str(detector_1_in_L1B14_group)],
-                                                e_prime_word=detector_to_word_tuples[str(detector_1_in_L2B_group)],
-                                                words_with_highest_energy=expected_highest_words)
+        expected_event_analysis = EventAnalysis(
+            range=rule_stub.range,
+            l1_detector=detector_1_in_L1B14_group,
+            l2_detector=detector_1_in_L2B_group,
+            e_delta_word=detector_to_word_tuples[str(detector_1_in_L1B14_group)],
+            e_prime_word=detector_to_word_tuples[str(detector_1_in_L2B_group)],
+            words_with_highest_energy=expected_highest_words,
+        )
 
         self.assertEqual(expected_event_analysis, event_analysis)
 
     def test_analyze_range_with_range_4(self):
-        detector_1_in_group_1 = Detector(layer=1, side="A", segment="b", address=10, group="L1A14")
-        detector_2_in_group_1 = Detector(layer=1, side="A", segment="c", address=11, group="L1A14")
-        detector_1_in_group_2 = Detector(layer=2, side="A", segment="c", address=15, group="L2A")
-        detector_1_in_group_3 = Detector(layer=3, side="A", segment="a", address=20, group="L3A")
-        detector_1_in_group_4 = Detector(layer=3, side="B", segment="a", address=21, group="L3B")
-        detector_1_in_group_5 = Detector(layer=2, side="B", segment="a", address=22, group="L2B")
-
-        detector_to_pha_value = [(detector_1_in_group_1, 10),
-                                 (detector_2_in_group_1, 20),
-                                 (detector_1_in_group_2, 20),
-                                 (detector_1_in_group_3, 11),
-                                 (detector_1_in_group_4, 12),
-                                 (detector_1_in_group_5, 13),
-                                 ]
-
-        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(detector_to_pha_value)
+        detector_1_in_group_1 = Detector(
+            layer=1, side="A", segment="b", address=10, group="L1A14"
+        )
+        detector_2_in_group_1 = Detector(
+            layer=1, side="A", segment="c", address=11, group="L1A14"
+        )
+        detector_1_in_group_2 = Detector(
+            layer=2, side="A", segment="c", address=15, group="L2A"
+        )
+        detector_1_in_group_3 = Detector(
+            layer=3, side="A", segment="a", address=20, group="L3A"
+        )
+        detector_1_in_group_4 = Detector(
+            layer=3, side="B", segment="a", address=21, group="L3B"
+        )
+        detector_1_in_group_5 = Detector(
+            layer=2, side="B", segment="a", address=22, group="L2B"
+        )
+
+        detector_to_pha_value = [
+            (detector_1_in_group_1, 10),
+            (detector_2_in_group_1, 20),
+            (detector_1_in_group_2, 20),
+            (detector_1_in_group_3, 11),
+            (detector_1_in_group_4, 12),
+            (detector_1_in_group_5, 13),
+        ]
+
+        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(
+            detector_to_pha_value
+        )
         words = [word for _, word in detector_to_word_tuples.items()]
         raw_pha_event = create_raw_pha_event(pha_words=words)
 
-        rule_stub = Rule(range=DetectedRange(DetectorRange.R4, DetectorSide.A),
-                         included_detector_groups=["L1A14", "L2A", "L3A", "L3B", "L2B"],
-                         excluded_detector_groups=["L1B"]
-                         )
+        rule_stub = Rule(
+            range=DetectedRange(DetectorRange.R4, DetectorSide.A),
+            included_detector_groups=["L1A14", "L2A", "L3A", "L3B", "L2B"],
+            excluded_detector_groups=["L1B"],
+        )
         range_lookup_table = Mock()
         range_lookup_table.lookup_range.return_value = rule_stub
 
         event_analysis = analyze_event(raw_pha_event, range_lookup_table)
 
-        range_lookup_table.lookup_range.assert_called_with({"L1A14", "L2A", "L3A", "L3B", "L2B"})
-
-        expected_highest_words = [detector_to_word_tuples[str(detector_2_in_group_1)],
-                                  detector_to_word_tuples[str(detector_1_in_group_2)],
-                                  detector_to_word_tuples[str(detector_1_in_group_3)],
-                                  detector_to_word_tuples[str(detector_1_in_group_4)],
-                                  detector_to_word_tuples[str(detector_1_in_group_5)],
-                                  ]
-        expected_event_analysis = EventAnalysis(range=rule_stub.range,
-                                                l1_detector=detector_2_in_group_1,
-                                                l2_detector=detector_1_in_group_2,
-                                                e_delta_word=detector_to_word_tuples[str(detector_1_in_group_3)],
-                                                e_prime_word=detector_to_word_tuples[str(detector_1_in_group_4)],
-                                                words_with_highest_energy=expected_highest_words)
+        range_lookup_table.lookup_range.assert_called_with(
+            {"L1A14", "L2A", "L3A", "L3B", "L2B"}
+        )
+
+        expected_highest_words = [
+            detector_to_word_tuples[str(detector_2_in_group_1)],
+            detector_to_word_tuples[str(detector_1_in_group_2)],
+            detector_to_word_tuples[str(detector_1_in_group_3)],
+            detector_to_word_tuples[str(detector_1_in_group_4)],
+            detector_to_word_tuples[str(detector_1_in_group_5)],
+        ]
+        expected_event_analysis = EventAnalysis(
+            range=rule_stub.range,
+            l1_detector=detector_2_in_group_1,
+            l2_detector=detector_1_in_group_2,
+            e_delta_word=detector_to_word_tuples[str(detector_1_in_group_3)],
+            e_prime_word=detector_to_word_tuples[str(detector_1_in_group_4)],
+            words_with_highest_energy=expected_highest_words,
+        )
 
         self.assertEqual(expected_event_analysis, event_analysis)
 
     def test_analyze_event_uses_detectors_with_highest_adc_values(self):
 
-        detector_l1ab = Detector(layer=1, side="A", segment="b", address=10, group="L1A14")
-        detector_l1ac = Detector(layer=1, side="A", segment="c", address=11, group="L1A14")
-        detector_l2ac = Detector(layer=2, side="A", segment="c", address=15, group="L2A")
+        detector_l1ab = Detector(
+            layer=1, side="A", segment="b", address=10, group="L1A14"
+        )
+        detector_l1ac = Detector(
+            layer=1, side="A", segment="c", address=11, group="L1A14"
+        )
+        detector_l2ac = Detector(
+            layer=2, side="A", segment="c", address=15, group="L2A"
+        )
 
-        word_l1ab = PHAWord(adc_value=1, detector=detector_l1ab, is_low_gain=False, adc_overflow=False,
-                            is_last_pha=False)
-        word_l1ac = PHAWord(adc_value=2, detector=detector_l1ac, is_low_gain=False, adc_overflow=False,
-                            is_last_pha=False)
-        word_l2ac = PHAWord(adc_value=3, detector=detector_l2ac, is_low_gain=False, adc_overflow=False,
-                            is_last_pha=False)
+        word_l1ab = PHAWord(
+            adc_value=1,
+            detector=detector_l1ab,
+            is_low_gain=False,
+            adc_overflow=False,
+            is_last_pha=False,
+        )
+        word_l1ac = PHAWord(
+            adc_value=2,
+            detector=detector_l1ac,
+            is_low_gain=False,
+            adc_overflow=False,
+            is_last_pha=False,
+        )
+        word_l2ac = PHAWord(
+            adc_value=3,
+            detector=detector_l2ac,
+            is_low_gain=False,
+            adc_overflow=False,
+            is_last_pha=False,
+        )
 
         test_cases = [
-            ("Higher ADC value is used both high gain", 1, False, 2, False, detector_l1ac, word_l1ac),
-            ("Higher ADC value is used both low gain", 2, True, 1, True, detector_l1ab, word_l1ab),
-            ("L1ab Low gain ADC * 20 is greater than L1ac ADC", 1, True, 19, False, detector_l1ab, word_l1ab),
-            ("L1ab Low gain ADC * 20 is less than L1ac ADC", 1, True, 21, False, detector_l1ac, word_l1ac),
-            ("Filters out saturated high gain words", 2047, False, 1, False, detector_l1ac, word_l1ac),
+            (
+                "Higher ADC value is used both high gain",
+                1,
+                False,
+                2,
+                False,
+                detector_l1ac,
+                word_l1ac,
+            ),
+            (
+                "Higher ADC value is used both low gain",
+                2,
+                True,
+                1,
+                True,
+                detector_l1ab,
+                word_l1ab,
+            ),
+            (
+                "L1ab Low gain ADC * 20 is greater than L1ac ADC",
+                1,
+                True,
+                19,
+                False,
+                detector_l1ab,
+                word_l1ab,
+            ),
+            (
+                "L1ab Low gain ADC * 20 is less than L1ac ADC",
+                1,
+                True,
+                21,
+                False,
+                detector_l1ac,
+                word_l1ac,
+            ),
+            (
+                "Filters out saturated high gain words",
+                2047,
+                False,
+                1,
+                False,
+                detector_l1ac,
+                word_l1ac,
+            ),
         ]
 
-        for name, l1ab_adc, l1ab_is_low_gain, l1ac_adc, l1ac_is_low_gain, expected_l1_detector, expected_l1_word in test_cases:
+        for (
+            name,
+            l1ab_adc,
+            l1ab_is_low_gain,
+            l1ac_adc,
+            l1ac_is_low_gain,
+            expected_l1_detector,
+            expected_l1_word,
+        ) in test_cases:
             with self.subTest(name):
                 word_l1ab.adc_value = l1ab_adc
                 word_l1ab.is_low_gain = l1ab_is_low_gain
                 word_l1ac.adc_value = l1ac_adc
                 word_l1ac.is_low_gain = l1ac_is_low_gain
 
-                raw_pha_event = create_raw_pha_event(pha_words=[word_l1ab, word_l1ac, word_l2ac])
+                raw_pha_event = create_raw_pha_event(
+                    pha_words=[word_l1ab, word_l1ac, word_l2ac]
+                )
 
-                rule_stub = Rule(range=DetectedRange(DetectorRange.R2, DetectorSide.A),
-                                 included_detector_groups=["L1A14", "L2A"],
-                                 excluded_detector_groups=["L1B"]
-                                 )
+                rule_stub = Rule(
+                    range=DetectedRange(DetectorRange.R2, DetectorSide.A),
+                    included_detector_groups=["L1A14", "L2A"],
+                    excluded_detector_groups=["L1B"],
+                )
                 range_lookup_table = Mock()
                 range_lookup_table.lookup_range.return_value = rule_stub
 
                 event_analysis = analyze_event(raw_pha_event, range_lookup_table)
 
-                expected_event_analysis = EventAnalysis(range=rule_stub.range,
-                                                        l1_detector=expected_l1_detector,
-                                                        l2_detector=detector_l2ac,
-                                                        e_delta_word=expected_l1_word,
-                                                        e_prime_word=word_l2ac,
-                                                        words_with_highest_energy=[expected_l1_word, word_l2ac])
+                expected_event_analysis = EventAnalysis(
+                    range=rule_stub.range,
+                    l1_detector=expected_l1_detector,
+                    l2_detector=detector_l2ac,
+                    e_delta_word=expected_l1_word,
+                    e_prime_word=word_l2ac,
+                    words_with_highest_energy=[expected_l1_word, word_l2ac],
+                )
 
                 self.assertEqual(expected_event_analysis.range, event_analysis.range)
-                self.assertEqual(expected_event_analysis.l1_detector, event_analysis.l1_detector)
-                self.assertEqual(expected_event_analysis.l2_detector, event_analysis.l2_detector)
-                self.assertEqual(expected_event_analysis.e_delta_word, event_analysis.e_delta_word)
-                self.assertEqual(expected_event_analysis.e_prime_word, event_analysis.e_prime_word)
-                self.assertEqual(expected_event_analysis.words_with_highest_energy, event_analysis.words_with_highest_energy)
+                self.assertEqual(
+                    expected_event_analysis.l1_detector, event_analysis.l1_detector
+                )
+                self.assertEqual(
+                    expected_event_analysis.l2_detector, event_analysis.l2_detector
+                )
+                self.assertEqual(
+                    expected_event_analysis.e_delta_word, event_analysis.e_delta_word
+                )
+                self.assertEqual(
+                    expected_event_analysis.e_prime_word, event_analysis.e_prime_word
+                )
+                self.assertEqual(
+                    expected_event_analysis.words_with_highest_energy,
+                    event_analysis.words_with_highest_energy,
+                )
 
     def test_analyze_event_filters_out_events_with_all_saturated_data(self):
-        detector_l1ab = Detector(layer=1, side="A", segment="b", address=10, group="L1A14")
-        detector_l1ac = Detector(layer=1, side="A", segment="c", address=11, group="L1A14")
-        detector_l2ac = Detector(layer=2, side="A", segment="c", address=15, group="L2A")
-
-        word_l1ab = PHAWord(adc_value=2047, detector=detector_l1ab, is_low_gain=False, adc_overflow=False,
-                            is_last_pha=False)
-        word_l1ac = PHAWord(adc_value=2047, detector=detector_l1ac, is_low_gain=False, adc_overflow=False,
-                            is_last_pha=False)
-        word_l2ac = PHAWord(adc_value=1, detector=detector_l2ac, is_low_gain=False, adc_overflow=False,
-                            is_last_pha=False)
-
-        raw_pha_event = create_raw_pha_event(pha_words=[word_l1ab, word_l1ac, word_l2ac])
-
-        rule_stub = Rule(range=DetectedRange(DetectorRange.R2, DetectorSide.A),
-                         included_detector_groups=["L1A14", "L2A"],
-                         excluded_detector_groups=["L1B"]
-                         )
+        detector_l1ab = Detector(
+            layer=1, side="A", segment="b", address=10, group="L1A14"
+        )
+        detector_l1ac = Detector(
+            layer=1, side="A", segment="c", address=11, group="L1A14"
+        )
+        detector_l2ac = Detector(
+            layer=2, side="A", segment="c", address=15, group="L2A"
+        )
+
+        word_l1ab = PHAWord(
+            adc_value=2047,
+            detector=detector_l1ab,
+            is_low_gain=False,
+            adc_overflow=False,
+            is_last_pha=False,
+        )
+        word_l1ac = PHAWord(
+            adc_value=2047,
+            detector=detector_l1ac,
+            is_low_gain=False,
+            adc_overflow=False,
+            is_last_pha=False,
+        )
+        word_l2ac = PHAWord(
+            adc_value=1,
+            detector=detector_l2ac,
+            is_low_gain=False,
+            adc_overflow=False,
+            is_last_pha=False,
+        )
+
+        raw_pha_event = create_raw_pha_event(
+            pha_words=[word_l1ab, word_l1ac, word_l2ac]
+        )
+
+        rule_stub = Rule(
+            range=DetectedRange(DetectorRange.R2, DetectorSide.A),
+            included_detector_groups=["L1A14", "L2A"],
+            excluded_detector_groups=["L1B"],
+        )
         range_lookup_table = Mock()
         range_lookup_table.lookup_range.return_value = rule_stub
 
@@ -244,17 +434,29 @@ def test_analyze_event_filters_out_events_with_all_saturated_data(self):
         self.assertIsNone(event_analysis)
 
     def test_analyze_event_handles_no_calc_rules(self):
-        detector_1_in_group_1 = Detector(layer=1, side="A", segment="b", address=10, group="L1A14")
-        detector_2_in_group_1 = Detector(layer=1, side="A", segment="c", address=11, group="L1A14")
-        detector_1_in_group_2 = Detector(layer=2, side="A", segment="c", address=15, group="L2A")
-        detector_1_in_group_3 = Detector(layer=3, side="A", segment="a", address=20, group="L3A")
+        detector_1_in_group_1 = Detector(
+            layer=1, side="A", segment="b", address=10, group="L1A14"
+        )
+        detector_2_in_group_1 = Detector(
+            layer=1, side="A", segment="c", address=11, group="L1A14"
+        )
+        detector_1_in_group_2 = Detector(
+            layer=2, side="A", segment="c", address=15, group="L2A"
+        )
+        detector_1_in_group_3 = Detector(
+            layer=3, side="A", segment="a", address=20, group="L3A"
+        )
 
-        detector_to_pha_value = [(detector_1_in_group_1, 10),
-                                 (detector_2_in_group_1, 20),
-                                 (detector_1_in_group_2, 20),
-                                 (detector_1_in_group_3, 10)]
+        detector_to_pha_value = [
+            (detector_1_in_group_1, 10),
+            (detector_2_in_group_1, 20),
+            (detector_1_in_group_2, 20),
+            (detector_1_in_group_3, 10),
+        ]
 
-        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(detector_to_pha_value)
+        detector_to_word_tuples = self._create_event_from_detector_to_pha_value_dict(
+            detector_to_pha_value
+        )
         words = [word for _, word in detector_to_word_tuples.items()]
         raw_pha_event = create_raw_pha_event(pha_words=words)
 
@@ -269,13 +471,27 @@ def test_analyze_event_handles_no_calc_rules(self):
 
     @patch("imap_l3_processing.hit.l3.pha.science.calculate_pha.analyze_event")
     @patch("imap_l3_processing.hit.l3.pha.science.calculate_pha.calculate_mev")
-    def test_process_event_for_event_with_invalid_detector_sequences(self, mock_calculate_mev, mock_analyze_event):
-        raw_pha_event = create_raw_pha_event(pha_words=[
-            PHAWord(detector=Detector.from_address(12), adc_value=10, adc_overflow=False,
-                    is_low_gain=True, is_last_pha=False),
-            PHAWord(detector=Detector.from_address(28), adc_value=20,
+    def test_process_event_for_event_with_invalid_detector_sequences(
+        self, mock_calculate_mev, mock_analyze_event
+    ):
+        raw_pha_event = create_raw_pha_event(
+            pha_words=[
+                PHAWord(
+                    detector=Detector.from_address(12),
+                    adc_value=10,
+                    adc_overflow=False,
+                    is_low_gain=True,
+                    is_last_pha=False,
+                ),
+                PHAWord(
+                    detector=Detector.from_address(28),
+                    adc_value=20,
                     adc_overflow=False,
-                    is_low_gain=False, is_last_pha=False)])
+                    is_low_gain=False,
+                    is_last_pha=False,
+                ),
+            ]
+        )
 
         word1_mev = 101.3
         word2_mev = 66.8
@@ -285,69 +501,119 @@ def test_process_event_for_event_with_invalid_detector_sequences(self, mock_calc
 
         event_output = process_pha_event(raw_pha_event, Mock(), Mock(), Mock(), Mock())
 
-        expected_event_output = EventOutput(original_event=raw_pha_event, energies=[word1_mev, word2_mev], charge=None,
-                                            total_energy=None, detected_range=None, e_delta=None, e_prime=None, )
+        expected_event_output = EventOutput(
+            original_event=raw_pha_event,
+            energies=[word1_mev, word2_mev],
+            charge=None,
+            total_energy=None,
+            detected_range=None,
+            e_delta=None,
+            e_prime=None,
+        )
         self.assertEqual(expected_event_output, event_output)
 
     @patch("imap_l3_processing.hit.l3.pha.science.calculate_pha.compute_charge")
-    @patch("imap_l3_processing.hit.l3.pha.science.calculate_pha.analyze_event", spec_set=True)
+    @patch(
+        "imap_l3_processing.hit.l3.pha.science.calculate_pha.analyze_event",
+        spec_set=True,
+    )
     def test_process_pha_event(self, mock_analyze_event, mock_compute_charge):
         pha_word1_adc_value = 53
         pha_word2_adc_value = 92
         pha_word3_adc_value = 48
 
-        l1_word = PHAWord(detector=Detector.from_address(12), adc_value=pha_word1_adc_value, adc_overflow=False,
-                          is_low_gain=True, is_last_pha=False)
-        l2_high_energy_word = PHAWord(detector=Detector.from_address(28), adc_value=pha_word2_adc_value,
-                                      adc_overflow=False,
-                                      is_low_gain=False, is_last_pha=False)
-        l2_low_energy_word = PHAWord(detector=Detector.from_address(29), adc_value=pha_word3_adc_value,
-                                     adc_overflow=False,
-                                     is_low_gain=False, is_last_pha=False)
+        l1_word = PHAWord(
+            detector=Detector.from_address(12),
+            adc_value=pha_word1_adc_value,
+            adc_overflow=False,
+            is_low_gain=True,
+            is_last_pha=False,
+        )
+        l2_high_energy_word = PHAWord(
+            detector=Detector.from_address(28),
+            adc_value=pha_word2_adc_value,
+            adc_overflow=False,
+            is_low_gain=False,
+            is_last_pha=False,
+        )
+        l2_low_energy_word = PHAWord(
+            detector=Detector.from_address(29),
+            adc_value=pha_word3_adc_value,
+            adc_overflow=False,
+            is_low_gain=False,
+            is_last_pha=False,
+        )
 
-        raw_pha_event = create_raw_pha_event(pha_words=[l1_word, l2_high_energy_word, l2_low_energy_word])
+        raw_pha_event = create_raw_pha_event(
+            pha_words=[l1_word, l2_high_energy_word, l2_low_energy_word]
+        )
 
         word1_gain = Gain(a=10.76, b=14.3)
         word2_gain = Gain(a=25.89, b=11.2)
         word3_gain = Gain(a=31.3, b=5.9)
 
-        mock_analyze_event.return_value = EventAnalysis(range=sentinel.detected_range, l1_detector=sentinel.l1_detector,
-                                                        l2_detector=sentinel.l2_detector,
-                                                        e_delta_word=l1_word,
-                                                        e_prime_word=l2_high_energy_word,
-                                                        words_with_highest_energy=[l1_word, l2_high_energy_word])
+        mock_analyze_event.return_value = EventAnalysis(
+            range=sentinel.detected_range,
+            l1_detector=sentinel.l1_detector,
+            l2_detector=sentinel.l2_detector,
+            e_delta_word=l1_word,
+            e_prime_word=l2_high_energy_word,
+            words_with_highest_energy=[l1_word, l2_high_energy_word],
+        )
 
         gain_lookup_table = {
             DetectorGain.HIGH: {28: word2_gain, 29: word3_gain},
-            DetectorGain.LOW: {12: word1_gain}
+            DetectorGain.LOW: {12: word1_gain},
         }
 
         cosine_correction = 12.2
 
         mock_cosine_correction_table = Mock()
-        mock_cosine_correction_table.get_cosine_correction.return_value = cosine_correction
+        mock_cosine_correction_table.get_cosine_correction.return_value = (
+            cosine_correction
+        )
 
-        event_output = process_pha_event(raw_pha_event, mock_cosine_correction_table, gain_lookup_table,
-                                         sentinel.range_fit_lookup, sentinel.rule_lookup)
+        event_output = process_pha_event(
+            raw_pha_event,
+            mock_cosine_correction_table,
+            gain_lookup_table,
+            sentinel.range_fit_lookup,
+            sentinel.rule_lookup,
+        )
 
         mock_analyze_event.assert_called_once_with(raw_pha_event, sentinel.rule_lookup)
 
-        mock_cosine_correction_table.get_cosine_correction.assert_called_once_with(sentinel.detected_range,
-                                                                                   sentinel.l1_detector,
-                                                                                   sentinel.l2_detector)
+        mock_cosine_correction_table.get_cosine_correction.assert_called_once_with(
+            sentinel.detected_range, sentinel.l1_detector, sentinel.l2_detector
+        )
 
         self.assertIsInstance(event_output, EventOutput)
 
-        expected_l1_energy = cosine_correction * (word1_gain.a * pha_word1_adc_value + word1_gain.b)
-        expected_l2_higher_energy = cosine_correction * (word2_gain.a * pha_word2_adc_value + word2_gain.b)
-        expected_energy3 = cosine_correction * (word3_gain.a * pha_word3_adc_value + word3_gain.b)
-        self.assertEqual([expected_l1_energy, expected_l2_higher_energy, expected_energy3], event_output.energies)
+        expected_l1_energy = cosine_correction * (
+            word1_gain.a * pha_word1_adc_value + word1_gain.b
+        )
+        expected_l2_higher_energy = cosine_correction * (
+            word2_gain.a * pha_word2_adc_value + word2_gain.b
+        )
+        expected_energy3 = cosine_correction * (
+            word3_gain.a * pha_word3_adc_value + word3_gain.b
+        )
+        self.assertEqual(
+            [expected_l1_energy, expected_l2_higher_energy, expected_energy3],
+            event_output.energies,
+        )
 
-        self.assertEqual(expected_l1_energy + expected_l2_higher_energy, event_output.total_energy)
+        self.assertEqual(
+            expected_l1_energy + expected_l2_higher_energy, event_output.total_energy
+        )
         self.assertEqual(raw_pha_event, event_output.original_event)
 
-        mock_compute_charge.assert_called_once_with(sentinel.detected_range, expected_l1_energy,
-                                                    expected_l2_higher_energy, sentinel.range_fit_lookup)
+        mock_compute_charge.assert_called_once_with(
+            sentinel.detected_range,
+            expected_l1_energy,
+            expected_l2_higher_energy,
+            sentinel.range_fit_lookup,
+        )
         self.assertEqual(mock_compute_charge.return_value, event_output.charge)
         self.assertEqual(sentinel.detected_range, event_output.detected_range)
         self.assertEqual(expected_l1_energy, event_output.e_delta)
@@ -367,33 +633,55 @@ def test_compute_charge(self):
         for delta_e, index_1, index_2 in cases:
             with self.subTest(delta_e):
                 mock_double_power_law_fit_params = Mock()
-                mock_double_power_law_fit_params.evaluate_e_prime.return_value = (charges, delta_e_losses)
-
-                detected_range = DetectedRange(range=DetectorRange.R2, side=DetectorSide.A)
+                mock_double_power_law_fit_params.evaluate_e_prime.return_value = (
+                    charges,
+                    delta_e_losses,
+                )
+
+                detected_range = DetectedRange(
+                    range=DetectorRange.R2, side=DetectorSide.A
+                )
                 e_prime = 200
-                charge = compute_charge(detected_range, delta_e, e_prime,
-                                        mock_double_power_law_fit_params)
+                charge = compute_charge(
+                    detected_range, delta_e, e_prime, mock_double_power_law_fit_params
+                )
 
-                mock_double_power_law_fit_params.evaluate_e_prime.assert_called_once_with(detected_range,
-                                                                                          e_prime)
+                mock_double_power_law_fit_params.evaluate_e_prime.assert_called_once_with(
+                    detected_range, e_prime
+                )
 
                 B = math.log(charges[index_2] / charges[index_1]) / math.log(
-                    delta_e_losses[index_2] / delta_e_losses[index_1])
+                    delta_e_losses[index_2] / delta_e_losses[index_1]
+                )
                 A = charges[index_1] / (delta_e_losses[index_1] ** B)
 
-                self.assertAlmostEqual(charge, A * delta_e ** B)
+                self.assertAlmostEqual(charge, A * delta_e**B)
 
-    def test_compute_charge_returns_nan_if_e_prime_or_delta_e_are_outside_of_range(self):
+    def test_compute_charge_returns_nan_if_e_prime_or_delta_e_are_outside_of_range(
+        self,
+    ):
         detected_range_1 = DetectedRange(range=DetectorRange.R2, side=DetectorSide.A)
         detected_range_2 = DetectedRange(range=DetectorRange.R3, side=DetectorSide.B)
         detected_range_3 = DetectedRange(range=DetectorRange.R4, side=DetectorSide.A)
         range_fit_lookup_table = RangeFitLookup.from_files(
-            get_test_data_path("hit/pha_events/imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"
+            ),
         )
         test_cases = [
             ("detector 1 - delta_e over max", detected_range_1, 430.1, 860, True),
@@ -423,35 +711,65 @@ def test_compute_charge_returns_nan_if_e_prime_or_delta_e_are_outside_of_range(s
         ]
         for test_name, detected_range, delta_e, e_prime, expected_nan in test_cases:
             with self.subTest(test_name):
-                result = compute_charge(detected_range, delta_e=delta_e, e_prime=e_prime,
-                                        double_power_law_lookup=range_fit_lookup_table)
+                result = compute_charge(
+                    detected_range,
+                    delta_e=delta_e,
+                    e_prime=e_prime,
+                    double_power_law_lookup=range_fit_lookup_table,
+                )
                 self.assertEqual(expected_nan, np.isnan(result))
 
-    def _create_detector_from_string(self, detector_string: str, detector_group: str = None):
+    def _create_detector_from_string(
+        self, detector_string: str, detector_group: str = None
+    ):
         layer = int(detector_string[1])
         group = detector_group if detector_group is not None else "L1A4c"
 
-        return Detector(layer=layer,
-                        side=detector_string[2],
-                        segment=detector_string[3:],
-                        address=0,
-                        group=group)
+        return Detector(
+            layer=layer,
+            side=detector_string[2],
+            segment=detector_string[3:],
+            address=0,
+            group=group,
+        )
 
-    def _create_event_from_detectors_and_groups(self, detectors: list[str], groups: list[str] = None):
+    def _create_event_from_detectors_and_groups(
+        self, detectors: list[str], groups: list[str] = None
+    ):
         if groups and len(detectors) != len(groups):
-            self.assertTrue(False, msg="Detectors and groups must have the same length in setup")
+            self.assertTrue(
+                False, msg="Detectors and groups must have the same length in setup"
+            )
 
-        detector_objects = [self._create_detector_from_string(d, g) for d, g in zip(detectors, groups)]
-        pha_words = [PHAWord(adc_value=0, adc_overflow=False, detector=d, is_low_gain=False, is_last_pha=False) for d in
-                     detector_objects]
+        detector_objects = [
+            self._create_detector_from_string(d, g) for d, g in zip(detectors, groups)
+        ]
+        pha_words = [
+            PHAWord(
+                adc_value=0,
+                adc_overflow=False,
+                detector=d,
+                is_low_gain=False,
+                is_last_pha=False,
+            )
+            for d in detector_objects
+        ]
         raw_pha_event = create_raw_pha_event(pha_words=pha_words)
         return raw_pha_event
 
-    def _create_event_from_detector_to_pha_value_dict(self, detector_to_pha_tuples: list[tuple[Detector, int]]):
+    def _create_event_from_detector_to_pha_value_dict(
+        self, detector_to_pha_tuples: list[tuple[Detector, int]]
+    ):
         pha_words = []
         detector_to_word_tuples = {}
         for detector, value in detector_to_pha_tuples:
-            word = PHAWord(adc_value=value, adc_overflow=False, detector=detector, is_low_gain=False, is_last_pha=False)
+            word = PHAWord(
+                adc_value=value,
+                adc_overflow=False,
+                detector=detector,
+                is_low_gain=False,
+                is_last_pha=False,
+            )
             pha_words.append(detector)
             detector_to_word_tuples[str(detector)] = word
 
diff --git a/tests/hit/l3/pha/science/test_cosine_correction_lookup_table.py b/tests/hit/l3/pha/science/test_cosine_correction_lookup_table.py
index c4329b1da..83cfcb6f2 100644
--- a/tests/hit/l3/pha/science/test_cosine_correction_lookup_table.py
+++ b/tests/hit/l3/pha/science/test_cosine_correction_lookup_table.py
@@ -1,19 +1,36 @@
 import unittest
 
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import CosineCorrectionLookupTable, Detector, \
-    DetectedRange, DetectorSide, DetectorRange
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    CosineCorrectionLookupTable,
+    DetectedRange,
+    Detector,
+    DetectorRange,
+    DetectorSide,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestCosineCorrectionLookupTable(unittest.TestCase):
     def test_can_create_from_file(self):
-        lookup_table = CosineCorrectionLookupTable(get_test_data_path(
-            "hit/pha_events/imap_hit_range-2A-cosine-lookup_20250203_v000.csv"), get_test_data_path(
-            "hit/pha_events/imap_hit_range-3A-cosine-lookup_20250203_v000.csv"), get_test_data_path(
-            "hit/pha_events/imap_hit_range-4A-cosine-lookup_20250203_v000.csv"), get_test_data_path(
-            "hit/pha_events/imap_hit_range-2B-cosine-lookup_20250203_v000.csv"), get_test_data_path(
-            "hit/pha_events/imap_hit_range-3B-cosine-lookup_20250203_v000.csv"), get_test_data_path(
-            "hit/pha_events/imap_hit_range-4B-cosine-lookup_20250203_v000.csv")
+        lookup_table = CosineCorrectionLookupTable(
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-2A-cosine-lookup_20250203_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-3A-cosine-lookup_20250203_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-4A-cosine-lookup_20250203_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-2B-cosine-lookup_20250203_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-3B-cosine-lookup_20250203_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-4B-cosine-lookup_20250203_v000.csv"
+            ),
         )
 
         self.assertEqual(150, len(lookup_table._range2A_corrections))
@@ -24,23 +41,67 @@ def test_can_create_from_file(self):
         self.assertEqual(150, len(lookup_table._range3B_corrections))
         self.assertEqual(150, len(lookup_table._range4B_corrections))
 
-        L1A0b_detector = Detector(layer=1, side="A", segment="0b", address=1234, group="L1A")
-        L2A0_detector = Detector(layer=2, side="A", segment="0", address=5678, group="L2A")
+        L1A0b_detector = Detector(
+            layer=1, side="A", segment="0b", address=1234, group="L1A"
+        )
+        L2A0_detector = Detector(
+            layer=2, side="A", segment="0", address=5678, group="L2A"
+        )
 
-        L1B0b_detector = Detector(layer=1, side="B", segment="0b", address=1234, group="L1A")
-        L2B0_detector = Detector(layer=2, side="B", segment="0", address=5678, group="L2A")
+        L1B0b_detector = Detector(
+            layer=1, side="B", segment="0b", address=1234, group="L1A"
+        )
+        L2B0_detector = Detector(
+            layer=2, side="B", segment="0", address=5678, group="L2A"
+        )
 
-        self.assertEqual(0.978643, lookup_table.get_cosine_correction(DetectedRange(DetectorRange.R2, DetectorSide.A),
-                                                                      L1A0b_detector, L2A0_detector))
-        self.assertEqual(0.978643, lookup_table.get_cosine_correction(DetectedRange(DetectorRange.R2, DetectorSide.B),
-                                                                      L1B0b_detector, L2B0_detector))
+        self.assertEqual(
+            0.978643,
+            lookup_table.get_cosine_correction(
+                DetectedRange(DetectorRange.R2, DetectorSide.A),
+                L1A0b_detector,
+                L2A0_detector,
+            ),
+        )
+        self.assertEqual(
+            0.978643,
+            lookup_table.get_cosine_correction(
+                DetectedRange(DetectorRange.R2, DetectorSide.B),
+                L1B0b_detector,
+                L2B0_detector,
+            ),
+        )
 
-        self.assertEqual(0.674600, lookup_table.get_cosine_correction(DetectedRange(DetectorRange.R3, DetectorSide.A),
-                                                                      L1A0b_detector, L2A0_detector))
-        self.assertEqual(0.674600, lookup_table.get_cosine_correction(DetectedRange(DetectorRange.R3, DetectorSide.B),
-                                                                      L1B0b_detector, L2B0_detector))
+        self.assertEqual(
+            0.674600,
+            lookup_table.get_cosine_correction(
+                DetectedRange(DetectorRange.R3, DetectorSide.A),
+                L1A0b_detector,
+                L2A0_detector,
+            ),
+        )
+        self.assertEqual(
+            0.674600,
+            lookup_table.get_cosine_correction(
+                DetectedRange(DetectorRange.R3, DetectorSide.B),
+                L1B0b_detector,
+                L2B0_detector,
+            ),
+        )
 
-        self.assertEqual(0.705122, lookup_table.get_cosine_correction(DetectedRange(DetectorRange.R4, DetectorSide.A),
-                                                                      L1A0b_detector, L2A0_detector))
-        self.assertEqual(0.705122, lookup_table.get_cosine_correction(DetectedRange(DetectorRange.R4, DetectorSide.B),
-                                                                      L1B0b_detector, L2B0_detector))
+        self.assertEqual(
+            0.705122,
+            lookup_table.get_cosine_correction(
+                DetectedRange(DetectorRange.R4, DetectorSide.A),
+                L1A0b_detector,
+                L2A0_detector,
+            ),
+        )
+        self.assertEqual(
+            0.705122,
+            lookup_table.get_cosine_correction(
+                DetectedRange(DetectorRange.R4, DetectorSide.B),
+                L1B0b_detector,
+                L2B0_detector,
+            ),
+        )
diff --git a/tests/hit/l3/pha/science/test_gain_lookup_table.py b/tests/hit/l3/pha/science/test_gain_lookup_table.py
index 5ac4eb3eb..99afb90e7 100644
--- a/tests/hit/l3/pha/science/test_gain_lookup_table.py
+++ b/tests/hit/l3/pha/science/test_gain_lookup_table.py
@@ -1,6 +1,9 @@
 import unittest
 
-from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import GainLookupTable, DetectorGain
+from imap_l3_processing.hit.l3.pha.science.gain_lookup_table import (
+    DetectorGain,
+    GainLookupTable,
+)
 from tests.test_helpers import get_test_data_path
 
 
@@ -8,9 +11,12 @@ class TestGainLookupTable(unittest.TestCase):
     def test_loads_from_file(self):
         lookup = GainLookupTable.from_file(
             high_gain_file_path=get_test_data_path(
-                'hit/pha_events/imap_hit_hi-gain-lookup_20250203_v000.csv'),
+                "hit/pha_events/imap_hit_hi-gain-lookup_20250203_v000.csv"
+            ),
             low_gain_file_path=get_test_data_path(
-                'hit/pha_events/imap_hit_lo-gain-lookup_20250203_v000.csv'))
+                "hit/pha_events/imap_hit_lo-gain-lookup_20250203_v000.csv"
+            ),
+        )
 
         self.assertEqual(2, len(lookup))
 
diff --git a/tests/hit/l3/pha/science/test_hit_event_type_lookup.py b/tests/hit/l3/pha/science/test_hit_event_type_lookup.py
index 893621bf6..abade9518 100644
--- a/tests/hit/l3/pha/science/test_hit_event_type_lookup.py
+++ b/tests/hit/l3/pha/science/test_hit_event_type_lookup.py
@@ -1,8 +1,15 @@
 import os
 import unittest
 
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, DetectorRange, DetectorSide
-from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import HitEventTypeLookup, Rule
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    DetectedRange,
+    DetectorRange,
+    DetectorSide,
+)
+from imap_l3_processing.hit.l3.pha.science.hit_event_type_lookup import (
+    HitEventTypeLookup,
+    Rule,
+)
 
 
 class TestHitEventTypeLookup(unittest.TestCase):
@@ -15,12 +22,18 @@ def tearDown(self):
 
     def test_hit_event_type_lookup_from_csv(self):
         with open(self.fake_lookup_path, "w") as csvfile:
-            csvfile.write("\n".join(["L1A4,L1B14,L2A,L3A,L2B,L40B,L4B14,Range",
-                                     "1,0,1,0,0,0,0,2A",
-                                     "1,0,1,1,0,*,0,3A",
-                                     "1,0,1,1,1,*,0,3A",
-                                     "0,1,0,*,1,*,1,2B",
-                                     "*,0,0,1,1,*,0,NOCALC"]))
+            csvfile.write(
+                "\n".join(
+                    [
+                        "L1A4,L1B14,L2A,L3A,L2B,L40B,L4B14,Range",
+                        "1,0,1,0,0,0,0,2A",
+                        "1,0,1,1,0,*,0,3A",
+                        "1,0,1,1,1,*,0,3A",
+                        "0,1,0,*,1,*,1,2B",
+                        "*,0,0,1,1,*,0,NOCALC",
+                    ]
+                )
+            )
 
         lookup = HitEventTypeLookup.from_csv(self.fake_lookup_path)
         self.assertEqual(4, len(lookup._rules))
@@ -30,12 +43,25 @@ def test_hit_event_type_lookup_from_csv(self):
         Range2B = DetectedRange(DetectorRange.R2, DetectorSide.B)
 
         test_cases = [
-            (Rule(Range2A, ["L1A4", "L2A"], ["L1B14", "L3A", "L2B", "L40B", "L4B14"]), {"L1A4", "L2A"}),
-            (Rule(Range3A, ["L1A4", "L2A", "L3A"], ["L1B14", "L2B", "L4B14"]), {"L1A4", "L2A", "L3A"}),
-            (Rule(Range3A, ["L1A4", "L2A", "L3A", "L2B"], ["L1B14", "L4B14"]),
-             {"L1A4", "L2A", "L3A", "L2B", "L40B"}),
-            (Rule(Range2B, ["L1B14", "L2B", "L4B14"], ["L1A4", "L2A"]), {"L1B14", "L2B", "L4B14"}),
-            (None, {"L3A", "L2B", "L40B"})
+            (
+                Rule(
+                    Range2A, ["L1A4", "L2A"], ["L1B14", "L3A", "L2B", "L40B", "L4B14"]
+                ),
+                {"L1A4", "L2A"},
+            ),
+            (
+                Rule(Range3A, ["L1A4", "L2A", "L3A"], ["L1B14", "L2B", "L4B14"]),
+                {"L1A4", "L2A", "L3A"},
+            ),
+            (
+                Rule(Range3A, ["L1A4", "L2A", "L3A", "L2B"], ["L1B14", "L4B14"]),
+                {"L1A4", "L2A", "L3A", "L2B", "L40B"},
+            ),
+            (
+                Rule(Range2B, ["L1B14", "L2B", "L4B14"], ["L1A4", "L2A"]),
+                {"L1B14", "L2B", "L4B14"},
+            ),
+            (None, {"L3A", "L2B", "L40B"}),
         ]
         for expected, detector_groups in test_cases:
             with self.subTest(detector_groups):
diff --git a/tests/hit/l3/pha/science/test_range_fit_lookup.py b/tests/hit/l3/pha/science/test_range_fit_lookup.py
index c81dd4043..02ac54f4f 100644
--- a/tests/hit/l3/pha/science/test_range_fit_lookup.py
+++ b/tests/hit/l3/pha/science/test_range_fit_lookup.py
@@ -5,24 +5,32 @@
 
 import numpy as np
 
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, DetectorSide, \
-    DetectorRange
-from imap_l3_processing.hit.l3.pha.science.range_fit_lookup import double_power_law, RangeFitLookup
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    DetectedRange,
+    DetectorRange,
+    DetectorSide,
+)
+from imap_l3_processing.hit.l3.pha.science.range_fit_lookup import (
+    RangeFitLookup,
+    double_power_law,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestRangeFitLookup(unittest.TestCase):
-
     def test_double_power_law(self):
-        a1 = 1.182819e+01
+        a1 = 1.182819e01
         b1 = 1.865991e-02
-        a2 = 6.508012e+01
+        a2 = 6.508012e01
         b2 = 8.420725e-01
-        gamma = 1.508828e+00
+        gamma = 1.508828e00
         e_prime = 5
         result = double_power_law(e_prime, a1, b1, a2, b2, gamma)
 
-        self.assertAlmostEqual(result, ((a1 * e_prime ** b1) ** gamma + (a2 * e_prime ** b2) ** gamma) ** (1 / gamma))
+        self.assertAlmostEqual(
+            result,
+            ((a1 * e_prime**b1) ** gamma + (a2 * e_prime**b2) ** gamma) ** (1 / gamma),
+        )
 
     def test_loads_csv_into_array(self):
         test_cases = [
@@ -47,13 +55,20 @@ def test_loads_csv_into_array(self):
                     charge1_parameters = [1, 2, 3, 4, 5]
                     charge2_parameters = [4, 5, 6, 7, 8]
 
-                    for path in [range2A_path, range3A_path, range4A_path, range2B_path, range3B_path, range4B_path]:
-                        with open(path, 'w') as f:
+                    for path in [
+                        range2A_path,
+                        range3A_path,
+                        range4A_path,
+                        range2B_path,
+                        range3B_path,
+                        range4B_path,
+                    ]:
+                        with open(path, "w") as f:
                             csv_writer = csv.writer(f)
                             csv_writer.writerow([0, 0, 0, 0, 0, 0])
 
                     path_to_populate = os.path.join(tempdir, file_to_populate)
-                    with open(path_to_populate, 'w') as range_file:
+                    with open(path_to_populate, "w") as range_file:
                         range_file.write("""#
 # Format: ion_charge, a1, b1, a2, b2, gamma
 #
@@ -68,25 +83,44 @@ def test_loads_csv_into_array(self):
                         range4A_file=range4A_path,
                         range2B_file=range2B_path,
                         range3B_file=range3B_path,
-                        range4B_file=range4B_path
+                        range4B_file=range4B_path,
                     )
 
                     e_prime = 11.2
 
-                    charges, delta_e_loss = lookup_table.evaluate_e_prime(range, e_prime)
+                    charges, delta_e_loss = lookup_table.evaluate_e_prime(
+                        range, e_prime
+                    )
 
                     np.testing.assert_equal(charges, expected_charges)
-                    np.testing.assert_allclose([double_power_law(e_prime, *charge1_parameters),
-                                                double_power_law(e_prime, *charge2_parameters)], delta_e_loss)
+                    np.testing.assert_allclose(
+                        [
+                            double_power_law(e_prime, *charge1_parameters),
+                            double_power_law(e_prime, *charge2_parameters),
+                        ],
+                        delta_e_loss,
+                    )
 
     def test_load_from_real_example_files(self):
         range_fit_lookup = RangeFitLookup.from_files(
-            get_test_data_path("hit/pha_events/imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"),
-            get_test_data_path("hit/pha_events/imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-2A-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-3A-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-4A-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-2B-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-3B-charge-fit-lookup_20250319_v000.csv"
+            ),
+            get_test_data_path(
+                "hit/pha_events/imap_hit_range-4B-charge-fit-lookup_20250319_v000.csv"
+            ),
         )
 
         self.assertIsInstance(range_fit_lookup, RangeFitLookup)
diff --git a/tests/hit/l3/pha/test_l3_pha_dependencies.py b/tests/hit/l3/pha/test_l3_pha_dependencies.py
index 27655f684..5c3cd0a2b 100644
--- a/tests/hit/l3/pha/test_l3_pha_dependencies.py
+++ b/tests/hit/l3/pha/test_l3_pha_dependencies.py
@@ -1,31 +1,60 @@
 import unittest
-from unittest.mock import patch, call, sentinel
+from unittest.mock import call, patch, sentinel
 
 import imap_data_access
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
-
-from imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies import HitL3PhaDependencies, HIT_L1A_EVENT_DESCRIPTOR, \
-    HIT_L3_LO_GAIN_LOOKUP_DESCRIPTOR, HIT_L3_HI_GAIN_LOOKUP_DESCRIPTOR, \
-    HIT_L3_EVENT_TYPE_LOOKUP_DESCRIPTOR, \
-    HIT_L3_RANGE_2A_COSINE_LOOKUP_DESCRIPTOR, HIT_L3_RANGE_3A_COSINE_LOOKUP_DESCRIPTOR, \
-    HIT_L3_RANGE_4A_COSINE_LOOKUP_DESCRIPTOR, HIT_L3_RANGE_2B_COSINE_LOOKUP_DESCRIPTOR, \
-    HIT_L3_RANGE_3B_COSINE_LOOKUP_DESCRIPTOR, HIT_L3_RANGE_4B_COSINE_LOOKUP_DESCRIPTOR, \
-    HIT_L3_RANGE_2A_CHARGE_FIT_LOOKUP_DESCRIPTOR, HIT_L3_RANGE_3A_CHARGE_FIT_LOOKUP_DESCRIPTOR, \
-    HIT_L3_RANGE_4A_CHARGE_FIT_LOOKUP_DESCRIPTOR, HIT_L3_RANGE_2B_CHARGE_FIT_LOOKUP_DESCRIPTOR, \
-    HIT_L3_RANGE_3B_CHARGE_FIT_LOOKUP_DESCRIPTOR, HIT_L3_RANGE_4B_CHARGE_FIT_LOOKUP_DESCRIPTOR
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
+
+from imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies import (
+    HIT_L1A_EVENT_DESCRIPTOR,
+    HIT_L3_EVENT_TYPE_LOOKUP_DESCRIPTOR,
+    HIT_L3_HI_GAIN_LOOKUP_DESCRIPTOR,
+    HIT_L3_LO_GAIN_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_2A_CHARGE_FIT_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_2A_COSINE_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_2B_CHARGE_FIT_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_2B_COSINE_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_3A_CHARGE_FIT_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_3A_COSINE_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_3B_CHARGE_FIT_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_3B_COSINE_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_4A_CHARGE_FIT_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_4A_COSINE_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_4B_CHARGE_FIT_LOOKUP_DESCRIPTOR,
+    HIT_L3_RANGE_4B_COSINE_LOOKUP_DESCRIPTOR,
+    HitL3PhaDependencies,
+)
 
 
 class TestHitL3PhaDependencies(unittest.TestCase):
-
     @patch("imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.download")
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.HitL1Data.read_from_cdf')
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.CosineCorrectionLookupTable')
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.GainLookupTable.from_file')
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.RangeFitLookup.from_files')
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.HitEventTypeLookup.from_csv')
-    def test_fetch_dependencies(self, mock_hit_event_type_from_lookup, mock_range_fit_from_lookup,
-                                mock_gain_lookup_from_file,
-                                mock_cosine_correction_lookup_from_file, mock_read_from_cdf, mock_download):
+    @patch(
+        "imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.HitL1Data.read_from_cdf"
+    )
+    @patch(
+        "imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.CosineCorrectionLookupTable"
+    )
+    @patch(
+        "imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.GainLookupTable.from_file"
+    )
+    @patch(
+        "imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.RangeFitLookup.from_files"
+    )
+    @patch(
+        "imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.HitEventTypeLookup.from_csv"
+    )
+    def test_fetch_dependencies(
+        self,
+        mock_hit_event_type_from_lookup,
+        mock_range_fit_from_lookup,
+        mock_gain_lookup_from_file,
+        mock_cosine_correction_lookup_from_file,
+        mock_read_from_cdf,
+        mock_download,
+    ):
         mock_download.side_effect = [
             sentinel.l1_data_cdf_path,
             sentinel.range_2A_cosine_lookup_cdf_path,
@@ -47,76 +76,109 @@ def test_fetch_dependencies(self, mock_hit_event_type_from_lookup, mock_range_fi
 
         input_collection = ProcessingInputCollection()
 
-        start_date = '20100105'
-        mission = 'imap'
-        instrument = 'hit'
-        data_level = 'l2'
-        version = 'v001'
+        start_date = "20100105"
+        mission = "imap"
+        instrument = "hit"
+        data_level = "l2"
+        version = "v001"
 
         science_l1a_event_path = f"{mission}_{instrument}_{data_level}_{HIT_L1A_EVENT_DESCRIPTOR}_{start_date}_{version}.cdf"
 
-        file_paths = [f"{mission}_{instrument}_{HIT_L3_RANGE_2A_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_3A_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_4A_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_2B_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_3B_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_4B_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_LO_GAIN_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_HI_GAIN_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_2A_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_3A_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_4A_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_2B_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_3B_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_RANGE_4B_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      f"{mission}_{instrument}_{HIT_L3_EVENT_TYPE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
-                      ]
+        file_paths = [
+            f"{mission}_{instrument}_{HIT_L3_RANGE_2A_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_3A_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_4A_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_2B_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_3B_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_4B_COSINE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_LO_GAIN_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_HI_GAIN_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_2A_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_3A_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_4A_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_2B_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_3B_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_RANGE_4B_CHARGE_FIT_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+            f"{mission}_{instrument}_{HIT_L3_EVENT_TYPE_LOOKUP_DESCRIPTOR}_{start_date}_{version}.csv",
+        ]
 
         ancillary_input_file_paths = [AncillaryInput(path) for path in file_paths]
         science_input = ScienceInput(science_l1a_event_path)
         input_collection.add([science_input, *ancillary_input_file_paths])
 
-        hit_l3_pha_dependencies = HitL3PhaDependencies.fetch_dependencies(input_collection)
+        hit_l3_pha_dependencies = HitL3PhaDependencies.fetch_dependencies(
+            input_collection
+        )
 
-        science_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'hit' / 'l2' / '2010' / '01'
-        ancillary_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'ancillary' / 'hit'
+        science_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "hit" / "l2" / "2010" / "01"
+        )
+        ancillary_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "ancillary" / "hit"
+        )
         expected_file_paths = [ancillary_dir / path for path in file_paths]
 
-        mock_download.assert_has_calls([
-            call(science_dir / science_l1a_event_path),
-            *[call(file_path) for file_path in expected_file_paths],
-        ])
+        mock_download.assert_has_calls(
+            [
+                call(science_dir / science_l1a_event_path),
+                *[call(file_path) for file_path in expected_file_paths],
+            ]
+        )
         mock_read_from_cdf.assert_called_with(sentinel.l1_data_cdf_path)
 
-        mock_cosine_correction_lookup_from_file.assert_called_once_with(sentinel.range_2A_cosine_lookup_cdf_path,
-                                                                        sentinel.range_3A_cosine_lookup_cdf_path,
-                                                                        sentinel.range_4A_cosine_lookup_cdf_path,
-                                                                        sentinel.range_2B_cosine_lookup_cdf_path,
-                                                                        sentinel.range_3B_cosine_lookup_cdf_path,
-                                                                        sentinel.range_4B_cosine_lookup_cdf_path)
-        mock_gain_lookup_from_file.assert_called_once_with(sentinel.hi_gain_lookup_cdf_path,
-                                                           sentinel.lo_gain_lookup_cdf_path, )
-        mock_range_fit_from_lookup.assert_called_once_with(sentinel.range_2A_charge_fit_lookup_cdf_path,
-                                                           sentinel.range_3A_charge_fit_lookup_cdf_path,
-                                                           sentinel.range_4A_charge_fit_lookup_cdf_path,
-                                                           sentinel.range_2B_charge_fit_lookup_cdf_path,
-                                                           sentinel.range_3B_charge_fit_lookup_cdf_path,
-                                                           sentinel.range_4B_charge_fit_lookup_cdf_path,
-                                                           )
-
-        mock_hit_event_type_from_lookup.assert_called_once_with(sentinel.event_type_lookup_path)
-
-        self.assertEqual(mock_read_from_cdf.return_value, hit_l3_pha_dependencies.hit_l1_data)
-        self.assertEqual(mock_cosine_correction_lookup_from_file.return_value,
-                         hit_l3_pha_dependencies.cosine_correction_lookup)
-        self.assertEqual(mock_gain_lookup_from_file.return_value, hit_l3_pha_dependencies.gain_lookup)
-        self.assertEqual(mock_range_fit_from_lookup.return_value, hit_l3_pha_dependencies.range_fit_lookup)
-        self.assertEqual(mock_hit_event_type_from_lookup.return_value, hit_l3_pha_dependencies.event_type_lookup)
-
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.download')
-    @patch('imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.HitL1Data.read_from_cdf')
-    def test_fetch_dependencies_throws_when_no_data_dependency_provided(self, mock_read_from_cdf,
-                                                                        mock_download_dependency):
+        mock_cosine_correction_lookup_from_file.assert_called_once_with(
+            sentinel.range_2A_cosine_lookup_cdf_path,
+            sentinel.range_3A_cosine_lookup_cdf_path,
+            sentinel.range_4A_cosine_lookup_cdf_path,
+            sentinel.range_2B_cosine_lookup_cdf_path,
+            sentinel.range_3B_cosine_lookup_cdf_path,
+            sentinel.range_4B_cosine_lookup_cdf_path,
+        )
+        mock_gain_lookup_from_file.assert_called_once_with(
+            sentinel.hi_gain_lookup_cdf_path,
+            sentinel.lo_gain_lookup_cdf_path,
+        )
+        mock_range_fit_from_lookup.assert_called_once_with(
+            sentinel.range_2A_charge_fit_lookup_cdf_path,
+            sentinel.range_3A_charge_fit_lookup_cdf_path,
+            sentinel.range_4A_charge_fit_lookup_cdf_path,
+            sentinel.range_2B_charge_fit_lookup_cdf_path,
+            sentinel.range_3B_charge_fit_lookup_cdf_path,
+            sentinel.range_4B_charge_fit_lookup_cdf_path,
+        )
+
+        mock_hit_event_type_from_lookup.assert_called_once_with(
+            sentinel.event_type_lookup_path
+        )
+
+        self.assertEqual(
+            mock_read_from_cdf.return_value, hit_l3_pha_dependencies.hit_l1_data
+        )
+        self.assertEqual(
+            mock_cosine_correction_lookup_from_file.return_value,
+            hit_l3_pha_dependencies.cosine_correction_lookup,
+        )
+        self.assertEqual(
+            mock_gain_lookup_from_file.return_value, hit_l3_pha_dependencies.gain_lookup
+        )
+        self.assertEqual(
+            mock_range_fit_from_lookup.return_value,
+            hit_l3_pha_dependencies.range_fit_lookup,
+        )
+        self.assertEqual(
+            mock_hit_event_type_from_lookup.return_value,
+            hit_l3_pha_dependencies.event_type_lookup,
+        )
+
+    @patch("imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.download")
+    @patch(
+        "imap_l3_processing.hit.l3.pha.hit_l3_pha_dependencies.HitL1Data.read_from_cdf"
+    )
+    def test_fetch_dependencies_throws_when_no_data_dependency_provided(
+        self, mock_read_from_cdf, mock_download_dependency
+    ):
         with self.assertRaises(ValueError) as e:
             _ = HitL3PhaDependencies.fetch_dependencies(ProcessingInputCollection())
-        self.assertEqual(str(e.exception), f"Missing {HIT_L1A_EVENT_DESCRIPTOR} dependency.")
+        self.assertEqual(
+            str(e.exception), f"Missing {HIT_L1A_EVENT_DESCRIPTOR} dependency."
+        )
diff --git a/tests/hit/l3/pha/test_pha_event_reader.py b/tests/hit/l3/pha/test_pha_event_reader.py
index 370bfbdd6..5cfba65d7 100644
--- a/tests/hit/l3/pha/test_pha_event_reader.py
+++ b/tests/hit/l3/pha/test_pha_event_reader.py
@@ -4,22 +4,48 @@
 from bitstring import BitArray, BitStream
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.hit.l3.pha.pha_event_reader import PHAEventReader, RawPHAEvent, PHAWord, Detector
+from imap_l3_processing.hit.l3.pha.pha_event_reader import (
+    Detector,
+    PHAEventReader,
+    PHAWord,
+    RawPHAEvent,
+)
 from tests.hit.l3.hit_test_builders import create_raw_pha_event
 from tests.test_helpers import get_test_data_path
 
 
 class TestPHAEventReader(unittest.TestCase):
-
     def test_can_create_detector_from_address(self):
         address_to_expected_detector = [
-            (35, "L1B1a", Detector(layer=1, side="B", segment="1a", address=35, group="L1B14")),
-            (6, "L2A3", Detector(layer=2, side="A", segment="3", address=6, group="L2A")),
-            (31, "L4Ao", Detector(layer=4, side="A", segment="o", address=31, group="L4oA")),
-            (10, "L4AUnknown_10", Detector(layer=4, side="A", segment="UNKNOWN_10", address=10, group="Unknown")),
+            (
+                35,
+                "L1B1a",
+                Detector(layer=1, side="B", segment="1a", address=35, group="L1B14"),
+            ),
+            (
+                6,
+                "L2A3",
+                Detector(layer=2, side="A", segment="3", address=6, group="L2A"),
+            ),
+            (
+                31,
+                "L4Ao",
+                Detector(layer=4, side="A", segment="o", address=31, group="L4oA"),
+            ),
+            (
+                10,
+                "L4AUnknown_10",
+                Detector(
+                    layer=4, side="A", segment="UNKNOWN_10", address=10, group="Unknown"
+                ),
+            ),
         ]
 
-        for address, expected_detector_name, expected_detector in address_to_expected_detector:
+        for (
+            address,
+            expected_detector_name,
+            expected_detector,
+        ) in address_to_expected_detector:
             with self.subTest(f"{address} should become {expected_detector_name}"):
                 detector = Detector.from_address(address)
                 self.assertEqual(expected_detector, detector)
@@ -68,18 +94,38 @@ def test_read_l1_pha_data(self):
         pha2_is_last_event = True
         pha2_is_last_pha_event = "1"
 
-        event_record_header_parts = [particle_id_binary, priority_number_binary, stim_tag_binary, haz_tag_binary,
-                                     time_tag_binary, a_b_side_binary, has_unread_adcs_binary, long_event_flag_binary,
-                                     culling_flag_binary, spare_binary]
-        pha1_data_parts = [pha1_adc_value_binary, pha1_adc_overflow_binary, pha1_detector_address_binary,
-                           pha1_is_low_gain_binary,
-                           pha1_is_last_pha_event]
-        pha2_data_parts = [pha2_adc_value_binary, pha2_adc_overflow_binary, pha2_detector_address_binary,
-                           pha2_is_low_gain_binary,
-                           pha2_is_last_pha_event]
+        event_record_header_parts = [
+            particle_id_binary,
+            priority_number_binary,
+            stim_tag_binary,
+            haz_tag_binary,
+            time_tag_binary,
+            a_b_side_binary,
+            has_unread_adcs_binary,
+            long_event_flag_binary,
+            culling_flag_binary,
+            spare_binary,
+        ]
+        pha1_data_parts = [
+            pha1_adc_value_binary,
+            pha1_adc_overflow_binary,
+            pha1_detector_address_binary,
+            pha1_is_low_gain_binary,
+            pha1_is_last_pha_event,
+        ]
+        pha2_data_parts = [
+            pha2_adc_value_binary,
+            pha2_adc_overflow_binary,
+            pha2_detector_address_binary,
+            pha2_is_low_gain_binary,
+            pha2_is_last_pha_event,
+        ]
 
-        binary = "".join(event_record_header_parts[::-1]) + "".join(pha1_data_parts[::-1]) + "".join(
-            pha2_data_parts[::-1])
+        binary = (
+            "".join(event_record_header_parts[::-1])
+            + "".join(pha1_data_parts[::-1])
+            + "".join(pha2_data_parts[::-1])
+        )
 
         bitstream = BitStream("0b" + binary)
         event_data = PHAEventReader.read_pha_event(bitstream)
@@ -99,13 +145,17 @@ def test_read_l1_pha_data(self):
         self.assertEqual(2, len(event_data.pha_words))
         self.assertEqual(pha1_adc_value, event_data.pha_words[0].adc_value)
         self.assertEqual(pha1_adc_overflow, event_data.pha_words[0].adc_overflow)
-        self.assertEqual(pha1_adc_detector_address, event_data.pha_words[0].detector.address)
+        self.assertEqual(
+            pha1_adc_detector_address, event_data.pha_words[0].detector.address
+        )
         self.assertEqual(pha1_is_low_gain, event_data.pha_words[0].is_low_gain)
         self.assertEqual(pha1_is_last_event, event_data.pha_words[0].is_last_pha)
 
         self.assertEqual(pha2_adc_value, event_data.pha_words[1].adc_value)
         self.assertEqual(pha2_adc_overflow, event_data.pha_words[1].adc_overflow)
-        self.assertEqual(pha2_adc_detector_address, event_data.pha_words[1].detector.address)
+        self.assertEqual(
+            pha2_adc_detector_address, event_data.pha_words[1].detector.address
+        )
         self.assertEqual(pha2_is_low_gain, event_data.pha_words[1].is_low_gain)
         self.assertEqual(pha2_is_last_event, event_data.pha_words[1].is_last_pha)
 
@@ -115,21 +165,31 @@ def test_reads_extended_header_section_when_long_event_flag_set(self):
         expected_delta_e_index = 108
         expected_e_prime_index = 105
 
-        extended_header_binary = "".join([
-            BitArray(uint=expected_delta_e_index, length=9).bin,
-            BitArray(uint=expected_e_prime_index, length=7).bin,
-            BitArray(uint=expected_detector_flags, length=8).bin,
-        ])
+        extended_header_binary = "".join(
+            [
+                BitArray(uint=expected_delta_e_index, length=9).bin,
+                BitArray(uint=expected_e_prime_index, length=7).bin,
+                BitArray(uint=expected_detector_flags, length=8).bin,
+            ]
+        )
 
         pha_word1 = self._create_pha_data(is_last_event=False)
         pha_word2 = self._create_pha_data(is_last_event=True)
-        bitstream = BitStream("0b" + header_binary + extended_header_binary + pha_word1 + pha_word2)
+        bitstream = BitStream(
+            "0b" + header_binary + extended_header_binary + pha_word1 + pha_word2
+        )
         event_data = PHAEventReader.read_pha_event(bitstream)
 
         self.assertIsNone(event_data.extended_stim_header)
-        self.assertEqual(expected_detector_flags, event_data.extended_header.detector_flags)
-        self.assertEqual(expected_delta_e_index, event_data.extended_header.delta_e_index)
-        self.assertEqual(expected_e_prime_index, event_data.extended_header.e_prime_index)
+        self.assertEqual(
+            expected_detector_flags, event_data.extended_header.detector_flags
+        )
+        self.assertEqual(
+            expected_delta_e_index, event_data.extended_header.delta_e_index
+        )
+        self.assertEqual(
+            expected_e_prime_index, event_data.extended_header.e_prime_index
+        )
 
     def test_reads_extended_stim_header_section_when_long_event_and_stim_flag_set(self):
         header_binary = self._create_pha_header(stim_tag=True, long_event_flag=True)
@@ -139,22 +199,32 @@ def test_reads_extended_stim_header_section_when_long_event_and_stim_flag_set(se
         expected_stim_step = 12
         expected_stim_gain = False
         expected_a_l_stim = 0
-        stim_block_binary = "".join([
-            BitArray(uint=expected_a_l_stim, length=1).bin,
-            BitArray(uint=0, length=2).bin,
-            BitArray(uint=expected_stim_gain, length=1).bin,
-            BitArray(uint=expected_stim_step, length=4).bin,
-        ])
-
-        extended_stim_header_binary = "".join([
-            BitArray(uint=expected_tbd, length=12).bin,
-            BitArray(uint=expected_dac_value, length=12).bin,
-        ])
+        stim_block_binary = "".join(
+            [
+                BitArray(uint=expected_a_l_stim, length=1).bin,
+                BitArray(uint=0, length=2).bin,
+                BitArray(uint=expected_stim_gain, length=1).bin,
+                BitArray(uint=expected_stim_step, length=4).bin,
+            ]
+        )
+
+        extended_stim_header_binary = "".join(
+            [
+                BitArray(uint=expected_tbd, length=12).bin,
+                BitArray(uint=expected_dac_value, length=12).bin,
+            ]
+        )
 
         pha_word1 = self._create_pha_data(is_last_event=False)
         pha_word2 = self._create_pha_data(is_last_event=True)
         bitstream = BitStream(
-            "0b" + header_binary + extended_stim_header_binary + stim_block_binary + pha_word1 + pha_word2)
+            "0b"
+            + header_binary
+            + extended_stim_header_binary
+            + stim_block_binary
+            + pha_word1
+            + pha_word2
+        )
         event_data = PHAEventReader.read_pha_event(bitstream)
 
         self.assertIsNone(event_data.extended_header)
@@ -171,16 +241,20 @@ def test_reads_stim_block_when_stim_flag_set(self):
         expected_stim_step = 11
         expected_stim_gain = True
         expected_a_l_stim = True
-        stim_block_binary = "".join([
-            BitArray(uint=expected_a_l_stim, length=1).bin,
-            BitArray(uint=0, length=2).bin,
-            BitArray(uint=expected_stim_gain, length=1).bin,
-            BitArray(uint=expected_stim_step, length=4).bin,
-        ])
+        stim_block_binary = "".join(
+            [
+                BitArray(uint=expected_a_l_stim, length=1).bin,
+                BitArray(uint=0, length=2).bin,
+                BitArray(uint=expected_stim_gain, length=1).bin,
+                BitArray(uint=expected_stim_step, length=4).bin,
+            ]
+        )
 
         pha_word1 = self._create_pha_data(is_last_event=False)
         pha_word2 = self._create_pha_data(is_last_event=True)
-        bitstream = BitStream("0b" + header_binary + stim_block_binary + pha_word1 + pha_word2)
+        bitstream = BitStream(
+            "0b" + header_binary + stim_block_binary + pha_word1 + pha_word2
+        )
         event_data = PHAEventReader.read_pha_event(bitstream)
 
         self.assertIsNone(event_data.extended_header)
@@ -209,9 +283,17 @@ def test_read_all_pha_events_stops_reading_events_when_it_finds_all_zeros(self):
         self.assertEqual(event3, events[2])
 
     def test_read_events_adds_extra_bits_for_odd_length_pha_words(self):
-        event1 = create_raw_pha_event(pha_words=[
-            PHAWord(detector=Detector.from_address(14), adc_value=1000, adc_overflow=True, is_low_gain=True,
-                    is_last_pha=True)])
+        event1 = create_raw_pha_event(
+            pha_words=[
+                PHAWord(
+                    detector=Detector.from_address(14),
+                    adc_value=1000,
+                    adc_overflow=True,
+                    is_low_gain=True,
+                    is_last_pha=True,
+                )
+            ]
+        )
 
         event2 = create_raw_pha_event()
 
@@ -225,7 +307,13 @@ def test_read_events_adds_extra_bits_for_odd_length_pha_words(self):
         self.assertEqual(event2, events[1])
 
     def test_reads_pha_events_from_cdf(self):
-        cdf = CDF(str(get_test_data_path("hit/pha_events/imap_hit_l1a_direct-events_20100105_v009.cdf")))
+        cdf = CDF(
+            str(
+                get_test_data_path(
+                    "hit/pha_events/imap_hit_l1a_direct-events_20100105_v009.cdf"
+                )
+            )
+        )
 
         event_total = 0
         for event_buffer in cdf["pha_raw"][...]:
@@ -235,11 +323,15 @@ def test_reads_pha_events_from_cdf(self):
         self.assertEqual(6967, event_total)
 
     def test_verify_against_HIT_script_output(self):
-        bitstream = BitStream(filename=get_test_data_path("hit/pha_events/full_event_record_buffer.bin"))
+        bitstream = BitStream(
+            filename=get_test_data_path("hit/pha_events/full_event_record_buffer.bin")
+        )
 
         events = PHAEventReader.read_all_pha_events(bitstream.bin)
 
-        with open(get_test_data_path("hit/pha_events/expected_pha_data_for_all_events.csv")) as expected_file:
+        with open(
+            get_test_data_path("hit/pha_events/expected_pha_data_for_all_events.csv")
+        ) as expected_file:
             reader = csv.reader(expected_file)
             for expected_row, event in zip(reader, events):
                 row = ["      " for _ in range(128)]
@@ -248,66 +340,89 @@ def test_verify_against_HIT_script_output(self):
                         continue
                     if not pha_word.is_low_gain:
                         index = pha_word.detector.address
-                        row[index] = "{: 6}".format(pha_word.adc_value)
+                        row[index] = f"{pha_word.adc_value: 6}"
                     else:
                         index = pha_word.detector.address + 64
-                        row[index] = "{: 6}".format(pha_word.adc_value)
+                        row[index] = f"{pha_word.adc_value: 6}"
                 self.assertEqual(expected_row[2:], row)
 
     def _create_pha_event_binary(self, pha_event: RawPHAEvent) -> str:
         binary_string = ""
 
-        binary_string += self._create_pha_header(particle_id=pha_event.particle_id,
-                                                 buf_priority_num=pha_event.priority_buffer_num,
-                                                 stim_tag=pha_event.stim_tag, haz_tag=pha_event.haz_tag,
-                                                 time_tag=pha_event.time_tag,
-                                                 a_b_side=pha_event.a_b_side_flag,
-                                                 has_unread_adcs=pha_event.has_unread_adcs,
-                                                 long_event_flag=pha_event.long_event_flag,
-                                                 culling_flag=pha_event.culling_flag,
-                                                 spare=pha_event.spare)
+        binary_string += self._create_pha_header(
+            particle_id=pha_event.particle_id,
+            buf_priority_num=pha_event.priority_buffer_num,
+            stim_tag=pha_event.stim_tag,
+            haz_tag=pha_event.haz_tag,
+            time_tag=pha_event.time_tag,
+            a_b_side=pha_event.a_b_side_flag,
+            has_unread_adcs=pha_event.has_unread_adcs,
+            long_event_flag=pha_event.long_event_flag,
+            culling_flag=pha_event.culling_flag,
+            spare=pha_event.spare,
+        )
 
         if pha_event.extended_header is not None:
-            binary_string += "".join([
-                BitArray(uint=pha_event.extended_header.e_prime_index, length=8).bin,
-                BitArray(uint=pha_event.extended_header.delta_e_index, length=8).bin,
-                BitArray(uint=pha_event.extended_header.detector_flags, length=8).bin,
-            ])
+            binary_string += "".join(
+                [
+                    BitArray(
+                        uint=pha_event.extended_header.e_prime_index, length=8
+                    ).bin,
+                    BitArray(
+                        uint=pha_event.extended_header.delta_e_index, length=8
+                    ).bin,
+                    BitArray(
+                        uint=pha_event.extended_header.detector_flags, length=8
+                    ).bin,
+                ]
+            )
 
         if pha_event.stim_block is not None:
-            binary_string += "".join([
-                BitArray(uint=pha_event.stim_block.a_l_stim, length=1).bin,
-                BitArray(uint=0, length=2).bin,
-                BitArray(uint=pha_event.stim_block.stim_gain, length=1).bin,
-                BitArray(uint=pha_event.stim_block.stim_step, length=4).bin,
-            ])
+            binary_string += "".join(
+                [
+                    BitArray(uint=pha_event.stim_block.a_l_stim, length=1).bin,
+                    BitArray(uint=0, length=2).bin,
+                    BitArray(uint=pha_event.stim_block.stim_gain, length=1).bin,
+                    BitArray(uint=pha_event.stim_block.stim_step, length=4).bin,
+                ]
+            )
 
         if pha_event.extended_stim_header is not None:
-            binary_string += "".join([
-                BitArray(uint=pha_event.extended_stim_header.tbd, length=12).bin,
-                BitArray(uint=pha_event.extended_stim_header.dac_value, length=12).bin,
-            ])
+            binary_string += "".join(
+                [
+                    BitArray(uint=pha_event.extended_stim_header.tbd, length=12).bin,
+                    BitArray(
+                        uint=pha_event.extended_stim_header.dac_value, length=12
+                    ).bin,
+                ]
+            )
 
         for word in pha_event.pha_words:
-            binary_string += self._create_pha_data(adc_value=word.adc_value, adc_overflow=word.adc_overflow,
-                                                   adc_detector_address=word.detector.address,
-                                                   is_low_gain=word.is_low_gain, is_last_event=word.is_last_pha)
+            binary_string += self._create_pha_data(
+                adc_value=word.adc_value,
+                adc_overflow=word.adc_overflow,
+                adc_detector_address=word.detector.address,
+                is_low_gain=word.is_low_gain,
+                is_last_event=word.is_last_pha,
+            )
         if len(pha_event.pha_words) % 2 == 1:
             binary_string += "0000"
 
         return binary_string
 
-    def _create_pha_header(self,
-                           particle_id: int = 0,
-                           buf_priority_num: int = 0,
-                           stim_tag: bool = False,
-                           haz_tag: bool = False,
-                           time_tag: int = 0,
-                           a_b_side: bool = False,
-                           has_unread_adcs: bool = False,
-                           long_event_flag: bool = False,
-                           culling_flag: bool = False,
-                           spare: bool = False):
+    def _create_pha_header(
+        self,
+        particle_id: int = 0,
+        buf_priority_num: int = 0,
+        stim_tag: bool = False,
+        haz_tag: bool = False,
+        time_tag: int = 0,
+        a_b_side: bool = False,
+        has_unread_adcs: bool = False,
+        long_event_flag: bool = False,
+        culling_flag: bool = False,
+        spare: bool = False,
+    ):
         bit_arrays = [
             BitArray(uint=int(spare), length=1),
             BitArray(uint=int(culling_flag), length=1),
@@ -322,13 +437,14 @@ def _create_pha_header(self,
         ]
         return "".join([bit_array.bin for bit_array in bit_arrays])
 
-    def _create_pha_data(self,
-                         adc_value=0,
-                         adc_overflow=False,
-                         adc_detector_address=0,
-                         is_low_gain=True,
-                         is_last_event=True,
-                         ):
+    def _create_pha_data(
+        self,
+        adc_value=0,
+        adc_overflow=False,
+        adc_detector_address=0,
+        is_low_gain=True,
+        is_last_event=True,
+    ):
         bit_arrays = [
             BitArray(uint=int(is_last_event), length=1),
             BitArray(uint=int(is_low_gain), length=1),
diff --git a/tests/hit/l3/sectored_products/science/test_sectored_products_algorithms.py b/tests/hit/l3/sectored_products/science/test_sectored_products_algorithms.py
index 85dde9c03..37c262c4e 100644
--- a/tests/hit/l3/sectored_products/science/test_sectored_products_algorithms.py
+++ b/tests/hit/l3/sectored_products/science/test_sectored_products_algorithms.py
@@ -2,39 +2,42 @@
 
 import numpy as np
 
-from imap_l3_processing.hit.l3.sectored_products.science.sectored_products_algorithms import get_sector_unit_vectors, \
-    get_hit_bin_polar_coordinates
+from imap_l3_processing.hit.l3.sectored_products.science.sectored_products_algorithms import (
+    get_hit_bin_polar_coordinates,
+    get_sector_unit_vectors,
+)
 
 
 class TestSectoredProductsAlgorithms(TestCase):
-
     def test_get_sector_unit_vectors(self):
         test_cases = [
-
             ([0], [[0], [270]], [[[0, 0, 1]], [[0, 0, 1]]]),
             ([0], [[90], [180]], [[[0, 0, 1]], [[0, 0, 1]]]),
             ([0], [[180], [90]], [[[0, 0, 1]], [[0, 0, 1]]]),
             ([0], [[270], [0]], [[[0, 0, 1]], [[0, 0, 1]]]),
             # ([0], [360], [[[0, 0, 1]]]),
-
             ([90], [[0], [270]], [[[1, 0, 0]], [[0, -1, 0]]]),
             ([90], [[90], [180]], [[[0, 1, 0]], [[-1, 0, 0]]]),
             ([90], [[180], [90]], [[[-1, 0, 0]], [[0, 1, 0]]]),
             ([90], [[270], [0]], [[[0, -1, 0]], [[1, 0, 0]]]),
-
             ([180], [[0], [90]], [[[0, 0, -1]], [[0, 0, -1]]]),
             ([180], [[90], [0]], [[[0, 0, -1]], [[0, 0, -1]]]),
-
         ]
 
         for elevation, azimuth, expected in test_cases:
             with self.subTest(elevation=elevation, azimuth=azimuth, expected=expected):
-                np.testing.assert_array_almost_equal(get_sector_unit_vectors(elevation, azimuth), expected)
+                np.testing.assert_array_almost_equal(
+                    get_sector_unit_vectors(elevation, azimuth), expected
+                )
 
     def test_get_hit_bin_polar_coordinates(self):
-        declinations, azimuths, declination_delta, azimuth_delta = get_hit_bin_polar_coordinates()
+        declinations, azimuths, declination_delta, azimuth_delta = (
+            get_hit_bin_polar_coordinates()
+        )
 
         np.testing.assert_array_equal(declination_delta, [11.25] * 8, strict=True)
         np.testing.assert_array_equal(azimuth_delta, [12.0] * 15, strict=True)
-        np.testing.assert_array_almost_equal(declinations, 11.25 + np.arange(0, 8) * 22.5)
+        np.testing.assert_array_almost_equal(
+            declinations, 11.25 + np.arange(0, 8) * 22.5
+        )
         np.testing.assert_array_almost_equal(azimuths, 12 + np.arange(15) * 24)
diff --git a/tests/hit/l3/sectored_products/test_models.py b/tests/hit/l3/sectored_products/test_models.py
index 078b6af0d..e8b3b07db 100644
--- a/tests/hit/l3/sectored_products/test_models.py
+++ b/tests/hit/l3/sectored_products/test_models.py
@@ -1,6 +1,6 @@
 from datetime import timedelta
 from unittest import TestCase
-from unittest.mock import sentinel, Mock
+from unittest.mock import Mock, sentinel
 
 import numpy as np
 
@@ -24,64 +24,65 @@ def test_to_data_product_variables(self):
         mock_measurement_pitch_angle = Mock()
         mock_measurement_pitch_angle.shape = (1, 8, 15)
 
-        data = HitPitchAngleDataProduct(sentinel.input_meta_data,
-                                        sentinel.epoch,
-                                        epoch_deltas,
-                                        pitch_angles,
-                                        sentinel.pitch_angles_deltas,
-                                        gyrophases,
-                                        sentinel.gyrophase_deltas,
-                                        sentinel.h_macropixel_intensity,
-                                        sentinel.h_macropixel_intensity_delta_plus,
-                                        sentinel.h_macropixel_intensity_delta_minus,
-                                        sentinel.h_pa_intensity,
-                                        sentinel.h_pa_intensity_delta_plus,
-                                        sentinel.h_pa_intensity_delta_minus,
-                                        h_energy,
-                                        sentinel.h_energy_delta_plus,
-                                        sentinel.h_energy_delta_minus,
-                                        sentinel.he4_macropixel_intensity,
-                                        sentinel.he4_macropixel_intensity_delta_plus,
-                                        sentinel.he4_macropixel_intensity_delta_minus,
-                                        sentinel.he4_pa_intensity,
-                                        sentinel.he4_pa_intensity_delta_plus,
-                                        sentinel.he4_pa_intensity_delta_minus,
-                                        he_energy,
-                                        sentinel.he4_energy_delta_plus,
-                                        sentinel.he4_energy_delta_minus,
-                                        sentinel.cno_macropixel_intensity,
-                                        sentinel.cno_macropixel_intensity_delta_plus,
-                                        sentinel.cno_macropixel_intensity_delta_minus,
-                                        sentinel.cno_pa_intensity,
-                                        sentinel.cno_pa_intensity_delta_plus,
-                                        sentinel.cno_pa_intensity_delta_minus,
-                                        cno_energy,
-                                        sentinel.cno_energy_delta_plus,
-                                        sentinel.cno_energy_delta_minus,
-                                        sentinel.ne_mg_si_macropixel_intensity,
-                                        sentinel.ne_mg_si_macropixel_intensity_delta_plus,
-                                        sentinel.ne_mg_si_macropixel_intensity_delta_minus,
-                                        sentinel.ne_mg_si_pa_intensity,
-                                        sentinel.ne_mg_si_pa_intensity_delta_plus,
-                                        sentinel.ne_mg_si_pa_intensity_delta_minus,
-                                        nemgsi_energy,
-                                        sentinel.ne_mg_si_energy_delta_plus,
-                                        sentinel.ne_mg_si_energy_delta_minus,
-                                        sentinel.iron_macropixel_intensity,
-                                        sentinel.iron_macropixel_intensity_delta_plus,
-                                        sentinel.iron_macropixel_intensity_delta_minus,
-                                        sentinel.iron_pa_intensity,
-                                        sentinel.iron_pa_intensity_delta_plus,
-                                        sentinel.iron_pa_intensity_delta_minus,
-                                        fe_energy,
-                                        sentinel.iron_energy_delta_plus,
-                                        sentinel.iron_energy_delta_minus,
-                                        mock_measurement_pitch_angle,
-                                        sentinel.measurement_gyrophase,
-                                        np.array([10, 180, 350]),
-                                        np.array([10.25, 90.75, 170.5]),
-                                        sentinel.quality_flags,
-                                        )
+        data = HitPitchAngleDataProduct(
+            sentinel.input_meta_data,
+            sentinel.epoch,
+            epoch_deltas,
+            pitch_angles,
+            sentinel.pitch_angles_deltas,
+            gyrophases,
+            sentinel.gyrophase_deltas,
+            sentinel.h_macropixel_intensity,
+            sentinel.h_macropixel_intensity_delta_plus,
+            sentinel.h_macropixel_intensity_delta_minus,
+            sentinel.h_pa_intensity,
+            sentinel.h_pa_intensity_delta_plus,
+            sentinel.h_pa_intensity_delta_minus,
+            h_energy,
+            sentinel.h_energy_delta_plus,
+            sentinel.h_energy_delta_minus,
+            sentinel.he4_macropixel_intensity,
+            sentinel.he4_macropixel_intensity_delta_plus,
+            sentinel.he4_macropixel_intensity_delta_minus,
+            sentinel.he4_pa_intensity,
+            sentinel.he4_pa_intensity_delta_plus,
+            sentinel.he4_pa_intensity_delta_minus,
+            he_energy,
+            sentinel.he4_energy_delta_plus,
+            sentinel.he4_energy_delta_minus,
+            sentinel.cno_macropixel_intensity,
+            sentinel.cno_macropixel_intensity_delta_plus,
+            sentinel.cno_macropixel_intensity_delta_minus,
+            sentinel.cno_pa_intensity,
+            sentinel.cno_pa_intensity_delta_plus,
+            sentinel.cno_pa_intensity_delta_minus,
+            cno_energy,
+            sentinel.cno_energy_delta_plus,
+            sentinel.cno_energy_delta_minus,
+            sentinel.ne_mg_si_macropixel_intensity,
+            sentinel.ne_mg_si_macropixel_intensity_delta_plus,
+            sentinel.ne_mg_si_macropixel_intensity_delta_minus,
+            sentinel.ne_mg_si_pa_intensity,
+            sentinel.ne_mg_si_pa_intensity_delta_plus,
+            sentinel.ne_mg_si_pa_intensity_delta_minus,
+            nemgsi_energy,
+            sentinel.ne_mg_si_energy_delta_plus,
+            sentinel.ne_mg_si_energy_delta_minus,
+            sentinel.iron_macropixel_intensity,
+            sentinel.iron_macropixel_intensity_delta_plus,
+            sentinel.iron_macropixel_intensity_delta_minus,
+            sentinel.iron_pa_intensity,
+            sentinel.iron_pa_intensity_delta_plus,
+            sentinel.iron_pa_intensity_delta_minus,
+            fe_energy,
+            sentinel.iron_energy_delta_plus,
+            sentinel.iron_energy_delta_minus,
+            mock_measurement_pitch_angle,
+            sentinel.measurement_gyrophase,
+            np.array([10, 180, 350]),
+            np.array([10.25, 90.75, 170.5]),
+            sentinel.quality_flags,
+        )
 
         data_product_variables = data.to_data_product_variables()
         expected_epoch_deltas = np.array([5e9])
@@ -92,78 +93,222 @@ def test_to_data_product_variables(self):
             DataProductVariable("pitch_angle_delta", sentinel.pitch_angles_deltas),
             DataProductVariable("gyrophase", NumpyArrayMatcher(gyrophases)),
             DataProductVariable("gyrophase_delta", sentinel.gyrophase_deltas),
-            DataProductVariable("h_macropixel_intensity", sentinel.h_macropixel_intensity),
-            DataProductVariable("h_macropixel_intensity_delta_plus", sentinel.h_macropixel_intensity_delta_plus),
-            DataProductVariable("h_macropixel_intensity_delta_minus", sentinel.h_macropixel_intensity_delta_minus),
+            DataProductVariable(
+                "h_macropixel_intensity", sentinel.h_macropixel_intensity
+            ),
+            DataProductVariable(
+                "h_macropixel_intensity_delta_plus",
+                sentinel.h_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "h_macropixel_intensity_delta_minus",
+                sentinel.h_macropixel_intensity_delta_minus,
+            ),
             DataProductVariable("h_macropixel_intensity_pa", sentinel.h_pa_intensity),
-            DataProductVariable("h_macropixel_intensity_pa_delta_plus", sentinel.h_pa_intensity_delta_plus),
-            DataProductVariable("h_macropixel_intensity_pa_delta_minus", sentinel.h_pa_intensity_delta_minus),
+            DataProductVariable(
+                "h_macropixel_intensity_pa_delta_plus",
+                sentinel.h_pa_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "h_macropixel_intensity_pa_delta_minus",
+                sentinel.h_pa_intensity_delta_minus,
+            ),
             DataProductVariable("h_energy", NumpyArrayMatcher(h_energy)),
             DataProductVariable("h_energy_delta_plus", sentinel.h_energy_delta_plus),
             DataProductVariable("h_energy_delta_minus", sentinel.h_energy_delta_minus),
-            DataProductVariable("he4_macropixel_intensity", sentinel.he4_macropixel_intensity),
-            DataProductVariable("he4_macropixel_intensity_delta_plus", sentinel.he4_macropixel_intensity_delta_plus),
-            DataProductVariable("he4_macropixel_intensity_delta_minus", sentinel.he4_macropixel_intensity_delta_minus),
-            DataProductVariable("he4_macropixel_intensity_pa", sentinel.he4_pa_intensity),
-            DataProductVariable("he4_macropixel_intensity_pa_delta_plus", sentinel.he4_pa_intensity_delta_plus),
-            DataProductVariable("he4_macropixel_intensity_pa_delta_minus", sentinel.he4_pa_intensity_delta_minus),
+            DataProductVariable(
+                "he4_macropixel_intensity", sentinel.he4_macropixel_intensity
+            ),
+            DataProductVariable(
+                "he4_macropixel_intensity_delta_plus",
+                sentinel.he4_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "he4_macropixel_intensity_delta_minus",
+                sentinel.he4_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                "he4_macropixel_intensity_pa", sentinel.he4_pa_intensity
+            ),
+            DataProductVariable(
+                "he4_macropixel_intensity_pa_delta_plus",
+                sentinel.he4_pa_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "he4_macropixel_intensity_pa_delta_minus",
+                sentinel.he4_pa_intensity_delta_minus,
+            ),
             DataProductVariable("he4_energy", NumpyArrayMatcher(he_energy)),
-            DataProductVariable("he4_energy_delta_plus", sentinel.he4_energy_delta_plus),
-            DataProductVariable("he4_energy_delta_minus", sentinel.he4_energy_delta_minus),
-            DataProductVariable("cno_macropixel_intensity", sentinel.cno_macropixel_intensity),
-            DataProductVariable("cno_macropixel_intensity_delta_plus", sentinel.cno_macropixel_intensity_delta_plus),
-            DataProductVariable("cno_macropixel_intensity_delta_minus", sentinel.cno_macropixel_intensity_delta_minus),
-            DataProductVariable("cno_macropixel_intensity_pa", sentinel.cno_pa_intensity),
-            DataProductVariable("cno_macropixel_intensity_pa_delta_plus", sentinel.cno_pa_intensity_delta_plus),
-            DataProductVariable("cno_macropixel_intensity_pa_delta_minus", sentinel.cno_pa_intensity_delta_minus),
+            DataProductVariable(
+                "he4_energy_delta_plus", sentinel.he4_energy_delta_plus
+            ),
+            DataProductVariable(
+                "he4_energy_delta_minus", sentinel.he4_energy_delta_minus
+            ),
+            DataProductVariable(
+                "cno_macropixel_intensity", sentinel.cno_macropixel_intensity
+            ),
+            DataProductVariable(
+                "cno_macropixel_intensity_delta_plus",
+                sentinel.cno_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "cno_macropixel_intensity_delta_minus",
+                sentinel.cno_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                "cno_macropixel_intensity_pa", sentinel.cno_pa_intensity
+            ),
+            DataProductVariable(
+                "cno_macropixel_intensity_pa_delta_plus",
+                sentinel.cno_pa_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "cno_macropixel_intensity_pa_delta_minus",
+                sentinel.cno_pa_intensity_delta_minus,
+            ),
             DataProductVariable("cno_energy", NumpyArrayMatcher(cno_energy)),
-            DataProductVariable("cno_energy_delta_plus", sentinel.cno_energy_delta_plus),
-            DataProductVariable("cno_energy_delta_minus", sentinel.cno_energy_delta_minus),
-            DataProductVariable("nemgsi_macropixel_intensity", sentinel.ne_mg_si_macropixel_intensity),
-            DataProductVariable("nemgsi_macropixel_intensity_delta_plus",
-                                sentinel.ne_mg_si_macropixel_intensity_delta_plus),
-            DataProductVariable("nemgsi_macropixel_intensity_delta_minus",
-                                sentinel.ne_mg_si_macropixel_intensity_delta_minus),
-            DataProductVariable("nemgsi_macropixel_intensity_pa", sentinel.ne_mg_si_pa_intensity),
-            DataProductVariable("nemgsi_macropixel_intensity_pa_delta_plus", sentinel.ne_mg_si_pa_intensity_delta_plus),
-            DataProductVariable("nemgsi_macropixel_intensity_pa_delta_minus",
-                                sentinel.ne_mg_si_pa_intensity_delta_minus),
+            DataProductVariable(
+                "cno_energy_delta_plus", sentinel.cno_energy_delta_plus
+            ),
+            DataProductVariable(
+                "cno_energy_delta_minus", sentinel.cno_energy_delta_minus
+            ),
+            DataProductVariable(
+                "nemgsi_macropixel_intensity", sentinel.ne_mg_si_macropixel_intensity
+            ),
+            DataProductVariable(
+                "nemgsi_macropixel_intensity_delta_plus",
+                sentinel.ne_mg_si_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "nemgsi_macropixel_intensity_delta_minus",
+                sentinel.ne_mg_si_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                "nemgsi_macropixel_intensity_pa", sentinel.ne_mg_si_pa_intensity
+            ),
+            DataProductVariable(
+                "nemgsi_macropixel_intensity_pa_delta_plus",
+                sentinel.ne_mg_si_pa_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "nemgsi_macropixel_intensity_pa_delta_minus",
+                sentinel.ne_mg_si_pa_intensity_delta_minus,
+            ),
             DataProductVariable("nemgsi_energy", NumpyArrayMatcher(nemgsi_energy)),
-            DataProductVariable("nemgsi_energy_delta_plus", sentinel.ne_mg_si_energy_delta_plus),
-            DataProductVariable("nemgsi_energy_delta_minus", sentinel.ne_mg_si_energy_delta_minus),
-            DataProductVariable("fe_macropixel_intensity", sentinel.iron_macropixel_intensity),
-            DataProductVariable("fe_macropixel_intensity_delta_plus", sentinel.iron_macropixel_intensity_delta_plus),
-            DataProductVariable("fe_macropixel_intensity_delta_minus", sentinel.iron_macropixel_intensity_delta_minus),
-            DataProductVariable("fe_macropixel_intensity_pa", sentinel.iron_pa_intensity),
-            DataProductVariable("fe_macropixel_intensity_pa_delta_plus", sentinel.iron_pa_intensity_delta_plus),
-            DataProductVariable("fe_macropixel_intensity_pa_delta_minus", sentinel.iron_pa_intensity_delta_minus),
+            DataProductVariable(
+                "nemgsi_energy_delta_plus", sentinel.ne_mg_si_energy_delta_plus
+            ),
+            DataProductVariable(
+                "nemgsi_energy_delta_minus", sentinel.ne_mg_si_energy_delta_minus
+            ),
+            DataProductVariable(
+                "fe_macropixel_intensity", sentinel.iron_macropixel_intensity
+            ),
+            DataProductVariable(
+                "fe_macropixel_intensity_delta_plus",
+                sentinel.iron_macropixel_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "fe_macropixel_intensity_delta_minus",
+                sentinel.iron_macropixel_intensity_delta_minus,
+            ),
+            DataProductVariable(
+                "fe_macropixel_intensity_pa", sentinel.iron_pa_intensity
+            ),
+            DataProductVariable(
+                "fe_macropixel_intensity_pa_delta_plus",
+                sentinel.iron_pa_intensity_delta_plus,
+            ),
+            DataProductVariable(
+                "fe_macropixel_intensity_pa_delta_minus",
+                sentinel.iron_pa_intensity_delta_minus,
+            ),
             DataProductVariable("fe_energy", NumpyArrayMatcher(fe_energy)),
-            DataProductVariable("fe_energy_delta_plus", sentinel.iron_energy_delta_plus),
-            DataProductVariable("fe_energy_delta_minus", sentinel.iron_energy_delta_minus),
-            DataProductVariable("measurement_pitch_angle", mock_measurement_pitch_angle),
-            DataProductVariable("measurement_gyrophase", sentinel.measurement_gyrophase),
-            DataProductVariable("pitch_angle_label",
-                                ["Pitch Angle Label 1", "Pitch Angle Label 2", "Pitch Angle Label 3",
-                                 "Pitch Angle Label 4", "Pitch Angle Label 5", "Pitch Angle Label 6"]),
-            DataProductVariable("gyrophase_label",
-                                ["Gyrophase Label 1", "Gyrophase Label 2", "Gyrophase Label 3", "Gyrophase Label 4"]),
-            DataProductVariable("h_energy_label",
-                                ["H Energy Label 1", "H Energy Label 2", "H Energy Label 3", "H Energy Label 4"]),
-            DataProductVariable("he4_energy_label", ["He4 Energy Label 1", "He4 Energy Label 2", "He4 Energy Label 3",
-                                                     "He4 Energy Label 4"]),
-            DataProductVariable("cno_energy_label", ["CNO Energy Label 1", "CNO Energy Label 2", "CNO Energy Label 3",
-                                                     "CNO Energy Label 4"]),
-            DataProductVariable("nemgsi_energy_label",
-                                ["NeMgSi Energy Label 1", "NeMgSi Energy Label 2", "NeMgSi Energy Label 3",
-                                 "NeMgSi Energy Label 4"]),
-            DataProductVariable("fe_energy_label",
-                                ["Fe Energy Label 1", "Fe Energy Label 2", "Fe Energy Label 3", "Fe Energy Label 4"]),
+            DataProductVariable(
+                "fe_energy_delta_plus", sentinel.iron_energy_delta_plus
+            ),
+            DataProductVariable(
+                "fe_energy_delta_minus", sentinel.iron_energy_delta_minus
+            ),
+            DataProductVariable(
+                "measurement_pitch_angle", mock_measurement_pitch_angle
+            ),
+            DataProductVariable(
+                "measurement_gyrophase", sentinel.measurement_gyrophase
+            ),
+            DataProductVariable(
+                "pitch_angle_label",
+                [
+                    "Pitch Angle Label 1",
+                    "Pitch Angle Label 2",
+                    "Pitch Angle Label 3",
+                    "Pitch Angle Label 4",
+                    "Pitch Angle Label 5",
+                    "Pitch Angle Label 6",
+                ],
+            ),
+            DataProductVariable(
+                "gyrophase_label",
+                [
+                    "Gyrophase Label 1",
+                    "Gyrophase Label 2",
+                    "Gyrophase Label 3",
+                    "Gyrophase Label 4",
+                ],
+            ),
+            DataProductVariable(
+                "h_energy_label",
+                [
+                    "H Energy Label 1",
+                    "H Energy Label 2",
+                    "H Energy Label 3",
+                    "H Energy Label 4",
+                ],
+            ),
+            DataProductVariable(
+                "he4_energy_label",
+                [
+                    "He4 Energy Label 1",
+                    "He4 Energy Label 2",
+                    "He4 Energy Label 3",
+                    "He4 Energy Label 4",
+                ],
+            ),
+            DataProductVariable(
+                "cno_energy_label",
+                [
+                    "CNO Energy Label 1",
+                    "CNO Energy Label 2",
+                    "CNO Energy Label 3",
+                    "CNO Energy Label 4",
+                ],
+            ),
+            DataProductVariable(
+                "nemgsi_energy_label",
+                [
+                    "NeMgSi Energy Label 1",
+                    "NeMgSi Energy Label 2",
+                    "NeMgSi Energy Label 3",
+                    "NeMgSi Energy Label 4",
+                ],
+            ),
+            DataProductVariable(
+                "fe_energy_label",
+                [
+                    "Fe Energy Label 1",
+                    "Fe Energy Label 2",
+                    "Fe Energy Label 3",
+                    "Fe Energy Label 4",
+                ],
+            ),
             DataProductVariable("azimuth", NumpyArrayMatcher(np.array([10, 180, 350]))),
-            DataProductVariable("zenith", NumpyArrayMatcher(np.array([10.25, 90.75, 170.5]))),
-            DataProductVariable("azimuth_label",
-                                ["10.0", "180.0", "350.0"]),
-            DataProductVariable("zenith_label",
-                                ["10.25", "90.75", "170.5"]),
+            DataProductVariable(
+                "zenith", NumpyArrayMatcher(np.array([10.25, 90.75, 170.5]))
+            ),
+            DataProductVariable("azimuth_label", ["10.0", "180.0", "350.0"]),
+            DataProductVariable("zenith_label", ["10.25", "90.75", "170.5"]),
             DataProductVariable("hit_flags", sentinel.quality_flags),
         ]
 
diff --git a/tests/hit/l3/test_hit_processor.py b/tests/hit/l3/test_hit_processor.py
index 5d307f5e0..14a85d3c5 100644
--- a/tests/hit/l3/test_hit_processor.py
+++ b/tests/hit/l3/test_hit_processor.py
@@ -3,50 +3,71 @@
 from pathlib import Path
 from typing import TypeVar
 from unittest import TestCase
-from unittest.mock import sentinel, patch, call, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
 
-from imap_l3_processing.constants import UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT1_FILL_VALUE
-from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import HITL3SectoredDependencies
+from imap_l3_processing.constants import (
+    UNSIGNED_INT1_FILL_VALUE,
+    UNSIGNED_INT2_FILL_VALUE,
+)
+from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import (
+    HITL3SectoredDependencies,
+)
 from imap_l3_processing.hit.l3.hit_processor import HitProcessor
-from imap_l3_processing.hit.l3.models import HitL2Data, HitL1Data
-from imap_l3_processing.hit.l3.pha.pha_event_reader import PHAWord, Detector, RawPHAEvent, PHAExtendedHeader, StimBlock, \
-    ExtendedStimHeader
+from imap_l3_processing.hit.l3.models import HitL1Data, HitL2Data
+from imap_l3_processing.hit.l3.pha.pha_event_reader import (
+    Detector,
+    ExtendedStimHeader,
+    PHAExtendedHeader,
+    PHAWord,
+    RawPHAEvent,
+    StimBlock,
+)
 from imap_l3_processing.hit.l3.pha.science.calculate_pha import EventOutput
-from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import DetectedRange, DetectorSide, \
-    DetectorRange
+from imap_l3_processing.hit.l3.pha.science.cosine_correction_lookup_table import (
+    DetectedRange,
+    DetectorRange,
+    DetectorSide,
+)
 from imap_l3_processing.hit.l3.sectored_products.models import HitPitchAngleDataProduct
 from imap_l3_processing.hit.quality_flags import HitL3Flags
-from imap_l3_processing.models import MagData, InputMetadata
+from imap_l3_processing.models import InputMetadata, MagData
 from imap_l3_processing.processor import Processor
 from tests.test_helpers import NumpyArrayMatcher, create_dataclass_mock
 
 
 class TestHitProcessor(TestCase):
     def test_is_a_processor(self):
-        self.assertIsInstance(
-            HitProcessor(Mock(), Mock()),
-            Processor
-        )
+        self.assertIsInstance(HitProcessor(Mock(), Mock()), Processor)
 
     @patch("imap_l3_processing.utils.spiceypy")
-    @patch('imap_l3_processing.hit.l3.hit_processor.save_data')
-    @patch('imap_l3_processing.hit.l3.hit_processor.HITL3SectoredDependencies.fetch_dependencies')
-    @patch('imap_l3_processing.hit.l3.hit_processor.calculate_unit_vector')
-    @patch('imap_l3_processing.hit.l3.hit_processor.get_hit_bin_polar_coordinates')
-    @patch('imap_l3_processing.hit.l3.hit_processor.get_sector_unit_vectors')
-    @patch('imap_l3_processing.hit.l3.hit_processor.calculate_pitch_angle')
-    @patch('imap_l3_processing.hit.l3.hit_processor.calculate_gyrophase')
-    @patch('imap_l3_processing.hit.l3.hit_processor.rebin_by_pitch_angle_and_gyrophase')
-    @patch('imap_l3_processing.hit.l3.hit_processor.rotate_particle_vectors_from_hit_despun_to_imap_despun')
-    def test_process_pitch_angle_product(self, mock_rotate_particle_vectors_from_hit_despun_to_imap_despun,
-                                         mock_hit_rebin, mock_calculate_gyrophase,
-                                         mock_calculate_pitch_angle,
-                                         mock_get_sector_unit_vectors, mock_get_hit_bin_polar_coordinates,
-                                         mock_calculate_unit_vector,
-                                         mock_fetch_dependencies, mock_save_data,
-                                         mock_spiceypy):
+    @patch("imap_l3_processing.hit.l3.hit_processor.save_data")
+    @patch(
+        "imap_l3_processing.hit.l3.hit_processor.HITL3SectoredDependencies.fetch_dependencies"
+    )
+    @patch("imap_l3_processing.hit.l3.hit_processor.calculate_unit_vector")
+    @patch("imap_l3_processing.hit.l3.hit_processor.get_hit_bin_polar_coordinates")
+    @patch("imap_l3_processing.hit.l3.hit_processor.get_sector_unit_vectors")
+    @patch("imap_l3_processing.hit.l3.hit_processor.calculate_pitch_angle")
+    @patch("imap_l3_processing.hit.l3.hit_processor.calculate_gyrophase")
+    @patch("imap_l3_processing.hit.l3.hit_processor.rebin_by_pitch_angle_and_gyrophase")
+    @patch(
+        "imap_l3_processing.hit.l3.hit_processor.rotate_particle_vectors_from_hit_despun_to_imap_despun"
+    )
+    def test_process_pitch_angle_product(
+        self,
+        mock_rotate_particle_vectors_from_hit_despun_to_imap_despun,
+        mock_hit_rebin,
+        mock_calculate_gyrophase,
+        mock_calculate_pitch_angle,
+        mock_get_sector_unit_vectors,
+        mock_get_hit_bin_polar_coordinates,
+        mock_calculate_unit_vector,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_spiceypy,
+    ):
         mock_spiceypy.ktotal.return_value = 0
 
         input_metadata = InputMetadata(
@@ -55,11 +76,13 @@ def test_process_pitch_angle_product(self, mock_rotate_particle_vectors_from_hit
             start_date=None,
             end_date=None,
             version="",
-            descriptor="macropixel"
+            descriptor="macropixel",
         )
 
         mock_processing_input_collection = Mock()
-        mock_processing_input_collection.get_file_paths.return_value = [Path("test/parent_path")]
+        mock_processing_input_collection.get_file_paths.return_value = [
+            Path("test/parent_path")
+        ]
 
         epochs = np.array([123, 234])
         epoch_deltas = np.array([12, 13])
@@ -107,35 +130,70 @@ def test_process_pitch_angle_product(self, mock_rotate_particle_vectors_from_hit
         delta_minus_NeMgSi_time2 = np.full(2, 102)
 
         mock_hit_data.cno = np.array([CNO_time1, CNO_time2])
-        mock_hit_data.delta_plus_cno = np.array([delta_plus_CNO_time1, delta_plus_CNO_time2])
-        mock_hit_data.delta_minus_cno = np.array([delta_minus_CNO_time1, delta_minus_CNO_time2])
+        mock_hit_data.delta_plus_cno = np.array(
+            [delta_plus_CNO_time1, delta_plus_CNO_time2]
+        )
+        mock_hit_data.delta_minus_cno = np.array(
+            [delta_minus_CNO_time1, delta_minus_CNO_time2]
+        )
         mock_hit_data.he4 = np.array([helium4_time1, helium4_time2])
-        mock_hit_data.delta_plus_he4 = np.array([delta_plus_helium4_time1, delta_plus_helium4_time2])
-        mock_hit_data.delta_minus_he4 = np.array([delta_minus_helium4_time1, delta_minus_helium4_time2])
+        mock_hit_data.delta_plus_he4 = np.array(
+            [delta_plus_helium4_time1, delta_plus_helium4_time2]
+        )
+        mock_hit_data.delta_minus_he4 = np.array(
+            [delta_minus_helium4_time1, delta_minus_helium4_time2]
+        )
         mock_hit_data.h = np.array([hydrogen_time1, hydrogen_time2])
-        mock_hit_data.delta_plus_h = np.array([delta_plus_hydrogen_time1, delta_plus_hydrogen_time2])
-        mock_hit_data.delta_minus_h = np.array([delta_minus_hydrogen_time1, delta_minus_hydrogen_time2])
+        mock_hit_data.delta_plus_h = np.array(
+            [delta_plus_hydrogen_time1, delta_plus_hydrogen_time2]
+        )
+        mock_hit_data.delta_minus_h = np.array(
+            [delta_minus_hydrogen_time1, delta_minus_hydrogen_time2]
+        )
         mock_hit_data.fe = np.array([iron_time1, iron_time2])
-        mock_hit_data.delta_plus_fe = np.array([delta_plus_iron_time1, delta_plus_iron_time2])
-        mock_hit_data.delta_minus_fe = np.array([delta_minus_iron_time1, delta_minus_iron_time2])
+        mock_hit_data.delta_plus_fe = np.array(
+            [delta_plus_iron_time1, delta_plus_iron_time2]
+        )
+        mock_hit_data.delta_minus_fe = np.array(
+            [delta_minus_iron_time1, delta_minus_iron_time2]
+        )
         mock_hit_data.nemgsi = np.array([NeMgSi_time1, NeMgSi_time2])
-        mock_hit_data.delta_plus_nemgsi = np.array([delta_plus_NeMgSi_time1, delta_plus_NeMgSi_time2])
-        mock_hit_data.delta_minus_nemgsi = np.array([delta_minus_NeMgSi_time1, delta_minus_NeMgSi_time2])
+        mock_hit_data.delta_plus_nemgsi = np.array(
+            [delta_plus_NeMgSi_time1, delta_plus_NeMgSi_time2]
+        )
+        mock_hit_data.delta_minus_nemgsi = np.array(
+            [delta_minus_NeMgSi_time1, delta_minus_NeMgSi_time2]
+        )
 
         mock_dependencies.data = mock_hit_data
         mock_fetch_dependencies.return_value = mock_dependencies
 
-        mock_calculate_unit_vector.side_effect = [sentinel.mag_unit_vector1, sentinel.mag_unit_vector2]
+        mock_calculate_unit_vector.side_effect = [
+            sentinel.mag_unit_vector1,
+            sentinel.mag_unit_vector2,
+        ]
         mock_get_hit_bin_polar_coordinates.return_value = (
-            sentinel.pitch_angles, sentinel.gyrophases, sentinel.pitch_angle_deltas, sentinel.gyrophase_deltas)
+            sentinel.pitch_angles,
+            sentinel.gyrophases,
+            sentinel.pitch_angle_deltas,
+            sentinel.gyrophase_deltas,
+        )
 
         sector_unit_vectors_zenith_first = np.full((8, 15, 3), np.sqrt(1 / 3))
         mock_get_sector_unit_vectors.return_value = sector_unit_vectors_zenith_first
         expected_sector_unit_vectors = np.full((15, 8, 3), np.sqrt(1 / 3))
 
-        mock_rotate_particle_vectors_from_hit_despun_to_imap_despun.return_value = sentinel.rotated_particle_vector
-        mock_calculate_pitch_angle.side_effect = [sentinel.pitch_angle1, sentinel.pitch_angle2]
-        mock_calculate_gyrophase.side_effect = [sentinel.gyrophase1, sentinel.gyrophase2]
+        mock_rotate_particle_vectors_from_hit_despun_to_imap_despun.return_value = (
+            sentinel.rotated_particle_vector
+        )
+        mock_calculate_pitch_angle.side_effect = [
+            sentinel.pitch_angle1,
+            sentinel.pitch_angle2,
+        ]
+        mock_calculate_gyrophase.side_effect = [
+            sentinel.gyrophase1,
+            sentinel.gyrophase2,
+        ]
 
         rebinned_pa_gyro_CNO_time1 = np.full((2, 8, 15), 1)
         rebinned_pa_gyro_CNO_delta_plus_time1 = np.full((2, 8, 15), 100)
@@ -200,294 +258,622 @@ def test_process_pitch_angle_product(self, mock_rotate_particle_vectors_from_hit
         rebinned_pa_NeMgSi_delta_minus_time2 = np.full((2, 8), 2001)
 
         mock_hit_rebin.side_effect = [
-            (rebinned_pa_gyro_hydrogen_time1, rebinned_pa_gyro_hydrogen_delta_plus_time1,
-             rebinned_pa_gyro_hydrogen_delta_minus_time1,
-             rebinned_pa_hydrogen_time1, rebinned_pa_hydrogen_delta_plus_time1, rebinned_pa_hydrogen_delta_minus_time1),
-            (rebinned_pa_gyro_helium4_time1, rebinned_pa_gyro_helium4_delta_plus_time1,
-             rebinned_pa_gyro_helium4_delta_minus_time1,
-             rebinned_pa_helium4_time1, rebinned_pa_helium4_delta_plus_time1, rebinned_pa_helium4_delta_minus_time1),
-            (rebinned_pa_gyro_CNO_time1, rebinned_pa_gyro_CNO_delta_plus_time1, rebinned_pa_gyro_CNO_delta_minus_time1,
-             rebinned_pa_CNO_time1, rebinned_pa_CNO_delta_plus_time1, rebinned_pa_CNO_delta_minus_time1),
-            (rebinned_pa_gyro_NeMgSi_time1, rebinned_pa_gyro_NeMgSi_delta_plus_time1,
-             rebinned_pa_gyro_NeMgSi_delta_minus_time1,
-             rebinned_pa_NeMgSi_time1, rebinned_pa_NeMgSi_delta_plus_time1, rebinned_pa_NeMgSi_delta_minus_time1),
-            (rebinned_pa_gyro_iron_time1, rebinned_pa_gyro_iron_delta_plus_time1,
-             rebinned_pa_gyro_iron_delta_minus_time1,
-             rebinned_pa_iron_time1, rebinned_pa_iron_delta_plus_time1, rebinned_pa_iron_delta_minus_time1),
-
-            (rebinned_pa_gyro_hydrogen_time2, rebinned_pa_gyro_hydrogen_delta_plus_time2,
-             rebinned_pa_gyro_hydrogen_delta_minus_time2,
-             rebinned_pa_hydrogen_time2, rebinned_pa_hydrogen_delta_plus_time2, rebinned_pa_hydrogen_delta_minus_time2),
-            (rebinned_pa_gyro_helium4_time2, rebinned_pa_gyro_helium4_delta_plus_time2,
-             rebinned_pa_gyro_helium4_delta_minus_time2,
-             rebinned_pa_helium4_time2, rebinned_pa_helium4_delta_plus_time2, rebinned_pa_helium4_delta_minus_time2),
-            (rebinned_pa_gyro_CNO_time2, rebinned_pa_gyro_CNO_delta_plus_time2, rebinned_pa_gyro_CNO_delta_minus_time2,
-             rebinned_pa_CNO_time2, rebinned_pa_CNO_delta_plus_time2, rebinned_pa_CNO_delta_minus_time2),
-            (rebinned_pa_gyro_NeMgSi_time2, rebinned_pa_gyro_NeMgSi_delta_plus_time2,
-             rebinned_pa_gyro_NeMgSi_delta_minus_time2,
-             rebinned_pa_NeMgSi_time2, rebinned_pa_NeMgSi_delta_plus_time2, rebinned_pa_NeMgSi_delta_minus_time2),
-            (rebinned_pa_gyro_iron_time2, rebinned_pa_gyro_iron_delta_plus_time2,
-             rebinned_pa_gyro_iron_delta_minus_time2,
-             rebinned_pa_iron_time2, rebinned_pa_iron_delta_plus_time2, rebinned_pa_iron_delta_minus_time2),
+            (
+                rebinned_pa_gyro_hydrogen_time1,
+                rebinned_pa_gyro_hydrogen_delta_plus_time1,
+                rebinned_pa_gyro_hydrogen_delta_minus_time1,
+                rebinned_pa_hydrogen_time1,
+                rebinned_pa_hydrogen_delta_plus_time1,
+                rebinned_pa_hydrogen_delta_minus_time1,
+            ),
+            (
+                rebinned_pa_gyro_helium4_time1,
+                rebinned_pa_gyro_helium4_delta_plus_time1,
+                rebinned_pa_gyro_helium4_delta_minus_time1,
+                rebinned_pa_helium4_time1,
+                rebinned_pa_helium4_delta_plus_time1,
+                rebinned_pa_helium4_delta_minus_time1,
+            ),
+            (
+                rebinned_pa_gyro_CNO_time1,
+                rebinned_pa_gyro_CNO_delta_plus_time1,
+                rebinned_pa_gyro_CNO_delta_minus_time1,
+                rebinned_pa_CNO_time1,
+                rebinned_pa_CNO_delta_plus_time1,
+                rebinned_pa_CNO_delta_minus_time1,
+            ),
+            (
+                rebinned_pa_gyro_NeMgSi_time1,
+                rebinned_pa_gyro_NeMgSi_delta_plus_time1,
+                rebinned_pa_gyro_NeMgSi_delta_minus_time1,
+                rebinned_pa_NeMgSi_time1,
+                rebinned_pa_NeMgSi_delta_plus_time1,
+                rebinned_pa_NeMgSi_delta_minus_time1,
+            ),
+            (
+                rebinned_pa_gyro_iron_time1,
+                rebinned_pa_gyro_iron_delta_plus_time1,
+                rebinned_pa_gyro_iron_delta_minus_time1,
+                rebinned_pa_iron_time1,
+                rebinned_pa_iron_delta_plus_time1,
+                rebinned_pa_iron_delta_minus_time1,
+            ),
+            (
+                rebinned_pa_gyro_hydrogen_time2,
+                rebinned_pa_gyro_hydrogen_delta_plus_time2,
+                rebinned_pa_gyro_hydrogen_delta_minus_time2,
+                rebinned_pa_hydrogen_time2,
+                rebinned_pa_hydrogen_delta_plus_time2,
+                rebinned_pa_hydrogen_delta_minus_time2,
+            ),
+            (
+                rebinned_pa_gyro_helium4_time2,
+                rebinned_pa_gyro_helium4_delta_plus_time2,
+                rebinned_pa_gyro_helium4_delta_minus_time2,
+                rebinned_pa_helium4_time2,
+                rebinned_pa_helium4_delta_plus_time2,
+                rebinned_pa_helium4_delta_minus_time2,
+            ),
+            (
+                rebinned_pa_gyro_CNO_time2,
+                rebinned_pa_gyro_CNO_delta_plus_time2,
+                rebinned_pa_gyro_CNO_delta_minus_time2,
+                rebinned_pa_CNO_time2,
+                rebinned_pa_CNO_delta_plus_time2,
+                rebinned_pa_CNO_delta_minus_time2,
+            ),
+            (
+                rebinned_pa_gyro_NeMgSi_time2,
+                rebinned_pa_gyro_NeMgSi_delta_plus_time2,
+                rebinned_pa_gyro_NeMgSi_delta_minus_time2,
+                rebinned_pa_NeMgSi_time2,
+                rebinned_pa_NeMgSi_delta_plus_time2,
+                rebinned_pa_NeMgSi_delta_minus_time2,
+            ),
+            (
+                rebinned_pa_gyro_iron_time2,
+                rebinned_pa_gyro_iron_delta_plus_time2,
+                rebinned_pa_gyro_iron_delta_minus_time2,
+                rebinned_pa_iron_time2,
+                rebinned_pa_iron_delta_plus_time2,
+                rebinned_pa_iron_delta_minus_time2,
+            ),
         ]
 
         processor = HitProcessor(mock_processing_input_collection, input_metadata)
         product = processor.process()
-        mock_fetch_dependencies.assert_called_once_with(mock_processing_input_collection)
+        mock_fetch_dependencies.assert_called_once_with(
+            mock_processing_input_collection
+        )
 
-        mock_mag_data.rebin_to.assert_called_once_with(mock_hit_data.epoch, mock_hit_data.epoch_delta)
+        mock_mag_data.rebin_to.assert_called_once_with(
+            mock_hit_data.epoch, mock_hit_data.epoch_delta
+        )
 
-        mock_calculate_unit_vector.assert_has_calls([
-            call(sentinel.mag_vector1),
-            call(sentinel.mag_vector2)
-        ])
-        mock_get_sector_unit_vectors.assert_called_once_with(mock_hit_data.zenith, mock_hit_data.azimuth)
+        mock_calculate_unit_vector.assert_has_calls(
+            [call(sentinel.mag_vector1), call(sentinel.mag_vector2)]
+        )
+        mock_get_sector_unit_vectors.assert_called_once_with(
+            mock_hit_data.zenith, mock_hit_data.azimuth
+        )
 
-        self.assertEqual(1, mock_rotate_particle_vectors_from_hit_despun_to_imap_despun.call_count)
+        self.assertEqual(
+            1, mock_rotate_particle_vectors_from_hit_despun_to_imap_despun.call_count
+        )
         np.testing.assert_array_equal(
-            mock_rotate_particle_vectors_from_hit_despun_to_imap_despun.call_args_list[0].args[0],
-            -expected_sector_unit_vectors)
+            mock_rotate_particle_vectors_from_hit_despun_to_imap_despun.call_args_list[
+                0
+            ].args[0],
+            -expected_sector_unit_vectors,
+        )
 
         self.assertEqual(2, mock_calculate_pitch_angle.call_count)
-        np.testing.assert_array_equal(mock_calculate_pitch_angle.call_args_list[0].args[0],
-                                      sentinel.rotated_particle_vector)
-        self.assertEqual(mock_calculate_pitch_angle.call_args_list[0].args[1], sentinel.mag_unit_vector1)
+        np.testing.assert_array_equal(
+            mock_calculate_pitch_angle.call_args_list[0].args[0],
+            sentinel.rotated_particle_vector,
+        )
+        self.assertEqual(
+            mock_calculate_pitch_angle.call_args_list[0].args[1],
+            sentinel.mag_unit_vector1,
+        )
 
-        np.testing.assert_array_equal(mock_calculate_pitch_angle.call_args_list[1].args[0],
-                                      sentinel.rotated_particle_vector)
-        self.assertEqual(mock_calculate_pitch_angle.call_args_list[1].args[1], sentinel.mag_unit_vector2)
+        np.testing.assert_array_equal(
+            mock_calculate_pitch_angle.call_args_list[1].args[0],
+            sentinel.rotated_particle_vector,
+        )
+        self.assertEqual(
+            mock_calculate_pitch_angle.call_args_list[1].args[1],
+            sentinel.mag_unit_vector2,
+        )
 
         self.assertEqual(2, mock_calculate_gyrophase.call_count)
-        np.testing.assert_array_equal(mock_calculate_gyrophase.call_args_list[0].args[0],
-                                      sentinel.rotated_particle_vector)
-        self.assertEqual(mock_calculate_gyrophase.call_args_list[0].args[1], sentinel.mag_unit_vector1)
+        np.testing.assert_array_equal(
+            mock_calculate_gyrophase.call_args_list[0].args[0],
+            sentinel.rotated_particle_vector,
+        )
+        self.assertEqual(
+            mock_calculate_gyrophase.call_args_list[0].args[1],
+            sentinel.mag_unit_vector1,
+        )
 
-        np.testing.assert_array_equal(mock_calculate_gyrophase.call_args_list[1].args[0],
-                                      sentinel.rotated_particle_vector)
-        self.assertEqual(mock_calculate_gyrophase.call_args_list[1].args[1], sentinel.mag_unit_vector2)
+        np.testing.assert_array_equal(
+            mock_calculate_gyrophase.call_args_list[1].args[0],
+            sentinel.rotated_particle_vector,
+        )
+        self.assertEqual(
+            mock_calculate_gyrophase.call_args_list[1].args[1],
+            sentinel.mag_unit_vector2,
+        )
 
         number_of_pitch_angle_bins = 8
         number_of_gyrophase_bins = 15
 
-        mock_hit_rebin.assert_has_calls([
-            call(NumpyArrayMatcher(hydrogen_time1), NumpyArrayMatcher(delta_plus_hydrogen_time1),
-                 NumpyArrayMatcher(delta_minus_hydrogen_time1), sentinel.pitch_angle1, sentinel.gyrophase1,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(helium4_time1), NumpyArrayMatcher(delta_plus_helium4_time1),
-                 NumpyArrayMatcher(delta_minus_helium4_time1), sentinel.pitch_angle1, sentinel.gyrophase1,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(CNO_time1), NumpyArrayMatcher(delta_plus_CNO_time1),
-                 NumpyArrayMatcher(delta_minus_CNO_time1), sentinel.pitch_angle1, sentinel.gyrophase1,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(NeMgSi_time1), NumpyArrayMatcher(delta_plus_NeMgSi_time1),
-                 NumpyArrayMatcher(delta_minus_NeMgSi_time1), sentinel.pitch_angle1, sentinel.gyrophase1,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(iron_time1), NumpyArrayMatcher(delta_plus_iron_time1),
-                 NumpyArrayMatcher(delta_minus_iron_time1), sentinel.pitch_angle1, sentinel.gyrophase1,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(hydrogen_time2), NumpyArrayMatcher(delta_plus_hydrogen_time2),
-                 NumpyArrayMatcher(delta_minus_hydrogen_time2), sentinel.pitch_angle2, sentinel.gyrophase2,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(helium4_time2), NumpyArrayMatcher(delta_plus_helium4_time2),
-                 NumpyArrayMatcher(delta_minus_helium4_time2), sentinel.pitch_angle2, sentinel.gyrophase2,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(CNO_time2), NumpyArrayMatcher(delta_plus_CNO_time2),
-                 NumpyArrayMatcher(delta_minus_CNO_time2), sentinel.pitch_angle2, sentinel.gyrophase2,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(NeMgSi_time2), NumpyArrayMatcher(delta_plus_NeMgSi_time2),
-                 NumpyArrayMatcher(delta_minus_NeMgSi_time2), sentinel.pitch_angle2, sentinel.gyrophase2,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-            call(NumpyArrayMatcher(iron_time2), NumpyArrayMatcher(delta_plus_iron_time2),
-                 NumpyArrayMatcher(delta_minus_iron_time2), sentinel.pitch_angle2, sentinel.gyrophase2,
-                 number_of_pitch_angle_bins,
-                 number_of_gyrophase_bins),
-        ])
-
-        saved_data_product: HitPitchAngleDataProduct = mock_save_data.call_args_list[0].args[0]
+        mock_hit_rebin.assert_has_calls(
+            [
+                call(
+                    NumpyArrayMatcher(hydrogen_time1),
+                    NumpyArrayMatcher(delta_plus_hydrogen_time1),
+                    NumpyArrayMatcher(delta_minus_hydrogen_time1),
+                    sentinel.pitch_angle1,
+                    sentinel.gyrophase1,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(helium4_time1),
+                    NumpyArrayMatcher(delta_plus_helium4_time1),
+                    NumpyArrayMatcher(delta_minus_helium4_time1),
+                    sentinel.pitch_angle1,
+                    sentinel.gyrophase1,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(CNO_time1),
+                    NumpyArrayMatcher(delta_plus_CNO_time1),
+                    NumpyArrayMatcher(delta_minus_CNO_time1),
+                    sentinel.pitch_angle1,
+                    sentinel.gyrophase1,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(NeMgSi_time1),
+                    NumpyArrayMatcher(delta_plus_NeMgSi_time1),
+                    NumpyArrayMatcher(delta_minus_NeMgSi_time1),
+                    sentinel.pitch_angle1,
+                    sentinel.gyrophase1,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(iron_time1),
+                    NumpyArrayMatcher(delta_plus_iron_time1),
+                    NumpyArrayMatcher(delta_minus_iron_time1),
+                    sentinel.pitch_angle1,
+                    sentinel.gyrophase1,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(hydrogen_time2),
+                    NumpyArrayMatcher(delta_plus_hydrogen_time2),
+                    NumpyArrayMatcher(delta_minus_hydrogen_time2),
+                    sentinel.pitch_angle2,
+                    sentinel.gyrophase2,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(helium4_time2),
+                    NumpyArrayMatcher(delta_plus_helium4_time2),
+                    NumpyArrayMatcher(delta_minus_helium4_time2),
+                    sentinel.pitch_angle2,
+                    sentinel.gyrophase2,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(CNO_time2),
+                    NumpyArrayMatcher(delta_plus_CNO_time2),
+                    NumpyArrayMatcher(delta_minus_CNO_time2),
+                    sentinel.pitch_angle2,
+                    sentinel.gyrophase2,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(NeMgSi_time2),
+                    NumpyArrayMatcher(delta_plus_NeMgSi_time2),
+                    NumpyArrayMatcher(delta_minus_NeMgSi_time2),
+                    sentinel.pitch_angle2,
+                    sentinel.gyrophase2,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+                call(
+                    NumpyArrayMatcher(iron_time2),
+                    NumpyArrayMatcher(delta_plus_iron_time2),
+                    NumpyArrayMatcher(delta_minus_iron_time2),
+                    sentinel.pitch_angle2,
+                    sentinel.gyrophase2,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                ),
+            ]
+        )
+
+        saved_data_product: HitPitchAngleDataProduct = mock_save_data.call_args_list[
+            0
+        ].args[0]
 
         self.assertEqual(input_metadata, saved_data_product.input_metadata)
         self.assertEqual(["parent_path"], saved_data_product.parent_file_names)
         np.testing.assert_array_equal(saved_data_product.epochs, epochs)
         np.testing.assert_array_equal(saved_data_product.epoch_deltas, epoch_deltas)
 
-        np.testing.assert_array_equal(saved_data_product.pitch_angles, sentinel.pitch_angles)
-        np.testing.assert_array_equal(saved_data_product.pitch_angle_deltas, sentinel.pitch_angle_deltas)
-        np.testing.assert_array_equal(saved_data_product.gyrophases, sentinel.gyrophases)
-        np.testing.assert_array_equal(saved_data_product.gyrophase_deltas, sentinel.gyrophase_deltas)
-
-        np.testing.assert_array_equal(saved_data_product.h_macropixel_intensity,
-                                      [rebinned_pa_gyro_hydrogen_time1,
-                                       rebinned_pa_gyro_hydrogen_time2])
-        np.testing.assert_array_equal(saved_data_product.h_macropixel_intensity_delta_plus,
-                                      [rebinned_pa_gyro_hydrogen_delta_plus_time1,
-                                       rebinned_pa_gyro_hydrogen_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.h_macropixel_intensity_delta_minus,
-                                      [rebinned_pa_gyro_hydrogen_delta_minus_time1,
-                                       rebinned_pa_gyro_hydrogen_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.he4_macropixel_intensity,
-                                      [rebinned_pa_gyro_helium4_time1,
-                                       rebinned_pa_gyro_helium4_time2])
-        np.testing.assert_array_equal(saved_data_product.he4_macropixel_intensity_delta_plus,
-                                      [rebinned_pa_gyro_helium4_delta_plus_time1,
-                                       rebinned_pa_gyro_helium4_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.he4_macropixel_intensity_delta_minus,
-                                      [rebinned_pa_gyro_helium4_delta_minus_time1,
-                                       rebinned_pa_gyro_helium4_delta_minus_time2])
-        np.testing.assert_array_equal(saved_data_product.cno_macropixel_intensity,
-                                      [rebinned_pa_gyro_CNO_time1,
-                                       rebinned_pa_gyro_CNO_time2])
-        np.testing.assert_array_equal(saved_data_product.cno_macropixel_intensity_delta_plus,
-                                      [rebinned_pa_gyro_CNO_delta_plus_time1,
-                                       rebinned_pa_gyro_CNO_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.cno_macropixel_intensity_delta_minus,
-                                      [rebinned_pa_gyro_CNO_delta_minus_time1,
-                                       rebinned_pa_gyro_CNO_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.ne_mg_si_macropixel_intensity,
-                                      [rebinned_pa_gyro_NeMgSi_time1,
-                                       rebinned_pa_gyro_NeMgSi_time2])
-        np.testing.assert_array_equal(saved_data_product.ne_mg_si_macropixel_intensity_delta_plus,
-                                      [rebinned_pa_gyro_NeMgSi_delta_plus_time1,
-                                       rebinned_pa_gyro_NeMgSi_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.ne_mg_si_macropixel_intensity_delta_minus,
-                                      [rebinned_pa_gyro_NeMgSi_delta_minus_time1,
-                                       rebinned_pa_gyro_NeMgSi_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.iron_macropixel_intensity,
-                                      [rebinned_pa_gyro_iron_time1,
-                                       rebinned_pa_gyro_iron_time2])
-        np.testing.assert_array_equal(saved_data_product.iron_macropixel_intensity_delta_plus,
-                                      [rebinned_pa_gyro_iron_delta_plus_time1,
-                                       rebinned_pa_gyro_iron_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.iron_macropixel_intensity_delta_minus,
-                                      [rebinned_pa_gyro_iron_delta_minus_time1,
-                                       rebinned_pa_gyro_iron_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.h_macropixel_intensity_pa,
-                                      [rebinned_pa_hydrogen_time1, rebinned_pa_hydrogen_time2])
-        np.testing.assert_array_equal(saved_data_product.h_macropixel_intensity_pa_delta_plus,
-                                      [rebinned_pa_hydrogen_delta_plus_time1,
-                                       rebinned_pa_hydrogen_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.h_macropixel_intensity_pa_delta_minus,
-                                      [rebinned_pa_hydrogen_delta_minus_time1,
-                                       rebinned_pa_hydrogen_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.he4_macropixel_intensity_pa,
-                                      [rebinned_pa_helium4_time1, rebinned_pa_helium4_time2])
-        np.testing.assert_array_equal(saved_data_product.he4_macropixel_intensity_pa_delta_plus,
-                                      [rebinned_pa_helium4_delta_plus_time1, rebinned_pa_helium4_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.he4_macropixel_intensity_pa_delta_minus,
-                                      [rebinned_pa_helium4_delta_minus_time1,
-                                       rebinned_pa_helium4_delta_minus_time2])
-        np.testing.assert_array_equal(saved_data_product.cno_macropixel_intensity_pa,
-                                      [rebinned_pa_CNO_time1, rebinned_pa_CNO_time2])
-        np.testing.assert_array_equal(saved_data_product.cno_macropixel_intensity_pa_delta_plus,
-                                      [rebinned_pa_CNO_delta_plus_time1, rebinned_pa_CNO_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.cno_macropixel_intensity_pa_delta_minus,
-                                      [rebinned_pa_CNO_delta_minus_time1, rebinned_pa_CNO_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.ne_mg_si_macropixel_intensity_pa,
-                                      [rebinned_pa_NeMgSi_time1, rebinned_pa_NeMgSi_time2])
-        np.testing.assert_array_equal(saved_data_product.ne_mg_si_macropixel_intensity_pa_delta_plus,
-                                      [rebinned_pa_NeMgSi_delta_plus_time1, rebinned_pa_NeMgSi_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.ne_mg_si_macropixel_intensity_pa_delta_minus,
-                                      [rebinned_pa_NeMgSi_delta_minus_time1, rebinned_pa_NeMgSi_delta_minus_time2])
-
-        np.testing.assert_array_equal(saved_data_product.iron_macropixel_intensity_pa,
-                                      [rebinned_pa_iron_time1, rebinned_pa_iron_time2])
-        np.testing.assert_array_equal(saved_data_product.iron_macropixel_intensity_pa_delta_plus,
-                                      [rebinned_pa_iron_delta_plus_time1, rebinned_pa_iron_delta_plus_time2])
-        np.testing.assert_array_equal(saved_data_product.iron_macropixel_intensity_pa_delta_minus,
-                                      [rebinned_pa_iron_delta_minus_time1, rebinned_pa_iron_delta_minus_time2])
+        np.testing.assert_array_equal(
+            saved_data_product.pitch_angles, sentinel.pitch_angles
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.pitch_angle_deltas, sentinel.pitch_angle_deltas
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.gyrophases, sentinel.gyrophases
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.gyrophase_deltas, sentinel.gyrophase_deltas
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.h_macropixel_intensity,
+            [rebinned_pa_gyro_hydrogen_time1, rebinned_pa_gyro_hydrogen_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.h_macropixel_intensity_delta_plus,
+            [
+                rebinned_pa_gyro_hydrogen_delta_plus_time1,
+                rebinned_pa_gyro_hydrogen_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.h_macropixel_intensity_delta_minus,
+            [
+                rebinned_pa_gyro_hydrogen_delta_minus_time1,
+                rebinned_pa_gyro_hydrogen_delta_minus_time2,
+            ],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.he4_macropixel_intensity,
+            [rebinned_pa_gyro_helium4_time1, rebinned_pa_gyro_helium4_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.he4_macropixel_intensity_delta_plus,
+            [
+                rebinned_pa_gyro_helium4_delta_plus_time1,
+                rebinned_pa_gyro_helium4_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.he4_macropixel_intensity_delta_minus,
+            [
+                rebinned_pa_gyro_helium4_delta_minus_time1,
+                rebinned_pa_gyro_helium4_delta_minus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.cno_macropixel_intensity,
+            [rebinned_pa_gyro_CNO_time1, rebinned_pa_gyro_CNO_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.cno_macropixel_intensity_delta_plus,
+            [
+                rebinned_pa_gyro_CNO_delta_plus_time1,
+                rebinned_pa_gyro_CNO_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.cno_macropixel_intensity_delta_minus,
+            [
+                rebinned_pa_gyro_CNO_delta_minus_time1,
+                rebinned_pa_gyro_CNO_delta_minus_time2,
+            ],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.ne_mg_si_macropixel_intensity,
+            [rebinned_pa_gyro_NeMgSi_time1, rebinned_pa_gyro_NeMgSi_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.ne_mg_si_macropixel_intensity_delta_plus,
+            [
+                rebinned_pa_gyro_NeMgSi_delta_plus_time1,
+                rebinned_pa_gyro_NeMgSi_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.ne_mg_si_macropixel_intensity_delta_minus,
+            [
+                rebinned_pa_gyro_NeMgSi_delta_minus_time1,
+                rebinned_pa_gyro_NeMgSi_delta_minus_time2,
+            ],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.iron_macropixel_intensity,
+            [rebinned_pa_gyro_iron_time1, rebinned_pa_gyro_iron_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.iron_macropixel_intensity_delta_plus,
+            [
+                rebinned_pa_gyro_iron_delta_plus_time1,
+                rebinned_pa_gyro_iron_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.iron_macropixel_intensity_delta_minus,
+            [
+                rebinned_pa_gyro_iron_delta_minus_time1,
+                rebinned_pa_gyro_iron_delta_minus_time2,
+            ],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.h_macropixel_intensity_pa,
+            [rebinned_pa_hydrogen_time1, rebinned_pa_hydrogen_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.h_macropixel_intensity_pa_delta_plus,
+            [
+                rebinned_pa_hydrogen_delta_plus_time1,
+                rebinned_pa_hydrogen_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.h_macropixel_intensity_pa_delta_minus,
+            [
+                rebinned_pa_hydrogen_delta_minus_time1,
+                rebinned_pa_hydrogen_delta_minus_time2,
+            ],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.he4_macropixel_intensity_pa,
+            [rebinned_pa_helium4_time1, rebinned_pa_helium4_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.he4_macropixel_intensity_pa_delta_plus,
+            [
+                rebinned_pa_helium4_delta_plus_time1,
+                rebinned_pa_helium4_delta_plus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.he4_macropixel_intensity_pa_delta_minus,
+            [
+                rebinned_pa_helium4_delta_minus_time1,
+                rebinned_pa_helium4_delta_minus_time2,
+            ],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.cno_macropixel_intensity_pa,
+            [rebinned_pa_CNO_time1, rebinned_pa_CNO_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.cno_macropixel_intensity_pa_delta_plus,
+            [rebinned_pa_CNO_delta_plus_time1, rebinned_pa_CNO_delta_plus_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.cno_macropixel_intensity_pa_delta_minus,
+            [rebinned_pa_CNO_delta_minus_time1, rebinned_pa_CNO_delta_minus_time2],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.ne_mg_si_macropixel_intensity_pa,
+            [rebinned_pa_NeMgSi_time1, rebinned_pa_NeMgSi_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.ne_mg_si_macropixel_intensity_pa_delta_plus,
+            [rebinned_pa_NeMgSi_delta_plus_time1, rebinned_pa_NeMgSi_delta_plus_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.ne_mg_si_macropixel_intensity_pa_delta_minus,
+            [
+                rebinned_pa_NeMgSi_delta_minus_time1,
+                rebinned_pa_NeMgSi_delta_minus_time2,
+            ],
+        )
+
+        np.testing.assert_array_equal(
+            saved_data_product.iron_macropixel_intensity_pa,
+            [rebinned_pa_iron_time1, rebinned_pa_iron_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.iron_macropixel_intensity_pa_delta_plus,
+            [rebinned_pa_iron_delta_plus_time1, rebinned_pa_iron_delta_plus_time2],
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.iron_macropixel_intensity_pa_delta_minus,
+            [rebinned_pa_iron_delta_minus_time1, rebinned_pa_iron_delta_minus_time2],
+        )
 
         self.assertIs(mock_hit_data.h_energy, saved_data_product.h_energies)
-        self.assertIs(mock_hit_data.h_energy_delta_plus, saved_data_product.h_energy_delta_plus)
-        self.assertIs(mock_hit_data.h_energy_delta_minus, saved_data_product.h_energy_delta_minus)
+        self.assertIs(
+            mock_hit_data.h_energy_delta_plus, saved_data_product.h_energy_delta_plus
+        )
+        self.assertIs(
+            mock_hit_data.h_energy_delta_minus, saved_data_product.h_energy_delta_minus
+        )
         self.assertIs(mock_hit_data.he4_energy, saved_data_product.he4_energies)
-        self.assertIs(mock_hit_data.he4_energy_delta_plus, saved_data_product.he4_energy_delta_plus)
-        self.assertIs(mock_hit_data.he4_energy_delta_minus, saved_data_product.he4_energy_delta_minus)
+        self.assertIs(
+            mock_hit_data.he4_energy_delta_plus,
+            saved_data_product.he4_energy_delta_plus,
+        )
+        self.assertIs(
+            mock_hit_data.he4_energy_delta_minus,
+            saved_data_product.he4_energy_delta_minus,
+        )
         self.assertIs(mock_hit_data.cno_energy, saved_data_product.cno_energies)
-        self.assertIs(mock_hit_data.cno_energy_delta_plus, saved_data_product.cno_energy_delta_plus)
-        self.assertIs(mock_hit_data.cno_energy_delta_minus, saved_data_product.cno_energy_delta_minus)
+        self.assertIs(
+            mock_hit_data.cno_energy_delta_plus,
+            saved_data_product.cno_energy_delta_plus,
+        )
+        self.assertIs(
+            mock_hit_data.cno_energy_delta_minus,
+            saved_data_product.cno_energy_delta_minus,
+        )
         self.assertIs(mock_hit_data.nemgsi_energy, saved_data_product.ne_mg_si_energies)
-        self.assertIs(mock_hit_data.nemgsi_energy_delta_plus, saved_data_product.ne_mg_si_energy_delta_plus)
-        self.assertIs(mock_hit_data.nemgsi_energy_delta_minus, saved_data_product.ne_mg_si_energy_delta_minus)
+        self.assertIs(
+            mock_hit_data.nemgsi_energy_delta_plus,
+            saved_data_product.ne_mg_si_energy_delta_plus,
+        )
+        self.assertIs(
+            mock_hit_data.nemgsi_energy_delta_minus,
+            saved_data_product.ne_mg_si_energy_delta_minus,
+        )
         self.assertIs(mock_hit_data.fe_energy, saved_data_product.iron_energies)
-        self.assertIs(mock_hit_data.fe_energy_delta_plus, saved_data_product.iron_energy_delta_plus)
-        self.assertIs(mock_hit_data.fe_energy_delta_minus, saved_data_product.iron_energy_delta_minus)
+        self.assertIs(
+            mock_hit_data.fe_energy_delta_plus,
+            saved_data_product.iron_energy_delta_plus,
+        )
+        self.assertIs(
+            mock_hit_data.fe_energy_delta_minus,
+            saved_data_product.iron_energy_delta_minus,
+        )
         self.assertIs(mock_hit_data.azimuth, saved_data_product.azimuth)
         self.assertIs(mock_hit_data.zenith, saved_data_product.zenith)
 
-        np.testing.assert_array_equal(saved_data_product.measurement_pitch_angle,
-                                      np.array([sentinel.pitch_angle1, sentinel.pitch_angle2]))
-        np.testing.assert_array_equal(saved_data_product.measurement_gyrophase,
-                                      np.array([sentinel.gyrophase1, sentinel.gyrophase2]))
+        np.testing.assert_array_equal(
+            saved_data_product.measurement_pitch_angle,
+            np.array([sentinel.pitch_angle1, sentinel.pitch_angle2]),
+        )
+        np.testing.assert_array_equal(
+            saved_data_product.measurement_gyrophase,
+            np.array([sentinel.gyrophase1, sentinel.gyrophase2]),
+        )
 
-        np.testing.assert_array_equal(saved_data_product.hit_flags,
-                                      np.array([HitL3Flags.PRELIMINARY_MAG, HitL3Flags.PRELIMINARY_MAG]))
+        np.testing.assert_array_equal(
+            saved_data_product.hit_flags,
+            np.array([HitL3Flags.PRELIMINARY_MAG, HitL3Flags.PRELIMINARY_MAG]),
+        )
 
         self.assertEqual([mock_save_data.return_value], product)
 
     @patch("imap_l3_processing.utils.spiceypy")
     @patch("imap_l3_processing.hit.l3.hit_processor.save_data")
     @patch("imap_l3_processing.hit.l3.hit_processor.process_pha_event", autospec=True)
-    @patch("imap_l3_processing.hit.l3.hit_processor.HitL3PhaDependencies.fetch_dependencies")
+    @patch(
+        "imap_l3_processing.hit.l3.hit_processor.HitL3PhaDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.hit.l3.hit_processor.PHAEventReader.read_all_pha_events")
-    def test_process_direct_event_product(self, mock_read_all_events, mock_fetch_dependencies, mock_process_pha_event,
-                                          mock_save_data, mock_spiceypy):
+    def test_process_direct_event_product(
+        self,
+        mock_read_all_events,
+        mock_fetch_dependencies,
+        mock_process_pha_event,
+        mock_save_data,
+        mock_spiceypy,
+    ):
         mock_spiceypy.ktotal.return_value = 0
-        pha_word_1 = PHAWord(adc_overflow=False, adc_value=11,
-                             detector=Detector(layer=1, side="A", segment="1A", address=2, group="L1A4"),
-                             is_last_pha=True,
-                             is_low_gain=True)
-
-        raw_pha_event_1 = RawPHAEvent(particle_id=1, priority_buffer_num=2, stim_tag=False, haz_tag=False, time_tag=20,
-                                      a_b_side_flag=False, has_unread_adcs=True, long_event_flag=False,
-                                      culling_flag=True,
-                                      spare=True, pha_words=[pha_word_1])
-
-        raw_pha_event_2 = RawPHAEvent(particle_id=1, priority_buffer_num=2, stim_tag=False, haz_tag=False, time_tag=20,
-                                      a_b_side_flag=False, has_unread_adcs=True, long_event_flag=False,
-                                      culling_flag=True,
-                                      spare=True, pha_words=[deepcopy(pha_word_1)])
-
-        pha_word_2 = PHAWord(adc_overflow=True, adc_value=12,
-                             detector=Detector(layer=2, side="B", segment="1B", address=5, group="L2B"),
-                             is_last_pha=False,
-                             is_low_gain=False)
-
-        pha_word_3 = PHAWord(adc_overflow=False, adc_value=11,
-                             detector=Detector(layer=1, side="A", segment="2A", address=8, group="L1A1"),
-                             is_last_pha=True,
-                             is_low_gain=True)
-
-        raw_pha_event_3 = RawPHAEvent(particle_id=2, priority_buffer_num=3, stim_tag=True, haz_tag=True, time_tag=30,
-                                      a_b_side_flag=True, has_unread_adcs=False, long_event_flag=True,
-                                      culling_flag=False,
-                                      spare=False, pha_words=[pha_word_2, pha_word_3],
-                                      extended_header=PHAExtendedHeader(detector_flags=2, delta_e_index=2,
-                                                                        e_prime_index=True),
-                                      stim_block=StimBlock(stim_step=1, stim_gain=2, unused=3, a_l_stim=True),
-                                      extended_stim_header=ExtendedStimHeader(dac_value=123, tbd=666))
-
-        event_output_1 = EventOutput(original_event=raw_pha_event_1, charge=9.0, energies=[1], total_energy=99,
-                                     detected_range=DetectedRange(DetectorRange.R2, DetectorSide.A), e_delta=103.7,
-                                     e_prime=63.27)
-        event_output_2 = EventOutput(original_event=raw_pha_event_2, charge=10.0, energies=[4], total_energy=100,
-                                     detected_range=None, e_delta=None, e_prime=None)
-        event_output_3 = EventOutput(original_event=raw_pha_event_3, charge=12.0, energies=[9, 8], total_energy=200,
-                                     detected_range=DetectedRange(DetectorRange.R3, DetectorSide.A), e_delta=106.7,
-                                     e_prime=69.27)
+        pha_word_1 = PHAWord(
+            adc_overflow=False,
+            adc_value=11,
+            detector=Detector(layer=1, side="A", segment="1A", address=2, group="L1A4"),
+            is_last_pha=True,
+            is_low_gain=True,
+        )
+
+        raw_pha_event_1 = RawPHAEvent(
+            particle_id=1,
+            priority_buffer_num=2,
+            stim_tag=False,
+            haz_tag=False,
+            time_tag=20,
+            a_b_side_flag=False,
+            has_unread_adcs=True,
+            long_event_flag=False,
+            culling_flag=True,
+            spare=True,
+            pha_words=[pha_word_1],
+        )
+
+        raw_pha_event_2 = RawPHAEvent(
+            particle_id=1,
+            priority_buffer_num=2,
+            stim_tag=False,
+            haz_tag=False,
+            time_tag=20,
+            a_b_side_flag=False,
+            has_unread_adcs=True,
+            long_event_flag=False,
+            culling_flag=True,
+            spare=True,
+            pha_words=[deepcopy(pha_word_1)],
+        )
+
+        pha_word_2 = PHAWord(
+            adc_overflow=True,
+            adc_value=12,
+            detector=Detector(layer=2, side="B", segment="1B", address=5, group="L2B"),
+            is_last_pha=False,
+            is_low_gain=False,
+        )
+
+        pha_word_3 = PHAWord(
+            adc_overflow=False,
+            adc_value=11,
+            detector=Detector(layer=1, side="A", segment="2A", address=8, group="L1A1"),
+            is_last_pha=True,
+            is_low_gain=True,
+        )
+
+        raw_pha_event_3 = RawPHAEvent(
+            particle_id=2,
+            priority_buffer_num=3,
+            stim_tag=True,
+            haz_tag=True,
+            time_tag=30,
+            a_b_side_flag=True,
+            has_unread_adcs=False,
+            long_event_flag=True,
+            culling_flag=False,
+            spare=False,
+            pha_words=[pha_word_2, pha_word_3],
+            extended_header=PHAExtendedHeader(
+                detector_flags=2, delta_e_index=2, e_prime_index=True
+            ),
+            stim_block=StimBlock(stim_step=1, stim_gain=2, unused=3, a_l_stim=True),
+            extended_stim_header=ExtendedStimHeader(dac_value=123, tbd=666),
+        )
+
+        event_output_1 = EventOutput(
+            original_event=raw_pha_event_1,
+            charge=9.0,
+            energies=[1],
+            total_energy=99,
+            detected_range=DetectedRange(DetectorRange.R2, DetectorSide.A),
+            e_delta=103.7,
+            e_prime=63.27,
+        )
+        event_output_2 = EventOutput(
+            original_event=raw_pha_event_2,
+            charge=10.0,
+            energies=[4],
+            total_energy=100,
+            detected_range=None,
+            e_delta=None,
+            e_prime=None,
+        )
+        event_output_3 = EventOutput(
+            original_event=raw_pha_event_3,
+            charge=12.0,
+            energies=[9, 8],
+            total_energy=200,
+            detected_range=DetectedRange(DetectorRange.R3, DetectorSide.A),
+            e_delta=106.7,
+            e_prime=69.27,
+        )
 
         input_metadata = InputMetadata(
             instrument="hit",
@@ -495,37 +881,70 @@ def test_process_direct_event_product(self, mock_read_all_events, mock_fetch_dep
             start_date=None,
             end_date=None,
             version="",
-            descriptor="direct-events"
+            descriptor="direct-events",
         )
 
         mock_processing_input_collection = Mock()
-        mock_processing_input_collection.get_file_paths.return_value = [Path("test/parent_path")]
+        mock_processing_input_collection.get_file_paths.return_value = [
+            Path("test/parent_path")
+        ]
 
         mock_hit_l3_pha_dependencies = mock_fetch_dependencies.return_value
-        mock_hit_l3_pha_dependencies.hit_l1_data.epoch = [datetime(year=2020, month=2, day=1),
-                                                          datetime(year=2020, month=2, day=1, hour=1)]
-        mock_hit_l3_pha_dependencies.hit_l1_data.event_binary = [sentinel.binary_stream_1, sentinel.binary_stream_2]
+        mock_hit_l3_pha_dependencies.hit_l1_data.epoch = [
+            datetime(year=2020, month=2, day=1),
+            datetime(year=2020, month=2, day=1, hour=1),
+        ]
+        mock_hit_l3_pha_dependencies.hit_l1_data.event_binary = [
+            sentinel.binary_stream_1,
+            sentinel.binary_stream_2,
+        ]
 
-        mock_process_pha_event.side_effect = [event_output_1, event_output_2, event_output_3]
-        mock_read_all_events.side_effect = [[raw_pha_event_1, raw_pha_event_2], [raw_pha_event_3]]
+        mock_process_pha_event.side_effect = [
+            event_output_1,
+            event_output_2,
+            event_output_3,
+        ]
+        mock_read_all_events.side_effect = [
+            [raw_pha_event_1, raw_pha_event_2],
+            [raw_pha_event_3],
+        ]
 
         processor = HitProcessor(mock_processing_input_collection, input_metadata)
         product = processor.process()
 
-        mock_fetch_dependencies.assert_called_once_with(mock_processing_input_collection)
-        mock_read_all_events.assert_has_calls([call(sentinel.binary_stream_1), call(sentinel.binary_stream_2)])
+        mock_fetch_dependencies.assert_called_once_with(
+            mock_processing_input_collection
+        )
+        mock_read_all_events.assert_has_calls(
+            [call(sentinel.binary_stream_1), call(sentinel.binary_stream_2)]
+        )
 
         mock_process_pha_event.assert_has_calls(
-            [call(raw_pha_event_1, mock_hit_l3_pha_dependencies.cosine_correction_lookup,
-                  mock_hit_l3_pha_dependencies.gain_lookup, mock_hit_l3_pha_dependencies.range_fit_lookup,
-                  mock_hit_l3_pha_dependencies.event_type_lookup),
-             call(raw_pha_event_2, mock_hit_l3_pha_dependencies.cosine_correction_lookup,
-                  mock_hit_l3_pha_dependencies.gain_lookup, mock_hit_l3_pha_dependencies.range_fit_lookup,
-                  mock_hit_l3_pha_dependencies.event_type_lookup),
-             call(raw_pha_event_3, mock_hit_l3_pha_dependencies.cosine_correction_lookup,
-                  mock_hit_l3_pha_dependencies.gain_lookup, mock_hit_l3_pha_dependencies.range_fit_lookup,
-                  mock_hit_l3_pha_dependencies.event_type_lookup)],
-            any_order=False)
+            [
+                call(
+                    raw_pha_event_1,
+                    mock_hit_l3_pha_dependencies.cosine_correction_lookup,
+                    mock_hit_l3_pha_dependencies.gain_lookup,
+                    mock_hit_l3_pha_dependencies.range_fit_lookup,
+                    mock_hit_l3_pha_dependencies.event_type_lookup,
+                ),
+                call(
+                    raw_pha_event_2,
+                    mock_hit_l3_pha_dependencies.cosine_correction_lookup,
+                    mock_hit_l3_pha_dependencies.gain_lookup,
+                    mock_hit_l3_pha_dependencies.range_fit_lookup,
+                    mock_hit_l3_pha_dependencies.event_type_lookup,
+                ),
+                call(
+                    raw_pha_event_3,
+                    mock_hit_l3_pha_dependencies.cosine_correction_lookup,
+                    mock_hit_l3_pha_dependencies.gain_lookup,
+                    mock_hit_l3_pha_dependencies.range_fit_lookup,
+                    mock_hit_l3_pha_dependencies.event_type_lookup,
+                ),
+            ],
+            any_order=False,
+        )
 
         mock_save_data.assert_called_once()
 
@@ -535,26 +954,61 @@ def test_process_direct_event_product(self, mock_read_all_events, mock_fetch_dep
         self.assertEqual(input_metadata, direct_event_product.input_metadata)
         self.assertEqual(["parent_path"], direct_event_product.parent_file_names)
 
-        np.testing.assert_array_equal(direct_event_product.epoch, np.array(
-            [datetime(year=2020, month=2, day=1), datetime(year=2020, month=2, day=1),
-             datetime(year=2020, month=2, day=1, hour=1)]))
-        np.testing.assert_array_equal(direct_event_product.charge, np.array([9.0, 10.0, 12.0]))
-        np.testing.assert_array_equal(direct_event_product.energy, np.array([99, 100, 200]))
-        np.testing.assert_array_equal(direct_event_product.particle_id, np.array([1, 1, 2]))
+        np.testing.assert_array_equal(
+            direct_event_product.epoch,
+            np.array(
+                [
+                    datetime(year=2020, month=2, day=1),
+                    datetime(year=2020, month=2, day=1),
+                    datetime(year=2020, month=2, day=1, hour=1),
+                ]
+            ),
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.charge, np.array([9.0, 10.0, 12.0])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.energy, np.array([99, 100, 200])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.particle_id, np.array([1, 1, 2])
+        )
 
-        np.testing.assert_array_equal(direct_event_product.e_delta, np.array([103.7, np.nan, 106.7]))
-        np.testing.assert_array_equal(direct_event_product.e_prime, np.array([63.27, np.nan, 69.27]))
+        np.testing.assert_array_equal(
+            direct_event_product.e_delta, np.array([103.7, np.nan, 106.7])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.e_prime, np.array([63.27, np.nan, 69.27])
+        )
 
-        np.testing.assert_array_equal(direct_event_product.detected_range, np.array([2, np.nan, 3]))
+        np.testing.assert_array_equal(
+            direct_event_product.detected_range, np.array([2, np.nan, 3])
+        )
 
-        np.testing.assert_array_equal(direct_event_product.priority_buffer_number, np.array([2, 2, 3]))
-        np.testing.assert_array_equal(direct_event_product.latency, np.array([20, 20, 30]))
-        np.testing.assert_array_equal(direct_event_product.stim_tag, np.array([False, False, True]))
-        np.testing.assert_array_equal(direct_event_product.long_event_flag, np.array([False, False, True]))
-        np.testing.assert_array_equal(direct_event_product.haz_tag, np.array([False, False, True]))
-        np.testing.assert_array_equal(direct_event_product.a_b_side, np.array([0, np.nan, 0]))
-        np.testing.assert_array_equal(direct_event_product.has_unread_adcs, np.array([True, True, False]))
-        np.testing.assert_array_equal(direct_event_product.culling_flag, np.array([True, True, False]))
+        np.testing.assert_array_equal(
+            direct_event_product.priority_buffer_number, np.array([2, 2, 3])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.latency, np.array([20, 20, 30])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.stim_tag, np.array([False, False, True])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.long_event_flag, np.array([False, False, True])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.haz_tag, np.array([False, False, True])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.a_b_side, np.array([0, np.nan, 0])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.has_unread_adcs, np.array([True, True, False])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.culling_flag, np.array([True, True, False])
+        )
 
         self.assertEqual((3, 64), direct_event_product.pha_value.shape)
         expected_values = {
@@ -587,10 +1041,16 @@ def test_process_direct_event_product(self, mock_read_all_events, mock_fetch_dep
                 adc_val,
                 err_msg=f"Did not match at index {idx}",
             )
-            np.testing.assert_array_equal(direct_event_product.energy_at_detector[idx], energy,
-                                          err_msg=f"Did not match at index {idx}")
-            np.testing.assert_array_equal(direct_event_product.is_low_gain[idx], is_low_gain,
-                                          err_msg=f"Did not match at index {idx}")
+            np.testing.assert_array_equal(
+                direct_event_product.energy_at_detector[idx],
+                energy,
+                err_msg=f"Did not match at index {idx}",
+            )
+            np.testing.assert_array_equal(
+                direct_event_product.is_low_gain[idx],
+                is_low_gain,
+                err_msg=f"Did not match at index {idx}",
+            )
             mask[idx] = False
 
         self.assertTrue(
@@ -603,8 +1063,10 @@ def test_process_direct_event_product(self, mock_read_all_events, mock_fetch_dep
             direct_event_product.detector_flags,
             np.array([UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE, 2]),
         )
-        np.testing.assert_array_equal(direct_event_product.deindex,
-                                      np.array([UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE, 2]))
+        np.testing.assert_array_equal(
+            direct_event_product.deindex,
+            np.array([UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE, 2]),
+        )
         np.testing.assert_array_equal(
             direct_event_product.epindex,
             np.array([UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE, True]),
@@ -612,19 +1074,37 @@ def test_process_direct_event_product(self, mock_read_all_events, mock_fetch_dep
         np.testing.assert_array_equal(
             direct_event_product.stim_gain, np.array([False, False, True])
         )
-        np.testing.assert_array_equal(direct_event_product.a_l_stim, np.array([False, False, True]))
-        np.testing.assert_array_equal(direct_event_product.stim_step,
-                                      np.array([UNSIGNED_INT1_FILL_VALUE, UNSIGNED_INT1_FILL_VALUE, 1]))
-        np.testing.assert_array_equal(direct_event_product.dac_value,
-                                      np.array([UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE, 123]))
-        np.testing.assert_array_equal(direct_event_product.hit_flags, np.zeros_like(direct_event_product.epoch, dtype=np.uint16), strict=True)
+        np.testing.assert_array_equal(
+            direct_event_product.a_l_stim, np.array([False, False, True])
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.stim_step,
+            np.array([UNSIGNED_INT1_FILL_VALUE, UNSIGNED_INT1_FILL_VALUE, 1]),
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.dac_value,
+            np.array([UNSIGNED_INT2_FILL_VALUE, UNSIGNED_INT2_FILL_VALUE, 123]),
+        )
+        np.testing.assert_array_equal(
+            direct_event_product.hit_flags,
+            np.zeros_like(direct_event_product.epoch, dtype=np.uint16),
+            strict=True,
+        )
 
     @patch("imap_l3_processing.hit.l3.hit_processor.PHAEventReader.read_all_pha_events")
     @patch("imap_l3_processing.hit.l3.hit_processor.process_pha_event", autospec=True)
-    def test_direct_event_parsing_errors_result_in_skipped_event(self, mock_process_pha_event, mock_read_all_events):
-        hit_l1_data = HitL1Data(epoch=np.array([datetime(year=2020, month=2, day=1),
-                                                datetime(year=2020, month=2, day=1, hour=1)]),
-                                event_binary=np.array([sentinel.binary_stream_1, sentinel.binary_stream_2]))
+    def test_direct_event_parsing_errors_result_in_skipped_event(
+        self, mock_process_pha_event, mock_read_all_events
+    ):
+        hit_l1_data = HitL1Data(
+            epoch=np.array(
+                [
+                    datetime(year=2020, month=2, day=1),
+                    datetime(year=2020, month=2, day=1, hour=1),
+                ]
+            ),
+            event_binary=np.array([sentinel.binary_stream_1, sentinel.binary_stream_2]),
+        )
         mock_direct_event_dependencies = Mock()
         mock_direct_event_dependencies.hit_l1_data = hit_l1_data
 
@@ -639,7 +1119,7 @@ def test_direct_event_parsing_errors_result_in_skipped_event(self, mock_process_
             has_unread_adcs=False,
             culling_flag=False,
             spare=False,
-            pha_words=[]
+            pha_words=[],
         )
 
         mock_read_all_events.side_effect = [Exception("error parsing"), [event]]
@@ -650,15 +1130,19 @@ def test_direct_event_parsing_errors_result_in_skipped_event(self, mock_process_
             start_date=None,
             end_date=None,
             version="",
-            descriptor="direct-events"
+            descriptor="direct-events",
         )
 
         processor = HitProcessor(Mock(), input_metadata)
         with self.assertLogs():
-            actual_product = processor.process_direct_event_product(mock_direct_event_dependencies)
+            actual_product = processor.process_direct_event_product(
+                mock_direct_event_dependencies
+            )
 
         self.assertEqual(1, len(actual_product.epoch))
-        self.assertEqual(datetime(year=2020, month=2, day=1, hour=1), actual_product.epoch[0])
+        self.assertEqual(
+            datetime(year=2020, month=2, day=1, hour=1), actual_product.epoch[0]
+        )
 
     def test_raise_error_if_descriptor_doesnt_match(self):
         input_metadata = InputMetadata(
@@ -667,14 +1151,16 @@ def test_raise_error_if_descriptor_doesnt_match(self):
             start_date=None,
             end_date=None,
             version="",
-            descriptor="spectral-index"
+            descriptor="spectral-index",
         )
         processor = HitProcessor(Mock(), input_metadata)
 
         with self.assertRaises(ValueError) as e:
             processor.process()
 
-        self.assertEqual(e.exception.args[0],
-                         f"Don't know how to generate 'spectral-index' /n Known HIT l3 data products: 'macropixel', 'direct-events'.")
+        self.assertEqual(
+            e.exception.args[0],
+            "Don't know how to generate 'spectral-index' /n Known HIT l3 data products: 'macropixel', 'direct-events'.",
+        )
 
     T = TypeVar("T")
diff --git a/tests/hit/l3/test_l3_sectored_dependencies.py b/tests/hit/l3/test_l3_sectored_dependencies.py
index 479488e41..fdf95b2a0 100644
--- a/tests/hit/l3/test_l3_sectored_dependencies.py
+++ b/tests/hit/l3/test_l3_sectored_dependencies.py
@@ -1,41 +1,54 @@
 from pathlib import Path
 from unittest import TestCase
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
 import imap_data_access
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection
+from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import HITL3SectoredDependencies, HIT_L2_DESCRIPTOR
+from imap_l3_processing.hit.l3.hit_l3_sectored_dependencies import (
+    HIT_L2_DESCRIPTOR,
+    HITL3SectoredDependencies,
+)
 
 
 class TestHITL3SectoredDependencies(TestCase):
     @patch("imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.read_mag_data")
     @patch("imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.read_l2_hit_data")
-    @patch('imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.imap_data_access.download')
-    def test_fetch_dependencies(self, mock_download, mock_read_hit_data, mock_read_mag_data):
-        hit_l2_data_dependency = ScienceInput(f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf")
-        mag_data_dependency = ScienceInput(f"imap_mag_l1d_norm-dsrf_20240906_v001.cdf")
-
-        hit_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'hit' / 'l2' / '2024' / '09'
-        mag_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'mag' / 'l1d' / '2024' / '09'
-
-        expected_file_paths = [hit_data_dir / f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf",
-                               mag_data_dir / f"imap_mag_l1d_norm-dsrf_20240906_v001.cdf"]
-        processing_input_collection = ProcessingInputCollection(hit_l2_data_dependency, mag_data_dependency)
+    @patch(
+        "imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.imap_data_access.download"
+    )
+    def test_fetch_dependencies(
+        self, mock_download, mock_read_hit_data, mock_read_mag_data
+    ):
+        hit_l2_data_dependency = ScienceInput(
+            f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf"
+        )
+        mag_data_dependency = ScienceInput("imap_mag_l1d_norm-dsrf_20240906_v001.cdf")
+
+        hit_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "hit" / "l2" / "2024" / "09"
+        )
+        mag_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "mag" / "l1d" / "2024" / "09"
+        )
+
+        expected_file_paths = [
+            hit_data_dir / f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf",
+            mag_data_dir / "imap_mag_l1d_norm-dsrf_20240906_v001.cdf",
+        ]
+        processing_input_collection = ProcessingInputCollection(
+            hit_l2_data_dependency, mag_data_dependency
+        )
 
         hit_data_path = Path("hit")
         mag_data_path = Path("mag")
-        mock_download.side_effect = [
-            hit_data_path,
-            mag_data_path
-        ]
+        mock_download.side_effect = [hit_data_path, mag_data_path]
 
         deps = HITL3SectoredDependencies.fetch_dependencies(processing_input_collection)
 
-        mock_download.assert_has_calls([
-            call(expected_file_paths[0]),
-            call(expected_file_paths[1])
-        ])
+        mock_download.assert_has_calls(
+            [call(expected_file_paths[0]), call(expected_file_paths[1])]
+        )
 
         mock_read_hit_data.assert_called_with(hit_data_path)
         mock_read_mag_data.assert_called_with(mag_data_path)
@@ -46,33 +59,42 @@ def test_fetch_dependencies(self, mock_download, mock_read_hit_data, mock_read_m
 
     @patch("imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.read_mag_data")
     @patch("imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.read_l2_hit_data")
-    @patch('imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.imap_data_access.download')
-    def test_fetch_dependencies_uses_mag_l2_if_available(self, mock_download, mock_read_hit_data, mock_read_mag_data):
-        hit_l2_data_dependency = ScienceInput(f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf")
-        mag_data_dependency = ScienceInput(f"imap_mag_l1d_norm-dsrf_20240906_v001.cdf")
-        mag_l2_data_dependency = ScienceInput(f"imap_mag_l2_norm-dsrf_20240906_v001.cdf")
-
-        hit_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'hit' / 'l2' / '2024' / '09'
-        mag_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'mag' / 'l2' / '2024' / '09'
-
-        expected_file_paths = [hit_data_dir / f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf",
-                               mag_data_dir / f"imap_mag_l2_norm-dsrf_20240906_v001.cdf"]
-        processing_input_collection = ProcessingInputCollection(hit_l2_data_dependency, mag_data_dependency,
-                                                                mag_l2_data_dependency)
+    @patch(
+        "imap_l3_processing.hit.l3.hit_l3_sectored_dependencies.imap_data_access.download"
+    )
+    def test_fetch_dependencies_uses_mag_l2_if_available(
+        self, mock_download, mock_read_hit_data, mock_read_mag_data
+    ):
+        hit_l2_data_dependency = ScienceInput(
+            f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf"
+        )
+        mag_data_dependency = ScienceInput("imap_mag_l1d_norm-dsrf_20240906_v001.cdf")
+        mag_l2_data_dependency = ScienceInput("imap_mag_l2_norm-dsrf_20240906_v001.cdf")
+
+        hit_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "hit" / "l2" / "2024" / "09"
+        )
+        mag_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "mag" / "l2" / "2024" / "09"
+        )
+
+        expected_file_paths = [
+            hit_data_dir / f"imap_hit_l2_{HIT_L2_DESCRIPTOR}_20240908_v001.cdf",
+            mag_data_dir / "imap_mag_l2_norm-dsrf_20240906_v001.cdf",
+        ]
+        processing_input_collection = ProcessingInputCollection(
+            hit_l2_data_dependency, mag_data_dependency, mag_l2_data_dependency
+        )
 
         hit_data_path = Path("hit")
         mag_data_path = Path("mag")
-        mock_download.side_effect = [
-            hit_data_path,
-            mag_data_path
-        ]
+        mock_download.side_effect = [hit_data_path, mag_data_path]
 
         deps = HITL3SectoredDependencies.fetch_dependencies(processing_input_collection)
 
-        mock_download.assert_has_calls([
-            call(expected_file_paths[0]),
-            call(expected_file_paths[1])
-        ])
+        mock_download.assert_has_calls(
+            [call(expected_file_paths[0]), call(expected_file_paths[1])]
+        )
 
         mock_read_hit_data.assert_called_with(hit_data_path)
         mock_read_mag_data.assert_called_with(mag_data_path)
diff --git a/tests/hit/l3/test_models.py b/tests/hit/l3/test_models.py
index 9a1f54178..f226f3d28 100644
--- a/tests/hit/l3/test_models.py
+++ b/tests/hit/l3/test_models.py
@@ -13,12 +13,12 @@
 
 class TestModels(CdfModelTestCase):
     def setUp(self) -> None:
-        if os.path.exists('test_cdf.cdf'):
-            os.remove('test_cdf.cdf')
+        if os.path.exists("test_cdf.cdf"):
+            os.remove("test_cdf.cdf")
 
     def tearDown(self) -> None:
-        if os.path.exists('test_cdf.cdf'):
-            os.remove('test_cdf.cdf')
+        if os.path.exists("test_cdf.cdf"):
+            os.remove("test_cdf.cdf")
 
     def test_direct_events_to_data_product_variables_with_multiple_events(self):
         input_metadata = InputMetadata(
@@ -27,7 +27,7 @@ def test_direct_events_to_data_product_variables_with_multiple_events(self):
             start_date=datetime.min,
             end_date=datetime.max,
             version="1",
-            descriptor="direct-events"
+            descriptor="direct-events",
         )
 
         epoch = [datetime.now(), datetime.now() + timedelta(hours=1)]
@@ -55,35 +55,36 @@ def test_direct_events_to_data_product_variables_with_multiple_events(self):
         a_l_stim = np.array([None, True])
         stim_step = np.array([None, 1])
         dac_value = np.array([None, 123])
-        hit_flag_values = np.array([0,0])
+        hit_flag_values = np.array([0, 0])
 
-        direct_event = HitDirectEventDataProduct(input_metadata=input_metadata,
-                                                 epoch=epoch,
-                                                 charge=charge,
-                                                 energy=energy,
-                                                 e_delta=e_delta,
-                                                 e_prime=e_prime,
-                                                 detected_range=detected_range,
-                                                 particle_id=particle,
-                                                 priority_buffer_number=priority_buffer_number,
-                                                 latency=latency,
-                                                 stim_tag=stim_tag,
-                                                 long_event_flag=long_event_flag,
-                                                 haz_tag=haz_tag,
-                                                 a_b_side=a_b_side,
-                                                 has_unread_adcs=has_unread_adcs,
-                                                 culling_flag=culling_flag,
-                                                 pha_value=pha_value,
-                                                 energy_at_detector=energy_at_detector,
-                                                 is_low_gain=is_low_gain,
-                                                 detector_flags=detector_flags,
-                                                 deindex=deindex,
-                                                 epindex=epindex,
-                                                 stim_gain=stim_gain,
-                                                 a_l_stim=a_l_stim,
-                                                 stim_step=stim_step,
-                                                 dac_value=dac_value,
-                                                 hit_flags=hit_flag_values,
+        direct_event = HitDirectEventDataProduct(
+            input_metadata=input_metadata,
+            epoch=epoch,
+            charge=charge,
+            energy=energy,
+            e_delta=e_delta,
+            e_prime=e_prime,
+            detected_range=detected_range,
+            particle_id=particle,
+            priority_buffer_number=priority_buffer_number,
+            latency=latency,
+            stim_tag=stim_tag,
+            long_event_flag=long_event_flag,
+            haz_tag=haz_tag,
+            a_b_side=a_b_side,
+            has_unread_adcs=has_unread_adcs,
+            culling_flag=culling_flag,
+            pha_value=pha_value,
+            energy_at_detector=energy_at_detector,
+            is_low_gain=is_low_gain,
+            detector_flags=detector_flags,
+            deindex=deindex,
+            epindex=epindex,
+            stim_gain=stim_gain,
+            a_l_stim=a_l_stim,
+            stim_step=stim_step,
+            dac_value=dac_value,
+            hit_flags=hit_flag_values,
         )
 
         expected_variables = [
@@ -95,7 +96,9 @@ def test_direct_events_to_data_product_variables_with_multiple_events(self):
             DataProductVariable(models.E_PRIME_VAR_NAME, e_prime),
             DataProductVariable(models.DETECTED_RANGE_VAR_NAME, detected_range),
             DataProductVariable(models.PARTICLE_ID_VAR_NAME, particle),
-            DataProductVariable(models.PRIORITY_BUFFER_NUMBER_VAR_NAME, priority_buffer_number),
+            DataProductVariable(
+                models.PRIORITY_BUFFER_NUMBER_VAR_NAME, priority_buffer_number
+            ),
             DataProductVariable(models.LATENCY_VAR_NAME, latency),
             DataProductVariable(models.STIM_TAG_VAR_NAME, stim_tag),
             DataProductVariable(models.LONG_EVENT_FLAG_VAR_NAME, long_event_flag),
@@ -114,26 +117,102 @@ def test_direct_events_to_data_product_variables_with_multiple_events(self):
             DataProductVariable(models.STIM_STEP_VAR_NAME, stim_step),
             DataProductVariable(models.DAC_VALUE_VAR_NAME, dac_value),
             DataProductVariable(models.HIT_FLAGS_CDF_VAR_NAME, hit_flag_values),
-            DataProductVariable(models.DETECTOR_ID_VAR_NAME, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
-                                                              17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-                                                              31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
-                                                              45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
-                                                              59, 60, 61, 62, 63]),
+            DataProductVariable(
+                models.DETECTOR_ID_VAR_NAME,
+                [
+                    0,
+                    1,
+                    2,
+                    3,
+                    4,
+                    5,
+                    6,
+                    7,
+                    8,
+                    9,
+                    10,
+                    11,
+                    12,
+                    13,
+                    14,
+                    15,
+                    16,
+                    17,
+                    18,
+                    19,
+                    20,
+                    21,
+                    22,
+                    23,
+                    24,
+                    25,
+                    26,
+                    27,
+                    28,
+                    29,
+                    30,
+                    31,
+                    32,
+                    33,
+                    34,
+                    35,
+                    36,
+                    37,
+                    38,
+                    39,
+                    40,
+                    41,
+                    42,
+                    43,
+                    44,
+                    45,
+                    46,
+                    47,
+                    48,
+                    49,
+                    50,
+                    51,
+                    52,
+                    53,
+                    54,
+                    55,
+                    56,
+                    57,
+                    58,
+                    59,
+                    60,
+                    61,
+                    62,
+                    63,
+                ],
+            ),
         ]
 
         actual_variables = direct_event.to_data_product_variables()
 
         self.assertEqual(28, len(actual_variables))
-        for expected_variable, actual_variable in zip(expected_variables, actual_variables):
-            self.assert_variable_attributes(actual_variable, expected_variable.value, expected_variable.name,
-                                            expected_variable.cdf_data_type, expected_variable.record_varying)
+        for expected_variable, actual_variable in zip(
+            expected_variables, actual_variables
+        ):
+            self.assert_variable_attributes(
+                actual_variable,
+                expected_variable.value,
+                expected_variable.name,
+                expected_variable.cdf_data_type,
+                expected_variable.record_varying,
+            )
 
     def test_hit_l1_data_read_from_cdf(self):
-        expected_epochs = np.array([datetime(year=2020, month=1, day=1), datetime(year=2020, month=1, day=1, hour=2)])
+        expected_epochs = np.array(
+            [
+                datetime(year=2020, month=1, day=1),
+                datetime(year=2020, month=1, day=1, hour=2),
+            ]
+        )
         expected_raw_binaries = ["10101011", "10101001010101"]
 
-        pathname = 'test_cdf'
-        with CDF(pathname, '') as cdf:
+        pathname = "test_cdf"
+        with CDF(pathname, "") as cdf:
             cdf["epoch"] = expected_epochs
             cdf["pha_raw"] = expected_raw_binaries
 
@@ -142,4 +221,6 @@ def test_hit_l1_data_read_from_cdf(self):
                 hit_l1_data = HitL1Data.read_from_cdf(file_path)
 
                 np.testing.assert_array_equal(hit_l1_data.epoch, expected_epochs)
-                np.testing.assert_array_equal(expected_raw_binaries, hit_l1_data.event_binary)
+                np.testing.assert_array_equal(
+                    expected_raw_binaries, hit_l1_data.event_binary
+                )
diff --git a/tests/hit/l3/test_utils.py b/tests/hit/l3/test_utils.py
index 07c31e2a8..5705f92c9 100644
--- a/tests/hit/l3/test_utils.py
+++ b/tests/hit/l3/test_utils.py
@@ -17,14 +17,43 @@ def setUp(self) -> None:
 
     def test_read_l2_hit_data(self):
         rng = np.random.default_rng()
-        expected_azimuth_data = [12., 36., 60., 84., 108., 132., 156., 180., 204., 228., 252.,
-                                 276., 300., 324., 348.]
-        expected_zenith_data = [11.25, 33.75, 56.25, 78.75, 101.25, 123.75, 146.25, 168.75]
+        expected_azimuth_data = [
+            12.0,
+            36.0,
+            60.0,
+            84.0,
+            108.0,
+            132.0,
+            156.0,
+            180.0,
+            204.0,
+            228.0,
+            252.0,
+            276.0,
+            300.0,
+            324.0,
+            348.0,
+        ]
+        expected_zenith_data = [
+            11.25,
+            33.75,
+            56.25,
+            78.75,
+            101.25,
+            123.75,
+            146.25,
+            168.75,
+        ]
 
         start_time = datetime(2010, 1, 1, 0, 5)
 
         epoch_data = np.array(
-            [start_time, start_time + timedelta(minutes=5), start_time + timedelta(minutes=10, microseconds=999)])
+            [
+                start_time,
+                start_time + timedelta(minutes=5),
+                start_time + timedelta(minutes=10, microseconds=999),
+            ]
+        )
 
         hydrogen_data = rng.random((3, 3, 15, 8))
         helium_data = rng.random((3, 2, 15, 8))
@@ -38,13 +67,19 @@ def test_read_l2_hit_data(self):
         nemgsi_delta = nemgsi_data * 0.1
         iron_delta = iron_data * 0.1
 
-        pathname = self.write_test_data_file(epoch_data, hydrogen_data, helium_data, cno_data, nemgsi_data, iron_data,
-                                             hydrogen_delta,
-                                             helium_delta,
-                                             cno_delta,
-                                             nemgsi_delta,
-                                             iron_delta,
-                                             )
+        pathname = self.write_test_data_file(
+            epoch_data,
+            hydrogen_data,
+            helium_data,
+            cno_data,
+            nemgsi_data,
+            iron_data,
+            hydrogen_delta,
+            helium_delta,
+            cno_delta,
+            nemgsi_delta,
+            iron_delta,
+        )
 
         for path in [pathname, Path(pathname)]:
             with self.subTest(path):
@@ -57,8 +92,10 @@ def test_read_l2_hit_data(self):
                 np.testing.assert_array_equal(iron_data, result.fe)
 
                 np.testing.assert_array_equal(epoch_data, result.epoch)
-                np.testing.assert_array_equal([timedelta(minutes=5), timedelta(minutes=5), timedelta(minutes=5)],
-                                              result.epoch_delta)
+                np.testing.assert_array_equal(
+                    [timedelta(minutes=5), timedelta(minutes=5), timedelta(minutes=5)],
+                    result.epoch_delta,
+                )
 
                 np.testing.assert_array_equal([0, 1, 2], result.h_energy)
                 np.testing.assert_array_equal([0, 1], result.he4_energy)
@@ -104,42 +141,114 @@ def test_read_l2_hit_data_handles_fill_values(self):
         nemgsi_data = np.full((1, 2, 15, 8), fill_value=fill_val)
         iron_data = np.full((1, 1, 15, 8), fill_value=fill_val)
 
-        pathname = self.write_test_data_file(epoch_data, hydrogen_data, helium_data, cno_data, nemgsi_data, iron_data,
-                                             hydrogen_data, helium_data, cno_data, nemgsi_data, iron_data)
+        pathname = self.write_test_data_file(
+            epoch_data,
+            hydrogen_data,
+            helium_data,
+            cno_data,
+            nemgsi_data,
+            iron_data,
+            hydrogen_data,
+            helium_data,
+            cno_data,
+            nemgsi_data,
+            iron_data,
+        )
 
         for path in [pathname, Path(pathname)]:
             with self.subTest(path):
                 result = read_l2_hit_data(path)
 
-                np.testing.assert_array_equal(result.h, np.full_like(hydrogen_data, np.nan))
-                np.testing.assert_array_equal(result.he4, np.full_like(helium_data, np.nan))
-                np.testing.assert_array_equal(result.cno, np.full_like(cno_data, np.nan))
-                np.testing.assert_array_equal(result.nemgsi, np.full_like(nemgsi_data, np.nan))
-                np.testing.assert_array_equal(result.fe, np.full_like(iron_data, np.nan))
-
-                np.testing.assert_array_equal(result.delta_plus_h, np.full_like(hydrogen_data, np.nan))
-                np.testing.assert_array_equal(result.delta_minus_h, np.full_like(hydrogen_data, np.nan))
-                np.testing.assert_array_equal(result.delta_plus_he4, np.full_like(helium_data, np.nan))
-                np.testing.assert_array_equal(result.delta_minus_he4, np.full_like(helium_data, np.nan))
-                np.testing.assert_array_equal(result.delta_plus_cno, np.full_like(cno_data, np.nan))
-                np.testing.assert_array_equal(result.delta_minus_cno, np.full_like(cno_data, np.nan))
-                np.testing.assert_array_equal(result.delta_plus_nemgsi, np.full_like(nemgsi_data, np.nan))
-                np.testing.assert_array_equal(result.delta_minus_nemgsi, np.full_like(nemgsi_data, np.nan))
-                np.testing.assert_array_equal(result.delta_plus_fe, np.full_like(iron_data, np.nan))
-                np.testing.assert_array_equal(result.delta_minus_fe, np.full_like(iron_data, np.nan))
-
-    def write_test_data_file(self, epoch_data, hydrogen_data, helium_data, cno_data, nemgsi_data, iron_data,
-                             hydrogen_delta,
-                             helium_delta,
-                             cno_delta,
-                             nemgsi_delta,
-                             iron_delta,
-                             ) -> str:
-        pathname = os.path.join(self.temp_dir.name, 'test_cdf.cdf')
-        expected_azimuth_data = [12., 36., 60., 84., 108., 132., 156., 180., 204., 228., 252.,
-                                 276., 300., 324., 348.]
-        expected_zenith_data = [11.25, 33.75, 56.25, 78.75, 101.25, 123.75, 146.25, 168.75]
-        with CDF(pathname, '') as cdf:
+                np.testing.assert_array_equal(
+                    result.h, np.full_like(hydrogen_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.he4, np.full_like(helium_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.cno, np.full_like(cno_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.nemgsi, np.full_like(nemgsi_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.fe, np.full_like(iron_data, np.nan)
+                )
+
+                np.testing.assert_array_equal(
+                    result.delta_plus_h, np.full_like(hydrogen_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_minus_h, np.full_like(hydrogen_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_plus_he4, np.full_like(helium_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_minus_he4, np.full_like(helium_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_plus_cno, np.full_like(cno_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_minus_cno, np.full_like(cno_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_plus_nemgsi, np.full_like(nemgsi_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_minus_nemgsi, np.full_like(nemgsi_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_plus_fe, np.full_like(iron_data, np.nan)
+                )
+                np.testing.assert_array_equal(
+                    result.delta_minus_fe, np.full_like(iron_data, np.nan)
+                )
+
+    def write_test_data_file(
+        self,
+        epoch_data,
+        hydrogen_data,
+        helium_data,
+        cno_data,
+        nemgsi_data,
+        iron_data,
+        hydrogen_delta,
+        helium_delta,
+        cno_delta,
+        nemgsi_delta,
+        iron_delta,
+    ) -> str:
+        pathname = os.path.join(self.temp_dir.name, "test_cdf.cdf")
+        expected_azimuth_data = [
+            12.0,
+            36.0,
+            60.0,
+            84.0,
+            108.0,
+            132.0,
+            156.0,
+            180.0,
+            204.0,
+            228.0,
+            252.0,
+            276.0,
+            300.0,
+            324.0,
+            348.0,
+        ]
+        expected_zenith_data = [
+            11.25,
+            33.75,
+            56.25,
+            78.75,
+            101.25,
+            123.75,
+            146.25,
+            168.75,
+        ]
+        with CDF(pathname, "") as cdf:
             cdf.col_major(True)
 
             cdf["h_macropixel_intensity"] = hydrogen_data
diff --git a/tests/integration/integration_test_helpers.py b/tests/integration/integration_test_helpers.py
index 21c3231d5..4606f43ed 100644
--- a/tests/integration/integration_test_helpers.py
+++ b/tests/integration/integration_test_helpers.py
@@ -2,19 +2,18 @@
 import os
 import shutil
 from collections import defaultdict
+from collections.abc import Callable
 from pathlib import Path
-from typing import Callable
-from unittest.mock import patch, Mock
+from unittest.mock import Mock, patch
 from urllib.parse import urlparse
 
 import imap_data_access
 import requests
 from imap_data_access import AncillaryFilePath
 from imap_data_access.file_validation import (
-    generate_imap_file_path,
     ScienceFilePath,
     SPICEFilePath,
-    Version,
+    generate_imap_file_path,
 )
 from requests import Response
 
@@ -26,7 +25,7 @@
 class ImapQueryPatcher:
     def __init__(self, input_files: list[Path | str]):
         self.input_files = input_files
-        self.patcher = patch.object(imap_data_access, 'query', new=self)
+        self.patcher = patch.object(imap_data_access, "query", new=self)
 
     def start(self):
         self.patcher.start()
@@ -36,8 +35,7 @@ def stop(self):
 
     def __call__(self, **kwargs):
         table = kwargs.get("table") or "science"
-        if "table" in kwargs:
-            del kwargs["table"]
+        kwargs.pop("table", None)
 
         use_latest = kwargs.get("version") == "latest"
         if kwargs.get("version") == "latest":
@@ -58,9 +56,13 @@ def __call__(self, **kwargs):
                     raise ValueError(f"Unexpected file type: {input_file}")
 
         query_results = create_mock_query_results(filtered_by_type)
-        filtered_query_results = [qr for qr in query_results if desired_attributes.issubset(set(qr.items()))]
+        filtered_query_results = [
+            qr for qr in query_results if desired_attributes.issubset(set(qr.items()))
+        ]
         if use_latest:
-            filtered_query_results = self.filter_results_for_latest(filtered_query_results)
+            filtered_query_results = self.filter_results_for_latest(
+                filtered_query_results
+            )
         return filtered_query_results
 
     @staticmethod
@@ -68,7 +70,11 @@ def filter_results_for_latest(query_results: list[dict]):
         query_results_by_date = defaultdict(list)
         for qr in query_results:
             query_results_by_date[qr["start_date"]].append(qr)
-        return [max(qrs, key=get_version_from_query_result) for qrs in query_results_by_date.values()]
+        return [
+            max(qrs, key=get_version_from_query_result)
+            for qrs in query_results_by_date.values()
+        ]
+
 
 def fake_download(file: Path | str):
     filename = Path(file).name
@@ -92,6 +98,7 @@ def stage_input_file(file_path: Path) -> Path:
     shutil.copy(file_path, destination)
     return destination
 
+
 METAKERNEL_TEMPLATE = """
 \\begindata
 
@@ -101,20 +108,24 @@ def stage_input_file(file_path: Path) -> Path:
 """
 
 
-def create_metakernel(path_to_spice_dir: str, requested_spice_paths: list[SPICEFilePath]) -> str:
+def create_metakernel(
+    path_to_spice_dir: str, requested_spice_paths: list[SPICEFilePath]
+) -> str:
     all_spice_paths = []
     for spice_file in requested_spice_paths:
         spice_subdir_name = spice_file.construct_path().parent.name
         spice_file_name = spice_file.construct_path().name
         spice_path = str(Path(path_to_spice_dir) / spice_subdir_name / spice_file_name)
 
-        chunked_spice_path = [spice_path[i:i + 79] for i in range(0, len(spice_path), 79)]
+        chunked_spice_path = [
+            spice_path[i : i + 79] for i in range(0, len(spice_path), 79)
+        ]
         for i, chunk in enumerate(chunked_spice_path[:-1]):
             chunked_spice_path[i] = f"{chunk}+"
 
         all_spice_paths.extend(chunked_spice_path)
 
-    formatted_spice_file_names = ',\n'.join([f"'{spice}'" for spice in all_spice_paths])
+    formatted_spice_file_names = ",\n".join([f"'{spice}'" for spice in all_spice_paths])
     return METAKERNEL_TEMPLATE.format(kernels=formatted_spice_file_names)
 
 
@@ -137,9 +148,9 @@ def __call__(self, url, **kwargs):
         parsed_url = urlparse(url)
         if "imap-mission" in url:
             if parsed_url.path == "/metakernel":
-                if 'params' in kwargs and kwargs['params'].get('list_files') == 'true':
+                if "params" in kwargs and kwargs["params"].get("list_files") == "true":
                     response.text = json.dumps(self.spice_file_names)
-                elif 'params' in kwargs:
+                elif "params" in kwargs:
                     prefix = kwargs["params"].get("spice_path") or ""
 
                     spice_file_paths = [
@@ -165,7 +176,9 @@ def __call__(self, url, **kwargs):
 
 
 class mock_imap_data_access:
-    def __init__(self, data_dir: Path, input_files: list[Path], omni_file: Path | None = None):
+    def __init__(
+        self, data_dir: Path, input_files: list[Path], omni_file: Path | None = None
+    ):
         self.data_dir = data_dir
 
         valid_files = []
@@ -183,8 +196,12 @@ def __init__(self, data_dir: Path, input_files: list[Path], omni_file: Path | No
         self.input_files = valid_files
 
         self.env_patcher = patch.dict(os.environ, {"IMAP_DATA_DIR": str(self.data_dir)})
-        self.data_dir_patcher = patch.dict(imap_data_access.config, {"DATA_DIR": self.data_dir})
-        self.download_patcher = patch.object(imap_data_access, "download", new=fake_download)
+        self.data_dir_patcher = patch.dict(
+            imap_data_access.config, {"DATA_DIR": self.data_dir}
+        )
+        self.download_patcher = patch.object(
+            imap_data_access, "download", new=fake_download
+        )
         self.query_patcher = ImapQueryPatcher(self.input_files)
         self.requests_get_patcher = RequestsGetPatcher(spice_file_paths, omni_file)
 
@@ -195,7 +212,8 @@ def __enter__(self):
         self.query_patcher.start()
         self.requests_get_patcher.start()
 
-        if self.data_dir.exists(): shutil.rmtree(self.data_dir)
+        if self.data_dir.exists():
+            shutil.rmtree(self.data_dir)
         self.data_dir.mkdir(exist_ok=True, parents=True)
 
         for file_path in self.input_files:
@@ -220,7 +238,14 @@ def wrapped(obj, *args, **kwargs):
 
         return wrapped
 
+
 def run_istp_compliance_check(cdf_path: Path):
-    cdf_as_json = requests.post("https://skteditor.heliophysics.net/cgi-bin/cdf2json.cgi", files={"file": open(cdf_path, "rb")}).json()
+    cdf_as_json = requests.post(
+        "https://skteditor.heliophysics.net/cgi-bin/cdf2json.cgi",
+        files={"file": open(cdf_path, "rb")},
+    ).json()
     cdf_json = list(cdf_as_json.values())[0]
-    return requests.post("https://skteditor.heliophysics.net/cgi-bin/validate.cgi", json={cdf_path.name: cdf_json}).text
+    return requests.post(
+        "https://skteditor.heliophysics.net/cgi-bin/validate.cgi",
+        json={cdf_path.name: cdf_json},
+    ).text
diff --git a/tests/integration/test_codice_processor_integration.py b/tests/integration/test_codice_processor_integration.py
index 135493904..1933acd62 100644
--- a/tests/integration/test_codice_processor_integration.py
+++ b/tests/integration/test_codice_processor_integration.py
@@ -21,8 +21,13 @@
 class CodiceProcessorIntegration(unittest.TestCase):
     @patch("imap_l3_data_processor._parse_cli_arguments")
     def test_codice_lo_direct_events(self, mock_parse_cli_arguments):
-        energy_per_charge_path = CODICE_TEST_DATA_DIR / "imap_codice_lo-energy-per-charge_20241110_v002.csv"
-        mass_coefficient_path = CODICE_TEST_DATA_DIR / "imap_codice_mass-coefficient-lookup_20241110_v003.csv"
+        energy_per_charge_path = (
+            CODICE_TEST_DATA_DIR / "imap_codice_lo-energy-per-charge_20241110_v002.csv"
+        )
+        mass_coefficient_path = (
+            CODICE_TEST_DATA_DIR
+            / "imap_codice_mass-coefficient-lookup_20241110_v003.csv"
+        )
         input_files = [
             CODICE_TEST_DATA_DIR / "imap_codice_l2_lo-direct-events_20260307_v004.cdf",
             CODICE_TEST_DATA_DIR / "imap_codice_l1a_lo-nsw-priority_20260307_v004.cdf",
@@ -35,10 +40,12 @@ def test_codice_lo_direct_events(self, mock_parse_cli_arguments):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
-            processing_input_collection = ProcessingInputCollection(*(generate_imap_input(f.name) for f in input_files))
+            processing_input_collection = ProcessingInputCollection(
+                *(generate_imap_input(f.name) for f in input_files)
+            )
 
             mock_arguments = Mock()
             mock_arguments.instrument = "codice"
@@ -56,8 +63,12 @@ def test_codice_lo_direct_events(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_output_path = ScienceFilePath(
-                "imap_codice_l3a_lo-direct-events_20260307_v001.cdf").construct_path()
-            self.assertTrue(expected_output_path.exists(), f"Expected file {expected_output_path.name} not found")
+                "imap_codice_l3a_lo-direct-events_20260307_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_output_path.exists(),
+                f"Expected file {expected_output_path.name} not found",
+            )
 
             expected_parents = {
                 "imap_codice_l2_lo-direct-events_20260307_v004.cdf",
@@ -71,14 +82,19 @@ def test_codice_lo_direct_events(self, mock_parse_cli_arguments):
                 self.assertEqual(expected_parents, set(cdf.attrs["Parents"]))
 
     @patch("imap_l3_data_processor._parse_cli_arguments")
-    def test_codice_lo_partial_densities_and_sw_products(self, mock_parse_cli_arguments):
+    def test_codice_lo_partial_densities_and_sw_products(
+        self, mock_parse_cli_arguments
+    ):
         input_files = [
             INTEGRATION_DATA_DIR / "imap_codice_l2_lo-sw-species_20260301_v001.cdf",
             INTEGRATION_DATA_DIR / "imap_codice_mass-per-charge_20241110_v003.csv",
-            INTEGRATION_DATA_DIR / "imap_codice_l3a_lo-partial-densities-25ccf871_20260301_v001.json",
-            INTEGRATION_DATA_DIR / "imap_codice_l3a_lo-sw-ratios-25ccf871_20260301_v001.json",
+            INTEGRATION_DATA_DIR
+            / "imap_codice_l3a_lo-partial-densities-25ccf871_20260301_v001.json",
+            INTEGRATION_DATA_DIR
+            / "imap_codice_l3a_lo-sw-ratios-25ccf871_20260301_v001.json",
         ]
         with mock_imap_data_access(OUTPUT_DIR, input_files):
+
             def run_processor(descriptor: str, dependency_file: str):
                 mock_arguments = Mock()
                 mock_arguments.instrument = "codice"
@@ -127,10 +143,13 @@ def test_codice_lo_3d_distributions(self, mock_parse_cli_arguments):
             CODICE_TEST_DATA_DIR / "imap_codice_lo-energy-per-charge_20241110_v002.csv",
             CODICE_TEST_DATA_DIR / "imap_codice_l3-lo-efficiency_20251008_v003.csv",
             CODICE_TEST_DATA_DIR / "imap_codice_l2-lo-gfactor_20251212_v003.csv",
-            CODICE_TEST_DATA_DIR / "imap_codice_lo-mass-species-bin-lookup_20250309_v003.csv",
+            CODICE_TEST_DATA_DIR
+            / "imap_codice_lo-mass-species-bin-lookup_20250309_v003.csv",
         ]
 
-        processing_input = ProcessingInputCollection(*(generate_imap_input(f.name) for f in input_files))
+        processing_input = ProcessingInputCollection(
+            *(generate_imap_input(f.name) for f in input_files)
+        )
         dependency_json = processing_input.serialize()
 
         with mock_imap_data_access(OUTPUT_DIR, input_files):
@@ -159,10 +178,14 @@ def test_codice_lo_3d_distributions(self, mock_parse_cli_arguments):
     @patch("imap_l3_data_processor._parse_cli_arguments")
     def test_codice_hi_direct_events(self, mock_parse_cli_arguments):
         input_files = [
-            CODICE_TEST_DATA_DIR / "imap_codice_l1a_hi-direct-events_20260831_v001.0003.cdf",
-            CODICE_TEST_DATA_DIR / "imap_codice_l2_hi-direct-events_20260831_v001.0003.cdf",
-            CODICE_TEST_DATA_DIR / "imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx",
-            INTEGRATION_DATA_DIR / "imap_codice_l3a_hi-direct-events-e968219e_20260831_v001.json",
+            CODICE_TEST_DATA_DIR
+            / "imap_codice_l1a_hi-direct-events_20260831_v001.0003.cdf",
+            CODICE_TEST_DATA_DIR
+            / "imap_codice_l2_hi-direct-events_20260831_v001.0003.cdf",
+            CODICE_TEST_DATA_DIR
+            / "imap_codice_l3-hi-mass-correction-lut-truncated_20251008_v001.xlsx",
+            INTEGRATION_DATA_DIR
+            / "imap_codice_l3a_hi-direct-events-e968219e_20260831_v001.json",
         ]
         with mock_imap_data_access(OUTPUT_DIR, input_files):
             mock_arguments = Mock()
@@ -173,7 +196,9 @@ def test_codice_hi_direct_events(self, mock_parse_cli_arguments):
             mock_arguments.end_date = None
             mock_arguments.repointing = None
             mock_arguments.version = "v001"
-            mock_arguments.dependency = "imap_codice_l3a_hi-direct-events-e968219e_20260831_v001.json"
+            mock_arguments.dependency = (
+                "imap_codice_l3a_hi-direct-events-e968219e_20260831_v001.json"
+            )
             mock_arguments.upload_to_sdc = False
             mock_parse_cli_arguments.return_value = mock_arguments
 
@@ -200,12 +225,16 @@ def test_codice_hi_pitch_angle(self, mock_parse_cli_arguments):
             mock_arguments.end_date = None
             mock_arguments.repointing = None
             mock_arguments.version = "v001"
-            processing_input = ProcessingInputCollection(*[generate_imap_input(f.name) for f in input_files])
+            processing_input = ProcessingInputCollection(
+                *[generate_imap_input(f.name) for f in input_files]
+            )
             mock_arguments.dependency = processing_input.serialize()
             mock_arguments.upload_to_sdc = False
             mock_parse_cli_arguments.return_value = mock_arguments
 
             imap_l3_data_processor.imap_l3_processor()
 
-            expected_output_path = ScienceFilePath("imap_codice_l3b_hi-pitch-angle_20260120_v001.cdf").construct_path()
+            expected_output_path = ScienceFilePath(
+                "imap_codice_l3b_hi-pitch-angle_20260120_v001.cdf"
+            ).construct_path()
             self.assertTrue(expected_output_path.exists())
diff --git a/tests/integration/test_glows_processor_integration.py b/tests/integration/test_glows_processor_integration.py
index 274ae6396..7666cbe9f 100644
--- a/tests/integration/test_glows_processor_integration.py
+++ b/tests/integration/test_glows_processor_integration.py
@@ -5,18 +5,22 @@
 import shutil
 import subprocess
 import unittest
-from datetime import timedelta, datetime
+from collections.abc import Callable
+from datetime import datetime, timedelta
 from functools import wraps
 from pathlib import Path
-from typing import Callable
-from unittest import skipIf, skip
-from unittest.mock import patch, Mock
+from unittest import skipIf
+from unittest.mock import Mock, patch
 
 import imap_data_access
 import numpy as np
-from imap_data_access import ProcessingInputCollection, RepointInput
-from imap_data_access import ScienceInput, AncillaryInput
-from imap_data_access.file_validation import ScienceFilePath, AncillaryFilePath, Version
+from imap_data_access import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    RepointInput,
+    ScienceInput,
+)
+from imap_data_access.file_validation import AncillaryFilePath, ScienceFilePath, Version
 from spacepy.pycdf import CDF
 
 import imap_l3_data_processor
@@ -24,13 +28,25 @@
 from imap_l3_processing.glows.descriptors import GLOWS_L3BCDE_DESCRIPTORS
 from imap_l3_processing.glows.glows_processor import GlowsProcessor
 from imap_l3_processing.glows.l3a.glows_l3a_dependencies import GlowsL3ADependencies
-from imap_l3_processing.glows.l3a.utils import read_l2_glows_data, create_glows_l3a_from_dictionary
+from imap_l3_processing.glows.l3a.utils import (
+    create_glows_l3a_from_dictionary,
+    read_l2_glows_data,
+)
 from imap_l3_processing.glows.l3d.utils import PATH_TO_L3D_TOOLKIT
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.utils import save_data
-from tests.integration.integration_test_helpers import mock_imap_data_access, run_istp_compliance_check
-from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path, \
-    with_tempdir, get_run_local_data_path, run_periodically, create_mock_version_map
+from tests.integration.integration_test_helpers import (
+    mock_imap_data_access,
+    run_istp_compliance_check,
+)
+from tests.test_helpers import (
+    create_mock_version_map,
+    get_run_local_data_path,
+    get_test_data_path,
+    get_test_instrument_team_data_path,
+    run_periodically,
+    with_tempdir,
+)
 
 GLOWS_INTEGRATION_DATA_DIR = get_run_local_data_path(
     "glows_l3bcde_integration_data_dir"
@@ -41,8 +57,12 @@
 
 def generate_test_function_import_path(fn: Callable) -> str:
     path_to_imap_processing_dir = Path(tests.__file__).parent.parent
-    path_to_file = Path(fn.__code__.co_filename).relative_to(path_to_imap_processing_dir)
-    path_in_import_style = str(path_to_file).replace('.py', '.').replace(os.path.sep, '.')
+    path_to_file = Path(fn.__code__.co_filename).relative_to(
+        path_to_imap_processing_dir
+    )
+    path_in_import_style = (
+        str(path_to_file).replace(".py", ".").replace(os.path.sep, ".")
+    )
     return path_in_import_style + fn.__qualname__
 
 
@@ -54,17 +74,34 @@ def decorated(self):
         else:
             l3_processing_dir = Path(tests.__file__).parent.parent
 
-            docker_build = subprocess.run(["docker", "build", "--platform", "linux/amd64", "-q", "-f", "Dockerfile_glows_integration", "."],
-                                          cwd=l3_processing_dir, capture_output=True)
-            image_hash = docker_build.stdout.strip().decode('utf-8')
+            docker_build = subprocess.run(
+                [
+                    "docker",
+                    "build",
+                    "--platform",
+                    "linux/amd64",
+                    "-q",
+                    "-f",
+                    "Dockerfile_glows_integration",
+                    ".",
+                ],
+                cwd=l3_processing_dir,
+                capture_output=True,
+            )
+            image_hash = docker_build.stdout.strip().decode("utf-8")
 
             print(f"Built docker container: {image_hash}")
 
             args = [
-                "docker", "run", "--rm",
-                "--platform", "linux/amd64",
-                "--mount", f'type=bind,src={l3_processing_dir}/temp_cdf_data,dst=/temp_cdf_data',
-                "--mount", f'type=bind,src={l3_processing_dir}/run_local_input_data,dst=/run_local_input_data',
+                "docker",
+                "run",
+                "--rm",
+                "--platform",
+                "linux/amd64",
+                "--mount",
+                f"type=bind,src={l3_processing_dir}/temp_cdf_data,dst=/temp_cdf_data",
+                "--mount",
+                f"type=bind,src={l3_processing_dir}/run_local_input_data,dst=/run_local_input_data",
             ]
 
             if imap_api_key := os.getenv("IMAP_API_KEY"):
@@ -79,6 +116,7 @@ def decorated(self):
 
     return decorated
 
+
 class TestGlowsProcessorIntegration(unittest.TestCase):
     @with_tempdir
     def test_glows_l3a(self, tmp_dir):
@@ -89,35 +127,46 @@ def test_glows_l3a(self, tmp_dir):
         date_in_path = l2_science_file_path.start_date
         start_date = datetime.strptime(date_in_path, "%Y%m%d")
         end_date = start_date + timedelta(days=1)
-        l3a_descriptor = 'hist'
+        l3a_descriptor = "hist"
         input_metadata = InputMetadata(
-            instrument='glows',
-            data_level='l3a',
+            instrument="glows",
+            data_level="l3a",
             descriptor=l3a_descriptor,
             start_date=start_date,
             end_date=end_date,
-            version=create_mock_version_map(descriptor=l3a_descriptor,minor_version=1),
-            repointing=l2_science_file_path.repointing
+            version=create_mock_version_map(descriptor=l3a_descriptor, minor_version=1),
+            repointing=l2_science_file_path.repointing,
         )
 
         expected_json_path = get_test_instrument_team_data_path(
-            "glows/imap_glows_l3a_20130908085214_orbX_modX_p_v00.json")
+            "glows/imap_glows_l3a_20130908085214_orbX_modX_p_v00.json"
+        )
         with open(expected_json_path) as f:
             instrument_team_dict = json.load(f)
-        expected_output = create_glows_l3a_from_dictionary(instrument_team_dict, input_metadata)
+        expected_output = create_glows_l3a_from_dictionary(
+            instrument_team_dict, input_metadata
+        )
 
         with CDF(str(input_l2_cdf_path)) as cdf_data:
             l2_glows_data = read_l2_glows_data(cdf_data)
 
-        dependencies = GlowsL3ADependencies(l2_glows_data, {
-            "calibration_data": get_test_instrument_team_data_path(
-                "glows/imap_glows_calibration-data_20100101_v002.dat"),
-            "settings": get_test_instrument_team_data_path("glows/imap_glows_pipeline-settings_20100101_v001.json"),
-            "time_dependent_bckgrd": get_test_instrument_team_data_path(
-                "glows/imap_glows_time-dep-bckgrd_20100101_v001.dat"),
-            "extra_heliospheric_bckgrd": get_test_instrument_team_data_path(
-                "glows/imap_glows_map-of-extra-helio-bckgrd_20100101_v002.dat"),
-        })
+        dependencies = GlowsL3ADependencies(
+            l2_glows_data,
+            {
+                "calibration_data": get_test_instrument_team_data_path(
+                    "glows/imap_glows_calibration-data_20100101_v002.dat"
+                ),
+                "settings": get_test_instrument_team_data_path(
+                    "glows/imap_glows_pipeline-settings_20100101_v001.json"
+                ),
+                "time_dependent_bckgrd": get_test_instrument_team_data_path(
+                    "glows/imap_glows_time-dep-bckgrd_20100101_v001.dat"
+                ),
+                "extra_heliospheric_bckgrd": get_test_instrument_team_data_path(
+                    "glows/imap_glows_map-of-extra-helio-bckgrd_20100101_v002.dat"
+                ),
+            },
+        )
 
         processor = GlowsProcessor(ProcessingInputCollection(), input_metadata)
         l3a_data = processor.process_l3a(dependencies)
@@ -130,53 +179,108 @@ def test_glows_l3a(self, tmp_dir):
 
         self.assertEqual(input_metadata.repointing, l3a_data.identifier)
 
-        np.testing.assert_allclose(actual_dict['photon_flux'], expected_dict['photon_flux'], rtol=1e-3)
-        np.testing.assert_allclose(actual_dict['photon_flux_uncertainty'], expected_dict['photon_flux_uncertainty'],
-                                   rtol=1e-3)
-        np.testing.assert_allclose(actual_dict['raw_histogram'], expected_dict['raw_histogram'])
-        np.testing.assert_allclose(actual_dict['exposure_times'], expected_dict['exposure_times'], rtol=1e-3)
-        np.testing.assert_allclose(actual_dict['number_of_bins'], expected_dict['number_of_bins'])
-        self.assertEqual(actual_dict['epoch'], expected_dict['epoch'])
-        np.testing.assert_allclose(actual_dict['epoch_delta'], expected_dict['epoch_delta'])
-        np.testing.assert_allclose(actual_dict['spin_angle'], expected_dict['spin_angle'])
-        np.testing.assert_allclose(actual_dict['spin_angle_delta'], expected_dict['spin_angle_delta'])
-        np.testing.assert_allclose(actual_dict['latitude'], expected_dict['latitude'], atol=1e-3)
-        np.testing.assert_allclose(actual_dict['longitude'], expected_dict['longitude'], atol=1e-3)
-        np.testing.assert_allclose(actual_dict['filter_temperature_average'],
-                                   expected_dict['filter_temperature_average'])
-        np.testing.assert_allclose(actual_dict['filter_temperature_std_dev'],
-                                   expected_dict['filter_temperature_std_dev'])
-        np.testing.assert_allclose(actual_dict['hv_voltage_average'], expected_dict['hv_voltage_average'])
-        np.testing.assert_allclose(actual_dict['hv_voltage_std_dev'], expected_dict['hv_voltage_std_dev'])
-        np.testing.assert_allclose(actual_dict['spin_period_average'], expected_dict['spin_period_average'])
-        np.testing.assert_allclose(actual_dict['spin_period_std_dev'], expected_dict['spin_period_std_dev'])
-        np.testing.assert_allclose(actual_dict['spin_period_ground_average'],
-                                   expected_dict['spin_period_ground_average'])
-        np.testing.assert_allclose(actual_dict['spin_period_ground_std_dev'],
-                                   expected_dict['spin_period_ground_std_dev'])
-        np.testing.assert_allclose(actual_dict['pulse_length_average'], expected_dict['pulse_length_average'])
-        np.testing.assert_allclose(actual_dict['pulse_length_std_dev'], expected_dict['pulse_length_std_dev'])
-        np.testing.assert_allclose(actual_dict['position_angle_offset_average'],
-                                   expected_dict['position_angle_offset_average'])
-        np.testing.assert_allclose(actual_dict['position_angle_offset_std_dev'],
-                                   expected_dict['position_angle_offset_std_dev'])
-        np.testing.assert_allclose(actual_dict['spin_axis_orientation_average'],
-                                   expected_dict['spin_axis_orientation_average'])
-        np.testing.assert_allclose(actual_dict['spin_axis_orientation_std_dev'],
-                                   expected_dict['spin_axis_orientation_std_dev'])
-        np.testing.assert_allclose(actual_dict['spacecraft_location_average'],
-                                   expected_dict['spacecraft_location_average'])
-        np.testing.assert_allclose(actual_dict['spacecraft_location_std_dev'],
-                                   expected_dict['spacecraft_location_std_dev'])
-
-        self.assertEqual(actual_dict['input_metadata'], expected_dict['input_metadata'])
+        np.testing.assert_allclose(
+            actual_dict["photon_flux"], expected_dict["photon_flux"], rtol=1e-3
+        )
+        np.testing.assert_allclose(
+            actual_dict["photon_flux_uncertainty"],
+            expected_dict["photon_flux_uncertainty"],
+            rtol=1e-3,
+        )
+        np.testing.assert_allclose(
+            actual_dict["raw_histogram"], expected_dict["raw_histogram"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["exposure_times"], expected_dict["exposure_times"], rtol=1e-3
+        )
+        np.testing.assert_allclose(
+            actual_dict["number_of_bins"], expected_dict["number_of_bins"]
+        )
+        self.assertEqual(actual_dict["epoch"], expected_dict["epoch"])
+        np.testing.assert_allclose(
+            actual_dict["epoch_delta"], expected_dict["epoch_delta"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_angle"], expected_dict["spin_angle"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_angle_delta"], expected_dict["spin_angle_delta"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["latitude"], expected_dict["latitude"], atol=1e-3
+        )
+        np.testing.assert_allclose(
+            actual_dict["longitude"], expected_dict["longitude"], atol=1e-3
+        )
+        np.testing.assert_allclose(
+            actual_dict["filter_temperature_average"],
+            expected_dict["filter_temperature_average"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["filter_temperature_std_dev"],
+            expected_dict["filter_temperature_std_dev"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["hv_voltage_average"], expected_dict["hv_voltage_average"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["hv_voltage_std_dev"], expected_dict["hv_voltage_std_dev"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_period_average"], expected_dict["spin_period_average"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_period_std_dev"], expected_dict["spin_period_std_dev"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_period_ground_average"],
+            expected_dict["spin_period_ground_average"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_period_ground_std_dev"],
+            expected_dict["spin_period_ground_std_dev"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["pulse_length_average"], expected_dict["pulse_length_average"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["pulse_length_std_dev"], expected_dict["pulse_length_std_dev"]
+        )
+        np.testing.assert_allclose(
+            actual_dict["position_angle_offset_average"],
+            expected_dict["position_angle_offset_average"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["position_angle_offset_std_dev"],
+            expected_dict["position_angle_offset_std_dev"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_axis_orientation_average"],
+            expected_dict["spin_axis_orientation_average"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["spin_axis_orientation_std_dev"],
+            expected_dict["spin_axis_orientation_std_dev"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["spacecraft_location_average"],
+            expected_dict["spacecraft_location_average"],
+        )
+        np.testing.assert_allclose(
+            actual_dict["spacecraft_location_std_dev"],
+            expected_dict["spacecraft_location_std_dev"],
+        )
+
+        self.assertEqual(actual_dict["input_metadata"], expected_dict["input_metadata"])
 
         istp_compliance_message = run_istp_compliance_check(output_cdf_path)
         print("ISTP Compliance:\n", istp_compliance_message)
         self.assertIn("PASSED variable checks", istp_compliance_message)
 
     def test_l3a_handles_l2_input_that_is_all_flagged(self):
-        bad_l2_cdf_path = get_test_data_path("glows/imap_glows_l2_hist_20251113-repoint00047_v003.cdf")
+        bad_l2_cdf_path = get_test_data_path(
+            "glows/imap_glows_l2_hist_20251113-repoint00047_v003.cdf"
+        )
 
         l2_science_file_path = ScienceFilePath(bad_l2_cdf_path)
 
@@ -255,74 +359,138 @@ def test_l3bcde_first_time_processing(self):
             INTEGRATION_TEST_DATA / "spice" / "imap_recon_20250415_20260415_v01.bsp",
         ]
         with mock_imap_data_access(GLOWS_INTEGRATION_DATA_DIR, input_files):
-
-            logging.basicConfig(force=True, level=logging.INFO,
-                                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            logging.basicConfig(
+                force=True,
+                level=logging.INFO,
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+            )
 
             for folder in ["data_l3b", "data_l3c", "data_l3d", "data_l3d_txt"]:
                 path = PATH_TO_L3D_TOOLKIT / folder
                 if path.exists():
                     shutil.rmtree(path)
 
-            processing_input = ProcessingInputCollection(RepointInput("imap_2026_090_01.repoint"),
-                                                         AncillaryInput("imap_glows_force-reprocessing-config_20250101_v000.csv"))
-
-            version_map = VersionMap({descriptor: Version(i, 2) for i, descriptor in enumerate(GLOWS_L3BCDE_DESCRIPTORS)})
-            input_metadata = InputMetadata(instrument="glows", data_level="l3b", descriptor="ion-rate-profile",
-                                           version=version_map, start_date=datetime(2000, 1, 1),
-                                           end_date=datetime(2000, 1, 1))
+            processing_input = ProcessingInputCollection(
+                RepointInput("imap_2026_090_01.repoint"),
+                AncillaryInput(
+                    "imap_glows_force-reprocessing-config_20250101_v000.csv"
+                ),
+            )
+
+            version_map = VersionMap(
+                {
+                    descriptor: Version(i, 2)
+                    for i, descriptor in enumerate(GLOWS_L3BCDE_DESCRIPTORS)
+                }
+            )
+            input_metadata = InputMetadata(
+                instrument="glows",
+                data_level="l3b",
+                descriptor="ion-rate-profile",
+                version=version_map,
+                start_date=datetime(2000, 1, 1),
+                end_date=datetime(2000, 1, 1),
+            )
 
             processor = GlowsProcessor(processing_input, input_metadata)
             processor.process()
 
             expected_files = [
-                ScienceFilePath('imap_glows_l3b_ion-rate-profile_20251102-cr02304_v000.0001.cdf'),
-                ScienceFilePath('imap_glows_l3b_ion-rate-profile_20251130-cr02305_v000.0001.cdf'),
-
-                ScienceFilePath('imap_glows_l3c_sw-profile_20251102-cr02304_v000.0001.cdf'),
-                ScienceFilePath('imap_glows_l3c_sw-profile_20251130-cr02305_v000.0001.cdf'),
-
-                ScienceFilePath('imap_glows_l3d_solar-hist_19470303-cr02306_v002.0001.cdf'),
-                AncillaryFilePath('imap_glows_uv-anis_19470303_20260110_v001.dat'),
-                AncillaryFilePath('imap_glows_lya_19470303_20260110_v001.dat'),
-                AncillaryFilePath('imap_glows_e-dens_19470303_20260110_v001.dat'),
-                AncillaryFilePath('imap_glows_p-dens_19470303_20260110_v001.dat'),
-                AncillaryFilePath('imap_glows_speed_19470303_20260110_v001.dat'),
-                AncillaryFilePath('imap_glows_phion_19470303_20260110_v001.dat'),
-
-                ScienceFilePath('imap_glows_l3e_survival-probability-ul-sf_20251102-repoint00036_v006.0001.cdf'),
-                ScienceFilePath('imap_glows_l3e_survival-probability-ul-sf_20251115-repoint00049_v006.0001.cdf'),
-                AncillaryFilePath('imap_glows_survival-probability-ul-sf-raw_20251102-repoint00036_v001.dat'),
-                AncillaryFilePath('imap_glows_survival-probability-ul-sf-raw_20251115-repoint00049_v001.dat'),
-
-                ScienceFilePath('imap_glows_l3e_survival-probability-ul-hf_20251102-repoint00036_v007.0001.cdf'),
-                ScienceFilePath('imap_glows_l3e_survival-probability-ul-hf_20251115-repoint00049_v007.0001.cdf'),
-                AncillaryFilePath('imap_glows_survival-probability-ul-hf-raw_20251102-repoint00036_v001.dat'),
-                AncillaryFilePath('imap_glows_survival-probability-ul-hf-raw_20251115-repoint00049_v001.dat'),
-
-                ScienceFilePath('imap_glows_l3e_survival-probability-hi-45_20251102-repoint00036_v003.0001.cdf'),
-                ScienceFilePath('imap_glows_l3e_survival-probability-hi-45_20251115-repoint00049_v003.0001.cdf'),
-                AncillaryFilePath('imap_glows_survival-probability-hi-45-raw_20251102-repoint00036_v001.dat'),
-                AncillaryFilePath('imap_glows_survival-probability-hi-45-raw_20251115-repoint00049_v001.dat'),
-
-                ScienceFilePath('imap_glows_l3e_survival-probability-hi-90_20251102-repoint00036_v004.0001.cdf'),
-                ScienceFilePath('imap_glows_l3e_survival-probability-hi-90_20251115-repoint00049_v004.0001.cdf'),
-                AncillaryFilePath('imap_glows_survival-probability-hi-90-raw_20251102-repoint00036_v001.dat'),
-                AncillaryFilePath('imap_glows_survival-probability-hi-90-raw_20251115-repoint00049_v001.dat'),
-
-                ScienceFilePath('imap_glows_l3e_survival-probability-lo_20251102-repoint00036_v005.0001.cdf'),
-                ScienceFilePath('imap_glows_l3e_survival-probability-lo_20251115-repoint00049_v005.0001.cdf'),
-                AncillaryFilePath('imap_glows_survival-probability-lo-raw_20251102-repoint00036_v001.dat'),
-                AncillaryFilePath('imap_glows_survival-probability-lo-raw_20251115-repoint00049_v001.dat'),
+                ScienceFilePath(
+                    "imap_glows_l3b_ion-rate-profile_20251102-cr02304_v000.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3b_ion-rate-profile_20251130-cr02305_v000.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3c_sw-profile_20251102-cr02304_v000.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3c_sw-profile_20251130-cr02305_v000.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3d_solar-hist_19470303-cr02306_v002.0001.cdf"
+                ),
+                AncillaryFilePath("imap_glows_uv-anis_19470303_20260110_v001.dat"),
+                AncillaryFilePath("imap_glows_lya_19470303_20260110_v001.dat"),
+                AncillaryFilePath("imap_glows_e-dens_19470303_20260110_v001.dat"),
+                AncillaryFilePath("imap_glows_p-dens_19470303_20260110_v001.dat"),
+                AncillaryFilePath("imap_glows_speed_19470303_20260110_v001.dat"),
+                AncillaryFilePath("imap_glows_phion_19470303_20260110_v001.dat"),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-ul-sf_20251102-repoint00036_v006.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-ul-sf_20251115-repoint00049_v006.0001.cdf"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-ul-sf-raw_20251102-repoint00036_v001.dat"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-ul-sf-raw_20251115-repoint00049_v001.dat"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-ul-hf_20251102-repoint00036_v007.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-ul-hf_20251115-repoint00049_v007.0001.cdf"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-ul-hf-raw_20251102-repoint00036_v001.dat"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-ul-hf-raw_20251115-repoint00049_v001.dat"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-hi-45_20251102-repoint00036_v003.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-hi-45_20251115-repoint00049_v003.0001.cdf"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-hi-45-raw_20251102-repoint00036_v001.dat"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-hi-45-raw_20251115-repoint00049_v001.dat"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-hi-90_20251102-repoint00036_v004.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-hi-90_20251115-repoint00049_v004.0001.cdf"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-hi-90-raw_20251102-repoint00036_v001.dat"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-hi-90-raw_20251115-repoint00049_v001.dat"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-lo_20251102-repoint00036_v005.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-lo_20251115-repoint00049_v005.0001.cdf"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-lo-raw_20251102-repoint00036_v001.dat"
+                ),
+                AncillaryFilePath(
+                    "imap_glows_survival-probability-lo-raw_20251115-repoint00049_v001.dat"
+                ),
             ]
 
             for file_path in expected_files:
-                self.assertTrue(file_path.construct_path().exists(), msg=str(file_path.construct_path()))
+                self.assertTrue(
+                    file_path.construct_path().exists(),
+                    msg=str(file_path.construct_path()),
+                )
 
     @run_periodically(timedelta(days=14))
     @run_test_in_docker
     def test_l3bcde_automatic_reprocessing(self):
-        new_l3a_file = GLOWS_TEST_DATA / "imap_glows_l3a_hist_20251118-repoint00052_v001.cdf"
+        new_l3a_file = (
+            GLOWS_TEST_DATA / "imap_glows_l3a_hist_20251118-repoint00052_v001.cdf"
+        )
         l3bcde_input_files = [
             GLOWS_TEST_DATA / "imap_glows_l3a_hist_20251113-repoint00047_v001.cdf",
             GLOWS_TEST_DATA / "imap_glows_l3a_hist_20251129-repoint00063_v001.cdf",
@@ -373,41 +541,71 @@ def test_l3bcde_automatic_reprocessing(self):
             INTEGRATION_TEST_DATA / "spice" / "imap_recon_20250415_20260415_v01.bsp",
         ]
 
-        logging.basicConfig(force=True, level=logging.INFO,
-                            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        logging.basicConfig(
+            force=True,
+            level=logging.INFO,
+            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        )
 
-        with mock_imap_data_access(get_run_local_data_path("glows_reprocessing"), l3bcde_input_files):
-            processing_input = ProcessingInputCollection(RepointInput("imap_2026_090_01.repoint"),
-                                                         AncillaryInput("imap_glows_force-reprocessing-config_20250101_v000.csv"))
-            input_metadata = InputMetadata(instrument="glows", data_level="l3b", descriptor="ion-rate-profile",
-                                           version=VersionMap({}, Version(1, 0)), start_date=datetime(2000, 1, 1),
-                                           end_date=datetime(2000, 1, 1))
+        with mock_imap_data_access(
+            get_run_local_data_path("glows_reprocessing"), l3bcde_input_files
+        ):
+            processing_input = ProcessingInputCollection(
+                RepointInput("imap_2026_090_01.repoint"),
+                AncillaryInput(
+                    "imap_glows_force-reprocessing-config_20250101_v000.csv"
+                ),
+            )
+            input_metadata = InputMetadata(
+                instrument="glows",
+                data_level="l3b",
+                descriptor="ion-rate-profile",
+                version=VersionMap({}, Version(1, 0)),
+                start_date=datetime(2000, 1, 1),
+                end_date=datetime(2000, 1, 1),
+            )
 
             processor = GlowsProcessor(processing_input, input_metadata)
             processor.process()
 
             expected_files = [
-                ScienceFilePath('imap_glows_l3b_ion-rate-profile_20251102-cr02304_v001.0002.cdf'),
-
-                ScienceFilePath('imap_glows_l3c_sw-profile_20251102-cr02304_v001.0002.cdf'),
-
-                ScienceFilePath('imap_glows_l3d_solar-hist_19470303-cr02306_v001.0002.cdf'),
-                AncillaryFilePath('imap_glows_uv-anis_19470303_20260110_v002.dat'),
-                AncillaryFilePath('imap_glows_lya_19470303_20260110_v002.dat'),
-                AncillaryFilePath('imap_glows_e-dens_19470303_20260110_v002.dat'),
-                AncillaryFilePath('imap_glows_p-dens_19470303_20260110_v002.dat'),
-                AncillaryFilePath('imap_glows_speed_19470303_20260110_v002.dat'),
-                AncillaryFilePath('imap_glows_phion_19470303_20260110_v002.dat'),
-
-                ScienceFilePath("imap_glows_l3e_survival-probability-hi-45_20251102-repoint00036_v001.0001.cdf"),
-                ScienceFilePath("imap_glows_l3e_survival-probability-hi-90_20251102-repoint00036_v001.0001.cdf"),
-                ScienceFilePath("imap_glows_l3e_survival-probability-lo_20251102-repoint00036_v001.0001.cdf"),
-                ScienceFilePath("imap_glows_l3e_survival-probability-ul-hf_20251102-repoint00036_v001.0001.cdf"),
-                ScienceFilePath("imap_glows_l3e_survival-probability-ul-sf_20251102-repoint00036_v001.0001.cdf"),
+                ScienceFilePath(
+                    "imap_glows_l3b_ion-rate-profile_20251102-cr02304_v001.0002.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3c_sw-profile_20251102-cr02304_v001.0002.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3d_solar-hist_19470303-cr02306_v001.0002.cdf"
+                ),
+                AncillaryFilePath("imap_glows_uv-anis_19470303_20260110_v002.dat"),
+                AncillaryFilePath("imap_glows_lya_19470303_20260110_v002.dat"),
+                AncillaryFilePath("imap_glows_e-dens_19470303_20260110_v002.dat"),
+                AncillaryFilePath("imap_glows_p-dens_19470303_20260110_v002.dat"),
+                AncillaryFilePath("imap_glows_speed_19470303_20260110_v002.dat"),
+                AncillaryFilePath("imap_glows_phion_19470303_20260110_v002.dat"),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-hi-45_20251102-repoint00036_v001.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-hi-90_20251102-repoint00036_v001.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-lo_20251102-repoint00036_v001.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-ul-hf_20251102-repoint00036_v001.0001.cdf"
+                ),
+                ScienceFilePath(
+                    "imap_glows_l3e_survival-probability-ul-sf_20251102-repoint00036_v001.0001.cdf"
+                ),
             ]
 
             for file_path in expected_files:
-                self.assertTrue(file_path.construct_path().exists(), msg=str(file_path.construct_path()))
+                self.assertTrue(
+                    file_path.construct_path().exists(),
+                    msg=str(file_path.construct_path()),
+                )
 
     @run_periodically(timedelta(days=14))
     @run_test_in_docker
@@ -426,7 +624,7 @@ def test_glows_l3abcde_from_l2(self):
         input_files = l2_paths + ancillary_file_paths
 
         l3a_integration_data_dir = get_run_local_data_path("glows_l3a_with_prod_l2")
-        with (mock_imap_data_access(l3a_integration_data_dir, input_files)):
+        with mock_imap_data_access(l3a_integration_data_dir, input_files):
             for i, l2_path in enumerate(l2_paths):
                 l2_science_file_path = ScienceFilePath(l2_path)
                 l2_science_input = ScienceInput(l2_path.name)
@@ -443,9 +641,13 @@ def test_glows_l3abcde_from_l2(self):
                     repointing=l2_science_file_path.repointing,
                 )
 
-                ancillary_inputs = [AncillaryInput(ancillary.name) for ancillary in ancillary_file_paths]
+                ancillary_inputs = [
+                    AncillaryInput(ancillary.name) for ancillary in ancillary_file_paths
+                ]
                 processing_inputs = [l2_science_input, *ancillary_inputs]
-                processor = GlowsProcessor(ProcessingInputCollection(*processing_inputs), input_metadata)
+                processor = GlowsProcessor(
+                    ProcessingInputCollection(*processing_inputs), input_metadata
+                )
 
                 try:
                     _ = processor.process()
@@ -479,23 +681,43 @@ def test_glows_l3abcde_from_l2(self):
             INTEGRATION_TEST_DATA / "spice" / "imap_recon_20250925_20260520_v01.bsp",
         ]
 
-        lo_l1b_folder = Path("/Users/harrison/Development/imap_L3_processing/data/imap/lo/l1b")
+        lo_l1b_folder = Path(
+            "/Users/harrison/Development/imap_L3_processing/data/imap/lo/l1b"
+        )
         lo_l1b_inputs = list(lo_l1b_folder.rglob("*.cdf"))
 
         l3a_inputs = list((l3a_integration_data_dir / "imap/glows/l3a").rglob("*.cdf"))
 
-        repoint_file_path = [INTEGRATION_TEST_DATA / "spice" / "imap_2026_139_01.repoint"]
+        repoint_file_path = [
+            INTEGRATION_TEST_DATA / "spice" / "imap_2026_139_01.repoint"
+        ]
 
-        logging.basicConfig(force=True, level=logging.INFO,
-                            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        logging.basicConfig(
+            force=True,
+            level=logging.INFO,
+            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        )
 
-        input_paths = l3bcde_ancillary_inputs + l3a_inputs + repoint_file_path + lo_l1b_inputs
-        with mock_imap_data_access(get_run_local_data_path("glows_l3bcde_with_prod_l2"), input_paths):
-            processing_input = ProcessingInputCollection(RepointInput("imap_2026_139_01.repoint"),
-                                                         AncillaryInput("imap_glows_force-reprocessing-config_20250101_v000.csv"))
-            input_metadata = InputMetadata(instrument="glows", data_level="l3b", descriptor="ion-rate-profile",
-                                           version=VersionMap({}, Version(None, 1)), start_date=datetime(2000, 1, 1),
-                                           end_date=datetime(2000, 1, 1))
+        input_paths = (
+            l3bcde_ancillary_inputs + l3a_inputs + repoint_file_path + lo_l1b_inputs
+        )
+        with mock_imap_data_access(
+            get_run_local_data_path("glows_l3bcde_with_prod_l2"), input_paths
+        ):
+            processing_input = ProcessingInputCollection(
+                RepointInput("imap_2026_139_01.repoint"),
+                AncillaryInput(
+                    "imap_glows_force-reprocessing-config_20250101_v000.csv"
+                ),
+            )
+            input_metadata = InputMetadata(
+                instrument="glows",
+                data_level="l3b",
+                descriptor="ion-rate-profile",
+                version=VersionMap({}, Version(None, 1)),
+                start_date=datetime(2000, 1, 1),
+                end_date=datetime(2000, 1, 1),
+            )
 
             processor = GlowsProcessor(processing_input, input_metadata)
             processor.process()
@@ -503,16 +725,28 @@ def test_glows_l3abcde_from_l2(self):
     @skipIf(os.getenv("IMAP_API_KEY") is None, "Only runs with prod IMAP_API_KEY")
     @run_test_in_docker
     def test_run_glows_l3be_against_prod(self):
-        logging.basicConfig(force=True, level=logging.INFO,
-                            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        logging.basicConfig(
+            force=True,
+            level=logging.INFO,
+            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        )
 
-        imap_data_access.config["DATA_DIR"] = get_run_local_data_path("glows_l3bcde_prod")
+        imap_data_access.config["DATA_DIR"] = get_run_local_data_path(
+            "glows_l3bcde_prod"
+        )
 
-        test_reprocessing_file = get_test_data_path("glows/imap_glows_force-reprocessing-config_20250101_v000.csv")
-        shutil.copy(test_reprocessing_file, AncillaryFilePath(test_reprocessing_file.name).construct_path())
+        test_reprocessing_file = get_test_data_path(
+            "glows/imap_glows_force-reprocessing-config_20250101_v000.csv"
+        )
+        shutil.copy(
+            test_reprocessing_file,
+            AncillaryFilePath(test_reprocessing_file.name).construct_path(),
+        )
 
         repoint_file_name = "imap_2026_227_01.repoint"
-        processing_input = ProcessingInputCollection(RepointInput(repoint_file_name), AncillaryInput(test_reprocessing_file.name))
+        processing_input = ProcessingInputCollection(
+            RepointInput(repoint_file_name), AncillaryInput(test_reprocessing_file.name)
+        )
 
         descriptors_to_produce = [
             "ion-rate-profile",
@@ -531,11 +765,14 @@ def test_run_glows_l3be_against_prod(self):
                 desc: {
                     "major_version": 1,
                     "minor_version": 1,
-                } for desc in descriptors_to_produce
-            }
+                }
+                for desc in descriptors_to_produce
+            },
         }
 
-        with patch("imap_l3_data_processor._parse_cli_arguments") as mock_parse_cli_arguments:
+        with patch(
+            "imap_l3_data_processor._parse_cli_arguments"
+        ) as mock_parse_cli_arguments:
             mock_arguments = Mock()
             mock_arguments.instrument = "glows"
             mock_arguments.data_level = "l3b"
@@ -551,8 +788,12 @@ def test_run_glows_l3be_against_prod(self):
 
     @staticmethod
     def _fill_official_l2_cdf_with_json_values(output_folder: Path) -> Path:
-        official_l2_path = get_test_data_path("glows/imap_glows_l2_hist_20251224-repoint00088_v001.cdf")
-        json_file_path = get_test_instrument_team_data_path("glows/imap_glows_l2_20130908085214_orbX_modX_p_v00.json")
+        official_l2_path = get_test_data_path(
+            "glows/imap_glows_l2_hist_20251224-repoint00088_v001.cdf"
+        )
+        json_file_path = get_test_instrument_team_data_path(
+            "glows/imap_glows_l2_20130908085214_orbX_modX_p_v00.json"
+        )
 
         new_file_path = output_folder / official_l2_path.name
         new_file_path.unlink(missing_ok=True)
@@ -561,14 +802,18 @@ def _fill_official_l2_cdf_with_json_values(output_folder: Path) -> Path:
             with open(json_file_path) as f:
                 instrument_data = json.load(f)
 
-                start_of_epoch_window = datetime.fromisoformat(instrument_data["start_time"])
-                end_of_epoch_window = datetime.fromisoformat(instrument_data["end_time"])
+                start_of_epoch_window = datetime.fromisoformat(
+                    instrument_data["start_time"]
+                )
+                end_of_epoch_window = datetime.fromisoformat(
+                    instrument_data["end_time"]
+                )
                 epoch_window = end_of_epoch_window - start_of_epoch_window
                 epoch = start_of_epoch_window + epoch_window / 2
 
                 cdf["epoch"][0] = epoch
-                cdf['start_time'][0] = instrument_data["start_time"]
-                cdf['end_time'][0] = instrument_data["end_time"]
+                cdf["start_time"][0] = instrument_data["start_time"]
+                cdf["end_time"][0] = instrument_data["end_time"]
 
                 lightcurve_vars = [
                     "spin_angle",
@@ -579,11 +824,21 @@ def _fill_official_l2_cdf_with_json_values(output_folder: Path) -> Path:
                     "ecliptic_lat",
                 ]
                 for var in lightcurve_vars:
-                    cdf[var] = np.array(instrument_data["daily_lightcurve"][var])[np.newaxis, :]
+                    cdf[var] = np.array(instrument_data["daily_lightcurve"][var])[
+                        np.newaxis, :
+                    ]
 
-                cdf["raw_histograms"] = np.array(instrument_data["daily_lightcurve"]["raw_histogram"])[np.newaxis, :]
+                cdf["raw_histograms"] = np.array(
+                    instrument_data["daily_lightcurve"]["raw_histogram"]
+                )[np.newaxis, :]
                 cdf["histogram_flag_array"] = np.array(
-                    [int(f, 16) for f in instrument_data["daily_lightcurve"]["histogram_flag_array"]])[np.newaxis, :]
+                    [
+                        int(f, 16)
+                        for f in instrument_data["daily_lightcurve"][
+                            "histogram_flag_array"
+                        ]
+                    ]
+                )[np.newaxis, :]
 
                 single_value_vars = [
                     "filter_temperature_average",
@@ -598,7 +853,7 @@ def _fill_official_l2_cdf_with_json_values(output_folder: Path) -> Path:
                     "spin_period_ground_std_dev",
                     "position_angle_offset_average",
                     "position_angle_offset_std_dev",
-                    "identifier"
+                    "identifier",
                 ]
                 for var in single_value_vars:
                     cdf[var][0] = instrument_data[var]
@@ -609,17 +864,23 @@ def _fill_official_l2_cdf_with_json_values(output_folder: Path) -> Path:
                     "spacecraft_velocity_average",
                     "spacecraft_velocity_std_dev",
                     "spin_axis_orientation_average",
-                    "spin_axis_orientation_std_dev"
+                    "spin_axis_orientation_std_dev",
                 ]
                 for var in vector_vars:
                     cdf[var][0] = np.array(list(instrument_data[var].values()))
 
-                cdf["bad_time_flag_occurrences"][0] = list(instrument_data["bad_time_flag_occurences"].values())
-                cdf["number_of_good_l1b_inputs"][0] = instrument_data["header"]["number_of_l1b_files_used"]
-                cdf["total_l1b_inputs"][0] = instrument_data["header"]["number_of_all_l1b_files"]
+                cdf["bad_time_flag_occurrences"][0] = list(
+                    instrument_data["bad_time_flag_occurences"].values()
+                )
+                cdf["number_of_good_l1b_inputs"][0] = instrument_data["header"][
+                    "number_of_l1b_files_used"
+                ]
+                cdf["total_l1b_inputs"][0] = instrument_data["header"][
+                    "number_of_all_l1b_files"
+                ]
 
         return new_file_path
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/integration/test_hit_processor_integration.py b/tests/integration/test_hit_processor_integration.py
index 13cc99490..a9b6ece19 100644
--- a/tests/integration/test_hit_processor_integration.py
+++ b/tests/integration/test_hit_processor_integration.py
@@ -1,19 +1,25 @@
 import logging
 import unittest
-from unittest.mock import patch, Mock
+from unittest.mock import Mock, patch
 
-from imap_data_access.processing_input import ProcessingInputCollection, generate_imap_input, ScienceFilePath
+from imap_data_access.processing_input import (
+    ProcessingInputCollection,
+    ScienceFilePath,
+    generate_imap_input,
+)
 
 import imap_l3_data_processor
 from tests.integration.integration_test_helpers import mock_imap_data_access
-from tests.test_helpers import get_test_data_path, get_run_local_data_path
+from tests.test_helpers import get_run_local_data_path, get_test_data_path
 
 
 class HitProcessorIntegration(unittest.TestCase):
     @patch("imap_l3_data_processor._parse_cli_arguments")
     def test_hit_macropixel_product(self, mock_parse_cli_arguments):
         input_files = [
-            get_test_data_path("hit/imap_hit_l2_macropixel-intensity_20260228_v001.0002.cdf"),
+            get_test_data_path(
+                "hit/imap_hit_l2_macropixel-intensity_20260228_v001.0002.cdf"
+            ),
             get_test_data_path("hit/imap_mag_l1d_norm-dsrf_20260228_v005.cdf"),
             get_test_data_path("hit/imap_mag_l2_norm-dsrf_20260228_v002.cdf"),
         ]
@@ -24,10 +30,12 @@ def test_hit_macropixel_product(self, mock_parse_cli_arguments):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
-            processing_input_collection = ProcessingInputCollection(*(generate_imap_input(f.name) for f in input_files))
+            processing_input_collection = ProcessingInputCollection(
+                *(generate_imap_input(f.name) for f in input_files)
+            )
 
             mock_arguments = Mock()
             mock_arguments.instrument = "hit"
@@ -45,5 +53,9 @@ def test_hit_macropixel_product(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_output_path = ScienceFilePath(
-                "imap_hit_l3_macropixel_20260228_v001.cdf").construct_path()
-            self.assertTrue(expected_output_path.exists(), f"Expected file {expected_output_path.name} not found")
+                "imap_hit_l3_macropixel_20260228_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_output_path.exists(),
+                f"Expected file {expected_output_path.name} not found",
+            )
diff --git a/tests/integration/test_map_processor_integration.py b/tests/integration/test_map_processor_integration.py
index 844382af3..78ccd084c 100644
--- a/tests/integration/test_map_processor_integration.py
+++ b/tests/integration/test_map_processor_integration.py
@@ -3,18 +3,21 @@
 import unittest
 from datetime import timedelta
 from pathlib import Path
-from unittest.mock import patch, Mock
+from unittest.mock import Mock, patch
 
 import numpy as np
-from imap_data_access import ProcessingInputCollection, ScienceInput
-from imap_data_access import ScienceFilePath
+from imap_data_access import ProcessingInputCollection, ScienceFilePath, ScienceInput
 from spacepy.pycdf import CDF
 from spiceypy import spiceypy
 
 import imap_l3_data_processor
 from imap_l3_processing.hi.hi_combined_initializer import HI_COMBINED_DESCRIPTORS
 from tests.integration.integration_test_helpers import mock_imap_data_access
-from tests.test_helpers import get_run_local_data_path, run_periodically, get_test_data_path
+from tests.test_helpers import (
+    get_run_local_data_path,
+    get_test_data_path,
+    run_periodically,
+)
 
 INTEGRATION_TEST_DATA_PATH = Path(__file__).parent / "test_data"
 
@@ -35,13 +38,21 @@ def test_lo_l3_multiple_arcs(self, mock_parse_cli_arguments):
             INTEGRATION_TEST_DATA_PATH / "spice" / "naif020.tls",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_science_108.tf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_sclk_008.tsc",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_dps_2025_105_2026_105_009.ah.bc",
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_dps_2025_105_2026_105_009.ah.bc",
             INTEGRATION_TEST_DATA_PATH / "spice" / "de440.bsp",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_recon_20250415_20260415_v01.bsp"]
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_recon_20250415_20260415_v01.bsp",
+        ]
 
         with mock_imap_data_access(lo_imap_data_dir, input_files):
-            logging.basicConfig(force=True, level=logging.INFO,
-                                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            logging.basicConfig(
+                force=True,
+                level=logging.INFO,
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+            )
 
             mock_arguments = Mock()
             mock_arguments.instrument = "lo"
@@ -58,12 +69,20 @@ def test_lo_l3_multiple_arcs(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_map_path = ScienceFilePath(
-                'imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-1yr_20250415_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-1yr_20250415_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
             expected_map_path = ScienceFilePath(
-                'imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
     @patch("imap_l3_data_processor._parse_cli_arguments")
     def test_hi_all_sp_maps_single_sensor(self, mock_parse_cli_arguments):
@@ -71,38 +90,84 @@ def test_hi_all_sp_maps_single_sensor(self, mock_parse_cli_arguments):
         hi_imap_data_dir = get_run_local_data_path("hi/integration_data")
 
         input_files = [
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h45-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250415_v971.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l1c_90sensor-pset_20250415-repoint01000_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l1c_45sensor-pset_20250415-repoint01000_v971.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l1c_45sensor-pset_20251015-repoint01183_v971.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l1c_90sensor-pset_20251015-repoint01183_v971.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_glows_l3e_survival-probability-hi-45_20250415-repoint01000_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_glows_l3e_survival-probability-hi-45_20260418-repoint02000_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_glows_l3e_survival-probability-hi-90_20250415-repoint01000_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_glows_l3e_survival-probability-hi-90_20260418-repoint02000_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_glows_l3e_survival-probability-hi-90_20251015-repoint01183_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_glows_l3e_survival-probability-hi-45_20251015-repoint01183_v001.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf",
-            hi_test_data_dir / 'single-sensor' / "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf",
-
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h45-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250415_v971.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l1c_90sensor-pset_20250415-repoint01000_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l1c_45sensor-pset_20250415-repoint01000_v971.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l1c_45sensor-pset_20251015-repoint01183_v971.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l1c_90sensor-pset_20251015-repoint01183_v971.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_glows_l3e_survival-probability-hi-45_20250415-repoint01000_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_glows_l3e_survival-probability-hi-45_20260418-repoint02000_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_glows_l3e_survival-probability-hi-90_20250415-repoint01000_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_glows_l3e_survival-probability-hi-90_20260418-repoint02000_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_glows_l3e_survival-probability-hi-90_20251015-repoint01183_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_glows_l3e_survival-probability-hi-45_20251015-repoint01183_v001.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf",
+            hi_test_data_dir
+            / "single-sensor"
+            / "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "naif020.tls",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_science_108.tf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_sclk_008.tsc",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_dps_2025_105_2026_105_009.ah.bc",
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_dps_2025_105_2026_105_009.ah.bc",
             INTEGRATION_TEST_DATA_PATH / "spice" / "de440.bsp",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_recon_20250415_20260415_v01.bsp",
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_recon_20250415_20260415_v01.bsp",
         ]
 
         with mock_imap_data_access(hi_imap_data_dir, input_files):
-            logging.basicConfig(force=True, level=logging.INFO,
-                                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            logging.basicConfig(
+                force=True,
+                level=logging.INFO,
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+            )
 
             mock_arguments = Mock()
             mock_arguments.instrument = "hi"
@@ -120,8 +185,12 @@ def test_hi_all_sp_maps_single_sensor(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_h45-ena-h-sf-sp-ram-hae-4deg-1yr_20250415_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_h45-ena-h-sf-sp-ram-hae-4deg-1yr_20250415_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
             expected_parents = {
                 "imap_hi_l2_h45-ena-h-sf-nsp-ram-hae-4deg-1yr_20250415_v001.cdf",
@@ -133,62 +202,86 @@ def test_hi_all_sp_maps_single_sensor(self, mock_parse_cli_arguments):
                 self.assertEqual(expected_parents, set(cdf.attrs["Parents"]))
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_h45-ena-h-hf-sp-full-hae-4deg-6mo_20250415_v001.cdf').construct_path()
+                "imap_hi_l3_h45-ena-h-hf-sp-full-hae-4deg-6mo_20250415_v001.cdf"
+            ).construct_path()
 
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
-            expected_parents = {'imap_hi_l1c_45sensor-pset_20250415-repoint01000_v971.cdf',
-                                'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf',
-                                'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf',
-                                'imap_glows_l3e_survival-probability-hi-45_20250415-repoint01000_v001.cdf',
-                                }
+            expected_parents = {
+                "imap_hi_l1c_45sensor-pset_20250415-repoint01000_v971.cdf",
+                "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf",
+                "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf",
+                "imap_glows_l3e_survival-probability-hi-45_20250415-repoint01000_v001.cdf",
+            }
 
             with CDF(str(expected_map_path)) as cdf:
                 self.assertEqual(expected_parents, set(cdf.attrs["Parents"]))
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_h45-ena-h-hf-sp-full-hae-4deg-6mo_20251015_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_h45-ena-h-hf-sp-full-hae-4deg-6mo_20251015_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
-            expected_parents = {'imap_hi_l1c_45sensor-pset_20251015-repoint01183_v971.cdf',
-                                'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf',
-                                'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf',
-                                'imap_glows_l3e_survival-probability-hi-45_20251015-repoint01183_v001.cdf',
-                                }
+            expected_parents = {
+                "imap_hi_l1c_45sensor-pset_20251015-repoint01183_v971.cdf",
+                "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf",
+                "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf",
+                "imap_glows_l3e_survival-probability-hi-45_20251015-repoint01183_v001.cdf",
+            }
 
             with CDF(str(expected_map_path)) as cdf:
                 self.assertEqual(expected_parents, set(cdf.attrs["Parents"]))
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_h90-ena-h-hf-sp-full-hae-4deg-6mo_20250415_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_h90-ena-h-hf-sp-full-hae-4deg-6mo_20250415_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
-            expected_parents = {'imap_hi_l1c_90sensor-pset_20250415-repoint01000_v001.cdf',
-                                'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf',
-                                'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf',
-                                'imap_glows_l3e_survival-probability-hi-90_20250415-repoint01000_v001.cdf',
-                                }
+            expected_parents = {
+                "imap_hi_l1c_90sensor-pset_20250415-repoint01000_v001.cdf",
+                "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20250415_v000.cdf",
+                "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20250415_v000.cdf",
+                "imap_glows_l3e_survival-probability-hi-90_20250415-repoint01000_v001.cdf",
+            }
 
             with CDF(str(expected_map_path)) as cdf:
                 self.assertEqual(expected_parents, set(cdf.attrs["Parents"]))
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_h90-ena-h-hf-sp-full-hae-4deg-6mo_20251015_v001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_h90-ena-h-hf-sp-full-hae-4deg-6mo_20251015_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
-            expected_parents = {'imap_hi_l1c_90sensor-pset_20251015-repoint01183_v971.cdf',
-                                'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf',
-                                'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf',
-                                'imap_glows_l3e_survival-probability-hi-90_20251015-repoint01183_v001.cdf',
-                                }
+            expected_parents = {
+                "imap_hi_l1c_90sensor-pset_20251015-repoint01183_v971.cdf",
+                "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-4deg-6mo_20251015_v000.cdf",
+                "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-4deg-6mo_20251015_v000.cdf",
+                "imap_glows_l3e_survival-probability-hi-90_20251015-repoint01183_v001.cdf",
+            }
 
             with CDF(str(expected_map_path)) as cdf:
                 self.assertEqual(expected_parents, set(cdf.attrs["Parents"]))
-                self.assertTrue(np.any(cdf['quality_flags'][...] > 0))
+                self.assertTrue(np.any(cdf["quality_flags"][...] > 0))
 
     @patch("imap_l3_data_processor._parse_cli_arguments")
     def test_hi_combined_sensor(self, mock_parse_cli_arguments):
@@ -196,24 +289,29 @@ def test_hi_combined_sensor(self, mock_parse_cli_arguments):
         hi_imap_data_dir = get_run_local_data_path("hi/integration_data")
 
         sp_files = [
-            'imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf',
-            'imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250415_v001.cdf',
-            'imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf',
-            'imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250415_v001.cdf',
+            "imap_hi_l3_h45-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf",
+            "imap_hi_l3_h45-ena-h-hf-sp-anti-hae-6deg-1yr_20250415_v001.cdf",
+            "imap_hi_l3_h90-ena-h-hf-sp-ram-hae-6deg-1yr_20250415_v001.cdf",
+            "imap_hi_l3_h90-ena-h-hf-sp-anti-hae-6deg-1yr_20250415_v001.cdf",
         ]
 
         nsp_files = [
-            'imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20250415_v000.cdf',
-            'imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250415_v000.cdf',
-            'imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20250415_v000.cdf',
-            'imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20250415_v000.cdf',
+            "imap_hi_l2_h45-ena-h-hf-nsp-anti-hae-6deg-1yr_20250415_v000.cdf",
+            "imap_hi_l2_h45-ena-h-hf-nsp-ram-hae-6deg-1yr_20250415_v000.cdf",
+            "imap_hi_l2_h90-ena-h-hf-nsp-anti-hae-6deg-1yr_20250415_v000.cdf",
+            "imap_hi_l2_h90-ena-h-hf-nsp-ram-hae-6deg-1yr_20250415_v000.cdf",
         ]
 
-        input_files = [hi_test_data_dir / 'combined' / f for f in [*sp_files, *nsp_files]]
+        input_files = [
+            hi_test_data_dir / "combined" / f for f in [*sp_files, *nsp_files]
+        ]
 
         with mock_imap_data_access(hi_imap_data_dir, input_files):
-            logging.basicConfig(force=True, level=logging.INFO,
-                                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            logging.basicConfig(
+                force=True,
+                level=logging.INFO,
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+            )
 
             mock_arguments = Mock()
             mock_arguments.instrument = "hi"
@@ -224,10 +322,13 @@ def test_hi_combined_sensor(self, mock_parse_cli_arguments):
             mock_arguments.repointing = None
             mock_arguments.version = "v001"
 
-            version_descriptors = {descriptor: {"major_version": 4, "minor_version": 7} for descriptor in HI_COMBINED_DESCRIPTORS}
+            version_descriptors = {
+                descriptor: {"major_version": 4, "minor_version": 7}
+                for descriptor in HI_COMBINED_DESCRIPTORS
+            }
             dependency_with_version_info = {
                 "version": version_descriptors,
-                "dependency": []
+                "dependency": [],
             }
             mock_arguments.dependency = json.dumps(dependency_with_version_info)
             mock_arguments.upload_to_sdc = False
@@ -237,15 +338,23 @@ def test_hi_combined_sensor(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250415_v004.0001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_hic-ena-h-hf-sp-full-hae-6deg-1yr_20250415_v004.0001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
             with CDF(str(expected_map_path)) as cdf:
                 self.assertEqual(set(sp_files), set(cdf.attrs["Parents"]))
 
             expected_map_path = ScienceFilePath(
-                'imap_hi_l3_hic-ena-h-hf-nsp-full-hae-6deg-1yr_20250415_v004.0001.cdf').construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_hi_l3_hic-ena-h-hf-nsp-full-hae-6deg-1yr_20250415_v004.0001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
             with CDF(str(expected_map_path)) as cdf:
                 self.assertEqual(set(nsp_files), set(cdf.attrs["Parents"]))
@@ -281,7 +390,7 @@ def test_lo_all_sp_maps(self, mock_parse_cli_arguments):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
             mock_arguments = Mock()
@@ -300,11 +409,19 @@ def test_lo_all_sp_maps(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_ena_path = ScienceFilePath(
-                "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-1yr_20260101_v001.cdf").construct_path()
+                "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-1yr_20260101_v001.cdf"
+            ).construct_path()
             expected_hf_ena_path = ScienceFilePath(
-                "imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-1yr_20260101_v001.cdf").construct_path()
-            self.assertTrue(expected_ena_path.exists(), f"Expected file {expected_ena_path.name} not found")
-            self.assertTrue(expected_hf_ena_path.exists(), f"Expected file {expected_hf_ena_path.name} not found")
+                "imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-1yr_20260101_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_ena_path.exists(),
+                f"Expected file {expected_ena_path.name} not found",
+            )
+            self.assertTrue(
+                expected_hf_ena_path.exists(),
+                f"Expected file {expected_hf_ena_path.name} not found",
+            )
             expected_ena_parents = {
                 "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-1yr_20260101_v900.cdf",
                 "imap_lo_l1c_pset_20260101-repoint01261_v001.cdf",
@@ -329,17 +446,23 @@ def test_lo_isn_maps(self, mock_parse_cli_arguments):
         lo_test_data_dir = INTEGRATION_TEST_DATA_PATH / "lo"
         lo_imap_data_dir = get_run_local_data_path("lo/integration_data")
 
-        input_file = "imap_lo_l2_l090-enanbs-h-sf-nsp-ram-hae-6deg-1yr_20250415_v000.cdf"
+        input_file = (
+            "imap_lo_l2_l090-enanbs-h-sf-nsp-ram-hae-6deg-1yr_20250415_v000.cdf"
+        )
         descriptor = "l090-isn-h-sf-nsp-ram-hae-6deg-1yr"
-        expected_output_file = "imap_lo_l3_l090-isn-h-sf-nsp-ram-hae-6deg-1yr_20250415_v001.cdf"
+        expected_output_file = (
+            "imap_lo_l3_l090-isn-h-sf-nsp-ram-hae-6deg-1yr_20250415_v001.cdf"
+        )
 
-        processing_input_collection = ProcessingInputCollection(ScienceInput(input_file))
+        processing_input_collection = ProcessingInputCollection(
+            ScienceInput(input_file)
+        )
 
-        with (mock_imap_data_access(lo_imap_data_dir, [lo_test_data_dir / input_file])):
+        with mock_imap_data_access(lo_imap_data_dir, [lo_test_data_dir / input_file]):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
             mock_arguments = Mock()
@@ -357,8 +480,7 @@ def test_lo_isn_maps(self, mock_parse_cli_arguments):
 
             imap_l3_data_processor.imap_l3_processor()
 
-            expected_ena_path = ScienceFilePath(
-                expected_output_file).construct_path()
+            expected_ena_path = ScienceFilePath(expected_output_file).construct_path()
 
             self.assertTrue(
                 expected_ena_path.exists(),
@@ -380,24 +502,30 @@ def test_ultra_all_sp_maps(self, mock_parse_cli_arguments):
         ultra_imap_data_dir = get_run_local_data_path("ultra/integration_data")
 
         input_files = [
-            ultra_test_data_dir / "imap_glows_l3e_survival-probability-ul-sf_20250415-repoint00000_v001.cdf",
-            ultra_test_data_dir / "imap_glows_l3e_survival-probability-ul-sf_20261020-repoint02000_v001.cdf",
-            ultra_test_data_dir / "imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00000_v000.cdf",
-            ultra_test_data_dir / "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
-
+            ultra_test_data_dir
+            / "imap_glows_l3e_survival-probability-ul-sf_20250415-repoint00000_v001.cdf",
+            ultra_test_data_dir
+            / "imap_glows_l3e_survival-probability-ul-sf_20261020-repoint02000_v001.cdf",
+            ultra_test_data_dir
+            / "imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00000_v000.cdf",
+            ultra_test_data_dir
+            / "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "naif020.tls",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_science_108.tf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_sclk_008.tsc",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_dps_2025_105_2026_105_009.ah.bc",
-
-            get_test_data_path("ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"),
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_dps_2025_105_2026_105_009.ah.bc",
+            get_test_data_path(
+                "ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+            ),
         ]
 
         with mock_imap_data_access(ultra_imap_data_dir, input_files):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
             mock_arguments = Mock()
@@ -415,13 +543,17 @@ def test_ultra_all_sp_maps(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_map_path = ScienceFilePath(
-                "imap_ultra_l3_u45-ena-h-sf-sp-full-hae-6deg-3mo_20250416_v001.cdf").construct_path()
-            self.assertTrue(expected_map_path.exists(), f"Expected file {expected_map_path.name} not found")
+                "imap_ultra_l3_u45-ena-h-sf-sp-full-hae-6deg-3mo_20250416_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_map_path.exists(),
+                f"Expected file {expected_map_path.name} not found",
+            )
 
             expected_parents = {
                 "imap_glows_l3e_survival-probability-ul-sf_20250415-repoint00000_v001.cdf",
                 "imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00000_v000.cdf",
-                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-6deg-3mo_20250416_v001.cdf"
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
             }
 
             with CDF(str(expected_map_path)) as cdf:
@@ -438,26 +570,37 @@ def test_ultra_combined_sp_maps(self, mock_parse_cli_arguments):
             INTEGRATION_TEST_DATA_PATH / "spice" / "naif020.tls",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_science_108.tf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_sclk_008.tsc",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_dps_2025_105_2026_105_009.ah.bc",
-
-            get_test_data_path("ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"),
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_dps_2025_105_2026_105_009.ah.bc",
+            get_test_data_path(
+                "ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+            ),
         ]
         input_files = [
-            ultra_path / "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
-            ultra_path / "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
-            ultra_path / "imap_ultra_l1c_45sensor-heliopset_20250416-repoint00000_v000.cdf",
-            ultra_path / "imap_ultra_l1c_45sensor-heliopset_20251017-repoint00184_v000.cdf",
-            ultra_path / "imap_ultra_l1c_90sensor-heliopset_20250416-repoint00000_v000.cdf",
-            ultra_path / "imap_ultra_l1c_90sensor-heliopset_20251017-repoint00184_v000.cdf",
-            ultra_path / "imap_glows_l3e_survival-probability-ul-hf_20250415-repoint00000_v001.cdf",
-            ultra_path / "imap_glows_l3e_survival-probability-ul-hf_20261020-repoint02000_v001.cdf",
+            ultra_path
+            / "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
+            ultra_path
+            / "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
+            ultra_path
+            / "imap_ultra_l1c_45sensor-heliopset_20250416-repoint00000_v000.cdf",
+            ultra_path
+            / "imap_ultra_l1c_45sensor-heliopset_20251017-repoint00184_v000.cdf",
+            ultra_path
+            / "imap_ultra_l1c_90sensor-heliopset_20250416-repoint00000_v000.cdf",
+            ultra_path
+            / "imap_ultra_l1c_90sensor-heliopset_20251017-repoint00184_v000.cdf",
+            ultra_path
+            / "imap_glows_l3e_survival-probability-ul-hf_20250415-repoint00000_v001.cdf",
+            ultra_path
+            / "imap_glows_l3e_survival-probability-ul-hf_20261020-repoint02000_v001.cdf",
         ]
 
         with mock_imap_data_access(ultra_imap_data_dir, input_files + ancil_files):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
             mock_arguments = Mock()
@@ -475,8 +618,12 @@ def test_ultra_combined_sp_maps(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_sp_map_path = ScienceFilePath(
-                "imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-6deg-3mo_20250416_v001.cdf").construct_path()
-            self.assertTrue(expected_sp_map_path.exists(), f"Expected file {expected_sp_map_path.name} not found")
+                "imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-6deg-3mo_20250416_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_sp_map_path.exists(),
+                f"Expected file {expected_sp_map_path.name} not found",
+            )
 
             expected_parents = {
                 "imap_glows_l3e_survival-probability-ul-hf_20250415-repoint00000_v001.cdf",
@@ -499,24 +646,33 @@ def test_ultra_combined_nsp_maps(self, mock_parse_cli_arguments):
             INTEGRATION_TEST_DATA_PATH / "spice" / "naif020.tls",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_science_108.tf",
             INTEGRATION_TEST_DATA_PATH / "spice" / "imap_sclk_008.tsc",
-            INTEGRATION_TEST_DATA_PATH / "spice" / "imap_dps_2025_105_2026_105_009.ah.bc",
-
-            get_test_data_path("ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"),
+            INTEGRATION_TEST_DATA_PATH
+            / "spice"
+            / "imap_dps_2025_105_2026_105_009.ah.bc",
+            get_test_data_path(
+                "ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+            ),
         ]
         input_files = [
-            ultra_path / "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
-            ultra_path / "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
-            ultra_path / "imap_ultra_l1c_45sensor-heliopset_20250416-repoint00000_v000.cdf",
-            ultra_path / "imap_ultra_l1c_45sensor-heliopset_20251017-repoint00184_v000.cdf",
-            ultra_path / "imap_ultra_l1c_90sensor-heliopset_20250416-repoint00000_v000.cdf",
-            ultra_path / "imap_ultra_l1c_90sensor-heliopset_20251017-repoint00184_v000.cdf",
+            ultra_path
+            / "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
+            ultra_path
+            / "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf",
+            ultra_path
+            / "imap_ultra_l1c_45sensor-heliopset_20250416-repoint00000_v000.cdf",
+            ultra_path
+            / "imap_ultra_l1c_45sensor-heliopset_20251017-repoint00184_v000.cdf",
+            ultra_path
+            / "imap_ultra_l1c_90sensor-heliopset_20250416-repoint00000_v000.cdf",
+            ultra_path
+            / "imap_ultra_l1c_90sensor-heliopset_20251017-repoint00184_v000.cdf",
         ]
 
         with mock_imap_data_access(ultra_imap_data_dir, input_files + ancil_files):
             logging.basicConfig(
                 force=True,
                 level=logging.INFO,
-                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
             )
 
             mock_arguments = Mock()
@@ -534,8 +690,12 @@ def test_ultra_combined_nsp_maps(self, mock_parse_cli_arguments):
             imap_l3_data_processor.imap_l3_processor()
 
             expected_nsp_map_path = ScienceFilePath(
-                "imap_ultra_l3_ulc-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf").construct_path()
-            self.assertTrue(expected_nsp_map_path.exists(), f"Expected file {expected_nsp_map_path.name} not found")
+                "imap_ultra_l3_ulc-ena-h-hf-nsp-full-hae-6deg-3mo_20250416_v001.cdf"
+            ).construct_path()
+            self.assertTrue(
+                expected_nsp_map_path.exists(),
+                f"Expected file {expected_nsp_map_path.name} not found",
+            )
 
             expected_parents = {
                 "imap_ultra_l1c_45sensor-heliopset_20250416-repoint00000_v000.cdf",
diff --git a/tests/integration/test_swapi_processor_integration.py b/tests/integration/test_swapi_processor_integration.py
index 6ff96c651..5039c9f7c 100644
--- a/tests/integration/test_swapi_processor_integration.py
+++ b/tests/integration/test_swapi_processor_integration.py
@@ -13,6 +13,7 @@
 would just be duplicate data.
 """
 
+import datetime
 import os
 import subprocess
 import sys
@@ -25,7 +26,6 @@
 import numpy.testing
 from imap_data_access import ScienceFilePath
 from spacepy.pycdf import CDF
-import datetime
 
 import imap_l3_processing
 from imap_l3_processing.swapi.constants import SWAPI_COARSE_SWEEP_BINS
@@ -43,23 +43,37 @@ def test_proton_sw_with_production_data(self):
         """
 
         expected_values = {
-            'epoch': datetime.datetime(2025, 12, 31, 23, 59, 35, 6000),
-            'proton_sw_speed': 474.578857421875,
-            'proton_sw_speed_uncert': 0.36081013083457947,
-            'proton_sw_speed_sun': 475.4219055175781,
-            'proton_sw_speed_sun_uncert': 0.3551594018936157,
-            'epoch_delta': 30000000000,
-            'proton_sw_temperature': 55131.46875,
-            'proton_sw_temperature_uncert': 2007.7718505859375,
-            'proton_sw_density': 2.6932971477508545,
-            'proton_sw_density_uncert': 0.049651436507701874,
-            'proton_sw_velocity_rtn_sun': [474.1429748535156, 31.2506103515625, 15.421923637390137],
-            'proton_sw_velocity_rtn': [474.2013854980469, 1.6974985599517822, 18.847949981689453],
-            'proton_sw_velocity_rtn_covariance': [[0.11774054169654846, -0.029444590210914612, 0.13579009473323822],
-                                                           [-0.029444590210914612, 0.8430156111717224, -0.22263257205486298],
-                                                           [0.13579009473323822, -0.22263257205486298, 1.3417274951934814]],
-            'proton_sw_velocity_rtn_uncert': [0.34313341675877107, 0.9181588158764923, 1.1583296142262276],
-            'swp_flags': 0
+            "epoch": datetime.datetime(2025, 12, 31, 23, 59, 35, 6000),
+            "proton_sw_speed": 474.578857421875,
+            "proton_sw_speed_uncert": 0.36081013083457947,
+            "proton_sw_speed_sun": 475.4219055175781,
+            "proton_sw_speed_sun_uncert": 0.3551594018936157,
+            "epoch_delta": 30000000000,
+            "proton_sw_temperature": 55131.46875,
+            "proton_sw_temperature_uncert": 2007.7718505859375,
+            "proton_sw_density": 2.6932971477508545,
+            "proton_sw_density_uncert": 0.049651436507701874,
+            "proton_sw_velocity_rtn_sun": [
+                474.1429748535156,
+                31.2506103515625,
+                15.421923637390137,
+            ],
+            "proton_sw_velocity_rtn": [
+                474.2013854980469,
+                1.6974985599517822,
+                18.847949981689453,
+            ],
+            "proton_sw_velocity_rtn_covariance": [
+                [0.11774054169654846, -0.029444590210914612, 0.13579009473323822],
+                [-0.029444590210914612, 0.8430156111717224, -0.22263257205486298],
+                [0.13579009473323822, -0.22263257205486298, 1.3417274951934814],
+            ],
+            "proton_sw_velocity_rtn_uncert": [
+                0.34313341675877107,
+                0.9181588158764923,
+                1.1583296142262276,
+            ],
+            "swp_flags": 0,
         }
 
         root_dir = Path(imap_l3_processing.__file__).parent.parent
@@ -80,12 +94,18 @@ def test_proton_sw_with_production_data(self):
             [
                 sys.executable,
                 "imap_l3_data_processor.py",
-                "--instrument", "swapi",
-                "--data-level", "l3a",
-                "--descriptor", "proton-sw",
-                "--start-date", "20260101",
-                "--version", "v001",
-                "--dependency", dependency_filename,
+                "--instrument",
+                "swapi",
+                "--data-level",
+                "l3a",
+                "--descriptor",
+                "proton-sw",
+                "--start-date",
+                "20260101",
+                "--version",
+                "v001",
+                "--dependency",
+                dependency_filename,
             ],
             env={**os.environ, "IMAP_DATA_DIR": str(data_dir)},
         )
@@ -93,27 +113,28 @@ def test_proton_sw_with_production_data(self):
         self.assertEqual(0, result.returncode)
         self.assertTrue(expected_file_path.exists())
 
-
-        bulk_speed_atol = 1e-3 * float(expected_values['proton_sw_speed'])
+        bulk_speed_atol = 1e-3 * float(expected_values["proton_sw_speed"])
 
         with CDF(str(expected_file_path)) as cdf:
-            for key in expected_values.keys():
+            for key in expected_values:
                 actual_value = cdf[key][0]
-                if key.endswith('_uncert'):
+                if key.endswith("_uncert"):
                     rtol, atol = 1e-2, 0.0
-                elif 'velocity' in key:
+                elif "velocity" in key:
                     rtol, atol = 1e-3, bulk_speed_atol
                 else:
                     rtol, atol = 1e-3, 0.0
                 try:
                     numpy.testing.assert_allclose(
-                        actual_value, expected_values[key], rtol=rtol, atol=atol, err_msg=key
+                        actual_value,
+                        expected_values[key],
+                        rtol=rtol,
+                        atol=atol,
+                        err_msg=key,
                     )
                 except TypeError:
                     self.assertEqual(expected_values[key], actual_value, msg=key)
 
-
-
     @skipUnless(os.environ.get("IMAP_API_KEY"), "requires production API key")
     def test_alpha_sw_with_production_data(self):
         """
@@ -127,25 +148,37 @@ def test_alpha_sw_with_production_data(self):
         # quantity is fill; the first valid record is index 1.
         sample_index = 1
         expected_values = {
-            'epoch': datetime.datetime(2026, 1, 1, 0, 0, 35, 6000),
-            'epoch_delta': 30000000000,
-            'alpha_sw_speed': 473.3949279785156,
-            'alpha_sw_speed_uncert': 0.3672780692577362,
-            'alpha_sw_speed_sun': 473.53363037109375,
-            'alpha_sw_speed_sun_uncert': 0.4522981643676758,
-            'alpha_sw_density': 0.1577976644039154,
-            'alpha_sw_density_uncert': 0.2121371477842331,
-            'alpha_sw_temperature': 2338303.75,
-            'alpha_sw_temperature_uncert': 1141809.75,
-            'alpha_sw_velocity_rtn': [473.010009765625, -9.928609848022461, 16.30108070373535],
-            'alpha_sw_velocity_rtn_sun': [472.9515686035156, 19.624502182006836, 12.875075340270996],
-            'alpha_sw_velocity_rtn_covariance': [
+            "epoch": datetime.datetime(2026, 1, 1, 0, 0, 35, 6000),
+            "epoch_delta": 30000000000,
+            "alpha_sw_speed": 473.3949279785156,
+            "alpha_sw_speed_uncert": 0.3672780692577362,
+            "alpha_sw_speed_sun": 473.53363037109375,
+            "alpha_sw_speed_sun_uncert": 0.4522981643676758,
+            "alpha_sw_density": 0.1577976644039154,
+            "alpha_sw_density_uncert": 0.2121371477842331,
+            "alpha_sw_temperature": 2338303.75,
+            "alpha_sw_temperature_uncert": 1141809.75,
+            "alpha_sw_velocity_rtn": [
+                473.010009765625,
+                -9.928609848022461,
+                16.30108070373535,
+            ],
+            "alpha_sw_velocity_rtn_sun": [
+                472.9515686035156,
+                19.624502182006836,
+                12.875075340270996,
+            ],
+            "alpha_sw_velocity_rtn_covariance": [
                 [0.13386771082878113, 0.23742027580738068, 0.1489003300666809],
                 [0.23742027580738068, 13.923757553100586, 7.167418003082275],
                 [0.1489003300666809, 7.167418003082275, 4.364877223968506],
             ],
-            'alpha_sw_velocity_rtn_uncert': [0.36587936649773123, 3.7314551522295676, 2.0892288586865027],
-            'swp_flags': 0,
+            "alpha_sw_velocity_rtn_uncert": [
+                0.36587936649773123,
+                3.7314551522295676,
+                2.0892288586865027,
+            ],
+            "swp_flags": 0,
         }
 
         root_dir = Path(imap_l3_processing.__file__).parent.parent
@@ -166,12 +199,18 @@ def test_alpha_sw_with_production_data(self):
             [
                 sys.executable,
                 "imap_l3_data_processor.py",
-                "--instrument", "swapi",
-                "--data-level", "l3a",
-                "--descriptor", "alpha-sw",
-                "--start-date", "20260101",
-                "--version", "v001",
-                "--dependency", dependency_filename,
+                "--instrument",
+                "swapi",
+                "--data-level",
+                "l3a",
+                "--descriptor",
+                "alpha-sw",
+                "--start-date",
+                "20260101",
+                "--version",
+                "v001",
+                "--dependency",
+                dependency_filename,
             ],
             env={**os.environ, "IMAP_DATA_DIR": str(data_dir)},
         )
@@ -179,25 +218,28 @@ def test_alpha_sw_with_production_data(self):
         self.assertEqual(0, result.returncode)
         self.assertTrue(expected_file_path.exists())
 
-        bulk_speed_atol = 1e-3 * float(expected_values['alpha_sw_speed'])
+        bulk_speed_atol = 1e-3 * float(expected_values["alpha_sw_speed"])
 
         with CDF(str(expected_file_path)) as cdf:
-            for key in expected_values.keys():
+            for key in expected_values:
                 actual_value = cdf[key][sample_index]
-                if key.endswith('_uncert'):
+                if key.endswith("_uncert"):
                     rtol, atol = 3e-2, 0.0
-                elif 'velocity' in key:
+                elif "velocity" in key:
                     rtol, atol = 1e-3, bulk_speed_atol
                 else:
                     rtol, atol = 3e-2, 0.0
                 try:
                     numpy.testing.assert_allclose(
-                        actual_value, expected_values[key], rtol=rtol, atol=atol, err_msg=key
+                        actual_value,
+                        expected_values[key],
+                        rtol=rtol,
+                        atol=atol,
+                        err_msg=key,
                     )
                 except TypeError:
                     self.assertEqual(expected_values[key], actual_value, msg=key)
 
-
     @skipUnless(os.environ.get("IMAP_API_KEY"), "requires production API key")
     def test_pui_he_with_production_data(self):
         """
@@ -208,17 +250,17 @@ def test_pui_he_with_production_data(self):
 
         sample_index = 98
         expected_values = {
-            'epoch': datetime.datetime(2026, 1, 1, 16, 24, 4, 954000),
-            'epoch_delta': 300000000000,
-            'pui_ionization_rate': 9.177568216500731e-08,
-            'pui_ionization_rate_uncert': 2.7529767354650403e-09,
-            'pui_cutoff_speed': 487.5039978027344,
-            'pui_cutoff_speed_uncert': 4.466331958770752,
-            'pui_density': 0.0006356762023642659,
-            'pui_density_uncert': 1.9068251276621595e-05,
-            'pui_temperature': 19009084.0,
-            'pui_temperature_uncert': 346333.4375,
-            'swp_flags': 0,
+            "epoch": datetime.datetime(2026, 1, 1, 16, 24, 4, 954000),
+            "epoch_delta": 300000000000,
+            "pui_ionization_rate": 9.177568216500731e-08,
+            "pui_ionization_rate_uncert": 2.7529767354650403e-09,
+            "pui_cutoff_speed": 487.5039978027344,
+            "pui_cutoff_speed_uncert": 4.466331958770752,
+            "pui_density": 0.0006356762023642659,
+            "pui_density_uncert": 1.9068251276621595e-05,
+            "pui_temperature": 19009084.0,
+            "pui_temperature_uncert": 346333.4375,
+            "swp_flags": 0,
         }
 
         root_dir = Path(imap_l3_processing.__file__).parent.parent
@@ -239,12 +281,18 @@ def test_pui_he_with_production_data(self):
             [
                 sys.executable,
                 "imap_l3_data_processor.py",
-                "--instrument", "swapi",
-                "--data-level", "l3a",
-                "--descriptor", "pui-he",
-                "--start-date", "20260101",
-                "--version", "v001",
-                "--dependency", dependency_filename,
+                "--instrument",
+                "swapi",
+                "--data-level",
+                "l3a",
+                "--descriptor",
+                "pui-he",
+                "--start-date",
+                "20260101",
+                "--version",
+                "v001",
+                "--dependency",
+                dependency_filename,
             ],
             env={**os.environ, "IMAP_DATA_DIR": str(data_dir)},
         )
@@ -253,20 +301,23 @@ def test_pui_he_with_production_data(self):
         self.assertTrue(expected_file_path.exists())
 
         with CDF(str(expected_file_path)) as cdf:
-            for key in expected_values.keys():
+            for key in expected_values:
                 actual_value = cdf[key][sample_index]
-                if key.endswith('_uncert'):
+                if key.endswith("_uncert"):
                     rtol, atol = 1e-2, 0.0
                 else:
                     rtol, atol = 1e-3, 0.0
                 try:
                     numpy.testing.assert_allclose(
-                        actual_value, expected_values[key], rtol=rtol, atol=atol, err_msg=key
+                        actual_value,
+                        expected_values[key],
+                        rtol=rtol,
+                        atol=atol,
+                        err_msg=key,
                     )
                 except TypeError:
                     self.assertEqual(expected_values[key], actual_value, msg=key)
 
-
     @skipUnless(os.environ.get("IMAP_API_KEY"), "requires production API key")
     def test_l3b_with_production_data(self):
         """
@@ -293,11 +344,16 @@ def test_l3b_with_production_data(self):
             [
                 sys.executable,
                 "imap_l3_data_processor.py",
-                "--instrument", "swapi",
-                "--data-level", "l3b",
-                "--start-date", "20260101",
-                "--version", "v001",
-                "--dependency", dependency_filename,
+                "--instrument",
+                "swapi",
+                "--data-level",
+                "l3b",
+                "--start-date",
+                "20260101",
+                "--version",
+                "v001",
+                "--dependency",
+                dependency_filename,
             ],
             env={**os.environ, "IMAP_DATA_DIR": str(data_dir)},
         )
@@ -317,13 +373,17 @@ def test_l3b_with_production_data(self):
             ].mean(axis=0)
             combined_differential_flux = l3b_cdf["combined_differential_flux"][0]
 
-            geometric_factor = (
-                l2_coincidence_rate / combined_differential_flux
-            )
+            geometric_factor = l2_coincidence_rate / combined_differential_flux
 
             index = np.abs(l3b_cdf["combined_energy"][0] - 1000.0).argmin()
-            print(l3b_cdf["combined_energy"][0][index], l3b_cdf["combined_energy"].attrs["UNITS"])
-            print(l3b_cdf["combined_differential_flux"][0][index], l3b_cdf["combined_differential_flux"].attrs["UNITS"])
+            print(
+                l3b_cdf["combined_energy"][0][index],
+                l3b_cdf["combined_energy"].attrs["UNITS"],
+            )
+            print(
+                l3b_cdf["combined_differential_flux"][0][index],
+                l3b_cdf["combined_differential_flux"].attrs["UNITS"],
+            )
             numpy.testing.assert_allclose(
                 geometric_factor[index],
                 38.1,
diff --git a/tests/integration/test_swe_processor_integration.py b/tests/integration/test_swe_processor_integration.py
index f6268160f..84d1fbce7 100644
--- a/tests/integration/test_swe_processor_integration.py
+++ b/tests/integration/test_swe_processor_integration.py
@@ -11,8 +11,12 @@
 
 import imap_l3_processing
 from tests.integration.integration_test_helpers import mock_imap_data_access
-from tests.test_helpers import get_run_local_data_path, get_test_data_path, get_integration_test_data_path, \
-    run_periodically
+from tests.test_helpers import (
+    get_integration_test_data_path,
+    get_run_local_data_path,
+    get_test_data_path,
+    run_periodically,
+)
 
 SWE_OUTPUT_DATA_DIR = get_run_local_data_path("swe_integration")
 
@@ -39,7 +43,7 @@ def test_swe_processor_with_local_data(self):
             spice_test_data_dir / "imap_sclk_0171.tsc",
             spice_test_data_dir / "de440.bsp",
             spice_test_data_dir / "imap_recon_20250925_20260511_v01.bsp",
-            spice_test_data_dir / "imap_dps_2025_359_2026_131_002.ah.bc"
+            spice_test_data_dir / "imap_dps_2025_359_2026_131_002.ah.bc",
         ]
 
         with mock_imap_data_access(SWE_OUTPUT_DATA_DIR, input_files):
@@ -62,8 +66,9 @@ def test_swe_processor_with_local_data(self):
                 ],
             )
 
-            expected_file_path = ScienceFilePath("imap_swe_l3_sci_20260120_v001.cdf").construct_path()
+            expected_file_path = ScienceFilePath(
+                "imap_swe_l3_sci_20260120_v001.cdf"
+            ).construct_path()
 
             self.assertEqual(0, result.returncode)
             self.assertTrue(expected_file_path.exists())
-
diff --git a/tests/lo/l3/test_lo_combined_dependencies.py b/tests/lo/l3/test_lo_combined_dependencies.py
index 139acf89b..eedde2aa5 100644
--- a/tests/lo/l3/test_lo_combined_dependencies.py
+++ b/tests/lo/l3/test_lo_combined_dependencies.py
@@ -1,36 +1,36 @@
 import unittest
-from unittest.mock import sentinel, call, patch
+from unittest.mock import call, patch, sentinel
 
-from imap_data_access import ScienceInput, ProcessingInputCollection
+from imap_data_access import ProcessingInputCollection, ScienceInput
 
 from imap_l3_processing.lo.l3.lo_combined_dependencies import LoCombinedDependencies
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData
 
 
 class TestLoCombinedDependencies(unittest.TestCase):
-
-    @patch("imap_l3_processing.lo.l3.lo_combined_dependencies.RectangularIntensityMapData.read_from_path")
-    @patch("imap_l3_processing.lo.l3.lo_combined_dependencies.imap_data_access.download")
+    @patch(
+        "imap_l3_processing.lo.l3.lo_combined_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch(
+        "imap_l3_processing.lo.l3.lo_combined_dependencies.imap_data_access.download"
+    )
     def test_fetch_dependencies(self, mock_download, mock_read_from_path):
-        lo75_input = 'imap_lo_l3_l075-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf'
-        lo90_input = 'imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf'
-        lo105_input = 'imap_lo_l3_l105-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf'
+        lo75_input = "imap_lo_l3_l075-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf"
+        lo90_input = "imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf"
+        lo105_input = "imap_lo_l3_l105-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf"
 
         mock_lo_map_data = [sentinel.lo75, sentinel.lo90, sentinel.lo105]
         mock_read_from_path.side_effect = mock_lo_map_data
 
-        inputs = ProcessingInputCollection(ScienceInput(lo75_input), ScienceInput(lo90_input), ScienceInput(lo105_input))
+        inputs = ProcessingInputCollection(
+            ScienceInput(lo75_input),
+            ScienceInput(lo90_input),
+            ScienceInput(lo105_input),
+        )
         dependencies = LoCombinedDependencies.fetch_dependencies(inputs)
 
         mock_download.assert_has_calls(
-            [
-                call(lo75_input),
-                call(lo90_input),
-                call(lo105_input)
-            ]
+            [call(lo75_input), call(lo90_input), call(lo105_input)]
         )
 
         mock_read_from_path.assert_called_with(mock_download.return_value)
         self.assertEqual(mock_lo_map_data, dependencies.map_data)
-
-
diff --git a/tests/lo/l3/test_lo_l3_isn_background_subtracted_dependencies.py b/tests/lo/l3/test_lo_l3_isn_background_subtracted_dependencies.py
index 8cb40298c..adae8dc2f 100644
--- a/tests/lo/l3/test_lo_l3_isn_background_subtracted_dependencies.py
+++ b/tests/lo/l3/test_lo_l3_isn_background_subtracted_dependencies.py
@@ -1,22 +1,30 @@
 import unittest
 from unittest.mock import patch
 
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection
+from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies import \
-    LoL3ISNBackgroundSubtractedDependencies
+from imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies import (
+    LoL3ISNBackgroundSubtractedDependencies,
+)
 
 
 class TestLoL3ISNBackgroundSubtractedDependencies(unittest.TestCase):
-
-    @patch("imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies.ISNRateData.read_from_path")
-    @patch("imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies.download")
+    @patch(
+        "imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies.ISNRateData.read_from_path"
+    )
+    @patch(
+        "imap_l3_processing.lo.l3.lo_l3_isn_background_subtracted_dependencies.download"
+    )
     def test_fetch_dependencies(self, mock_download, mock_read_from_path):
-        file_name = "imap_lo_l2_l090-isnnbkgnd-h-sf-nsp-ram-hae-6deg-1yr_20250422_v001.cdf"
+        file_name = (
+            "imap_lo_l2_l090-isnnbkgnd-h-sf-nsp-ram-hae-6deg-1yr_20250422_v001.cdf"
+        )
         input = ScienceInput(file_name)
         processing_input_collection = ProcessingInputCollection(input)
 
-        dependencies = LoL3ISNBackgroundSubtractedDependencies.fetch_dependencies(processing_input_collection)
+        dependencies = LoL3ISNBackgroundSubtractedDependencies.fetch_dependencies(
+            processing_input_collection
+        )
 
         mock_download.assert_called_once_with(file_name)
 
diff --git a/tests/lo/l3/test_lo_l3_spectral_fit_dependencies.py b/tests/lo/l3/test_lo_l3_spectral_fit_dependencies.py
index 4f2928b9f..8b8b25e5c 100644
--- a/tests/lo/l3/test_lo_l3_spectral_fit_dependencies.py
+++ b/tests/lo/l3/test_lo_l3_spectral_fit_dependencies.py
@@ -1,14 +1,17 @@
 import unittest
 from unittest.mock import patch
 
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection
+from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies import LoL3SpectralFitDependencies
+from imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies import (
+    LoL3SpectralFitDependencies,
+)
 
 
 class TestLoL3SpectralFitDependencies(unittest.TestCase):
-
-    @patch("imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies.RectangularIntensityMapData.read_from_path")
+    @patch(
+        "imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies.RectangularIntensityMapData.read_from_path"
+    )
     @patch("imap_l3_processing.lo.l3.lo_l3_spectral_fit_dependencies.download")
     def test_fetch_dependencies(self, mock_download, mock_read_from_path):
         for input_data_level in ("l2", "l3"):
@@ -22,10 +25,14 @@ def test_fetch_dependencies(self, mock_download, mock_read_from_path):
 
                 input_deps = ProcessingInputCollection(ena_input, *extra_inputs)
 
-                dependencies = LoL3SpectralFitDependencies.fetch_dependencies(input_deps)
+                dependencies = LoL3SpectralFitDependencies.fetch_dependencies(
+                    input_deps
+                )
 
                 mock_download.assert_called_once_with(file_name)
-                self.assertEqual(dependencies.map_data, mock_read_from_path.return_value)
+                self.assertEqual(
+                    dependencies.map_data, mock_read_from_path.return_value
+                )
 
                 mock_read_from_path.assert_called_with(mock_download.return_value)
 
@@ -34,12 +41,25 @@ def test_raises_error_if_unexpected_number_of_files(self):
         l2_file_name = "imap_lo_l2_l090-ena-h-hf-nsp-ram-hae-4deg-6mo_20250422_v001.cdf"
         cases = [
             ("0 files", ProcessingInputCollection()),
-            ("2 L3 files", ProcessingInputCollection(
-                ScienceInput(l3_file_name),
-                ScienceInput(l3_file_name),
-            )),
-             ("1 L2 and 1 L3 file", ProcessingInputCollection(ScienceInput(l2_file_name), ScienceInput(l3_file_name))),
-             ("2 L2 files", ProcessingInputCollection(ScienceInput(l2_file_name), ScienceInput(l2_file_name)))
+            (
+                "2 L3 files",
+                ProcessingInputCollection(
+                    ScienceInput(l3_file_name),
+                    ScienceInput(l3_file_name),
+                ),
+            ),
+            (
+                "1 L2 and 1 L3 file",
+                ProcessingInputCollection(
+                    ScienceInput(l2_file_name), ScienceInput(l3_file_name)
+                ),
+            ),
+            (
+                "2 L2 files",
+                ProcessingInputCollection(
+                    ScienceInput(l2_file_name), ScienceInput(l2_file_name)
+                ),
+            ),
         ]
 
         for name, collection in cases:
diff --git a/tests/lo/l3/test_lo_sp_initializer.py b/tests/lo/l3/test_lo_sp_initializer.py
index 9a2668059..7a43c15bc 100644
--- a/tests/lo/l3/test_lo_sp_initializer.py
+++ b/tests/lo/l3/test_lo_sp_initializer.py
@@ -1,18 +1,23 @@
 import unittest
 from datetime import datetime
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.lo.l3.lo_sp_initializer import LoSPInitializer, LO_SP_MAP_KERNELS
-from imap_l3_processing.maps.map_initializer import PossibleMapToProduce, MapInitializer
+from imap_l3_processing.lo.l3.lo_sp_initializer import (
+    LO_SP_MAP_KERNELS,
+    LoSPInitializer,
+)
+from imap_l3_processing.maps.map_initializer import MapInitializer, PossibleMapToProduce
 from imap_l3_processing.models import InputMetadata, VersionMap
 from tests.integration.integration_test_helpers import ImapQueryPatcher
 
 
 class TestLoSPInitializer(unittest.TestCase):
     def setUp(self):
-        self.query_patcher = patch('imap_l3_processing.lo.l3.lo_sp_initializer.imap_data_access.query')
+        self.query_patcher = patch(
+            "imap_l3_processing.lo.l3.lo_sp_initializer.imap_data_access.query"
+        )
         self.mock_query = self.query_patcher.start()
 
     def tearDown(self):
@@ -22,91 +27,109 @@ def test_is_a_map_initializer(self):
         initializer = LoSPInitializer()
         self.assertIsInstance(initializer, MapInitializer)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced(self,  mock_read_cdf_parents):
-        self.mock_query.side_effect = ImapQueryPatcher([
-            'imap_glows_l3e_survival-probability-lo_20100101-repoint00001_v001.cdf',
-            'imap_glows_l3e_survival-probability-lo_20100102-repoint00002_v001.cdf',
-            'imap_glows_l3e_survival-probability-lo_20100103-repoint00003_v001.cdf',
-
-            'imap_glows_l3e_survival-probability-lo_20100401-repoint00101_v002.cdf',
-            'imap_glows_l3e_survival-probability-lo_20100402-repoint00102_v002.cdf',
-            'imap_glows_l3e_survival-probability-lo_20100403-repoint00103_v002.cdf',
-
-            'imap_glows_l3e_survival-probability-lo_20110410-repoint00201_v001.cdf',
-
-            'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100101_v001.cdf',
-            'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf',
-            'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v000.cdf',
-
-            'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100701_v001.cdf',
-            'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20101001_v001.cdf',
-
-            'imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100101_v003.0001.cdf',
-            'imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100401_v003.0001.cdf'
-        ])
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
+        self.mock_query.side_effect = ImapQueryPatcher(
+            [
+                "imap_glows_l3e_survival-probability-lo_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100102-repoint00002_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100103-repoint00003_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100401-repoint00101_v002.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100402-repoint00102_v002.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100403-repoint00103_v002.cdf",
+                "imap_glows_l3e_survival-probability-lo_20110410-repoint00201_v001.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100101_v001.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v000.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100701_v001.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20101001_v001.cdf",
+                "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100101_v003.0001.cdf",
+                "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100401_v003.0001.cdf",
+            ]
+        )
 
         mock_read_cdf_parents.side_effect = [
             [
                 "imap_lo_l1c_pset_20100101-repoint00001_v001.cdf",
                 "imap_lo_l1c_pset_20100102-repoint00002_v001.cdf",
-                "imap_lo_l1c_pset_20100103-repoint00003_v001.cdf"
+                "imap_lo_l1c_pset_20100103-repoint00003_v001.cdf",
             ],
             [
-                'imap_lo_l1c_pset_20100401-repoint00101_v001.cdf',
-                'imap_lo_l1c_pset_20100402-repoint00102_v001.cdf',
-                'imap_lo_l1c_pset_20100403-repoint00103_v001.cdf'
+                "imap_lo_l1c_pset_20100401-repoint00101_v001.cdf",
+                "imap_lo_l1c_pset_20100402-repoint00102_v001.cdf",
+                "imap_lo_l1c_pset_20100403-repoint00103_v001.cdf",
             ],
             [
-                'imap_lo_l1c_pset_20100701-repoint00201_v001.cdf',
-                'imap_lo_l1c_pset_20100702-repoint00202_v001.cdf',
-                'imap_lo_l1c_pset_20100703-repoint00203_v001.cdf'
+                "imap_lo_l1c_pset_20100701-repoint00201_v001.cdf",
+                "imap_lo_l1c_pset_20100702-repoint00202_v001.cdf",
+                "imap_lo_l1c_pset_20100703-repoint00203_v001.cdf",
             ],
             [
-                'imap_lo_l1c_pset_20101001-repoint00301_v001.cdf',
-                'imap_lo_l1c_pset_20101002-repoint00302_v001.cdf',
-                'imap_lo_l1c_pset_20101003-repoint00303_v001.cdf'
+                "imap_lo_l1c_pset_20101001-repoint00301_v001.cdf",
+                "imap_lo_l1c_pset_20101002-repoint00302_v001.cdf",
+                "imap_lo_l1c_pset_20101003-repoint00303_v001.cdf",
             ],
             [
-                'imap_glows_l3e_survival-probability-lo_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-lo_20100102-repoint00002_v001.cdf',
-                'imap_glows_l3e_survival-probability-lo_20100103-repoint00003_v001.cdf',
+                "imap_glows_l3e_survival-probability-lo_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100102-repoint00002_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100103-repoint00003_v001.cdf",
                 "imap_lo_l1c_pset_20100101-repoint00001_v001.cdf",
                 "imap_lo_l1c_pset_20100102-repoint00002_v001.cdf",
                 "imap_lo_l1c_pset_20100103-repoint00003_v001.cdf",
-                'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100101_v001.cdf'
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100101_v001.cdf",
             ],
             [
-                'imap_glows_l3e_survival-probability-lo_20100401-repoint00101_v001.cdf',
-                'imap_glows_l3e_survival-probability-lo_20100402-repoint00102_v001.cdf',
-                'imap_glows_l3e_survival-probability-lo_20100403-repoint00103_v001.cdf',
-                'imap_lo_l1c_pset_20100401-repoint00101_v001.cdf',
-                'imap_lo_l1c_pset_20100402-repoint00102_v001.cdf',
-                'imap_lo_l1c_pset_20100403-repoint00103_v001.cdf',
-                'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf'
-            ]
+                "imap_glows_l3e_survival-probability-lo_20100401-repoint00101_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100402-repoint00102_v001.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100403-repoint00103_v001.cdf",
+                "imap_lo_l1c_pset_20100401-repoint00101_v001.cdf",
+                "imap_lo_l1c_pset_20100402-repoint00102_v001.cdf",
+                "imap_lo_l1c_pset_20100403-repoint00103_v001.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf",
+            ],
         ]
 
         initializer = LoSPInitializer()
         descriptor = "l090-ena-h-sf-sp-ram-hae-6deg-12mo"
-        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(descriptor,
-                                                                              3)
-
-        self.mock_query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', descriptor='survival-probability-lo', version="latest"),
-            call(instrument='lo', data_level='l2'),
-            call(instrument='lo', data_level='l3'),
-        ])
+        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+            descriptor, 3
+        )
 
-        mock_read_cdf_parents.assert_has_calls([
-            call('imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100101_v001.cdf'),
-            call('imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf'),
-            call('imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100701_v001.cdf'),
-            call('imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20101001_v001.cdf'),
+        self.mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor="survival-probability-lo",
+                    version="latest",
+                ),
+                call(instrument="lo", data_level="l2"),
+                call(instrument="lo", data_level="l3"),
+            ]
+        )
 
-            call('imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100101_v003.0001.cdf'),
-            call('imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100401_v003.0001.cdf')
-        ])
+        mock_read_cdf_parents.assert_has_calls(
+            [
+                call(
+                    "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf"
+                ),
+                call(
+                    "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100701_v001.cdf"
+                ),
+                call(
+                    "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20101001_v001.cdf"
+                ),
+                call(
+                    "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100101_v003.0001.cdf"
+                ),
+                call(
+                    "imap_lo_l3_l090-ena-h-sf-sp-ram-hae-6deg-12mo_20100401_v003.0001.cdf"
+                ),
+            ]
+        )
 
         expected_possible_map_to_produce = PossibleMapToProduce(
             input_metadata=InputMetadata(
@@ -115,23 +138,23 @@ def test_get_maps_that_should_be_produced(self,  mock_read_cdf_parents):
                 start_date=datetime(2010, 4, 1),
                 end_date=datetime(2011, 4, 1, 6, 0),
                 version=VersionMap({descriptor: Version(3, 2)}),
-                descriptor=descriptor
+                descriptor=descriptor,
             ),
             input_files={
-                'imap_glows_l3e_survival-probability-lo_20100401-repoint00101_v002.cdf',
-                'imap_glows_l3e_survival-probability-lo_20100402-repoint00102_v002.cdf',
-                'imap_glows_l3e_survival-probability-lo_20100403-repoint00103_v002.cdf',
-                'imap_lo_l1c_pset_20100401-repoint00101_v001.cdf',
-                'imap_lo_l1c_pset_20100402-repoint00102_v001.cdf',
-                'imap_lo_l1c_pset_20100403-repoint00103_v001.cdf',
-                'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf'
-            }
+                "imap_glows_l3e_survival-probability-lo_20100401-repoint00101_v002.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100402-repoint00102_v002.cdf",
+                "imap_glows_l3e_survival-probability-lo_20100403-repoint00103_v002.cdf",
+                "imap_lo_l1c_pset_20100401-repoint00101_v001.cdf",
+                "imap_lo_l1c_pset_20100402-repoint00102_v001.cdf",
+                "imap_lo_l1c_pset_20100403-repoint00103_v001.cdf",
+                "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-12mo_20100401_v001.cdf",
+            },
         )
 
         self.assertIn(expected_possible_map_to_produce, actual_maps_to_produce)
         self.assertEqual(2, len(actual_maps_to_produce))
 
-    @patch('imap_l3_processing.lo.l3.lo_sp_initializer.furnish_spice_metakernel')
+    @patch("imap_l3_processing.lo.l3.lo_sp_initializer.furnish_spice_metakernel")
     def test_furnish_spice_dependencies(self, mock_furnish_metakernel):
         start_date = datetime(2025, 4, 15)
         end_date = datetime(2025, 7, 15)
@@ -149,5 +172,6 @@ def test_furnish_spice_dependencies(self, mock_furnish_metakernel):
         lo_initializer = LoSPInitializer()
         lo_initializer.furnish_spice_dependencies(map_to_produce)
 
-        mock_furnish_metakernel.assert_called_once_with(start_date=start_date, end_date=end_date,
-                                                        kernel_types=LO_SP_MAP_KERNELS)
+        mock_furnish_metakernel.assert_called_once_with(
+            start_date=start_date, end_date=end_date, kernel_types=LO_SP_MAP_KERNELS
+        )
diff --git a/tests/lo/test_lo_processor.py b/tests/lo/test_lo_processor.py
index 73c00204f..7e6f5b96b 100644
--- a/tests/lo/test_lo_processor.py
+++ b/tests/lo/test_lo_processor.py
@@ -9,64 +9,96 @@
 
 from imap_l3_processing.lo.l3.lo_combined_dependencies import LoCombinedDependencies
 from imap_l3_processing.lo.lo_processor import LoProcessor, isn_background_subtraction
-from imap_l3_processing.maps.map_models import RectangularSpectralIndexDataProduct, RectangularIntensityDataProduct, \
-    InputRectangularPointingSet, ISNRateData, ISNBackgroundSubtractedData, ISNBackgroundSubtractedMapData
+from imap_l3_processing.maps.map_models import (
+    InputRectangularPointingSet,
+    ISNBackgroundSubtractedData,
+    ISNBackgroundSubtractedMapData,
+    ISNRateData,
+    RectangularIntensityDataProduct,
+    RectangularSpectralIndexDataProduct,
+)
 from imap_l3_processing.models import InputMetadata, Instrument
 from tests.maps import test_builders
 
 
 class TestLoProcessor(unittest.TestCase):
-
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch('imap_l3_processing.lo.lo_processor.slice_energy_range_by_bin')
-    @patch('imap_l3_processing.lo.lo_processor.LoL3SpectralFitDependencies.fetch_dependencies')
-    @patch('imap_l3_processing.lo.lo_processor.fit_spectral_index_map')
-    @patch('imap_l3_processing.lo.lo_processor.save_data')
-    def test_process_spectral_index(self, mock_save_data, mock_fit_spectral_index_map, mock_fetch_dependencies,
-                                    mock_slice_energy_range_by_bin, mock_get_parent_file_names):
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch("imap_l3_processing.lo.lo_processor.slice_energy_range_by_bin")
+    @patch(
+        "imap_l3_processing.lo.lo_processor.LoL3SpectralFitDependencies.fetch_dependencies"
+    )
+    @patch("imap_l3_processing.lo.lo_processor.fit_spectral_index_map")
+    @patch("imap_l3_processing.lo.lo_processor.save_data")
+    def test_process_spectral_index(
+        self,
+        mock_save_data,
+        mock_fit_spectral_index_map,
+        mock_fetch_dependencies,
+        mock_slice_energy_range_by_bin,
+        mock_get_parent_file_names,
+    ):
         mock_get_parent_file_names.return_value = ["some_input_file_name"]
 
         input_collection = Mock()
         lo_l3_spectral_fit_dependency = Mock()
-        lo_l3_spectral_fit_dependency.map_data.intensity_map_data = test_builders.create_intensity_map_data()
+        lo_l3_spectral_fit_dependency.map_data.intensity_map_data = (
+            test_builders.create_intensity_map_data()
+        )
         lo_l3_spectral_fit_dependency.map_data.intensity_map_data.energy = np.array(
-            [1, 10, 100, 1000, 10000, 100000, 1000000, 10000000])
+            [1, 10, 100, 1000, 10000, 100000, 1000000, 10000000]
+        )
         mock_fetch_dependencies.return_value = lo_l3_spectral_fit_dependency
 
-        metadata = InputMetadata(instrument="lo",
-                                 data_level="l3",
-                                 version="v000",
-                                 start_date=datetime(2020, 1, 1, 1),
-                                 end_date=datetime(2020, 1, 1, 1),
-                                 descriptor="l090-spx-h-hf-sp-ram-hae-6deg-1yr")
+        metadata = InputMetadata(
+            instrument="lo",
+            data_level="l3",
+            version="v000",
+            start_date=datetime(2020, 1, 1, 1),
+            end_date=datetime(2020, 1, 1, 1),
+            descriptor="l090-spx-h-hf-sp-ram-hae-6deg-1yr",
+        )
 
         processor = LoProcessor(input_collection, input_metadata=metadata)
         product = processor.process(spice_frame_name=SpiceFrame.ECLIPJ2000)
 
         mock_fetch_dependencies.assert_called_once_with(input_collection)
         mock_slice_energy_range_by_bin.assert_called_once_with(
-            lo_l3_spectral_fit_dependency.map_data.intensity_map_data, 4, 7)
-        mock_fit_spectral_index_map.assert_called_once_with(mock_slice_energy_range_by_bin.return_value)
+            lo_l3_spectral_fit_dependency.map_data.intensity_map_data, 4, 7
+        )
+        mock_fit_spectral_index_map.assert_called_once_with(
+            mock_slice_energy_range_by_bin.return_value
+        )
 
         data_product = mock_save_data.call_args_list[0].args[0]
 
         self.assertIsInstance(data_product, RectangularSpectralIndexDataProduct)
-        self.assertEqual(data_product.data.spectral_index_map_data,
-                         mock_fit_spectral_index_map.return_value)
-        self.assertEqual(data_product.data.coords, lo_l3_spectral_fit_dependency.map_data.coords)
+        self.assertEqual(
+            data_product.data.spectral_index_map_data,
+            mock_fit_spectral_index_map.return_value,
+        )
+        self.assertEqual(
+            data_product.data.coords, lo_l3_spectral_fit_dependency.map_data.coords
+        )
         self.assertEqual(data_product.input_metadata, processor.input_metadata)
         self.assertEqual(data_product.parent_file_names, ["some_input_file_name"])
         self.assertEqual(data_product.spice_frame_name, SpiceFrame.ECLIPJ2000)
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names')
-    @patch('imap_l3_processing.lo.lo_processor.LoL3SpectralFitDependencies.fetch_dependencies')
-    @patch('imap_l3_processing.lo.lo_processor.fit_spectral_index_map')
-    @patch('imap_l3_processing.lo.lo_processor.save_data')
-    @patch('imap_l3_processing.lo.lo_processor.slice_energy_range_by_bin')
-    def test_process_spectral_index_with_range_specified(self, mock_slice_energy_range_by_bin, mock_save_data,
-                                                         mock_fit_spectral_index_map, mock_fetch_dependencies,
-                                                         mock_get_parent_file_names):
+    @patch("imap_l3_processing.hi.hi_processor.MapProcessor.get_parent_file_names")
+    @patch(
+        "imap_l3_processing.lo.lo_processor.LoL3SpectralFitDependencies.fetch_dependencies"
+    )
+    @patch("imap_l3_processing.lo.lo_processor.fit_spectral_index_map")
+    @patch("imap_l3_processing.lo.lo_processor.save_data")
+    @patch("imap_l3_processing.lo.lo_processor.slice_energy_range_by_bin")
+    def test_process_spectral_index_with_range_specified(
+        self,
+        mock_slice_energy_range_by_bin,
+        mock_save_data,
+        mock_fit_spectral_index_map,
+        mock_fetch_dependencies,
+        mock_get_parent_file_names,
+    ):
         test_cases = [
             ("l090-spx0104-h-hf-sp-ram-hae-6deg-1yr", (1, 4)),
             ("l090-spxnbs0105-h-hf-sp-ram-hae-6deg-1yr", (1, 5)),
@@ -85,15 +117,18 @@ def test_process_spectral_index_with_range_specified(self, mock_slice_energy_ran
 
                 input_collection = Mock()
                 lo_l3_spectral_fit_dependency = mock_fetch_dependencies.return_value
-                lo_l3_spectral_fit_dependency.map_data.intensity_map_data.energy = np.array(
-                    [1, 10, 1000, 10000, 100000, 1000000, 10000000])
+                lo_l3_spectral_fit_dependency.map_data.intensity_map_data.energy = (
+                    np.array([1, 10, 1000, 10000, 100000, 1000000, 10000000])
+                )
 
-                metadata = InputMetadata(instrument="lo",
-                                         data_level="l3",
-                                         version="v000",
-                                         start_date=datetime(2020, 1, 1, 1),
-                                         end_date=datetime(2020, 1, 1, 1),
-                                         descriptor=descriptor)
+                metadata = InputMetadata(
+                    instrument="lo",
+                    data_level="l3",
+                    version="v000",
+                    start_date=datetime(2020, 1, 1, 1),
+                    end_date=datetime(2020, 1, 1, 1),
+                    descriptor=descriptor,
+                )
 
                 processor = LoProcessor(input_collection, input_metadata=metadata)
                 product = processor.process()
@@ -102,94 +137,125 @@ def test_process_spectral_index_with_range_specified(self, mock_slice_energy_ran
                 mock_slice_energy_range_by_bin.assert_called_once_with(
                     lo_l3_spectral_fit_dependency.map_data.intensity_map_data,
                     expected_energy_start,
-                    expected_energy_end
+                    expected_energy_end,
                 )
 
-                mock_fit_spectral_index_map.assert_called_once_with(mock_slice_energy_range_by_bin.return_value)
+                mock_fit_spectral_index_map.assert_called_once_with(
+                    mock_slice_energy_range_by_bin.return_value
+                )
 
                 data_product = mock_save_data.call_args_list[0].args[0]
 
                 self.assertIsInstance(data_product, RectangularSpectralIndexDataProduct)
-                self.assertEqual(data_product.data.spectral_index_map_data,
-                                 mock_fit_spectral_index_map.return_value)
-                self.assertEqual(data_product.data.coords, lo_l3_spectral_fit_dependency.map_data.coords)
+                self.assertEqual(
+                    data_product.data.spectral_index_map_data,
+                    mock_fit_spectral_index_map.return_value,
+                )
+                self.assertEqual(
+                    data_product.data.coords,
+                    lo_l3_spectral_fit_dependency.map_data.coords,
+                )
                 self.assertEqual(data_product.input_metadata, processor.input_metadata)
                 self.assertEqual(data_product.spice_frame_name, SpiceFrame.ECLIPJ2000)
-                self.assertEqual(data_product.parent_file_names, ["some_input_file_name"])
+                self.assertEqual(
+                    data_product.parent_file_names, ["some_input_file_name"]
+                )
                 self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.lo.lo_processor.MapProcessor.get_parent_file_names')
-    @patch("imap_l3_processing.lo.lo_processor.HiLoL3SurvivalDependencies.fetch_dependencies")
+    @patch("imap_l3_processing.lo.lo_processor.MapProcessor.get_parent_file_names")
+    @patch(
+        "imap_l3_processing.lo.lo_processor.HiLoL3SurvivalDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.lo.lo_processor.process_survival_probabilities")
-    @patch('imap_l3_processing.lo.lo_processor.save_data')
-    def test_process_survival_probabilities(self, mock_save_data,
-                                            mock_process_survival_prob,
-                                            mock_fetch_survival_dependencies, mock_get_parent_file_names):
+    @patch("imap_l3_processing.lo.lo_processor.save_data")
+    def test_process_survival_probabilities(
+        self,
+        mock_save_data,
+        mock_process_survival_prob,
+        mock_fetch_survival_dependencies,
+        mock_get_parent_file_names,
+    ):
         mock_get_parent_file_names.return_value = ["somewhere"]
-        cases = {
-            "spacecraft": "sf",
-            "heliospheric": "hf"
-        }
+        cases = {"spacecraft": "sf", "heliospheric": "hf"}
 
         for case, reference_frame in cases.items():
             with self.subTest(case):
-                input_metadata = InputMetadata(instrument="lo",
-                                               data_level="l3",
-                                               start_date=datetime.now(),
-                                               end_date=datetime.now() + timedelta(days=1),
-                                               version="",
-                                               descriptor=f"l090-ena-h-{reference_frame}-sp-ram-hae-4deg-6mo",
-                                               )
+                input_metadata = InputMetadata(
+                    instrument="lo",
+                    data_level="l3",
+                    start_date=datetime.now(),
+                    end_date=datetime.now() + timedelta(days=1),
+                    version="",
+                    descriptor=f"l090-ena-h-{reference_frame}-sp-ram-hae-4deg-6mo",
+                )
 
                 dependencies = Mock(
-                    l1c_data=[InputRectangularPointingSet(
-                        epoch=datetime(2025, 1, 1),
-                        epoch_delta=None,
-                        repointing=1,
-                        epoch_j2000=np.array([10]),
-                        exposure_times=np.full((1, 7, 3600, 40), 2),
-                        esa_energy_step=np.arange(7),
-                        pointing_start_met=np.array([43200_000_000_000]),
-                        pointing_end_met=np.array([43200_000_100_000]),
-                        hae_longitude=np.ones(3600).reshape((1, 3600)),
-                        hae_latitude=np.ones(3600).reshape((1, 3600))
-                    )],
-                    dependency_file_paths=[Path("folder/map"), Path("folder/l1c")]
+                    l1c_data=[
+                        InputRectangularPointingSet(
+                            epoch=datetime(2025, 1, 1),
+                            epoch_delta=None,
+                            repointing=1,
+                            epoch_j2000=np.array([10]),
+                            exposure_times=np.full((1, 7, 3600, 40), 2),
+                            esa_energy_step=np.arange(7),
+                            pointing_start_met=np.array([43200_000_000_000]),
+                            pointing_end_met=np.array([43200_000_100_000]),
+                            hae_longitude=np.ones(3600).reshape((1, 3600)),
+                            hae_latitude=np.ones(3600).reshape((1, 3600)),
+                        )
+                    ],
+                    dependency_file_paths=[Path("folder/map"), Path("folder/l1c")],
                 )
                 mock_fetch_survival_dependencies.return_value = dependencies
 
-                mock_process_survival_prob.return_value = sentinel.survival_probabilities
+                mock_process_survival_prob.return_value = (
+                    sentinel.survival_probabilities
+                )
 
                 processor = LoProcessor(sentinel.input_dependencies, input_metadata)
                 product = processor.process(spice_frame_name=SpiceFrame.IMAP_DPS)
 
-                mock_fetch_survival_dependencies.assert_called_once_with(sentinel.input_dependencies,
-                                                                         Instrument.IMAP_LO)
+                mock_fetch_survival_dependencies.assert_called_once_with(
+                    sentinel.input_dependencies, Instrument.IMAP_LO
+                )
 
-                mock_process_survival_prob.assert_called_once_with(dependencies, SpiceFrame.IMAP_DPS, cg_corrected=False)
+                mock_process_survival_prob.assert_called_once_with(
+                    dependencies, SpiceFrame.IMAP_DPS, cg_corrected=False
+                )
 
-                np.testing.assert_array_equal(np.full((1, 7, 3600), 80), dependencies.l1c_data[0].exposure_times)
+                np.testing.assert_array_equal(
+                    np.full((1, 7, 3600), 80), dependencies.l1c_data[0].exposure_times
+                )
 
-                mock_save_data.assert_called_once_with(RectangularIntensityDataProduct(
-                    input_metadata=input_metadata,
-                    parent_file_names=["l1c", "map", "somewhere"],
-                    data=sentinel.survival_probabilities,
-                    spice_frame_name=SpiceFrame.IMAP_DPS,
-                ))
+                mock_save_data.assert_called_once_with(
+                    RectangularIntensityDataProduct(
+                        input_metadata=input_metadata,
+                        parent_file_names=["l1c", "map", "somewhere"],
+                        data=sentinel.survival_probabilities,
+                        spice_frame_name=SpiceFrame.IMAP_DPS,
+                    )
+                )
                 self.assertEqual([mock_save_data.return_value], product)
 
                 mock_fetch_survival_dependencies.reset_mock()
                 mock_process_survival_prob.reset_mock()
                 mock_save_data.reset_mock()
 
-    @patch("imap_l3_processing.lo.lo_processor.LoCombinedDependencies.fetch_dependencies")
+    @patch(
+        "imap_l3_processing.lo.lo_processor.LoCombinedDependencies.fetch_dependencies"
+    )
     @patch("imap_l3_processing.lo.lo_processor.ExposureWeightedCombination")
-    @patch('imap_l3_processing.lo.lo_processor.save_data')
-    def test_process_lo_combined(self, mock_save_data,
-                                            mock_exposure_weighted_combination_class,
-                                            mock_fetch_combined_dependencies):
-
-        mock_exposure_weighted_combination = mock_exposure_weighted_combination_class.return_value
+    @patch("imap_l3_processing.lo.lo_processor.save_data")
+    def test_process_lo_combined(
+        self,
+        mock_save_data,
+        mock_exposure_weighted_combination_class,
+        mock_fetch_combined_dependencies,
+    ):
+
+        mock_exposure_weighted_combination = (
+            mock_exposure_weighted_combination_class.return_value
+        )
 
         input_metadata = InputMetadata(
             instrument="lo",
@@ -205,27 +271,41 @@ def test_process_lo_combined(self, mock_save_data,
         )
 
         processing_input = ProcessingInputCollection(
-            ScienceInput('imap_lo_l3_l075-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf'),
-            ScienceInput('imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf'),
-            ScienceInput('imap_lo_l3_l105-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf'),
+            ScienceInput(
+                "imap_lo_l3_l075-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf"
+            ),
+            ScienceInput(
+                "imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf"
+            ),
+            ScienceInput(
+                "imap_lo_l3_l105-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf"
+            ),
         )
 
         lo_processor = LoProcessor(processing_input, input_metadata)
 
-        [combined_product] = lo_processor.process(spice_frame_name=SpiceFrame.ECLIPJ2000)
+        [combined_product] = lo_processor.process(
+            spice_frame_name=SpiceFrame.ECLIPJ2000
+        )
 
         mock_fetch_combined_dependencies.assert_called_once_with(processing_input)
 
-        mock_combine = mock_exposure_weighted_combination.combine_rectangular_intensity_map_data
+        mock_combine = (
+            mock_exposure_weighted_combination.combine_rectangular_intensity_map_data
+        )
 
-        mock_combine.assert_called_once_with([sentinel.l075, sentinel.l090, sentinel.l105])
+        mock_combine.assert_called_once_with(
+            [sentinel.l075, sentinel.l090, sentinel.l105]
+        )
 
         expected_parents = [
-            'imap_lo_l3_l075-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf',
-            'imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf',
-            'imap_lo_l3_l105-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf',
+            "imap_lo_l3_l075-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf",
+            "imap_lo_l3_l090-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf",
+            "imap_lo_l3_l105-ena-h-hf-sp-ram-hae-6deg-6mo_20250422_v001.cdf",
         ]
-        expected_data_product = RectangularIntensityDataProduct(input_metadata, mock_combine.return_value, SpiceFrame.ECLIPJ2000)
+        expected_data_product = RectangularIntensityDataProduct(
+            input_metadata, mock_combine.return_value, SpiceFrame.ECLIPJ2000
+        )
         expected_data_product.parent_file_names.extend(expected_parents)
         mock_save_data.assert_called_once_with(expected_data_product)
 
@@ -235,17 +315,27 @@ def test_rejects_unimplemented_descriptors(self):
         input_collection = ProcessingInputCollection()
 
         cases = [
-            ("not-a-valid-descriptor", ValueError, ("Could not parse descriptor not-a-valid-descriptor",)),
-            ("l090-ena-h-hf-nsp-ram-hae-6deg-1yr", NotImplementedError, ("l090-ena-h-hf-nsp-ram-hae-6deg-1yr",)),
+            (
+                "not-a-valid-descriptor",
+                ValueError,
+                ("Could not parse descriptor not-a-valid-descriptor",),
+            ),
+            (
+                "l090-ena-h-hf-nsp-ram-hae-6deg-1yr",
+                NotImplementedError,
+                ("l090-ena-h-hf-nsp-ram-hae-6deg-1yr",),
+            ),
         ]
         for descriptor, exception_class, exception_args in cases:
             with self.subTest(descriptor):
-                metadata = InputMetadata(instrument="lo",
-                                         data_level="l3",
-                                         version="v000",
-                                         start_date=datetime(2020, 1, 1, 1),
-                                         end_date=datetime(2020, 1, 1, 1),
-                                         descriptor=descriptor)
+                metadata = InputMetadata(
+                    instrument="lo",
+                    data_level="l3",
+                    version="v000",
+                    start_date=datetime(2020, 1, 1, 1),
+                    end_date=datetime(2020, 1, 1, 1),
+                    descriptor=descriptor,
+                )
 
                 processor = LoProcessor(input_collection, input_metadata=metadata)
                 with self.assertRaises(exception_class) as cm:
@@ -271,42 +361,86 @@ def test_isn_background_subtraction(self):
             obs_date=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30)) * 5,
             obs_date_range=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30)) * 6,
             ena_intensity=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30)) * 7,
-            ena_intensity_sys_err=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30)) * 8,
-            ena_intensity_stat_uncert=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30)) * 9,
+            ena_intensity_sys_err=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30))
+            * 8,
+            ena_intensity_stat_uncert=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30))
+            * 9,
             bg_rate_sys_err=np.arange(1 * 7 * 60 * 30).reshape((1, 7, 60, 30)) * 11,
             ena_count_rate=np.ones((1, 7, 60, 30)) * 3,
             bg_rate=np.ones((1, 7, 60, 30)) * 2,
             bg_rate_stat_uncert=np.ones((1, 7, 60, 30)) * 3,
             ena_count_rate_stat_uncert=np.ones((1, 7, 60, 30)) * 2,
         )
-        actual_map_data: ISNBackgroundSubtractedMapData = isn_background_subtraction(input_data)
-        actual_isn_rate_map_data: ISNBackgroundSubtractedData = actual_map_data.isn_rate_map_data
+        actual_map_data: ISNBackgroundSubtractedMapData = isn_background_subtraction(
+            input_data
+        )
+        actual_isn_rate_map_data: ISNBackgroundSubtractedData = (
+            actual_map_data.isn_rate_map_data
+        )
 
         expected_isn_bg_rate_subtracted = np.ones((1, 7, 60, 30))
-        expected_isn_bg_rate_subtracted_stat_uncert = np.ones((1, 7, 60, 30)) * np.sqrt(13)
-
-        np.testing.assert_array_equal(actual_isn_rate_map_data.isn_bg_rate_subtracted, expected_isn_bg_rate_subtracted)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.isn_bg_rate_subtracted_stat_uncert, expected_isn_bg_rate_subtracted_stat_uncert)
-
-        np.testing.assert_array_equal(actual_isn_rate_map_data.energy, input_data.energy)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.energy_delta_plus, input_data.energy_delta_plus)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.energy_delta_minus, input_data.energy_delta_minus)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.energy_label, input_data.energy_label)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.exposure_factor, input_data.exposure_factor)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.obs_date, input_data.obs_date)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.obs_date_range, input_data.obs_date_range)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.bg_rate, input_data.bg_rate)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.bg_rate_stat_uncert, input_data.bg_rate_stat_uncert)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.bg_rate_sys_err, input_data.bg_rate_sys_err)
-        np.testing.assert_array_equal(actual_isn_rate_map_data.bg_rate_sys_err,
-                                      actual_isn_rate_map_data.isn_bg_rate_subtracted_sys_err)
+        expected_isn_bg_rate_subtracted_stat_uncert = np.ones((1, 7, 60, 30)) * np.sqrt(
+            13
+        )
+
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.isn_bg_rate_subtracted,
+            expected_isn_bg_rate_subtracted,
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.isn_bg_rate_subtracted_stat_uncert,
+            expected_isn_bg_rate_subtracted_stat_uncert,
+        )
+
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.energy, input_data.energy
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.energy_delta_plus, input_data.energy_delta_plus
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.energy_delta_minus, input_data.energy_delta_minus
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.energy_label, input_data.energy_label
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.exposure_factor, input_data.exposure_factor
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.obs_date, input_data.obs_date
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.obs_date_range, input_data.obs_date_range
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.bg_rate, input_data.bg_rate
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.bg_rate_stat_uncert, input_data.bg_rate_stat_uncert
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.bg_rate_sys_err, input_data.bg_rate_sys_err
+        )
+        np.testing.assert_array_equal(
+            actual_isn_rate_map_data.bg_rate_sys_err,
+            actual_isn_rate_map_data.isn_bg_rate_subtracted_sys_err,
+        )
 
         self.assertEqual(actual_isn_rate_map_data.epoch, sentinel.epoch)
         self.assertEqual(actual_isn_rate_map_data.solid_angle, sentinel.solid_angle)
         self.assertEqual(actual_isn_rate_map_data.latitude, sentinel.latitude)
-        self.assertEqual(actual_isn_rate_map_data.latitude_delta, sentinel.latitude_delta)
-        self.assertEqual(actual_isn_rate_map_data.latitude_label, sentinel.latitude_label)
+        self.assertEqual(
+            actual_isn_rate_map_data.latitude_delta, sentinel.latitude_delta
+        )
+        self.assertEqual(
+            actual_isn_rate_map_data.latitude_label, sentinel.latitude_label
+        )
         self.assertEqual(actual_isn_rate_map_data.longitude, sentinel.longitude)
-        self.assertEqual(actual_isn_rate_map_data.longitude_delta, sentinel.longitude_delta)
-        self.assertEqual(actual_isn_rate_map_data.longitude_label, sentinel.longitude_label)
+        self.assertEqual(
+            actual_isn_rate_map_data.longitude_delta, sentinel.longitude_delta
+        )
+        self.assertEqual(
+            actual_isn_rate_map_data.longitude_label, sentinel.longitude_label
+        )
         self.assertEqual(actual_isn_rate_map_data.epoch_delta, sentinel.epoch_delta)
diff --git a/tests/maps/test_builders.py b/tests/maps/test_builders.py
index a76b7056e..6ad64ac7b 100644
--- a/tests/maps/test_builders.py
+++ b/tests/maps/test_builders.py
@@ -1,30 +1,50 @@
 from datetime import datetime
-from typing import Optional
 
 import numpy as np
 import spiceypy
 
-from imap_l3_processing.maps.map_models import RectangularIntensityMapData, IntensityMapData, RectangularCoords, \
-    InputRectangularPointingSet, GlowsL3eRectangularMapInputData, RectangularSpectralIndexMapData, SpectralIndexMapData
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
+    IntensityMapData,
+    RectangularCoords,
+    RectangularIntensityMapData,
+    RectangularSpectralIndexMapData,
+    SpectralIndexMapData,
+)
 from imap_l3_processing.maps.quality_flags import MapL3Flags
 
 
-def create_intensity_map_data(epoch=None, epoch_delta=None, lon=None, lat=None, energy=None,
-                              energy_delta=None, flux=None,
-                              intensity_stat_uncert=None) -> IntensityMapData:
+def create_intensity_map_data(
+    epoch=None,
+    epoch_delta=None,
+    lon=None,
+    lat=None,
+    energy=None,
+    energy_delta=None,
+    flux=None,
+    intensity_stat_uncert=None,
+) -> IntensityMapData:
     lon = lon if lon is not None else np.array([1.0])
     lat = lat if lat is not None else np.array([1.0])
     energy = energy if energy is not None else np.array([1.0])
-    energy_delta = energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    energy_delta = (
+        energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    )
     epoch = epoch if epoch is not None else np.ma.array([datetime.now()])
     epoch_delta = epoch_delta if epoch_delta is not None else np.ma.array([86400 * 1e9])
-    flux = flux if flux is not None else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
-    intensity_stat_uncert = intensity_stat_uncert if intensity_stat_uncert is not None else np.full(
-        (len(epoch), len(energy), len(lon), len(lat)),
-        fill_value=1)
+    flux = (
+        flux
+        if flux is not None
+        else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
+    )
+    intensity_stat_uncert = (
+        intensity_stat_uncert
+        if intensity_stat_uncert is not None
+        else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
+    )
 
     if isinstance(flux, np.ndarray):
-
         more_real_flux = flux
 
     else:
@@ -55,25 +75,44 @@ def create_intensity_map_data(epoch=None, epoch_delta=None, lon=None, lat=None,
     )
 
 
-def create_rectangular_intensity_map_data(epoch=None, epoch_delta=None, lon=None, lat=None, energy=None,
-                                          energy_delta=None, flux=None,
-                                          intensity_stat_uncert=None,
-                                          include_ena_intensity_sys_err_minus=False) -> RectangularIntensityMapData:
+def create_rectangular_intensity_map_data(
+    epoch=None,
+    epoch_delta=None,
+    lon=None,
+    lat=None,
+    energy=None,
+    energy_delta=None,
+    flux=None,
+    intensity_stat_uncert=None,
+    include_ena_intensity_sys_err_minus=False,
+) -> RectangularIntensityMapData:
     lon = lon if lon is not None else np.array([1.0])
     lat = lat if lat is not None else np.array([1.0])
     energy = energy if energy is not None else np.array([1.0])
-    energy_delta = energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    energy_delta = (
+        energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    )
     epoch = epoch if epoch is not None else np.ma.array([datetime.now()])
     epoch_delta = epoch_delta if epoch_delta is not None else np.ma.array([86400 * 1e9])
-    flux = flux if flux is not None else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
-    intensity_stat_uncert = intensity_stat_uncert if intensity_stat_uncert is not None else np.full(
-        (len(epoch), len(energy), len(lon), len(lat)),
-        fill_value=1)
+    flux = (
+        flux
+        if flux is not None
+        else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
+    )
+    intensity_stat_uncert = (
+        intensity_stat_uncert
+        if intensity_stat_uncert is not None
+        else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
+    )
     ena_intensity_sys_err_minus = None
     ena_intensity_sys_err_plus = None
     if include_ena_intensity_sys_err_minus:
-        ena_intensity_sys_err_minus = np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=2)
-        ena_intensity_sys_err_plus = np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=3)
+        ena_intensity_sys_err_minus = np.full(
+            (len(epoch), len(energy), len(lon), len(lat)), fill_value=2
+        )
+        ena_intensity_sys_err_plus = np.full(
+            (len(epoch), len(energy), len(lon), len(lat)), fill_value=3
+        )
 
     if isinstance(flux, np.ndarray):
         more_real_flux = flux
@@ -92,15 +131,17 @@ def create_rectangular_intensity_map_data(epoch=None, epoch_delta=None, lon=None
             latitude=lat,
             longitude=lon,
             exposure_factor=np.full_like(flux, 0),
-            obs_date=np.ma.array(np.full(more_real_flux.shape, datetime(year=2010, month=1, day=1))),
+            obs_date=np.ma.array(
+                np.full(more_real_flux.shape, datetime(year=2010, month=1, day=1))
+            ),
             obs_date_range=np.ma.array(np.full_like(more_real_flux, 0)),
             solid_angle=np.full_like(more_real_flux, 0),
             ena_intensity=flux,
             ena_intensity_stat_uncert=intensity_stat_uncert,
-            ena_intensity_sys_err=flux * .001,
-            bg_intensity=flux * .01,
-            bg_intensity_stat_uncert=intensity_stat_uncert * .01,
-            bg_intensity_sys_err=flux * .01 * .001,
+            ena_intensity_sys_err=flux * 0.001,
+            bg_intensity=flux * 0.01,
+            bg_intensity_stat_uncert=intensity_stat_uncert * 0.01,
+            bg_intensity_sys_err=flux * 0.01 * 0.001,
             ena_intensity_sys_err_minus=ena_intensity_sys_err_minus,
             ena_intensity_sys_err_plus=ena_intensity_sys_err_plus,
             quality_flags=np.full_like(flux, MapL3Flags.NONE),
@@ -110,13 +151,18 @@ def create_rectangular_intensity_map_data(epoch=None, epoch_delta=None, lon=None
             latitude_label=lat.astype(str),
             longitude_delta=np.full_like(lon, 0),
             longitude_label=lon.astype(str),
-        )
+        ),
     )
 
 
-def create_rectangular_intensity_map(intensity_map_data: IntensityMapData = None,
-                                     coords: RectangularCoords = None) -> RectangularIntensityMapData:
-    intensity_map = intensity_map_data if intensity_map_data is not None else create_intensity_map_data()
+def create_rectangular_intensity_map(
+    intensity_map_data: IntensityMapData = None, coords: RectangularCoords = None
+) -> RectangularIntensityMapData:
+    intensity_map = (
+        intensity_map_data
+        if intensity_map_data is not None
+        else create_intensity_map_data()
+    )
 
     if coords is None:
         lon = np.array([1.0])
@@ -131,7 +177,9 @@ def create_rectangular_intensity_map(intensity_map_data: IntensityMapData = None
     return RectangularIntensityMapData(intensity_map_data=intensity_map, coords=coords)
 
 
-def construct_intensity_data_with_all_zero_fields(num_pixels: int = 1) -> IntensityMapData:
+def construct_intensity_data_with_all_zero_fields(
+    num_pixels: int = 1,
+) -> IntensityMapData:
     return IntensityMapData(
         epoch=np.array([0]),
         epoch_delta=np.array([0]),
@@ -156,20 +204,34 @@ def construct_intensity_data_with_all_zero_fields(num_pixels: int = 1) -> Intens
     )
 
 
-def create_rectangular_spectral_index_map_data(epoch=None, epoch_delta=None, lon=None, lat=None, energy=None,
-                                               energy_delta=None, spectral_index=None,
-                                               spectral_index_stat_unc=None):
+def create_rectangular_spectral_index_map_data(
+    epoch=None,
+    epoch_delta=None,
+    lon=None,
+    lat=None,
+    energy=None,
+    energy_delta=None,
+    spectral_index=None,
+    spectral_index_stat_unc=None,
+):
     lon = lon if lon is not None else np.array([1.0])
     lat = lat if lat is not None else np.array([1.0])
     energy = energy if energy is not None else np.array([1.0])
-    energy_delta = energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    energy_delta = (
+        energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    )
     epoch = epoch if epoch is not None else np.ma.array([datetime.now()])
     epoch_delta = epoch_delta if epoch_delta is not None else np.ma.array([86400 * 1e9])
-    spectral_index = spectral_index if spectral_index is not None else np.full((len(epoch), len(lon), len(lat)),
-                                                                               fill_value=1)
-    spectral_index_stat_unc = spectral_index_stat_unc if spectral_index_stat_unc is not None else np.full(
-        (len(epoch), len(energy), len(lon), len(lat)),
-        fill_value=1)
+    spectral_index = (
+        spectral_index
+        if spectral_index is not None
+        else np.full((len(epoch), len(lon), len(lat)), fill_value=1)
+    )
+    spectral_index_stat_unc = (
+        spectral_index_stat_unc
+        if spectral_index_stat_unc is not None
+        else np.full((len(epoch), len(energy), len(lon), len(lat)), fill_value=1)
+    )
     ena_spectral_index_scalar_coefficient = np.full_like(spectral_index, 3)
     ena_spectral_index_scalar_coefficient_stat_unc = np.full_like(spectral_index, 4)
     ena_spectral_index_chisq = np.full_like(spectral_index, 5)
@@ -212,18 +274,22 @@ def create_rectangular_spectral_index_map_data(epoch=None, epoch_delta=None, lon
 
 
 def create_l1c_pset(
-        epoch: datetime = datetime(2025, 4, 15, 12),
-        epoch_delta: np.ndarray = np.array([43200_000_000_000]),
-        repointing: int = 1,
-        energy_steps: np.ndarray = np.array([1]),
-        exposures: Optional[np.ndarray] = None,
-        pointing_start_met=None,
-        pointing_end_met=None,
-        hae_longitude=None,
-        hae_latitude=None,
+    epoch: datetime = datetime(2025, 4, 15, 12),
+    epoch_delta: np.ndarray = np.array([43200_000_000_000]),
+    repointing: int = 1,
+    energy_steps: np.ndarray = np.array([1]),
+    exposures: np.ndarray | None = None,
+    pointing_start_met=None,
+    pointing_end_met=None,
+    hae_longitude=None,
+    hae_latitude=None,
 ) -> InputRectangularPointingSet:
     epoch_j2000 = np.array([spiceypy.datetime2et(epoch)]) * 1e9
-    exposures = exposures if exposures is not None else np.full(shape=(1, energy_steps.shape[0], 3600), fill_value=1.)
+    exposures = (
+        exposures
+        if exposures is not None
+        else np.full(shape=(1, energy_steps.shape[0], 3600), fill_value=1.0)
+    )
     ram_latitudes = np.linspace(89.95, -89.95, 1800)
     anti_latitudes = np.linspace(-89.95, 89.95, 1800)
     full_rotation_latitudes = np.concat([ram_latitudes, anti_latitudes])
@@ -250,10 +316,12 @@ def create_l3e_pset(
     repointing: int = 1,
     energy_steps=np.array([0.5, 5.0, 12.0]),
     spin_angle=np.arange(0, 360),
-    sp: Optional[np.array] = None,
-    flags: Optional[np.array] = None,
+    sp: np.array | None = None,
+    flags: np.array | None = None,
 ) -> GlowsL3eRectangularMapInputData:
     epoch_j2000 = np.array([spiceypy.datetime2et(epoch)]) * 1e9
     sp = sp or np.full(shape=(1, len(energy_steps), len(spin_angle)), fill_value=0.5)
     flags = flags or np.full(1, 0, dtype=np.uint16)
-    return GlowsL3eRectangularMapInputData(epoch, epoch_j2000, repointing, energy_steps, spin_angle, sp, flags)
+    return GlowsL3eRectangularMapInputData(
+        epoch, epoch_j2000, repointing, energy_steps, spin_angle, sp, flags
+    )
diff --git a/tests/maps/test_hilo_l3_survival_dependencies.py b/tests/maps/test_hilo_l3_survival_dependencies.py
index 8ca95ed56..294574aac 100644
--- a/tests/maps/test_hilo_l3_survival_dependencies.py
+++ b/tests/maps/test_hilo_l3_survival_dependencies.py
@@ -1,88 +1,137 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, Mock, sentinel, call
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies, \
-    HiL3SingleSensorFullSpinDependencies
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiL3SingleSensorFullSpinDependencies,
+    HiLoL3SurvivalDependencies,
+)
 from imap_l3_processing.models import Instrument
 
 
 class TestHiLoL3SurvivalDependencies(unittest.TestCase):
-
     @patch("imap_l3_processing.maps.hilo_l3_survival_dependencies.parse_map_descriptor")
-    @patch("imap_l3_processing.maps.hilo_l3_survival_dependencies.imap_data_access.download")
+    @patch(
+        "imap_l3_processing.maps.hilo_l3_survival_dependencies.imap_data_access.download"
+    )
     @patch("imap_l3_processing.maps.hilo_l3_survival_dependencies.read_glows_l3e_data")
-    @patch("imap_l3_processing.maps.hilo_l3_survival_dependencies.read_l1c_rectangular_pointing_set_data")
-    @patch("imap_l3_processing.maps.hilo_l3_survival_dependencies.RectangularIntensityMapData.read_from_path")
-    def test_fetch_dependencies(self, mock_read_from_path, mock_read_l1c, mock_read_glows_l3e,
-                                mock_imap_data_access_download, mock_parse_map_descriptor):
+    @patch(
+        "imap_l3_processing.maps.hilo_l3_survival_dependencies.read_l1c_rectangular_pointing_set_data"
+    )
+    @patch(
+        "imap_l3_processing.maps.hilo_l3_survival_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    def test_fetch_dependencies(
+        self,
+        mock_read_from_path,
+        mock_read_l1c,
+        mock_read_glows_l3e,
+        mock_imap_data_access_download,
+        mock_parse_map_descriptor,
+    ):
         for instrument in [Instrument.IMAP_HI, Instrument.IMAP_LO]:
             mock_read_from_path.reset_mock()
             mock_parse_map_descriptor.reset_mock()
 
-            l1c_file_names = [f"imap_{instrument.value}_l1c_45sensor-pset_20201001_v001.cdf",
-                              f"imap_{instrument.value}_l1c_45sensor-pset_20201002_v002.cdf",
-                              f"imap_{instrument.value}_l1c_45sensor-pset_20201003_v001.cdf"]
+            l1c_file_names = [
+                f"imap_{instrument.value}_l1c_45sensor-pset_20201001_v001.cdf",
+                f"imap_{instrument.value}_l1c_45sensor-pset_20201002_v002.cdf",
+                f"imap_{instrument.value}_l1c_45sensor-pset_20201003_v001.cdf",
+            ]
             glows_file_names = [
                 f"imap_glows_l3e_survival-probability-{instrument.value}_20201001_v001.cdf",
                 f"imap_glows_l3e_survival-probability-{instrument.value}_20201002_v002.cdf",
-                f"imap_glows_l3e_survival-probability-{instrument.value}_20201003_v003.cdf"]
+                f"imap_glows_l3e_survival-probability-{instrument.value}_20201003_v003.cdf",
+            ]
 
-            l2_map_descriptor = f"ena-h-sf-nsp-ram-hae-6deg-1yr"
-            l2_filename = f"imap_{instrument.value}_l2_{l2_map_descriptor}_20250415_v001.cdf"
+            l2_map_descriptor = "ena-h-sf-nsp-ram-hae-6deg-1yr"
+            l2_filename = (
+                f"imap_{instrument.value}_l2_{l2_map_descriptor}_20250415_v001.cdf"
+            )
 
             dependencies = ProcessingInputCollection(
                 ScienceInput(l2_filename),
                 *[ScienceInput(l1c) for l1c in l1c_file_names],
-                *[ScienceInput(l3e) for l3e in glows_file_names]
+                *[ScienceInput(l3e) for l3e in glows_file_names],
             )
 
-            downloaded_files = [sentinel.l2_file_path, sentinel.l1c_file_path_1,
-                                sentinel.l1c_file_path_2,
-                                sentinel.l1c_file_path_3, sentinel.glows_file_path_1,
-                                sentinel.glows_file_path_2, sentinel.glows_file_path_3]
+            downloaded_files = [
+                sentinel.l2_file_path,
+                sentinel.l1c_file_path_1,
+                sentinel.l1c_file_path_2,
+                sentinel.l1c_file_path_3,
+                sentinel.glows_file_path_1,
+                sentinel.glows_file_path_2,
+                sentinel.glows_file_path_3,
+            ]
 
             mock_imap_data_access_download.side_effect = downloaded_files
-            mock_read_l1c.side_effect = [sentinel.l1c_data_1, sentinel.l1c_data_2, sentinel.l1c_data_3]
-
-            mock_read_glows_l3e.side_effect = [sentinel.glows_data_1, sentinel.glows_data_2,
-                                               sentinel.glows_data_3]
-
-            actual = HiLoL3SurvivalDependencies.fetch_dependencies(dependencies, instrument)
+            mock_read_l1c.side_effect = [
+                sentinel.l1c_data_1,
+                sentinel.l1c_data_2,
+                sentinel.l1c_data_3,
+            ]
+
+            mock_read_glows_l3e.side_effect = [
+                sentinel.glows_data_1,
+                sentinel.glows_data_2,
+                sentinel.glows_data_3,
+            ]
+
+            actual = HiLoL3SurvivalDependencies.fetch_dependencies(
+                dependencies, instrument
+            )
 
-            expected_imap_data_access_calls = [call(l2_filename)] + [call(path) for path in
-                                                                     l1c_file_names + glows_file_names]
-            mock_imap_data_access_download.assert_has_calls(expected_imap_data_access_calls)
+            expected_imap_data_access_calls = [call(l2_filename)] + [
+                call(path) for path in l1c_file_names + glows_file_names
+            ]
+            mock_imap_data_access_download.assert_has_calls(
+                expected_imap_data_access_calls
+            )
 
             mock_read_from_path.assert_called_once_with(sentinel.l2_file_path)
 
-            mock_read_l1c.assert_has_calls([
-                call(sentinel.l1c_file_path_1),
-                call(sentinel.l1c_file_path_2),
-                call(sentinel.l1c_file_path_3)
-            ])
+            mock_read_l1c.assert_has_calls(
+                [
+                    call(sentinel.l1c_file_path_1),
+                    call(sentinel.l1c_file_path_2),
+                    call(sentinel.l1c_file_path_3),
+                ]
+            )
 
-            mock_read_glows_l3e.assert_has_calls([
-                call(sentinel.glows_file_path_1),
-                call(sentinel.glows_file_path_2),
-                call(sentinel.glows_file_path_3)
-            ])
+            mock_read_glows_l3e.assert_has_calls(
+                [
+                    call(sentinel.glows_file_path_1),
+                    call(sentinel.glows_file_path_2),
+                    call(sentinel.glows_file_path_3),
+                ]
+            )
 
             mock_parse_map_descriptor.assert_called_once_with(l2_map_descriptor)
 
             self.assertEqual(actual.l2_data, mock_read_from_path.return_value)
-            self.assertEqual(actual.l1c_data, [sentinel.l1c_data_1,
-                                               sentinel.l1c_data_2,
-                                               sentinel.l1c_data_3])
-            self.assertEqual(actual.glows_l3e_data, [sentinel.glows_data_1,
-                                                     sentinel.glows_data_2,
-                                                     sentinel.glows_data_3, ])
-            self.assertEqual(actual.l2_map_descriptor_parts, mock_parse_map_descriptor.return_value)
+            self.assertEqual(
+                actual.l1c_data,
+                [sentinel.l1c_data_1, sentinel.l1c_data_2, sentinel.l1c_data_3],
+            )
+            self.assertEqual(
+                actual.glows_l3e_data,
+                [
+                    sentinel.glows_data_1,
+                    sentinel.glows_data_2,
+                    sentinel.glows_data_3,
+                ],
+            )
+            self.assertEqual(
+                actual.l2_map_descriptor_parts, mock_parse_map_descriptor.return_value
+            )
             self.assertEqual(actual.dependency_file_paths, downloaded_files)
 
-    @patch("imap_l3_processing.maps.hilo_l3_survival_dependencies.HiLoL3SurvivalDependencies.fetch_dependencies")
+    @patch(
+        "imap_l3_processing.maps.hilo_l3_survival_dependencies.HiLoL3SurvivalDependencies.fetch_dependencies"
+    )
     def test_fetch_single_sensor_full_spin_dependencies(self, mock_fetch_dependencies):
         ram_map_descriptor = "h90-ena-h-hf-nsp-ram-hae-6deg-1yr"
         ram_l2_filename = f"imap_hi_l2_{ram_map_descriptor}_20250415_v001.cdf"
@@ -96,29 +145,55 @@ def test_fetch_single_sensor_full_spin_dependencies(self, mock_fetch_dependencie
         anti_input = ScienceInput(anti_l2_filename)
         glows_input = ScienceInput(glows_l3e_filename)
         l1c_input = ScienceInput(l1c_filename)
-        dependencies = ProcessingInputCollection(ram_input, anti_input, glows_input, l1c_input)
+        dependencies = ProcessingInputCollection(
+            ram_input, anti_input, glows_input, l1c_input
+        )
 
         mock_ram_survival_dependencies = Mock(spec=HiLoL3SurvivalDependencies)
-        mock_ram_survival_dependencies.dependency_file_paths = [Path("ram_input1"), Path("ram_input2")]
+        mock_ram_survival_dependencies.dependency_file_paths = [
+            Path("ram_input1"),
+            Path("ram_input2"),
+        ]
         mock_antiram_survival_dependencies = Mock(spec=HiLoL3SurvivalDependencies)
-        mock_antiram_survival_dependencies.dependency_file_paths = [Path("antiram_input1"), Path("antiram_input2")]
+        mock_antiram_survival_dependencies.dependency_file_paths = [
+            Path("antiram_input1"),
+            Path("antiram_input2"),
+        ]
 
-        mock_fetch_dependencies.side_effect = [mock_ram_survival_dependencies, mock_antiram_survival_dependencies]
+        mock_fetch_dependencies.side_effect = [
+            mock_ram_survival_dependencies,
+            mock_antiram_survival_dependencies,
+        ]
 
         result = HiL3SingleSensorFullSpinDependencies.fetch_dependencies(dependencies)
         self.assertIsInstance(result, HiL3SingleSensorFullSpinDependencies)
         self.assertEqual(result.ram_dependencies, mock_ram_survival_dependencies)
-        self.assertEqual(result.antiram_dependencies, mock_antiram_survival_dependencies)
+        self.assertEqual(
+            result.antiram_dependencies, mock_antiram_survival_dependencies
+        )
 
         [ram_dependencies, instrument] = mock_fetch_dependencies.call_args_list[0].args
         self.assertIsInstance(ram_dependencies, ProcessingInputCollection)
-        self.assertEqual([ram_input, glows_input, l1c_input], ram_dependencies.processing_input)
+        self.assertEqual(
+            [ram_input, glows_input, l1c_input], ram_dependencies.processing_input
+        )
         self.assertEqual(Instrument.IMAP_HI, instrument)
 
-        [antiram_dependencies, instrument] = mock_fetch_dependencies.call_args_list[1].args
+        [antiram_dependencies, instrument] = mock_fetch_dependencies.call_args_list[
+            1
+        ].args
         self.assertIsInstance(antiram_dependencies, ProcessingInputCollection)
-        self.assertEqual([anti_input, glows_input, l1c_input], antiram_dependencies.processing_input)
+        self.assertEqual(
+            [anti_input, glows_input, l1c_input], antiram_dependencies.processing_input
+        )
         self.assertEqual(Instrument.IMAP_HI, instrument)
 
-        self.assertEqual([Path("ram_input1"), Path("ram_input2"), Path("antiram_input1"), Path("antiram_input2")],
-                         result.dependency_file_paths)
+        self.assertEqual(
+            [
+                Path("ram_input1"),
+                Path("ram_input2"),
+                Path("antiram_input1"),
+                Path("antiram_input2"),
+            ],
+            result.dependency_file_paths,
+        )
diff --git a/tests/maps/test_map_combination.py b/tests/maps/test_map_combination.py
index 1d5578f4b..56bdae604 100644
--- a/tests/maps/test_map_combination.py
+++ b/tests/maps/test_map_combination.py
@@ -4,46 +4,73 @@
 
 import numpy as np
 
-from imap_l3_processing.maps.map_combination import UnweightedCombination, CombinationStrategy, \
-    ExposureWeightedCombination, UncertaintyWeightedCombination
-from imap_l3_processing.maps.map_models import IntensityMapData, RectangularIntensityMapData, RectangularCoords, \
-    HealPixIntensityMapData, HealPixCoords
+from imap_l3_processing.maps.map_combination import (
+    CombinationStrategy,
+    ExposureWeightedCombination,
+    UncertaintyWeightedCombination,
+    UnweightedCombination,
+)
+from imap_l3_processing.maps.map_models import (
+    HealPixCoords,
+    HealPixIntensityMapData,
+    IntensityMapData,
+    RectangularCoords,
+    RectangularIntensityMapData,
+)
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from tests.maps.test_builders import construct_intensity_data_with_all_zero_fields, create_rectangular_intensity_map
+from tests.maps.test_builders import (
+    construct_intensity_data_with_all_zero_fields,
+    create_rectangular_intensity_map,
+)
 
 
 class TestMapCombination(unittest.TestCase):
     def test_combine_maps_works_when_passed_all_nan_arrays(self):
-        for combination_strategy in [UnweightedCombination, ExposureWeightedCombination,
-                                     UncertaintyWeightedCombination]:
+        for combination_strategy in [
+            UnweightedCombination,
+            ExposureWeightedCombination,
+            UncertaintyWeightedCombination,
+        ]:
             with self.subTest(strategy=combination_strategy):
                 combination = combination_strategy()
                 self._combine_nan_maps(combination)
 
     def test_combine_maps_does_nothing_when_passed_a_single_map(self):
-        for combination_strategy in [UnweightedCombination, ExposureWeightedCombination,
-                                     UncertaintyWeightedCombination]:
+        for combination_strategy in [
+            UnweightedCombination,
+            ExposureWeightedCombination,
+            UncertaintyWeightedCombination,
+        ]:
             with self.subTest(strategy=combination_strategy):
                 combination = combination_strategy()
                 self._combine_single_map(combination)
 
     def test_combine_maps_works_for_maps_with_no_background_data(self):
-        for combination_strategy in [UnweightedCombination, ExposureWeightedCombination,
-                                     UncertaintyWeightedCombination]:
+        for combination_strategy in [
+            UnweightedCombination,
+            ExposureWeightedCombination,
+            UncertaintyWeightedCombination,
+        ]:
             with self.subTest(strategy=combination_strategy):
                 combination = combination_strategy()
                 self._combine_maps_with_no_background_data(combination)
 
     def test_combine_maps_works_for_maps_with_no_intensity_sys_errors(self):
-        for combination_strategy in [UnweightedCombination, ExposureWeightedCombination,
-                                     UncertaintyWeightedCombination]:
+        for combination_strategy in [
+            UnweightedCombination,
+            ExposureWeightedCombination,
+            UncertaintyWeightedCombination,
+        ]:
             with self.subTest(strategy=combination_strategy):
                 combination = combination_strategy()
                 self._combine_maps_with_no_intensity_sys_errors(combination)
 
     def test_check_maps_match(self):
-        for combination_strategy in [UnweightedCombination, ExposureWeightedCombination,
-                                     UncertaintyWeightedCombination]:
+        for combination_strategy in [
+            UnweightedCombination,
+            ExposureWeightedCombination,
+            UncertaintyWeightedCombination,
+        ]:
             with self.subTest(strategy=combination_strategy):
                 combination = combination_strategy()
                 self._check_maps_match(combination)
@@ -64,7 +91,9 @@ def _combine_single_map(self, combination: CombinationStrategy):
         map_1.ena_intensity_stat_uncert = np.array([1])
 
         combine_one = combination._combine([map_1])
-        np.testing.assert_equal(dataclasses.asdict(combine_one), dataclasses.asdict(map_1))
+        np.testing.assert_equal(
+            dataclasses.asdict(combine_one), dataclasses.asdict(map_1)
+        )
 
     def _combine_maps_with_no_background_data(self, combination: CombinationStrategy):
         map_1 = construct_intensity_data_with_all_zero_fields()
@@ -74,20 +103,26 @@ def _combine_maps_with_no_background_data(self, combination: CombinationStrategy
         map_1.bg_intensity_stat_uncert = None
 
         combine_one = combination._combine([map_1])
-        np.testing.assert_equal(dataclasses.asdict(combine_one), dataclasses.asdict(map_1))
+        np.testing.assert_equal(
+            dataclasses.asdict(combine_one), dataclasses.asdict(map_1)
+        )
 
-    def _combine_maps_with_no_intensity_sys_errors(self, combination: CombinationStrategy):
+    def _combine_maps_with_no_intensity_sys_errors(
+        self, combination: CombinationStrategy
+    ):
         map_1 = construct_intensity_data_with_all_zero_fields()
         map_1.ena_intensity_stat_uncert = np.array([1])
         map_1.ena_intensity_sys_err_plus = None
         map_1.ena_intensity_sys_err_minus = None
 
         combine_one = combination._combine([map_1])
-        np.testing.assert_equal(dataclasses.asdict(combine_one), dataclasses.asdict(map_1))
+        np.testing.assert_equal(
+            dataclasses.asdict(combine_one), dataclasses.asdict(map_1)
+        )
 
     def _check_maps_match(self, combination: CombinationStrategy):
         map_1 = construct_intensity_data_with_all_zero_fields()
-        map_1.ena_intensity_sys_err_plus = np.array([0] * 1),
+        map_1.ena_intensity_sys_err_plus = (np.array([0] * 1),)
         map_1.ena_intensity_sys_err_minus = np.array([0] * 2)
 
         fields_which_may_differ = {
@@ -112,8 +147,12 @@ def _check_maps_match(self, combination: CombinationStrategy):
         generic_value = 10
 
         for field in dataclasses.fields(map_1):
-            replacement_value = alternate_values_by_type.get(type(getattr(map_1, field.name)[0]), generic_value)
-            map_with_difference = dataclasses.replace(map_1, **{field.name: np.array([replacement_value])})
+            replacement_value = alternate_values_by_type.get(
+                type(getattr(map_1, field.name)[0]), generic_value
+            )
+            map_with_difference = dataclasses.replace(
+                map_1, **{field.name: np.array([replacement_value])}
+            )
             if field.name not in fields_which_may_differ:
                 with self.assertRaises(AssertionError, msg=field.name):
                     combination._check_maps_match([map_1, map_with_difference])
@@ -121,7 +160,9 @@ def _check_maps_match(self, combination: CombinationStrategy):
                 try:
                     combination._check_maps_match([map_1, map_with_difference])
                 except:
-                    self.fail(f"Differences in other fields should be alright: {field.name}")
+                    self.fail(
+                        f"Differences in other fields should be alright: {field.name}"
+                    )
 
     def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
         map_1 = construct_intensity_data_with_all_zero_fields()
@@ -166,12 +207,15 @@ def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
 
         map_2.exposure_factor = np.array([3, 1, 5, 2, 0])
         map_2.obs_date = np.ma.masked_equal(
-            [datetime(2025, 5, 9),
-             datetime(2025, 5, 10),
-             datetime(2025, 5, 11),
-             datetime(2025, 5, 12),
-             DATETIME_FILL],
-            DATETIME_FILL)
+            [
+                datetime(2025, 5, 9),
+                datetime(2025, 5, 10),
+                datetime(2025, 5, 11),
+                datetime(2025, 5, 12),
+                DATETIME_FILL,
+            ],
+            DATETIME_FILL,
+        )
         map_2.survival_probability = np.array([0, 0.5, np.nan, 1, 0])
 
         expected_combined_bg_stat_unc = [
@@ -179,7 +223,7 @@ def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
             np.sqrt((1 * 9) / 1),
             np.sqrt((25 * 100 + 25 * 4) / 100),
             np.sqrt((36 * 100 + 4 * 1) / 64),
-            np.nan
+            np.nan,
         ]
 
         expected_combined_exposure = [4, 1, 10, 8, 0]
@@ -189,14 +233,14 @@ def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
             (1 * 4) / 1,
             (5 * 10 + 5 * 2) / (5 + 5),
             (6 * 100 + 2 * 0) / (6 + 2),
-            np.nan
+            np.nan,
         ]
         expected_stat_unc = [
             np.sqrt((1 * 100 + 9 * 1) / 16),
             2,
             np.sqrt((25 * 100 + 25 * 9) / 100),
             np.sqrt((36 * 100 + 16 * 4) / 64),
-            np.nan
+            np.nan,
         ]
 
         expected_combined_bg_intensity = [7, 7, 4.5, 2, np.nan]
@@ -213,7 +257,7 @@ def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
             (1 * 6) / 1,
             (5 * 10 + 5 * 7) / (5 + 5),
             (6 * 10 + 2 * 8) / (6 + 2),
-            np.nan
+            np.nan,
         ]
 
         expected_combined_sys_err_plus = [
@@ -221,7 +265,7 @@ def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
             (1 * 9) / 1,
             (5 * 11 + 5 * 10) / (5 + 5),
             (6 * 11 + 2 * 11) / (6 + 2),
-            np.nan
+            np.nan,
         ]
 
         expected_obs_date = np.ma.array(
@@ -291,7 +335,7 @@ def test_combine_maps_does_a_time_weighted_average_of_intensity(self):
         )
 
     def test_combine_handles_maps_with_different_patterns_of_missing_intensity_and_bg_intensity(
-            self,
+        self,
     ):
         map_1 = construct_intensity_data_with_all_zero_fields(num_pixels=5)
         map_1.ena_intensity = np.array([1, np.nan, np.nan, np.nan, np.nan])
@@ -353,8 +397,10 @@ def test_combine_handles_maps_with_different_patterns_of_missing_intensity_and_b
         np.testing.assert_array_equal(
             combined_map.intensity_map_data.bg_intensity, expected_combined_bg_intensity
         )
-        np.testing.assert_array_equal(combined_map.intensity_map_data.bg_intensity_stat_uncert,
-                                      expected_combined_bg_stat_unc)
+        np.testing.assert_array_equal(
+            combined_map.intensity_map_data.bg_intensity_stat_uncert,
+            expected_combined_bg_stat_unc,
+        )
         np.testing.assert_array_equal(
             combined_map.intensity_map_data.bg_intensity_sys_err,
             expected_combined_bg_intensity_sys_err,
@@ -385,9 +431,12 @@ def test_combine_maps_handles_integer_obs_date(self):
         )
 
         combine_two = exposure_weighted_strategy.combine_rectangular_intensity_map_data(
-            [rectangular_map_1, rectangular_map_2])
+            [rectangular_map_1, rectangular_map_2]
+        )
 
-        np.testing.assert_equal(combine_two.intensity_map_data.obs_date.mask, expected_obs_date.mask)
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.obs_date.mask, expected_obs_date.mask
+        )
         np.testing.assert_equal(
             combine_two.intensity_map_data.obs_date, expected_obs_date
         )
@@ -418,47 +467,57 @@ def test_combine_unweighted_combination_strategy(self):
             DATETIME_FILL,
         )
         map_1.survival_probability = np.array([0.9, 1, 1, 1, np.nan, 1, 1, 1, 1])
-        map_1.quality_flags = np.array([
-            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE,
-            MapL3Flags.NONE,
-            MapL3Flags.NONE,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE,
-            MapL3Flags.NONE,
-            MapL3Flags.NONE
-        ])
+        map_1.quality_flags = np.array(
+            [
+                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NONE,
+                MapL3Flags.NONE,
+                MapL3Flags.NONE,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NONE,
+                MapL3Flags.NONE,
+                MapL3Flags.NONE,
+            ]
+        )
 
         map_2 = construct_intensity_data_with_all_zero_fields()
         map_2.ena_intensity = np.array([5, 6, 7, 8, np.nan, 100, 9, 10, 11])
         map_2.exposure_factor = np.array([10, 0, 10, 10, 10, 100, 100, np.nan, 30])
-        map_2.ena_intensity_sys_err = np.array([4, 1, 4, 5, np.nan, 1, np.nan, np.nan, np.nan])
-        map_2.ena_intensity_stat_uncert = np.array([8, 1, 8, 10, np.nan, 1, np.nan, np.nan, np.nan])
+        map_2.ena_intensity_sys_err = np.array(
+            [4, 1, 4, 5, np.nan, 1, np.nan, np.nan, np.nan]
+        )
+        map_2.ena_intensity_stat_uncert = np.array(
+            [8, 1, 8, 10, np.nan, 1, np.nan, np.nan, np.nan]
+        )
         map_2.obs_date = np.ma.masked_equal(
-            [datetime(2025, 5, 9),
-             datetime(2025, 5, 10),
-             datetime(2025, 5, 11),
-             datetime(2025, 5, 12),
-             DATETIME_FILL,
-             DATETIME_FILL,
-             DATETIME_FILL,
-             DATETIME_FILL,
-             datetime(2025, 5, 13),
-             ],
-            DATETIME_FILL)
+            [
+                datetime(2025, 5, 9),
+                datetime(2025, 5, 10),
+                datetime(2025, 5, 11),
+                datetime(2025, 5, 12),
+                DATETIME_FILL,
+                DATETIME_FILL,
+                DATETIME_FILL,
+                DATETIME_FILL,
+                datetime(2025, 5, 13),
+            ],
+            DATETIME_FILL,
+        )
         map_2.survival_probability = np.array([0.1, 1, 1, 1, 0.2, 0.2, 1, 0.8, 0])
-        map_2.quality_flags = np.array([
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE,
-            MapL3Flags.PERSISTED_LAST_POINT,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
-            MapL3Flags.PERSISTED_LAST_POINT,
-            MapL3Flags.NONE,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE
-        ])
+        map_2.quality_flags = np.array(
+            [
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NONE,
+                MapL3Flags.PERSISTED_LAST_POINT,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                MapL3Flags.PERSISTED_LAST_POINT,
+                MapL3Flags.NONE,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NONE,
+            ]
+        )
 
         expected_combined_intensity = [3, np.nan, 5, 6, np.nan, 100, 4.5, 5, 12]
         expected_sys_err = [2.5, np.nan, np.nan, 6.5, np.nan, 1, np.nan, np.nan, np.nan]
@@ -474,28 +533,54 @@ def test_combine_unweighted_combination_strategy(self):
             MapL3Flags.PREDICTIVE_EPHEMERIS | MapL3Flags.PERSISTED_LAST_POINT,
             MapL3Flags.NONE,
             MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE]
+            MapL3Flags.NONE,
+        ]
 
         exposure_unweighted_strategy = UnweightedCombination()
 
-        rectangular_map_1: RectangularIntensityMapData = create_rectangular_intensity_map(map_1)
-        rectangular_map_2: RectangularIntensityMapData = create_rectangular_intensity_map(map_2)
+        rectangular_map_1: RectangularIntensityMapData = (
+            create_rectangular_intensity_map(map_1)
+        )
+        rectangular_map_2: RectangularIntensityMapData = (
+            create_rectangular_intensity_map(map_2)
+        )
 
-        combine_two = exposure_unweighted_strategy.combine_rectangular_intensity_map_data(
-            [rectangular_map_1, rectangular_map_2])
+        combine_two = (
+            exposure_unweighted_strategy.combine_rectangular_intensity_map_data(
+                [rectangular_map_1, rectangular_map_2]
+            )
+        )
 
-        np.testing.assert_equal(combine_two.intensity_map_data.ena_intensity, expected_combined_intensity)
-        np.testing.assert_equal(combine_two.intensity_map_data.ena_intensity_sys_err, expected_sys_err)
-        np.testing.assert_equal(combine_two.intensity_map_data.ena_intensity_stat_uncert, expected_stat_unc)
-        np.testing.assert_equal(combine_two.intensity_map_data.obs_date[-1], expected_obs_date)
-        np.testing.assert_equal(combine_two.intensity_map_data.survival_probability, expected_survival_probability)
-        np.testing.assert_equal(combine_two.intensity_map_data.quality_flags, expected_quality_flags)
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.ena_intensity, expected_combined_intensity
+        )
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.ena_intensity_sys_err, expected_sys_err
+        )
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.ena_intensity_stat_uncert, expected_stat_unc
+        )
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.obs_date[-1], expected_obs_date
+        )
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.survival_probability,
+            expected_survival_probability,
+        )
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.quality_flags, expected_quality_flags
+        )
 
     def test_combine_rectangular_intensity_map_data_errors_if_coords_not_matching(self):
         delta_array = np.array([1])
         label_array = np.array(["one"])
 
-        def make_data(lat_delta=delta_array, lon_delta=delta_array, lat_label=label_array, lon_label=label_array):
+        def make_data(
+            lat_delta=delta_array,
+            lon_delta=delta_array,
+            lat_label=label_array,
+            lon_label=label_array,
+        ):
             return RectangularIntensityMapData(
                 intensity_map_data=construct_intensity_data_with_all_zero_fields(),
                 coords=RectangularCoords(
@@ -503,7 +588,7 @@ def make_data(lat_delta=delta_array, lon_delta=delta_array, lat_label=label_arra
                     longitude_delta=lon_delta,
                     latitude_label=lat_label,
                     longitude_label=lon_label,
-                )
+                ),
             )
 
         base_map = make_data()
@@ -517,7 +602,9 @@ def make_data(lat_delta=delta_array, lon_delta=delta_array, lat_label=label_arra
             with self.subTest(name):
                 with self.assertRaises(AssertionError):
                     strategy = UnweightedCombination()
-                    strategy.combine_rectangular_intensity_map_data([base_map, not_matching_map])
+                    strategy.combine_rectangular_intensity_map_data(
+                        [base_map, not_matching_map]
+                    )
 
     def test_combine_healpix_intensity_map_data_errors_if_coords_not_matching(self):
         index_array = np.array([1])
@@ -529,19 +616,29 @@ def make_data(pixel_index=index_array, pixel_index_label=label_array):
                 coords=HealPixCoords(
                     pixel_index=pixel_index,
                     pixel_index_label=pixel_index_label,
-                )
+                ),
             )
 
         base_map = make_data()
         cases = [
-            ("pixel_index", make_data(pixel_index=np.array([2]), pixel_index_label=label_array)),
-            ("pixel_index_label", make_data(pixel_index=index_array, pixel_index_label=np.array(["diff label"]))),
+            (
+                "pixel_index",
+                make_data(pixel_index=np.array([2]), pixel_index_label=label_array),
+            ),
+            (
+                "pixel_index_label",
+                make_data(
+                    pixel_index=index_array, pixel_index_label=np.array(["diff label"])
+                ),
+            ),
         ]
         for name, not_matching_map in cases:
             with self.subTest(name):
                 with self.assertRaises(AssertionError):
                     strategy = UnweightedCombination()
-                    strategy.combine_healpix_intensity_map_data([base_map, not_matching_map])
+                    strategy.combine_healpix_intensity_map_data(
+                        [base_map, not_matching_map]
+                    )
 
     def test_calculate_weighted_sys_err(self):
         uncertainties_1 = np.array([10, 20, 30, 40])
@@ -551,8 +648,10 @@ def test_calculate_weighted_sys_err(self):
         exposures_2 = np.array([4, 3, 2, 1])
 
         expected = np.array([15, 30, 45, 60])
-        actual = CombinationStrategy.calculate_weighted_sys_err(np.array([uncertainties_1, uncertainties_2]),
-                                                                np.array([exposures_1, exposures_2]))
+        actual = CombinationStrategy.calculate_weighted_sys_err(
+            np.array([uncertainties_1, uncertainties_2]),
+            np.array([exposures_1, exposures_2]),
+        )
         np.testing.assert_array_equal(actual, expected)
 
     def test_uncertainty_weighted_combination(self):
@@ -563,12 +662,15 @@ def test_uncertainty_weighted_combination(self):
         map_1.ena_intensity_stat_uncert = np.array([10, np.nan, 10, 10, np.nan])
         DATETIME_FILL = datetime(9999, 12, 31, 23, 59, 59, 999999)
         map_1.obs_date = np.ma.masked_equal(
-            [datetime(2025, 5, 5),
-             DATETIME_FILL,
-             datetime(2025, 5, 7),
-             datetime(2025, 5, 8),
-             DATETIME_FILL],
-            DATETIME_FILL)
+            [
+                datetime(2025, 5, 5),
+                DATETIME_FILL,
+                datetime(2025, 5, 7),
+                datetime(2025, 5, 8),
+                DATETIME_FILL,
+            ],
+            DATETIME_FILL,
+        )
         map_1.survival_probability = np.array([1, 0.2, 0.8, 1, np.nan])
 
         map_2 = construct_intensity_data_with_all_zero_fields()
@@ -577,57 +679,72 @@ def test_uncertainty_weighted_combination(self):
         map_2.ena_intensity_sys_err = np.array([9, 4, np.inf, 0, np.nan])
         map_2.ena_intensity_stat_uncert = np.array([1, 2, np.inf, 4, np.nan])
         map_2.obs_date = np.ma.masked_equal(
-            [datetime(2025, 5, 9),
-             datetime(2025, 5, 10),
-             datetime(2025, 5, 11),
-             datetime(2025, 5, 12),
-             DATETIME_FILL],
-            DATETIME_FILL)
+            [
+                datetime(2025, 5, 9),
+                datetime(2025, 5, 10),
+                datetime(2025, 5, 11),
+                datetime(2025, 5, 12),
+                DATETIME_FILL,
+            ],
+            DATETIME_FILL,
+        )
         map_2.survival_probability = np.array([1, 0.5, 0.2, 0.25, np.nan])
 
         expected_combined_exposure = [4, 1, 5, 8, 0]
         expected_combined_intensity = [
-            (1 / (10 ** 2) + 5 / (1 ** 2)) / (1 / (10 ** 2) + 1 / (1 ** 2)),
-            (6 / (2 ** 2)) / (1 / (2 ** 2)),
-            (3 / (10 ** 2)) / (1 / (10 ** 2)),
-            (4 / (10 ** 2) + 8 / (4 ** 2)) / (1 / (10 ** 2) + 1 / (4 ** 2)),
-            np.nan
+            (1 / (10**2) + 5 / (1**2)) / (1 / (10**2) + 1 / (1**2)),
+            (6 / (2**2)) / (1 / (2**2)),
+            (3 / (10**2)) / (1 / (10**2)),
+            (4 / (10**2) + 8 / (4**2)) / (1 / (10**2) + 1 / (4**2)),
+            np.nan,
         ]
         expected_stat_unc = [
-            np.sqrt(1 / (1 / (10 ** 2) + 1 / (1 ** 2))),
-            np.sqrt(1 / (1 / (2 ** 2))),
-            np.sqrt(1 / (1 / (10 ** 2))),
-            np.sqrt(1 / (1 / (10 ** 2) + 1 / (4 ** 2))),
-            np.nan
+            np.sqrt(1 / (1 / (10**2) + 1 / (1**2))),
+            np.sqrt(1 / (1 / (2**2))),
+            np.sqrt(1 / (1 / (10**2))),
+            np.sqrt(1 / (1 / (10**2) + 1 / (4**2))),
+            np.nan,
         ]
         expected_sys_err = [
             (1 * 1 + 3 * 9) / (1 + 3),
             (1 * 4) / 1,
             (5 * 10) / (5),
             (6 * 100 + 2 * 0) / (6 + 2),
-            np.nan
+            np.nan,
         ]
         expected_obs_date = np.ma.array(
-            [datetime(2025, 5, 8),
-             datetime(2025, 5, 10),
-             datetime(2025, 5, 7),
-             datetime(2025, 5, 9), np.ma.masked])
+            [
+                datetime(2025, 5, 8),
+                datetime(2025, 5, 10),
+                datetime(2025, 5, 7),
+                datetime(2025, 5, 9),
+                np.ma.masked,
+            ]
+        )
         expected_survival_probability = np.array(
-            [1, 0.5, 0.8, (1 / 10 ** 2 + 0.25 / 4 ** 2) / (1 / 10 ** 2 + 1 / 4 ** 2), np.nan])
+            [1, 0.5, 0.8, (1 / 10**2 + 0.25 / 4**2) / (1 / 10**2 + 1 / 4**2), np.nan]
+        )
 
         exposure_weighted_strategy = UncertaintyWeightedCombination()
 
         rectangular_map_1 = create_rectangular_intensity_map(map_1)
         rectangular_map_2 = create_rectangular_intensity_map(map_2)
         combine_two = exposure_weighted_strategy.combine_rectangular_intensity_map_data(
-            [rectangular_map_1, rectangular_map_2])
+            [rectangular_map_1, rectangular_map_2]
+        )
 
-        np.testing.assert_equal(combine_two.intensity_map_data.ena_intensity, expected_combined_intensity)
-        np.testing.assert_equal(combine_two.intensity_map_data.ena_intensity_sys_err, expected_sys_err)
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.ena_intensity, expected_combined_intensity
+        )
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.ena_intensity_sys_err, expected_sys_err
+        )
         np.testing.assert_equal(
             combine_two.intensity_map_data.ena_intensity_stat_uncert, expected_stat_unc
         )
-        np.testing.assert_equal(combine_two.intensity_map_data.exposure_factor, expected_combined_exposure)
+        np.testing.assert_equal(
+            combine_two.intensity_map_data.exposure_factor, expected_combined_exposure
+        )
         np.testing.assert_equal(
             combine_two.intensity_map_data.obs_date.mask, expected_obs_date.mask
         )
@@ -635,14 +752,15 @@ def test_uncertainty_weighted_combination(self):
             combine_two.intensity_map_data.obs_date, expected_obs_date
         )
         np.testing.assert_equal(
-            combine_two.intensity_map_data.survival_probability, expected_survival_probability
+            combine_two.intensity_map_data.survival_probability,
+            expected_survival_probability,
         )
 
     def test_combination_handles_missing_sp_data(self):
         test_cases = [
             ExposureWeightedCombination,
             UncertaintyWeightedCombination,
-            UnweightedCombination
+            UnweightedCombination,
         ]
 
         for combination_strategy in test_cases:
@@ -653,10 +771,14 @@ def test_combination_handles_missing_sp_data(self):
                 map_2 = construct_intensity_data_with_all_zero_fields()
                 map_2.survival_probability = None
 
-                combined_map = combination_strategy().combine_rectangular_intensity_map_data([
-                    create_rectangular_intensity_map(map_1),
-                    create_rectangular_intensity_map(map_2)
-                ])
+                combined_map = (
+                    combination_strategy().combine_rectangular_intensity_map_data(
+                        [
+                            create_rectangular_intensity_map(map_1),
+                            create_rectangular_intensity_map(map_2),
+                        ]
+                    )
+                )
 
                 self.assertIsNone(combined_map.intensity_map_data.survival_probability)
 
@@ -664,41 +786,53 @@ def test_combination_handles_quality_flag(self):
         test_cases = [
             ExposureWeightedCombination,
             UncertaintyWeightedCombination,
-            UnweightedCombination
+            UnweightedCombination,
         ]
 
         for combination_strategy in test_cases:
             with self.subTest(combination_strategy.__name__):
                 map_1 = construct_intensity_data_with_all_zero_fields()
-                map_1.quality_flags = np.array([
-                    MapL3Flags.NONE,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.NONE,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.NONE,
-                    MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
-                ])
+                map_1.quality_flags = np.array(
+                    [
+                        MapL3Flags.NONE,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.NONE,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.NONE,
+                        MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                    ]
+                )
 
                 map_2 = construct_intensity_data_with_all_zero_fields()
-                map_2.quality_flags = np.array([
-                    MapL3Flags.NONE,
-                    MapL3Flags.NONE,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                ])
-
-                combined_map = combination_strategy().combine_rectangular_intensity_map_data([
-                    create_rectangular_intensity_map(map_1),
-                    create_rectangular_intensity_map(map_2)
-                ])
-
-                np.testing.assert_equal(combined_map.intensity_map_data.quality_flags, [
-                    MapL3Flags.NONE,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.PREDICTIVE_EPHEMERIS,
-                    MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
-                    MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS,
-                ])
+                map_2.quality_flags = np.array(
+                    [
+                        MapL3Flags.NONE,
+                        MapL3Flags.NONE,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                    ]
+                )
+
+                combined_map = (
+                    combination_strategy().combine_rectangular_intensity_map_data(
+                        [
+                            create_rectangular_intensity_map(map_1),
+                            create_rectangular_intensity_map(map_2),
+                        ]
+                    )
+                )
+
+                np.testing.assert_equal(
+                    combined_map.intensity_map_data.quality_flags,
+                    [
+                        MapL3Flags.NONE,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.PREDICTIVE_EPHEMERIS,
+                        MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                        MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                        | MapL3Flags.PREDICTIVE_EPHEMERIS,
+                    ],
+                )
diff --git a/tests/maps/test_map_descriptors.py b/tests/maps/test_map_descriptors.py
index 513c80873..5b02ca092 100644
--- a/tests/maps/test_map_descriptors.py
+++ b/tests/maps/test_map_descriptors.py
@@ -1,11 +1,19 @@
 import unittest
 
-from imap_l3_processing.maps.map_descriptors import ReferenceFrame, SurvivalCorrection, MapDescriptorParts, Sensor, \
-    MapQuantity, SpinPhase, PixelSize, parse_map_descriptor, map_descriptor_parts_to_string
+from imap_l3_processing.maps.map_descriptors import (
+    MapDescriptorParts,
+    MapQuantity,
+    PixelSize,
+    ReferenceFrame,
+    Sensor,
+    SpinPhase,
+    SurvivalCorrection,
+    map_descriptor_parts_to_string,
+    parse_map_descriptor,
+)
 
 
 class TestMapDescriptors(unittest.TestCase):
-
     def test_parse_map_descriptor(self):
         cg = ReferenceFrame.Heliospheric
         no_cg = ReferenceFrame.Spacecraft
@@ -13,138 +21,407 @@ def test_parse_map_descriptor(self):
         no_sp = SurvivalCorrection.NotSurvivalCorrected
 
         test_cases = [
-            ("h45-ena-h-hf-sp-ram-hae-4deg-3mo",
-             MapDescriptorParts(sensor=Sensor.Hi45, quantity=MapQuantity.Intensity, quantity_suffix="",
-                                survival_correction=sp, reference_frame=cg,
-                                spin_phase=SpinPhase.RamOnly, coord="hae",
-                                duration="3mo",
-                                grid=PixelSize.FourDegrees)),
-            ("h45-ena-h-sf-sp-anti-hae-4deg-3mo",
-             MapDescriptorParts(Sensor.Hi45, no_cg, sp, SpinPhase.AntiRamOnly, "hae",
-                                PixelSize.FourDegrees, "3mo",
-                                MapQuantity.Intensity, "")),
-            ("h90-ena-h-hf-nsp-ram-hae-6deg-1yr", MapDescriptorParts(Sensor.Hi90, cg, no_sp, SpinPhase.RamOnly, "hae",
-                                                                     PixelSize.SixDegrees, "1yr",
-                                                                     MapQuantity.Intensity, "")),
-            ("h90-ena-h-hf-nsp-full-hae-6deg-6mo", MapDescriptorParts(Sensor.Hi90, cg, no_sp, SpinPhase.FullSpin, "hae",
-                                                                      PixelSize.SixDegrees, "6mo",
-                                                                      MapQuantity.Intensity, "")),
-            ("h45-spx-h-hf-sp-full-hae-4deg-6mo", MapDescriptorParts(Sensor.Hi45, cg, sp, SpinPhase.FullSpin, "hae",
-                                                                     PixelSize.FourDegrees, "6mo",
-                                                                     MapQuantity.SpectralIndex, "")),
-            ("hic-ena-h-hf-nsp-full-hae-6deg-6mo",
-             MapDescriptorParts(Sensor.HiCombined, cg, no_sp, SpinPhase.FullSpin, "hae",
-                                PixelSize.SixDegrees, "6mo",
-                                MapQuantity.Intensity, "")),
+            (
+                "h45-ena-h-hf-sp-ram-hae-4deg-3mo",
+                MapDescriptorParts(
+                    sensor=Sensor.Hi45,
+                    quantity=MapQuantity.Intensity,
+                    quantity_suffix="",
+                    survival_correction=sp,
+                    reference_frame=cg,
+                    spin_phase=SpinPhase.RamOnly,
+                    coord="hae",
+                    duration="3mo",
+                    grid=PixelSize.FourDegrees,
+                ),
+            ),
+            (
+                "h45-ena-h-sf-sp-anti-hae-4deg-3mo",
+                MapDescriptorParts(
+                    Sensor.Hi45,
+                    no_cg,
+                    sp,
+                    SpinPhase.AntiRamOnly,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "3mo",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "h90-ena-h-hf-nsp-ram-hae-6deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Hi90,
+                    cg,
+                    no_sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "1yr",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "h90-ena-h-hf-nsp-full-hae-6deg-6mo",
+                MapDescriptorParts(
+                    Sensor.Hi90,
+                    cg,
+                    no_sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "6mo",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "h45-spx-h-hf-sp-full-hae-4deg-6mo",
+                MapDescriptorParts(
+                    Sensor.Hi45,
+                    cg,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                ),
+            ),
+            (
+                "hic-ena-h-hf-nsp-full-hae-6deg-6mo",
+                MapDescriptorParts(
+                    Sensor.HiCombined,
+                    cg,
+                    no_sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "6mo",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
             (
                 "l090-ena-h-hf-nsp-full-hae-6deg-6mo",
-                MapDescriptorParts(Sensor.Lo90, cg, no_sp, SpinPhase.FullSpin, "hae",
-                                   PixelSize.SixDegrees, "6mo",
-                                   MapQuantity.Intensity, "")),
-            ("l090-ena-h-hk-sp-ram-hae-4deg-1yr", MapDescriptorParts(Sensor.Lo90, ReferenceFrame.HeliosphericKinematic,
-                                                                     sp, SpinPhase.RamOnly, "hae",
-                                                                     PixelSize.FourDegrees, "1yr",
-                                                                     MapQuantity.Intensity, "")),
-            ("l075-ena-h-hk-sp-ram-hae-4deg-1yr", MapDescriptorParts(Sensor.Lo75, ReferenceFrame.HeliosphericKinematic,
-                                                                     sp, SpinPhase.RamOnly, "hae",
-                                                                     PixelSize.FourDegrees, "1yr",
-                                                                     MapQuantity.Intensity, "")),
-            ("l105-ena-h-hk-sp-ram-hae-4deg-1yr", MapDescriptorParts(Sensor.Lo105, ReferenceFrame.HeliosphericKinematic,
-                                                                     sp, SpinPhase.RamOnly, "hae",
-                                                                     PixelSize.FourDegrees, "1yr",
-                                                                     MapQuantity.Intensity, "")),
-            ("u90-spx-h-sf-sp-full-hae-nside8-6mo", MapDescriptorParts(Sensor.Ultra90, ReferenceFrame.Spacecraft,
-                                                                       sp, SpinPhase.FullSpin, "hae", PixelSize.Nside8,
-                                                                       "6mo", MapQuantity.SpectralIndex, "")),
-            ("u45-spx-h-sf-sp-full-hae-2deg-6mo", MapDescriptorParts(Sensor.Ultra45, ReferenceFrame.Spacecraft,
-                                                                     sp, SpinPhase.FullSpin, "hae",
-                                                                     PixelSize.TwoDegrees,
-                                                                     "6mo", MapQuantity.SpectralIndex, "")),
-            ("ulc-spx-h-sf-sp-full-hae-nside16-6mo", MapDescriptorParts(Sensor.UltraCombined, ReferenceFrame.Spacecraft,
-                                                                        sp, SpinPhase.FullSpin, "hae",
-                                                                        PixelSize.Nside16,
-                                                                        "6mo", MapQuantity.SpectralIndex, "")),
-            ("u45-spx-h-sf-sp-full-hae-nside16-6mo", MapDescriptorParts(Sensor.Ultra45, ReferenceFrame.Spacecraft,
-                                                                        sp, SpinPhase.FullSpin, "hae",
-                                                                        PixelSize.Nside16,
-                                                                        "6mo", MapQuantity.SpectralIndex, "")),
-            ("u45-spx-h-sf-sp-full-custom-nside16-6mo", MapDescriptorParts(Sensor.Ultra45, ReferenceFrame.Spacecraft,
-                                                                           sp, SpinPhase.FullSpin, "custom",
-                                                                           PixelSize.Nside16,
-                                                                           "6mo", MapQuantity.SpectralIndex, "")),
-            ("ilo-ena-h-sf-sp-ram-imaphae-2deg-3mo", MapDescriptorParts(Sensor.LoCombined, ReferenceFrame.Spacecraft,
-                                                                        sp, SpinPhase.RamOnly, "imaphae",
-                                                                        PixelSize.TwoDegrees,
-                                                                        "3mo", MapQuantity.Intensity, "")),
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    cg,
+                    no_sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "6mo",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "l090-ena-h-hk-sp-ram-hae-4deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    ReferenceFrame.HeliosphericKinematic,
+                    sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "1yr",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "l075-ena-h-hk-sp-ram-hae-4deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo75,
+                    ReferenceFrame.HeliosphericKinematic,
+                    sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "1yr",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "l105-ena-h-hk-sp-ram-hae-4deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo105,
+                    ReferenceFrame.HeliosphericKinematic,
+                    sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "1yr",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
+            (
+                "u90-spx-h-sf-sp-full-hae-nside8-6mo",
+                MapDescriptorParts(
+                    Sensor.Ultra90,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.Nside8,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                ),
+            ),
+            (
+                "u45-spx-h-sf-sp-full-hae-2deg-6mo",
+                MapDescriptorParts(
+                    Sensor.Ultra45,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.TwoDegrees,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                ),
+            ),
+            (
+                "ulc-spx-h-sf-sp-full-hae-nside16-6mo",
+                MapDescriptorParts(
+                    Sensor.UltraCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.Nside16,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                ),
+            ),
+            (
+                "u45-spx-h-sf-sp-full-hae-nside16-6mo",
+                MapDescriptorParts(
+                    Sensor.Ultra45,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.Nside16,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                ),
+            ),
+            (
+                "u45-spx-h-sf-sp-full-custom-nside16-6mo",
+                MapDescriptorParts(
+                    Sensor.Ultra45,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "custom",
+                    PixelSize.Nside16,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                ),
+            ),
+            (
+                "ilo-ena-h-sf-sp-ram-imaphae-2deg-3mo",
+                MapDescriptorParts(
+                    Sensor.LoCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.RamOnly,
+                    "imaphae",
+                    PixelSize.TwoDegrees,
+                    "3mo",
+                    MapQuantity.Intensity,
+                    "",
+                ),
+            ),
             ("not-valid-at-all", None),
             ("invalid_prefix-hic-ena-h-hf-nsp-full-hae-6deg-6mo", None),
             ("hic-ena-h-hf-nsp-full-hae-6deg-6mo-invalid-suffix", None),
             ("hic-ena-h-hf-nsp-full-imap?-6deg-6mo-invalid-suffix", None),
             ("hic-ena-h-hf-nsp-full-imap_v1-6deg-6mo-invalid-suffix", None),
-            ("hic-spxwItHcUsToMsUfFiX-h-sf-sp-full-hae-6deg-6mo", MapDescriptorParts(Sensor.HiCombined,
-                                                                                     ReferenceFrame.Spacecraft, sp,
-                                                                                     SpinPhase.FullSpin, "hae",
-                                                                                     PixelSize.SixDegrees,
-                                                                                     "6mo",
-                                                                                     MapQuantity.SpectralIndex,
-                                                                                     "wItHcUsToMsUfFiX")),
-            ("hic-enaCUSTOM-h-sf-sp-full-CUSTOM1-6deg-6mo", MapDescriptorParts(Sensor.HiCombined,
-                                                                               ReferenceFrame.Spacecraft, sp,
-                                                                               SpinPhase.FullSpin, "CUSTOM1",
-                                                                               PixelSize.SixDegrees,
-                                                                               "6mo",
-                                                                               MapQuantity.Intensity, "CUSTOM")),
-            ("hic-enaCUSTOM-h-sf-sp-full-imapeclipj2000-6deg-6mo", MapDescriptorParts(Sensor.HiCombined,
-                                                                                      ReferenceFrame.Spacecraft, sp,
-                                                                                      SpinPhase.FullSpin,
-                                                                                      "imapeclipj2000",
-                                                                                      PixelSize.SixDegrees,
-                                                                                      "6mo",
-                                                                                      MapQuantity.Intensity, "CUSTOM")),
-            ("hic-enaCUSTOM-h-sf-sp-full-hae-6deg-0mo", MapDescriptorParts(Sensor.HiCombined,
-                                                                           ReferenceFrame.Spacecraft, sp,
-                                                                           SpinPhase.FullSpin, "hae",
-                                                                           PixelSize.SixDegrees,
-                                                                           "0mo",
-                                                                           MapQuantity.Intensity, "CUSTOM")),
-            ("hic-enaCUSTOM-h-sf-sp-full-hae-6deg-50yr", MapDescriptorParts(Sensor.HiCombined,
-                                                                            ReferenceFrame.Spacecraft, sp,
-                                                                            SpinPhase.FullSpin, "hae",
-                                                                            PixelSize.SixDegrees,
-                                                                            "50yr",
-                                                                            MapQuantity.Intensity, "CUSTOM")),
-            ("l090-isn-h-sf-nsp-ram-hae-6deg-1yr", MapDescriptorParts(Sensor.Lo90, ReferenceFrame.Spacecraft,
-                                                                      no_sp, SpinPhase.RamOnly, "hae",
-                                                                      PixelSize.SixDegrees,
-                                                                      "1yr",
-                                                                      MapQuantity.ISNBackgroundSubtracted, "")),
-            ("l090-spxnbs-h-sf-nsp-ram-hae-6deg-1yr", MapDescriptorParts(Sensor.Lo90, ReferenceFrame.Spacecraft,
-                                                                      no_sp, SpinPhase.RamOnly, "hae",
-                                                                      PixelSize.SixDegrees,
-                                                                      "1yr",
-                                                                      MapQuantity.SpectralIndexNBS, "")),
-            ("l090-spxnbs1111-h-sf-nsp-ram-hae-6deg-1yr", MapDescriptorParts(Sensor.Lo90, ReferenceFrame.Spacecraft,
-                                                                         no_sp, SpinPhase.RamOnly, "hae",
-                                                                         PixelSize.SixDegrees,
-                                                                         "1yr",
-                                                                         MapQuantity.SpectralIndexNBS, "", (11, 11))),
-            ("l090-spxnbs1111thisisdense-h-sf-nsp-ram-hae-6deg-1yr", MapDescriptorParts(Sensor.Lo90, ReferenceFrame.Spacecraft,
-                                                                             no_sp, SpinPhase.RamOnly, "hae",
-                                                                             PixelSize.SixDegrees,
-                                                                             "1yr",
-                                                                             MapQuantity.SpectralIndexNBS, "thisisdense",
-                                                                             (11, 11))),
-            ("l090-spxnbs-h-hk-nsp-ram-hae-6deg-1yr", MapDescriptorParts(Sensor.Lo90, ReferenceFrame.HeliosphericKinematic,
-                                                                         no_sp, SpinPhase.RamOnly, "hae",
-                                                                         PixelSize.SixDegrees,
-                                                                         "1yr",
-                                                                         MapQuantity.SpectralIndexNBS, "")),
-            ("h45-spx0607-h-hf-sp-full-hae-4deg-6mo", MapDescriptorParts(Sensor.Hi45, cg, sp, SpinPhase.FullSpin, "hae",
-                                                                     PixelSize.FourDegrees, "6mo",
-                                                                     MapQuantity.SpectralIndex, "", (6, 7))),
-            ("h45-spx1234customsuffix-h-hf-sp-full-hae-4deg-6mo", MapDescriptorParts(Sensor.Hi45, cg, sp, SpinPhase.FullSpin, "hae",
-                                                                     PixelSize.FourDegrees, "6mo",
-                                                                     MapQuantity.SpectralIndex, "customsuffix", (12, 34))),
+            (
+                "hic-spxwItHcUsToMsUfFiX-h-sf-sp-full-hae-6deg-6mo",
+                MapDescriptorParts(
+                    Sensor.HiCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "wItHcUsToMsUfFiX",
+                ),
+            ),
+            (
+                "hic-enaCUSTOM-h-sf-sp-full-CUSTOM1-6deg-6mo",
+                MapDescriptorParts(
+                    Sensor.HiCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "CUSTOM1",
+                    PixelSize.SixDegrees,
+                    "6mo",
+                    MapQuantity.Intensity,
+                    "CUSTOM",
+                ),
+            ),
+            (
+                "hic-enaCUSTOM-h-sf-sp-full-imapeclipj2000-6deg-6mo",
+                MapDescriptorParts(
+                    Sensor.HiCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "imapeclipj2000",
+                    PixelSize.SixDegrees,
+                    "6mo",
+                    MapQuantity.Intensity,
+                    "CUSTOM",
+                ),
+            ),
+            (
+                "hic-enaCUSTOM-h-sf-sp-full-hae-6deg-0mo",
+                MapDescriptorParts(
+                    Sensor.HiCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "0mo",
+                    MapQuantity.Intensity,
+                    "CUSTOM",
+                ),
+            ),
+            (
+                "hic-enaCUSTOM-h-sf-sp-full-hae-6deg-50yr",
+                MapDescriptorParts(
+                    Sensor.HiCombined,
+                    ReferenceFrame.Spacecraft,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "50yr",
+                    MapQuantity.Intensity,
+                    "CUSTOM",
+                ),
+            ),
+            (
+                "l090-isn-h-sf-nsp-ram-hae-6deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    ReferenceFrame.Spacecraft,
+                    no_sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "1yr",
+                    MapQuantity.ISNBackgroundSubtracted,
+                    "",
+                ),
+            ),
+            (
+                "l090-spxnbs-h-sf-nsp-ram-hae-6deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    ReferenceFrame.Spacecraft,
+                    no_sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "1yr",
+                    MapQuantity.SpectralIndexNBS,
+                    "",
+                ),
+            ),
+            (
+                "l090-spxnbs1111-h-sf-nsp-ram-hae-6deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    ReferenceFrame.Spacecraft,
+                    no_sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "1yr",
+                    MapQuantity.SpectralIndexNBS,
+                    "",
+                    (11, 11),
+                ),
+            ),
+            (
+                "l090-spxnbs1111thisisdense-h-sf-nsp-ram-hae-6deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    ReferenceFrame.Spacecraft,
+                    no_sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "1yr",
+                    MapQuantity.SpectralIndexNBS,
+                    "thisisdense",
+                    (11, 11),
+                ),
+            ),
+            (
+                "l090-spxnbs-h-hk-nsp-ram-hae-6deg-1yr",
+                MapDescriptorParts(
+                    Sensor.Lo90,
+                    ReferenceFrame.HeliosphericKinematic,
+                    no_sp,
+                    SpinPhase.RamOnly,
+                    "hae",
+                    PixelSize.SixDegrees,
+                    "1yr",
+                    MapQuantity.SpectralIndexNBS,
+                    "",
+                ),
+            ),
+            (
+                "h45-spx0607-h-hf-sp-full-hae-4deg-6mo",
+                MapDescriptorParts(
+                    Sensor.Hi45,
+                    cg,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "",
+                    (6, 7),
+                ),
+            ),
+            (
+                "h45-spx1234customsuffix-h-hf-sp-full-hae-4deg-6mo",
+                MapDescriptorParts(
+                    Sensor.Hi45,
+                    cg,
+                    sp,
+                    SpinPhase.FullSpin,
+                    "hae",
+                    PixelSize.FourDegrees,
+                    "6mo",
+                    MapQuantity.SpectralIndex,
+                    "customsuffix",
+                    (12, 34),
+                ),
+            ),
             ("h45-spx012-h-hf-sp-full-hae-4deg-6mo", None),
             ("h45-spx12345-h-hf-sp-full-hae-4deg-6mo", None),
         ]
@@ -155,8 +432,10 @@ def test_parse_map_descriptor(self):
                 self.assertEqual(expected, descriptor_parts)
 
                 if descriptor_parts is not None:
-                    self.assertEqual(descriptor, map_descriptor_parts_to_string(descriptor_parts))
+                    self.assertEqual(
+                        descriptor, map_descriptor_parts_to_string(descriptor_parts)
+                    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/maps/test_map_models.py b/tests/maps/test_map_models.py
index a36127f23..2fd4684fd 100644
--- a/tests/maps/test_map_models.py
+++ b/tests/maps/test_map_models.py
@@ -4,7 +4,7 @@
 import unittest
 from datetime import datetime, timedelta
 from pathlib import Path
-from unittest.mock import sentinel, Mock
+from unittest.mock import Mock, sentinel
 
 import numpy as np
 from imap_processing.ena_maps.ena_maps import HealpixSkyMap
@@ -14,40 +14,45 @@
 from spacepy.pycdf import CDF
 from xarray import Dataset
 
-from imap_l3_processing.cdf.cdf_utils import read_variable_and_mask_fill_values, read_numeric_variable
-from imap_l3_processing.constants import ONE_SECOND_IN_NANOSECONDS, SECONDS_PER_DAY, FIVE_MINUTES_IN_NANOSECONDS, \
-    TT2000_EPOCH
+from imap_l3_processing.cdf.cdf_utils import (
+    read_numeric_variable,
+    read_variable_and_mask_fill_values,
+)
+from imap_l3_processing.constants import (
+    FIVE_MINUTES_IN_NANOSECONDS,
+    ONE_SECOND_IN_NANOSECONDS,
+    SECONDS_PER_DAY,
+    TT2000_EPOCH,
+)
 from imap_l3_processing.maps import map_models
 from imap_l3_processing.maps.map_models import (
-    RectangularCoords,
-    SpectralIndexMapData,
-    RectangularSpectralIndexMapData,
-    RectangularSpectralIndexDataProduct,
-    RectangularIntensityMapData,
-    IntensityMapData,
-    RectangularIntensityDataProduct,
-    HealPixIntensityMapData,
+    QUALITY_FLAGS_VAR_NAME,
     HealPixCoords,
-    convert_tt2000_time_to_datetime,
-    _read_intensity_map_data_from_open_cdf,
-    calculate_datetime_weighted_average,
-    ISNRateData,
+    HealPixIntensityMapData,
+    IntensityMapData,
     ISNBackgroundSubtractedData,
     ISNBackgroundSubtractedDataProduct,
     ISNBackgroundSubtractedMapData,
-    QUALITY_FLAGS_VAR_NAME,
+    ISNRateData,
+    RectangularCoords,
+    RectangularIntensityDataProduct,
+    RectangularIntensityMapData,
+    RectangularSpectralIndexDataProduct,
+    RectangularSpectralIndexMapData,
+    SpectralIndexMapData,
+    _read_intensity_map_data_from_open_cdf,
+    calculate_datetime_weighted_average,
+    convert_tt2000_time_to_datetime,
 )
 from imap_l3_processing.maps.quality_flags import MapL3Flags
 from imap_l3_processing.models import DataProductVariable
 from tests.test_helpers import (
-    get_test_data_folder,
     get_integration_test_data_path,
-    NumpyArrayMatcher,
+    get_test_data_folder,
 )
 
 
 class TestMapModels(unittest.TestCase):
-
     def test_rectangular_spectral_index_to_data_product_variables(self):
         input_metadata = Mock()
 
@@ -129,8 +134,7 @@ def test_rectangular_spectral_index_to_data_product_variables(self):
                 sentinel.ena_spectral_index_chisq,
             ),
             DataProductVariable(
-                map_models.QUALITY_FLAGS_VAR_NAME,
-                sentinel.quality_flags
+                map_models.QUALITY_FLAGS_VAR_NAME, sentinel.quality_flags
             ),
             DataProductVariable(
                 map_models.LATITUDE_DELTA_VAR_NAME, sentinel.latitude_delta
@@ -204,9 +208,9 @@ def test_rectangular_intensity_to_data_product_variables(self):
         ]
 
         for (
-                test_name,
-                additional_inputs,
-                expected_additional_data_products_vars,
+            test_name,
+            additional_inputs,
+            expected_additional_data_products_vars,
         ) in test_cases:
             with self.subTest(test_name):
                 input_metadata = sentinel.input_metadata
@@ -231,15 +235,14 @@ def test_rectangular_intensity_to_data_product_variables(self):
                             ena_intensity_stat_uncert=sentinel.ena_intensity_stat_uncert,
                             ena_intensity_sys_err=sentinel.ena_intensity_sys_err,
                             quality_flags=sentinel.quality_flags,
-                            **additional_inputs
+                            **additional_inputs,
                         ),
                         coords=RectangularCoords(
                             longitude_delta=sentinel.longitude_delta,
                             longitude_label=sentinel.longitude_label,
                             latitude_delta=sentinel.latitude_delta,
                             latitude_label=sentinel.latitude_label,
-
-                        )
+                        ),
                     ),
                     spice_frame_name=SpiceFrame.ECLIPJ2000,
                 )
@@ -403,7 +406,9 @@ def test_isn_background_subtracted_to_data_product_variables(self):
         self.assertEqual(expected_variables, actual_variables)
 
     def test_healpix_map_nside_property(self):
-        path_to_cdf = get_test_data_folder() / 'ultra' / 'fake_l2_maps' / 'test_l2_map.cdf'
+        path_to_cdf = (
+            get_test_data_folder() / "ultra" / "fake_l2_maps" / "test_l2_map.cdf"
+        )
         actual = HealPixIntensityMapData.read_from_path(path_to_cdf)
         expected_nside = 2
 
@@ -411,41 +416,88 @@ def test_healpix_map_nside_property(self):
         self.assertEqual(expected_nside, actual.coords.nside)
 
     def test_ultra_l2_map_read_from_file(self):
-        path_to_cdf = get_test_data_folder() / 'ultra' / 'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-2deg-6mo_20250415_v102.cdf'
+        path_to_cdf = (
+            get_test_data_folder()
+            / "ultra"
+            / "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-2deg-6mo_20250415_v102.cdf"
+        )
 
         map_data = RectangularIntensityMapData.read_from_path(path_to_cdf)
         actual_intensity_data = map_data.intensity_map_data
         actual_coords = map_data.coords
 
         with CDF(str(path_to_cdf)) as expected:
-            date_range_var = read_variable_and_mask_fill_values(expected["obs_date_range"])
+            date_range_var = read_variable_and_mask_fill_values(
+                expected["obs_date_range"]
+            )
             obs_date = convert_tt2000_time_to_datetime(
-                read_variable_and_mask_fill_values(expected["obs_date"]).filled(0))
-
-            self.assertEqual(expected['epoch'][...], actual_intensity_data.epoch)
-            np.testing.assert_array_equal(expected["epoch_delta"][...], actual_intensity_data.epoch_delta)
-            np.testing.assert_array_equal(expected["energy"][...], actual_intensity_data.energy)
-            np.testing.assert_array_equal(expected["energy_delta_plus"][...], actual_intensity_data.energy_delta_plus)
-            np.testing.assert_array_equal(expected["energy_delta_minus"][...], actual_intensity_data.energy_delta_minus)
-            np.testing.assert_array_equal(expected["energy_label"][...], actual_intensity_data.energy_label)
-            np.testing.assert_array_equal(expected["latitude"][...], actual_intensity_data.latitude)
-            np.testing.assert_array_equal(expected["latitude_delta"][...], actual_coords.latitude_delta)
-            np.testing.assert_array_equal(expected["latitude_label"][...], actual_coords.latitude_label)
-            np.testing.assert_array_equal(expected["longitude"][...], actual_intensity_data.longitude)
-            np.testing.assert_array_equal(expected["longitude_delta"][...], actual_coords.longitude_delta)
-            np.testing.assert_array_equal(expected["longitude_label"][...], actual_coords.longitude_label)
-            np.testing.assert_array_equal(expected["exposure_factor"][...], actual_intensity_data.exposure_factor)
+                read_variable_and_mask_fill_values(expected["obs_date"]).filled(0)
+            )
+
+            self.assertEqual(expected["epoch"][...], actual_intensity_data.epoch)
+            np.testing.assert_array_equal(
+                expected["epoch_delta"][...], actual_intensity_data.epoch_delta
+            )
+            np.testing.assert_array_equal(
+                expected["energy"][...], actual_intensity_data.energy
+            )
+            np.testing.assert_array_equal(
+                expected["energy_delta_plus"][...],
+                actual_intensity_data.energy_delta_plus,
+            )
+            np.testing.assert_array_equal(
+                expected["energy_delta_minus"][...],
+                actual_intensity_data.energy_delta_minus,
+            )
+            np.testing.assert_array_equal(
+                expected["energy_label"][...], actual_intensity_data.energy_label
+            )
+            np.testing.assert_array_equal(
+                expected["latitude"][...], actual_intensity_data.latitude
+            )
+            np.testing.assert_array_equal(
+                expected["latitude_delta"][...], actual_coords.latitude_delta
+            )
+            np.testing.assert_array_equal(
+                expected["latitude_label"][...], actual_coords.latitude_label
+            )
+            np.testing.assert_array_equal(
+                expected["longitude"][...], actual_intensity_data.longitude
+            )
+            np.testing.assert_array_equal(
+                expected["longitude_delta"][...], actual_coords.longitude_delta
+            )
+            np.testing.assert_array_equal(
+                expected["longitude_label"][...], actual_coords.longitude_label
+            )
+            np.testing.assert_array_equal(
+                expected["exposure_factor"][...], actual_intensity_data.exposure_factor
+            )
             np.testing.assert_array_equal(obs_date, actual_intensity_data.obs_date)
-            np.testing.assert_array_equal(date_range_var, actual_intensity_data.obs_date_range)
-            np.testing.assert_array_equal(expected["solid_angle"][...], actual_intensity_data.solid_angle)
-            np.testing.assert_array_equal(expected["ena_intensity"][...], actual_intensity_data.ena_intensity)
-            np.testing.assert_array_equal(expected["ena_intensity_stat_uncert"][...],
-                                          actual_intensity_data.ena_intensity_stat_uncert)
-            np.testing.assert_array_equal(expected["ena_intensity_sys_err"][...],
-                                          actual_intensity_data.ena_intensity_sys_err)
+            np.testing.assert_array_equal(
+                date_range_var, actual_intensity_data.obs_date_range
+            )
+            np.testing.assert_array_equal(
+                expected["solid_angle"][...], actual_intensity_data.solid_angle
+            )
+            np.testing.assert_array_equal(
+                expected["ena_intensity"][...], actual_intensity_data.ena_intensity
+            )
+            np.testing.assert_array_equal(
+                expected["ena_intensity_stat_uncert"][...],
+                actual_intensity_data.ena_intensity_stat_uncert,
+            )
+            np.testing.assert_array_equal(
+                expected["ena_intensity_sys_err"][...],
+                actual_intensity_data.ena_intensity_sys_err,
+            )
 
     def test_ultra_healpix_intensity_read_from_xarray(self):
-        full_shape = [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.HEALPIX_INDEX.value]
+        full_shape = [
+            CoordNames.TIME.value,
+            CoordNames.ENERGY_L2.value,
+            CoordNames.HEALPIX_INDEX.value,
+        ]
         input_xarray: Dataset = Dataset(
             data_vars={
                 "latitude": ([CoordNames.HEALPIX_INDEX.value], np.full((12,), 4)),
@@ -525,7 +577,10 @@ def test_ultra_healpix_intensity_read_from_xarray(self):
         np.testing.assert_array_equal(
             input_xarray[CoordNames.ENERGY_L2.value], output.intensity_map_data.energy
         )
-        np.testing.assert_array_equal(input_xarray["energy_delta_minus"], output.intensity_map_data.energy_delta_minus)
+        np.testing.assert_array_equal(
+            input_xarray["energy_delta_minus"],
+            output.intensity_map_data.energy_delta_minus,
+        )
         np.testing.assert_array_equal(
             input_xarray["energy_delta_plus"],
             output.intensity_map_data.energy_delta_plus,
@@ -533,7 +588,11 @@ def test_ultra_healpix_intensity_read_from_xarray(self):
         np.testing.assert_array_equal(
             input_xarray["energy_label"], output.intensity_map_data.energy_label
         )
-        np.testing.assert_array_equal(output.intensity_map_data.quality_flags, np.full((2, 15, 12),MapL3Flags.NONE), strict=True)
+        np.testing.assert_array_equal(
+            output.intensity_map_data.quality_flags,
+            np.full((2, 15, 12), MapL3Flags.NONE),
+            strict=True,
+        )
 
     def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
 
@@ -549,7 +608,7 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                 obs_date_datetime,
                 np.full(map_data_shape, False),
                 False,
-                True
+                True,
             ),
             (
                 "obs date is int",
@@ -557,7 +616,7 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                 np.full(map_data_shape, TT2000_EPOCH) + timedelta(seconds=1),
                 np.full(map_data_shape, False),
                 True,
-                False
+                False,
             ),
             (
                 "obs date is all fill",
@@ -565,14 +624,21 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                 np.full(map_data_shape, TT2000_EPOCH),
                 np.full(map_data_shape, True),
                 True,
-                True
+                True,
             ),
         ]
 
-        for test_name, obs_date_in_cdf, expected_obs_date, expected_obs_date_mask, include_bg, include_ena_sys_err_minus in test_cases:
+        for (
+            test_name,
+            obs_date_in_cdf,
+            expected_obs_date,
+            expected_obs_date_mask,
+            include_bg,
+            include_ena_sys_err_minus,
+        ) in test_cases:
             with tempfile.TemporaryDirectory() as temp_dir:
                 pathname = os.path.join(temp_dir, "test_cdf")
-                with CDF(pathname, '') as cdf:
+                with CDF(pathname, "") as cdf:
                     cdf.col_major(True)
 
                     ena_intensity = rng.random(map_data_shape)
@@ -603,7 +669,10 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                     lon_label = [f"{x} deg" for x in lon]
 
                     obs_date = obs_date_in_cdf
-                    obs_date_range = np.full(ena_intensity.shape, ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY * 2)
+                    obs_date_range = np.full(
+                        ena_intensity.shape,
+                        ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY * 2,
+                    )
                     solid_angle = build_solid_angle_map(4)
                     solid_angle = solid_angle[np.newaxis, ...]
 
@@ -616,8 +685,14 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                     cdf.new("longitude_delta", lon_delta, recVary=False)
                     cdf.new("longitude_label", lon_label, recVary=False)
                     cdf.new("ena_intensity", ena_intensity, recVary=True)
-                    cdf.new("ena_intensity_stat_uncert", ena_intensity_stat_uncert, recVary=True)
-                    cdf.new("ena_intensity_sys_err", ena_intensity_sys_err, recVary=True)
+                    cdf.new(
+                        "ena_intensity_stat_uncert",
+                        ena_intensity_stat_uncert,
+                        recVary=True,
+                    )
+                    cdf.new(
+                        "ena_intensity_sys_err", ena_intensity_sys_err, recVary=True
+                    )
                     cdf.new("exposure_factor", exposure, recVary=True)
                     cdf.new("obs_date", obs_date, recVary=True)
                     cdf.new("obs_date_range", obs_date_range, recVary=True)
@@ -629,15 +704,29 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                     cdf.new("survival_probability", survival_probability, recVary=False)
                     if include_bg:
                         cdf.new("bg_intensity", bg_intensity, recVary=True)
-                        cdf.new("bg_intensity_stat_uncert", bg_intensity_stat_unc, recVary=True)
-                        cdf.new("bg_intensity_sys_err", bg_intensity_sys_err, recVary=True)
+                        cdf.new(
+                            "bg_intensity_stat_uncert",
+                            bg_intensity_stat_unc,
+                            recVary=True,
+                        )
+                        cdf.new(
+                            "bg_intensity_sys_err", bg_intensity_sys_err, recVary=True
+                        )
 
                     if include_ena_sys_err_minus:
-                        cdf.new("ena_intensity_sys_err_minus", ena_intensity_sys_err_minus, recVary=True)
-                        cdf.new("ena_intensity_sys_err_plus", ena_intensity_sys_err_plus, recVary=True)
+                        cdf.new(
+                            "ena_intensity_sys_err_minus",
+                            ena_intensity_sys_err_minus,
+                            recVary=True,
+                        )
+                        cdf.new(
+                            "ena_intensity_sys_err_plus",
+                            ena_intensity_sys_err_plus,
+                            recVary=True,
+                        )
 
                     for var in cdf:
-                        cdf[var].attrs['FILLVAL'] = 1000000
+                        cdf[var].attrs["FILLVAL"] = 1000000
 
                     cdf["obs_date"].attrs["FILLVAL"] = obs_date_fillval
 
@@ -652,7 +741,9 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                         np.testing.assert_array_equal(map_data.epoch, epoch)
                         np.testing.assert_array_equal(map_data.epoch_delta, epoch_delta)
                         np.testing.assert_array_equal(map_data.energy, energy)
-                        np.testing.assert_array_equal(map_data.energy_delta_plus, energy_delta_plus)
+                        np.testing.assert_array_equal(
+                            map_data.energy_delta_plus, energy_delta_plus
+                        )
                         np.testing.assert_array_equal(
                             map_data.energy_delta_minus, energy_delta_minus
                         )
@@ -663,10 +754,16 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                         np.testing.assert_array_equal(
                             rectangular_coords.latitude_delta, lat_delta
                         )
-                        np.testing.assert_array_equal(rectangular_coords.latitude_label, lat_label)
+                        np.testing.assert_array_equal(
+                            rectangular_coords.latitude_label, lat_label
+                        )
                         np.testing.assert_array_equal(map_data.longitude, lon)
-                        np.testing.assert_array_equal(rectangular_coords.longitude_delta, lon_delta)
-                        np.testing.assert_array_equal(rectangular_coords.longitude_label, lon_label)
+                        np.testing.assert_array_equal(
+                            rectangular_coords.longitude_delta, lon_delta
+                        )
+                        np.testing.assert_array_equal(
+                            rectangular_coords.longitude_label, lon_label
+                        )
                         np.testing.assert_array_equal(
                             map_data.ena_intensity, ena_intensity
                         )
@@ -674,11 +771,21 @@ def test_read_intensity_map_with_rectangular_cords_data_from_cdf(self):
                             map_data.ena_intensity_stat_uncert,
                             ena_intensity_stat_uncert,
                         )
-                        np.testing.assert_array_equal(map_data.ena_intensity_sys_err, ena_intensity_sys_err)
-                        np.testing.assert_array_equal(map_data.exposure_factor, exposure)
-                        np.testing.assert_array_equal(map_data.obs_date.data, expected_obs_date)
-                        np.testing.assert_array_equal(map_data.obs_date.mask, expected_obs_date_mask)
-                        np.testing.assert_array_equal(map_data.obs_date_range, obs_date_range)
+                        np.testing.assert_array_equal(
+                            map_data.ena_intensity_sys_err, ena_intensity_sys_err
+                        )
+                        np.testing.assert_array_equal(
+                            map_data.exposure_factor, exposure
+                        )
+                        np.testing.assert_array_equal(
+                            map_data.obs_date.data, expected_obs_date
+                        )
+                        np.testing.assert_array_equal(
+                            map_data.obs_date.mask, expected_obs_date_mask
+                        )
+                        np.testing.assert_array_equal(
+                            map_data.obs_date_range, obs_date_range
+                        )
                         np.testing.assert_array_equal(map_data.solid_angle, solid_angle)
                         np.testing.assert_array_equal(
                             map_data.survival_probability, survival_probability
@@ -728,17 +835,21 @@ def test_read_intensity_map_with_rectangular_coords_data_from_cdf_missing_flags(
         input_quality_flags[0, :, 83, :] = MapL3Flags.PREDICTIVE_EPHEMERIS
         input_quality_flags[0, :, 84, :] = MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
         input_quality_flags[0, :, 85, :] = MapL3Flags.PERSISTED_LAST_POINT
-        input_quality_flags[0, :, 86, :] = MapL3Flags.PREDICTIVE_EPHEMERIS | MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PERSISTED_LAST_POINT
+        input_quality_flags[0, :, 86, :] = (
+            MapL3Flags.PREDICTIVE_EPHEMERIS
+            | MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+            | MapL3Flags.PERSISTED_LAST_POINT
+        )
 
         cases = (
             ("includes quality flags", input_quality_flags, input_quality_flags),
-            ("does not include quality flags", None, np.full(map_data_shape, MapL3Flags.NONE)),
+            (
+                "does not include quality flags",
+                None,
+                np.full(map_data_shape, MapL3Flags.NONE),
+            ),
         )
-        for (
-            case_name,
-            input_quality_flags,
-            expected_quality_flags
-        ) in cases:
+        for case_name, input_quality_flags, expected_quality_flags in cases:
             with tempfile.TemporaryDirectory() as temp_dir, self.subTest(case_name):
                 pathname = os.path.join(temp_dir, "test_cdf")
                 with CDF(pathname, "") as cdf:
@@ -821,7 +932,9 @@ def test_read_intensity_map_with_rectangular_coords_data_from_cdf_missing_flags(
                         recVary=True,
                     )
                     if input_quality_flags is not None:
-                        cdf.new(QUALITY_FLAGS_VAR_NAME, input_quality_flags, recVary=True)
+                        cdf.new(
+                            QUALITY_FLAGS_VAR_NAME, input_quality_flags, recVary=True
+                        )
 
                     for var in cdf:
                         cdf[var].attrs["FILLVAL"] = 1000000
@@ -881,13 +994,22 @@ def test_read_intensity_map_with_rectangular_coords_data_from_cdf_missing_flags(
                         map_data.survival_probability, survival_probability
                     )
                     np.testing.assert_array_equal(map_data.bg_intensity, bg_intensity)
-                    np.testing.assert_array_equal(map_data.bg_intensity_sys_err, bg_intensity_sys_err)
-                    np.testing.assert_array_equal(map_data.bg_intensity_stat_uncert, bg_intensity_stat_unc)
-                    np.testing.assert_array_equal(map_data.ena_intensity_sys_err_minus,
-                                                  ena_intensity_sys_err_minus)
-                    np.testing.assert_array_equal(map_data.ena_intensity_sys_err_plus,
-                                                  ena_intensity_sys_err_plus)
-                    np.testing.assert_array_equal(map_data.quality_flags, expected_quality_flags, strict=True)
+                    np.testing.assert_array_equal(
+                        map_data.bg_intensity_sys_err, bg_intensity_sys_err
+                    )
+                    np.testing.assert_array_equal(
+                        map_data.bg_intensity_stat_uncert, bg_intensity_stat_unc
+                    )
+                    np.testing.assert_array_equal(
+                        map_data.ena_intensity_sys_err_minus,
+                        ena_intensity_sys_err_minus,
+                    )
+                    np.testing.assert_array_equal(
+                        map_data.ena_intensity_sys_err_plus, ena_intensity_sys_err_plus
+                    )
+                    np.testing.assert_array_equal(
+                        map_data.quality_flags, expected_quality_flags, strict=True
+                    )
 
     def test_fill_values_in_read_rectangular_intensity_map_data_from_cdf(self):
         path = get_test_data_folder() / "hi" / "map_with_fill_values.cdf"
@@ -904,10 +1026,20 @@ def test_fill_values_in_read_rectangular_intensity_map_data_from_cdf(self):
             self.assertTrue(np.all(map_data.obs_date.mask))
             self.assertTrue(np.all(map_data.obs_date_range.mask))
 
-            np.testing.assert_array_equal(map_data.energy, np.full_like(cdf["energy"], np.nan))
-            np.testing.assert_array_equal(map_data.energy_delta_plus, np.full_like(cdf["energy_delta_plus"], np.nan))
-            np.testing.assert_array_equal(map_data.energy_delta_minus, np.full_like(cdf["energy_delta_minus"], np.nan))
-            np.testing.assert_array_equal(map_data.latitude, np.full_like(cdf["latitude"], np.nan))
+            np.testing.assert_array_equal(
+                map_data.energy, np.full_like(cdf["energy"], np.nan)
+            )
+            np.testing.assert_array_equal(
+                map_data.energy_delta_plus,
+                np.full_like(cdf["energy_delta_plus"], np.nan),
+            )
+            np.testing.assert_array_equal(
+                map_data.energy_delta_minus,
+                np.full_like(cdf["energy_delta_minus"], np.nan),
+            )
+            np.testing.assert_array_equal(
+                map_data.latitude, np.full_like(cdf["latitude"], np.nan)
+            )
             np.testing.assert_array_equal(
                 map_data.longitude, np.full_like(cdf["longitude"], np.nan)
             )
@@ -918,13 +1050,23 @@ def test_fill_values_in_read_rectangular_intensity_map_data_from_cdf(self):
                 map_data.ena_intensity_stat_uncert,
                 np.full_like(cdf["ena_intensity_stat_uncert"], np.nan),
             )
-            np.testing.assert_array_equal(map_data.ena_intensity_sys_err,
-                                          np.full_like(cdf["ena_intensity_sys_err"], np.nan))
-            np.testing.assert_array_equal(map_data.exposure_factor, np.full_like(cdf["exposure_factor"], np.nan))
-            np.testing.assert_array_equal(map_data.solid_angle, np.full_like(cdf["solid_angle"], np.nan))
-            np.testing.assert_array_equal(map_data.bg_intensity, np.full_like(cdf["bg_intensity"], np.nan))
-            np.testing.assert_array_equal(map_data.bg_intensity_sys_err,
-                                          np.full_like(cdf["bg_intensity_sys_err"], np.nan))
+            np.testing.assert_array_equal(
+                map_data.ena_intensity_sys_err,
+                np.full_like(cdf["ena_intensity_sys_err"], np.nan),
+            )
+            np.testing.assert_array_equal(
+                map_data.exposure_factor, np.full_like(cdf["exposure_factor"], np.nan)
+            )
+            np.testing.assert_array_equal(
+                map_data.solid_angle, np.full_like(cdf["solid_angle"], np.nan)
+            )
+            np.testing.assert_array_equal(
+                map_data.bg_intensity, np.full_like(cdf["bg_intensity"], np.nan)
+            )
+            np.testing.assert_array_equal(
+                map_data.bg_intensity_sys_err,
+                np.full_like(cdf["bg_intensity_sys_err"], np.nan),
+            )
             np.testing.assert_array_equal(
                 map_data.bg_intensity_stat_uncert,
                 np.full_like(cdf["bg_intensity_stat_uncert"], np.nan),
@@ -934,10 +1076,12 @@ def test_fill_values_in_read_rectangular_intensity_map_data_from_cdf(self):
                 np.full_like(cdf["survival_probability"], np.nan),
             )
             np.testing.assert_array_equal(
-                map_data.ena_intensity_sys_err_plus, np.full_like(cdf["ena_intensity_sys_err_plus"], np.nan)
+                map_data.ena_intensity_sys_err_plus,
+                np.full_like(cdf["ena_intensity_sys_err_plus"], np.nan),
             )
             np.testing.assert_array_equal(
-                map_data.ena_intensity_sys_err_minus, np.full_like(cdf["ena_intensity_sys_err_minus"], np.nan)
+                map_data.ena_intensity_sys_err_minus,
+                np.full_like(cdf["ena_intensity_sys_err_minus"], np.nan),
             )
 
     def test_healpix_intensity_map_data_to_skymap(self):
@@ -945,10 +1089,11 @@ def test_healpix_intensity_map_data_to_skymap(self):
 
         num_epochs = 1
         num_energies = 5
-        num_healpix_indices = 12 * (expected_nside ** 2)
+        num_healpix_indices = 12 * (expected_nside**2)
 
-        fake_data_per_energy_per_pixel = np.arange(num_epochs * num_energies * num_healpix_indices) \
-            .reshape(num_epochs, num_energies, num_healpix_indices)
+        fake_data_per_energy_per_pixel = np.arange(
+            num_epochs * num_energies * num_healpix_indices
+        ).reshape(num_epochs, num_energies, num_healpix_indices)
 
         fake_data_per_pixel = np.arange(num_healpix_indices)
 
@@ -958,11 +1103,13 @@ def test_healpix_intensity_map_data_to_skymap(self):
         quality_flags[0, :, 18] = MapL3Flags.PERSISTED_LAST_POINT
 
         expected_pred_ephem = quality_flags & MapL3Flags.PREDICTIVE_EPHEMERIS
-        expected_nominal_alpha_proton = quality_flags & MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+        expected_nominal_alpha_proton = (
+            quality_flags & MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+        )
         expected_persisted_last_point = quality_flags & MapL3Flags.PERSISTED_LAST_POINT
 
         intensity_map_data = IntensityMapData(
-            epoch=np.array([np.datetime64('1970-01-01T00:00:00')]),
+            epoch=np.array([np.datetime64("1970-01-01T00:00:00")]),
             epoch_delta=np.array([999]),
             energy=np.arange(num_energies) * 1.1,
             energy_delta_plus=np.arange(num_energies) * 1.2,
@@ -971,7 +1118,8 @@ def test_healpix_intensity_map_data_to_skymap(self):
             latitude=fake_data_per_pixel * 2.2,
             longitude=fake_data_per_pixel * 2.3,
             exposure_factor=fake_data_per_energy_per_pixel * 2.2,
-            obs_date=np.datetime64('1970-01-01T00:00:00') + fake_data_per_energy_per_pixel * 10000,
+            obs_date=np.datetime64("1970-01-01T00:00:00")
+            + fake_data_per_energy_per_pixel * 10000,
             obs_date_range=fake_data_per_energy_per_pixel * 3.2,
             solid_angle=fake_data_per_pixel * 3.4,
             ena_intensity=fake_data_per_energy_per_pixel * 2.2,
@@ -986,7 +1134,7 @@ def test_healpix_intensity_map_data_to_skymap(self):
             coords=HealPixCoords(
                 pixel_index=np.arange(num_healpix_indices),
                 pixel_index_label=np.arange(num_healpix_indices).astype(str),
-            )
+            ),
         )
 
         skymap = healpix_intensity_map_data.to_healpix_skymap()
@@ -997,31 +1145,97 @@ def test_healpix_intensity_map_data_to_skymap(self):
         actual_dataset = skymap.data_1d
 
         # @formatter:off
-        np.testing.assert_array_equal(actual_dataset.coords[CoordNames.TIME.value].values, intensity_map_data.epoch)
-        np.testing.assert_array_equal(actual_dataset.coords[CoordNames.ENERGY_L2.value].values, intensity_map_data.energy)
-        np.testing.assert_array_equal(actual_dataset.coords[CoordNames.GENERIC_PIXEL.value].values, healpix_intensity_map_data.coords.pixel_index)
-
-        np.testing.assert_array_equal(actual_dataset.data_vars["latitude"].values, intensity_map_data.latitude)
-        np.testing.assert_array_equal(actual_dataset.data_vars["longitude"].values, intensity_map_data.longitude)
-        np.testing.assert_array_equal(actual_dataset.data_vars["solid_angle"].values, intensity_map_data.solid_angle)
-
-        np.testing.assert_array_equal(actual_dataset.data_vars["obs_date_range"].values, intensity_map_data.obs_date_range)
-        np.testing.assert_array_equal(actual_dataset.data_vars["obs_date"].values, intensity_map_data.obs_date.astype(np.float64))
-
-        np.testing.assert_array_equal(actual_dataset.data_vars["exposure_factor"].values, intensity_map_data.exposure_factor)
-        np.testing.assert_array_equal(actual_dataset.data_vars["ena_intensity"].values, intensity_map_data.ena_intensity)
-        np.testing.assert_array_equal(actual_dataset.data_vars["ena_intensity_stat_uncert"].values, intensity_map_data.ena_intensity_stat_uncert)
-        np.testing.assert_array_equal(actual_dataset.data_vars["ena_intensity_sys_err"].values, intensity_map_data.ena_intensity_sys_err)
-
-        np.testing.assert_array_equal(actual_dataset.data_vars["survival_probability"].values, intensity_map_data.survival_probability)
-        np.testing.assert_array_equal(actual_dataset.data_vars["predicted_ephemeris_flag"].values, expected_pred_ephem)
-        np.testing.assert_array_equal(actual_dataset.data_vars["nominal_alpha_proton_ratio_flag"].values, expected_nominal_alpha_proton)
-        np.testing.assert_array_equal(actual_dataset.data_vars["persisted_last_point_flag"].values, expected_persisted_last_point)
-
-        for key in [ "obs_date", "obs_date_range", "exposure_factor", "ena_intensity", "ena_intensity_stat_uncert", "ena_intensity_sys_err", "survival_probability", "predicted_ephemeris_flag", "nominal_alpha_proton_ratio_flag", "persisted_last_point_flag" ]:
-            self.assertEqual((CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.GENERIC_PIXEL.value), actual_dataset.data_vars[key].dims)
-        for key in [ "latitude", "longitude", "solid_angle" ]:
-            self.assertEqual((CoordNames.GENERIC_PIXEL.value,), actual_dataset.data_vars[key].dims)
+        np.testing.assert_array_equal(
+            actual_dataset.coords[CoordNames.TIME.value].values,
+            intensity_map_data.epoch,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.coords[CoordNames.ENERGY_L2.value].values,
+            intensity_map_data.energy,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.coords[CoordNames.GENERIC_PIXEL.value].values,
+            healpix_intensity_map_data.coords.pixel_index,
+        )
+
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["latitude"].values, intensity_map_data.latitude
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["longitude"].values, intensity_map_data.longitude
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["solid_angle"].values,
+            intensity_map_data.solid_angle,
+        )
+
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["obs_date_range"].values,
+            intensity_map_data.obs_date_range,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["obs_date"].values,
+            intensity_map_data.obs_date.astype(np.float64),
+        )
+
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["exposure_factor"].values,
+            intensity_map_data.exposure_factor,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["ena_intensity"].values,
+            intensity_map_data.ena_intensity,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["ena_intensity_stat_uncert"].values,
+            intensity_map_data.ena_intensity_stat_uncert,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["ena_intensity_sys_err"].values,
+            intensity_map_data.ena_intensity_sys_err,
+        )
+
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["survival_probability"].values,
+            intensity_map_data.survival_probability,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["predicted_ephemeris_flag"].values,
+            expected_pred_ephem,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["nominal_alpha_proton_ratio_flag"].values,
+            expected_nominal_alpha_proton,
+        )
+        np.testing.assert_array_equal(
+            actual_dataset.data_vars["persisted_last_point_flag"].values,
+            expected_persisted_last_point,
+        )
+
+        for key in [
+            "obs_date",
+            "obs_date_range",
+            "exposure_factor",
+            "ena_intensity",
+            "ena_intensity_stat_uncert",
+            "ena_intensity_sys_err",
+            "survival_probability",
+            "predicted_ephemeris_flag",
+            "nominal_alpha_proton_ratio_flag",
+            "persisted_last_point_flag",
+        ]:
+            self.assertEqual(
+                (
+                    CoordNames.TIME.value,
+                    CoordNames.ENERGY_L2.value,
+                    CoordNames.GENERIC_PIXEL.value,
+                ),
+                actual_dataset.data_vars[key].dims,
+            )
+        for key in ["latitude", "longitude", "solid_angle"]:
+            self.assertEqual(
+                (CoordNames.GENERIC_PIXEL.value,), actual_dataset.data_vars[key].dims
+            )
         # @formatter:on
 
     def test_healpix_intensity_map_data_to_skymap_with_no_sp(self):
@@ -1085,16 +1299,14 @@ def test_read_intensity_data_handles_missing_obs_date_and_missing_sp(self):
 
     def test_calculate_datetime_weighted_average(self):
 
-        map_1_obs_date = np.ma.array(data=[
-            datetime(2000, 1, 1),
-            datetime(2000, 1, 1),
-            datetime(9999, 12, 31)],
-            mask=[False, False, True])
-        map_2_obs_date = np.ma.array(data=[
-            datetime(9999, 12, 31),
-            datetime(2000, 1, 4),
-            datetime(9999, 12, 31)],
-            mask=[True, False, True])
+        map_1_obs_date = np.ma.array(
+            data=[datetime(2000, 1, 1), datetime(2000, 1, 1), datetime(9999, 12, 31)],
+            mask=[False, False, True],
+        )
+        map_2_obs_date = np.ma.array(
+            data=[datetime(9999, 12, 31), datetime(2000, 1, 4), datetime(9999, 12, 31)],
+            mask=[True, False, True],
+        )
 
         map_1_weights = [1, 1, 1]
         map_2_weights = [1, 2, 1]
@@ -1102,8 +1314,8 @@ def test_calculate_datetime_weighted_average(self):
         weights = np.array([map_1_weights, map_2_weights])
         original_weights = weights.copy()
         average_obs_date: np.ma.masked_array = calculate_datetime_weighted_average(
-            np.ma.masked_array([map_1_obs_date, map_2_obs_date]),
-            weights, axis=0)
+            np.ma.masked_array([map_1_obs_date, map_2_obs_date]), weights, axis=0
+        )
 
         self.assertIsInstance(average_obs_date, np.ma.masked_array)
 
@@ -1116,38 +1328,68 @@ def test_calculate_datetime_weighted_average(self):
         self.assertEqual(TT2000_EPOCH, average_obs_date.data[2])
         self.assertTrue(average_obs_date.mask[2])
 
-        np.testing.assert_equal(weights, original_weights, "should not mutate the input weights")
-
+        np.testing.assert_equal(
+            weights, original_weights, "should not mutate the input weights"
+        )
 
     def test_lo_isn_data_read_from_path(self):
-        path_to_cdf = get_test_data_folder() / 'lo' / 'imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-1yr_20250415_v000'
+        path_to_cdf = (
+            get_test_data_folder()
+            / "lo"
+            / "imap_lo_l2_l090-ena-h-sf-nsp-ram-hae-6deg-1yr_20250415_v000"
+        )
 
         actual = ISNRateData.read_from_path(path_to_cdf)
 
         with CDF(str(path_to_cdf)) as expected:
-            np.testing.assert_array_equal(actual.epoch, expected['epoch'][...])
-
-            np.testing.assert_array_equal(actual.bg_rate, read_numeric_variable(expected['bg_rate']))
-            np.testing.assert_array_equal(actual.bg_rate_stat_uncert,
-                                          read_numeric_variable(expected['bg_rate_stat_uncert']))
-            np.testing.assert_array_equal(actual.bg_rate_sys_err, read_numeric_variable(expected['bg_rate_sys_err']))
-            np.testing.assert_array_equal(actual.ena_count_rate, read_numeric_variable(expected['ena_count_rate']))
-            np.testing.assert_array_equal(actual.ena_count_rate_stat_uncert,
-                                          read_numeric_variable(expected['ena_count_rate_stat_uncert']))
-            np.testing.assert_array_equal(actual.ena_intensity, read_numeric_variable(expected['ena_intensity']))
-            np.testing.assert_array_equal(actual.ena_intensity_stat_uncert,
-                                          read_numeric_variable(expected['ena_intensity_stat_uncert']))
-            np.testing.assert_array_equal(actual.ena_intensity_sys_err,
-                                          read_numeric_variable(expected['ena_intensity_sys_err']))
-            np.testing.assert_array_equal(actual.energy, expected['energy'][...])
-            np.testing.assert_array_equal(actual.exposure_factor,
-                                          read_numeric_variable(expected['exposure_factor']))
-            np.testing.assert_array_equal(actual.latitude_delta, expected['latitude_delta'][...])
-            np.testing.assert_array_equal(actual.latitude, expected['latitude'][...])
-            np.testing.assert_array_equal(actual.longitude_delta, expected['longitude_delta'][...])
-            np.testing.assert_array_equal(actual.longitude, expected['longitude'][...])
-            np.testing.assert_array_equal(actual.solid_angle, read_numeric_variable(expected['solid_angle']))
-
-
-if __name__ == '__main__':
+            np.testing.assert_array_equal(actual.epoch, expected["epoch"][...])
+
+            np.testing.assert_array_equal(
+                actual.bg_rate, read_numeric_variable(expected["bg_rate"])
+            )
+            np.testing.assert_array_equal(
+                actual.bg_rate_stat_uncert,
+                read_numeric_variable(expected["bg_rate_stat_uncert"]),
+            )
+            np.testing.assert_array_equal(
+                actual.bg_rate_sys_err,
+                read_numeric_variable(expected["bg_rate_sys_err"]),
+            )
+            np.testing.assert_array_equal(
+                actual.ena_count_rate, read_numeric_variable(expected["ena_count_rate"])
+            )
+            np.testing.assert_array_equal(
+                actual.ena_count_rate_stat_uncert,
+                read_numeric_variable(expected["ena_count_rate_stat_uncert"]),
+            )
+            np.testing.assert_array_equal(
+                actual.ena_intensity, read_numeric_variable(expected["ena_intensity"])
+            )
+            np.testing.assert_array_equal(
+                actual.ena_intensity_stat_uncert,
+                read_numeric_variable(expected["ena_intensity_stat_uncert"]),
+            )
+            np.testing.assert_array_equal(
+                actual.ena_intensity_sys_err,
+                read_numeric_variable(expected["ena_intensity_sys_err"]),
+            )
+            np.testing.assert_array_equal(actual.energy, expected["energy"][...])
+            np.testing.assert_array_equal(
+                actual.exposure_factor,
+                read_numeric_variable(expected["exposure_factor"]),
+            )
+            np.testing.assert_array_equal(
+                actual.latitude_delta, expected["latitude_delta"][...]
+            )
+            np.testing.assert_array_equal(actual.latitude, expected["latitude"][...])
+            np.testing.assert_array_equal(
+                actual.longitude_delta, expected["longitude_delta"][...]
+            )
+            np.testing.assert_array_equal(actual.longitude, expected["longitude"][...])
+            np.testing.assert_array_equal(
+                actual.solid_angle, read_numeric_variable(expected["solid_angle"])
+            )
+
+
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/maps/test_rectangular_survival_probability.py b/tests/maps/test_rectangular_survival_probability.py
index 1238a4b1d..ac6cc6751 100644
--- a/tests/maps/test_rectangular_survival_probability.py
+++ b/tests/maps/test_rectangular_survival_probability.py
@@ -1,36 +1,45 @@
-import dataclasses
 import copy
+import dataclasses
 from datetime import datetime
-from unittest.mock import patch, sentinel, call, MagicMock
+from unittest.mock import MagicMock, call, patch, sentinel
 
 import numpy as np
 import spiceypy
 import xarray as xr
-from imap_processing.ena_maps.ena_maps import RectangularSkyMap, PointingSet
+from imap_processing.ena_maps.ena_maps import PointingSet, RectangularSkyMap
 from imap_processing.ena_maps.utils.coordinates import CoordNames
 from imap_processing.spice import geometry
 from imap_processing.spice.geometry import SpiceFrame
 
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.maps.map_descriptors import SpinPhase
-from imap_l3_processing.maps.map_models import GlowsL3eRectangularMapInputData, InputRectangularPointingSet
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
+)
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.maps.rectangular_survival_probability import Sensor, \
-    RectangularSurvivalProbabilitySkyMap, RectangularSurvivalProbabilityPointingSet, \
-    interpolate_angular_data_to_nearest_neighbor
+from imap_l3_processing.maps.rectangular_survival_probability import (
+    RectangularSurvivalProbabilityPointingSet,
+    RectangularSurvivalProbabilitySkyMap,
+    Sensor,
+    interpolate_angular_data_to_nearest_neighbor,
+)
 from tests.maps import test_builders
 from tests.maps.test_builders import create_l1c_pset
 from tests.spice_test_case import SpiceTestCase
 
 
 class TestRectangularSurvivalProbability(SpiceTestCase):
-
     def setUp(self):
         self.num_energies = 2
         self.l1c_epoch = datetime(2025, 1, 1, 0, 30)
         self.l1c_epoch_delta = np.array([86400_000_000_000])
-        self.l1c_hae_longitude = (0.05 + np.linspace(0, 360, 3600, endpoint=False) + 90).reshape((1, -1)) % 360
-        self.l1c_hae_latitude = np.linspace(-90, 90, 3600, endpoint=False).reshape((1, -1))
+        self.l1c_hae_longitude = (
+            0.05 + np.linspace(0, 360, 3600, endpoint=False) + 90
+        ).reshape((1, -1)) % 360
+        self.l1c_hae_latitude = np.linspace(-90, 90, 3600, endpoint=False).reshape(
+            (1, -1)
+        )
 
         self.hi_energies = np.geomspace(1, 10000, self.num_energies)
 
@@ -38,13 +47,18 @@ def setUp(self):
             epoch=self.l1c_epoch,
             epoch_delta=self.l1c_epoch_delta,
             repointing=1,
-            epoch_j2000=np.array(np.array([spiceypy.datetime2et(self.l1c_epoch)]) * 1e9),
-            exposure_times=np.arange(self.num_energies * 3600).reshape((1, self.num_energies, 3600)) + 1.1,
+            epoch_j2000=np.array(
+                np.array([spiceypy.datetime2et(self.l1c_epoch)]) * 1e9
+            ),
+            exposure_times=np.arange(self.num_energies * 3600).reshape(
+                (1, self.num_energies, 3600)
+            )
+            + 1.1,
             esa_energy_step=np.arange(self.num_energies),
             pointing_start_met=None,
             pointing_end_met=None,
             hae_longitude=self.l1c_hae_longitude,
-            hae_latitude=self.l1c_hae_latitude
+            hae_latitude=self.l1c_hae_latitude,
         )
 
         self.glows_data = GlowsL3eRectangularMapInputData(
@@ -72,13 +86,17 @@ def _mock_calculate_ram_mask(dataset):
             return dataset
 
         self.mock_add_spacecraft_position_and_velocity_to_pset_patcher = patch(
-            'imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset')
+            "imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset"
+        )
 
-        self.mock_add_sc_velocity_to_pset = self.mock_add_spacecraft_position_and_velocity_to_pset_patcher.start()
+        self.mock_add_sc_velocity_to_pset = (
+            self.mock_add_spacecraft_position_and_velocity_to_pset_patcher.start()
+        )
         self.mock_add_sc_velocity_to_pset.side_effect = _mock_add_spacecraft_velocity
 
         self.mock_calculate_ram_mask_patcher = patch(
-            'imap_l3_processing.maps.rectangular_survival_probability.calculate_ram_mask')
+            "imap_l3_processing.maps.rectangular_survival_probability.calculate_ram_mask"
+        )
         self.mock_calculate_ram_mask = self.mock_calculate_ram_mask_patcher.start()
         self.mock_calculate_ram_mask.side_effect = _mock_calculate_ram_mask
 
@@ -86,33 +104,54 @@ def tearDown(self):
         self.mock_add_spacecraft_position_and_velocity_to_pset_patcher.stop()
         self.mock_calculate_ram_mask_patcher.stop()
 
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.PointingSet.__init__')
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset')
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.calculate_ram_mask')
-    def test_survival_probability_pointing_set_calls_parent_constructor(self, _,
-                                                                        __,
-                                                                        mock_rectangular_pointing_set_constructor):
-        pointing_set = RectangularSurvivalProbabilityPointingSet(self.l1c_hi_dataset, Sensor.Hi45, SpinPhase.RamOnly,
-                                                                 self.glows_data,
-                                                                 self.hi_energies)
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.PointingSet.__init__"
+    )
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset"
+    )
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.calculate_ram_mask"
+    )
+    def test_survival_probability_pointing_set_calls_parent_constructor(
+        self, _, __, mock_rectangular_pointing_set_constructor
+    ):
+        pointing_set = RectangularSurvivalProbabilityPointingSet(
+            self.l1c_hi_dataset,
+            Sensor.Hi45,
+            SpinPhase.RamOnly,
+            self.glows_data,
+            self.hi_energies,
+        )
         self.assertIsInstance(pointing_set, PointingSet)
 
         mock_rectangular_pointing_set_constructor.assert_called_once()
 
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset')
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.calculate_ram_mask')
-    def test_survival_probability_pointing_set_no_cg(self, mock_calculate_ram_mask, mock_add_sc_velocity_to_pset):
-        mock_calculate_ram_mask.return_value = xr.Dataset({
-            "ram_mask": [True]
-        })
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset"
+    )
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.calculate_ram_mask"
+    )
+    def test_survival_probability_pointing_set_no_cg(
+        self, mock_calculate_ram_mask, mock_add_sc_velocity_to_pset
+    ):
+        mock_calculate_ram_mask.return_value = xr.Dataset({"ram_mask": [True]})
 
-        pointing_set = RectangularSurvivalProbabilityPointingSet(self.l1c_hi_dataset, Sensor.Hi45, SpinPhase.RamOnly,
-                                                                 self.glows_data,
-                                                                 self.hi_energies, False)
+        pointing_set = RectangularSurvivalProbabilityPointingSet(
+            self.l1c_hi_dataset,
+            Sensor.Hi45,
+            SpinPhase.RamOnly,
+            self.glows_data,
+            self.hi_energies,
+            False,
+        )
 
-        mock_calculate_ram_mask.assert_called_once_with(mock_add_sc_velocity_to_pset.return_value)
+        mock_calculate_ram_mask.assert_called_once_with(
+            mock_add_sc_velocity_to_pset.return_value
+        )
 
-        self.assertEqual(pointing_set.data['directional_mask'], [True])
+        self.assertEqual(pointing_set.data["directional_mask"], [True])
 
     def test_survival_probability_pointing_set(self):
         test_cases = [
@@ -123,46 +162,68 @@ def test_survival_probability_pointing_set(self):
             (Sensor.Lo90, 0, SpinPhase.AntiRamOnly, ~self.ram_mask),
         ]
 
-        expected_repointing_midpoint = self.l1c_hi_dataset.epoch_j2000 + self.l1c_epoch_delta / 2
+        expected_repointing_midpoint = (
+            self.l1c_hi_dataset.epoch_j2000 + self.l1c_epoch_delta / 2
+        )
 
         for sensor, expected_sensor_angle, spin_phase, expected_mask in test_cases:
             with self.subTest(f"{sensor.value}"):
-                pointing_set = RectangularSurvivalProbabilityPointingSet(self.l1c_hi_dataset, sensor, spin_phase,
-                                                                         self.glows_data,
-                                                                         self.hi_energies)
+                pointing_set = RectangularSurvivalProbabilityPointingSet(
+                    self.l1c_hi_dataset,
+                    sensor,
+                    spin_phase,
+                    self.glows_data,
+                    self.hi_energies,
+                )
                 self.assertIsInstance(pointing_set, PointingSet)
-                self.assertEqual(pointing_set.spice_reference_frame, geometry.SpiceFrame.IMAP_HAE)
+                self.assertEqual(
+                    pointing_set.spice_reference_frame, geometry.SpiceFrame.IMAP_HAE
+                )
 
                 self.assertIn("exposure", pointing_set.data.data_vars)
                 np.testing.assert_array_equal(
                     pointing_set.data["exposure"].values,
-                    self.l1c_hi_dataset.exposure_times)
+                    self.l1c_hi_dataset.exposure_times,
+                )
 
                 np.testing.assert_array_equal(
-                    pointing_set.data["directional_mask"].values,
-                    expected_mask
+                    pointing_set.data["directional_mask"].values, expected_mask
                 )
 
                 self.assertIn(CoordNames.AZIMUTH_L1C.value, pointing_set.data.coords)
                 np.testing.assert_array_almost_equal(
                     np.arange(0, 360, 0.1) + 0.05,
-                    pointing_set.data[CoordNames.AZIMUTH_L1C.value].values)
+                    pointing_set.data[CoordNames.AZIMUTH_L1C.value].values,
+                )
 
-                self.assertIn(CoordNames.ENERGY_ULTRA_L1C.value, pointing_set.data.coords)
-                np.testing.assert_array_equal(self.l1c_hi_dataset.esa_energy_step,
-                                              pointing_set.data[CoordNames.ENERGY_ULTRA_L1C.value].values)
+                self.assertIn(
+                    CoordNames.ENERGY_ULTRA_L1C.value, pointing_set.data.coords
+                )
+                np.testing.assert_array_equal(
+                    self.l1c_hi_dataset.esa_energy_step,
+                    pointing_set.data[CoordNames.ENERGY_ULTRA_L1C.value].values,
+                )
 
                 self.assertIn(CoordNames.TIME.value, pointing_set.data.coords)
-                np.testing.assert_array_equal(pointing_set.data[CoordNames.TIME.value].values,
-                                              expected_repointing_midpoint)
+                np.testing.assert_array_equal(
+                    pointing_set.data[CoordNames.TIME.value].values,
+                    expected_repointing_midpoint,
+                )
 
-                np.testing.assert_array_equal(pointing_set.az_el_points[:, 0],
-                                              self.l1c_hae_longitude[0])
+                np.testing.assert_array_equal(
+                    pointing_set.az_el_points[:, 0], self.l1c_hae_longitude[0]
+                )
 
-                np.testing.assert_array_equal(pointing_set.az_el_points[:, 1], self.l1c_hae_latitude[0])
+                np.testing.assert_array_equal(
+                    pointing_set.az_el_points[:, 1], self.l1c_hae_latitude[0]
+                )
 
-    @patch("imap_l3_processing.maps.rectangular_survival_probability.apply_compton_getting_correction")
-    def test_hi_cg_corrected_survival_probability_pointing_set(self, mock_cg_correction):
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.apply_compton_getting_correction"
+    )
+    def test_hi_cg_corrected_survival_probability_pointing_set(
+        self, mock_cg_correction
+    ):
         self.mock_add_sc_velocity_to_pset.side_effect = None
         corrected_hae_longitude = np.full((1, 3, 3600), 2)
         corrected_hae_latitude = np.full((1, 3, 3600), 1)
@@ -170,10 +231,14 @@ def test_hi_cg_corrected_survival_probability_pointing_set(self, mock_cg_correct
         l1c_dataset = test_builders.create_l1c_pset(
             epoch=self.l1c_hi_dataset.epoch,
             epoch_delta=self.l1c_hi_dataset.epoch_delta,
-            exposures=np.array([[np.full((3600,), 1), np.full((3600,), 2), np.full((3600,), 3)]]),
+            exposures=np.array(
+                [[np.full((3600,), 1), np.full((3600,), 2), np.full((3600,), 3)]]
+            ),
         )
 
-        corrected_az_el_pairs = np.stack([corrected_hae_longitude[0], corrected_hae_latitude[0]], axis=2)
+        corrected_az_el_pairs = np.stack(
+            [corrected_hae_longitude[0], corrected_hae_latitude[0]], axis=2
+        )
 
         energy_sc = np.ones((1, 3, 3600))
         energy_sc[0, 0, :1800] = 10
@@ -185,69 +250,129 @@ def test_hi_cg_corrected_survival_probability_pointing_set(self, mock_cg_correct
 
         sc_velocity = np.arange(3) + 1000
 
-        hi_hf_energies = np.array([.001, .1, 1])
-        expected_exposures = np.array([
-            [np.concatenate((l1c_dataset.exposure_times[0, 0, :1800], l1c_dataset.exposure_times[0, 1, 1800:3600])),
-             np.concatenate((l1c_dataset.exposure_times[0, 1, :1800], l1c_dataset.exposure_times[0, 2, 1800:3600])),
-             l1c_dataset.exposure_times[0, 2, 0:3600]],
-        ])
+        hi_hf_energies = np.array([0.001, 0.1, 1])
+        expected_exposures = np.array(
+            [
+                [
+                    np.concatenate(
+                        (
+                            l1c_dataset.exposure_times[0, 0, :1800],
+                            l1c_dataset.exposure_times[0, 1, 1800:3600],
+                        )
+                    ),
+                    np.concatenate(
+                        (
+                            l1c_dataset.exposure_times[0, 1, :1800],
+                            l1c_dataset.exposure_times[0, 2, 1800:3600],
+                        )
+                    ),
+                    l1c_dataset.exposure_times[0, 2, 0:3600],
+                ],
+            ]
+        )
 
-        mock_cg_correction.return_value = xr.Dataset({
-            "energy_sc": (
-                [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.AZIMUTH_L2.value], energy_sc
-            ),
-            "hae_latitude": (
-                [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.AZIMUTH_L2.value], corrected_hae_latitude
-            ),
-            "hae_longitude": (
-                [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.AZIMUTH_L2.value],
-                corrected_hae_longitude
-            ),
-            "sc_velocity": (
-                [CoordNames.CARTESIAN_VECTOR.value],
-                sc_velocity
-            )
-        },
+        mock_cg_correction.return_value = xr.Dataset(
+            {
+                "energy_sc": (
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_L2.value,
+                        CoordNames.AZIMUTH_L2.value,
+                    ],
+                    energy_sc,
+                ),
+                "hae_latitude": (
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_L2.value,
+                        CoordNames.AZIMUTH_L2.value,
+                    ],
+                    corrected_hae_latitude,
+                ),
+                "hae_longitude": (
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_L2.value,
+                        CoordNames.AZIMUTH_L2.value,
+                    ],
+                    corrected_hae_longitude,
+                ),
+                "sc_velocity": ([CoordNames.CARTESIAN_VECTOR.value], sc_velocity),
+            },
             coords={
                 CoordNames.TIME.value: [1_000_000_000],
                 CoordNames.ENERGY_L2.value: hi_hf_energies,
                 CoordNames.AZIMUTH_L1C.value: np.arange(3600),
-                CoordNames.CARTESIAN_VECTOR.value: np.arange(3)
-            }
+                CoordNames.CARTESIAN_VECTOR.value: np.arange(3),
+            },
         )
 
-        cg_pointing_set = RectangularSurvivalProbabilityPointingSet(l1c_dataset, Sensor.Hi90, SpinPhase.RamOnly,
-                                                                    self.glows_data, hi_hf_energies,
-                                                                    cg_corrected=True)
+        cg_pointing_set = RectangularSurvivalProbabilityPointingSet(
+            l1c_dataset,
+            Sensor.Hi90,
+            SpinPhase.RamOnly,
+            self.glows_data,
+            hi_hf_energies,
+            cg_corrected=True,
+        )
 
         [actual_uncorrected_pset] = self.mock_add_sc_velocity_to_pset.call_args[0]
 
-        np.testing.assert_array_equal(actual_uncorrected_pset['hae_longitude'].values[0], l1c_dataset.hae_longitude[0])
-        np.testing.assert_array_equal(actual_uncorrected_pset['hae_latitude'].values[0], l1c_dataset.hae_latitude[0])
+        np.testing.assert_array_equal(
+            actual_uncorrected_pset["hae_longitude"].values[0],
+            l1c_dataset.hae_longitude[0],
+        )
+        np.testing.assert_array_equal(
+            actual_uncorrected_pset["hae_latitude"].values[0],
+            l1c_dataset.hae_latitude[0],
+        )
 
-        np.testing.assert_array_equal(actual_uncorrected_pset['epoch'].values, self.l1c_hi_dataset.epoch_j2000)
-        np.testing.assert_array_equal(actual_uncorrected_pset['epoch_delta'].values, self.l1c_hi_dataset.epoch_delta)
+        np.testing.assert_array_equal(
+            actual_uncorrected_pset["epoch"].values, self.l1c_hi_dataset.epoch_j2000
+        )
+        np.testing.assert_array_equal(
+            actual_uncorrected_pset["epoch_delta"].values,
+            self.l1c_hi_dataset.epoch_delta,
+        )
 
         pset_with_sc_velocity, actual_hf_energies = mock_cg_correction.call_args[0]
-        self.assertEqual(pset_with_sc_velocity, self.mock_add_sc_velocity_to_pset.return_value)
-        self.mock_calculate_ram_mask.assert_called_once_with(mock_cg_correction.return_value)
+        self.assertEqual(
+            pset_with_sc_velocity, self.mock_add_sc_velocity_to_pset.return_value
+        )
+        self.mock_calculate_ram_mask.assert_called_once_with(
+            mock_cg_correction.return_value
+        )
 
         expected_energies_in_eV = hi_hf_energies * 1000
         np.testing.assert_array_equal(actual_hf_energies, expected_energies_in_eV)
         self.assertEqual((CoordNames.ENERGY_ULTRA_L1C.value,), actual_hf_energies.dims)
 
-        np.testing.assert_array_equal(cg_pointing_set.data['epoch'],
-                                      self.l1c_hi_dataset.epoch_j2000 + (self.l1c_hi_dataset.epoch_delta / 2))
-        np.testing.assert_array_equal(cg_pointing_set.az_el_points, corrected_az_el_pairs)
+        np.testing.assert_array_equal(
+            cg_pointing_set.data["epoch"],
+            self.l1c_hi_dataset.epoch_j2000 + (self.l1c_hi_dataset.epoch_delta / 2),
+        )
+        np.testing.assert_array_equal(
+            cg_pointing_set.az_el_points, corrected_az_el_pairs
+        )
 
-        np.testing.assert_array_equal(cg_pointing_set.data['exposure'], expected_exposures)
+        np.testing.assert_array_equal(
+            cg_pointing_set.data["exposure"], expected_exposures
+        )
 
-    @patch("imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset")
-    @patch("imap_l3_processing.maps.rectangular_survival_probability.apply_compton_getting_correction")
-    def test_lo_cg_corrected_survival_probability_pointing_set(self, mock_cg_correction, mock_add_sc_velocity_to_pset):
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.add_spacecraft_position_and_velocity_to_pset"
+    )
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.apply_compton_getting_correction"
+    )
+    def test_lo_cg_corrected_survival_probability_pointing_set(
+        self, mock_cg_correction, mock_add_sc_velocity_to_pset
+    ):
         corrected_hae_longitude = np.full((1, 3, 3600), 2)
         corrected_hae_latitude = np.full((1, 3, 3600), 1)
-        corrected_az_el_pairs = np.stack([corrected_hae_longitude[0], corrected_hae_latitude[0]], axis=2)
+        corrected_az_el_pairs = np.stack(
+            [corrected_hae_longitude[0], corrected_hae_latitude[0]], axis=2
+        )
 
         uncorrected_hae_lon = np.ones((1, 3600))
         uncorrected_hae_lat = np.full((1, 3600), 2)
@@ -255,7 +380,9 @@ def test_lo_cg_corrected_survival_probability_pointing_set(self, mock_cg_correct
         l1c_dataset = test_builders.create_l1c_pset(
             epoch=self.l1c_hi_dataset.epoch,
             epoch_delta=None,
-            exposures=np.array([[np.full((3600,), 1), np.full((3600,), 2), np.full((3600,), 3)]]),
+            exposures=np.array(
+                [[np.full((3600,), 1), np.full((3600,), 2), np.full((3600,), 3)]]
+            ),
             pointing_start_met=np.array([800_000_000]),
             pointing_end_met=np.array([800_100_000]),
         )
@@ -273,71 +400,127 @@ def test_lo_cg_corrected_survival_probability_pointing_set(self, mock_cg_correct
 
         sc_velocity = np.arange(3) + 1000
 
-        lo_hf_energies = np.array([.001, .1, 1])
-        expected_exposures = np.array([
-            [np.concatenate((l1c_dataset.exposure_times[0, 0, :1800], l1c_dataset.exposure_times[0, 1, 1800:3600])),
-             np.concatenate((l1c_dataset.exposure_times[0, 1, :1800], l1c_dataset.exposure_times[0, 2, 1800:3600])),
-             l1c_dataset.exposure_times[0, 2, 0:3600]],
-        ])
+        lo_hf_energies = np.array([0.001, 0.1, 1])
+        expected_exposures = np.array(
+            [
+                [
+                    np.concatenate(
+                        (
+                            l1c_dataset.exposure_times[0, 0, :1800],
+                            l1c_dataset.exposure_times[0, 1, 1800:3600],
+                        )
+                    ),
+                    np.concatenate(
+                        (
+                            l1c_dataset.exposure_times[0, 1, :1800],
+                            l1c_dataset.exposure_times[0, 2, 1800:3600],
+                        )
+                    ),
+                    l1c_dataset.exposure_times[0, 2, 0:3600],
+                ],
+            ]
+        )
 
-        mock_cg_correction.return_value = xr.Dataset({
-            "energy_sc": (
-                [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.AZIMUTH_L2.value], energy_sc
-            ),
-            "hae_latitude": (
-                [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.AZIMUTH_L2.value], corrected_hae_latitude
-            ),
-            "hae_longitude": (
-                [CoordNames.TIME.value, CoordNames.ENERGY_L2.value, CoordNames.AZIMUTH_L2.value],
-                corrected_hae_longitude
-            ),
-            "sc_velocity": (
-                [CoordNames.CARTESIAN_VECTOR.value],
-                sc_velocity
-            )
-        },
+        mock_cg_correction.return_value = xr.Dataset(
+            {
+                "energy_sc": (
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_L2.value,
+                        CoordNames.AZIMUTH_L2.value,
+                    ],
+                    energy_sc,
+                ),
+                "hae_latitude": (
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_L2.value,
+                        CoordNames.AZIMUTH_L2.value,
+                    ],
+                    corrected_hae_latitude,
+                ),
+                "hae_longitude": (
+                    [
+                        CoordNames.TIME.value,
+                        CoordNames.ENERGY_L2.value,
+                        CoordNames.AZIMUTH_L2.value,
+                    ],
+                    corrected_hae_longitude,
+                ),
+                "sc_velocity": ([CoordNames.CARTESIAN_VECTOR.value], sc_velocity),
+            },
             coords={
                 CoordNames.TIME.value: [1_000_000_000],
                 CoordNames.ENERGY_L2.value: lo_hf_energies,
                 CoordNames.AZIMUTH_L1C.value: np.arange(3600),
-                CoordNames.CARTESIAN_VECTOR.value: np.arange(3)
-            }
+                CoordNames.CARTESIAN_VECTOR.value: np.arange(3),
+            },
         )
 
-        cases = {
-            "lo90": Sensor.Lo90,
-            "lo75": Sensor.Lo75,
-            "lo105": Sensor.Lo105
-        }
+        cases = {"lo90": Sensor.Lo90, "lo75": Sensor.Lo75, "lo105": Sensor.Lo105}
         for name, sensor in cases.items():
             with self.subTest(name):
-                cg_pointing_set = RectangularSurvivalProbabilityPointingSet(l1c_dataset, sensor, SpinPhase.RamOnly,
-                                                                            self.glows_data, lo_hf_energies,
-                                                                            cg_corrected=True)
+                cg_pointing_set = RectangularSurvivalProbabilityPointingSet(
+                    l1c_dataset,
+                    sensor,
+                    SpinPhase.RamOnly,
+                    self.glows_data,
+                    lo_hf_energies,
+                    cg_corrected=True,
+                )
 
-                expected_pointing_epoch_midpoint = self.l1c_hi_dataset.epoch_j2000 + (100_000 * 1e9 / 2)
+                expected_pointing_epoch_midpoint = self.l1c_hi_dataset.epoch_j2000 + (
+                    100_000 * 1e9 / 2
+                )
 
                 [actual_uncorrected_pset] = mock_add_sc_velocity_to_pset.call_args[0]
 
-                np.testing.assert_array_equal(actual_uncorrected_pset['hae_longitude'], uncorrected_hae_lon)
-                np.testing.assert_array_equal(actual_uncorrected_pset['hae_latitude'], uncorrected_hae_lat)
+                np.testing.assert_array_equal(
+                    actual_uncorrected_pset["hae_longitude"], uncorrected_hae_lon
+                )
+                np.testing.assert_array_equal(
+                    actual_uncorrected_pset["hae_latitude"], uncorrected_hae_lat
+                )
 
-                np.testing.assert_array_equal(actual_uncorrected_pset['epoch'].values, self.l1c_hi_dataset.epoch_j2000)
-                np.testing.assert_array_equal(actual_uncorrected_pset['epoch_delta'].values, None)
-                np.testing.assert_array_equal(actual_uncorrected_pset['pointing_start_met'].values, [800_000_000])
-                np.testing.assert_array_equal(actual_uncorrected_pset['pointing_end_met'].values, [800_100_000])
-                self.assertEqual(actual_uncorrected_pset.attrs['Logical_source'], "imap_lo")
+                np.testing.assert_array_equal(
+                    actual_uncorrected_pset["epoch"].values,
+                    self.l1c_hi_dataset.epoch_j2000,
+                )
+                np.testing.assert_array_equal(
+                    actual_uncorrected_pset["epoch_delta"].values, None
+                )
+                np.testing.assert_array_equal(
+                    actual_uncorrected_pset["pointing_start_met"].values, [800_000_000]
+                )
+                np.testing.assert_array_equal(
+                    actual_uncorrected_pset["pointing_end_met"].values, [800_100_000]
+                )
+                self.assertEqual(
+                    actual_uncorrected_pset.attrs["Logical_source"], "imap_lo"
+                )
 
-                pset_with_sc_velocity, actual_hf_energies = mock_cg_correction.call_args[0]
-                self.assertEqual(pset_with_sc_velocity, mock_add_sc_velocity_to_pset.return_value)
+                pset_with_sc_velocity, actual_hf_energies = (
+                    mock_cg_correction.call_args[0]
+                )
+                self.assertEqual(
+                    pset_with_sc_velocity, mock_add_sc_velocity_to_pset.return_value
+                )
 
                 expected_energies_in_eV = lo_hf_energies * 1000
-                np.testing.assert_array_equal(actual_hf_energies, expected_energies_in_eV)
+                np.testing.assert_array_equal(
+                    actual_hf_energies, expected_energies_in_eV
+                )
 
-                np.testing.assert_array_equal(cg_pointing_set.data['epoch'], expected_pointing_epoch_midpoint)
-                np.testing.assert_array_equal(cg_pointing_set.az_el_points, corrected_az_el_pairs)
+                np.testing.assert_array_equal(
+                    cg_pointing_set.data["epoch"], expected_pointing_epoch_midpoint
+                )
+                np.testing.assert_array_equal(
+                    cg_pointing_set.az_el_points, corrected_az_el_pairs
+                )
 
-                np.testing.assert_array_equal(cg_pointing_set.data['exposure'], expected_exposures)
+                np.testing.assert_array_equal(
+                    cg_pointing_set.data["exposure"], expected_exposures
+                )
 
     def test_exposure_weighting_with_interpolated_survival_probabilities(self):
         test_cases = [
@@ -346,44 +529,66 @@ def test_exposure_weighting_with_interpolated_survival_probabilities(self):
         ]
 
         for spin_phase, expected_mask in test_cases:
-            with (self.subTest(spin_phase)):
+            with self.subTest(spin_phase):
                 self.hi_energies = np.array([10, 10_000])
                 self.glows_data.energy = np.array([1, 100, 100_000])
-                self.glows_data.probability_of_survival = np.repeat([2, 4, 7], 360).reshape(1, 3, 360)
+                self.glows_data.probability_of_survival = np.repeat(
+                    [2, 4, 7], 360
+                ).reshape(1, 3, 360)
 
-                expected_interpolated_survival_probabilities = \
-                    np.repeat([3, 6], 3600).reshape(1, 2, 3600) * self.l1c_hi_dataset.exposure_times
+                expected_interpolated_survival_probabilities = (
+                    np.repeat([3, 6], 3600).reshape(1, 2, 3600)
+                    * self.l1c_hi_dataset.exposure_times
+                )
 
                 sensor, expected_skygrid_elevation_index = Sensor.Hi90, 901
-                pointing_set = RectangularSurvivalProbabilityPointingSet(self.l1c_hi_dataset, sensor, spin_phase,
-                                                                         self.glows_data,
-                                                                         self.hi_energies)
+                pointing_set = RectangularSurvivalProbabilityPointingSet(
+                    self.l1c_hi_dataset,
+                    sensor,
+                    spin_phase,
+                    self.glows_data,
+                    self.hi_energies,
+                )
 
-                self.assertIn("survival_probability_times_exposure", pointing_set.data.data_vars)
-                self.assertEqual((1, self.num_energies, 3600),
-                                 pointing_set.data["survival_probability_times_exposure"].values.shape)
+                self.assertIn(
+                    "survival_probability_times_exposure", pointing_set.data.data_vars
+                )
+                self.assertEqual(
+                    (1, self.num_energies, 3600),
+                    pointing_set.data[
+                        "survival_probability_times_exposure"
+                    ].values.shape,
+                )
 
                 np.testing.assert_array_equal(
                     expected_interpolated_survival_probabilities,
-                    pointing_set.data["survival_probability_times_exposure"].values)
+                    pointing_set.data["survival_probability_times_exposure"].values,
+                )
 
                 np.testing.assert_array_equal(
-                    pointing_set.data["directional_mask"],
-                    expected_mask
+                    pointing_set.data["directional_mask"], expected_mask
                 )
 
     def test_exposure_weighted_survivals_are_repeated_to_match_l1c_shape(self):
-        pointing_set = RectangularSurvivalProbabilityPointingSet(self.l1c_hi_dataset, Sensor.Hi90, SpinPhase.RamOnly,
-                                                                 self.glows_data,
-                                                                 self.hi_energies)
+        pointing_set = RectangularSurvivalProbabilityPointingSet(
+            self.l1c_hi_dataset,
+            Sensor.Hi90,
+            SpinPhase.RamOnly,
+            self.glows_data,
+            self.hi_energies,
+        )
 
-        survivals = pointing_set.data[
-                        "survival_probability_times_exposure"].values / self.l1c_hi_dataset.exposure_times
+        survivals = (
+            pointing_set.data["survival_probability_times_exposure"].values
+            / self.l1c_hi_dataset.exposure_times
+        )
         every_tenth_value = survivals[:, :, ::10, np.newaxis]
         groups_of_ten = survivals.reshape(1, self.num_energies, 360, 10)
         self.assertTrue(np.all(np.isclose(every_tenth_value, groups_of_ten)))
 
-    @patch("imap_l3_processing.maps.rectangular_survival_probability.interpolate_angular_data_to_nearest_neighbor")
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.interpolate_angular_data_to_nearest_neighbor"
+    )
     def test_survivals_matched_with_corresponding_exposures(self, mock_interpolate):
         self.hi_energies = np.array([1, 100])
         self.glows_data.energy = np.array([1, 100, 100_000])
@@ -391,12 +596,20 @@ def test_survivals_matched_with_corresponding_exposures(self, mock_interpolate):
         first_energy_corresponding_glows_data = np.linspace(0, 1, 3600)
         second_energy_corresponding_glows_data = np.linspace(0, 1, 3600) + 100.2
 
-        mock_interpolate.return_value = np.array([first_energy_corresponding_glows_data,
-                                                  second_energy_corresponding_glows_data])
+        mock_interpolate.return_value = np.array(
+            [
+                first_energy_corresponding_glows_data,
+                second_energy_corresponding_glows_data,
+            ]
+        )
 
-        pointing_set = RectangularSurvivalProbabilityPointingSet(self.l1c_hi_dataset, Sensor.Hi90, SpinPhase.RamOnly,
-                                                                 self.glows_data,
-                                                                 self.hi_energies)
+        pointing_set = RectangularSurvivalProbabilityPointingSet(
+            self.l1c_hi_dataset,
+            Sensor.Hi90,
+            SpinPhase.RamOnly,
+            self.glows_data,
+            self.hi_energies,
+        )
 
         pset_spin_angles = np.linspace(0, 360, 3600, endpoint=False) + 0.05
         pset_azimuths = np.mod(pset_spin_angles, 360)
@@ -404,20 +617,38 @@ def test_survivals_matched_with_corresponding_exposures(self, mock_interpolate):
         mock_interpolate.assert_called_once()
         get_interpolated_glows_data_args = mock_interpolate.call_args_list[0].args
 
-        np.testing.assert_array_equal(pset_azimuths, get_interpolated_glows_data_args[0])
-        np.testing.assert_array_equal(self.glows_data.spin_angle, get_interpolated_glows_data_args[1])
-        np.testing.assert_array_equal(self.glows_data.probability_of_survival[0, :2],
-                                      get_interpolated_glows_data_args[2])
+        np.testing.assert_array_equal(
+            pset_azimuths, get_interpolated_glows_data_args[0]
+        )
+        np.testing.assert_array_equal(
+            self.glows_data.spin_angle, get_interpolated_glows_data_args[1]
+        )
+        np.testing.assert_array_equal(
+            self.glows_data.probability_of_survival[0, :2],
+            get_interpolated_glows_data_args[2],
+        )
 
         corresponding_glows_data = np.array(
-            [first_energy_corresponding_glows_data, second_energy_corresponding_glows_data])[np.newaxis, ...]
-
-        np.testing.assert_array_almost_equal(pointing_set.data["survival_probability_times_exposure"].values,
-                                             corresponding_glows_data * self.l1c_hi_dataset.exposure_times)
+            [
+                first_energy_corresponding_glows_data,
+                second_energy_corresponding_glows_data,
+            ]
+        )[np.newaxis, ...]
+
+        np.testing.assert_array_almost_equal(
+            pointing_set.data["survival_probability_times_exposure"].values,
+            corresponding_glows_data * self.l1c_hi_dataset.exposure_times,
+        )
 
-    @patch("imap_l3_processing.maps.rectangular_survival_probability.interpolate_angular_data_to_nearest_neighbor")
-    @patch("imap_l3_processing.maps.rectangular_survival_probability.apply_compton_getting_correction")
-    def test_survivals_matched_with_corresponding_exposures_cg_corrected(self, mock_cg_correction, mock_interpolate):
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.interpolate_angular_data_to_nearest_neighbor"
+    )
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.apply_compton_getting_correction"
+    )
+    def test_survivals_matched_with_corresponding_exposures_cg_corrected(
+        self, mock_cg_correction, mock_interpolate
+    ):
         test_cases = [
             Sensor.Hi90,
             Sensor.Hi45,
@@ -514,7 +745,11 @@ def test_survivals_matched_with_corresponding_exposures_cg_corrected(self, mock_
                 )
 
                 corresponding_glows_data = np.array(
-                    [first_energy_corresponding_glows_data, second_energy_corresponding_glows_data])[np.newaxis, ...]
+                    [
+                        first_energy_corresponding_glows_data,
+                        second_energy_corresponding_glows_data,
+                    ]
+                )[np.newaxis, ...]
 
                 np.testing.assert_array_almost_equal(
                     pointing_set.data["survival_probability_times_exposure"].values,
@@ -529,41 +764,100 @@ def test_survival_probability_pointing_set_propagates_flags(
             (GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO, 0.0, 1.0, 0.0),
             (GlowsL3Flags.PREDICTIVE_EPHEMERIS, 1.0, 0.0, 0.0),
             (GlowsL3Flags.PERSISTED_LAST_POINT, 0.0, 0.0, 1.0),
-            (GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PREDICTIVE_EPHEMERIS, 1.0, 1.0, 0.0),
-            (GlowsL3Flags.PERSISTED_LAST_POINT | GlowsL3Flags.PREDICTIVE_EPHEMERIS, 1.0, 0.0, 1.0),
-            (GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PERSISTED_LAST_POINT, 0.0, 1.0, 1.0),
-            (GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PERSISTED_LAST_POINT | GlowsL3Flags.PREDICTIVE_EPHEMERIS, 1.0, 1.0, 1.0),
+            (
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                | GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                1.0,
+                1.0,
+                0.0,
+            ),
+            (
+                GlowsL3Flags.PERSISTED_LAST_POINT | GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                1.0,
+                0.0,
+                1.0,
+            ),
+            (
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                | GlowsL3Flags.PERSISTED_LAST_POINT,
+                0.0,
+                1.0,
+                1.0,
+            ),
+            (
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                | GlowsL3Flags.PERSISTED_LAST_POINT
+                | GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                1.0,
+                1.0,
+                1.0,
+            ),
         ]
-        for flag_value, expected_pred_ephem_value, expected_nominal_alpha_proton_value, expected_persisted_last_point_value in cases:
+        for (
+            flag_value,
+            expected_pred_ephem_value,
+            expected_nominal_alpha_proton_value,
+            expected_persisted_last_point_value,
+        ) in cases:
             with self.subTest(flag_value):
                 exposure_array = np.full(self.l1c_hi_dataset.exposure_times.shape, 1.0)
                 exposure_array[:, :, 1000] = 0.0
                 self.glows_data.flags[0] = flag_value
                 self.l1c_hi_dataset.exposure_times = exposure_array
                 pointing_set = RectangularSurvivalProbabilityPointingSet(
-                    self.l1c_hi_dataset, Sensor.Hi90, SpinPhase.RamOnly,
-                    glows_dataset=self.glows_data, energies=self.hi_energies)
+                    self.l1c_hi_dataset,
+                    Sensor.Hi90,
+                    SpinPhase.RamOnly,
+                    glows_dataset=self.glows_data,
+                    energies=self.hi_energies,
+                )
 
                 actual_pred_ephem = pointing_set.data["predicted_ephemeris_flag"].values
-                expected_pred_ephem = np.full((1, self.num_energies, 3600), expected_pred_ephem_value)
+                expected_pred_ephem = np.full(
+                    (1, self.num_energies, 3600), expected_pred_ephem_value
+                )
                 expected_pred_ephem[:, :, 1000] = 0
-                np.testing.assert_array_equal(actual_pred_ephem, expected_pred_ephem, strict=True)
-                self.assertEqual(pointing_set.data["predicted_ephemeris_flag"].dims,
-                                 pointing_set.data["survival_probability_times_exposure"].dims)
+                np.testing.assert_array_equal(
+                    actual_pred_ephem, expected_pred_ephem, strict=True
+                )
+                self.assertEqual(
+                    pointing_set.data["predicted_ephemeris_flag"].dims,
+                    pointing_set.data["survival_probability_times_exposure"].dims,
+                )
 
-                actual_nominal_alpha_proton = pointing_set.data["nominal_alpha_proton_ratio_flag"].values
-                expected_nominal_alpha_proton = np.full((1, self.num_energies, 3600), expected_nominal_alpha_proton_value)
+                actual_nominal_alpha_proton = pointing_set.data[
+                    "nominal_alpha_proton_ratio_flag"
+                ].values
+                expected_nominal_alpha_proton = np.full(
+                    (1, self.num_energies, 3600), expected_nominal_alpha_proton_value
+                )
                 expected_nominal_alpha_proton[:, :, 1000] = 0
-                np.testing.assert_array_equal(actual_nominal_alpha_proton, expected_nominal_alpha_proton, strict=True)
-                self.assertEqual(pointing_set.data["nominal_alpha_proton_ratio_flag"].dims,
-                                 pointing_set.data["survival_probability_times_exposure"].dims)
+                np.testing.assert_array_equal(
+                    actual_nominal_alpha_proton,
+                    expected_nominal_alpha_proton,
+                    strict=True,
+                )
+                self.assertEqual(
+                    pointing_set.data["nominal_alpha_proton_ratio_flag"].dims,
+                    pointing_set.data["survival_probability_times_exposure"].dims,
+                )
 
-                actual_persisted_last_point = pointing_set.data["persisted_last_point_flag"].values
-                expected_persisted_last_point = np.full((1, self.num_energies, 3600), expected_persisted_last_point_value)
+                actual_persisted_last_point = pointing_set.data[
+                    "persisted_last_point_flag"
+                ].values
+                expected_persisted_last_point = np.full(
+                    (1, self.num_energies, 3600), expected_persisted_last_point_value
+                )
                 expected_persisted_last_point[:, :, 1000] = 0
-                np.testing.assert_array_equal(actual_persisted_last_point, expected_persisted_last_point, strict=True)
-                self.assertEqual(pointing_set.data["persisted_last_point_flag"].dims,
-                                 pointing_set.data["survival_probability_times_exposure"].dims)
+                np.testing.assert_array_equal(
+                    actual_persisted_last_point,
+                    expected_persisted_last_point,
+                    strict=True,
+                )
+                self.assertEqual(
+                    pointing_set.data["persisted_last_point_flag"].dims,
+                    pointing_set.data["survival_probability_times_exposure"].dims,
+                )
 
     def test_interpolate_angular_data_to_nearest_neighbor(self):
         input_cases = [
@@ -581,143 +875,206 @@ def test_interpolate_angular_data_to_nearest_neighbor(self):
         basic_glows_data = np.array([np.linspace(1, 360, 360, endpoint=True) + 1000])
         glows_cases = [
             ("basic", basic_glows_spin_angles, basic_glows_data),
-            ("0 instead of 360", np.mod(basic_glows_spin_angles, 360), basic_glows_data),
-            ("with 0 at start", np.roll(np.mod(basic_glows_spin_angles, 360), 1), np.roll(basic_glows_data, 1)),
-            ("rolled", np.roll(basic_glows_spin_angles, 90), np.roll(basic_glows_data, 90)),
+            (
+                "0 instead of 360",
+                np.mod(basic_glows_spin_angles, 360),
+                basic_glows_data,
+            ),
+            (
+                "with 0 at start",
+                np.roll(np.mod(basic_glows_spin_angles, 360), 1),
+                np.roll(basic_glows_data, 1),
+            ),
+            (
+                "rolled",
+                np.roll(basic_glows_spin_angles, 90),
+                np.roll(basic_glows_data, 90),
+            ),
         ]
         for name, glows_spin_angles, glows_data in glows_cases:
             with self.subTest(name):
                 input_angles, expected_output = np.array(input_cases).T
-                result = interpolate_angular_data_to_nearest_neighbor(input_angles, glows_spin_angles, glows_data)
+                result = interpolate_angular_data_to_nearest_neighbor(
+                    input_angles, glows_spin_angles, glows_data
+                )
                 np.testing.assert_array_equal(expected_output + 1000, result[0])
 
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.RectangularSkyMap.project_pset_values_to_map')
-    @patch('imap_l3_processing.maps.rectangular_survival_probability.RectangularSkyMap.__init__')
-    def test_survival_probability_map_construction(self, mock_skymap_constructor, mock_project_pset):
-        class TestableRectangularSurvivalProbabilitySkyMap(RectangularSurvivalProbabilitySkyMap):
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.RectangularSkyMap.project_pset_values_to_map"
+    )
+    @patch(
+        "imap_l3_processing.maps.rectangular_survival_probability.RectangularSkyMap.__init__"
+    )
+    def test_survival_probability_map_construction(
+        self, mock_skymap_constructor, mock_project_pset
+    ):
+        class TestableRectangularSurvivalProbabilitySkyMap(
+            RectangularSurvivalProbabilitySkyMap
+        ):
             def __init__(self, *args):
                 self.data_1d = MagicMock()
                 super().__init__(*args)
 
         pset_1 = MagicMock()
-        pset_1.data['directional_mask'] = sentinel.directional_mask_1
+        pset_1.data["directional_mask"] = sentinel.directional_mask_1
         pset_2 = MagicMock()
-        pset_2.data['directional_mask'] = sentinel.directional_mask_2
+        pset_2.data["directional_mask"] = sentinel.directional_mask_2
 
-        actual_sky_map = TestableRectangularSurvivalProbabilitySkyMap([pset_1, pset_2],
-                                                                      sentinel.spacing_deg,
-                                                                      sentinel.spice_frame)
+        actual_sky_map = TestableRectangularSurvivalProbabilitySkyMap(
+            [pset_1, pset_2], sentinel.spacing_deg, sentinel.spice_frame
+        )
         self.assertIsInstance(actual_sky_map, RectangularSkyMap)
 
-        mock_skymap_constructor.assert_called_with(sentinel.spacing_deg, sentinel.spice_frame)
-
-        expected_values_to_project = ["survival_probability_times_exposure", "exposure", "predicted_ephemeris_flag",
-                 "nominal_alpha_proton_ratio_flag", "persisted_last_point_flag"]
-        mock_project_pset.assert_has_calls([
-            call(pset_1, expected_values_to_project, pset_valid_mask=pset_1.data['directional_mask']),
-            call(pset_2, expected_values_to_project, pset_valid_mask=pset_2.data['directional_mask']),
-        ])
-
-    def test_survival_probability_sky_map_returns_exposure_weighted_survival_probabilities(self):
-        self.l1c_hi_dataset.hae_longitude = np.concat([
-            np.full((1, 1800), 30.05),
-            np.full((1, 1800), 210.05)
-        ], axis=1)
-
-        self.l1c_hi_dataset.hae_latitude = np.concat([
-            np.linspace(90, -90, 1800, endpoint=False) - 0.05,
-            np.linspace(-90, 90, 1800, endpoint=False) + 0.05,
-        ]).reshape(1, -1)
+        mock_skymap_constructor.assert_called_with(
+            sentinel.spacing_deg, sentinel.spice_frame
+        )
+
+        expected_values_to_project = [
+            "survival_probability_times_exposure",
+            "exposure",
+            "predicted_ephemeris_flag",
+            "nominal_alpha_proton_ratio_flag",
+            "persisted_last_point_flag",
+        ]
+        mock_project_pset.assert_has_calls(
+            [
+                call(
+                    pset_1,
+                    expected_values_to_project,
+                    pset_valid_mask=pset_1.data["directional_mask"],
+                ),
+                call(
+                    pset_2,
+                    expected_values_to_project,
+                    pset_valid_mask=pset_2.data["directional_mask"],
+                ),
+            ]
+        )
+
+    def test_survival_probability_sky_map_returns_exposure_weighted_survival_probabilities(
+        self,
+    ):
+        self.l1c_hi_dataset.hae_longitude = np.concat(
+            [np.full((1, 1800), 30.05), np.full((1, 1800), 210.05)], axis=1
+        )
+
+        self.l1c_hi_dataset.hae_latitude = np.concat(
+            [
+                np.linspace(90, -90, 1800, endpoint=False) - 0.05,
+                np.linspace(-90, 90, 1800, endpoint=False) + 0.05,
+            ]
+        ).reshape(1, -1)
         self.ram_mask = np.concat((np.full(1800, True), np.full(1800, False)))
 
         pset1_glows = self.glows_data
         pset1_l1c = self.l1c_hi_dataset
 
-        pset2_glows = dataclasses.replace(self.glows_data,
-                                          probability_of_survival=4 * self.glows_data.probability_of_survival)
-        pset2_l1c = dataclasses.replace(self.l1c_hi_dataset, exposure_times=2 * self.l1c_hi_dataset.exposure_times)
+        pset2_glows = dataclasses.replace(
+            self.glows_data,
+            probability_of_survival=4 * self.glows_data.probability_of_survival,
+        )
+        pset2_l1c = dataclasses.replace(
+            self.l1c_hi_dataset, exposure_times=2 * self.l1c_hi_dataset.exposure_times
+        )
 
-        pset1 = RectangularSurvivalProbabilityPointingSet(pset1_l1c, Sensor.Hi90, SpinPhase.RamOnly,
-                                                          pset1_glows, self.hi_energies)
+        pset1 = RectangularSurvivalProbabilityPointingSet(
+            pset1_l1c, Sensor.Hi90, SpinPhase.RamOnly, pset1_glows, self.hi_energies
+        )
 
-        pset2 = RectangularSurvivalProbabilityPointingSet(pset2_l1c, Sensor.Hi90, SpinPhase.RamOnly,
-                                                          pset2_glows, self.hi_energies)
+        pset2 = RectangularSurvivalProbabilityPointingSet(
+            pset2_l1c, Sensor.Hi90, SpinPhase.RamOnly, pset2_glows, self.hi_energies
+        )
 
         spice_frame = SpiceFrame.IMAP_HAE
-        actual_skymap = RectangularSurvivalProbabilitySkyMap([pset1, pset2],
-                                                             0.1, spice_frame)
+        actual_skymap = RectangularSurvivalProbabilitySkyMap(
+            [pset1, pset2], 0.1, spice_frame
+        )
         survival_probability_dataset = actual_skymap.to_dataset()
-        expected = 3 * np.repeat(self.glows_data.probability_of_survival[:, [0, -1], 0:180], 10, axis=2)
+        expected = 3 * np.repeat(
+            self.glows_data.probability_of_survival[:, [0, -1], 0:180], 10, axis=2
+        )
         expected = np.flip(expected, axis=2)
 
         survival_prob_in_skygrid_shape = np.full((1, 2, 3600, 1800), np.nan)
         survival_prob_in_skygrid_shape[:, :, 300, :] = expected
 
-        self.assertIn("exposure_weighted_survival_probabilities", survival_probability_dataset)
-        self.assertEqual((1, 2, 3600, 1800),
-                         survival_probability_dataset["exposure_weighted_survival_probabilities"].values.shape)
+        self.assertIn(
+            "exposure_weighted_survival_probabilities", survival_probability_dataset
+        )
+        self.assertEqual(
+            (1, 2, 3600, 1800),
+            survival_probability_dataset[
+                "exposure_weighted_survival_probabilities"
+            ].values.shape,
+        )
 
-        actual_sp_at_relevant_long = survival_probability_dataset["exposure_weighted_survival_probabilities"].values[:,
-                                     :, 300, :]
+        actual_sp_at_relevant_long = survival_probability_dataset[
+            "exposure_weighted_survival_probabilities"
+        ].values[:, :, 300, :]
 
         np.testing.assert_array_almost_equal(expected, actual_sp_at_relevant_long)
 
-        np.testing.assert_array_almost_equal(survival_prob_in_skygrid_shape,
-                                             survival_probability_dataset[
-                                                 "exposure_weighted_survival_probabilities"].values)
+        np.testing.assert_array_almost_equal(
+            survival_prob_in_skygrid_shape,
+            survival_probability_dataset[
+                "exposure_weighted_survival_probabilities"
+            ].values,
+        )
 
     def test_survival_probability_sky_map_returns_flags(self):
-        self.l1c_hi_dataset.hae_longitude = np.concat([
-            np.full((1, 1800), 30.05),
-            np.full((1, 1800), 210.05)
-        ], axis=1)
-
-        self.l1c_hi_dataset.hae_latitude = np.concat([
-            np.linspace(90, -90, 1800, endpoint=False) - 0.05,
-            np.linspace(-90, 90, 1800, endpoint=False) + 0.05,
-        ]).reshape(1, -1)
+        self.l1c_hi_dataset.hae_longitude = np.concat(
+            [np.full((1, 1800), 30.05), np.full((1, 1800), 210.05)], axis=1
+        )
+
+        self.l1c_hi_dataset.hae_latitude = np.concat(
+            [
+                np.linspace(90, -90, 1800, endpoint=False) - 0.05,
+                np.linspace(-90, 90, 1800, endpoint=False) + 0.05,
+            ]
+        ).reshape(1, -1)
         self.ram_mask = np.concat((np.full(1800, True), np.full(1800, False)))
 
         cases = {
-            (
-                GlowsL3Flags.NONE,
-                GlowsL3Flags.NONE,
-                MapL3Flags.NONE
-            ),
+            (GlowsL3Flags.NONE, GlowsL3Flags.NONE, MapL3Flags.NONE),
             (
                 GlowsL3Flags.PREDICTIVE_EPHEMERIS,
                 GlowsL3Flags.NONE,
-                MapL3Flags.PREDICTIVE_EPHEMERIS
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
             ),
             (
                 GlowsL3Flags.PREDICTIVE_EPHEMERIS,
                 GlowsL3Flags.PREDICTIVE_EPHEMERIS,
-                MapL3Flags.PREDICTIVE_EPHEMERIS
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
             ),
             (
                 GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
                 GlowsL3Flags.NONE,
-                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
             ),
             (
-                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                | GlowsL3Flags.PREDICTIVE_EPHEMERIS,
                 GlowsL3Flags.NONE,
-                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS
+                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS,
             ),
             (
                 GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
                 GlowsL3Flags.PREDICTIVE_EPHEMERIS,
-                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS
+                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS,
             ),
             (
                 GlowsL3Flags.PREDICTIVE_EPHEMERIS,
                 GlowsL3Flags.PERSISTED_LAST_POINT,
-                MapL3Flags.PREDICTIVE_EPHEMERIS | MapL3Flags.PERSISTED_LAST_POINT
-            )
+                MapL3Flags.PREDICTIVE_EPHEMERIS | MapL3Flags.PERSISTED_LAST_POINT,
+            ),
         }
 
         for pset1_flags, pset2_flags, expected_quality_flags in cases:
-            with self.subTest(pset1_flags=pset1_flags, pset2_flags=pset2_flags, expected=expected_quality_flags):
+            with self.subTest(
+                pset1_flags=pset1_flags,
+                pset2_flags=pset2_flags,
+                expected=expected_quality_flags,
+            ):
                 pset1_glows = copy.deepcopy(self.glows_data)
                 pset1_glows.flags[0] = pset1_flags
                 pset1_l1c = pset2_l1c = self.l1c_hi_dataset
@@ -725,21 +1082,40 @@ def test_survival_probability_sky_map_returns_flags(self):
                 pset2_glows = copy.deepcopy(self.glows_data)
                 pset2_glows.flags[0] = pset2_flags
 
-                pset1 = RectangularSurvivalProbabilityPointingSet(pset1_l1c, Sensor.Hi90, SpinPhase.RamOnly,
-                                                                  pset1_glows, self.hi_energies)
+                pset1 = RectangularSurvivalProbabilityPointingSet(
+                    pset1_l1c,
+                    Sensor.Hi90,
+                    SpinPhase.RamOnly,
+                    pset1_glows,
+                    self.hi_energies,
+                )
 
-                pset2 = RectangularSurvivalProbabilityPointingSet(pset2_l1c, Sensor.Hi90, SpinPhase.RamOnly,
-                                                                  pset2_glows, self.hi_energies)
+                pset2 = RectangularSurvivalProbabilityPointingSet(
+                    pset2_l1c,
+                    Sensor.Hi90,
+                    SpinPhase.RamOnly,
+                    pset2_glows,
+                    self.hi_energies,
+                )
 
                 spice_frame = SpiceFrame.IMAP_HAE
-                actual_skymap = RectangularSurvivalProbabilitySkyMap([pset1, pset2],
-                                                                     0.1, spice_frame)
+                actual_skymap = RectangularSurvivalProbabilitySkyMap(
+                    [pset1, pset2], 0.1, spice_frame
+                )
                 survival_probability_dataset = actual_skymap.to_dataset()
 
-                expected_flags_in_full_shape = np.full((1, 2, 3600, 1800), MapL3Flags.NONE)
+                expected_flags_in_full_shape = np.full(
+                    (1, 2, 3600, 1800), MapL3Flags.NONE
+                )
                 expected_flags_in_full_shape[:, :, 300, :] = expected_quality_flags
 
                 self.assertIn("quality_flags", survival_probability_dataset)
-                self.assertEqual((1, 2, 3600, 1800), survival_probability_dataset["quality_flags"].values.shape)
+                self.assertEqual(
+                    (1, 2, 3600, 1800),
+                    survival_probability_dataset["quality_flags"].values.shape,
+                )
 
-                np.testing.assert_array_equal(expected_flags_in_full_shape, survival_probability_dataset["quality_flags"].values)
+                np.testing.assert_array_equal(
+                    expected_flags_in_full_shape,
+                    survival_probability_dataset["quality_flags"].values,
+                )
diff --git a/tests/maps/test_spectral_fit.py b/tests/maps/test_spectral_fit.py
index 269ad4ba3..7b3f012c6 100644
--- a/tests/maps/test_spectral_fit.py
+++ b/tests/maps/test_spectral_fit.py
@@ -1,16 +1,24 @@
 import dataclasses
 import unittest
 from datetime import datetime
-from unittest.mock import patch, Mock, sentinel
+from unittest.mock import Mock, patch, sentinel
 
 import numpy as np
 from scipy.stats import linregress
 
-from imap_l3_processing.maps.map_models import IntensityMapData, RectangularIntensityMapData, QUALITY_FLAGS_VAR_NAME
+from imap_l3_processing.maps.map_models import (
+    IntensityMapData,
+    RectangularIntensityMapData,
+)
 from imap_l3_processing.maps.mpfit import mpfit
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.maps.spectral_fit import power_law, fit_arrays_to_power_law, fit_spectral_index_map, \
-    calculate_spectral_index_for_multiple_ranges, slice_energy_range_by_bin
+from imap_l3_processing.maps.spectral_fit import (
+    calculate_spectral_index_for_multiple_ranges,
+    fit_arrays_to_power_law,
+    fit_spectral_index_map,
+    power_law,
+    slice_energy_range_by_bin,
+)
 from tests.test_helpers import get_test_data_path
 
 
@@ -20,7 +28,7 @@ def test_power_law_function(self):
         x = np.array([1, 2, 3])
         y = np.array([4, 10, 22])
         err = np.array([2, 2, 2])
-        keywords = {'xval': x, 'yval': y, 'errval': err}
+        keywords = {"xval": x, "yval": y, "errval": err}
 
         expected_residual = np.array([1, 1, 2])
         status, actual_residuals = power_law(params, **keywords)
@@ -35,65 +43,102 @@ def test_finds_best_fit(self):
 
         errors = 0.2 * np.abs(flux_data)
 
-        cases = [
-            ("rectangular", (1, 1)),
-            ("healpix", (1,))
-        ]
+        cases = [("rectangular", (1, 1)), ("healpix", (1,))]
 
         for name, spacial_dimension_shape in cases:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
-
-                result_A, result_A_error, result_gamma, result_gamma_error, result_chi_square = fit_arrays_to_power_law(flux, uncertainty, energies)
-                np.testing.assert_array_equal(result_gamma,
-                                              np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(result_gamma_error,
-                                                     np.array([0.060068]).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(result_A, np.array(true_A).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(result_A_error,
-                                                     np.array([0.161515]).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal( result_chi_square ,np.zeros((1,1, *spacial_dimension_shape)))
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+
+                (
+                    result_A,
+                    result_A_error,
+                    result_gamma,
+                    result_gamma_error,
+                    result_chi_square,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                np.testing.assert_array_equal(
+                    result_gamma,
+                    np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_almost_equal(
+                    result_gamma_error,
+                    np.array([0.060068]).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    result_A, np.array(true_A).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_array_almost_equal(
+                    result_A_error,
+                    np.array([0.161515]).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_almost_equal(
+                    result_chi_square, np.zeros((1, 1, *spacial_dimension_shape))
+                )
 
     def test_finds_chi_squared_for_fit(self):
         energies = np.array([1, 10, 30, 100])
         flux_data = np.array([99, 10, 3, 1])
-        errors = np.array([1,1, 0.1, 1e-3])
+        errors = np.array([1, 1, 0.1, 1e-3])
 
-        cases = [
-            ("rectangular", (1, 1)),
-            ("healpix", (1,))
-        ]
+        cases = [("rectangular", (1, 1)), ("healpix", (1,))]
 
         for name, spacial_dimension_shape in cases:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
 
-                result_A, result_A_error, result_gamma, result_gamma_error, result_chi_square = fit_arrays_to_power_law(flux, uncertainty, energies)
-                fitted_flux = result_A * np.power(energies.reshape((-1,*spacial_dimension_shape)), -result_gamma)
+                (
+                    result_A,
+                    result_A_error,
+                    result_gamma,
+                    result_gamma_error,
+                    result_chi_square,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                fitted_flux = result_A * np.power(
+                    energies.reshape((-1, *spacial_dimension_shape)), -result_gamma
+                )
                 residual = fitted_flux - flux
-                chisquared = np.sum(np.square(residual/uncertainty))
+                chisquared = np.sum(np.square(residual / uncertainty))
                 reduced_chisquared = chisquared / (len(energies) - 2)
-                np.testing.assert_array_almost_equal(result_chi_square, reduced_chisquared)
+                np.testing.assert_array_almost_equal(
+                    result_chi_square, reduced_chisquared
+                )
 
     def test_handles_small_fluxes(self):
-        energies = np.array([5.65826829,  8.45453724, 12.70203527])
+        energies = np.array([5.65826829, 8.45453724, 12.70203527])
         flux_data = np.array([0.12815728, 0.00414464, 0.0383057])
         errors = np.array([0.12532869, 0.00414464, 0.02755597])
 
-        cases = [
-            ("rectangular", (1, 1)),
-            ("healpix", (1,))
-        ]
+        cases = [("rectangular", (1, 1)), ("healpix", (1,))]
 
         for name, spacial_dimension_shape in cases:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
 
-                result_a, result_a_error, result_gamma, result_gamma_error, result_chi_square = fit_arrays_to_power_law(flux, uncertainty, energies)
-                fitted_flux = result_a * np.power(energies.reshape((-1,*spacial_dimension_shape)), -result_gamma)
+                (
+                    result_a,
+                    result_a_error,
+                    result_gamma,
+                    result_gamma_error,
+                    result_chi_square,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                fitted_flux = result_a * np.power(
+                    energies.reshape((-1, *spacial_dimension_shape)), -result_gamma
+                )
                 self.assertGreater(result_a, 0)
 
     def test_spectral_fit_map(self):
@@ -132,24 +177,44 @@ def test_spectral_fit_map(self):
         expected_energy_midpoint = np.sqrt(0.5 * 10.5)
         expected_energy_minus = expected_energy_midpoint - 0.5
         expected_energy_plus = 10.5 - expected_energy_midpoint
-        expected_energy_label = f"0.5 - 10.5"
-        np.testing.assert_array_equal(spectral_intensity_map.ena_spectral_index,
-                                      np.array(true_gamma).reshape(1, 1, 1, 1))
+        expected_energy_label = "0.5 - 10.5"
+        np.testing.assert_array_equal(
+            spectral_intensity_map.ena_spectral_index,
+            np.array(true_gamma).reshape(1, 1, 1, 1),
+        )
 
-        np.testing.assert_array_almost_equal(spectral_intensity_map.ena_spectral_index_stat_uncert,
-                                             np.array([0.060068]).reshape(1, 1, 1, 1))
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.ena_spectral_index_stat_uncert,
+            np.array([0.060068]).reshape(1, 1, 1, 1),
+        )
 
-        np.testing.assert_array_almost_equal(spectral_intensity_map.ena_spectral_index_scalar_coefficient_stat_uncert,
-                                             np.array([0.161515]).reshape(1, 1, 1, 1))
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.ena_spectral_index_scalar_coefficient_stat_uncert,
+            np.array([0.161515]).reshape(1, 1, 1, 1),
+        )
 
-        np.testing.assert_array_almost_equal(spectral_intensity_map.energy, [expected_energy_midpoint])
-        np.testing.assert_array_almost_equal(spectral_intensity_map.energy_delta_minus, [expected_energy_minus])
-        np.testing.assert_array_almost_equal(spectral_intensity_map.energy_delta_plus, [expected_energy_plus])
-        np.testing.assert_equal(spectral_intensity_map.energy_label, [expected_energy_label])
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.energy, [expected_energy_midpoint]
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.energy_delta_minus, [expected_energy_minus]
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.energy_delta_plus, [expected_energy_plus]
+        )
+        np.testing.assert_equal(
+            spectral_intensity_map.energy_label, [expected_energy_label]
+        )
 
-        np.testing.assert_array_almost_equal(spectral_intensity_map.longitude, data.longitude)
-        np.testing.assert_array_almost_equal(spectral_intensity_map.latitude, data.latitude)
-        np.testing.assert_array_almost_equal(spectral_intensity_map.solid_angle, data.solid_angle)
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.longitude, data.longitude
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.latitude, data.latitude
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.solid_angle, data.solid_angle
+        )
 
     def test_spectral_fit_fields_other_than_fit_fields(self):
         input_energies = np.array([1, 10, 99]) + 0.5
@@ -182,7 +247,7 @@ def test_spectral_fit_fields_other_than_fit_fields(self):
             ena_intensity=np.full(input_shape, 1),
             ena_intensity_sys_err=np.full(input_shape, 1),
             ena_intensity_stat_uncert=np.full(input_shape, 1),
-            quality_flags=quality_flags
+            quality_flags=quality_flags,
         )
 
         input_map.obs_date[0, 0] = datetime(2025, 1, 1)
@@ -207,14 +272,21 @@ def test_spectral_fit_fields_other_than_fit_fields(self):
         self.assertEqual("1.0 - 100.0", output.energy_label[0])
 
         expected_ena_shape = np.array([1, 1, len(longitude), len(latitude)])
-        np.testing.assert_array_equal(output.obs_date,
-                                      np.full(expected_ena_shape, datetime(2026, 1, 1)))
-        np.testing.assert_array_equal(output.obs_date_range, np.full(expected_ena_shape, 2))
-        np.testing.assert_array_equal(output.exposure_factor, np.full(expected_ena_shape, 6))
+        np.testing.assert_array_equal(
+            output.obs_date, np.full(expected_ena_shape, datetime(2026, 1, 1))
+        )
+        np.testing.assert_array_equal(
+            output.obs_date_range, np.full(expected_ena_shape, 2)
+        )
+        np.testing.assert_array_equal(
+            output.exposure_factor, np.full(expected_ena_shape, 6)
+        )
 
         expected_quality_flags = np.full(expected_ena_shape, MapL3Flags.NONE)
         expected_quality_flags[0, 0, 0] = MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
-        expected_quality_flags[0, 0, 1] = MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS
+        expected_quality_flags[0, 0, 1] = (
+            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS
+        )
         expected_quality_flags[0, 0, 2:4] = MapL3Flags.PREDICTIVE_EPHEMERIS
         expected_quality_flags[0, 0, 4] = MapL3Flags.PERSISTED_LAST_POINT
 
@@ -230,22 +302,34 @@ def test_finds_best_fit_with_nan_in_flux(self):
 
         errors = 0.2 * np.abs(flux_data)
 
-        cases = [
-            ("rectangular", (1, 1)),
-            ("healpix", (1,))
-        ]
+        cases = [("rectangular", (1, 1)), ("healpix", (1,))]
 
         for name, spacial_dimension_shape in cases:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
-
-                scalar_coefficients, scalar_coefficient_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-                np.testing.assert_array_equal(result, np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_allclose(scalar_coefficients,
-                                           np.array(true_A).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(chisq, np.zeros((1, 1, *spacial_dimension_shape)))
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
 
+                (
+                    scalar_coefficients,
+                    scalar_coefficient_error,
+                    result,
+                    result_error,
+                    chisq,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                np.testing.assert_array_equal(
+                    result, np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_allclose(
+                    scalar_coefficients,
+                    np.array(true_A).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    chisq, np.zeros((1, 1, *spacial_dimension_shape))
+                )
 
     def test_spectral_fit_map_negative_gammas(self):
         epoch = np.array([datetime.now()])
@@ -283,24 +367,46 @@ def test_spectral_fit_map_negative_gammas(self):
         expected_energy_midpoint = np.sqrt(0.5 * 10.5)
         expected_energy_minus = expected_energy_midpoint - 0.5
         expected_energy_plus = 10.5 - expected_energy_midpoint
-        expected_energy_label = f"0.5 - 10.5"
-        np.testing.assert_array_equal(spectral_intensity_map.ena_spectral_index_stat_uncert,
-                                      np.full(shape=(1, 1, 1, 1), fill_value=np.nan))
-        np.testing.assert_array_equal(spectral_intensity_map.ena_spectral_index_scalar_coefficient,
-                                      np.full(shape=(1, 1, 1, 1), fill_value=np.nan))
-        np.testing.assert_array_equal(spectral_intensity_map.ena_spectral_index,
-                                      np.full(shape=(1, 1, 1, 1), fill_value=np.nan))
-        np.testing.assert_array_equal(spectral_intensity_map.ena_spectral_index_chisq,
-                                      np.full(shape=(1, 1, 1, 1), fill_value=np.nan))
-
-        np.testing.assert_array_almost_equal(spectral_intensity_map.energy, [expected_energy_midpoint])
-        np.testing.assert_array_almost_equal(spectral_intensity_map.energy_delta_minus, [expected_energy_minus])
-        np.testing.assert_array_almost_equal(spectral_intensity_map.energy_delta_plus, [expected_energy_plus])
-        np.testing.assert_equal(spectral_intensity_map.energy_label, [expected_energy_label])
-
-        np.testing.assert_array_almost_equal(spectral_intensity_map.longitude, data.longitude)
-        np.testing.assert_array_almost_equal(spectral_intensity_map.latitude, data.latitude)
-        np.testing.assert_array_almost_equal(spectral_intensity_map.solid_angle, data.solid_angle)
+        expected_energy_label = "0.5 - 10.5"
+        np.testing.assert_array_equal(
+            spectral_intensity_map.ena_spectral_index_stat_uncert,
+            np.full(shape=(1, 1, 1, 1), fill_value=np.nan),
+        )
+        np.testing.assert_array_equal(
+            spectral_intensity_map.ena_spectral_index_scalar_coefficient,
+            np.full(shape=(1, 1, 1, 1), fill_value=np.nan),
+        )
+        np.testing.assert_array_equal(
+            spectral_intensity_map.ena_spectral_index,
+            np.full(shape=(1, 1, 1, 1), fill_value=np.nan),
+        )
+        np.testing.assert_array_equal(
+            spectral_intensity_map.ena_spectral_index_chisq,
+            np.full(shape=(1, 1, 1, 1), fill_value=np.nan),
+        )
+
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.energy, [expected_energy_midpoint]
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.energy_delta_minus, [expected_energy_minus]
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.energy_delta_plus, [expected_energy_plus]
+        )
+        np.testing.assert_equal(
+            spectral_intensity_map.energy_label, [expected_energy_label]
+        )
+
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.longitude, data.longitude
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.latitude, data.latitude
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_intensity_map.solid_angle, data.solid_angle
+        )
 
     def test_finds_best_fit_with_nan_in_uncertainty(self):
         energies = np.geomspace(1, 10, 23)
@@ -312,16 +418,35 @@ def test_finds_best_fit_with_nan_in_uncertainty(self):
         errors[0] = np.nan
         errors[-1] = np.nan
 
-        for name, spacial_dimension_shape in [("rectangular", (1, 1)), ("healpix", (1,))]:
+        for name, spacial_dimension_shape in [
+            ("rectangular", (1, 1)),
+            ("healpix", (1,)),
+        ]:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
 
-                scalar_coefficients, scalar_coefficient_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-                np.testing.assert_array_equal(result, np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_allclose(scalar_coefficients,
-                                           np.array(true_A).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(chisq, np.zeros((1, 1, *spacial_dimension_shape)))
+                (
+                    scalar_coefficients,
+                    scalar_coefficient_error,
+                    result,
+                    result_error,
+                    chisq,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                np.testing.assert_array_equal(
+                    result, np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_allclose(
+                    scalar_coefficients,
+                    np.array(true_A).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    chisq, np.zeros((1, 1, *spacial_dimension_shape))
+                )
 
     def test_finds_best_fit_with_zero_in_flux_and_uncertainty(self):
         energies = np.geomspace(1, 10, 23)
@@ -332,51 +457,84 @@ def test_finds_best_fit_with_zero_in_flux_and_uncertainty(self):
         flux_data[0:3] = 0
         errors[0:3] = 0
 
-        for name, spacial_dimension_shape in [("rectangular", (1, 1)), ("healpix", (1,))]:
+        for name, spacial_dimension_shape in [
+            ("rectangular", (1, 1)),
+            ("healpix", (1,)),
+        ]:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
 
-                scalar_coefficients, scalar_coefficient_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-                np.testing.assert_array_equal(result, np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(scalar_coefficients,
-                                              np.array(true_A).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(chisq, np.zeros((1, 1, *spacial_dimension_shape)))
+                (
+                    scalar_coefficients,
+                    scalar_coefficient_error,
+                    result,
+                    result_error,
+                    chisq,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                np.testing.assert_array_equal(
+                    result, np.array(true_gamma).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_array_equal(
+                    scalar_coefficients,
+                    np.array(true_A).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    chisq, np.zeros((1, 1, *spacial_dimension_shape))
+                )
 
     def test_finds_best_fit_with_ibex_data(self):
         energies = np.array([0.71, 1.11, 1.74, 2.73, 4.29])
-        flux_data = np.array([[[[46.710853, 61.45169],
-                                [60.682266, 60.523616]],
-                               [[34.896639, 11.987692],
-                                [29.323209, 19.239248]],
-                               [[18.899919, 17.140896],
-                                [16.12169, 14.099767]],
-                               [[14.709325, 12.165362],
-                                [13.114869, 13.726924]],
-                               [[5.815468, 5.071331],
-                                [6.060111, 6.499908]]]])
-        errors = np.array([[[[4.17239236e+02, 5.87577556e+02],
-                             [4.59947913e+02, 7.02472212e+02]],
-                            [[6.38942760e+01, 3.62103490e+01],
-                             [2.87564900e+01, 5.06517210e+01]],
-                            [[1.58932960e+01, 1.41284380e+01],
-                             [1.34992710e+01, 9.66067900e+00]],
-                            [[2.01988700e+00, 2.75456600e+00],
-                             [1.83946100e+00, 2.25438200e+00]],
-                            [[3.00438000e-01, 3.76740000e-01],
-                             [2.15943000e-01, 3.76271000e-01]]]])
-
-        scalar_coefficients, scalar_coefficient_errors, index, index_error, chisq = fit_arrays_to_power_law(flux_data, errors, energies)
-        np.testing.assert_array_almost_equal(index, np.array([[[[1.811566, 1.489658],
-                                                                 [1.480259, 1.317993]]]]))
-        np.testing.assert_array_almost_equal(index_error, np.array([[[[0.279224, 0.409522],
-                                                                       [0.260374, 0.318162]]]]))
-        np.testing.assert_array_almost_equal(scalar_coefficients, np.array([[[[81.92187 , 44.822599],
-                                                                            [52.494651, 44.647085]]]]))
-        np.testing.assert_array_almost_equal(scalar_coefficient_errors, np.array([[[[32.245376, 26.075033],
-         [19.584559, 20.080747]]]]))
-        np.testing.assert_array_almost_equal(chisq, np.array([[[[0.44621 , 0.391638],
-         [0.344915, 0.475525]]]]))
+        flux_data = np.array(
+            [
+                [
+                    [[46.710853, 61.45169], [60.682266, 60.523616]],
+                    [[34.896639, 11.987692], [29.323209, 19.239248]],
+                    [[18.899919, 17.140896], [16.12169, 14.099767]],
+                    [[14.709325, 12.165362], [13.114869, 13.726924]],
+                    [[5.815468, 5.071331], [6.060111, 6.499908]],
+                ]
+            ]
+        )
+        errors = np.array(
+            [
+                [
+                    [[4.17239236e02, 5.87577556e02], [4.59947913e02, 7.02472212e02]],
+                    [[6.38942760e01, 3.62103490e01], [2.87564900e01, 5.06517210e01]],
+                    [[1.58932960e01, 1.41284380e01], [1.34992710e01, 9.66067900e00]],
+                    [[2.01988700e00, 2.75456600e00], [1.83946100e00, 2.25438200e00]],
+                    [
+                        [3.00438000e-01, 3.76740000e-01],
+                        [2.15943000e-01, 3.76271000e-01],
+                    ],
+                ]
+            ]
+        )
+
+        scalar_coefficients, scalar_coefficient_errors, index, index_error, chisq = (
+            fit_arrays_to_power_law(flux_data, errors, energies)
+        )
+        np.testing.assert_array_almost_equal(
+            index, np.array([[[[1.811566, 1.489658], [1.480259, 1.317993]]]])
+        )
+        np.testing.assert_array_almost_equal(
+            index_error, np.array([[[[0.279224, 0.409522], [0.260374, 0.318162]]]])
+        )
+        np.testing.assert_array_almost_equal(
+            scalar_coefficients,
+            np.array([[[[81.92187, 44.822599], [52.494651, 44.647085]]]]),
+        )
+        np.testing.assert_array_almost_equal(
+            scalar_coefficient_errors,
+            np.array([[[[32.245376, 26.075033], [19.584559, 20.080747]]]]),
+        )
+        np.testing.assert_array_almost_equal(
+            chisq, np.array([[[[0.44621, 0.391638], [0.344915, 0.475525]]]])
+        )
 
     def test_finds_best_fit_with_zeros_in_flux_and_not_uncertainty(self):
         energies = np.geomspace(1, 1e10, 23)
@@ -387,17 +545,35 @@ def test_finds_best_fit_with_zeros_in_flux_and_not_uncertainty(self):
 
         flux_data[0:3] = 0
 
-        for name, spacial_dimension_shape in [("rectangular", (1, 1)), ("healpix", (1,))]:
+        for name, spacial_dimension_shape in [
+            ("rectangular", (1, 1)),
+            ("healpix", (1,)),
+        ]:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
 
-                scalar_coefficients, scalar_coefficient_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-                np.testing.assert_array_almost_equal(result,
-                                                     np.array([1.472697]).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(scalar_coefficients,
-                                                     np.array([1.251819]).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(chisq, np.array([2.218472]).reshape(1, 1, *spacial_dimension_shape))
+                (
+                    scalar_coefficients,
+                    scalar_coefficient_error,
+                    result,
+                    result_error,
+                    chisq,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                np.testing.assert_array_almost_equal(
+                    result, np.array([1.472697]).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_array_almost_equal(
+                    scalar_coefficients,
+                    np.array([1.251819]).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_almost_equal(
+                    chisq, np.array([2.218472]).reshape(1, 1, *spacial_dimension_shape)
+                )
 
     def test_returns_nan_when_only_one_point_is_valid(self):
         energies = np.geomspace(1, 1e10, 5)
@@ -409,21 +585,43 @@ def test_returns_nan_when_only_one_point_is_valid(self):
         flux_data[1:] = 0
         errors[1:] = 0
 
-        for name, spacial_dimension_shape in [("rectangular", (1, 1)), ("healpix", (1,))]:
+        for name, spacial_dimension_shape in [
+            ("rectangular", (1, 1)),
+            ("healpix", (1,)),
+        ]:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
-                scalar_coefficients, scalar_coefficient_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-
-                np.testing.assert_array_almost_equal(result,
-                                                     np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(result_error,
-                                                     np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(scalar_coefficients,
-                                              np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(scalar_coefficient_error,
-                                              np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(chisq, np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                (
+                    scalar_coefficients,
+                    scalar_coefficient_error,
+                    result,
+                    result_error,
+                    chisq,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+
+                np.testing.assert_array_almost_equal(
+                    result, np.array(np.nan).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_array_almost_equal(
+                    result_error,
+                    np.array(np.nan).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    scalar_coefficients,
+                    np.array(np.nan).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    scalar_coefficient_error,
+                    np.array(np.nan).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    chisq, np.array(np.nan).reshape(1, 1, *spacial_dimension_shape)
+                )
 
     def test_returns_nan_when_there_are_less_than_2_positive_fluxes(self):
         energies = np.geomspace(1, 1e10, 5)
@@ -431,21 +629,43 @@ def test_returns_nan_when_there_are_less_than_2_positive_fluxes(self):
         flux_data = np.full_like(energies, 0)
         errors = np.full_like(flux_data, 2)
 
-        for name, spacial_dimension_shape in [("rectangular", (1, 1)), ("healpix", (1,))]:
+        for name, spacial_dimension_shape in [
+            ("rectangular", (1, 1)),
+            ("healpix", (1,)),
+        ]:
             with self.subTest(name):
-                flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
-
-                scalar_coefficients, scalar_coefficient_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-                np.testing.assert_array_almost_equal(result,
-                                                     np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(result_error,
-                                                     np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(scalar_coefficients,
-                                                     np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_almost_equal(scalar_coefficient_error,
-                                                     np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
-                np.testing.assert_array_equal(chisq, np.array(np.nan).reshape(1, 1, *spacial_dimension_shape))
+                flux = np.array(flux_data).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+                uncertainty = np.array(errors).reshape(
+                    1, len(energies), *spacial_dimension_shape
+                )
+
+                (
+                    scalar_coefficients,
+                    scalar_coefficient_error,
+                    result,
+                    result_error,
+                    chisq,
+                ) = fit_arrays_to_power_law(flux, uncertainty, energies)
+                np.testing.assert_array_almost_equal(
+                    result, np.array(np.nan).reshape(1, 1, *spacial_dimension_shape)
+                )
+                np.testing.assert_array_almost_equal(
+                    result_error,
+                    np.array(np.nan).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_almost_equal(
+                    scalar_coefficients,
+                    np.array(np.nan).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_almost_equal(
+                    scalar_coefficient_error,
+                    np.array(np.nan).reshape(1, 1, *spacial_dimension_shape),
+                )
+                np.testing.assert_array_equal(
+                    chisq, np.array(np.nan).reshape(1, 1, *spacial_dimension_shape)
+                )
 
     def test_spectral_fit_can_fit_multiple_energy_ranges(self):
         epoch = np.array([datetime.now()])
@@ -453,8 +673,12 @@ def test_spectral_fit_can_fit_multiple_energy_ranges(self):
         input_energy_range_2 = np.geomspace(101, 10000, 12)
         true_A_range_1, true_gamma_range_1 = 2.0, 1.5
         true_A_range_2, true_gamma_range_2 = 0.1, 3.5
-        flux_data_range_1 = true_A_range_1 * np.power(input_energy_range_1, -true_gamma_range_1)
-        flux_data_range_2 = true_A_range_2 * np.power(input_energy_range_2, -true_gamma_range_2)
+        flux_data_range_1 = true_A_range_1 * np.power(
+            input_energy_range_1, -true_gamma_range_1
+        )
+        flux_data_range_2 = true_A_range_2 * np.power(
+            input_energy_range_2, -true_gamma_range_2
+        )
         longitude = np.array([45])
         latitude = np.array([90])
         errors_range_1 = 0.1 * np.abs(flux_data_range_1)
@@ -470,12 +694,16 @@ def test_spectral_fit_can_fit_multiple_energy_ranges(self):
         quality_flags_range_2 = np.full((12,), MapL3Flags.NONE)
         quality_flags_range_2[1] = MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
         quality_flags_range_2[2] = MapL3Flags.PREDICTIVE_EPHEMERIS
-        quality_flags = np.concat((quality_flags_range_1, quality_flags_range_2)).reshape(full_shape)
+        quality_flags = np.concat(
+            (quality_flags_range_1, quality_flags_range_2)
+        ).reshape(full_shape)
 
-        expected_quality_flags = np.array([
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS
-        ]).reshape((1, 2, 1, 1))
+        expected_quality_flags = np.array(
+            [
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PREDICTIVE_EPHEMERIS,
+            ]
+        ).reshape((1, 2, 1, 1))
 
         data = IntensityMapData(
             epoch=epoch,
@@ -499,52 +727,84 @@ def test_spectral_fit_can_fit_multiple_energy_ranges(self):
         )
 
         output_energies = np.array([[1, 100.5], [100.5, 10000.5]])
-        spectral_index_map_data = calculate_spectral_index_for_multiple_ranges(data, output_energies)
-        np.testing.assert_array_equal(spectral_index_map_data.ena_spectral_index[0, 0, 0, 0], true_gamma_range_1)
-        np.testing.assert_array_equal(spectral_index_map_data.ena_spectral_index[0, 1, 0, 0], true_gamma_range_2)
-        np.testing.assert_almost_equal(spectral_index_map_data.ena_spectral_index_scalar_coefficient[0, 0, 0, 0],
-                                       true_A_range_1)
-        np.testing.assert_almost_equal(spectral_index_map_data.ena_spectral_index_scalar_coefficient[0, 1, 0, 0],
-                                       true_A_range_2)
-        np.testing.assert_array_almost_equal(spectral_index_map_data.ena_spectral_index_stat_uncert[0, 0, 0, 0],
-                                             0.020704)
-        np.testing.assert_array_almost_equal(spectral_index_map_data.ena_spectral_index_stat_uncert[0, 1, 0, 0],
-                                             2.00179e-05)
-
-        np.testing.assert_almost_equal(spectral_index_map_data.energy,
-                                       np.array([7.088723439378913, 1002.5219448969683]))
-        np.testing.assert_almost_equal(spectral_index_map_data.energy_delta_plus, np.array([93.4112766, 8997.9780551]))
-        np.testing.assert_almost_equal(spectral_index_map_data.energy_delta_minus, np.array([6.5887234, 902.0219449]))
+        spectral_index_map_data = calculate_spectral_index_for_multiple_ranges(
+            data, output_energies
+        )
+        np.testing.assert_array_equal(
+            spectral_index_map_data.ena_spectral_index[0, 0, 0, 0], true_gamma_range_1
+        )
+        np.testing.assert_array_equal(
+            spectral_index_map_data.ena_spectral_index[0, 1, 0, 0], true_gamma_range_2
+        )
+        np.testing.assert_almost_equal(
+            spectral_index_map_data.ena_spectral_index_scalar_coefficient[0, 0, 0, 0],
+            true_A_range_1,
+        )
+        np.testing.assert_almost_equal(
+            spectral_index_map_data.ena_spectral_index_scalar_coefficient[0, 1, 0, 0],
+            true_A_range_2,
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_index_map_data.ena_spectral_index_stat_uncert[0, 0, 0, 0], 0.020704
+        )
+        np.testing.assert_array_almost_equal(
+            spectral_index_map_data.ena_spectral_index_stat_uncert[0, 1, 0, 0],
+            2.00179e-05,
+        )
+
+        np.testing.assert_almost_equal(
+            spectral_index_map_data.energy,
+            np.array([7.088723439378913, 1002.5219448969683]),
+        )
+        np.testing.assert_almost_equal(
+            spectral_index_map_data.energy_delta_plus,
+            np.array([93.4112766, 8997.9780551]),
+        )
+        np.testing.assert_almost_equal(
+            spectral_index_map_data.energy_delta_minus,
+            np.array([6.5887234, 902.0219449]),
+        )
         np.testing.assert_array_equal(spectral_index_map_data.epoch, epoch)
-        np.testing.assert_array_equal(spectral_index_map_data.epoch_delta, data.epoch_delta)
+        np.testing.assert_array_equal(
+            spectral_index_map_data.epoch_delta, data.epoch_delta
+        )
         np.testing.assert_array_equal(spectral_index_map_data.latitude, latitude)
         np.testing.assert_array_equal(spectral_index_map_data.longitude, longitude)
-        np.testing.assert_array_equal(spectral_index_map_data.solid_angle, data.solid_angle)
+        np.testing.assert_array_equal(
+            spectral_index_map_data.solid_angle, data.solid_angle
+        )
 
         np.testing.assert_array_equal(
             spectral_index_map_data.energy_label, ["0.5 - 100.5", "100.5 - 10000.5"]
         )
 
-        np.testing.assert_array_equal(spectral_index_map_data.exposure_factor, np.reshape([11, 12], (1, 2, 1, 1)))
-        np.testing.assert_array_equal(spectral_index_map_data.obs_date,
-                                      np.full((1, 2, 1, 1), datetime(year=2010, month=1, day=1)))
-        np.testing.assert_array_equal(spectral_index_map_data.obs_date_range, np.full((1, 2, 1, 1), 100000))
+        np.testing.assert_array_equal(
+            spectral_index_map_data.exposure_factor, np.reshape([11, 12], (1, 2, 1, 1))
+        )
+        np.testing.assert_array_equal(
+            spectral_index_map_data.obs_date,
+            np.full((1, 2, 1, 1), datetime(year=2010, month=1, day=1)),
+        )
+        np.testing.assert_array_equal(
+            spectral_index_map_data.obs_date_range, np.full((1, 2, 1, 1), 100000)
+        )
 
         self.assertEqual((1, 2, 1, 1), spectral_index_map_data.quality_flags.shape)
-        np.testing.assert_array_equal(spectral_index_map_data.quality_flags, expected_quality_flags)
+        np.testing.assert_array_equal(
+            spectral_index_map_data.quality_flags, expected_quality_flags
+        )
 
-    @patch('imap_l3_processing.maps.spectral_fit.mpfit')
-    def test_spectral_fit_returns_nan_if_fit_status_is_not_positive_or_equal_to_five(self, mock_mpfit):
+    @patch("imap_l3_processing.maps.spectral_fit.mpfit")
+    def test_spectral_fit_returns_nan_if_fit_status_is_not_positive_or_equal_to_five(
+        self, mock_mpfit
+    ):
         energies = np.geomspace(1, 10, 23)
         true_A, true_gamma = 2.0, 1.5
         flux_data = true_A * np.power(energies, -true_gamma)
 
         errors = 0.2 * np.abs(flux_data)
 
-        cases = [
-            ("rectangular", (1, 1)),
-            ("healpix", (1,))
-        ]
+        cases = [("rectangular", (1, 1)), ("healpix", (1,))]
 
         invalid_status_cases = [-1, 0, 5]
 
@@ -552,20 +812,32 @@ def test_spectral_fit_returns_nan_if_fit_status_is_not_positive_or_equal_to_five
             for status_code in invalid_status_cases:
                 with self.subTest(f"Pixelization: {name}, status_code: {status_code}"):
                     mock_mpfit.return_value = Mock(status=status_code, params=(0, 0))
-                    flux = np.array(flux_data).reshape(1, len(energies), *spacial_dimension_shape)
-                    uncertainty = np.array(errors).reshape(1, len(energies), *spacial_dimension_shape)
-
-                    scalar, scalar_error, result, result_error, chisq = fit_arrays_to_power_law(flux, uncertainty, energies)
-                    expected_nan_result = np.full((1, 1, *spacial_dimension_shape), np.nan)
+                    flux = np.array(flux_data).reshape(
+                        1, len(energies), *spacial_dimension_shape
+                    )
+                    uncertainty = np.array(errors).reshape(
+                        1, len(energies), *spacial_dimension_shape
+                    )
+
+                    scalar, scalar_error, result, result_error, chisq = (
+                        fit_arrays_to_power_law(flux, uncertainty, energies)
+                    )
+                    expected_nan_result = np.full(
+                        (1, 1, *spacial_dimension_shape), np.nan
+                    )
                     np.testing.assert_array_equal(result, expected_nan_result)
                     np.testing.assert_array_equal(result_error, expected_nan_result)
                     np.testing.assert_array_equal(scalar, expected_nan_result)
                     np.testing.assert_array_equal(scalar_error, expected_nan_result)
                     np.testing.assert_array_equal(chisq, expected_nan_result)
 
-    @patch('imap_l3_processing.maps.spectral_fit.mpfit', wraps=mpfit)
-    @patch('imap_l3_processing.maps.spectral_fit.scipy.stats.linregress', wraps=linregress)
-    def test_passes_initial_guess_to_mpfit_based_on_linear_fit_in_log_space(self, mock_linregress, mock_mpfit):
+    @patch("imap_l3_processing.maps.spectral_fit.mpfit", wraps=mpfit)
+    @patch(
+        "imap_l3_processing.maps.spectral_fit.scipy.stats.linregress", wraps=linregress
+    )
+    def test_passes_initial_guess_to_mpfit_based_on_linear_fit_in_log_space(
+        self, mock_linregress, mock_mpfit
+    ):
         energies = np.geomspace(1e1, 1e4, 6)
         flux_data = np.array([1e7, 1e5, 1e6, 1e4, 1e5, 1e3])
 
@@ -612,7 +884,9 @@ def test_spectral_fit_against_validation_data(self):
                     delimiter=",",
                     dtype=np.float64,
                 ).T
-                expected_a = np.loadtxt(get_test_data_path(expected_a_path), delimiter=",", dtype=np.float64).T
+                expected_a = np.loadtxt(
+                    get_test_data_path(expected_a_path), delimiter=",", dtype=np.float64
+                ).T
                 expected_gamma_sigma = np.loadtxt(
                     get_test_data_path(expected_sigma_path),
                     delimiter=",",
@@ -623,31 +897,44 @@ def test_spectral_fit_against_validation_data(self):
                     delimiter=",",
                     dtype=np.float64,
                 ).T
-                expected_chisq = np.loadtxt(get_test_data_path(expected_chisq_path), delimiter=",", dtype=np.float64).T
+                expected_chisq = np.loadtxt(
+                    get_test_data_path(expected_chisq_path),
+                    delimiter=",",
+                    dtype=np.float64,
+                ).T
 
-                output_data = calculate_spectral_index_for_multiple_ranges(input_data, [[0, np.inf]])
+                output_data = calculate_spectral_index_for_multiple_ranges(
+                    input_data, [[0, np.inf]]
+                )
 
-                np.testing.assert_allclose(output_data.ena_spectral_index[0, 0],
-                                           expected_gamma, atol=1e-3)
+                np.testing.assert_allclose(
+                    output_data.ena_spectral_index[0, 0], expected_gamma, atol=1e-3
+                )
                 np.testing.assert_allclose(
                     output_data.ena_spectral_index_stat_uncert[0, 0],
                     expected_gamma_sigma,
                     atol=1e-3,
                 )
-                np.testing.assert_allclose(output_data.ena_spectral_index_scalar_coefficient[0, 0],
-                                           expected_a, atol=1e-3)
-                np.testing.assert_allclose(output_data.ena_spectral_index_scalar_coefficient_stat_uncert[0, 0],
-                                           expected_a_sigma, atol=1e-3)
-                np.testing.assert_allclose(output_data.ena_spectral_index_chisq[0, 0], expected_chisq)
-                np.testing.assert_allclose(output_data.quality_flags, np.full((1,1,90,45), MapL3Flags.NONE))
+                np.testing.assert_allclose(
+                    output_data.ena_spectral_index_scalar_coefficient[0, 0],
+                    expected_a,
+                    atol=1e-3,
+                )
+                np.testing.assert_allclose(
+                    output_data.ena_spectral_index_scalar_coefficient_stat_uncert[0, 0],
+                    expected_a_sigma,
+                    atol=1e-3,
+                )
+                np.testing.assert_allclose(
+                    output_data.ena_spectral_index_chisq[0, 0], expected_chisq
+                )
+                np.testing.assert_allclose(
+                    output_data.quality_flags, np.full((1, 1, 90, 45), MapL3Flags.NONE)
+                )
 
     def test_slice_energy_range_by_bin(self):
         def build_array(*values):
-            return np.array([
-                [
-                    [[a]] for a in values
-                ]
-            ])
+            return np.array([[[[a]] for a in values]])
 
         input_data = IntensityMapData(
             epoch=sentinel.epoch,
@@ -659,12 +946,13 @@ def build_array(*values):
             latitude=sentinel.latitude,
             longitude=sentinel.longitude,
             exposure_factor=build_array(1, 2, 3, 4, 5),
-            obs_date=build_array(datetime(2026, 1, 1),
-                                 datetime(2026, 1, 2),
-                                 datetime(2026, 1, 3),
-                                 datetime(2026, 1, 4),
-                                 datetime(2026, 1, 5),
-                                 ),
+            obs_date=build_array(
+                datetime(2026, 1, 1),
+                datetime(2026, 1, 2),
+                datetime(2026, 1, 3),
+                datetime(2026, 1, 4),
+                datetime(2026, 1, 5),
+            ),
             obs_date_range=build_array(1000, 2000, 3000, 4000, 5000),
             solid_angle=sentinel.solid_angle,
             ena_intensity=build_array(100, 200, 300, 400, 500),
@@ -675,7 +963,7 @@ def build_array(*values):
                 MapL3Flags.NONE,
                 MapL3Flags.PREDICTIVE_EPHEMERIS,
                 MapL3Flags.PREDICTIVE_EPHEMERIS,
-                MapL3Flags.NONE
+                MapL3Flags.NONE,
             ),
         )
 
@@ -689,31 +977,32 @@ def build_array(*values):
             latitude=sentinel.latitude,
             longitude=sentinel.longitude,
             exposure_factor=build_array(1, 2, 3),
-            obs_date=build_array(datetime(2026, 1, 1),
-                                 datetime(2026, 1, 2),
-                                 datetime(2026, 1, 3),
-                                 ),
+            obs_date=build_array(
+                datetime(2026, 1, 1),
+                datetime(2026, 1, 2),
+                datetime(2026, 1, 3),
+            ),
             obs_date_range=build_array(1000, 2000, 3000),
             solid_angle=sentinel.solid_angle,
             ena_intensity=build_array(100, 200, 300),
             ena_intensity_stat_uncert=build_array(11, 12, 13),
             ena_intensity_sys_err=build_array(21, 22, 23),
-            quality_flags=build_array(MapL3Flags.NONE, MapL3Flags.NONE, MapL3Flags.PREDICTIVE_EPHEMERIS),
+            quality_flags=build_array(
+                MapL3Flags.NONE, MapL3Flags.NONE, MapL3Flags.PREDICTIVE_EPHEMERIS
+            ),
         )
 
         actual = slice_energy_range_by_bin(input_data, 1, 3)
         self.assertIsInstance(actual, IntensityMapData)
 
         for field in dataclasses.fields(expected_data):
-            np.testing.assert_equal(getattr(actual, field.name), getattr(expected_data, field.name))
+            np.testing.assert_equal(
+                getattr(actual, field.name), getattr(expected_data, field.name)
+            )
 
     def test_slice_energy_range_by_bin_raises_error(self):
         def build_array(*values):
-            return np.array([
-                [
-                    [[a]] for a in values
-                ]
-            ])
+            return np.array([[[[a]] for a in values]])
 
         input_data = IntensityMapData(
             epoch=sentinel.epoch,
@@ -725,12 +1014,13 @@ def build_array(*values):
             latitude=sentinel.latitude,
             longitude=sentinel.longitude,
             exposure_factor=build_array(1, 2, 3, 4, 5),
-            obs_date=build_array(datetime(2026, 1, 1),
-                                 datetime(2026, 1, 2),
-                                 datetime(2026, 1, 3),
-                                 datetime(2026, 1, 4),
-                                 datetime(2026, 1, 5),
-                                 ),
+            obs_date=build_array(
+                datetime(2026, 1, 1),
+                datetime(2026, 1, 2),
+                datetime(2026, 1, 3),
+                datetime(2026, 1, 4),
+                datetime(2026, 1, 5),
+            ),
             obs_date_range=build_array(1000, 2000, 3000, 4000, 5000),
             solid_angle=sentinel.solid_angle,
             ena_intensity=build_array(100, 200, 300, 400, 500),
@@ -741,19 +1031,16 @@ def build_array(*values):
                 MapL3Flags.NONE,
                 MapL3Flags.PREDICTIVE_EPHEMERIS,
                 MapL3Flags.PREDICTIVE_EPHEMERIS,
-                MapL3Flags.NONE
+                MapL3Flags.NONE,
             ),
         )
-        cases = [
-            (1, 10),
-            (0, 3),
-            (3, 0),
-            (1, 1),
-            (10, 3)
-        ]
+        cases = [(1, 10), (0, 3), (3, 0), (1, 1), (10, 3)]
         for start_bin, end_bin in cases:
             with self.subTest(f"{start_bin}-{end_bin}"):
                 with self.assertRaises(ValueError) as cm:
                     slice_energy_range_by_bin(input_data, start_bin, end_bin)
 
-                self.assertEqual(str(cm.exception), f"Error slicing energy bins {start_bin},{end_bin}")
+                self.assertEqual(
+                    str(cm.exception),
+                    f"Error slicing energy bins {start_bin},{end_bin}",
+                )
diff --git a/tests/maps/test_survival_probability_processing.py b/tests/maps/test_survival_probability_processing.py
index 7c569d353..69e151531 100644
--- a/tests/maps/test_survival_probability_processing.py
+++ b/tests/maps/test_survival_probability_processing.py
@@ -1,60 +1,104 @@
 from datetime import datetime
-from unittest.mock import sentinel, patch, call, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
 import xarray as xr
 from imap_processing.ena_maps.utils.coordinates import CoordNames
 from imap_processing.spice.geometry import SpiceFrame
 
-from imap_l3_processing.maps.hilo_l3_survival_dependencies import HiLoL3SurvivalDependencies
-from imap_l3_processing.maps.map_descriptors import PixelSize, parse_map_descriptor, ReferenceFrame
-from imap_l3_processing.maps.map_models import GlowsL3eRectangularMapInputData, InputRectangularPointingSet
+from imap_l3_processing.maps.hilo_l3_survival_dependencies import (
+    HiLoL3SurvivalDependencies,
+)
+from imap_l3_processing.maps.map_descriptors import (
+    PixelSize,
+    ReferenceFrame,
+    parse_map_descriptor,
+)
+from imap_l3_processing.maps.map_models import (
+    GlowsL3eRectangularMapInputData,
+    InputRectangularPointingSet,
+)
 from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.maps.survival_probability_processing import process_survival_probabilities, \
-    combine_glows_l3e_with_l1c_pointing, filter_bad_days
-from tests.maps.test_builders import create_rectangular_intensity_map_data, create_l1c_pset, create_l3e_pset
+from imap_l3_processing.maps.survival_probability_processing import (
+    combine_glows_l3e_with_l1c_pointing,
+    filter_bad_days,
+    process_survival_probabilities,
+)
+from tests.maps.test_builders import (
+    create_l1c_pset,
+    create_l3e_pset,
+    create_rectangular_intensity_map_data,
+)
 from tests.spice_test_case import SpiceTestCase
 from tests.test_helpers import create_dataclass_mock
 
 
 class TestSurvivalProbabilityProcessing(SpiceTestCase):
-    @patch('imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilitySkyMap')
-    @patch('imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilityPointingSet')
-    @patch('imap_l3_processing.maps.survival_probability_processing.combine_glows_l3e_with_l1c_pointing')
-    @patch('imap_l3_processing.maps.survival_probability_processing.filter_bad_days')
-    def test_process_survival_probability(self, mock_filter_bad_days, mock_combine_glows_l3e_with_l1c_pointing,
-                                          mock_survival_probability_pointing_set, mock_survival_skymap):
+    @patch(
+        "imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilitySkyMap"
+    )
+    @patch(
+        "imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilityPointingSet"
+    )
+    @patch(
+        "imap_l3_processing.maps.survival_probability_processing.combine_glows_l3e_with_l1c_pointing"
+    )
+    @patch("imap_l3_processing.maps.survival_probability_processing.filter_bad_days")
+    def test_process_survival_probability(
+        self,
+        mock_filter_bad_days,
+        mock_combine_glows_l3e_with_l1c_pointing,
+        mock_survival_probability_pointing_set,
+        mock_survival_skymap,
+    ):
         rng = np.random.default_rng()
         input_map_flux = rng.random((1, 9, 90, 45))
         epoch = datetime.now()
 
-        input_map = create_rectangular_intensity_map_data(epoch=[epoch], flux=input_map_flux,
-                                                          include_ena_intensity_sys_err_minus=True)
+        input_map = create_rectangular_intensity_map_data(
+            epoch=[epoch], flux=input_map_flux, include_ena_intensity_sys_err_minus=True
+        )
 
         intensity_map_data = input_map.intensity_map_data
         input_map.intensity_map_data.energy = sentinel.hi_l2_energies
 
         l2_grid = PixelSize.FourDegrees
-        l2_descriptor_parts = Mock(sensor=sentinel.l2_sensor, spin_phase=sentinel.l2_spin, grid=l2_grid,
-                                   reference_frame=ReferenceFrame.Heliospheric)
-        dependencies = HiLoL3SurvivalDependencies(l2_data=input_map,
-                                                  l1c_data=sentinel.l1c_data,
-                                                  glows_l3e_data=sentinel.glows_l3e_data,
-                                                  l2_map_descriptor_parts=l2_descriptor_parts,
-                                                  dependency_file_paths=[])
+        l2_descriptor_parts = Mock(
+            sensor=sentinel.l2_sensor,
+            spin_phase=sentinel.l2_spin,
+            grid=l2_grid,
+            reference_frame=ReferenceFrame.Heliospheric,
+        )
+        dependencies = HiLoL3SurvivalDependencies(
+            l2_data=input_map,
+            l1c_data=sentinel.l1c_data,
+            glows_l3e_data=sentinel.glows_l3e_data,
+            l2_map_descriptor_parts=l2_descriptor_parts,
+            dependency_file_paths=[],
+        )
 
         mock_filter_bad_days.return_value = sentinel.filtered_l1c
 
-        mock_combine_glows_l3e_with_l1c_pointing.return_value = [(sentinel.hi_l1c_1, sentinel.glows_l3e_1),
-                                                                 (sentinel.hi_l1c_2, sentinel.glows_l3e_2),
-                                                                 (sentinel.hi_l1c_3, sentinel.glows_l3e_3)]
+        mock_combine_glows_l3e_with_l1c_pointing.return_value = [
+            (sentinel.hi_l1c_1, sentinel.glows_l3e_1),
+            (sentinel.hi_l1c_2, sentinel.glows_l3e_2),
+            (sentinel.hi_l1c_3, sentinel.glows_l3e_3),
+        ]
 
-        mock_survival_probability_pointing_set.side_effect = [sentinel.pset_1, sentinel.pset_2, sentinel.pset_3]
+        mock_survival_probability_pointing_set.side_effect = [
+            sentinel.pset_1,
+            sentinel.pset_2,
+            sentinel.pset_3,
+        ]
 
         computed_survival_probabilities = rng.random((1, 9, 90, 45))
         quality_flags = np.full((1, 9, 90, 45), MapL3Flags.NONE)
-        quality_flags[rng.random((1, 9, 90, 45)) > 0.8] = MapL3Flags.PREDICTIVE_EPHEMERIS
-        quality_flags[rng.random((1, 9, 90, 45)) > 0.7] = MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+        quality_flags[rng.random((1, 9, 90, 45)) > 0.8] = (
+            MapL3Flags.PREDICTIVE_EPHEMERIS
+        )
+        quality_flags[rng.random((1, 9, 90, 45)) > 0.7] = (
+            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+        )
 
         mock_survival_skymap.return_value.to_dataset.return_value = xr.Dataset(
             data_vars={
@@ -65,7 +109,7 @@ def test_process_survival_probability(self, mock_filter_bad_days, mock_combine_g
                         CoordNames.AZIMUTH_L2.value,
                         CoordNames.ELEVATION_L2.value,
                     ],
-                    computed_survival_probabilities
+                    computed_survival_probabilities,
                 ),
                 "quality_flags": (
                     [
@@ -74,7 +118,7 @@ def test_process_survival_probability(self, mock_filter_bad_days, mock_combine_g
                         CoordNames.AZIMUTH_L2.value,
                         CoordNames.ELEVATION_L2.value,
                     ],
-                    quality_flags
+                    quality_flags,
                 ),
             },
             coords={
@@ -82,39 +126,74 @@ def test_process_survival_probability(self, mock_filter_bad_days, mock_combine_g
                 CoordNames.ENERGY_ULTRA_L1C.value: rng.random((9,)),
                 CoordNames.AZIMUTH_L2.value: rng.random((90,)),
                 CoordNames.ELEVATION_L2.value: rng.random((45,)),
-            }
+            },
         )
 
-        survival_data = process_survival_probabilities(dependencies, SpiceFrame.IMAP_DPS)
+        survival_data = process_survival_probabilities(
+            dependencies, SpiceFrame.IMAP_DPS
+        )
 
         mock_filter_bad_days.assert_called_once_with(sentinel.l1c_data)
 
-        mock_combine_glows_l3e_with_l1c_pointing.assert_called_once_with(sentinel.glows_l3e_data, sentinel.filtered_l1c)
+        mock_combine_glows_l3e_with_l1c_pointing.assert_called_once_with(
+            sentinel.glows_l3e_data, sentinel.filtered_l1c
+        )
 
-        mock_survival_probability_pointing_set.assert_has_calls([
-            call(sentinel.hi_l1c_1, sentinel.l2_sensor, sentinel.l2_spin, sentinel.glows_l3e_1,
-                 sentinel.hi_l2_energies, cg_corrected=True),
-            call(sentinel.hi_l1c_2, sentinel.l2_sensor, sentinel.l2_spin, sentinel.glows_l3e_2,
-                 sentinel.hi_l2_energies, cg_corrected=True),
-            call(sentinel.hi_l1c_3, sentinel.l2_sensor, sentinel.l2_spin, sentinel.glows_l3e_3,
-                 sentinel.hi_l2_energies, cg_corrected=True)
-        ])
+        mock_survival_probability_pointing_set.assert_has_calls(
+            [
+                call(
+                    sentinel.hi_l1c_1,
+                    sentinel.l2_sensor,
+                    sentinel.l2_spin,
+                    sentinel.glows_l3e_1,
+                    sentinel.hi_l2_energies,
+                    cg_corrected=True,
+                ),
+                call(
+                    sentinel.hi_l1c_2,
+                    sentinel.l2_sensor,
+                    sentinel.l2_spin,
+                    sentinel.glows_l3e_2,
+                    sentinel.hi_l2_energies,
+                    cg_corrected=True,
+                ),
+                call(
+                    sentinel.hi_l1c_3,
+                    sentinel.l2_sensor,
+                    sentinel.l2_spin,
+                    sentinel.glows_l3e_3,
+                    sentinel.hi_l2_energies,
+                    cg_corrected=True,
+                ),
+            ]
+        )
 
-        mock_survival_skymap.assert_called_once_with([sentinel.pset_1, sentinel.pset_2, sentinel.pset_3],
-                                                     l2_grid,
-                                                     SpiceFrame.IMAP_DPS)
+        mock_survival_skymap.assert_called_once_with(
+            [sentinel.pset_1, sentinel.pset_2, sentinel.pset_3],
+            l2_grid,
+            SpiceFrame.IMAP_DPS,
+        )
 
         mock_survival_skymap.return_value.to_dataset.assert_called_once()
 
-        np.testing.assert_array_equal(survival_data.intensity_map_data.ena_intensity,
-                                      intensity_map_data.ena_intensity / computed_survival_probabilities)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.ena_intensity_stat_uncert,
-                                      intensity_map_data.ena_intensity_stat_uncert / computed_survival_probabilities)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.ena_intensity_sys_err,
-                                      intensity_map_data.ena_intensity_sys_err / computed_survival_probabilities)
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.ena_intensity,
+            intensity_map_data.ena_intensity / computed_survival_probabilities,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.ena_intensity_stat_uncert,
+            intensity_map_data.ena_intensity_stat_uncert
+            / computed_survival_probabilities,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.ena_intensity_sys_err,
+            intensity_map_data.ena_intensity_sys_err / computed_survival_probabilities,
+        )
 
-        np.testing.assert_array_equal(survival_data.intensity_map_data.bg_intensity,
-                                      intensity_map_data.bg_intensity / computed_survival_probabilities)
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.bg_intensity,
+            intensity_map_data.bg_intensity / computed_survival_probabilities,
+        )
         np.testing.assert_array_equal(
             survival_data.intensity_map_data.bg_intensity_sys_err,
             intensity_map_data.bg_intensity_sys_err / computed_survival_probabilities,
@@ -130,108 +209,197 @@ def test_process_survival_probability(self, mock_filter_bad_days, mock_combine_g
             computed_survival_probabilities,
         )
 
-        np.testing.assert_array_equal(survival_data.intensity_map_data.ena_intensity_sys_err_minus,
-                                      intensity_map_data.ena_intensity_sys_err_minus / computed_survival_probabilities)
-
-        np.testing.assert_array_equal(survival_data.intensity_map_data.ena_intensity_sys_err_plus,
-                                      intensity_map_data.ena_intensity_sys_err_plus / computed_survival_probabilities)
-
-        np.testing.assert_array_equal(survival_data.intensity_map_data.epoch, intensity_map_data.epoch)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.epoch_delta, intensity_map_data.epoch_delta)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.energy, intensity_map_data.energy)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.energy_delta_plus,
-                                      intensity_map_data.energy_delta_plus)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.energy_delta_minus,
-                                      intensity_map_data.energy_delta_minus)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.energy_label, intensity_map_data.energy_label)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.latitude, intensity_map_data.latitude)
-        np.testing.assert_array_equal(survival_data.coords.latitude_delta, input_map.coords.latitude_delta)
-        np.testing.assert_array_equal(survival_data.coords.latitude_label, input_map.coords.latitude_label)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.longitude, intensity_map_data.longitude)
-        np.testing.assert_array_equal(survival_data.coords.longitude_delta, input_map.coords.longitude_delta)
-        np.testing.assert_array_equal(survival_data.coords.longitude_label, input_map.coords.longitude_label)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.exposure_factor,
-                                      intensity_map_data.exposure_factor)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.obs_date, intensity_map_data.obs_date)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.obs_date_range,
-                                      intensity_map_data.obs_date_range)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.solid_angle, intensity_map_data.solid_angle)
-        np.testing.assert_array_equal(survival_data.intensity_map_data.quality_flags, quality_flags)
-
-    @patch('imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilitySkyMap')
-    @patch('imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilityPointingSet')
-    @patch('imap_l3_processing.maps.survival_probability_processing.combine_glows_l3e_with_l1c_pointing')
-    @patch('imap_l3_processing.maps.survival_probability_processing.filter_bad_days')
-    def test_process_survival_probability_cg_correction_argument(self, _mock_filter_bad_days,
-                                                                 mock_combine_glows_l3e_with_l1c_pointing,
-                                                                 mock_survival_probability_pointing_set,
-                                                                 mock_survival_skymap):
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.ena_intensity_sys_err_minus,
+            intensity_map_data.ena_intensity_sys_err_minus
+            / computed_survival_probabilities,
+        )
+
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.ena_intensity_sys_err_plus,
+            intensity_map_data.ena_intensity_sys_err_plus
+            / computed_survival_probabilities,
+        )
+
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.epoch, intensity_map_data.epoch
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.epoch_delta, intensity_map_data.epoch_delta
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.energy, intensity_map_data.energy
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.energy_delta_plus,
+            intensity_map_data.energy_delta_plus,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.energy_delta_minus,
+            intensity_map_data.energy_delta_minus,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.energy_label,
+            intensity_map_data.energy_label,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.latitude, intensity_map_data.latitude
+        )
+        np.testing.assert_array_equal(
+            survival_data.coords.latitude_delta, input_map.coords.latitude_delta
+        )
+        np.testing.assert_array_equal(
+            survival_data.coords.latitude_label, input_map.coords.latitude_label
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.longitude, intensity_map_data.longitude
+        )
+        np.testing.assert_array_equal(
+            survival_data.coords.longitude_delta, input_map.coords.longitude_delta
+        )
+        np.testing.assert_array_equal(
+            survival_data.coords.longitude_label, input_map.coords.longitude_label
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.exposure_factor,
+            intensity_map_data.exposure_factor,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.obs_date, intensity_map_data.obs_date
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.obs_date_range,
+            intensity_map_data.obs_date_range,
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.solid_angle, intensity_map_data.solid_angle
+        )
+        np.testing.assert_array_equal(
+            survival_data.intensity_map_data.quality_flags, quality_flags
+        )
+
+    @patch(
+        "imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilitySkyMap"
+    )
+    @patch(
+        "imap_l3_processing.maps.survival_probability_processing.RectangularSurvivalProbabilityPointingSet"
+    )
+    @patch(
+        "imap_l3_processing.maps.survival_probability_processing.combine_glows_l3e_with_l1c_pointing"
+    )
+    @patch("imap_l3_processing.maps.survival_probability_processing.filter_bad_days")
+    def test_process_survival_probability_cg_correction_argument(
+        self,
+        _mock_filter_bad_days,
+        mock_combine_glows_l3e_with_l1c_pointing,
+        mock_survival_probability_pointing_set,
+        mock_survival_skymap,
+    ):
         test_cases = [True, False]
         for cg_correction_value in test_cases:
             rng = np.random.default_rng()
             input_map_flux = rng.random((1, 9, 90, 45))
             epoch = datetime.now()
 
-            input_map = create_rectangular_intensity_map_data(epoch=[epoch], flux=input_map_flux)
+            input_map = create_rectangular_intensity_map_data(
+                epoch=[epoch], flux=input_map_flux
+            )
 
             input_map.intensity_map_data.energy = sentinel.hi_l2_energies
 
             l2_grid = PixelSize.FourDegrees
-            l2_descriptor_parts = Mock(sensor=sentinel.l2_sensor, spin_phase=sentinel.l2_spin, grid=l2_grid,
-                                       reference_frame=ReferenceFrame.Heliospheric)
-            dependencies = HiLoL3SurvivalDependencies(l2_data=input_map,
-                                                      l1c_data=sentinel.l1c_data,
-                                                      glows_l3e_data=sentinel.glows_l3e_data,
-                                                      l2_map_descriptor_parts=l2_descriptor_parts,
-                                                      dependency_file_paths=[])
-
-            mock_combine_glows_l3e_with_l1c_pointing.return_value = [(sentinel.hi_l1c_1, sentinel.glows_l3e_1),
-                                                                     (sentinel.hi_l1c_2, sentinel.glows_l3e_2),
-                                                                     (sentinel.hi_l1c_3, sentinel.glows_l3e_3)]
-
-            mock_survival_probability_pointing_set.side_effect = [sentinel.pset_1, sentinel.pset_2, sentinel.pset_3]
+            l2_descriptor_parts = Mock(
+                sensor=sentinel.l2_sensor,
+                spin_phase=sentinel.l2_spin,
+                grid=l2_grid,
+                reference_frame=ReferenceFrame.Heliospheric,
+            )
+            dependencies = HiLoL3SurvivalDependencies(
+                l2_data=input_map,
+                l1c_data=sentinel.l1c_data,
+                glows_l3e_data=sentinel.glows_l3e_data,
+                l2_map_descriptor_parts=l2_descriptor_parts,
+                dependency_file_paths=[],
+            )
+
+            mock_combine_glows_l3e_with_l1c_pointing.return_value = [
+                (sentinel.hi_l1c_1, sentinel.glows_l3e_1),
+                (sentinel.hi_l1c_2, sentinel.glows_l3e_2),
+                (sentinel.hi_l1c_3, sentinel.glows_l3e_3),
+            ]
+
+            mock_survival_probability_pointing_set.side_effect = [
+                sentinel.pset_1,
+                sentinel.pset_2,
+                sentinel.pset_3,
+            ]
 
             computed_survival_probabilities = rng.random((1, 9, 90, 45))
-            quality_flags = np.full_like(computed_survival_probabilities, MapL3Flags.NONE)
-
-            mock_survival_skymap.return_value.to_dataset.return_value = xr.Dataset({
-                "exposure_weighted_survival_probabilities": (
-                    [
-                        CoordNames.TIME.value,
-                        CoordNames.ENERGY_ULTRA_L1C.value,
-                        CoordNames.AZIMUTH_L2.value,
-                        CoordNames.ELEVATION_L2.value,
-                    ],
-                    computed_survival_probabilities
-                ),
-                "quality_flags": (
-                    [
-                        CoordNames.TIME.value,
-                        CoordNames.ENERGY_ULTRA_L1C.value,
-                        CoordNames.AZIMUTH_L2.value,
-                        CoordNames.ELEVATION_L2.value,
-                    ],
-                    quality_flags
-                ),
-            },
+            quality_flags = np.full_like(
+                computed_survival_probabilities, MapL3Flags.NONE
+            )
+
+            mock_survival_skymap.return_value.to_dataset.return_value = xr.Dataset(
+                {
+                    "exposure_weighted_survival_probabilities": (
+                        [
+                            CoordNames.TIME.value,
+                            CoordNames.ENERGY_ULTRA_L1C.value,
+                            CoordNames.AZIMUTH_L2.value,
+                            CoordNames.ELEVATION_L2.value,
+                        ],
+                        computed_survival_probabilities,
+                    ),
+                    "quality_flags": (
+                        [
+                            CoordNames.TIME.value,
+                            CoordNames.ENERGY_ULTRA_L1C.value,
+                            CoordNames.AZIMUTH_L2.value,
+                            CoordNames.ELEVATION_L2.value,
+                        ],
+                        quality_flags,
+                    ),
+                },
                 coords={
                     CoordNames.TIME.value: [epoch],
                     CoordNames.ENERGY_ULTRA_L1C.value: rng.random((9,)),
                     CoordNames.AZIMUTH_L2.value: rng.random((90,)),
                     CoordNames.ELEVATION_L2.value: rng.random((45,)),
-                })
-
-            process_survival_probabilities(dependencies, SpiceFrame.IMAP_DPS,
-                                                           cg_corrected=cg_correction_value)
-
-            mock_survival_probability_pointing_set.assert_has_calls([
-                call(sentinel.hi_l1c_1, sentinel.l2_sensor, sentinel.l2_spin, sentinel.glows_l3e_1,
-                     sentinel.hi_l2_energies, cg_corrected=cg_correction_value),
-                call(sentinel.hi_l1c_2, sentinel.l2_sensor, sentinel.l2_spin, sentinel.glows_l3e_2,
-                     sentinel.hi_l2_energies, cg_corrected=cg_correction_value),
-                call(sentinel.hi_l1c_3, sentinel.l2_sensor, sentinel.l2_spin, sentinel.glows_l3e_3,
-                     sentinel.hi_l2_energies, cg_corrected=cg_correction_value)
-            ])
+                },
+            )
+
+            process_survival_probabilities(
+                dependencies, SpiceFrame.IMAP_DPS, cg_corrected=cg_correction_value
+            )
+
+            mock_survival_probability_pointing_set.assert_has_calls(
+                [
+                    call(
+                        sentinel.hi_l1c_1,
+                        sentinel.l2_sensor,
+                        sentinel.l2_spin,
+                        sentinel.glows_l3e_1,
+                        sentinel.hi_l2_energies,
+                        cg_corrected=cg_correction_value,
+                    ),
+                    call(
+                        sentinel.hi_l1c_2,
+                        sentinel.l2_sensor,
+                        sentinel.l2_spin,
+                        sentinel.glows_l3e_2,
+                        sentinel.hi_l2_energies,
+                        cg_corrected=cg_correction_value,
+                    ),
+                    call(
+                        sentinel.hi_l1c_3,
+                        sentinel.l2_sensor,
+                        sentinel.l2_spin,
+                        sentinel.glows_l3e_3,
+                        sentinel.hi_l2_energies,
+                        cg_corrected=cg_correction_value,
+                    ),
+                ]
+            )
 
     def test_integration_uses_fill_values_for_missing_l3e_data(self):
         t1 = datetime(2025, 4, 29, 12)
@@ -240,38 +408,66 @@ def test_integration_uses_fill_values_for_missing_l3e_data(self):
         t4 = datetime(2025, 5, 15, 12)
 
         l1c_psets = [
-            create_l1c_pset(epoch=t1, repointing=1,
-                            hae_longitude=np.concat([np.full((1800), 304 + 2), np.full((1800), 124 + 2)])[np.newaxis,
-                                          :]),
-            create_l1c_pset(epoch=t2, repointing=2,
-                            hae_longitude=np.concat([np.full((1800), 312 + 2), np.full((1800), 132 + 2)])[np.newaxis,
-                                          :]),
-            create_l1c_pset(epoch=t3, repointing=3,
-                            hae_longitude=np.concat([np.full((1800), 316 + 2), np.full((1800), 136 + 2)])[np.newaxis,
-                                          :]),
-            create_l1c_pset(epoch=t4, repointing=4,
-                            hae_longitude=np.concat([np.full((1800), 320 + 2), np.full((1800), 140 + 2)])[np.newaxis,
-                                          :])
+            create_l1c_pset(
+                epoch=t1,
+                repointing=1,
+                hae_longitude=np.concat(
+                    [np.full((1800), 304 + 2), np.full((1800), 124 + 2)]
+                )[np.newaxis, :],
+            ),
+            create_l1c_pset(
+                epoch=t2,
+                repointing=2,
+                hae_longitude=np.concat(
+                    [np.full((1800), 312 + 2), np.full((1800), 132 + 2)]
+                )[np.newaxis, :],
+            ),
+            create_l1c_pset(
+                epoch=t3,
+                repointing=3,
+                hae_longitude=np.concat(
+                    [np.full((1800), 316 + 2), np.full((1800), 136 + 2)]
+                )[np.newaxis, :],
+            ),
+            create_l1c_pset(
+                epoch=t4,
+                repointing=4,
+                hae_longitude=np.concat(
+                    [np.full((1800), 320 + 2), np.full((1800), 140 + 2)]
+                )[np.newaxis, :],
+            ),
         ]
         l3e_psets = [
             create_l3e_pset(epoch=t1, repointing=1),
             create_l3e_pset(epoch=t2, repointing=2),
-            create_l3e_pset(epoch=t4, repointing=4)
+            create_l3e_pset(epoch=t4, repointing=4),
         ]
         l2_intensity_map = create_rectangular_intensity_map_data()
 
         descriptor = parse_map_descriptor("h90-ena-h-sf-nsp-ram-hae-4deg-3mo")
-        survival_dependencies = HiLoL3SurvivalDependencies(l2_intensity_map, l1c_psets, l3e_psets, descriptor,
-                                                           Mock())
+        survival_dependencies = HiLoL3SurvivalDependencies(
+            l2_intensity_map, l1c_psets, l3e_psets, descriptor, Mock()
+        )
 
         expected_sky_strip = np.full(45, 2.0)
         default_sp_sky_strip = np.full(45, np.nan)
 
-        output_map = process_survival_probabilities(survival_dependencies, SpiceFrame.IMAP_HAE)
-        np.testing.assert_equal(output_map.intensity_map_data.ena_intensity[0, 0, 76, :], expected_sky_strip)
-        np.testing.assert_equal(output_map.intensity_map_data.ena_intensity[0, 0, 78, :], expected_sky_strip)
-        np.testing.assert_equal(output_map.intensity_map_data.ena_intensity[0, 0, 79, :], default_sp_sky_strip)
-        np.testing.assert_equal(output_map.intensity_map_data.ena_intensity[0, 0, 80, :], expected_sky_strip)
+        output_map = process_survival_probabilities(
+            survival_dependencies, SpiceFrame.IMAP_HAE
+        )
+        np.testing.assert_equal(
+            output_map.intensity_map_data.ena_intensity[0, 0, 76, :], expected_sky_strip
+        )
+        np.testing.assert_equal(
+            output_map.intensity_map_data.ena_intensity[0, 0, 78, :], expected_sky_strip
+        )
+        np.testing.assert_equal(
+            output_map.intensity_map_data.ena_intensity[0, 0, 79, :],
+            default_sp_sky_strip,
+        )
+        np.testing.assert_equal(
+            output_map.intensity_map_data.ena_intensity[0, 0, 80, :], expected_sky_strip
+        )
 
     def test_combine_glows_l3e_with_l1c_pointing(self):
         glows_l3e_data = [
@@ -285,7 +481,7 @@ def test_combine_glows_l3e_with_l1c_pointing(self):
             create_dataclass_mock(InputRectangularPointingSet, repointing=1),
             create_dataclass_mock(InputRectangularPointingSet, repointing=2),
             create_dataclass_mock(InputRectangularPointingSet, repointing=3),
-            create_dataclass_mock(InputRectangularPointingSet, repointing=4)
+            create_dataclass_mock(InputRectangularPointingSet, repointing=4),
         ]
 
         expected = [
@@ -302,10 +498,18 @@ def test_filter_bad_days(self):
         bad_day_exposures = np.zeros((1, 7, 3600, 40))
 
         hi_l1c_data = [
-            create_dataclass_mock(InputRectangularPointingSet, exposure_times=good_day_exposures.copy()),
-            create_dataclass_mock(InputRectangularPointingSet, exposure_times=bad_day_exposures.copy()),
-            create_dataclass_mock(InputRectangularPointingSet, exposure_times=good_day_exposures.copy()),
-            create_dataclass_mock(InputRectangularPointingSet, exposure_times=bad_day_exposures.copy())
+            create_dataclass_mock(
+                InputRectangularPointingSet, exposure_times=good_day_exposures.copy()
+            ),
+            create_dataclass_mock(
+                InputRectangularPointingSet, exposure_times=bad_day_exposures.copy()
+            ),
+            create_dataclass_mock(
+                InputRectangularPointingSet, exposure_times=good_day_exposures.copy()
+            ),
+            create_dataclass_mock(
+                InputRectangularPointingSet, exposure_times=bad_day_exposures.copy()
+            ),
         ]
 
         filtered_psets = filter_bad_days(hi_l1c_data)
diff --git a/tests/scripts/test_convert_product_definition_to_yaml.py b/tests/scripts/test_convert_product_definition_to_yaml.py
index 1efca334b..c103f6d61 100644
--- a/tests/scripts/test_convert_product_definition_to_yaml.py
+++ b/tests/scripts/test_convert_product_definition_to_yaml.py
@@ -5,13 +5,19 @@
 
 
 class TestConvertProductDefinitionToYamlTest(unittest.TestCase):
-
     def test_parse_required_for_data_with_multiple_depend(self):
         row_columns = {
-            "NAME": ["intensity_by_pitch_angle_and_gyrophase", "energy_label", "pitch_angle_label"],
+            "NAME": [
+                "intensity_by_pitch_angle_and_gyrophase",
+                "energy_label",
+                "pitch_angle_label",
+            ],
             "DATA_TYPE": ["float32", "str", "str"],
-            "CATDESC": ["Intensity organized by pitch angle and gyrophase",
-                        "energy label", "pitch angle label"],
+            "CATDESC": [
+                "Intensity organized by pitch angle and gyrophase",
+                "energy label",
+                "pitch angle label",
+            ],
             "VAR_TYPE": ["data", "metadata", "metadata"],
             "DEPEND_0": ["epoch", "", ""],
             "DEPEND_1": ["energy", "", ""],
@@ -26,18 +32,18 @@ def test_parse_required_for_data_with_multiple_depend(self):
             "LABL_PTR_1": ["energy_label", "", ""],
             "LABL_PTR_2": ["pitch_angle_label", "", ""],
             "VARIABLE_PURPOSE": ["primary var, summary", "", ""],
-            "RECORD_VARYING": ["rv", "nrv", "nrv"]
+            "RECORD_VARYING": ["rv", "nrv", "nrv"],
         }
 
         rows = [
-            [k for k in row_columns.keys()],
+            [k for k in row_columns],
             [v[0] for v in row_columns.values()],
             [v[1] for v in row_columns.values()],
             [v[2] for v in row_columns.values()],
         ]
 
         filePath = "csv_test_file.csv"
-        with open(filePath, "w", newline='') as csvfile:
+        with open(filePath, "w", newline="") as csvfile:
             test_csv_writer = csv.writer(csvfile)
             test_csv_writer.writerows(rows)
 
@@ -79,8 +85,22 @@ def test_parse_required_for_data_with_multiple_depend(self):
         self.assertEqual(expected_yaml, actual_yaml)
 
     def test_parse_required_for_data_only_depend_0(self):
-        required_for_data = ["NAME", "DATA_TYPE", "CATDESC", "VAR_TYPE", "RECORD_VARYING", "DEPEND_0", "DISPLAY_TYPE",
-                             "FIELDNAM", "FORMAT", "LABLAXIS", "UNITS", "VALIDMIN", "VALIDMAX", "FILLVAL"]
+        required_for_data = [
+            "NAME",
+            "DATA_TYPE",
+            "CATDESC",
+            "VAR_TYPE",
+            "RECORD_VARYING",
+            "DEPEND_0",
+            "DISPLAY_TYPE",
+            "FIELDNAM",
+            "FORMAT",
+            "LABLAXIS",
+            "UNITS",
+            "VALIDMIN",
+            "VALIDMAX",
+            "FILLVAL",
+        ]
 
         test_csv_values = [
             "core_fit_num_points",
@@ -96,10 +116,10 @@ def test_parse_required_for_data_only_depend_0(self):
             "",
             "",
             "",
-            "-1.00E+31"
+            "-1.00E+31",
         ]
         filePath = "csv_test_file.csv"
-        with open(filePath, "w", newline='') as csvfile:
+        with open(filePath, "w", newline="") as csvfile:
             test_csv_writer = csv.writer(csvfile)
             test_csv_writer.writerow(required_for_data)
             test_csv_writer.writerow(test_csv_values)
@@ -121,8 +141,21 @@ def test_parse_required_for_data_only_depend_0(self):
         self.assertEqual(expected_yaml, actual_yaml)
 
     def test_parse_required_for_support_data_non_rv(self):
-        required_for_data = ["NAME", "DATA_TYPE", "CATDESC", "VAR_TYPE", "RECORD_VARYING", "DISPLAY_TYPE",
-                             "FIELDNAM", "FORMAT", "LABLAXIS", "UNITS", "VALIDMIN", "VALIDMAX", "FILLVAL"]
+        required_for_data = [
+            "NAME",
+            "DATA_TYPE",
+            "CATDESC",
+            "VAR_TYPE",
+            "RECORD_VARYING",
+            "DISPLAY_TYPE",
+            "FIELDNAM",
+            "FORMAT",
+            "LABLAXIS",
+            "UNITS",
+            "VALIDMIN",
+            "VALIDMAX",
+            "FILLVAL",
+        ]
 
         test_csv_values = [
             "energy",
@@ -137,10 +170,10 @@ def test_parse_required_for_support_data_non_rv(self):
             "",
             "",
             "",
-            "-1.00E+31"
+            "-1.00E+31",
         ]
         filePath = "csv_test_file.csv"
-        with open(filePath, "w", newline='') as csvfile:
+        with open(filePath, "w", newline="") as csvfile:
             test_csv_writer = csv.writer(csvfile)
             test_csv_writer.writerow(required_for_data)
             test_csv_writer.writerow(test_csv_values)
@@ -161,9 +194,22 @@ def test_parse_required_for_support_data_non_rv(self):
         self.assertEqual(expected_yaml, actual_yaml)
 
     def test_parse_required_for_data_epoch(self):
-        required_for_data = ["NAME", "DATA_TYPE", "CATDESC", "VAR_TYPE", "DEPEND_0", "DISPLAY_TYPE",
-                             "FIELDNAM", "FORMAT", "LABLAXIS", "UNITS", "VALIDMIN", "VALIDMAX", "FILLVAL",
-                             "RECORD_VARYING"]
+        required_for_data = [
+            "NAME",
+            "DATA_TYPE",
+            "CATDESC",
+            "VAR_TYPE",
+            "DEPEND_0",
+            "DISPLAY_TYPE",
+            "FIELDNAM",
+            "FORMAT",
+            "LABLAXIS",
+            "UNITS",
+            "VALIDMIN",
+            "VALIDMAX",
+            "FILLVAL",
+            "RECORD_VARYING",
+        ]
 
         test_csv_values = [
             "epoch",
@@ -179,10 +225,10 @@ def test_parse_required_for_data_epoch(self):
             "",
             "",
             "-1.00E+31",
-            "rv"
+            "rv",
         ]
         filePath = "csv_test_file.csv"
-        with open(filePath, "w", newline='') as csvfile:
+        with open(filePath, "w", newline="") as csvfile:
             test_csv_writer = csv.writer(csvfile)
             test_csv_writer.writerow(required_for_data)
             test_csv_writer.writerow(test_csv_values)
@@ -203,47 +249,48 @@ def test_parse_required_for_data_epoch(self):
         self.assertEqual(expected_yaml, actual_yaml)
 
     def test_parse_does_not_include_columns_if_they_are_blank(self):
-        all_headers = ["NAME",
-                       "DATA_TYPE",
-                       "Data shape",
-                       "CATDESC",
-                       "VAR_TYPE",
-                       "RECORD_VARYING",
-                       "DEPEND_0",
-                       "DEPEND_1",
-                       "DEPEND_2",
-                       "DEPEND_3",
-                       "DISPLAY_TYPE",
-                       "FIELDNAM",
-                       "FORMAT",
-                       "LABLAXIS",
-                       "UNITS",
-                       "VALIDMIN",
-                       "VALIDMAX",
-                       "FILLVAL",
-                       "LABL_PTRS",
-                       "UNIT_PTR",
-                       "SCALE_TYP",
-                       "SCAL_PTR",
-                       "VAR_NOTES",
-                       "TIME_BASE",
-                       "TIME_SCALE",
-                       "LEAP_SECONDS_INCLUDED",
-                       "ABSOLUTE_ERROR",
-                       "AVG_TYPE",
-                       "BIN_LOCATION",
-                       "DELTA_PLUS_VAR",
-                       "DELTA_MINUS_VAR",
-                       "DERIVN",
-                       "DICT_KEY",
-                       "MONOTON",
-                       "SCALEMIN",
-                       "SCALEMAX",
-                       "REFERENCE_POSITION",
-                       "RELATIVE_ERROR",
-                       "RESOLUTION",
-                       "SI_CONVERSION"
-                       ]
+        all_headers = [
+            "NAME",
+            "DATA_TYPE",
+            "Data shape",
+            "CATDESC",
+            "VAR_TYPE",
+            "RECORD_VARYING",
+            "DEPEND_0",
+            "DEPEND_1",
+            "DEPEND_2",
+            "DEPEND_3",
+            "DISPLAY_TYPE",
+            "FIELDNAM",
+            "FORMAT",
+            "LABLAXIS",
+            "UNITS",
+            "VALIDMIN",
+            "VALIDMAX",
+            "FILLVAL",
+            "LABL_PTRS",
+            "UNIT_PTR",
+            "SCALE_TYP",
+            "SCAL_PTR",
+            "VAR_NOTES",
+            "TIME_BASE",
+            "TIME_SCALE",
+            "LEAP_SECONDS_INCLUDED",
+            "ABSOLUTE_ERROR",
+            "AVG_TYPE",
+            "BIN_LOCATION",
+            "DELTA_PLUS_VAR",
+            "DELTA_MINUS_VAR",
+            "DERIVN",
+            "DICT_KEY",
+            "MONOTON",
+            "SCALEMIN",
+            "SCALEMAX",
+            "REFERENCE_POSITION",
+            "RELATIVE_ERROR",
+            "RESOLUTION",
+            "SI_CONVERSION",
+        ]
 
         row_contents = [
             "stim_tag",
@@ -285,11 +332,11 @@ def test_parse_does_not_include_columns_if_they_are_blank(self):
             "",
             "",
             "",
-            ""
+            "",
         ]
 
         filePath = "csv_test_file.csv"
-        with open(filePath, "w", newline='') as csvfile:
+        with open(filePath, "w", newline="") as csvfile:
             test_csv_writer = csv.writer(csvfile)
             test_csv_writer.writerow(all_headers)
             test_csv_writer.writerow(row_contents)
diff --git a/tests/swapi/_helpers.py b/tests/swapi/_helpers.py
index 1ede531ae..6aed11f79 100644
--- a/tests/swapi/_helpers.py
+++ b/tests/swapi/_helpers.py
@@ -1,5 +1,4 @@
 from datetime import datetime
-from typing import Optional
 
 import numpy as np
 from spacepy import pycdf
@@ -16,7 +15,6 @@
 from imap_l3_processing.swapi.response.swapi_response import ResponseGrid, SwapiResponse
 from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
-
 # Calibration CSVs shipped with the repo. Loading the full SwapiResponse from
 # these triggers the same code path the production pipeline uses.
 SWAPI_AZIMUTHAL_TRANSMISSION_PATH = get_test_instrument_team_data_path(
@@ -34,7 +32,7 @@
 
 
 def load_swapi_response(
-    warm_cache_voltages: Optional[np.ndarray] = None,
+    warm_cache_voltages: np.ndarray | None = None,
 ) -> SwapiResponse:
     """Build a `SwapiResponse` from the CSV files."""
     response = SwapiResponse.from_files(
@@ -108,4 +106,5 @@ def synthesize_count_rates(ctx, sw_params: SolarWindParams) -> np.ndarray:
     ideal, _ = model_solar_wind_ideal_coincidence_rates(sw_params, ctx)
     return ideal * deadtime_factor(ideal)
 
+
 NOMINAL_TEST_EPOCH_TT2000 = pycdf.lib.datetime_to_tt2000(datetime(2026, 1, 1))
diff --git a/tests/swapi/cdf_model_test_case.py b/tests/swapi/cdf_model_test_case.py
index 207bf8abb..70e349053 100644
--- a/tests/swapi/cdf_model_test_case.py
+++ b/tests/swapi/cdf_model_test_case.py
@@ -6,12 +6,28 @@
 
 
 class CdfModelTestCase(unittest.TestCase):
-    def assert_variable_attributes(self, variable: DataProductVariable,
-                                   expected_data, expected_name,
-                                   expected_data_type=None,
-                                   expected_record_varying=None):
+    def assert_variable_attributes(
+        self,
+        variable: DataProductVariable,
+        expected_data,
+        expected_name,
+        expected_data_type=None,
+        expected_record_varying=None,
+    ):
         self.assertEqual(expected_name, variable.name)
-        np.testing.assert_array_equal(variable.value, expected_data, f"mismatch for var name: {variable.name}",
-                                      strict=True)
-        self.assertEqual(expected_data_type, variable.cdf_data_type, f"mismatch for var name: {variable.name}")
-        self.assertEqual(expected_record_varying, variable.record_varying, f"mismatch for var name: {variable.name}")
+        np.testing.assert_array_equal(
+            variable.value,
+            expected_data,
+            f"mismatch for var name: {variable.name}",
+            strict=True,
+        )
+        self.assertEqual(
+            expected_data_type,
+            variable.cdf_data_type,
+            f"mismatch for var name: {variable.name}",
+        )
+        self.assertEqual(
+            expected_record_varying,
+            variable.record_varying,
+            f"mismatch for var name: {variable.name}",
+        )
diff --git a/tests/swapi/l3a/science/pickup_ion/test_build_collapsed_response_grid.py b/tests/swapi/l3a/science/pickup_ion/test_build_collapsed_response_grid.py
index 27a967417..3b0fd3dca 100644
--- a/tests/swapi/l3a/science/pickup_ion/test_build_collapsed_response_grid.py
+++ b/tests/swapi/l3a/science/pickup_ion/test_build_collapsed_response_grid.py
@@ -83,7 +83,9 @@ def test_grid_matches_precomputed_shell_integral_reference(self):
         trusted = h_truth > 0.05 * peak
         self.assertGreater(trusted.sum(), 16, "trusted region too small")
 
-        rel_err = np.abs(collapsed.values[trusted] - h_truth[trusted]) / h_truth[trusted]
+        rel_err = (
+            np.abs(collapsed.values[trusted] - h_truth[trusted]) / h_truth[trusted]
+        )
         worst = int(np.argmax(rel_err))
         worst_index = int(np.flatnonzero(trusted)[worst])
         self.assertLess(
@@ -136,10 +138,11 @@ def test_matches_shell_integral_when_grid_cell_aligned_with_singularity(self):
         cos_elevation = np.cos(np.deg2rad(elevation_deg))
         cos_delta_azimuth_target = 1.0 - 5e-7
         cos_angle_target = cos_delta_azimuth_target * cos_elevation
-        v_singular_kms = float(np.sqrt(
-            speed ** 2 + bulk_speed ** 2
-            - 2 * cos_angle_target * speed * bulk_speed
-        ))
+        v_singular_kms = float(
+            np.sqrt(
+                speed**2 + bulk_speed**2 - 2 * cos_angle_target * speed * bulk_speed
+            )
+        )
 
         collapsed = build_collapsed_response_grid(
             response_grid,
diff --git a/tests/swapi/l3a/science/pickup_ion/test_calculate_coincidence_rate.py b/tests/swapi/l3a/science/pickup_ion/test_calculate_coincidence_rate.py
index 162385312..fd4a59292 100644
--- a/tests/swapi/l3a/science/pickup_ion/test_calculate_coincidence_rate.py
+++ b/tests/swapi/l3a/science/pickup_ion/test_calculate_coincidence_rate.py
@@ -11,6 +11,7 @@
 bulk-aligned geometry near its 1/sin(delta_azimuth) Jacobian singularity, and at
 very low rates (covered by `atol`).
 """
+
 import unittest
 
 import numpy as np
@@ -59,14 +60,14 @@ def test_matches_xarray_reference_per_voltage(self):
         # (−cos θ sin φ, −cos θ cos φ, −sin θ).
         sw_az_rad = np.radians(_SW_AZIMUTH_DEG)
         sw_el_rad = np.radians(_SW_ELEVATION_DEG)
-        bulk_vec = _SW_SPEED_KMS * np.array([
-            -np.cos(sw_el_rad) * np.sin(sw_az_rad),
-            -np.cos(sw_el_rad) * np.cos(sw_az_rad),
-            -np.sin(sw_el_rad),
-        ])
-        bulk_sw_per_bin = np.broadcast_to(
-            bulk_vec, (1, voltage_v.size, 3)
-        ).copy()
+        bulk_vec = _SW_SPEED_KMS * np.array(
+            [
+                -np.cos(sw_el_rad) * np.sin(sw_az_rad),
+                -np.cos(sw_el_rad) * np.cos(sw_az_rad),
+                -np.sin(sw_el_rad),
+            ]
+        )
+        bulk_sw_per_bin = np.broadcast_to(bulk_vec, (1, voltage_v.size, 3)).copy()
 
         swapi_response = load_swapi_response(
             warm_cache_voltages=voltage_v.astype(float)
diff --git a/tests/swapi/l3a/science/pickup_ion/test_density_of_neutral_helium_lookup_table.py b/tests/swapi/l3a/science/pickup_ion/test_density_of_neutral_helium_lookup_table.py
index 3e108c91b..60a367900 100644
--- a/tests/swapi/l3a/science/pickup_ion/test_density_of_neutral_helium_lookup_table.py
+++ b/tests/swapi/l3a/science/pickup_ion/test_density_of_neutral_helium_lookup_table.py
@@ -2,8 +2,9 @@
 
 import numpy as np
 
-from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import \
-    DensityOfNeutralHeliumLookupTable
+from imap_l3_processing.swapi.l3a.science.pickup_ion.density_of_neutral_helium_lookup_table import (
+    DensityOfNeutralHeliumLookupTable,
+)
 
 
 def _table_with_360_anchor():
diff --git a/tests/swapi/l3a/science/pickup_ion/test_goodness_of_fit.py b/tests/swapi/l3a/science/pickup_ion/test_goodness_of_fit.py
index 5deadf7db..a3d9638bd 100644
--- a/tests/swapi/l3a/science/pickup_ion/test_goodness_of_fit.py
+++ b/tests/swapi/l3a/science/pickup_ion/test_goodness_of_fit.py
@@ -4,12 +4,12 @@
 
 from imap_l3_processing.swapi.constants import SWAPI_BACKGROUND_RATE
 from imap_l3_processing.swapi.l3a.science.pickup_ion.goodness_of_fit import (
+    CUTOFF_DROP_RATIO,
     MAX_CUTOFF_SPEED_KMS,
     MAX_CUTOFF_SPEED_RATIO,
     MAX_IONIZATION_RATE,
     MIN_CUTOFF_SPEED_RATIO,
     MIN_IONIZATION_RATE,
-    CUTOFF_DROP_RATIO,
     is_good_fit,
 )
 
@@ -32,7 +32,7 @@ def _make_pui_model(
 
     # custom-shaped cutoff, with only one point below CUTOFF_DROP_RATIO = 0.4
     rolloff_start = int(np.argmax(~below_cutoff))
-    shape[rolloff_start:rolloff_start + 3] = np.array([0.9, 0.5, 0.2])
+    shape[rolloff_start : rolloff_start + 3] = np.array([0.9, 0.5, 0.2])
 
     # scale by peak rate
     model_rates = rate_at_cutoff * shape
@@ -58,12 +58,19 @@ def test_fit_acceptance_criteria(self):
 
         chunk_mean_model_rates = model_rates.mean(axis=0)
         peak_model_rate = chunk_mean_model_rates.max()
-        cutoff_mask_energy = esa_energies[chunk_mean_model_rates >= peak_model_rate * CUTOFF_DROP_RATIO].max()
+        cutoff_mask_energy = esa_energies[
+            chunk_mean_model_rates >= peak_model_rate * CUTOFF_DROP_RATIO
+        ].max()
         past_cutoff_mask = esa_energies > cutoff_mask_energy
-        assert past_cutoff_mask.sum() == 4  # 4 points by construction: three zero, one nonzero
+        assert (
+            past_cutoff_mask.sum() == 4
+        )  # 4 points by construction: three zero, one nonzero
 
         def extra_counts_past_cutoff(fraction: float) -> dict:
-            return dict(observed_rates=model_rates + past_cutoff_mask * peak_model_rate * fraction)
+            return dict(
+                observed_rates=model_rates
+                + past_cutoff_mask * peak_model_rate * fraction
+            )
 
         def poisson_sampled_observed_rates() -> dict:
             livetime = 0.145
@@ -73,7 +80,10 @@ def poisson_sampled_observed_rates() -> dict:
 
         cases = {
             "all good": (True, {}),
-            "cutoff just below maximum": (True, dict(cutoff_speed_kms=max_cutoff - 1.0)),
+            "cutoff just below maximum": (
+                True,
+                dict(cutoff_speed_kms=max_cutoff - 1.0),
+            ),
             "cutoff at maximum": (True, dict(cutoff_speed_kms=max_cutoff)),
             "cutoff above maximum": (False, dict(cutoff_speed_kms=max_cutoff + 1.0)),
             "model underpredicts a little": (True, dict(model_rates=model_rates * 0.9)),
@@ -84,7 +94,9 @@ def poisson_sampled_observed_rates() -> dict:
         }
         for case_name, (expected, overrides) in cases.items():
             with self.subTest(case_name):
-                self.assertEqual(expected, is_good_fit(**{**defaults, **overrides}), msg=case_name)
+                self.assertEqual(
+                    expected, is_good_fit(**{**defaults, **overrides}), msg=case_name
+                )
 
     def test_fitted_parameter_range_criteria(self):
         """A fit whose cutoff speed leaves the allowed multiple of the bulk
@@ -108,30 +120,56 @@ def test_fitted_parameter_range_criteria(self):
         cases = {
             "all good": (True, {}),
             "cutoff speed below the lowest allowed multiple": (
-                False, dict(cutoff_speed_kms=min_cutoff_speed - 1.0)),
+                False,
+                dict(cutoff_speed_kms=min_cutoff_speed - 1.0),
+            ),
             "cutoff speed at the lowest allowed multiple of the bulk speed": (
-                True, dict(cutoff_speed_kms=min_cutoff_speed)),
+                True,
+                dict(cutoff_speed_kms=min_cutoff_speed),
+            ),
             "cutoff speed above the lowest allowed multiple": (
-                True, dict(cutoff_speed_kms=min_cutoff_speed + 1.0)),
+                True,
+                dict(cutoff_speed_kms=min_cutoff_speed + 1.0),
+            ),
             "cutoff speed below the highest allowed multiple": (
-                True, dict(cutoff_speed_kms=max_cutoff_speed - 1.0)),
+                True,
+                dict(cutoff_speed_kms=max_cutoff_speed - 1.0),
+            ),
             "cutoff speed at the highest allowed multiple of the bulk speed": (
-                True, dict(cutoff_speed_kms=max_cutoff_speed)),
+                True,
+                dict(cutoff_speed_kms=max_cutoff_speed),
+            ),
             "cutoff speed above the highest allowed multiple": (
-                False, dict(cutoff_speed_kms=max_cutoff_speed + 1.0)),
+                False,
+                dict(cutoff_speed_kms=max_cutoff_speed + 1.0),
+            ),
             "ionization rate below the lower limit": (
-                False, dict(ionization_rate=MIN_IONIZATION_RATE * 0.99)),
+                False,
+                dict(ionization_rate=MIN_IONIZATION_RATE * 0.99),
+            ),
             "ionization rate at the lower limit": (
-                True, dict(ionization_rate=MIN_IONIZATION_RATE)),
+                True,
+                dict(ionization_rate=MIN_IONIZATION_RATE),
+            ),
             "ionization rate above the lower limit": (
-                True, dict(ionization_rate=MIN_IONIZATION_RATE * 1.01)),
+                True,
+                dict(ionization_rate=MIN_IONIZATION_RATE * 1.01),
+            ),
             "ionization rate below the upper limit": (
-                True, dict(ionization_rate=MAX_IONIZATION_RATE * 0.99)),
+                True,
+                dict(ionization_rate=MAX_IONIZATION_RATE * 0.99),
+            ),
             "ionization rate at the upper limit": (
-                True, dict(ionization_rate=MAX_IONIZATION_RATE)),
+                True,
+                dict(ionization_rate=MAX_IONIZATION_RATE),
+            ),
             "ionization rate above the upper limit": (
-                False, dict(ionization_rate=MAX_IONIZATION_RATE * 1.01)),
+                False,
+                dict(ionization_rate=MAX_IONIZATION_RATE * 1.01),
+            ),
         }
         for case_name, (expected, overrides) in cases.items():
             with self.subTest(case_name):
-                self.assertEqual(expected, is_good_fit(**{**defaults, **overrides}), msg=case_name)
+                self.assertEqual(
+                    expected, is_good_fit(**{**defaults, **overrides}), msg=case_name
+                )
diff --git a/tests/swapi/l3a/science/pickup_ion/test_utils.py b/tests/swapi/l3a/science/pickup_ion/test_utils.py
index e45960a8b..1ce8b3a62 100644
--- a/tests/swapi/l3a/science/pickup_ion/test_utils.py
+++ b/tests/swapi/l3a/science/pickup_ion/test_utils.py
@@ -15,9 +15,30 @@
         [-0.803319036, -0.595067395, -0.023944118, 0.0, 0.0, 0.0],
         [0.594803234, -0.803675802, 0.017728995, 0.0, 0.0, 0.0],
         [-0.029793255, 0.0, 0.999556082, 0.0, 0.0, 0.0],
-        [-1.16314295e-06, 1.56750981e-06, 6.68593934e-08, -0.803319036, -0.595067395, -0.023944118],
-        [-1.56457525e-06, -1.16063465e-06, -1.21809529e-07, 0.594803234, -0.803675802, 0.017728995],
-        [1.26218156e-07, 5.29395592e-23, 3.76211978e-09, -0.029793255, 0.0, 0.999556082],
+        [
+            -1.16314295e-06,
+            1.56750981e-06,
+            6.68593934e-08,
+            -0.803319036,
+            -0.595067395,
+            -0.023944118,
+        ],
+        [
+            -1.56457525e-06,
+            -1.16063465e-06,
+            -1.21809529e-07,
+            0.594803234,
+            -0.803675802,
+            0.017728995,
+        ],
+        [
+            1.26218156e-07,
+            5.29395592e-23,
+            3.76211978e-09,
+            -0.029793255,
+            0.0,
+            0.999556082,
+        ],
     ]
 )
 
@@ -42,7 +63,9 @@ def test_adds_imap_inertial_velocity_to_rotated_input(self, mock_spice):
             ]
         )
         np.testing.assert_array_almost_equal(output_velocity, expected)
-        mock_spice.sxform.assert_called_with("INPUT_FRAME", "OUTPUT_FRAME", ephemeris_time)
+        mock_spice.sxform.assert_called_with(
+            "INPUT_FRAME", "OUTPUT_FRAME", ephemeris_time
+        )
         mock_spice.spkezr.assert_called_with(
             "IMAP", ephemeris_time, "OUTPUT_FRAME", "NONE", "SUN"
         )
diff --git a/tests/swapi/l3a/science/pickup_ion/test_vasyliunas_siscoe_distribution.py b/tests/swapi/l3a/science/pickup_ion/test_vasyliunas_siscoe_distribution.py
index f17bc0837..c8fd14e25 100644
--- a/tests/swapi/l3a/science/pickup_ion/test_vasyliunas_siscoe_distribution.py
+++ b/tests/swapi/l3a/science/pickup_ion/test_vasyliunas_siscoe_distribution.py
@@ -13,11 +13,14 @@
 from imap_l3_processing.swapi.l3a.science.pickup_ion.uniform_speed_grid import (
     UniformSpeedGrid,
 )
-from imap_l3_processing.swapi.l3a.science.pickup_ion.vasyliunas_siscoe_distribution import \
-    vasyliunas_siscoe_vdf
+from imap_l3_processing.swapi.l3a.science.pickup_ion.vasyliunas_siscoe_distribution import (
+    vasyliunas_siscoe_vdf,
+)
 from tests.test_helpers import NumpyArrayMatcher
 
-_VASYLIUNAS_SISCOE_MODULE = "imap_l3_processing.swapi.l3a.science.pickup_ion.vasyliunas_siscoe_distribution"
+_VASYLIUNAS_SISCOE_MODULE = (
+    "imap_l3_processing.swapi.l3a.science.pickup_ion.vasyliunas_siscoe_distribution"
+)
 
 _IONIZATION_RATE_HZ = 0.47
 _SOLAR_WIND_SPEED_INERTIAL_KMS = 456.0
@@ -56,7 +59,8 @@ def _expected_uncut_filled_shell(cutoff_speed: float) -> np.ndarray:
         # closed form of the filled shell, with the mocked unit neutral
         # helium density converted from cm^-3 to km^-3.
         return (
-            SWAPI_PUI_COOLING_INDEX / (4 * np.pi)
+            SWAPI_PUI_COOLING_INDEX
+            / (4 * np.pi)
             * (_IONIZATION_RATE_HZ * ONE_AU_IN_KM**2)
             / (_DISTANCE_KM * _SOLAR_WIND_SPEED_INERTIAL_KMS * cutoff_speed**3)
             * (_SPEED_GRID.centers / cutoff_speed) ** (SWAPI_PUI_COOLING_INDEX - 3)
@@ -79,7 +83,7 @@ def test_scales_the_cutoff_cell_by_the_fraction_below_it_and_zeros_the_cells_abo
 
         expected = self._expected_uncut_filled_shell(cutoff_speed)
         expected[cutoff_index] *= fraction_below_cutoff
-        expected[cutoff_index + 1:] = 0.0
+        expected[cutoff_index + 1 :] = 0.0
         np.testing.assert_allclose(result, expected, rtol=1e-12)
 
         mock_density.assert_called_with(
diff --git a/tests/swapi/l3a/science/solar_wind/alpha/test_fit_solar_wind_alpha_model.py b/tests/swapi/l3a/science/solar_wind/alpha/test_fit_solar_wind_alpha_model.py
index 03511f3b3..6b6f0c5f7 100644
--- a/tests/swapi/l3a/science/solar_wind/alpha/test_fit_solar_wind_alpha_model.py
+++ b/tests/swapi/l3a/science/solar_wind/alpha/test_fit_solar_wind_alpha_model.py
@@ -1,21 +1,22 @@
 import unittest
 from unittest.mock import MagicMock, patch
 
-import numba
 import numpy as np
 from uncertainties import ufloat
 
 from imap_l3_processing.constants import (
     ALPHA_MASS_PER_CHARGE_M_P_PER_E,
-    ALPHA_PARTICLE_CHARGE_COULOMBS,
     ALPHA_PARTICLE_MASS_KG,
     PROTON_CHARGE_COULOMBS,
     PROTON_MASS_KG,
 )
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.l3a.science.solar_wind.alpha import (
-    fit_solar_wind_alpha_model as alpha_module,
     calculate_initial_guess as alpha_initial_guess_module,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha import (
+    fit_solar_wind_alpha_model as alpha_module,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.fit_solar_wind_alpha_model import (
     MIN_TOLERABLE_ALPHA_SPEED_RATIO,
     AlphaSolarWindFitResult,
@@ -29,18 +30,16 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
     model_solar_wind_ideal_coincidence_rates,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
     ProtonSolarWindFitResult,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
-from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
 from imap_l3_processing.swapi.species import Species
 from tests.swapi._helpers import NOMINAL_TEST_EPOCH_TT2000, load_swapi_response
 
-
 # ----- module-level fixture constants --------------------------------------
 
 # 5-sweep coarse-only voltage axis: 62 bins/sweep × 5 sweeps = 310 measurements,
@@ -51,10 +50,10 @@
 # physical voltages SWAPI sweeps in flight.
 _N_BINS_PER_SWEEP = 62
 _N_SWEEPS = 5
-_ONE_SWEEP_VOLTAGE = np.logspace(
-    np.log10(3500.0), np.log10(140.0), _N_BINS_PER_SWEEP
-)
-_FIVE_SWEEP_VOLTAGE = np.broadcast_to(_ONE_SWEEP_VOLTAGE, (_N_SWEEPS, _N_BINS_PER_SWEEP)).copy()
+_ONE_SWEEP_VOLTAGE = np.logspace(np.log10(3500.0), np.log10(140.0), _N_BINS_PER_SWEEP)
+_FIVE_SWEEP_VOLTAGE = np.broadcast_to(
+    _ONE_SWEEP_VOLTAGE, (_N_SWEEPS, _N_BINS_PER_SWEEP)
+).copy()
 _N_MEAS = _FIVE_SWEEP_VOLTAGE.size
 
 # Slow-wind ground-truth moments. RTN +R points sunward, so a sunward solar
@@ -232,7 +231,9 @@ def _synthesize_proton_plus_alpha_count_rate(
             species=Species.ALPHA,
             rotation_matrices=rotation_matrices,
         )
-        alpha_true, _ = model_solar_wind_ideal_coincidence_rates(alpha_params, alpha_ctx)
+        alpha_true, _ = model_solar_wind_ideal_coincidence_rates(
+            alpha_params, alpha_ctx
+        )
         total_true = proton_true + alpha_true
     else:
         total_true = proton_true
@@ -386,8 +387,7 @@ def test_alpha_velocity_equals_proton_velocity_plus_delta_v_along_bhat(self):
         """The post-fit `velocity_rtn` satisfies the algebraic identity v_α = v_p* + Δv·B̂ exactly (not approximately) — the dataclass stores the constraint, not a free vector."""
         v_alpha = self.result.velocity_rtn_nominal()
         expected = (
-            _TRUE_PROTON_VELOCITY_RTN
-            + self.result.delta_v.nominal_value * _B_HAT_RTN
+            _TRUE_PROTON_VELOCITY_RTN + self.result.delta_v.nominal_value * _B_HAT_RTN
         )
         np.testing.assert_allclose(v_alpha, expected, atol=1e-9)
 
@@ -400,9 +400,7 @@ def test_alpha_velocity_recovers_truth(self):
         )
 
 
-class TestAlphaPeakEnergyRatioGuard(
-    _SyntheticAlphaSpectrumFixture, unittest.TestCase
-):
+class TestAlphaPeakEnergyRatioGuard(_SyntheticAlphaSpectrumFixture, unittest.TestCase):
     """Only fitted alpha speeds below the tolerable ratio to the proton speed are rejected."""
 
     def test_alpha_speed_ratio_lower_bound(self):
@@ -472,9 +470,7 @@ def setUpClass(cls):
 
     def test_alpha_velocity_minus_proton_velocity_is_parallel_to_bhat(self):
         """For a tilted B̂, the recovered (v_α − v_p) lies along ±B̂ — equivalently, the cross product (v_α − v_p) × B̂ is zero up to numerical noise."""
-        delta = (
-            self.result.velocity_rtn_nominal() - self.proton_velocity_rtn
-        )
+        delta = self.result.velocity_rtn_nominal() - self.proton_velocity_rtn
         np.testing.assert_allclose(np.cross(delta, self.b_hat), 0.0, atol=1e-9)
 
     def test_recovered_delta_v_matches_dot_product_of_velocity_offset(self):
@@ -582,18 +578,22 @@ def test_fit_error_flag_set_when_least_squares_does_not_converge(self):
             count_rate=np.full(_FIVE_SWEEP_VOLTAGE.shape, 100.0),
         )
 
-        with patch.object(
-            alpha_module,
-            "calculate_initial_guess",
-            return_value=(0.2, 4.0e5, 0.0, np.array([10, 11, 12])),
-        ), patch.object(
-            alpha_module._AlphaEvaluator,
-            "residuals",
-            return_value=np.full(_N_MEAS, 1.0),
-        ), patch.object(
-            alpha_module.scipy.optimize,
-            "least_squares",
-            return_value=non_converged,
+        with (
+            patch.object(
+                alpha_module,
+                "calculate_initial_guess",
+                return_value=(0.2, 4.0e5, 0.0, np.array([10, 11, 12])),
+            ),
+            patch.object(
+                alpha_module._AlphaEvaluator,
+                "residuals",
+                return_value=np.full(_N_MEAS, 1.0),
+            ),
+            patch.object(
+                alpha_module.scipy.optimize,
+                "least_squares",
+                return_value=non_converged,
+            ),
         ):
             result = fit_solar_wind_alpha_model(
                 proton_ctx=proton_ctx,
@@ -757,13 +757,16 @@ def setUpClass(cls):
 
     def test_jacobian_shape_is_n_residuals_by_three(self):
         """The analytic Jacobian has shape (N_residuals, 3) — one row per measurement bin, one column per fit parameter (log n_α, log T_α, Δv)."""
-        self.assertEqual(self.analytic_jacobian.shape, (self.alpha_ctx.count_rate.size, 3))
+        self.assertEqual(
+            self.analytic_jacobian.shape, (self.alpha_ctx.count_rate.size, 3)
+        )
 
     def test_log_density_column_equals_alpha_only_rate_times_deadtime_squared(self):
         """Because the rate is linear in n_α, the analytic ∂(observable)/∂(log n_α) column equals 𝒟²(R_total) · R_α exactly with no quadrature slack — an identity check independent of finite-difference noise."""
         from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
             model_solar_wind_ideal_coincidence_rates,
         )
+
         alpha_only_params = SolarWindParams(
             density=_TRUE_ALPHA_DENSITY_CM3,
             velocity_rtn=_TRUE_PROTON_VELOCITY_RTN + _TRUE_DELTA_V_KM_S * _B_HAT_RTN,
@@ -893,24 +896,28 @@ def test_least_squares_receives_jac_callable_and_no_diff_step(self):
             rotation_matrices=self.rotation_matrices,
         )
 
-        with patch.object(
-            alpha_module,
-            "calculate_initial_guess",
-            return_value=(
-                _TRUE_ALPHA_DENSITY_CM3,
-                _TRUE_ALPHA_TEMPERATURE_K,
-                0.0,
-                peak_bin_idx,
+        with (
+            patch.object(
+                alpha_module,
+                "calculate_initial_guess",
+                return_value=(
+                    _TRUE_ALPHA_DENSITY_CM3,
+                    _TRUE_ALPHA_TEMPERATURE_K,
+                    0.0,
+                    peak_bin_idx,
+                ),
+            ),
+            patch.object(
+                alpha_module._AlphaEvaluator,
+                "residuals",
+                return_value=np.zeros(n_peak_residuals),
             ),
-        ), patch.object(
-            alpha_module._AlphaEvaluator,
-            "residuals",
-            return_value=np.zeros(n_peak_residuals),
-        ), patch.object(
-            alpha_module.scipy.optimize,
-            "least_squares",
-            return_value=mock_result,
-        ) as mock_lm:
+            patch.object(
+                alpha_module.scipy.optimize,
+                "least_squares",
+                return_value=mock_result,
+            ) as mock_lm,
+        ):
             fit_solar_wind_alpha_model(
                 proton_ctx=proton_ctx,
                 alpha_ctx=alpha_ctx,
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_calculate_initial_guess.py b/tests/swapi/l3a/science/solar_wind/proton/test_calculate_initial_guess.py
index 99ee495ed..62034866f 100644
--- a/tests/swapi/l3a/science/solar_wind/proton/test_calculate_initial_guess.py
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_calculate_initial_guess.py
@@ -8,31 +8,28 @@
     PROTON_CHARGE_COULOMBS,
     PROTON_MASS_KG,
 )
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
     model_solar_wind_ideal_coincidence_rates,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.calculate_initial_guess import (
     INITIAL_TEMPERATURE_FLOOR_K,
     calculate_initial_guess,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
-    SolarWindParams,
-)
 from imap_l3_processing.swapi.l3a.utils import optimal_density_scale
-from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.species import Species
 from tests.swapi._helpers import NOMINAL_TEST_EPOCH_TT2000, load_swapi_response
 
-
 # RTN → SWAPI rotation. Body +Y (the SWAPI boresight / spin axis) in RTN is
 # column 1 of the transpose, i.e. -R̂_RTN. The solar wind direction (anti-
 # parallel to the spin axis) is therefore +R̂.
-_R_BASE_RTN_TO_SWAPI = np.array(
-    [[0.0, 1.0, 0.0], [-1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]
-)
+_R_BASE_RTN_TO_SWAPI = np.array([[0.0, 1.0, 0.0], [-1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
 
 
 def _esa_voltage_for_proton_speed(speed_km_s: float) -> float:
@@ -71,17 +68,14 @@ def _build_proton_ctx(count_rate: np.ndarray, esa_voltage: np.ndarray):
     return ctx
 
 
-def _make_synthetic_ctx_at_known_truth(truth: SolarWindParams,
-                                       n_bins: int = 71):
+def _make_synthetic_ctx_at_known_truth(truth: SolarWindParams, n_bins: int = 71):
     """Build a context whose `count_rate` array is the noiseless ideal forward
     model evaluated at `truth`."""
     bulk_speed = float(np.linalg.norm(truth.velocity_rtn))
     # Wide enough to bracket ±5σ at T=1e5 K, narrow enough to keep all bins
     # on-instrument.
     speed_grid = np.linspace(0.4 * bulk_speed, 1.6 * bulk_speed, n_bins)
-    voltages = np.array(
-        [_esa_voltage_for_proton_speed(s) for s in speed_grid]
-    )
+    voltages = np.array([_esa_voltage_for_proton_speed(s) for s in speed_grid])
 
     # Build a placeholder ctx, evaluate the forward model at `truth` to get
     # ideal rates, then build the production ctx with those rates as
@@ -98,11 +92,8 @@ def _ctx_with_peak_at(self, peak_speed_kms: float):
         """Build a context whose count-rate spectrum has its maximum at a
         bin whose ESA voltage corresponds to `peak_speed_kms`. The shape of
         the spectrum doesn't matter — we patch out the refiner."""
-        speed_grid = np.linspace(0.4 * peak_speed_kms, 1.6 * peak_speed_kms,
-                                 31)
-        voltages = np.array(
-            [_esa_voltage_for_proton_speed(s) for s in speed_grid]
-        )
+        speed_grid = np.linspace(0.4 * peak_speed_kms, 1.6 * peak_speed_kms, 31)
+        voltages = np.array([_esa_voltage_for_proton_speed(s) for s in speed_grid])
         peak_idx = len(speed_grid) // 2  # middle bin
         voltages[peak_idx] = _esa_voltage_for_proton_speed(peak_speed_kms)
         count_rate = np.linspace(0.1, 0.5, len(voltages))
@@ -138,8 +129,9 @@ def test_temperature_seed_uses_documented_speed_squared_formula(self):
 
         temperature_seed_arg = patched_refine.call_args.args[3]
         expected_temperature = 60_000.0 * (peak_speed / 400.0) ** 2
-        np.testing.assert_allclose(temperature_seed_arg, expected_temperature,
-                                   rtol=1e-12)
+        np.testing.assert_allclose(
+            temperature_seed_arg, expected_temperature, rtol=1e-12
+        )
 
     def test_temperature_seed_floors_at_one_ev(self):
         """At a low peak speed where 60_000·(v/400)² is below 1 eV, the temperature seed handed to the refiner is clamped to `INITIAL_TEMPERATURE_FLOOR_K`."""
@@ -162,9 +154,7 @@ class TestCalculateInitialGuessDirection(unittest.TestCase):
     def test_initial_velocity_is_anti_parallel_to_spin_axis(self):
         """With every rotation matrix aligning body +Y to -R̂, the returned bulk velocity points along +R̂ — the negation of the chunk-mean spin axis."""
         speed_grid = np.linspace(300.0, 600.0, 31)
-        voltages = np.array(
-            [_esa_voltage_for_proton_speed(s) for s in speed_grid]
-        )
+        voltages = np.array([_esa_voltage_for_proton_speed(s) for s in speed_grid])
         count_rate = np.linspace(0.1, 0.5, len(voltages))
         peak_idx = len(speed_grid) // 2  # middle bin
         count_rate[peak_idx] = 100.0
@@ -175,9 +165,7 @@ def test_initial_velocity_is_anti_parallel_to_spin_axis(self):
         expected_axis = np.array([-1.0, 0.0, 0.0])
 
         guess = calculate_initial_guess(ctx)
-        v_unit = guess.velocity_rtn / np.linalg.norm(
-            guess.velocity_rtn
-        )
+        v_unit = guess.velocity_rtn / np.linalg.norm(guess.velocity_rtn)
         np.testing.assert_allclose(v_unit, -expected_axis, atol=1e-12)
 
     def test_velocity_magnitude_matches_truth_bulk_speed(self):
@@ -238,9 +226,7 @@ def test_wraps_runtime_error_on_pathological_edge_spike_spectrum(self):
         """A count-rate spectrum with an extreme isolated spike at the lowest-speed bin (1e10 surrounded by 1e-6) drives `curve_fit` past its `maxfev` budget; the refiner re-raises the resulting `RuntimeError` with a wrapped message that names the peak-bin seed, and the scipy error is preserved on `__cause__`."""
         peak_speed = 250.0
         speed_grid = np.linspace(peak_speed, peak_speed + 600.0, 71)
-        voltages = np.array(
-            [_esa_voltage_for_proton_speed(s) for s in speed_grid]
-        )
+        voltages = np.array([_esa_voltage_for_proton_speed(s) for s in speed_grid])
         count_rate = np.full(len(voltages), 1e-6)
         count_rate[0] = 1.0e10
         ctx = _build_proton_ctx(count_rate, voltages)
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_escape_local_minimum.py b/tests/swapi/l3a/science/solar_wind/proton/test_escape_local_minimum.py
index f1f2ed82e..311849f18 100644
--- a/tests/swapi/l3a/science/solar_wind/proton/test_escape_local_minimum.py
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_escape_local_minimum.py
@@ -7,6 +7,7 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     SolarWindFitContext,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton import (
     escape_local_minimum as escape_local_minimum_module,
 )
@@ -18,7 +19,6 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.optimize_solar_wind_proton_params import (
     OptimizeSolarWindProtonParamsResult,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
 from tests.swapi._helpers import proton_params as _shared_proton_params
 
 
@@ -78,13 +78,17 @@ def test_returns_lm1_unchanged_whenflipped_seed_far_worse(self):
         flipped_mse_far_above_gate = lm1.mse * self._GATE_FACTOR * 10.0
         flipped_params = _proton_params(velocity_rtn=(450.0, 0.0, 0.0))
 
-        with patch.object(
-            escape_local_minimum_module,
-            "flipped_seed",
-            return_value=(flipped_mse_far_above_gate, flipped_params),
-        ) as mock_flipped, patch.object(
-            escape_local_minimum_module, "_restart_from_flipped_params",
-        ) as mock_opt:
+        with (
+            patch.object(
+                escape_local_minimum_module,
+                "flipped_seed",
+                return_value=(flipped_mse_far_above_gate, flipped_params),
+            ) as mock_flipped,
+            patch.object(
+                escape_local_minimum_module,
+                "_restart_from_flipped_params",
+            ) as mock_opt,
+        ):
             out = escape_local_minimum(lm1, ctx)
 
         self.assertIs(out, lm1)
@@ -99,13 +103,16 @@ def test_returns_lm1_unchanged_whenflipped_seed_exactly_at_gate(self):
         lm1 = _result(_proton_params(), mse=1.0)
         flipped_mse_at_gate = lm1.mse * self._GATE_FACTOR
 
-        with patch.object(
-            escape_local_minimum_module,
-            "flipped_seed",
-            return_value=(flipped_mse_at_gate, _proton_params()),
-        ), patch.object(
-            escape_local_minimum_module, "_restart_from_flipped_params"
-        ) as mock_opt:
+        with (
+            patch.object(
+                escape_local_minimum_module,
+                "flipped_seed",
+                return_value=(flipped_mse_at_gate, _proton_params()),
+            ),
+            patch.object(
+                escape_local_minimum_module, "_restart_from_flipped_params"
+            ) as mock_opt,
+        ):
             out = escape_local_minimum(lm1, ctx)
 
         self.assertIs(out, lm1)
@@ -126,18 +133,21 @@ def test_returns_lm2_when_lm2_mse_is_lower(self):
         flipped_seed_mse_iter1 = lm1.mse * (self._GATE_FACTOR / 2.0)
         flipped_seed_mse_iter2 = better_lm2.mse * (self._GATE_FACTOR * 2.0)
 
-        with patch.object(
-            escape_local_minimum_module,
-            "flipped_seed",
-            side_effect=[
-                (flipped_seed_mse_iter1, better_params),
-                (flipped_seed_mse_iter2, better_params),
-            ],
-        ) as mock_flipped, patch.object(
-            escape_local_minimum_module,
-            "_restart_from_flipped_params",
-            return_value=better_lm2,
-        ) as mock_opt:
+        with (
+            patch.object(
+                escape_local_minimum_module,
+                "flipped_seed",
+                side_effect=[
+                    (flipped_seed_mse_iter1, better_params),
+                    (flipped_seed_mse_iter2, better_params),
+                ],
+            ) as mock_flipped,
+            patch.object(
+                escape_local_minimum_module,
+                "_restart_from_flipped_params",
+                return_value=better_lm2,
+            ) as mock_opt,
+        ):
             out = escape_local_minimum(lm1, ctx)
 
         self.assertIs(out, better_lm2)
@@ -157,15 +167,18 @@ def test_returns_lm1_when_lm2_mse_is_higher(self):
 
         # Flipped seed clears the cheap gate (within threshold²×current).
         flipped_seed_mse = lm1.mse * (self._GATE_FACTOR / 2.0)
-        with patch.object(
-            escape_local_minimum_module,
-            "flipped_seed",
-            return_value=(flipped_seed_mse, worse_params),
-        ), patch.object(
-            escape_local_minimum_module,
-            "_restart_from_flipped_params",
-            return_value=worse_lm2,
-        ) as mock_opt:
+        with (
+            patch.object(
+                escape_local_minimum_module,
+                "flipped_seed",
+                return_value=(flipped_seed_mse, worse_params),
+            ),
+            patch.object(
+                escape_local_minimum_module,
+                "_restart_from_flipped_params",
+                return_value=worse_lm2,
+            ) as mock_opt,
+        ):
             out = escape_local_minimum(lm1, ctx)
 
         self.assertIs(out, lm1)
@@ -189,19 +202,22 @@ def test_runs_at_most_max_basin_refine_iters_lm2_calls(self):
         ]
         lm1 = results[0]
         gate_factor = self._GATE_FACTOR
-        with patch.object(
-            escape_local_minimum_module,
-            "flipped_seed",
-            # Always within gate (factor / 2 < gate_factor), always with a fresh seed.
-            side_effect=[
-                (results[i].mse * (gate_factor / 2.0), results[i].sw_params)
-                for i in range(chain_length - 1)
-            ],
-        ), patch.object(
-            escape_local_minimum_module,
-            "_restart_from_flipped_params",
-            side_effect=results[1:],
-        ) as mock_opt:
+        with (
+            patch.object(
+                escape_local_minimum_module,
+                "flipped_seed",
+                # Always within gate (factor / 2 < gate_factor), always with a fresh seed.
+                side_effect=[
+                    (results[i].mse * (gate_factor / 2.0), results[i].sw_params)
+                    for i in range(chain_length - 1)
+                ],
+            ),
+            patch.object(
+                escape_local_minimum_module,
+                "_restart_from_flipped_params",
+                side_effect=results[1:],
+            ) as mock_opt,
+        ):
             out = escape_local_minimum(lm1, ctx)
 
         # Pin the cap from the source constant — never hardcode the number.
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_fit_solar_wind_proton_model.py b/tests/swapi/l3a/science/solar_wind/proton/test_fit_solar_wind_proton_model.py
index a294a40ba..2a1a5fdf4 100644
--- a/tests/swapi/l3a/science/solar_wind/proton/test_fit_solar_wind_proton_model.py
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_fit_solar_wind_proton_model.py
@@ -9,33 +9,91 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    N_STATE,
+    SolarWindParams,
+)
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_solar_wind_proton_model import (
     fit_solar_wind_proton_model,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.proton.optimize_solar_wind_proton_params import (
     OptimizeSolarWindProtonParamsResult,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
-    N_STATE,
-    SolarWindParams,
-)
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.swapi.species import Species
-from tests.swapi._helpers import NOMINAL_TEST_EPOCH_TT2000, load_swapi_response, synthesize_count_rates
+from tests.swapi._helpers import (
+    NOMINAL_TEST_EPOCH_TT2000,
+    load_swapi_response,
+    synthesize_count_rates,
+)
 
 # Mean SWAPI L2 coarse-sweep voltages (V), descending — a 62-bin sweep that
 # covers the proton speed range densely. Identical to the set used by
 # `docs/swapi/figure_src/plot_fit_accuracy.py`.
 _VOLTAGES_PER_SWEEP = np.array(
     [
-        9895.52, 9088.69, 8348.80, 7667.55, 7042.16, 6469.31, 5941.77, 5457.31,
-        5013.22, 4603.65, 4230.77, 3886.92, 3569.16, 3278.72, 3011.13, 2766.25,
-        2539.54, 2333.83, 2144.24, 1969.31, 1808.74, 1660.86, 1525.75, 1401.82,
-        1287.58, 1182.24, 1085.15, 995.55, 914.31, 839.94, 771.70, 709.46,
-        651.59, 598.47, 549.91, 505.12, 463.89, 425.92, 391.18, 359.35, 329.94,
-        303.02, 278.25, 255.55, 234.77, 215.61, 197.95, 181.82, 167.04, 153.46,
-        140.91, 129.50, 118.91, 109.20, 100.30, 92.11, 84.61, 77.73, 71.40,
-        65.59, 60.23, 55.34,
+        9895.52,
+        9088.69,
+        8348.80,
+        7667.55,
+        7042.16,
+        6469.31,
+        5941.77,
+        5457.31,
+        5013.22,
+        4603.65,
+        4230.77,
+        3886.92,
+        3569.16,
+        3278.72,
+        3011.13,
+        2766.25,
+        2539.54,
+        2333.83,
+        2144.24,
+        1969.31,
+        1808.74,
+        1660.86,
+        1525.75,
+        1401.82,
+        1287.58,
+        1182.24,
+        1085.15,
+        995.55,
+        914.31,
+        839.94,
+        771.70,
+        709.46,
+        651.59,
+        598.47,
+        549.91,
+        505.12,
+        463.89,
+        425.92,
+        391.18,
+        359.35,
+        329.94,
+        303.02,
+        278.25,
+        255.55,
+        234.77,
+        215.61,
+        197.95,
+        181.82,
+        167.04,
+        153.46,
+        140.91,
+        129.50,
+        118.91,
+        109.20,
+        100.30,
+        92.11,
+        84.61,
+        77.73,
+        71.40,
+        65.59,
+        60.23,
+        55.34,
     ]
 )
 _N_BINS_PER_SWEEP = len(_VOLTAGES_PER_SWEEP)
@@ -66,9 +124,9 @@
 # The +Y column of `_ANCHOR_ROTATION_MATRIX` is the SWAPI spin axis expressed
 # in RTN. For a proper rotation matrix it must be unit-norm; assert that here
 # so the bulk-velocity construction below can use it directly.
-assert np.isclose(
-    np.linalg.norm(_ANCHOR_ROTATION_MATRIX[:, 1]), 1.0, atol=1e-3
-), "expected +Y column of anchor rotation to be unit-norm"
+assert np.isclose(np.linalg.norm(_ANCHOR_ROTATION_MATRIX[:, 1]), 1.0, atol=1e-3), (
+    "expected +Y column of anchor rotation to be unit-norm"
+)
 
 # Ground-truth solar-wind parameters used for the parameter-recovery test.
 # Moderate-speed slow-stream proton population — well inside the SWAPI energy
@@ -83,23 +141,16 @@
 _TRUE_DENSITY_CM3 = 5.0
 _TRUE_TEMPERATURE_K = 1.0e5
 _TRUE_BULK_SPEED_KM_S = 450.0
-_TRUE_VELOCITY_RTN_KM_S = (
-    -_TRUE_BULK_SPEED_KM_S * _ANCHOR_ROTATION_MATRIX[:, 1]
-)
+_TRUE_VELOCITY_RTN_KM_S = -_TRUE_BULK_SPEED_KM_S * _ANCHOR_ROTATION_MATRIX[:, 1]
 
 
 def _per_bin_rotation_matrices() -> np.ndarray:
     """Synthesize plausible per-bin SWAPI→RTN matrices for `_N_SWEEPS` sweeps;
     details don't affect what's being tested."""
-    sweep_index = np.repeat(
-        np.arange(_N_SWEEPS), _N_BINS_PER_SWEEP
-    )
-    bin_index_in_sweep = np.tile(
-        np.arange(1, _N_BINS_PER_SWEEP + 1), _N_SWEEPS
-    )
+    sweep_index = np.repeat(np.arange(_N_SWEEPS), _N_BINS_PER_SWEEP)
+    bin_index_in_sweep = np.tile(np.arange(1, _N_BINS_PER_SWEEP + 1), _N_SWEEPS)
     sample_times_s = (
-        sweep_index * _SWEEP_DURATION_S
-        + bin_index_in_sweep * _SAMPLE_TIME_PER_BIN_S
+        sweep_index * _SWEEP_DURATION_S + bin_index_in_sweep * _SAMPLE_TIME_PER_BIN_S
     )
 
     spin_axis = _ANCHOR_ROTATION_MATRIX[:, 1] / np.linalg.norm(
@@ -131,9 +182,7 @@ def _build_synthetic_fit_context(truth_params: SolarWindParams):
     `SolarWindFitContext` whose count rates are forward-modelled from
     `truth_params`. Returns `(swapi_response, rotation_matrices, fit_ctx)`."""
     all_voltages_2d = np.tile(_VOLTAGES_PER_SWEEP, (_N_SWEEPS, 1))
-    swapi_response = load_swapi_response(
-        warm_cache_voltages=all_voltages_2d.ravel()
-    )
+    swapi_response = load_swapi_response(warm_cache_voltages=all_voltages_2d.ravel())
     rotation_matrices = _per_bin_rotation_matrices()
 
     # Build a context with placeholder rates first, then forward-model the
@@ -203,9 +252,7 @@ def test_recovers_temperature(self):
     def test_recovers_velocity_components(self):
         """Fitting noise-free synthesized rates recovers all three RTN bulk-velocity components within 1 km/s."""
         nominal = self.result.velocity_rtn_nominal()
-        np.testing.assert_allclose(
-            nominal, _TRUE_VELOCITY_RTN_KM_S, atol=1.0
-        )
+        np.testing.assert_allclose(nominal, _TRUE_VELOCITY_RTN_KM_S, atol=1.0)
 
     def test_quality_flag_is_none_on_successful_convergence(self):
         """A clean LM convergence on noise-free data leaves the bad-fit flag cleared (`SwapiL3Flags.NONE`)."""
@@ -244,12 +291,8 @@ def test_velocity_rtn_nominal_matches_per_component_nominal_values(
         self,
     ):
         """The nominal-velocity vector matches the per-component `.nominal_value` of the stored UFloat triple."""
-        per_component = np.array(
-            [v.nominal_value for v in self.result.velocity_rtn]
-        )
-        np.testing.assert_array_equal(
-            self.result.velocity_rtn_nominal(), per_component
-        )
+        per_component = np.array([v.nominal_value for v in self.result.velocity_rtn])
+        np.testing.assert_array_equal(self.result.velocity_rtn_nominal(), per_component)
 
     def test_velocity_rtn_covariance_is_three_by_three(self):
         """The covariance accessor returns a 3x3 matrix matching the RTN component count."""
@@ -271,9 +314,7 @@ def test_covariance_diagonal_matches_per_component_variance(self):
         """Each diagonal entry of the velocity covariance equals the square of the corresponding UFloat `std_dev`."""
         covariance = self.result.velocity_rtn_covariance()
         for i, ufloat_value in enumerate(self.result.velocity_rtn):
-            self.assertAlmostEqual(
-                covariance[i, i], ufloat_value.std_dev ** 2, places=10
-            )
+            self.assertAlmostEqual(covariance[i, i], ufloat_value.std_dev**2, places=10)
 
 
 class TestQualityFlagBranches(unittest.TestCase):
@@ -281,9 +322,7 @@ class TestQualityFlagBranches(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        _, _, cls.fit_ctx = _build_synthetic_fit_context(
-            truth_params=_truth_params()
-        )
+        _, _, cls.fit_ctx = _build_synthetic_fit_context(truth_params=_truth_params())
 
     def _patch_optimizer_with_result(self, optimize_result):
         return patch(
@@ -301,9 +340,7 @@ def test_fit_error_flag_when_optimizer_reports_failure(self):
                 mass=PROTON_MASS_KG,
             ),
             residuals=np.zeros(_N_BINS_PER_SWEEP * _N_SWEEPS),
-            jacobian=np.zeros(
-                (_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)
-            ),
+            jacobian=np.zeros((_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)),
             success=False,
         )
         with self._patch_optimizer_with_result(failed_result):
@@ -323,9 +360,7 @@ def test_quality_flag_when_temperature_above_threshold(self):
                 mass=PROTON_MASS_KG,
             ),
             residuals=np.zeros(_N_BINS_PER_SWEEP * _N_SWEEPS),
-            jacobian=np.zeros(
-                (_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)
-            ),
+            jacobian=np.zeros((_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)),
             success=True,
         )
         with patch(
@@ -345,9 +380,7 @@ class TestPipelineOrder(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        _, _, cls.fit_ctx = _build_synthetic_fit_context(
-            truth_params=_truth_params()
-        )
+        _, _, cls.fit_ctx = _build_synthetic_fit_context(truth_params=_truth_params())
 
     def test_construct_fit_result_uses_post_escape_local_minimum_result(self):
         """When `escape_local_minimum` returns a result distinct from LM-1, the final fit result carries the post-escape parameters."""
@@ -366,9 +399,7 @@ def test_construct_fit_result_uses_post_escape_local_minimum_result(self):
                 mass=PROTON_MASS_KG,
             ),
             residuals=np.zeros(_N_BINS_PER_SWEEP * _N_SWEEPS),
-            jacobian=np.zeros(
-                (_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)
-            ),
+            jacobian=np.zeros((_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)),
             success=True,
         )
 
@@ -380,9 +411,7 @@ def test_construct_fit_result_uses_post_escape_local_minimum_result(self):
 
         # density alone is decisive: it, temperature, and velocity_rtn
         # propagate together from the same OptimizeSolarWindProtonParamsResult.
-        self.assertAlmostEqual(
-            result.density.nominal_value, post_escape_density
-        )
+        self.assertAlmostEqual(result.density.nominal_value, post_escape_density)
 
 
 if __name__ == "__main__":
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_optimize_solar_wind_proton_params.py b/tests/swapi/l3a/science/solar_wind/proton/test_optimize_solar_wind_proton_params.py
index 482d28a66..9fa8c3e7a 100644
--- a/tests/swapi/l3a/science/solar_wind/proton/test_optimize_solar_wind_proton_params.py
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_optimize_solar_wind_proton_params.py
@@ -7,18 +7,18 @@
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
-from imap_l3_processing.swapi.l3a.science.solar_wind.proton.optimize_solar_wind_proton_params import (
-    OptimizeSolarWindProtonParamsResult,
-    optimize_solar_wind_proton_params,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     N_STATE,
     SolarWindParams,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.optimize_solar_wind_proton_params import (
+    OptimizeSolarWindProtonParamsResult,
+    optimize_solar_wind_proton_params,
+)
 from imap_l3_processing.swapi.species import Species
 from tests.swapi._helpers import (
-    NOMINAL_TEST_EPOCH_TT2000,
     NOMINAL_SWAPI_TO_RTN_ROTATION,
+    NOMINAL_TEST_EPOCH_TT2000,
     REALISTIC_ESA_VOLTAGES,
     load_swapi_response,
     proton_params,
@@ -190,7 +190,5 @@ def test_uses_lm_with_xtol_from_doc_spec(self):
             self.assertEqual(kwargs["xtol"], 1e-4)
 
 
-
-
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py b/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py
index 6b1aecd67..f474e6d5b 100644
--- a/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py
+++ b/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py
@@ -32,7 +32,8 @@ def test_open_aperture_positive_wing_flags_and_sign(self):
     def test_region_namedtuple_field_order_is_stable(self):
         """The AzimuthalRegion NamedTuple field order is locked so positional unpacking in numba forward-model loops keeps working."""
         self.assertEqual(
-            AzimuthalRegion._fields, ("is_sunglasses", "is_open_aperture", "azimuth_sign")
+            AzimuthalRegion._fields,
+            ("is_sunglasses", "is_open_aperture", "azimuth_sign"),
         )
 
 
diff --git a/tests/swapi/l3a/science/solar_wind/test_fit_context.py b/tests/swapi/l3a/science/solar_wind/test_fit_context.py
index 60abf894c..c03ebd228 100644
--- a/tests/swapi/l3a/science/solar_wind/test_fit_context.py
+++ b/tests/swapi/l3a/science/solar_wind/test_fit_context.py
@@ -54,12 +54,14 @@ def setUp(self):
             count_rate=np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]]),
             esa_voltage=np.array([[100.0, 200.0, 300.0], [400.0, 500.0, 600.0]]),
             response_grids=[
-                "s0b0", "s0b1", "s0b2",
-                "s1b0", "s1b1", "s1b2",
+                "s0b0",
+                "s0b1",
+                "s0b2",
+                "s1b0",
+                "s1b1",
+                "s1b2",
             ],
-            rotation_matrices=np.stack(
-                [np.eye(3) * (i + 1) for i in range(6)]
-            ),
+            rotation_matrices=np.stack([np.eye(3) * (i + 1) for i in range(6)]),
             mass_kg=PROTON_MASS_KG,
         )
 
@@ -78,16 +80,12 @@ def test_subset_with_all_bin_indices_returns_equivalent_context(self):
     def test_subset_picks_bin_axis_at_the_given_indices_preserving_sweeps(self):
         """Selecting bin indices [0, 2] yields a context with those two bin columns from each sweep; count_rate and esa_voltage stay 2D as (n_sweeps, n_kept_bins)."""
         kept = self.full_ctx.subset(np.array([0, 2]))
-        np.testing.assert_array_equal(
-            kept.count_rate, [[10.0, 30.0], [40.0, 60.0]]
-        )
+        np.testing.assert_array_equal(kept.count_rate, [[10.0, 30.0], [40.0, 60.0]])
         np.testing.assert_array_equal(
             kept.esa_voltage, [[100.0, 300.0], [400.0, 600.0]]
         )
         # Flat sweep-major bin-minor order: s0b0, s0b2, s1b0, s1b2
-        self.assertEqual(
-            list(kept.response_grids), ["s0b0", "s0b2", "s1b0", "s1b2"]
-        )
+        self.assertEqual(list(kept.response_grids), ["s0b0", "s0b2", "s1b0", "s1b2"])
         np.testing.assert_array_equal(
             kept.rotation_matrices,
             self.full_ctx.rotation_matrices[[0, 2, 3, 5]],
diff --git a/tests/swapi/l3a/science/solar_wind/test_forward_model.py b/tests/swapi/l3a/science/solar_wind/test_forward_model.py
index ab3f80217..0aced001c 100644
--- a/tests/swapi/l3a/science/solar_wind/test_forward_model.py
+++ b/tests/swapi/l3a/science/solar_wind/test_forward_model.py
@@ -7,6 +7,7 @@
     PROTON_CHARGE_COULOMBS,
     PROTON_MASS_KG,
 )
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
@@ -18,16 +19,14 @@
     LOG_DENSITY_IDX,
     LOG_TEMPERATURE_IDX,
     N_STATE,
-    SolarWindParams,
     VELOCITY_SLICE,
+    SolarWindParams,
 )
-from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
 from imap_l3_processing.swapi.species import Species
 from tests.swapi._helpers import NOMINAL_TEST_EPOCH_TT2000, proton_params
 from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
-
 # Reference bulk speed for the slow-wind fixture state shared by every test
 # in this module (5 cm⁻³, 100 kK protons under the default `proton_params`).
 _REF_BULK_SPEED_KM_S = 450.0
@@ -82,8 +81,6 @@ def _esa_voltage_at_speed(speed_km_s: float) -> float:
 _JACOBIAN_ATOL = 1e-3
 
 
-
-
 def _build_fit_context_for_voltages(
     response: SwapiResponse,
     esa_voltages: np.ndarray,
@@ -142,9 +139,7 @@ def test_returns_one_rate_and_one_jacobian_row_per_sweep(self):
                 ]
             ),
         )
-        rates, jacobian = model_solar_wind_ideal_coincidence_rates(
-            proton_params(), ctx
-        )
+        rates, jacobian = model_solar_wind_ideal_coincidence_rates(proton_params(), ctx)
         self.assertEqual(rates.shape, (3,))
         self.assertEqual(jacobian.shape, (3, N_STATE))
 
diff --git a/tests/swapi/l3a/science/solar_wind/test_integration_limits.py b/tests/swapi/l3a/science/solar_wind/test_integration_limits.py
index b043bd6cf..14a25f48e 100644
--- a/tests/swapi/l3a/science/solar_wind/test_integration_limits.py
+++ b/tests/swapi/l3a/science/solar_wind/test_integration_limits.py
@@ -3,6 +3,7 @@
 import numpy as np
 
 from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.l3a.science.solar_wind.azimuthal_regions import (
     REGION_OPEN_APERTURE_NEG,
     REGION_OPEN_APERTURE_POS,
@@ -15,7 +16,6 @@
     speed_window_misses_passband,
 )
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
-    SolarWindParams,
     bulk_speed,
     thermal_speed,
     thermal_speed_to_temperature,
@@ -24,7 +24,6 @@
     interpolate_passband,
     speed_ratio_range_at_elevation,
 )
-from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.species import Species
 from tests.swapi._helpers import (
     NOMINAL_SWAPI_TO_RTN_ROTATION,
@@ -205,9 +204,7 @@ def setUpClass(cls):
         cls.identity = np.eye(3)
         # Bulk pointed at az=+90°: identity rotation makes v_inst = (-v, 0, 0),
         # giving azimuth = atan2(v, 0) = 90° and elevation = 0°.
-        cls.sw_bulk_at_90 = proton_params(
-            velocity_rtn=(-_BULK_SPEED_KM_S, 0.0, 0.0)
-        )
+        cls.sw_bulk_at_90 = proton_params(velocity_rtn=(-_BULK_SPEED_KM_S, 0.0, 0.0))
 
     def test_oa_positive_region_is_active_for_bulk_at_90_deg_azimuth(self):
         """A bulk at az ≈ +90° lies inside the OA+ band, so the region is active and a quadrature is returned."""
@@ -230,12 +227,8 @@ def test_oa_positive_azimuth_window_is_inside_20_to_150_degrees(self):
             self.identity,
             0.0,
         )
-        self.assertGreaterEqual(
-            quadrature.azimuth_points.min(), _OA_AZIMUTH_INNER_DEG
-        )
-        self.assertLessEqual(
-            quadrature.azimuth_points.max(), _OA_AZIMUTH_OUTER_DEG
-        )
+        self.assertGreaterEqual(quadrature.azimuth_points.min(), _OA_AZIMUTH_INNER_DEG)
+        self.assertLessEqual(quadrature.azimuth_points.max(), _OA_AZIMUTH_OUTER_DEG)
 
     def test_oa_negative_region_is_skipped_for_bulk_at_plus_90_deg_azimuth(self):
         """A bulk at az ≈ +90° is too far from the OA- band for a narrow Maxwellian to reach, so the OA- region is skipped."""
@@ -286,12 +279,8 @@ def test_oa_negative_azimuth_window_is_inside_minus_150_to_minus_20(self):
             self.identity,
             0.0,
         )
-        self.assertGreaterEqual(
-            quadrature.azimuth_points.min(), -_OA_AZIMUTH_OUTER_DEG
-        )
-        self.assertLessEqual(
-            quadrature.azimuth_points.max(), -_OA_AZIMUTH_INNER_DEG
-        )
+        self.assertGreaterEqual(quadrature.azimuth_points.min(), -_OA_AZIMUTH_OUTER_DEG)
+        self.assertLessEqual(quadrature.azimuth_points.max(), -_OA_AZIMUTH_INNER_DEG)
 
     def test_oa_positive_region_is_skipped_for_bulk_at_minus_90_deg_azimuth(self):
         """A bulk at az ≈ -90° is too far from the OA+ band for a narrow Maxwellian to reach, so the OA+ region is skipped."""
@@ -374,9 +363,7 @@ def test_speed_quadrature_arrays_are_all_15_long(self):
 
     def test_returns_skip_when_bulk_speed_window_does_not_overlap_passband(self):
         """A bulk so far above the passband that `bulk - k·σ` exceeds the upper edge yields skip=True and a `None` quadrature."""
-        sw = proton_params(
-            velocity_rtn=(0.0, -1500.0, 0.0), temperature=10_000.0
-        )
+        sw = proton_params(velocity_rtn=(0.0, -1500.0, 0.0), temperature=10_000.0)
         skip, speed_quadrature = get_speed_quadrature(
             sw, self.response_grid, REGION_SUNGLASSES, 0.0
         )
@@ -408,15 +395,15 @@ def test_uses_region_specific_passband(self):
         # silently caught here.
         self.assertNotEqual(sg_quad.points.min(), oa_quad.points.min())
 
-    def test_speed_cubed_times_passband_is_v3_times_normalized_passband_at_each_node(self):
+    def test_speed_cubed_times_passband_is_v3_times_normalized_passband_at_each_node(
+        self,
+    ):
         """The `speed_cubed_times_passband` array equals `v³ · P(θ, v/v0) / P(0, 1)` reconstructed from `interpolate_passband` at every GL node."""
         sw = proton_params()
         _, speed_quadrature = get_speed_quadrature(
             sw, self.response_grid, REGION_SUNGLASSES, 0.0
         )
-        on_axis_peak = interpolate_passband(
-            self.response_grid.sg_passband, 0.0, 1.0
-        )
+        on_axis_peak = interpolate_passband(self.response_grid.sg_passband, 0.0, 1.0)
         expected = np.array(
             [
                 v**3
diff --git a/tests/swapi/l3a/science/solar_wind/test_params.py b/tests/swapi/l3a/science/solar_wind/test_params.py
index 63f3bf8b0..e1c834e3c 100644
--- a/tests/swapi/l3a/science/solar_wind/test_params.py
+++ b/tests/swapi/l3a/science/solar_wind/test_params.py
@@ -57,9 +57,7 @@ def test_from_vector_inverts_to_vector(self):
         )
         self.assertAlmostEqual(round_tripped.density, original.density)
         self.assertAlmostEqual(round_tripped.temperature, original.temperature)
-        np.testing.assert_array_equal(
-            round_tripped.velocity_rtn, original.velocity_rtn
-        )
+        np.testing.assert_array_equal(round_tripped.velocity_rtn, original.velocity_rtn)
         self.assertEqual(round_tripped.mass, original.mass)
 
     def test_from_vector_carries_mass_through_unchanged(self):
diff --git a/tests/swapi/l3a/science/solar_wind/test_trim_open_aperture.py b/tests/swapi/l3a/science/solar_wind/test_trim_open_aperture.py
index 6a26fc72f..6438c4b79 100644
--- a/tests/swapi/l3a/science/solar_wind/test_trim_open_aperture.py
+++ b/tests/swapi/l3a/science/solar_wind/test_trim_open_aperture.py
@@ -3,16 +3,16 @@
 
 import numpy as np
 
-from imap_l3_processing.swapi.l3a.science.solar_wind.trim_open_aperture import (
-    OA_SCAN_RESOLUTION,
-    OA_SCAN_THRESHOLD,
-    trim_open_aperture,
-)
 from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
     SolarWindParams,
     bulk_speed,
     thermal_speed,
 )
+from imap_l3_processing.swapi.l3a.science.solar_wind.trim_open_aperture import (
+    OA_SCAN_RESOLUTION,
+    OA_SCAN_THRESHOLD,
+    trim_open_aperture,
+)
 from imap_l3_processing.swapi.response.azimuthal_transmission import (
     AzimuthalTransmissionGrid,
 )
@@ -21,7 +21,6 @@
 from tests.swapi._helpers import NOMINAL_SWAPI_TO_RTN_ROTATION, proton_params
 from tests.swapi.response.test_passband_grid import _gaussian_values_df
 
-
 # --- fixtures ---------------------------------------------------------------
 
 
@@ -56,9 +55,7 @@ def _proton_params_at_elevation(
 ) -> SolarWindParams:
     v_x_swapi = -speed * math.cos(math.radians(bulk_el_deg))
     v_z_swapi = -speed * math.sin(math.radians(bulk_el_deg))
-    v_rtn = NOMINAL_SWAPI_TO_RTN_ROTATION @ np.array(
-        [v_x_swapi, 0.0, v_z_swapi]
-    )
+    v_rtn = NOMINAL_SWAPI_TO_RTN_ROTATION @ np.array([v_x_swapi, 0.0, v_z_swapi])
     return proton_params(
         velocity_rtn=tuple(v_rtn), density=density, temperature=temperature
     )
@@ -193,7 +190,9 @@ def test_endpoints_expand_one_node_past_threshold_crossings(self):
         # interior. The integrand reduces to the Maxwellian centered at the
         # bulk azimuth, which we recompute here.
         rg = _make_response_grid()
-        sw = proton_params(velocity_rtn=(0.0, -450.0, 0.0))  # bulk_az = +90°, bulk_el = 0°
+        sw = proton_params(
+            velocity_rtn=(0.0, -450.0, 0.0)
+        )  # bulk_az = +90°, bulk_el = 0°
 
         lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
 
@@ -202,7 +201,8 @@ def test_endpoints_expand_one_node_past_threshold_crossings(self):
         speed = bulk_speed(sw)
         central = rg.central_speed
         delta_v_sq = (
-            central**2 + speed**2
+            central**2
+            + speed**2
             - 2.0 * central * speed * np.cos(np.radians(scan - 90.0))
         )
         integrand = np.exp(-delta_v_sq / (2.0 * sigma**2))
@@ -227,9 +227,13 @@ def test_mirror_symmetric_bulk_yields_mirror_symmetric_windows(self):
         sw_pos = proton_params(velocity_rtn=(0.0, -450.0, 0.0))
         sw_neg = proton_params(velocity_rtn=(0.0, +450.0, 0.0))
 
-        lo_pos, hi_pos = _trim(rg, sw_pos, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
+        lo_pos, hi_pos = _trim(
+            rg, sw_pos, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0
+        )
         # OA-: same magnitudes, mirrored around 0.
-        lo_neg, hi_neg = _trim(rg, sw_neg, azimuth_lo=-150.0, azimuth_hi=-20.0, sg_rate=0.0)
+        lo_neg, hi_neg = _trim(
+            rg, sw_neg, azimuth_lo=-150.0, azimuth_hi=-20.0, sg_rate=0.0
+        )
 
         # The implementation picks scan-grid nodes by index, and
         # `linspace(20, 150, N)` mirrors `linspace(-150, -20, N)` up to a
@@ -246,9 +250,7 @@ def test_high_sg_rate_forces_skip_for_otherwise_aligned_bulk(self):
         rg = _make_response_grid()
         sw = proton_params(velocity_rtn=(0.0, -450.0, 0.0))  # az_inst = +90°
 
-        lo_open, hi_open = _trim(
-            rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0
-        )
+        lo_open, hi_open = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
         self.assertGreater(hi_open, lo_open)
 
         lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=1.0e15)
diff --git a/tests/swapi/l3a/science/solar_wind/test_uncertainty_calibration.py b/tests/swapi/l3a/science/solar_wind/test_uncertainty_calibration.py
index 7bf33b6e6..ea06f7c51 100644
--- a/tests/swapi/l3a/science/solar_wind/test_uncertainty_calibration.py
+++ b/tests/swapi/l3a/science/solar_wind/test_uncertainty_calibration.py
@@ -47,13 +47,12 @@
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
 from imap_l3_processing.swapi.species import Species
 from tests.swapi._helpers import (
-    NOMINAL_TEST_EPOCH_TT2000,
     NOMINAL_SWAPI_TO_RTN_ROTATION,
+    NOMINAL_TEST_EPOCH_TT2000,
     load_swapi_response,
 )
 from tests.test_helpers import run_periodically
 
-
 _N_BINS_PER_SWEEP = 62
 _N_SWEEPS = 5
 _VOLTAGE_AXIS = np.broadcast_to(
@@ -76,8 +75,7 @@ def _per_bin_rotation_matrices() -> np.ndarray:
     sweep_index = np.repeat(np.arange(_N_SWEEPS), _N_BINS_PER_SWEEP)
     bin_index_in_sweep = np.tile(np.arange(1, _N_BINS_PER_SWEEP + 1), _N_SWEEPS)
     sample_times_s = (
-        sweep_index * _SWEEP_DURATION_S
-        + bin_index_in_sweep * _SAMPLE_TIME_PER_BIN_S
+        sweep_index * _SWEEP_DURATION_S + bin_index_in_sweep * _SAMPLE_TIME_PER_BIN_S
     )
     spin_axis = NOMINAL_SWAPI_TO_RTN_ROTATION[:, 1]
     spin_axis = spin_axis / np.linalg.norm(spin_axis)
@@ -102,9 +100,7 @@ def _per_bin_rotation_matrices() -> np.ndarray:
 _TRUE_BULK_SPEED_KM_S = 450.0
 _TRUE_PROTON_DENSITY_CM3 = 5.0
 _TRUE_PROTON_TEMPERATURE_K = 1.0e5
-_TRUE_PROTON_VELOCITY_RTN = (
-    -_TRUE_BULK_SPEED_KM_S * NOMINAL_SWAPI_TO_RTN_ROTATION[:, 1]
-)
+_TRUE_PROTON_VELOCITY_RTN = -_TRUE_BULK_SPEED_KM_S * NOMINAL_SWAPI_TO_RTN_ROTATION[:, 1]
 _TRUE_ALPHA_DENSITY_CM3 = 0.2
 _TRUE_ALPHA_TEMPERATURE_K = 4.0e5
 _TRUE_DELTA_V_KM_S = 30.0
@@ -144,7 +140,9 @@ def _build_truth_count_rates(response, *, with_alpha: bool) -> np.ndarray:
         species=Species.PROTON,
         rotation_matrices=_ROTATION_MATRICES,
     )
-    proton_rates, _ = model_solar_wind_ideal_coincidence_rates(proton_params, proton_ctx)
+    proton_rates, _ = model_solar_wind_ideal_coincidence_rates(
+        proton_params, proton_ctx
+    )
     total_rates = proton_rates
     if with_alpha:
         alpha_velocity = _TRUE_PROTON_VELOCITY_RTN + _TRUE_DELTA_V_KM_S * _B_HAT_RTN
@@ -162,7 +160,9 @@ def _build_truth_count_rates(response, *, with_alpha: bool) -> np.ndarray:
             species=Species.ALPHA,
             rotation_matrices=_ROTATION_MATRICES,
         )
-        alpha_rates, _ = model_solar_wind_ideal_coincidence_rates(alpha_params, alpha_ctx)
+        alpha_rates, _ = model_solar_wind_ideal_coincidence_rates(
+            alpha_params, alpha_ctx
+        )
         total_rates = proton_rates + alpha_rates
     deadtime_applied = total_rates * deadtime_factor(total_rates)
     return deadtime_applied.reshape(_VOLTAGE_AXIS.shape)
@@ -349,7 +349,9 @@ def _run_trial(trial_seed: int) -> dict | None:
         b_hat_rtn=_B_HAT_RTN.copy(),
         quality_flag=int(alpha_moments.quality_flag),
     )
-    return _alpha_moments_from_fit(chunk_result, epoch=0, sc_velocity_rtn=_SC_VELOCITY_RTN)
+    return _alpha_moments_from_fit(
+        chunk_result, epoch=0, sc_velocity_rtn=_SC_VELOCITY_RTN
+    )
 
 
 def _run_mc_in_parallel(n_trials: int) -> tuple[dict, int]:
diff --git a/tests/swapi/l3a/science/solar_wind/test_utils.py b/tests/swapi/l3a/science/solar_wind/test_utils.py
index 16f041cd6..bfa99f8ff 100644
--- a/tests/swapi/l3a/science/solar_wind/test_utils.py
+++ b/tests/swapi/l3a/science/solar_wind/test_utils.py
@@ -31,9 +31,7 @@ def test_single_rotation_returns_its_y_axis(self):
     def test_constant_axis_across_sweeps_returns_that_axis(self):
         """Five identical identity rotations average to the same +Y_RTN unit vector, confirming the mean is over the sweep axis."""
         rotations = np.stack([np.eye(3)] * 5)
-        np.testing.assert_array_equal(
-            average_spin_axis_rtn(rotations), [0.0, 1.0, 0.0]
-        )
+        np.testing.assert_array_equal(average_spin_axis_rtn(rotations), [0.0, 1.0, 0.0])
 
     def test_averages_two_axes_at_pm_45_deg_to_a_unit_vector(self):
         """Two body-+Y directions tilted ±45° in the RT-plane average to +Y_RTN after renormalization, cancelling the R components."""
@@ -73,9 +71,7 @@ def _proton_params(
             mass=PROTON_MASS_KG,
         )
 
-    def _response_grid(
-        self, central_effective_area: float = 0.5
-    ) -> ResponseGrid:
+    def _response_grid(self, central_effective_area: float = 0.5) -> ResponseGrid:
         return ResponseGrid(
             sg_passband=None,
             oa_passband=None,
diff --git a/tests/swapi/l3a/science/test_inflow_vector.py b/tests/swapi/l3a/science/test_inflow_vector.py
index 393e813b1..238695834 100644
--- a/tests/swapi/l3a/science/test_inflow_vector.py
+++ b/tests/swapi/l3a/science/test_inflow_vector.py
@@ -6,7 +6,11 @@
 
 class TestInflowVector(unittest.TestCase):
     def test_from_file(self):
-        vector = InflowVector.from_file(get_test_data_path("swapi/imap_swapi_hydrogen-inflow-vector_20100101_v001.dat"))
+        vector = InflowVector.from_file(
+            get_test_data_path(
+                "swapi/imap_swapi_hydrogen-inflow-vector_20100101_v001.dat"
+            )
+        )
         self.assertIsInstance(vector, InflowVector)
         self.assertEqual(22, vector.speed_km_per_s)
         self.assertEqual(252.2, vector.longitude_deg_eclipj2000)
@@ -15,12 +19,16 @@ def test_from_file(self):
     def test_raises_parse_error(self):
         with self.assertRaises(AssertionError) as exc_ctx:
             InflowVector.from_file(
-                get_test_data_path("swapi/imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"))
+                get_test_data_path(
+                    "swapi/imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"
+                )
+            )
 
         self.assertEqual(
             "Failed to parse Inflow Vector from imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat",
-            str(exc_ctx.exception))
+            str(exc_ctx.exception),
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/swapi/l3a/test_chunk_fits.py b/tests/swapi/l3a/test_chunk_fits.py
index 9aee39df9..d32c53258 100644
--- a/tests/swapi/l3a/test_chunk_fits.py
+++ b/tests/swapi/l3a/test_chunk_fits.py
@@ -1,7 +1,7 @@
+import platform
 import unittest
 from datetime import datetime
-from unittest.mock import Mock, patch, create_autospec
-import platform
+from unittest.mock import Mock, create_autospec, patch
 
 import numpy as np
 from imap_processing.spice.time import str_yyyymmdd_to_ttj2000ns
@@ -16,7 +16,15 @@
     THIRTY_SECONDS_IN_NANOSECONDS,
 )
 from imap_l3_processing.models import InputMetadata, MagData
-from imap_l3_processing.swapi.swapi_processor import SwapiProcessor
+from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
+from imap_l3_processing.swapi.constants import (
+    SWAPI_BIN_PERIOD_S,
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_FINE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+    SWAPI_LIVETIME_CENTER_OFFSET_S,
+    SWAPI_SCIENCE_BINS,
+)
 from imap_l3_processing.swapi.l3a import chunk_fits
 from imap_l3_processing.swapi.l3a.chunk_fits import (
     AlphaChunkFitter,
@@ -25,15 +33,11 @@
     ProtonChunkFitter,
     PuiChunkFitter,
 )
-from imap_l3_processing.swapi.l3a.utils import pickup_ion_chunk_epoch
+from imap_l3_processing.swapi.l3a.models import SwapiL2Data
 from imap_l3_processing.swapi.l3a.science.pickup_ion.calculate_pickup_ion_values import (
     PickupIonFitInputData,
     PickupIonFitResult,
 )
-from imap_l3_processing.swapi.response.efficiency_calibration_table import (
-    EfficiencyCalibrationTable,
-)
-from imap_l3_processing.swapi.l3a.models import SwapiL2Data
 from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
     build_solar_wind_fit_context,
 )
@@ -45,21 +49,17 @@
     ProtonSolarWindFitResult,
 )
 from imap_l3_processing.swapi.l3a.utils import (
-    get_swapi_geometry,
     get_spacecraft_velocity_rtn,
+    get_swapi_geometry,
+    pickup_ion_chunk_epoch,
 )
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.swapi.response.deadtime import deadtime_factor
-from imap_l3_processing.swapi.constants import (
-    SWAPI_BIN_PERIOD_S,
-    SWAPI_COARSE_SWEEP_BINS,
-    SWAPI_FINE_SWEEP_BINS,
-    SWAPI_L2_K_FACTOR,
-    SWAPI_LIVETIME_CENTER_OFFSET_S,
-    SWAPI_SCIENCE_BINS,
+from imap_l3_processing.swapi.response.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
 )
-from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
 from imap_l3_processing.swapi.species import Species
+from imap_l3_processing.swapi.swapi_processor import SwapiProcessor
 from tests.spice_test_case import SpiceTestCase
 from tests.swapi._helpers import (
     REALISTIC_ESA_VOLTAGES,
diff --git a/tests/swapi/l3a/test_models.py b/tests/swapi/l3a/test_models.py
index 688414e28..c1ab618bd 100644
--- a/tests/swapi/l3a/test_models.py
+++ b/tests/swapi/l3a/test_models.py
@@ -1,39 +1,62 @@
 from datetime import datetime
-from unittest.mock import Mock, sentinel, patch
+from unittest.mock import Mock, patch, sentinel
 
 import imap_data_access
 import numpy as np
 from imap_data_access.file_validation import Version
 from uncertainties.unumpy import uarray
 
-from imap_l3_processing.constants import THIRTY_SECONDS_IN_NANOSECONDS, FIVE_MINUTES_IN_NANOSECONDS
+from imap_l3_processing.constants import (
+    FIVE_MINUTES_IN_NANOSECONDS,
+    THIRTY_SECONDS_IN_NANOSECONDS,
+)
 from imap_l3_processing.models import InputMetadata, VersionMap
-from imap_l3_processing.swapi.l3a.models import SwapiL3ProtonSolarWindData, EPOCH_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME, PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME, \
-    SwapiL3AlphaSolarWindData, PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME, ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME, ALPHA_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME, ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME, ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME, \
-    SwapiL3PickupIonData, \
-    PUI_IONIZATION_RATE_CDF_VAR_NAME, PUI_CUTOFF_SPEED_CDF_VAR_NAME, \
-    PUI_DENSITY_CDF_VAR_NAME, PUI_TEMPERATURE_CDF_VAR_NAME, \
-    PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME, PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME, \
-    PUI_DENSITY_UNCERTAINTY_CDF_VAR_NAME, \
-    PUI_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, VELOCITY_RTN_LABEL_CDF_VAR_NAME, \
-    ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME, ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME, PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME, \
-    SwapiL3aProtonDataFromCDF
+from imap_l3_processing.swapi.l3a.models import (
+    ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME,
+    ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME,
+    ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+    ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME,
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME,
+    PROTON_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME,
+    PUI_CUTOFF_SPEED_CDF_VAR_NAME,
+    PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+    PUI_DENSITY_CDF_VAR_NAME,
+    PUI_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+    PUI_IONIZATION_RATE_CDF_VAR_NAME,
+    PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME,
+    PUI_TEMPERATURE_CDF_VAR_NAME,
+    PUI_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+    SWAPI_QUALITY_FLAGS_CDF_VAR_NAME,
+    VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+    SwapiL3AlphaSolarWindData,
+    SwapiL3aProtonDataFromCDF,
+    SwapiL3PickupIonData,
+    SwapiL3ProtonSolarWindData,
+)
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.utils import save_data
 from tests.swapi.cdf_model_test_case import CdfModelTestCase
@@ -41,20 +64,19 @@
 
 
 class TestModels(CdfModelTestCase):
-
     def test_getting_proton_sw_data_product_variables(self):
         epoch_data = np.arange(20, step=2)
         epoch_delta = np.full_like(epoch_data, THIRTY_SECONDS_IN_NANOSECONDS)
         n = len(epoch_data)
 
         speed = np.arange(10, step=1.0)
-        speed_uncert = np.arange(5, step=.5)
+        speed_uncert = np.arange(5, step=0.5)
         speed_sun = np.arange(20, 30, step=1.0)
-        speed_sun_uncert = np.arange(2, 7, step=.5)
-        temperature = np.arange(1000, 2000, step=100.)
-        temperature_uncert = np.arange(50, step=5.)
-        density = np.arange(3, 13, step=1.)
-        density_uncert = np.arange(1, step=.1)
+        speed_sun_uncert = np.arange(2, 7, step=0.5)
+        temperature = np.arange(1000, 2000, step=100.0)
+        temperature_uncert = np.arange(50, step=5.0)
+        density = np.arange(3, 13, step=1.0)
+        density_uncert = np.arange(1, step=0.1)
         bulk_v_rtn_sun = np.arange(n * 3, dtype=float).reshape(n, 3)
         bulk_v_rtn_sc = np.arange(100, 100 + n * 3, dtype=float).reshape(n, 3)
         bulk_v_rtn_cov = np.arange(200, 200 + n * 9, dtype=float).reshape(n, 3, 3)
@@ -63,12 +85,19 @@ def test_getting_proton_sw_data_product_variables(self):
         quality_flags[3:5] |= SwapiL3Flags.FIT_ERROR
 
         data = SwapiL3ProtonSolarWindData(
-            Mock(), epoch_data,
-            speed, speed_uncert,
-            speed_sun, speed_sun_uncert,
-            temperature, temperature_uncert,
-            density, density_uncert,
-            bulk_v_rtn_sun, bulk_v_rtn_sc, bulk_v_rtn_cov,
+            Mock(),
+            epoch_data,
+            speed,
+            speed_uncert,
+            speed_sun,
+            speed_sun_uncert,
+            temperature,
+            temperature_uncert,
+            density,
+            density_uncert,
+            bulk_v_rtn_sun,
+            bulk_v_rtn_sc,
+            bulk_v_rtn_cov,
             quality_flags,
         )
 
@@ -77,31 +106,78 @@ def test_getting_proton_sw_data_product_variables(self):
         var_iter = iter(variables)
 
         self.assert_variable_attributes(next(var_iter), epoch_data, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed, PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed_uncert, PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed_sun, PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed_sun_uncert,
-                                        PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), temperature, PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), temperature_uncert,
-                                        PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), density, PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), density_uncert,
-                                        PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), bulk_v_rtn_sun,
-                                        PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), bulk_v_rtn_sc,
-                                        PROTON_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), bulk_v_rtn_cov,
-                                        PROTON_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME)
-        expected_velocity_uncert = np.sqrt(np.diagonal(bulk_v_rtn_cov, axis1=1, axis2=2))
-        self.assert_variable_attributes(next(var_iter), expected_velocity_uncert,
-                                        PROTON_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), ["R", "T", "N"], VELOCITY_RTN_LABEL_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), ["Vp SC R", "Vp SC T", "Vp SC N"], PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), ["Vp Sun R", "Vp Sun T", "Vp Sun N"], PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(var_iter), speed, PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            speed_uncert,
+            PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), speed_sun, PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            speed_sun_uncert,
+            PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter), temperature, PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            temperature_uncert,
+            PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), density, PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            density_uncert,
+            PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            bulk_v_rtn_sun,
+            PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), bulk_v_rtn_sc, PROTON_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            bulk_v_rtn_cov,
+            PROTON_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME,
+        )
+        expected_velocity_uncert = np.sqrt(
+            np.diagonal(bulk_v_rtn_cov, axis1=1, axis2=2)
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            expected_velocity_uncert,
+            PROTON_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter), ["R", "T", "N"], VELOCITY_RTN_LABEL_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            ["Vp SC R", "Vp SC T", "Vp SC N"],
+            PROTON_SOLAR_WIND_VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            ["Vp Sun R", "Vp Sun T", "Vp Sun N"],
+            PROTON_SOLAR_WIND_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME,
+        )
 
         self.assertIs(sentinel.done, next(var_iter, sentinel.done))
 
@@ -114,118 +190,200 @@ def test_getting_alpha_sw_data_product_variables(self):
         speed_uncert = np.arange(1, 1 + n, dtype=float)
         speed_sun = np.arange(420, 420 + n, dtype=float)
         speed_sun_uncert = np.arange(2, 2 + n, dtype=float)
-        density = np.arange(2, step=.2)
+        density = np.arange(2, step=0.2)
         density_uncert = np.arange(1, step=0.1)
-        temperature = np.arange(300000, step=30000.)
-        temperature_uncert = np.arange(50000, step=5000.)
+        temperature = np.arange(300000, step=30000.0)
+        temperature_uncert = np.arange(50000, step=5000.0)
         velocity_rtn_sun = np.arange(n * 3, dtype=float).reshape(n, 3)
         velocity_rtn_sc = np.arange(100, 100 + n * 3, dtype=float).reshape(n, 3)
         velocity_rtn_cov = np.arange(n * 9, dtype=float).reshape(n, 3, 3)
 
         quality_flags = np.full_like(epoch_data, SwapiL3Flags.NONE)
-        quality_flags[:n // 2] = SwapiL3Flags.BAD_FIT
+        quality_flags[: n // 2] = SwapiL3Flags.BAD_FIT
 
         data = SwapiL3AlphaSolarWindData(
-            Mock(), epoch_data,
-            speed, speed_uncert,
-            speed_sun, speed_sun_uncert,
-            density, density_uncert,
-            temperature, temperature_uncert,
-            velocity_rtn_sun, velocity_rtn_sc, velocity_rtn_cov,
+            Mock(),
+            epoch_data,
+            speed,
+            speed_uncert,
+            speed_sun,
+            speed_sun_uncert,
+            density,
+            density_uncert,
+            temperature,
+            temperature_uncert,
+            velocity_rtn_sun,
+            velocity_rtn_sc,
+            velocity_rtn_cov,
             quality_flags,
         )
         variables = data.to_data_product_variables()
 
         var_iter = iter(variables)
         self.assert_variable_attributes(next(var_iter), epoch_data, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed, ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed_uncert,
-                                        ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed_sun, ALPHA_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), speed_sun_uncert,
-                                        ALPHA_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), density, ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), density_uncert,
-                                        ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), temperature, ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), temperature_uncert,
-                                        ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), velocity_rtn_sun,
-                                        ALPHA_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), velocity_rtn_sc,
-                                        ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), velocity_rtn_cov,
-                                        ALPHA_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME)
-        expected_velocity_uncert = np.sqrt(np.diagonal(velocity_rtn_cov, axis1=1, axis2=2))
-        self.assert_variable_attributes(next(var_iter), expected_velocity_uncert,
-                                        ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), ["R", "T", "N"], VELOCITY_RTN_LABEL_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), ['Va SC R', 'Va SC T', 'Va SC N'], ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(var_iter), ['Va Sun R', 'Va Sun T', 'Va Sun N'], ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(var_iter), speed, ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            speed_uncert,
+            ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), speed_sun, ALPHA_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            speed_sun_uncert,
+            ALPHA_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter), density, ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            density_uncert,
+            ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), temperature, ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            temperature_uncert,
+            ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            velocity_rtn_sun,
+            ALPHA_SOLAR_WIND_VELOCITY_RTN_SUN_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), velocity_rtn_sc, ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            velocity_rtn_cov,
+            ALPHA_SOLAR_WIND_VELOCITY_RTN_COVARIANCE_CDF_VAR_NAME,
+        )
+        expected_velocity_uncert = np.sqrt(
+            np.diagonal(velocity_rtn_cov, axis1=1, axis2=2)
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            expected_velocity_uncert,
+            ALPHA_SOLAR_WIND_VELOCITY_RTN_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter), quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter), ["R", "T", "N"], VELOCITY_RTN_LABEL_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            ["Va SC R", "Va SC T", "Va SC N"],
+            ALPHA_VELOCITY_RTN_LABEL_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(var_iter),
+            ["Va Sun R", "Va Sun T", "Va Sun N"],
+            ALPHA_VELOCITY_RTN_SUN_LABEL_CDF_VAR_NAME,
+        )
 
         self.assertIs(sentinel.done, next(var_iter, sentinel.done))
 
     def test_getting_pui_data_product_variables(self):
         epoch_data = np.arange(20, step=2)
         expected_epoch_delta = np.full(10, FIVE_MINUTES_IN_NANOSECONDS)
-        expected_ionization_rate_nominal = np.arange(300000, step=30000.)
-        expected_ionization_rate_std_dev = np.arange(10, step=1.)
-        expected_ionization_rate = uarray(expected_ionization_rate_nominal, expected_ionization_rate_std_dev)
+        expected_ionization_rate_nominal = np.arange(300000, step=30000.0)
+        expected_ionization_rate_std_dev = np.arange(10, step=1.0)
+        expected_ionization_rate = uarray(
+            expected_ionization_rate_nominal, expected_ionization_rate_std_dev
+        )
 
-        expected_cutoff_speed_nominal = np.arange(50000, step=5000.)
-        expected_cutoff_speed_std_dev = np.arange(5, step=.5)
-        expected_cutoff_speed = uarray(expected_cutoff_speed_nominal, expected_cutoff_speed_std_dev)
+        expected_cutoff_speed_nominal = np.arange(50000, step=5000.0)
+        expected_cutoff_speed_std_dev = np.arange(5, step=0.5)
+        expected_cutoff_speed = uarray(
+            expected_cutoff_speed_nominal, expected_cutoff_speed_std_dev
+        )
 
         expected_density_nominal = np.arange(0.001, step=0.0001)
         expected_density_std_dev = np.arange(0.0001, step=0.00001)
         expected_density = uarray(expected_density_nominal, expected_density_std_dev)
 
-        expected_temperature_nominal = np.arange(1e5, step=10000.)
-        expected_temperature_std_dev = np.arange(100, step=10.)
-        expected_temperature = uarray(expected_temperature_nominal, expected_temperature_std_dev)
+        expected_temperature_nominal = np.arange(1e5, step=10000.0)
+        expected_temperature_std_dev = np.arange(100, step=10.0)
+        expected_temperature = uarray(
+            expected_temperature_nominal, expected_temperature_std_dev
+        )
 
         expected_quality_flags = np.full(20, 0)
 
-        data = SwapiL3PickupIonData(Mock(), epoch_data, expected_ionization_rate,
-                                    expected_cutoff_speed, expected_density, expected_temperature,
-                                    expected_quality_flags)
+        data = SwapiL3PickupIonData(
+            Mock(),
+            epoch_data,
+            expected_ionization_rate,
+            expected_cutoff_speed,
+            expected_density,
+            expected_temperature,
+            expected_quality_flags,
+        )
         variables = data.to_data_product_variables()
 
         self.assertEqual(11, len(variables))
         self.assert_variable_attributes(variables[0], epoch_data, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[1], expected_epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[2], expected_ionization_rate_nominal,
-                                        PUI_IONIZATION_RATE_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[3], expected_ionization_rate_std_dev,
-                                        PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[4], expected_cutoff_speed_nominal,
-                                        PUI_CUTOFF_SPEED_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[5], expected_cutoff_speed_std_dev,
-                                        PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[6], expected_density_nominal,
-                                        PUI_DENSITY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[7], expected_density_std_dev,
-                                        PUI_DENSITY_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[8], expected_temperature_nominal,
-                                        PUI_TEMPERATURE_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[9], expected_temperature_std_dev,
-                                        PUI_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[10], expected_quality_flags,
-                                        SWAPI_QUALITY_FLAGS_CDF_VAR_NAME)
-
-
+        self.assert_variable_attributes(
+            variables[1], expected_epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            variables[2],
+            expected_ionization_rate_nominal,
+            PUI_IONIZATION_RATE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[3],
+            expected_ionization_rate_std_dev,
+            PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[4], expected_cutoff_speed_nominal, PUI_CUTOFF_SPEED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            variables[5],
+            expected_cutoff_speed_std_dev,
+            PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[6], expected_density_nominal, PUI_DENSITY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            variables[7], expected_density_std_dev, PUI_DENSITY_UNCERTAINTY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            variables[8], expected_temperature_nominal, PUI_TEMPERATURE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            variables[9],
+            expected_temperature_std_dev,
+            PUI_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[10], expected_quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME
+        )
 
     @with_tempdir
     def test_reads_data_into_model(self, tempdir):
-        with patch.dict(imap_data_access.config, {"DATA_DIR":tempdir}):
+        with patch.dict(imap_data_access.config, {"DATA_DIR": tempdir}):
             input_metadata = InputMetadata(
                 instrument="swapi",
                 data_level="l3a",
-                start_date=datetime(2026,8,21),
+                start_date=datetime(2026, 8, 21),
                 end_date=None,
-                version=VersionMap({},Version(None, 1)),
+                version=VersionMap({}, Version(None, 1)),
                 descriptor="proton-sw",
             )
             l2_parent_file_name = "imap_swapi_l2_sci_20260120_v002.cdf"
@@ -234,9 +392,9 @@ def test_reads_data_into_model(self, tempdir):
                 parent_file_names=[
                     "imap_sclk_0170.tsc",
                     l2_parent_file_name,
-                    "imap_swapi_efficiency-lut_20241020_v001.dat"
+                    "imap_swapi_efficiency-lut_20241020_v001.dat",
                 ],
-                epoch=[datetime(2026,8,21), datetime(2026,8,22)],
+                epoch=[datetime(2026, 8, 21), datetime(2026, 8, 22)],
                 proton_sw_speed=[500, 501],
                 proton_sw_speed_uncert=[5, 6],
                 proton_sw_speed_sun=[400, 401],
@@ -248,16 +406,23 @@ def test_reads_data_into_model(self, tempdir):
                 proton_sw_velocity_rtn_sun=[[500, 15, 16], [501, 17, 18]],
                 proton_sw_velocity_rtn=[[440, 13, np.nan], [441, 12, 11]],
                 proton_sw_velocity_rtn_covariance=[
-                    [[40, 20, 10],[30, 30, 30],[np.nan, 14, 18]],
-                    [[40, 20, 10],[30, 30, 30],[np.nan, 14, 18]],
+                    [[40, 20, 10], [30, 30, 30], [np.nan, 14, 18]],
+                    [[40, 20, 10], [30, 30, 30], [np.nan, 14, 18]],
                 ],
-                quality_flags = [0, SwapiL3Flags.PREDICTIVE_EPHEMERIS],
+                quality_flags=[0, SwapiL3Flags.PREDICTIVE_EPHEMERIS],
             )
             path = save_data(initial_data)
             loaded = SwapiL3aProtonDataFromCDF.from_file(path)
             np.testing.assert_equal(loaded.l2_parent_file_name, l2_parent_file_name)
-            np.testing.assert_equal(loaded.velocity_rtn, initial_data.proton_sw_velocity_rtn)
-            np.testing.assert_equal(loaded.velocity_rtn_covariance, initial_data.proton_sw_velocity_rtn_covariance)
+            np.testing.assert_equal(
+                loaded.velocity_rtn, initial_data.proton_sw_velocity_rtn
+            )
+            np.testing.assert_equal(
+                loaded.velocity_rtn_covariance,
+                initial_data.proton_sw_velocity_rtn_covariance,
+            )
             np.testing.assert_equal(loaded.density, initial_data.proton_sw_density)
-            np.testing.assert_equal(loaded.temperature, initial_data.proton_sw_temperature)
+            np.testing.assert_equal(
+                loaded.temperature, initial_data.proton_sw_temperature
+            )
             np.testing.assert_equal(loaded.quality_flags, initial_data.quality_flags)
diff --git a/tests/swapi/l3a/test_swapi_l3a_dependencies.py b/tests/swapi/l3a/test_swapi_l3a_dependencies.py
index a91d7031d..8799fbca0 100644
--- a/tests/swapi/l3a/test_swapi_l3a_dependencies.py
+++ b/tests/swapi/l3a/test_swapi_l3a_dependencies.py
@@ -1,31 +1,44 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, call, sentinel
+from unittest.mock import call, patch, sentinel
 
 import imap_data_access
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, AncillaryInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
-from imap_l3_processing.swapi.descriptors import SWAPI_L2_DESCRIPTOR, DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, \
-    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
-    HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, HELIUM_INFLOW_VECTOR_DESCRIPTOR, \
-    AZIMUTHAL_TRANSMISSION_DESCRIPTOR, CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR, \
-    MAG_RTN_DESCRIPTOR
+from imap_l3_processing.swapi.descriptors import (
+    AZIMUTHAL_TRANSMISSION_DESCRIPTOR,
+    CENTRAL_EFFECTIVE_AREA_DESCRIPTOR,
+    DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR,
+    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR,
+    HELIUM_INFLOW_VECTOR_DESCRIPTOR,
+    HYDROGEN_INFLOW_VECTOR_DESCRIPTOR,
+    MAG_RTN_DESCRIPTOR,
+    PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR,
+    SWAPI_L2_DESCRIPTOR,
+)
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
 
 
 class TestSwapiL3ADependencies(unittest.TestCase):
-
     @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.select_mag_path")
-    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths")
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths"
+    )
     @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.download")
-    def test_fetch_dependencies(self, mock_download, mock_from_file_paths, mock_select_mag_path):
+    def test_fetch_dependencies(
+        self, mock_download, mock_from_file_paths, mock_select_mag_path
+    ):
         input_collection = ProcessingInputCollection()
 
-        start_date = '20100105'
-        mission = 'imap'
-        instrument = 'swapi'
-        data_level = 'l2'
-        version = 'v010'
+        start_date = "20100105"
+        mission = "imap"
+        instrument = "swapi"
+        data_level = "l2"
+        version = "v010"
 
         mock_download.side_effect = [
             sentinel.swapi_l2_data,
@@ -50,40 +63,66 @@ def test_fetch_dependencies(self, mock_download, mock_from_file_paths, mock_sele
 
         science_input = ScienceInput(swapi_science_file_download_path)
         efficiency_ancillary = AncillaryInput(swapi_efficiency_file_name)
-        density_of_neutral_helium_ancillary = AncillaryInput(swapi_density_of_neutral_helium_lookup)
+        density_of_neutral_helium_ancillary = AncillaryInput(
+            swapi_density_of_neutral_helium_lookup
+        )
         hydrogen_inflow_ancillary = AncillaryInput(swapi_hydrogen_inflow_filename)
         helium_inflow_ancillary = AncillaryInput(swapi_helium_inflow_filename)
-        azimuthal_transmission_ancillary = AncillaryInput(swapi_azimuthal_transmission_filename)
-        central_effective_area_ancillary = AncillaryInput(swapi_central_effective_area_filename)
-        passband_fit_coefficients_ancillary = AncillaryInput(swapi_passband_fit_coefficients_filename)
+        azimuthal_transmission_ancillary = AncillaryInput(
+            swapi_azimuthal_transmission_filename
+        )
+        central_effective_area_ancillary = AncillaryInput(
+            swapi_central_effective_area_filename
+        )
+        passband_fit_coefficients_ancillary = AncillaryInput(
+            swapi_passband_fit_coefficients_filename
+        )
 
         input_collection.add(
-            [science_input, efficiency_ancillary,
-             density_of_neutral_helium_ancillary,
-             hydrogen_inflow_ancillary,
-             helium_inflow_ancillary,
-             azimuthal_transmission_ancillary,
-             central_effective_area_ancillary,
-             passband_fit_coefficients_ancillary,
-             ])
-
-        actual_swapi_l3_dependencies = SwapiL3ADependencies.fetch_dependencies(input_collection)
-
-        science_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'swapi' / 'l2' / '2010' / '01'
-        ancillary_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'ancillary' / 'swapi'
-
-        mock_download.assert_has_calls([
-            call(science_data_dir / swapi_science_file_download_path),
-            call(ancillary_data_dir / swapi_efficiency_file_name),
-            call(ancillary_data_dir / swapi_density_of_neutral_helium_lookup),
-            call(ancillary_data_dir / swapi_hydrogen_inflow_filename),
-            call(ancillary_data_dir / swapi_helium_inflow_filename),
-            call(ancillary_data_dir / swapi_azimuthal_transmission_filename),
-            call(ancillary_data_dir / swapi_central_effective_area_filename),
-            call(ancillary_data_dir / swapi_passband_fit_coefficients_filename),
-        ])
-
-        mock_select_mag_path.assert_called_once_with(input_collection, MAG_RTN_DESCRIPTOR)
+            [
+                science_input,
+                efficiency_ancillary,
+                density_of_neutral_helium_ancillary,
+                hydrogen_inflow_ancillary,
+                helium_inflow_ancillary,
+                azimuthal_transmission_ancillary,
+                central_effective_area_ancillary,
+                passband_fit_coefficients_ancillary,
+            ]
+        )
+
+        actual_swapi_l3_dependencies = SwapiL3ADependencies.fetch_dependencies(
+            input_collection
+        )
+
+        science_data_dir = (
+            imap_data_access.config["DATA_DIR"]
+            / "imap"
+            / "swapi"
+            / "l2"
+            / "2010"
+            / "01"
+        )
+        ancillary_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "ancillary" / "swapi"
+        )
+
+        mock_download.assert_has_calls(
+            [
+                call(science_data_dir / swapi_science_file_download_path),
+                call(ancillary_data_dir / swapi_efficiency_file_name),
+                call(ancillary_data_dir / swapi_density_of_neutral_helium_lookup),
+                call(ancillary_data_dir / swapi_hydrogen_inflow_filename),
+                call(ancillary_data_dir / swapi_helium_inflow_filename),
+                call(ancillary_data_dir / swapi_azimuthal_transmission_filename),
+                call(ancillary_data_dir / swapi_central_effective_area_filename),
+                call(ancillary_data_dir / swapi_passband_fit_coefficients_filename),
+            ]
+        )
+
+        mock_select_mag_path.assert_called_once_with(
+            input_collection, MAG_RTN_DESCRIPTOR
+        )
 
         mock_from_file_paths.assert_called_with(
             sentinel.swapi_l2_data,
@@ -98,12 +137,18 @@ def test_fetch_dependencies(self, mock_download, mock_from_file_paths, mock_sele
             False,
         )
 
-        self.assertEqual(mock_from_file_paths.return_value, actual_swapi_l3_dependencies)
+        self.assertEqual(
+            mock_from_file_paths.return_value, actual_swapi_l3_dependencies
+        )
 
     @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.select_mag_path")
-    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths")
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths"
+    )
     @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.download")
-    def test_fetch_dependencies_with_l1d_mag(self, mock_download, mock_from_file_paths, mock_select_mag_path):
+    def test_fetch_dependencies_with_l1d_mag(
+        self, mock_download, mock_from_file_paths, mock_select_mag_path
+    ):
         input_collection = ProcessingInputCollection()
 
         mock_download.side_effect = [
@@ -118,8 +163,8 @@ def test_fetch_dependencies_with_l1d_mag(self, mock_download, mock_from_file_pat
         ]
         mock_select_mag_path.return_value = (sentinel.mag_path, "l1d")
 
-        start_date = '20100105'
-        version = 'v010'
+        start_date = "20100105"
+        version = "v010"
         descriptors = [
             ("imap_swapi_l2", SWAPI_L2_DESCRIPTOR, ScienceInput),
             ("imap_swapi", EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, AncillaryInput),
@@ -140,31 +185,52 @@ def test_fetch_dependencies_with_l1d_mag(self, mock_download, mock_from_file_pat
         self.assertEqual(sentinel.mag_path, args[8])
         self.assertEqual(True, args[9])
 
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_mag_rtn_data')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiResponse.from_files')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.CDF')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InflowVector.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.EfficiencyCalibrationTable')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.DensityOfNeutralHeliumLookupTable.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_l2_swapi_data')
-    def test_from_file_paths(self, mock_read_l2_swapi, mock_neutral_helium_from_file,
-                             mock_efficiency_lookup_class,
-                             mock_inflow_vector_from_file, mock_CDF,
-                             mock_swapi_response_from_files, mock_read_mag_rtn_data):
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_mag_rtn_data")
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiResponse.from_files"
+    )
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.CDF")
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InflowVector.from_file")
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.EfficiencyCalibrationTable"
+    )
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.DensityOfNeutralHeliumLookupTable.from_file"
+    )
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_l2_swapi_data")
+    def test_from_file_paths(
+        self,
+        mock_read_l2_swapi,
+        mock_neutral_helium_from_file,
+        mock_efficiency_lookup_class,
+        mock_inflow_vector_from_file,
+        mock_CDF,
+        mock_swapi_response_from_files,
+        mock_read_mag_rtn_data,
+    ):
         science_path = Path("imap_swapi_l2_sci_20100105_v010.cdf")
         efficiency_path = Path("imap_swapi_efficiency_20100105_v010.cdf")
         neutral_helium_path = Path("imap_swapi_neutral_he_20100105_v010.cdf")
         hydrogen_vector_path = Path("imap_swapi_h_inflow_20100105_v010.dat")
         helium_vector_path = Path("imap_swapi_he_inflow_20100105_v010.dat")
-        azimuthal_transmission_path = Path("imap_swapi_azimuthal_transmission_20100105_v010.cdf")
-        central_effective_area_path = Path("imap_swapi_central_effective_area_20100105_v010.cdf")
-        passband_fit_coefficients_path = Path("imap_swapi_passband_fit_coefficients_20100105_v010.cdf")
+        azimuthal_transmission_path = Path(
+            "imap_swapi_azimuthal_transmission_20100105_v010.cdf"
+        )
+        central_effective_area_path = Path(
+            "imap_swapi_central_effective_area_20100105_v010.cdf"
+        )
+        passband_fit_coefficients_path = Path(
+            "imap_swapi_passband_fit_coefficients_20100105_v010.cdf"
+        )
         mag_path = Path("imap_mag_l2_norm-rtn_20100105_v010.cdf")
 
         mock_read_l2_swapi.return_value = sentinel.swapi_l2_data
         mock_efficiency_lookup_class.return_value = sentinel.efficiency_lookup
         mock_neutral_helium_from_file.return_value = sentinel.neutral_helium_data
-        mock_inflow_vector_from_file.side_effect = [sentinel.hydrogen_vector, sentinel.helium_vector]
+        mock_inflow_vector_from_file.side_effect = [
+            sentinel.hydrogen_vector,
+            sentinel.helium_vector,
+        ]
         mock_swapi_response_from_files.return_value = sentinel.swapi_response
         mock_read_mag_rtn_data.return_value = sentinel.mag_data
 
@@ -197,27 +263,45 @@ def test_from_file_paths(self, mock_read_l2_swapi, mock_neutral_helium_from_file
         mock_efficiency_lookup_class.assert_called_once_with(efficiency_path)
         mock_neutral_helium_from_file.assert_called_once_with(neutral_helium_path)
         mock_inflow_vector_from_file.assert_has_calls(
-            [call(hydrogen_vector_path), call(helium_vector_path)])
+            [call(hydrogen_vector_path), call(helium_vector_path)]
+        )
         mock_swapi_response_from_files.assert_called_once_with(
             azimuthal_transmission_path=azimuthal_transmission_path,
             central_effective_area_path=central_effective_area_path,
             passband_fit_coefficients_path=passband_fit_coefficients_path,
-            efficiency_table_path=efficiency_path)
+            efficiency_table_path=efficiency_path,
+        )
         mock_read_mag_rtn_data.assert_called_once_with(mag_path)
 
         self.assertEqual(expected_dependencies, actual_dependencies)
 
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_mag_rtn_data')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiResponse.from_files')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.CDF')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InflowVector.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.EfficiencyCalibrationTable')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.DensityOfNeutralHeliumLookupTable.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_l2_swapi_data')
-    def test_from_file_paths_without_mag(self, mock_read_l2_swapi, mock_neutral_helium_from_file,
-                                         mock_efficiency_lookup_class, mock_inflow_vector_from_file, mock_CDF,
-                                         mock_swapi_response_from_files, mock_read_mag_rtn_data):
-        mock_inflow_vector_from_file.side_effect = [sentinel.hydrogen_vector, sentinel.helium_vector]
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_mag_rtn_data")
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiResponse.from_files"
+    )
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.CDF")
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InflowVector.from_file")
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.EfficiencyCalibrationTable"
+    )
+    @patch(
+        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.DensityOfNeutralHeliumLookupTable.from_file"
+    )
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_l2_swapi_data")
+    def test_from_file_paths_without_mag(
+        self,
+        mock_read_l2_swapi,
+        mock_neutral_helium_from_file,
+        mock_efficiency_lookup_class,
+        mock_inflow_vector_from_file,
+        mock_CDF,
+        mock_swapi_response_from_files,
+        mock_read_mag_rtn_data,
+    ):
+        mock_inflow_vector_from_file.side_effect = [
+            sentinel.hydrogen_vector,
+            sentinel.helium_vector,
+        ]
 
         actual = SwapiL3ADependencies.from_file_paths(
             Path("science.cdf"),
diff --git a/tests/swapi/l3a/test_utils.py b/tests/swapi/l3a/test_utils.py
index 3fb513e67..346592eeb 100644
--- a/tests/swapi/l3a/test_utils.py
+++ b/tests/swapi/l3a/test_utils.py
@@ -25,7 +25,6 @@
 )
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
 from imap_l3_processing.swapi.l3a.utils import (
-    velocity_components_to_angles_in_instrument_frame,
     calculate_sw_speed,
     chunk_l2_data,
     esa_voltage_to_alpha_speed,
@@ -36,15 +35,14 @@
     pickup_ion_chunk_epoch,
     read_l2_swapi_data,
     read_mag_rtn_data,
+    velocity_components_to_angles_in_instrument_frame,
     velocity_to_angles_in_instrument_frame,
 )
 from tests.spice_test_case import SpiceTestCase
 from tests.swapi._helpers import proton_params
 
 
-def _analytic_speed_km_per_s(
-    voltage: float, mass_kg: float, charge_c: float
-) -> float:
+def _analytic_speed_km_per_s(voltage: float, mass_kg: float, charge_c: float) -> float:
     return float(
         np.sqrt(2 * SWAPI_K_FACTOR * charge_c * abs(voltage) / mass_kg)
         / METERS_PER_KILOMETER
@@ -57,43 +55,75 @@ class TestChunkL2Data(TestCase):
     def test_chunk_l2_data(self):
         """chunk_l2_data splits a 4-sweep L2 dataset into two 2-sweep chunks, copying epoch, energy, count rate, and uncertainty into each chunk."""
         epoch = np.array([0, 1, 2, 3])
-        energy = np.array([[15000, 16000, 17000, 18000, 19000],
-                           [25000, 26000, 27000, 28000, 29000],
-                           [35000, 36000, 37000, 38000, 39000],
-                           [45000, 46000, 47000, 48000, 49000], ]
-                          )
+        energy = np.array(
+            [
+                [15000, 16000, 17000, 18000, 19000],
+                [25000, 26000, 27000, 28000, 29000],
+                [35000, 36000, 37000, 38000, 39000],
+                [45000, 46000, 47000, 48000, 49000],
+            ]
+        )
         coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
+            [
+                [4, 5, 6, 7, 8],
+                [9, 10, 11, 12, 13],
+                [14, 15, 16, 17, 18],
+                [19, 20, 21, 22, 23],
+            ]
+        )
         coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
+            [
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+            ]
+        )
 
-        data = SwapiL2Data(epoch, energy, coincidence_count_rate, coincidence_count_rate_uncertainty)
+        data = SwapiL2Data(
+            epoch, energy, coincidence_count_rate, coincidence_count_rate_uncertainty
+        )
         chunks = list(chunk_l2_data(data, 2))
 
-        expected_energy_chunk_1 = np.array([[15000, 16000, 17000, 18000, 19000],
-                                            [25000, 26000, 27000, 28000, 29000]])
-        expected_energy_chunk_2 = np.array([[35000, 36000, 37000, 38000, 39000],
-                                            [45000, 46000, 47000, 48000, 49000]])
+        expected_energy_chunk_1 = np.array(
+            [[15000, 16000, 17000, 18000, 19000], [25000, 26000, 27000, 28000, 29000]]
+        )
+        expected_energy_chunk_2 = np.array(
+            [[35000, 36000, 37000, 38000, 39000], [45000, 46000, 47000, 48000, 49000]]
+        )
 
         expected_count_rate_chunk_1 = np.array([[4, 5, 6, 7, 8], [9, 10, 11, 12, 13]])
-        expected_count_rate_uncertainty_chunk_1 = np.array([[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5]])
+        expected_count_rate_uncertainty_chunk_1 = np.array(
+            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5]]
+        )
         first_chunk = chunks[0]
 
         np.testing.assert_array_equal(first_chunk.sci_start_time, np.array([0, 1]))
         np.testing.assert_array_equal(expected_energy_chunk_1, first_chunk.energy)
-        np.testing.assert_array_equal(expected_count_rate_chunk_1, first_chunk.coincidence_count_rate)
-        np.testing.assert_array_equal(expected_count_rate_uncertainty_chunk_1,
-                                      first_chunk.coincidence_count_rate_uncertainty)
+        np.testing.assert_array_equal(
+            expected_count_rate_chunk_1, first_chunk.coincidence_count_rate
+        )
+        np.testing.assert_array_equal(
+            expected_count_rate_uncertainty_chunk_1,
+            first_chunk.coincidence_count_rate_uncertainty,
+        )
 
-        expected_count_rate_chunk_2 = np.array([[14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        expected_count_rate_uncertainty_chunk_2 = np.array([[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5]])
+        expected_count_rate_chunk_2 = np.array(
+            [[14, 15, 16, 17, 18], [19, 20, 21, 22, 23]]
+        )
+        expected_count_rate_uncertainty_chunk_2 = np.array(
+            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5]]
+        )
         second_chunk = chunks[1]
         np.testing.assert_array_equal(np.array([2, 3]), second_chunk.sci_start_time)
         np.testing.assert_array_equal(expected_energy_chunk_2, second_chunk.energy)
-        np.testing.assert_array_equal(expected_count_rate_chunk_2, second_chunk.coincidence_count_rate)
-        np.testing.assert_array_equal(expected_count_rate_uncertainty_chunk_2,
-                                      second_chunk.coincidence_count_rate_uncertainty)
+        np.testing.assert_array_equal(
+            expected_count_rate_chunk_2, second_chunk.coincidence_count_rate
+        )
+        np.testing.assert_array_equal(
+            expected_count_rate_uncertainty_chunk_2,
+            second_chunk.coincidence_count_rate_uncertainty,
+        )
 
     def test_chunk_l2_data_partial_trailing_chunk_is_dropped(self):
         """When the sweep count is not a multiple of the chunk size, the trailing partial chunk is dropped rather than yielded short."""
@@ -120,7 +150,9 @@ def test_each_bin_is_offset_from_its_sweep_start_by_the_livetime_center(self):
         times = measurement_times(sci_start_time)
 
         def expected(sweep_start, bin_index):
-            seconds_into_sweep = bin_index * SWAPI_BIN_PERIOD_S + SWAPI_LIVETIME_CENTER_OFFSET_S
+            seconds_into_sweep = (
+                bin_index * SWAPI_BIN_PERIOD_S + SWAPI_LIVETIME_CENTER_OFFSET_S
+            )
             return sweep_start + seconds_into_sweep * ONE_SECOND_IN_NANOSECONDS
 
         self.assertEqual(times.shape, (2, SWAPI_SWEEP_BIN_COUNT))
@@ -131,30 +163,36 @@ def expected(sweep_start, bin_index):
     def test_offset_is_the_livetime_center_not_its_start(self):
         """The per-bin offset lands half a livetime past the end of the ramp-up, i.e. at the center of the livetime window rather than its start."""
         ramp_up_s = SWAPI_BIN_PERIOD_S - SWAPI_LIVETIME_S
-        self.assertAlmostEqual(SWAPI_LIVETIME_CENTER_OFFSET_S, ramp_up_s + SWAPI_LIVETIME_S / 2)
-        self.assertAlmostEqual(SWAPI_LIVETIME_CENTER_OFFSET_S, SWAPI_BIN_PERIOD_S - SWAPI_LIVETIME_S / 2)
+        self.assertAlmostEqual(
+            SWAPI_LIVETIME_CENTER_OFFSET_S, ramp_up_s + SWAPI_LIVETIME_S / 2
+        )
+        self.assertAlmostEqual(
+            SWAPI_LIVETIME_CENTER_OFFSET_S, SWAPI_BIN_PERIOD_S - SWAPI_LIVETIME_S / 2
+        )
 
 
 class TestReadL2SwapiData(TestCase):
     """Tests for `read_l2_swapi_data`."""
 
     def tearDown(self) -> None:
-        if os.path.exists('temp_cdf.cdf'):
-            os.remove('temp_cdf.cdf')
+        if os.path.exists("temp_cdf.cdf"):
+            os.remove("temp_cdf.cdf")
 
     def test_reading_l2_data_into_model(self):
         """read_l2_swapi_data parses a CDF into SwapiL2Data, decoding the start time to TT2000 and replacing each variable's FILLVAL entries with NaN."""
-        path = Path('temp_cdf.cdf')
+        path = Path("temp_cdf.cdf")
         if path.exists():
             os.remove(path)
 
-        temp_cdf = CDF('temp_cdf', '')
-        temp_cdf["sci_start_time"] = np.array(['2010-01-01T00:00:46.000'])
+        temp_cdf = CDF("temp_cdf", "")
+        temp_cdf["sci_start_time"] = np.array(["2010-01-01T00:00:46.000"])
         temp_cdf["esa_energy"] = np.array([1, -1e31, 3, 4], dtype=float)
         temp_cdf["swp_coin_rate"] = np.array([5, 6, 7, -1e31], dtype=float)
-        temp_cdf["swp_coin_rate_stat_uncert_plus"] = np.array([2, 2, -1e31, 2, 2, 2, 2, 2], dtype=float)
+        temp_cdf["swp_coin_rate_stat_uncert_plus"] = np.array(
+            [2, 2, -1e31, 2, 2, 2, 2, 2], dtype=float
+        )
 
-        temp_cdf["sci_start_time"].attrs["FILLVAL"] = '0'
+        temp_cdf["sci_start_time"].attrs["FILLVAL"] = "0"
         temp_cdf["esa_energy"].attrs["FILLVAL"] = -1e31
         temp_cdf["swp_coin_rate"].attrs["FILLVAL"] = -1e31
         temp_cdf["swp_coin_rate_stat_uncert_plus"].attrs["FILLVAL"] = -1e31
@@ -164,11 +202,19 @@ def test_reading_l2_data_into_model(self):
         actual_swapi_l2_data = read_l2_swapi_data(CDF("temp_cdf.cdf"))
 
         epoch_as_tt2000 = 315576112184000000
-        np.testing.assert_array_equal(np.array(epoch_as_tt2000), actual_swapi_l2_data.sci_start_time)
-        np.testing.assert_array_equal(np.array([1, np.nan, 3, 4]), actual_swapi_l2_data.energy)
-        np.testing.assert_array_equal(np.array([5, 6, 7, np.nan]), actual_swapi_l2_data.coincidence_count_rate)
-        np.testing.assert_array_equal(np.array([2, 2, np.nan, 2, 2, 2, 2, 2]),
-                                      actual_swapi_l2_data.coincidence_count_rate_uncertainty)
+        np.testing.assert_array_equal(
+            np.array(epoch_as_tt2000), actual_swapi_l2_data.sci_start_time
+        )
+        np.testing.assert_array_equal(
+            np.array([1, np.nan, 3, 4]), actual_swapi_l2_data.energy
+        )
+        np.testing.assert_array_equal(
+            np.array([5, 6, 7, np.nan]), actual_swapi_l2_data.coincidence_count_rate
+        )
+        np.testing.assert_array_equal(
+            np.array([2, 2, np.nan, 2, 2, 2, 2, 2]),
+            actual_swapi_l2_data.coincidence_count_rate_uncertainty,
+        )
 
 
 class TestCalculateSwSpeed(TestCase):
@@ -232,7 +278,7 @@ class TestReadMagRtnData(TestCase):
     """Tests for `read_mag_rtn_data`."""
 
     def setUp(self) -> None:
-        self.cdf_path = Path('temp_mag_cdf.cdf')
+        self.cdf_path = Path("temp_mag_cdf.cdf")
         if self.cdf_path.exists():
             os.remove(self.cdf_path)
 
@@ -242,13 +288,13 @@ def tearDown(self) -> None:
 
     def test_reads_b_rtn_and_epoch_into_mag_data(self):
         """read_mag_rtn_data converts CDF epochs to TT2000 and keeps only the leading three vector components of b_rtn (dropping the magnitude column)."""
-        epochs = np.array([datetime(2026, 1, 1, 0, 0, 0),
-                           datetime(2026, 1, 1, 0, 0, 1)])
+        epochs = np.array(
+            [datetime(2026, 1, 1, 0, 0, 0), datetime(2026, 1, 1, 0, 0, 1)]
+        )
         b_rtn = np.array(
-            [[1.0, 2.0, 3.0, 0.0],
-             [4.0, 5.0, 6.0, 0.0]],
+            [[1.0, 2.0, 3.0, 0.0], [4.0, 5.0, 6.0, 0.0]],
         )
-        cdf = CDF(str(self.cdf_path.with_suffix("")), '')
+        cdf = CDF(str(self.cdf_path.with_suffix("")), "")
         cdf["epoch"] = epochs
         cdf["b_rtn"] = b_rtn
         cdf["b_rtn"].attrs["FILLVAL"] = -1e31
@@ -307,40 +353,54 @@ class TestVelocityComponentsToAnglesInInstrumentFrame(TestCase):
 
     def test_flow_along_minus_y_returns_zero_angles(self):
         """A flow along -Y means SWAPI looks toward +Y, which is the (azimuth=0, elevation=0) bore-sight in the instrument frame."""
-        azimuth, elevation = velocity_components_to_angles_in_instrument_frame(0.0, -450.0, 0.0)
+        azimuth, elevation = velocity_components_to_angles_in_instrument_frame(
+            0.0, -450.0, 0.0
+        )
         self.assertAlmostEqual(azimuth, 0.0)
         self.assertAlmostEqual(elevation, 0.0)
 
     def test_positive_x_flow_yields_negative_azimuth(self):
         """A flow with a positive X component (look direction in -X) gives a negative azimuth, since azimuth = atan2(-vx, -vy) < 0 when vx > 0 and vy < 0."""
-        azimuth, _ = velocity_components_to_angles_in_instrument_frame(50.0, -450.0, 0.0)
+        azimuth, _ = velocity_components_to_angles_in_instrument_frame(
+            50.0, -450.0, 0.0
+        )
         self.assertLess(azimuth, 0.0)
 
     def test_positive_z_flow_yields_negative_elevation(self):
         """A flow with a positive Z component (look direction in -Z) gives a negative elevation, since elevation = asin(-vz/|v|) < 0 when vz > 0."""
-        _, elevation = velocity_components_to_angles_in_instrument_frame(0.0, -450.0, 50.0)
+        _, elevation = velocity_components_to_angles_in_instrument_frame(
+            0.0, -450.0, 50.0
+        )
         self.assertLess(elevation, 0.0)
 
     def test_negative_z_flow_yields_positive_elevation(self):
         """A flow with a negative Z component (look direction in +Z) gives a positive elevation."""
-        _, elevation = velocity_components_to_angles_in_instrument_frame(0.0, -450.0, -50.0)
+        _, elevation = velocity_components_to_angles_in_instrument_frame(
+            0.0, -450.0, -50.0
+        )
         self.assertGreater(elevation, 0.0)
 
     def test_pure_plus_x_flow_yields_azimuth_minus_90_elevation_zero(self):
         """A pure +X flow has look direction along -X; azimuth = atan2(-450, 0) = -90 deg, elevation = 0."""
-        azimuth, elevation = velocity_components_to_angles_in_instrument_frame(450.0, 0.0, 0.0)
+        azimuth, elevation = velocity_components_to_angles_in_instrument_frame(
+            450.0, 0.0, 0.0
+        )
         self.assertAlmostEqual(azimuth, -90.0)
         self.assertAlmostEqual(elevation, 0.0)
 
     def test_pure_plus_y_flow_yields_azimuth_180(self):
         """A pure +Y flow (away from SWAPI) has look direction along -Y; azimuth = atan2(0, -450) = +/-180 deg, elevation = 0."""
-        azimuth, elevation = velocity_components_to_angles_in_instrument_frame(0.0, 450.0, 0.0)
+        azimuth, elevation = velocity_components_to_angles_in_instrument_frame(
+            0.0, 450.0, 0.0
+        )
         self.assertAlmostEqual(abs(azimuth), 180.0)
         self.assertAlmostEqual(elevation, 0.0)
 
     def test_pure_plus_z_flow_yields_elevation_minus_90(self):
         """A pure +Z flow has look direction along -Z; elevation = asin(-1) = -90 deg."""
-        _, elevation = velocity_components_to_angles_in_instrument_frame(0.0, 0.0, 450.0)
+        _, elevation = velocity_components_to_angles_in_instrument_frame(
+            0.0, 0.0, 450.0
+        )
         self.assertAlmostEqual(elevation, -90.0)
 
 
@@ -388,9 +448,7 @@ def test_matches_analytical_formula(self):
             with self.subTest(voltage=V):
                 np.testing.assert_allclose(
                     esa_voltage_to_proton_speed(V),
-                    _analytic_speed_km_per_s(
-                        V, PROTON_MASS_KG, PROTON_CHARGE_COULOMBS
-                    ),
+                    _analytic_speed_km_per_s(V, PROTON_MASS_KG, PROTON_CHARGE_COULOMBS),
                     rtol=1e-12,
                 )
 
@@ -441,9 +499,8 @@ def test_ten_minute_chunk_is_centered_five_minutes_in(self):
         start, so its center is 5 minutes in — matching the +/- 5 minute
         `epoch_delta` the chunk is reported with."""
         first_start = 800_000_000_000_000_000
-        sweep_starts = (
-            first_start
-            + np.arange(50, dtype=np.int64) * int(12 * ONE_SECOND_IN_NANOSECONDS)
+        sweep_starts = first_start + np.arange(50, dtype=np.int64) * int(
+            12 * ONE_SECOND_IN_NANOSECONDS
         )
         empty_sweeps = np.zeros((50, SWAPI_SWEEP_BIN_COUNT))
         chunk = SwapiL2Data(
@@ -454,7 +511,8 @@ def test_ten_minute_chunk_is_centered_five_minutes_in(self):
         )
 
         self.assertEqual(
-            pickup_ion_chunk_epoch(chunk), first_start + 5 * 60 * ONE_SECOND_IN_NANOSECONDS
+            pickup_ion_chunk_epoch(chunk),
+            first_start + 5 * 60 * ONE_SECOND_IN_NANOSECONDS,
         )
 
     def test_center_is_half_a_sweep_past_the_midpoint_of_the_sweep_starts(self):
@@ -462,9 +520,8 @@ def test_center_is_half_a_sweep_past_the_midpoint_of_the_sweep_starts(self):
         center sits half a sweep beyond the midpoint of the start times — the
         half sweep that separates a sweep's start from its own center."""
         first_start = 800_000_000_000_000_000
-        sweep_starts = (
-            first_start
-            + np.arange(50, dtype=np.int64) * int(12 * ONE_SECOND_IN_NANOSECONDS)
+        sweep_starts = first_start + np.arange(50, dtype=np.int64) * int(
+            12 * ONE_SECOND_IN_NANOSECONDS
         )
         empty_sweeps = np.zeros((50, SWAPI_SWEEP_BIN_COUNT))
         chunk = SwapiL2Data(
diff --git a/tests/swapi/l3b/science/test_calculate_solar_wind_differential_flux.py b/tests/swapi/l3b/science/test_calculate_solar_wind_differential_flux.py
index 2f9464f05..1b03ae14a 100644
--- a/tests/swapi/l3b/science/test_calculate_solar_wind_differential_flux.py
+++ b/tests/swapi/l3b/science/test_calculate_solar_wind_differential_flux.py
@@ -3,9 +3,12 @@
 
 import numpy as np
 
-from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_differential_flux import \
-    calculate_combined_solar_wind_differential_flux
-from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
+from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_differential_flux import (
+    calculate_combined_solar_wind_differential_flux,
+)
+from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import (
+    GeometricFactorCalibrationTable,
+)
 
 
 class TestCalculateSolarWindDifferentialFlux(TestCase):
@@ -14,10 +17,15 @@ def test_calculate_combined_differential_flux(self):
         count_rates = np.array([10, 20, 30])
         efficiency = 0.0882
         mock_geometric_factor_table = create_autospec(GeometricFactorCalibrationTable)
-        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array([1e-12, 1e-13, 1e-14])
+        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array(
+            [1e-12, 1e-13, 1e-14]
+        )
 
-        differential_flux = calculate_combined_solar_wind_differential_flux(energies, count_rates, efficiency,
-                                                                            mock_geometric_factor_table)
-        expected_flux = np.array([11.337868480725623, 302.3431594860166, 6802.721088435374])
+        differential_flux = calculate_combined_solar_wind_differential_flux(
+            energies, count_rates, efficiency, mock_geometric_factor_table
+        )
+        expected_flux = np.array(
+            [11.337868480725623, 302.3431594860166, 6802.721088435374]
+        )
         np.testing.assert_array_almost_equal(expected_flux, differential_flux)
         mock_geometric_factor_table.lookup_geometric_factor.assert_called_with(energies)
diff --git a/tests/swapi/l3b/science/test_calculate_solar_wind_vdf.py b/tests/swapi/l3b/science/test_calculate_solar_wind_vdf.py
index d8efb146e..b671d798c 100644
--- a/tests/swapi/l3b/science/test_calculate_solar_wind_vdf.py
+++ b/tests/swapi/l3b/science/test_calculate_solar_wind_vdf.py
@@ -3,9 +3,13 @@
 
 import numpy as np
 
-from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import calculate_proton_solar_wind_vdf, \
-    GeometricFactorCalibrationTable, calculate_alpha_solar_wind_vdf, calculate_pui_solar_wind_vdf, \
-    calculate_delta_minus_plus
+from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import (
+    GeometricFactorCalibrationTable,
+    calculate_alpha_solar_wind_vdf,
+    calculate_delta_minus_plus,
+    calculate_proton_solar_wind_vdf,
+    calculate_pui_solar_wind_vdf,
+)
 
 
 class TestCalculateSolarWindVDF(TestCase):
@@ -14,13 +18,16 @@ def test_calculate_proton_solar_wind_vdf(self):
         count_rates = np.array([10, 20, 30])
         efficiency = 0.0882
         mock_geometric_factor_table = create_autospec(GeometricFactorCalibrationTable)
-        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array([1e-12, 1e-13, 1e-14])
+        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array(
+            [1e-12, 1e-13, 1e-14]
+        )
 
         expected_velocities = [437.6947142244463, 379.05474162054054, 309.496900517614]
         expected_probabilities = [0.001487, 0.039664, 0.892442]
 
-        velocities, probabilities = calculate_proton_solar_wind_vdf(energies, count_rates, efficiency,
-                                                                    mock_geometric_factor_table)
+        velocities, probabilities = calculate_proton_solar_wind_vdf(
+            energies, count_rates, efficiency, mock_geometric_factor_table
+        )
 
         np.testing.assert_array_equal(velocities, expected_velocities)
         np.testing.assert_array_almost_equal(probabilities, expected_probabilities)
@@ -49,13 +56,16 @@ def test_calculate_alpha_solar_wind_vdf(self):
         count_rates = np.array([10, 20, 30])
         efficiency = 0.0882
         mock_geometric_factor_table = create_autospec(GeometricFactorCalibrationTable)
-        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array([1e-12, 1e-13, 1e-14])
+        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array(
+            [1e-12, 1e-13, 1e-14]
+        )
 
         expected_velocities = [310.5624166704235, 268.95494229727456, 219.6007908093385]
         expected_probabilities = [0.002954, 0.078785, 1.772657]
 
-        velocities, probabilities = calculate_alpha_solar_wind_vdf(energies, count_rates, efficiency,
-                                                                   mock_geometric_factor_table)
+        velocities, probabilities = calculate_alpha_solar_wind_vdf(
+            energies, count_rates, efficiency, mock_geometric_factor_table
+        )
 
         np.testing.assert_array_equal(velocities, expected_velocities)
         np.testing.assert_array_almost_equal(probabilities, expected_probabilities)
@@ -66,13 +76,20 @@ def test_calculate_pickup_ion_solar_wind_vdf(self):
         count_rates = np.array([10, 20, 30])
         efficiency = 0.0882
         mock_geometric_factor_table = create_autospec(GeometricFactorCalibrationTable)
-        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array([1e-12, 1e-13, 1e-14])
-
-        expected_velocities = [219.58573945228636, 190.16682867447082, 155.27056541857408]
+        mock_geometric_factor_table.lookup_geometric_factor.return_value = np.array(
+            [1e-12, 1e-13, 1e-14]
+        )
+
+        expected_velocities = [
+            219.58573945228636,
+            190.16682867447082,
+            155.27056541857408,
+        ]
         expected_probabilities = [0.00591, 0.157591, 3.5458]
 
-        velocities, probabilities = calculate_pui_solar_wind_vdf(energies, count_rates, efficiency,
-                                                                 mock_geometric_factor_table)
+        velocities, probabilities = calculate_pui_solar_wind_vdf(
+            energies, count_rates, efficiency, mock_geometric_factor_table
+        )
 
         np.testing.assert_array_equal(velocities, expected_velocities)
         np.testing.assert_array_almost_equal(probabilities, expected_probabilities)
diff --git a/tests/swapi/l3b/science/test_geometric_factor_calibration_table.py b/tests/swapi/l3b/science/test_geometric_factor_calibration_table.py
index 40a9ab242..d83605c18 100644
--- a/tests/swapi/l3b/science/test_geometric_factor_calibration_table.py
+++ b/tests/swapi/l3b/science/test_geometric_factor_calibration_table.py
@@ -1,12 +1,16 @@
 import unittest
 
-from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
+from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import (
+    GeometricFactorCalibrationTable,
+)
 from tests.test_helpers import get_test_data_path
 
 
 class TestGeometricFactorTable(unittest.TestCase):
     def test_geometric_factor_table_from_file(self):
-        file_path = get_test_data_path("swapi/imap_swapi_energy-gf-sw-lut_20100101_v001.csv")
+        file_path = get_test_data_path(
+            "swapi/imap_swapi_energy-gf-sw-lut_20100101_v001.csv"
+        )
 
         calibration_table = GeometricFactorCalibrationTable.from_file(file_path)
 
@@ -15,5 +19,11 @@ def test_geometric_factor_table_from_file(self):
 
         known_energy = 8165.393844536367
         energy_to_interpolate = 14194.87288073211
-        self.assertEqual(6.419796603112413e-13, calibration_table.lookup_geometric_factor(known_energy))
-        self.assertAlmostEqual(5.711128783363629e-13, calibration_table.lookup_geometric_factor(energy_to_interpolate))
+        self.assertEqual(
+            6.419796603112413e-13,
+            calibration_table.lookup_geometric_factor(known_energy),
+        )
+        self.assertAlmostEqual(
+            5.711128783363629e-13,
+            calibration_table.lookup_geometric_factor(energy_to_interpolate),
+        )
diff --git a/tests/swapi/l3b/test_models.py b/tests/swapi/l3b/test_models.py
index 4b86fd8cf..6df21d150 100644
--- a/tests/swapi/l3b/test_models.py
+++ b/tests/swapi/l3b/test_models.py
@@ -11,23 +11,22 @@
     EPOCH_DELTA_CDF_VAR_NAME,
 )
 from imap_l3_processing.swapi.l3b.models import (
-    SwapiL3BCombinedVDF,
     COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_CDF_VAR_NAME,
     COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME,
-    SOLAR_WIND_ENERGY_CDF_VAR_NAME,
     SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME,
     SOLAR_WIND_COMBINED_ENERGY_DELTA_PLUS_CDF_VAR_NAME,
+    SOLAR_WIND_ENERGY_CDF_VAR_NAME,
     SOLAR_WIND_ENERGY_LABEL_CDF_VAR_NAME,
+    SwapiL3BCombinedVDF,
 )
 from tests.swapi.cdf_model_test_case import CdfModelTestCase
 
 
 class TestModels(CdfModelTestCase):
     def test_combined_vdf_data_products(self):
-        input_metadata = InputMetadata("swapi", "l3b",
-                                                datetime(2024, 9, 8),
-                                                datetime(2024, 9, 9),
-                                                "v001", "")
+        input_metadata = InputMetadata(
+            "swapi", "l3b", datetime(2024, 9, 8), datetime(2024, 9, 9), "v001", ""
+        )
         epoch = np.array([1, 2, 3])
         epoch_delta = np.full_like(epoch, FIVE_MINUTES_IN_NANOSECONDS)
         proton_velocities = np.array([4, 5, 6])
@@ -41,16 +40,28 @@ def test_combined_vdf_data_products(self):
         alpha_velocities_delta_plus = np.array([0.11, 0.12, 0.13])
         alpha_velocities_delta_minus = 1 + np.array([0.11, 0.12, 0.13])
         alpha_vdf = np.array([[14, 15, 16, 17], [17, 18, 19, 20], [20, 21, 22, 23]])
-        alpha_vdf_uncertainties = np.array([[0.7, 0.8, 0.9, 1.0], [0.10, 0.11, 0.12, 0.13], [0.13, 0.14, 0.15, 0.16]])
+        alpha_vdf_uncertainties = np.array(
+            [[0.7, 0.8, 0.9, 1.0], [0.10, 0.11, 0.12, 0.13], [0.13, 0.14, 0.15, 0.16]]
+        )
         pui_velocities = np.array([23, 24, 25])
         pui_velocities_delta_plus = np.array([0.23, 0.24, 0.25])
         pui_velocities_delta_minus = 1 + np.array([0.23, 0.24, 0.25])
         pui_vdf = np.array([[26, 27, 28, 29], [29, 30, 31, 32], [32, 33, 34, 35]])
-        pui_vdf_uncertainties = np.array([[0.72, 0.8, 0.9, 1.0], [0.10, 0.121, 0.12, 0.13], [0.13, 0.142, 0.15, 0.16]])
-        combined_energies = np.array([[100, 300, 1000, 3000], [100, 300, 1000, 3000],[100, 300, 1000, 3000]])
+        pui_vdf_uncertainties = np.array(
+            [
+                [0.72, 0.8, 0.9, 1.0],
+                [0.10, 0.121, 0.12, 0.13],
+                [0.13, 0.142, 0.15, 0.16],
+            ]
+        )
+        combined_energies = np.array(
+            [[100, 300, 1000, 3000], [100, 300, 1000, 3000], [100, 300, 1000, 3000]]
+        )
         combined_energies_delta_plus = np.array([44, 55, 66])
         combined_energies_delta_minus = 1 + np.array([44, 55, 66])
-        combined_differential_flux = np.array([[26, 27.2, 28, 29], [29.2, 30, 31, 32.1], [32, 33.5, 34, 36]])
+        combined_differential_flux = np.array(
+            [[26, 27.2, 28, 29], [29.2, 30, 31, 32.1], [32, 33.5, 34, 36]]
+        )
         combined_differential_flux_uncertainties = np.array(
             [
                 [0.725, 0.8, 0.9, 1.0],
@@ -59,7 +70,9 @@ def test_combined_vdf_data_products(self):
             ]
         )
 
-        expected_combined_energy_labels = np.array(["ESA 01", "ESA 02", "ESA 03", "ESA 04"])
+        expected_combined_energy_labels = np.array(
+            ["ESA 01", "ESA 02", "ESA 03", "ESA 04"]
+        )
 
         vdf = SwapiL3BCombinedVDF(
             input_metadata=input_metadata,
@@ -79,21 +92,46 @@ def test_combined_vdf_data_products(self):
             combined_energy=combined_energies,
             combined_energy_delta_minus=combined_energies_delta_minus,
             combined_energy_delta_plus=combined_energies_delta_plus,
-            combined_differential_flux=uarray(combined_differential_flux, combined_differential_flux_uncertainties))
+            combined_differential_flux=uarray(
+                combined_differential_flux, combined_differential_flux_uncertainties
+            ),
+        )
 
         variables = vdf.to_data_product_variables()
 
         self.assertEqual(8, len(variables))
-        self.assert_variable_attributes(variables[0], epoch, EPOCH_CDF_VAR_NAME, pycdf.const.CDF_TIME_TT2000)
-        self.assert_variable_attributes(variables[1], epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            variables[0], epoch, EPOCH_CDF_VAR_NAME, pycdf.const.CDF_TIME_TT2000
+        )
+        self.assert_variable_attributes(
+            variables[1], epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
 
-        self.assert_variable_attributes(variables[2], combined_energies, SOLAR_WIND_ENERGY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[3], combined_energies_delta_minus,
-                                        SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[4], combined_energies_delta_plus,
-                                        SOLAR_WIND_COMBINED_ENERGY_DELTA_PLUS_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[5], expected_combined_energy_labels, SOLAR_WIND_ENERGY_LABEL_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[6], combined_differential_flux,
-                                        COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[7], combined_differential_flux_uncertainties,
-                                        COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            variables[2], combined_energies, SOLAR_WIND_ENERGY_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            variables[3],
+            combined_energies_delta_minus,
+            SOLAR_WIND_COMBINED_ENERGY_DELTA_MINUS_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[4],
+            combined_energies_delta_plus,
+            SOLAR_WIND_COMBINED_ENERGY_DELTA_PLUS_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[5],
+            expected_combined_energy_labels,
+            SOLAR_WIND_ENERGY_LABEL_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[6],
+            combined_differential_flux,
+            COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            variables[7],
+            combined_differential_flux_uncertainties,
+            COMBINED_SOLAR_WIND_DIFFERENTIAL_FLUX_DELTA_CDF_VAR_NAME,
+        )
diff --git a/tests/swapi/l3b/test_swapi_l3b_dependencies.py b/tests/swapi/l3b/test_swapi_l3b_dependencies.py
index bd3236c5b..94a8241ae 100644
--- a/tests/swapi/l3b/test_swapi_l3b_dependencies.py
+++ b/tests/swapi/l3b/test_swapi_l3b_dependencies.py
@@ -1,56 +1,80 @@
 import unittest
 from datetime import datetime
 from pathlib import Path
-from unittest.mock import patch, call, sentinel
+from unittest.mock import call, patch, sentinel
 
 import imap_data_access
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, AncillaryInput
-
-from imap_l3_processing.swapi.descriptors import SWAPI_L2_DESCRIPTOR, GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR, \
-    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
+
+from imap_l3_processing.swapi.descriptors import (
+    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR,
+    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR,
+    SWAPI_L2_DESCRIPTOR,
+)
 from imap_l3_processing.swapi.l3b.swapi_l3b_dependencies import SwapiL3BDependencies
 
 
 class TestSwapiL3BDependencies(unittest.TestCase):
     def setUp(self) -> None:
-        self.mock_imap_patcher = patch('imap_l3_processing.utils.imap_data_access')
+        self.mock_imap_patcher = patch("imap_l3_processing.utils.imap_data_access")
         self.mock_imap_api = self.mock_imap_patcher.start()
 
     def tearDown(self) -> None:
         self.mock_imap_patcher.stop()
 
     @patch(
-        "imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.SwapiL3BDependencies.from_file_paths")
-    @patch(
-        "imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.download")
+        "imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.SwapiL3BDependencies.from_file_paths"
+    )
+    @patch("imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.download")
     def test_fetch_dependencies(self, mock_download, mock_from_file_paths):
-        incoming_data_level = 'l2'
-        version = 'v002'
+        incoming_data_level = "l2"
+        version = "v002"
         start_date = datetime(2025, 1, 1).strftime("%Y%m%d")
 
-        science_file_path = f'imap_swapi_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{start_date}_{version}.cdf'
-        geometric_calibration_path = f'imap_swapi_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf'
-        efficiency_table_path = f'imap_swapi_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf'
+        science_file_path = f"imap_swapi_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{start_date}_{version}.cdf"
+        geometric_calibration_path = f"imap_swapi_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
+        efficiency_table_path = f"imap_swapi_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
 
         science_input = ScienceInput(science_file_path)
         geometric_calibration_input = AncillaryInput(geometric_calibration_path)
         efficiency_table_input = AncillaryInput(efficiency_table_path)
-        dependencies = ProcessingInputCollection(science_input, geometric_calibration_input, efficiency_table_input)
+        dependencies = ProcessingInputCollection(
+            science_input, geometric_calibration_input, efficiency_table_input
+        )
 
-        actual_swapi_l3b_dependencies = SwapiL3BDependencies.fetch_dependencies(dependencies)
+        actual_swapi_l3b_dependencies = SwapiL3BDependencies.fetch_dependencies(
+            dependencies
+        )
 
-        sci_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'swapi' / 'l2' / '2025' / '01'
-        ancillary_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'ancillary' / 'swapi'
+        sci_data_dir = (
+            imap_data_access.config["DATA_DIR"]
+            / "imap"
+            / "swapi"
+            / "l2"
+            / "2025"
+            / "01"
+        )
+        ancillary_data_dir = (
+            imap_data_access.config["DATA_DIR"] / "imap" / "ancillary" / "swapi"
+        )
 
         expected_download_science_path = sci_data_dir / science_file_path
-        expected_geometric_calibration_path = ancillary_data_dir / geometric_calibration_path
+        expected_geometric_calibration_path = (
+            ancillary_data_dir / geometric_calibration_path
+        )
         expected_efficiency_table_path = ancillary_data_dir / efficiency_table_path
 
-        mock_download.assert_has_calls([
-            call(expected_download_science_path),
-            call(expected_geometric_calibration_path),
-            call(expected_efficiency_table_path),
-        ])
+        mock_download.assert_has_calls(
+            [
+                call(expected_download_science_path),
+                call(expected_geometric_calibration_path),
+                call(expected_efficiency_table_path),
+            ]
+        )
 
         mock_from_file_paths.assert_called_with(
             expected_download_science_path,
@@ -58,42 +82,64 @@ def test_fetch_dependencies(self, mock_download, mock_from_file_paths):
             expected_efficiency_table_path,
         )
 
-        self.assertEqual(mock_from_file_paths.return_value, actual_swapi_l3b_dependencies)
-
-    @patch('imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.CDF')
-    @patch('imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.EfficiencyCalibrationTable')
-    @patch('imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.GeometricFactorCalibrationTable.from_file')
-    @patch('imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.read_l2_swapi_data')
-    def test_from_file_paths(self, mock_read_l2_swapi, mock_read_geometric_factor, mock_efficiency_calibration,
-                             mock_cdf):
-        start_date = '20100105'
-        mission = 'imap'
-        instrument = 'swapi'
-        data_level = 'l2'
-        version = 'v010'
+        self.assertEqual(
+            mock_from_file_paths.return_value, actual_swapi_l3b_dependencies
+        )
 
-        swapi_science_file_download_path = Path(f"{mission}_{instrument}_{data_level}_{SWAPI_L2_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_geometric_factor_calibration_file_path = Path(f"{mission}_{instrument}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_efficiency_calibration_file_path = Path(f"{mission}_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
+    @patch("imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.CDF")
+    @patch(
+        "imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.EfficiencyCalibrationTable"
+    )
+    @patch(
+        "imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.GeometricFactorCalibrationTable.from_file"
+    )
+    @patch("imap_l3_processing.swapi.l3b.swapi_l3b_dependencies.read_l2_swapi_data")
+    def test_from_file_paths(
+        self,
+        mock_read_l2_swapi,
+        mock_read_geometric_factor,
+        mock_efficiency_calibration,
+        mock_cdf,
+    ):
+        start_date = "20100105"
+        mission = "imap"
+        instrument = "swapi"
+        data_level = "l2"
+        version = "v010"
+
+        swapi_science_file_download_path = Path(
+            f"{mission}_{instrument}_{data_level}_{SWAPI_L2_DESCRIPTOR}_{start_date}_{version}.cdf"
+        )
+        swapi_geometric_factor_calibration_file_path = Path(
+            f"{mission}_{instrument}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
+        )
+        swapi_efficiency_calibration_file_path = Path(
+            f"{mission}_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
+        )
 
         mock_read_l2_swapi.return_value = sentinel.swapi_l2_data
         mock_read_geometric_factor.return_value = sentinel.geometric_factor_data
         mock_efficiency_calibration.return_value = sentinel.efficiency_calibration_data
 
-        expected_dependencies = SwapiL3BDependencies(sentinel.swapi_l2_data,
-                                                     sentinel.geometric_factor_data,
-                                                     sentinel.efficiency_calibration_data,
-                                                     )
+        expected_dependencies = SwapiL3BDependencies(
+            sentinel.swapi_l2_data,
+            sentinel.geometric_factor_data,
+            sentinel.efficiency_calibration_data,
+        )
 
         actual_dependencies = SwapiL3BDependencies.from_file_paths(
             swapi_science_file_download_path,
             swapi_geometric_factor_calibration_file_path,
-            swapi_efficiency_calibration_file_path
+            swapi_efficiency_calibration_file_path,
         )
 
         mock_read_l2_swapi.assert_called_once_with(mock_cdf.return_value)
-        mock_read_geometric_factor.assert_called_once_with(swapi_geometric_factor_calibration_file_path)
-        mock_efficiency_calibration.assert_called_once_with(swapi_efficiency_calibration_file_path)
+        mock_read_geometric_factor.assert_called_once_with(
+            swapi_geometric_factor_calibration_file_path
+        )
+        mock_efficiency_calibration.assert_called_once_with(
+            swapi_efficiency_calibration_file_path
+        )
 
         mock_cdf.assert_called_once_with(str(swapi_science_file_download_path))
 
diff --git a/tests/swapi/response/test_azimuthal_transmission.py b/tests/swapi/response/test_azimuthal_transmission.py
index 456d37b45..36cdc973d 100644
--- a/tests/swapi/response/test_azimuthal_transmission.py
+++ b/tests/swapi/response/test_azimuthal_transmission.py
@@ -6,17 +6,17 @@
 import pandas as pd
 
 from imap_l3_processing.swapi.response.azimuthal_transmission import (
-    AzimuthalTransmissionGrid,
     OA_PLATEAU_AZIMUTH_MAX_DEG,
     OA_PLATEAU_AZIMUTH_MIN_DEG,
     OA_PLATEAU_TRANSMISSION,
     SG_PLATEAU_AZIMUTH_MAX_DEG,
     SG_PLATEAU_TRANSMISSION,
+    AzimuthalTransmissionGrid,
     interpolate_azimuthal_transmission,
     validate_azimuthal_transmission_values,
 )
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
-from tests.test_helpers import get_test_instrument_team_data_path, get_test_data_path
+from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
 _TRANSMISSION_CSV = get_test_instrument_team_data_path(
     "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
@@ -79,7 +79,9 @@ def test_linear_interpolation_off_center_between_grid_points(self):
         for fraction in [0.25, 0.7, 0.9]:
             with self.subTest(fraction=fraction):
                 az = az_lower + fraction * _SPACING_DEG
-                expected = (1 - fraction) * self.values[idx] + fraction * self.values[idx + 1]
+                expected = (1 - fraction) * self.values[idx] + fraction * self.values[
+                    idx + 1
+                ]
                 got = interpolate_azimuthal_transmission(self.grid, az)
                 self.assertAlmostEqual(got, float(expected))
 
@@ -170,7 +172,9 @@ def test_csv_layout_matches_production_spacing(self):
         self.assertAlmostEqual(float(grid.values[0]), 1e-3)
 
 
-def _build_grid_satisfying_invariants(spacing: float = 0.1, n: int = 1801) -> np.ndarray:
+def _build_grid_satisfying_invariants(
+    spacing: float = 0.1, n: int = 1801
+) -> np.ndarray:
     """Build a synthetic values array that respects the SG-floor and OA-plateau
     invariants the interpolator's short-circuit branches assume. Cells outside
     those two ranges are set to a neutral 0.5 so a test can corrupt a single
@@ -178,7 +182,9 @@ def _build_grid_satisfying_invariants(spacing: float = 0.1, n: int = 1801) -> np
     azimuths = np.arange(n) * spacing
     values = np.full(n, 0.5)
     values[azimuths <= SG_PLATEAU_AZIMUTH_MAX_DEG] = SG_PLATEAU_TRANSMISSION
-    in_plateau = (azimuths >= OA_PLATEAU_AZIMUTH_MIN_DEG) & (azimuths <= OA_PLATEAU_AZIMUTH_MAX_DEG)
+    in_plateau = (azimuths >= OA_PLATEAU_AZIMUTH_MIN_DEG) & (
+        azimuths <= OA_PLATEAU_AZIMUTH_MAX_DEG
+    )
     values[in_plateau] = OA_PLATEAU_TRANSMISSION
     return values
 
@@ -239,9 +245,11 @@ class TestSwapiResponseRejectsInvalidTransmissionCsv(unittest.TestCase):
     silently driving the short-circuit branches in the interpolator to lie."""
 
     def test_from_files_raises_when_csv_violates_invariants(self):
-        df = pd.read_csv(get_test_instrument_team_data_path(
-            "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
-        ))
+        df = pd.read_csv(
+            get_test_instrument_team_data_path(
+                "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+            )
+        )
         # Corrupt a single cell inside the OA plateau range.
         df.loc[500, "transmission"] = 0.5
 
diff --git a/tests/swapi/response/test_efficiency_calibration_table.py b/tests/swapi/response/test_efficiency_calibration_table.py
index 283ce7389..9ce85a64c 100644
--- a/tests/swapi/response/test_efficiency_calibration_table.py
+++ b/tests/swapi/response/test_efficiency_calibration_table.py
@@ -1,10 +1,11 @@
 import unittest
 from datetime import datetime
 
-from scipy.constants import milli
 from spacepy import pycdf
 
-from imap_l3_processing.swapi.response.efficiency_calibration_table import EfficiencyCalibrationTable
+from imap_l3_processing.swapi.response.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
 from imap_l3_processing.swapi.species import Species
 from tests.test_helpers import get_test_data_path
 
@@ -12,72 +13,139 @@
 class TestEfficiencyCalibrationTable(unittest.TestCase):
     def test_relative_efficiency_for_protons(self):
         """Each queried time returns the proton efficiency from the most recent row that precedes it, unscaled."""
-        calibration_table_path = get_test_data_path("swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat")
+        calibration_table_path = get_test_data_path(
+            "swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat"
+        )
         efficiency_table = EfficiencyCalibrationTable(calibration_table_path)
 
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1)), Species.PROTON), 0.1)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1)),
+                Species.PROTON,
+            ),
+            0.1,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2013, month=10, day=1)), Species.PROTON),
-            0.1)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2013, month=10, day=1)),
+                Species.PROTON,
+            ),
+            0.1,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2014, month=10, day=3)), Species.PROTON),
-            0.09)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2014, month=10, day=3)),
+                Species.PROTON,
+            ),
+            0.09,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=1)), Species.PROTON),
-            0.09)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=1)),
+                Species.PROTON,
+            ),
+            0.09,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=3)), Species.PROTON),
-            0.0882)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=3)),
+                Species.PROTON,
+            ),
+            0.0882,
+        )
 
     def test_relative_efficiency_for_helium(self):
         """A helium species reads the helium column by the same most-recent-preceding-row rule as protons."""
-        calibration_table_path = get_test_data_path("swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat")
+        calibration_table_path = get_test_data_path(
+            "swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat"
+        )
         efficiency_table = EfficiencyCalibrationTable(calibration_table_path)
 
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1)), Species.ALPHA), 0.9)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1)),
+                Species.ALPHA,
+            ),
+            0.9,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2013, month=10, day=1)), Species.ALPHA),
-            0.9)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2013, month=10, day=1)),
+                Species.ALPHA,
+            ),
+            0.9,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2014, month=10, day=3)), Species.ALPHA),
-            0.95)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2014, month=10, day=3)),
+                Species.ALPHA,
+            ),
+            0.95,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=1)), Species.ALPHA),
-            0.95)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=1)),
+                Species.ALPHA,
+            ),
+            0.95,
+        )
         self.assertEqual(
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=3)), Species.ALPHA),
-            0.99)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(datetime(year=2024, month=10, day=3)),
+                Species.ALPHA,
+            ),
+            0.99,
+        )
 
     def test_relative_efficiency_handles_float_input(self):
         """A TT2000 passed as a float rather than an int resolves to the same row."""
-        calibration_table_path = get_test_data_path("swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat")
+        calibration_table_path = get_test_data_path(
+            "swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat"
+        )
         efficiency_table = EfficiencyCalibrationTable(calibration_table_path)
 
-        time_as_float = float(pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1)))
-        self.assertEqual(efficiency_table.relative_efficiency(time_as_float, Species.ALPHA), 0.9)
-        self.assertEqual(efficiency_table.relative_efficiency(time_as_float, Species.PROTON), 0.1)
+        time_as_float = float(
+            pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1))
+        )
+        self.assertEqual(
+            efficiency_table.relative_efficiency(time_as_float, Species.ALPHA), 0.9
+        )
+        self.assertEqual(
+            efficiency_table.relative_efficiency(time_as_float, Species.PROTON), 0.1
+        )
 
     def test_raises_exception_if_ask_for_time_before_the_table_starts(self):
         """A time earlier than every row raises rather than falling back to the first row."""
-        calibration_table_path = get_test_data_path("swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat")
+        calibration_table_path = get_test_data_path(
+            "swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat"
+        )
         efficiency_table = EfficiencyCalibrationTable(calibration_table_path)
 
         with self.assertRaises(ValueError) as content_manager:
             time = datetime(year=1999, month=1, day=4)
-            efficiency_table.relative_efficiency(pycdf.lib.datetime_to_tt2000(time), Species.PROTON)
+            efficiency_table.relative_efficiency(
+                pycdf.lib.datetime_to_tt2000(time), Species.PROTON
+            )
 
-        self.assertEqual((f"No efficiency data for {time}",), content_manager.exception.args)
+        self.assertEqual(
+            (f"No efficiency data for {time}",), content_manager.exception.args
+        )
 
     def test_loads_calibration_table_with_single_row(self):
         """A one-row table still parses and serves both species for any later time."""
         calibration_table_path = get_test_data_path(
-            "swapi/imap_swapi_efficiency-lut-single-row-test_20241020_v001.dat")
+            "swapi/imap_swapi_efficiency-lut-single-row-test_20241020_v001.dat"
+        )
         efficiency_table = EfficiencyCalibrationTable(calibration_table_path)
 
         # file has one row, with 0.1 for H, 0.9 for He.
 
-        time_after_row = pycdf.lib.datetime_to_tt2000(datetime(year=2001, month=2, day=1))
-        self.assertEqual(efficiency_table.relative_efficiency(time_after_row, Species.PROTON), 0.1)
-        self.assertEqual(efficiency_table.relative_efficiency(time_after_row, Species.ALPHA), 0.9)
+        time_after_row = pycdf.lib.datetime_to_tt2000(
+            datetime(year=2001, month=2, day=1)
+        )
+        self.assertEqual(
+            efficiency_table.relative_efficiency(time_after_row, Species.PROTON), 0.1
+        )
+        self.assertEqual(
+            efficiency_table.relative_efficiency(time_after_row, Species.ALPHA), 0.9
+        )
diff --git a/tests/swapi/response/test_passband_grid.py b/tests/swapi/response/test_passband_grid.py
index 32a773757..b1b42a663 100644
--- a/tests/swapi/response/test_passband_grid.py
+++ b/tests/swapi/response/test_passband_grid.py
@@ -3,6 +3,7 @@
 import numpy as np
 import pandas as pd
 
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.response.passband_grid import (
     _PASSBAND_BOUNDARY_THRESHOLD,
     _TARGET_ELEVATIONS,
@@ -11,7 +12,6 @@
     interpolate_passband,
     speed_ratio_range_at_elevation,
 )
-from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 
 
 def _gaussian_values_df(
@@ -43,8 +43,7 @@ def _gaussian_values_df(
                 # round so that there are true zeros like the real SIMION-derived passband
                 np.exp(
                     -((elev - peak_elevation) ** 2) / (2.0 * sigma_el**2)
-                    - ((SWAPI_K_FACTOR * sr**2 - peak_er) ** 2)
-                    / (2.0 * sigma_er**2)
+                    - ((SWAPI_K_FACTOR * sr**2 - peak_er) ** 2) / (2.0 * sigma_er**2)
                 )
             ),
         }
@@ -96,11 +95,15 @@ def test_boundary_marks_cutoff_crossing(self):
                 continue
             with self.subTest(row=i):
                 np.testing.assert_allclose(
-                    np.interp(grid.min_boundary[i], _TARGET_SPEED_RATIOS, grid.values[i]),
+                    np.interp(
+                        grid.min_boundary[i], _TARGET_SPEED_RATIOS, grid.values[i]
+                    ),
                     cutoff,
                 )
                 np.testing.assert_allclose(
-                    np.interp(grid.max_boundary[i], _TARGET_SPEED_RATIOS, grid.values[i]),
+                    np.interp(
+                        grid.max_boundary[i], _TARGET_SPEED_RATIOS, grid.values[i]
+                    ),
                     cutoff,
                 )
 
@@ -159,9 +162,7 @@ def test_speed_ratio_indexing_via_k_factor(self):
         must produce a grid peaking at speed_ratio = 1.0."""
         grid = self.default_grid
 
-        row_at_peak_elevation = grid.values[
-            _row_index_for_elevation(grid, 0.0)
-        ]
+        row_at_peak_elevation = grid.values[_row_index_for_elevation(grid, 0.0)]
         peak_speed_ratio = _TARGET_SPEED_RATIOS[int(np.argmax(row_at_peak_elevation))]
 
         self.assertAlmostEqual(peak_speed_ratio, 1.0, places=2)
@@ -176,7 +177,7 @@ def setUp(self):
 
     def test_matches_corner_weighted_sum(self):
         """`interpolate_passband` returns the bilinear-weighted sum of the four
-            enclosing corner values."""
+        enclosing corner values."""
         cases = [
             # (elevation, speed_ratio, label)
             (0.0, 1.0, "on-node"),
@@ -194,9 +195,7 @@ def test_matches_corner_weighted_sum(self):
                 i_lo, j_lo = int(i_float), int(j_float)
                 i_weight, j_weight = i_float - i_lo, j_float - j_lo
 
-                corners = self.grid.values[
-                    i_lo : i_lo + 2, j_lo : j_lo + 2
-                ]
+                corners = self.grid.values[i_lo : i_lo + 2, j_lo : j_lo + 2]
                 weights = np.array(
                     [
                         [(1 - i_weight) * (1 - j_weight), (1 - i_weight) * j_weight],
diff --git a/tests/swapi/response/test_swapi_response.py b/tests/swapi/response/test_swapi_response.py
index 677092fca..4f2e04415 100644
--- a/tests/swapi/response/test_swapi_response.py
+++ b/tests/swapi/response/test_swapi_response.py
@@ -9,7 +9,7 @@
 from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
 from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
 from imap_l3_processing.swapi.species import Species
-from tests.test_helpers import get_test_instrument_team_data_path, get_test_data_path
+from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
 
 def _load_response() -> SwapiResponse:
@@ -25,7 +25,7 @@ def _load_response() -> SwapiResponse:
         ),
         efficiency_table_path=get_test_data_path(
             "swapi/imap_swapi_efficiency-lut-test_20241020_v001.dat"
-        )
+        ),
     )
 
 
@@ -49,7 +49,9 @@ def test_populates_cache_with_unique_finite_voltages(self):
         """Duplicates collapse, NaN/inf are skipped."""
         voltages = np.array([100.0, 100.0, 200.0, 300.0, np.nan, np.inf])
         self.response.warm_cache(voltages)
-        self.assertEqual(set(self.response._passband_grid_cache.keys()), {100.0, 200.0, 300.0})
+        self.assertEqual(
+            set(self.response._passband_grid_cache.keys()), {100.0, 200.0, 300.0}
+        )
         self.assertEqual(len(self.response._passband_grid_cache), 3)
 
     def test_warming_twice_at_same_voltage_is_a_noop(self):
@@ -57,19 +59,25 @@ def test_warming_twice_at_same_voltage_is_a_noop(self):
         PassbandGrid objects — it does not rebuild them."""
         self.response.warm_cache([750.0])
         first_grids = {
-            region: self.response._passband_grid_cache[750.0][region] for region in ("SG", "OA")
+            region: self.response._passband_grid_cache[750.0][region]
+            for region in ("SG", "OA")
         }
         self.response.warm_cache([750.0])
 
         for region in ("SG", "OA"):
             with self.subTest(region=region):
-                self.assertIs(self.response._passband_grid_cache[750.0][region], first_grids[region])
+                self.assertIs(
+                    self.response._passband_grid_cache[750.0][region],
+                    first_grids[region],
+                )
 
     def test_accepts_2d_voltage_array(self):
         """Multidimensional voltage inputs are flattened."""
         voltages = np.array([[100.0, 200.0], [200.0, 300.0]])
         self.response.warm_cache(voltages)
-        self.assertEqual(set(self.response._passband_grid_cache.keys()), {100.0, 200.0, 300.0})
+        self.assertEqual(
+            set(self.response._passband_grid_cache.keys()), {100.0, 200.0, 300.0}
+        )
 
 
 class TestPassbandInterpolation(_RealResponseFixture):
@@ -99,18 +107,18 @@ def test_values_match_manual_polyval(self):
                 log_beam_energy = np.log(SWAPI_K_FACTOR * voltage)
                 n_degrees = coeffs.shape[1]
                 degrees = np.arange(n_degrees - 1, -1, -1)
-                expected_exponent = (coeffs.values * log_beam_energy ** degrees).sum(
+                expected_exponent = (coeffs.values * log_beam_energy**degrees).sum(
                     axis=1
                 )
                 expected = np.exp(expected_exponent)
 
-                npt.assert_allclose(
-                    returned["value"].to_numpy(), expected, rtol=1e-12
-                )
+                npt.assert_allclose(returned["value"].to_numpy(), expected, rtol=1e-12)
 
     def _assert_grid_value_bounds(self, esa_voltage):
         self.response.warm_cache([esa_voltage])
-        cached = self.response._passband_grid_cache[self.response._cache_key(esa_voltage)]
+        cached = self.response._passband_grid_cache[
+            self.response._cache_key(esa_voltage)
+        ]
         for region in ("SG", "OA"):
             grid = cached[region]
             self.assertGreaterEqual(
@@ -141,32 +149,65 @@ def test_get_response_grid(self):
         response = _load_response()
         response.warm_cache(esa_voltage)
 
-        response_grid = response.get_response_grid(time_as_tt2000, esa_voltage, Species.PROTON)
-        self.assertAlmostEqual(response_grid.central_effective_area, 0.37553722631276887)
-        self.assertIs(response_grid.azimuthal_transmission, response._azimuthal_transmission_grid)
-        self.assertIs(response_grid.oa_passband, response._passband_grid_cache[round(esa_voltage, 3)]["OA"])
-        self.assertIs(response_grid.sg_passband, response._passband_grid_cache[round(esa_voltage, 3)]["SG"])
-        expected_central_speed_km_per_s = np.sqrt(
-            2 * 1.89 * 552.1339894 * constants.PROTON_CHARGE_COULOMBS / constants.PROTON_MASS_KG
-        ) / constants.METERS_PER_KILOMETER
-        self.assertAlmostEqual(response_grid.central_speed, expected_central_speed_km_per_s)
-
-        response_grid2 = response.get_response_grid(time_as_tt2000, esa_voltage, Species.PROTON)
+        response_grid = response.get_response_grid(
+            time_as_tt2000, esa_voltage, Species.PROTON
+        )
+        self.assertAlmostEqual(
+            response_grid.central_effective_area, 0.37553722631276887
+        )
+        self.assertIs(
+            response_grid.azimuthal_transmission, response._azimuthal_transmission_grid
+        )
+        self.assertIs(
+            response_grid.oa_passband,
+            response._passband_grid_cache[round(esa_voltage, 3)]["OA"],
+        )
+        self.assertIs(
+            response_grid.sg_passband,
+            response._passband_grid_cache[round(esa_voltage, 3)]["SG"],
+        )
+        expected_central_speed_km_per_s = (
+            np.sqrt(
+                2
+                * 1.89
+                * 552.1339894
+                * constants.PROTON_CHARGE_COULOMBS
+                / constants.PROTON_MASS_KG
+            )
+            / constants.METERS_PER_KILOMETER
+        )
+        self.assertAlmostEqual(
+            response_grid.central_speed, expected_central_speed_km_per_s
+        )
+
+        response_grid2 = response.get_response_grid(
+            time_as_tt2000, esa_voltage, Species.PROTON
+        )
         self.assertIs(response_grid, response_grid2, "cache miss")
 
         for species, mass_per_charge in [
             (Species.ALPHA, constants.ALPHA_MASS_PER_CHARGE_M_P_PER_E),
             (Species.HELIUM_PLUS, constants.HE_PUI_PARTICLE_MASS_PER_CHARGE_M_P_PER_E),
         ]:
-            response_grid_species = response.get_response_grid(time_as_tt2000, esa_voltage, species)
-            self.assertAlmostEqual(response_grid_species.central_effective_area / response_grid.central_effective_area, 1.05)
+            response_grid_species = response.get_response_grid(
+                time_as_tt2000, esa_voltage, species
+            )
+            self.assertAlmostEqual(
+                response_grid_species.central_effective_area
+                / response_grid.central_effective_area,
+                1.05,
+            )
             self.assertAlmostEqual(
                 response_grid_species.central_speed / response_grid.central_speed,
                 np.sqrt(constants.PROTON_MASS_PER_CHARGE_M_P_PER_E / mass_per_charge),
-                msg="central speed should be lower by the sqrt mass per charge ratio"
+                msg="central speed should be lower by the sqrt mass per charge ratio",
             )
             for attr in ["sg_passband", "oa_passband", "azimuthal_transmission"]:
-                self.assertIs(getattr(response_grid, attr), getattr(response_grid_species, attr), msg="shared attribute mismatch")
+                self.assertIs(
+                    getattr(response_grid, attr),
+                    getattr(response_grid_species, attr),
+                    msg="shared attribute mismatch",
+                )
 
 
 if __name__ == "__main__":
diff --git a/tests/swapi/test_swapi_processor.py b/tests/swapi/test_swapi_processor.py
index 9eaeeda81..3fd8c85cd 100644
--- a/tests/swapi/test_swapi_processor.py
+++ b/tests/swapi/test_swapi_processor.py
@@ -1,25 +1,38 @@
 from dataclasses import replace
-from datetime import datetime, timedelta, date
+from datetime import date, datetime, timedelta
 from unittest import TestCase
-from unittest.mock import patch, sentinel, call, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
 from imap_data_access import config
 from imap_data_access.file_validation import Version
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
-from uncertainties.unumpy import uarray, nominal_values, std_devs
-
-from imap_l3_processing.constants import THIRTY_SECONDS_IN_NANOSECONDS, \
-    FIVE_MINUTES_IN_NANOSECONDS
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
+from uncertainties.unumpy import nominal_values, std_devs, uarray
+
+from imap_l3_processing.constants import (
+    FIVE_MINUTES_IN_NANOSECONDS,
+    THIRTY_SECONDS_IN_NANOSECONDS,
+)
 from imap_l3_processing.models import InputMetadata, VersionMap
-from imap_l3_processing.swapi.descriptors import DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, \
-    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
-    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR, \
-    HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, HELIUM_INFLOW_VECTOR_DESCRIPTOR, \
-    AZIMUTHAL_TRANSMISSION_DESCRIPTOR, CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, \
-    PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR
+from imap_l3_processing.swapi.descriptors import (
+    AZIMUTHAL_TRANSMISSION_DESCRIPTOR,
+    CENTRAL_EFFECTIVE_AREA_DESCRIPTOR,
+    DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR,
+    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR,
+    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR,
+    HELIUM_INFLOW_VECTOR_DESCRIPTOR,
+    HYDROGEN_INFLOW_VECTOR_DESCRIPTOR,
+    PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR,
+)
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
-from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SWAPI_L2_DESCRIPTOR, SwapiL3ADependencies
+from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import (
+    SWAPI_L2_DESCRIPTOR,
+    SwapiL3ADependencies,
+)
 from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import DeltaMinusPlus
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.swapi.species import Species
@@ -28,24 +41,29 @@
 
 
 class TestSwapiProcessor(TestCase):
-    @patch('imap_l3_processing.utils.ImapAttributeManager')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3PickupIonData')
-    @patch('imap_l3_processing.utils.write_cdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.PuiChunkFitter')
-    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3a_pui(self, mock_spicepy,
-                             mock_swapi_l3_dependencies_class,
-                             mock_parallel_chunk_runner_class,
-                             mock_pui_chunk_fitter_class,
-                             mock_chunk_l2_data, mock_write_cdf,
-                             mock_pickup_ion_data_constructor, mock_imap_attribute_manager):
-        instrument = 'swapi'
-        incoming_data_level = 'l2'
+    @patch("imap_l3_processing.utils.ImapAttributeManager")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3PickupIonData")
+    @patch("imap_l3_processing.utils.write_cdf")
+    @patch("imap_l3_processing.swapi.swapi_processor.chunk_l2_data")
+    @patch("imap_l3_processing.swapi.swapi_processor.PuiChunkFitter")
+    @patch("imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_l3a_pui(
+        self,
+        mock_spicepy,
+        mock_swapi_l3_dependencies_class,
+        mock_parallel_chunk_runner_class,
+        mock_pui_chunk_fitter_class,
+        mock_chunk_l2_data,
+        mock_write_cdf,
+        mock_pickup_ion_data_constructor,
+        mock_imap_attribute_manager,
+    ):
+        instrument = "swapi"
+        incoming_data_level = "l2"
         dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
-        version = 'v001'
+        version = "v001"
         end_date = datetime(2025, 9, 26)
         outgoing_data_level = "l3a"
         start_date = datetime(2025, 9, 25)
@@ -71,20 +89,35 @@ def test_process_l3a_pui(self, mock_spicepy,
         epoch_for_fifty_sweeps = np.arange(initial_epoch, initial_epoch + 50)
         energy_1d = np.array([15000, 16000, 17000, 18000, 19000])
         coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
+            [
+                [4, 5, 6, 7, 8],
+                [9, 10, 11, 12, 13],
+                [14, 15, 16, 17, 18],
+                [19, 20, 21, 22, 23],
+            ]
+        )
         coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
+            [
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+            ]
+        )
 
-        chunk_of_five = SwapiL2Data(epoch, energy_1d, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
+        chunk_of_five = SwapiL2Data(
+            epoch, energy_1d, coincidence_count_rate, coincidence_count_rate_uncertainty
+        )
 
         energy_fifty = np.tile(energy_1d * 2, (50, 1))
         coincidence_count_rate_fifty = np.full((50, 5), 7.0)
         coincidence_count_rate_uncertainty_fifty = np.full((50, 5), 0.2)
-        chunk_of_fifty = SwapiL2Data(epoch_for_fifty_sweeps, energy_fifty,
-                                     coincidence_count_rate_fifty,
-                                     coincidence_count_rate_uncertainty_fifty)
+        chunk_of_fifty = SwapiL2Data(
+            epoch_for_fifty_sweeps,
+            energy_fifty,
+            coincidence_count_rate_fifty,
+            coincidence_count_rate_uncertainty_fifty,
+        )
 
         pui_runner_result = dict(
             epoch=np.array([initial_epoch + FIVE_MINUTES_IN_NANOSECONDS]),
@@ -98,18 +131,23 @@ def test_process_l3a_pui(self, mock_spicepy,
         mock_runner.run.side_effect = [proton_runner_result, pui_runner_result]
 
         science_input = ScienceInput(
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf"
+        )
 
         input_file_names = [
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
         ]
 
-        ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
+        ancillary_inputs = [
+            AncillaryInput(file_name) for file_name in input_file_names[1:]
+        ]
 
         dependencies = ProcessingInputCollection(science_input, *ancillary_inputs)
 
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
+        input_metadata = InputMetadata(
+            instrument, outgoing_data_level, start_date, end_date, input_version
+        )
 
         pickup_ion_data = mock_pickup_ion_data_constructor.return_value
         expected_pickup_ion_metadata = replace(input_metadata, descriptor=descriptor)
@@ -117,40 +155,57 @@ def test_process_l3a_pui(self, mock_spicepy,
 
         input_metadata.descriptor = descriptor
 
-        expected_cdf_path = (config["DATA_DIR"] / "imap" / "swapi" / "l3a" / "2025" / "09" /
-                             f"imap_swapi_l3a_pui-he_{start_date_as_str}_v123.cdf")
+        expected_cdf_path = (
+            config["DATA_DIR"]
+            / "imap"
+            / "swapi"
+            / "l3a"
+            / "2025"
+            / "09"
+            / f"imap_swapi_l3a_pui-he_{start_date_as_str}_v123.cdf"
+        )
 
         mock_chunk_l2_data.side_effect = [
             [chunk_of_five],
             [chunk_of_fifty],
         ]
 
-        mock_l3a_dependencies = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value
+        mock_l3a_dependencies = (
+            mock_swapi_l3_dependencies_class.fetch_dependencies.return_value
+        )
         mock_l3a_dependencies.data = Mock(energy=energy_1d)
 
         mock_manager = mock_imap_attribute_manager.return_value
 
-        swapi_processor = SwapiProcessor(
-            dependencies, input_metadata)
+        swapi_processor = SwapiProcessor(dependencies, input_metadata)
         product = swapi_processor.process()
 
         actual_science_input = swapi_processor.dependencies.get_science_inputs()[0]
-        self.assertEqual(actual_science_input.get_time_range()[0].strftime("%Y%m%d"), dependency_start_date)
+        self.assertEqual(
+            actual_science_input.get_time_range()[0].strftime("%Y%m%d"),
+            dependency_start_date,
+        )
 
-        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
+        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(
+            dependencies
+        )
 
         mock_swapi_response = mock_l3a_dependencies.swapi_response
         mock_efficiency_lut = mock_l3a_dependencies.efficiency_calibration_table
-        mock_density_of_neutral_helium_calibration_table = mock_l3a_dependencies.density_of_neutral_helium_calibration_table
+        mock_density_of_neutral_helium_calibration_table = (
+            mock_l3a_dependencies.density_of_neutral_helium_calibration_table
+        )
         mock_hydrogen_inflow_vector = mock_l3a_dependencies.hydrogen_inflow_vector
         mock_helium_inflow_vector = mock_l3a_dependencies.helium_inflow_vector
 
-        mock_chunk_l2_data.assert_has_calls([call(mock_l3a_dependencies.data, 5),
-                                             call(mock_l3a_dependencies.data, 50)])
+        mock_chunk_l2_data.assert_has_calls(
+            [call(mock_l3a_dependencies.data, 5), call(mock_l3a_dependencies.data, 50)]
+        )
 
         mock_swapi_response.warm_cache.assert_called_once()
         mock_parallel_chunk_runner_class.assert_called_once_with(
-            mock_swapi_response, mock_efficiency_lut,
+            mock_swapi_response,
+            mock_efficiency_lut,
         )
         self.assertEqual(mock_runner.run.call_count, 2)
 
@@ -167,18 +222,24 @@ def test_process_l3a_pui(self, mock_spicepy,
             pui_fitter_kwargs["density_of_neutral_helium_lookup_table"],
             mock_density_of_neutral_helium_calibration_table,
         )
-        self.assertIs(pui_fitter_kwargs["hydrogen_inflow_vector"], mock_hydrogen_inflow_vector)
-        self.assertIs(pui_fitter_kwargs["helium_inflow_vector"], mock_helium_inflow_vector)
+        self.assertIs(
+            pui_fitter_kwargs["hydrogen_inflow_vector"], mock_hydrogen_inflow_vector
+        )
+        self.assertIs(
+            pui_fitter_kwargs["helium_inflow_vector"], mock_helium_inflow_vector
+        )
         self.assertIs(pui_fitter_kwargs["proton_sw_results"], proton_runner_result)
 
-        mock_manager.add_global_attribute.assert_has_calls([call("Data_version", outgoing_version),
-                                                            call("Generation_date",
-                                                                 date.today().strftime("%Y%m%d")),
-                                                            call("Logical_source",
-                                                                 f"imap_swapi_l3a_pui-he"),
-                                                            call("Logical_file_id",
-                                                                 f"imap_swapi_l3a_pui-he_{start_date_as_str}_v123"),
-                                                            ])
+        mock_manager.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", outgoing_version),
+                call("Generation_date", date.today().strftime("%Y%m%d")),
+                call("Logical_source", "imap_swapi_l3a_pui-he"),
+                call(
+                    "Logical_file_id", f"imap_swapi_l3a_pui-he_{start_date_as_str}_v123"
+                ),
+            ]
+        )
 
         actual_positional = mock_pickup_ion_data_constructor.call_args.args
         actual_kwargs = mock_pickup_ion_data_constructor.call_args.kwargs
@@ -186,30 +247,37 @@ def test_process_l3a_pui(self, mock_spicepy,
         for key, expected_val in pui_runner_result.items():
             np.testing.assert_array_equal(expected_val, actual_kwargs[key])
 
-        mock_manager.add_instrument_attrs.assert_called_once_with("swapi", "l3a", descriptor)
+        mock_manager.add_instrument_attrs.assert_called_once_with(
+            "swapi", "l3a", descriptor
+        )
 
         self.assertEqual(input_file_names, pickup_ion_data.parent_file_names)
-        mock_write_cdf.assert_called_once_with(str(expected_cdf_path), pickup_ion_data, mock_manager)
+        mock_write_cdf.assert_called_once_with(
+            str(expected_cdf_path), pickup_ion_data, mock_manager
+        )
         self.assertEqual([expected_cdf_path], product)
 
-    @patch('imap_l3_processing.utils.ImapAttributeManager')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ProtonSolarWindData')
-    @patch('imap_l3_processing.utils.write_cdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
-    @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3a_proton(self, mock_spicepy,
-                                mock_swapi_l3_dependencies_class,
-                                mock_chunk_l2_data,
-                                mock_parallel_chunk_runner_class,
-                                mock_write_cdf,
-                                mock_proton_solar_wind_data_constructor,
-                                mock_imap_attribute_manager):
-        instrument = 'swapi'
-        incoming_data_level = 'l2'
+    @patch("imap_l3_processing.utils.ImapAttributeManager")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3ProtonSolarWindData")
+    @patch("imap_l3_processing.utils.write_cdf")
+    @patch("imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner")
+    @patch("imap_l3_processing.swapi.swapi_processor.chunk_l2_data")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_l3a_proton(
+        self,
+        mock_spicepy,
+        mock_swapi_l3_dependencies_class,
+        mock_chunk_l2_data,
+        mock_parallel_chunk_runner_class,
+        mock_write_cdf,
+        mock_proton_solar_wind_data_constructor,
+        mock_imap_attribute_manager,
+    ):
+        instrument = "swapi"
+        incoming_data_level = "l2"
         dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
-        version = 'v001'
+        version = "v001"
         end_date = datetime(2025, 6, 13)
         outgoing_data_level = "l3a"
         start_date = datetime(2025, 6, 12)
@@ -225,16 +293,29 @@ def test_process_l3a_proton(self, mock_spicepy,
         epoch = np.array([initial_epoch, 11, 12, 13])
         energy = np.array([15000, 16000, 17000, 18000, 19000])
         coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
+            [
+                [4, 5, 6, 7, 8],
+                [9, 10, 11, 12, 13],
+                [14, 15, 16, 17, 18],
+                [19, 20, 21, 22, 23],
+            ]
+        )
         coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
+            [
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+                [0.1, 0.2, 0.3, 0.4, 0.5],
+            ]
+        )
 
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
+        chunk_of_five = SwapiL2Data(
+            epoch, energy, coincidence_count_rate, coincidence_count_rate_uncertainty
+        )
 
         science_input = ScienceInput(
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf"
+        )
 
         runner_result = dict(
             epoch=np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
@@ -259,21 +340,25 @@ def test_process_l3a_proton(self, mock_spicepy,
         mock_runner.run.return_value = runner_result
 
         input_file_names = [
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{HYDROGEN_INFLOW_VECTOR_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{HELIUM_INFLOW_VECTOR_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{HYDROGEN_INFLOW_VECTOR_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{HELIUM_INFLOW_VECTOR_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
         ]
 
-        ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
+        ancillary_inputs = [
+            AncillaryInput(file_name) for file_name in input_file_names[1:]
+        ]
 
         dependencies = ProcessingInputCollection(science_input, *ancillary_inputs)
 
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
+        input_metadata = InputMetadata(
+            instrument, outgoing_data_level, start_date, end_date, input_version
+        )
 
         proton_solar_wind_data = mock_proton_solar_wind_data_constructor.return_value
 
@@ -282,21 +367,32 @@ def test_process_l3a_proton(self, mock_spicepy,
 
         input_metadata.descriptor = descriptor
 
-        expected_cdf_path = (config["DATA_DIR"] / "imap" / "swapi" / "l3a" / "2025" / "06" /
-                             f"imap_swapi_l3a_proton-sw_{start_date_as_str}_v123.cdf")
+        expected_cdf_path = (
+            config["DATA_DIR"]
+            / "imap"
+            / "swapi"
+            / "l3a"
+            / "2025"
+            / "06"
+            / f"imap_swapi_l3a_proton-sw_{start_date_as_str}_v123.cdf"
+        )
 
         mock_chunk_l2_data.return_value = [chunk_of_five]
 
         swapi_l3a_dependencies = create_swapi_l3a_dependencies_with_mocks()
         swapi_l3a_dependencies.data = Mock(energy=energy)
-        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = swapi_l3a_dependencies
+        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = (
+            swapi_l3a_dependencies
+        )
 
         mock_manager = mock_imap_attribute_manager.return_value
 
         swapi_processor = SwapiProcessor(dependencies, input_metadata)
         product = swapi_processor.process()
 
-        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
+        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(
+            dependencies
+        )
 
         swapi_l3a_dependencies.swapi_response.warm_cache.assert_called_once()
 
@@ -314,44 +410,56 @@ def test_process_l3a_proton(self, mock_spicepy,
         for key, expected_val in runner_result.items():
             np.testing.assert_array_equal(expected_val, actual_kwargs[key])
 
-        mock_manager.add_global_attribute.assert_has_calls([call("Data_version", outgoing_version),
-                                                            call("Generation_date",
-                                                                 date.today().strftime("%Y%m%d")),
-                                                            call("Logical_source",
-                                                                 f"imap_swapi_l3a_proton-sw"),
-                                                            call("Logical_file_id",
-                                                                 f"imap_swapi_l3a_proton-sw_{start_date_as_str}_v123"),
-                                                            ])
+        mock_manager.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", outgoing_version),
+                call("Generation_date", date.today().strftime("%Y%m%d")),
+                call("Logical_source", "imap_swapi_l3a_proton-sw"),
+                call(
+                    "Logical_file_id",
+                    f"imap_swapi_l3a_proton-sw_{start_date_as_str}_v123",
+                ),
+            ]
+        )
 
-        mock_manager.add_instrument_attrs.assert_called_once_with("swapi", "l3a", descriptor)
+        mock_manager.add_instrument_attrs.assert_called_once_with(
+            "swapi", "l3a", descriptor
+        )
 
         self.assertEqual(input_file_names, proton_solar_wind_data.parent_file_names)
-        mock_write_cdf.assert_called_once_with(str(expected_cdf_path), proton_solar_wind_data, mock_manager)
+        mock_write_cdf.assert_called_once_with(
+            str(expected_cdf_path), proton_solar_wind_data, mock_manager
+        )
         self.assertEqual([expected_cdf_path], product)
 
-    @patch('imap_l3_processing.utils.ImapAttributeManager')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3AlphaSolarWindData')
-    @patch('imap_l3_processing.utils.write_cdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
-    @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3a_alpha(self, mock_spicepy,
-                               mock_swapi_l3_dependencies_class,
-                               mock_chunk_l2_data,
-                               mock_parallel_chunk_runner_class,
-                               mock_write_cdf,
-                               mock_alpha_solar_wind_data_constructor,
-                               mock_imap_attribute_manager):
-        instrument = 'swapi'
-        incoming_data_level = 'l2'
+    @patch("imap_l3_processing.utils.ImapAttributeManager")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3AlphaSolarWindData")
+    @patch("imap_l3_processing.utils.write_cdf")
+    @patch("imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner")
+    @patch("imap_l3_processing.swapi.swapi_processor.chunk_l2_data")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_l3a_alpha(
+        self,
+        mock_spicepy,
+        mock_swapi_l3_dependencies_class,
+        mock_chunk_l2_data,
+        mock_parallel_chunk_runner_class,
+        mock_write_cdf,
+        mock_alpha_solar_wind_data_constructor,
+        mock_imap_attribute_manager,
+    ):
+        instrument = "swapi"
+        incoming_data_level = "l2"
         dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
-        version = 'v001'
+        version = "v001"
         end_date = datetime(2025, 8, 29)
         outgoing_data_level = "l3a"
         start_date = datetime(2025, 8, 28)
         l3a_descriptor = "alpha-sw"
-        input_version = create_mock_version_map(descriptor=l3a_descriptor, minor_version=123)
+        input_version = create_mock_version_map(
+            descriptor=l3a_descriptor, minor_version=123
+        )
         outgoing_version = "123"
         start_date_as_str = datetime.strftime(start_date, "%Y%m%d")
 
@@ -361,11 +469,18 @@ def test_process_l3a_alpha(self, mock_spicepy,
         epoch = np.array([initial_epoch, 11, 12, 13])
         energy = np.array([15000, 16000, 17000, 18000, 19000])
         coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
+            [
+                [4, 5, 6, 7, 8],
+                [9, 10, 11, 12, 13],
+                [14, 15, 16, 17, 18],
+                [19, 20, 21, 22, 23],
+            ]
+        )
         coincidence_count_rate_uncertainty = np.array([[0.1, 0.2, 0.3, 0.4, 0.5]] * 4)
 
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
+        chunk_of_five = SwapiL2Data(
+            epoch, energy, coincidence_count_rate, coincidence_count_rate_uncertainty
+        )
 
         runner_result = dict(
             epoch=np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
@@ -386,20 +501,25 @@ def test_process_l3a_alpha(self, mock_spicepy,
         mock_runner.run.return_value = runner_result
 
         science_input = ScienceInput(
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf"
+        )
 
         input_file_names = [
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
         ]
 
-        ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
+        ancillary_inputs = [
+            AncillaryInput(file_name) for file_name in input_file_names[1:]
+        ]
         dependencies = ProcessingInputCollection(science_input, *ancillary_inputs)
 
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
+        input_metadata = InputMetadata(
+            instrument, outgoing_data_level, start_date, end_date, input_version
+        )
 
         alpha_solar_wind_data = mock_alpha_solar_wind_data_constructor.return_value
         expected_alpha_metadata = replace(input_metadata, descriptor=l3a_descriptor)
@@ -407,8 +527,15 @@ def test_process_l3a_alpha(self, mock_spicepy,
 
         input_metadata.descriptor = l3a_descriptor
 
-        expected_cdf_path = (config["DATA_DIR"] / "imap" / "swapi" / "l3a" / "2025" / "08" /
-                             f"imap_swapi_l3a_alpha-sw_{start_date_as_str}_v123.cdf")
+        expected_cdf_path = (
+            config["DATA_DIR"]
+            / "imap"
+            / "swapi"
+            / "l3a"
+            / "2025"
+            / "08"
+            / f"imap_swapi_l3a_alpha-sw_{start_date_as_str}_v123.cdf"
+        )
 
         mock_chunk_l2_data.return_value = [chunk_of_five]
 
@@ -416,14 +543,18 @@ def test_process_l3a_alpha(self, mock_spicepy,
         swapi_l3a_dependencies.data = Mock(energy=energy)
         swapi_l3a_dependencies.mag_data = Mock()
         swapi_l3a_dependencies.mag_is_preliminary = False
-        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = swapi_l3a_dependencies
+        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = (
+            swapi_l3a_dependencies
+        )
 
         mock_manager = mock_imap_attribute_manager.return_value
 
         swapi_processor = SwapiProcessor(dependencies, input_metadata)
         product = swapi_processor.process()
 
-        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
+        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(
+            dependencies
+        )
 
         swapi_l3a_dependencies.swapi_response.warm_cache.assert_called_once()
 
@@ -441,41 +572,56 @@ def test_process_l3a_alpha(self, mock_spicepy,
         for key, expected_val in runner_result.items():
             np.testing.assert_array_equal(expected_val, actual_kwargs[key])
 
-        mock_manager.add_global_attribute.assert_has_calls([
-            call("Data_version", outgoing_version),
-            call("Generation_date", date.today().strftime("%Y%m%d")),
-            call("Logical_source", "imap_swapi_l3a_alpha-sw"),
-            call("Logical_file_id", f"imap_swapi_l3a_alpha-sw_{start_date_as_str}_v123"),
-        ])
-        mock_manager.add_instrument_attrs.assert_called_once_with("swapi", "l3a", l3a_descriptor)
+        mock_manager.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", outgoing_version),
+                call("Generation_date", date.today().strftime("%Y%m%d")),
+                call("Logical_source", "imap_swapi_l3a_alpha-sw"),
+                call(
+                    "Logical_file_id",
+                    f"imap_swapi_l3a_alpha-sw_{start_date_as_str}_v123",
+                ),
+            ]
+        )
+        mock_manager.add_instrument_attrs.assert_called_once_with(
+            "swapi", "l3a", l3a_descriptor
+        )
 
         self.assertEqual(input_file_names, alpha_solar_wind_data.parent_file_names)
-        mock_write_cdf.assert_called_once_with(str(expected_cdf_path), alpha_solar_wind_data, mock_manager)
+        mock_write_cdf.assert_called_once_with(
+            str(expected_cdf_path), alpha_solar_wind_data, mock_manager
+        )
         self.assertEqual([expected_cdf_path], product)
 
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_delta_minus_plus')
-    @patch('imap_l3_processing.swapi.swapi_processor.save_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3BCombinedVDF')
-    @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3BDependencies')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_alpha_solar_wind_vdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_vdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_pui_solar_wind_vdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_combined_solar_wind_differential_flux')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_differential_flux,
-                         mock_calculate_pui_solar_wind_vdf,
-                         mock_calculate_proton_solar_wind_vdf,
-                         mock_calculate_alpha_solar_wind_vdf,
-                         mock_swapi_l3b_dependencies_class,
-                         mock_chunk_l2_data,
-                         mock_combined_vdf_data,
-                         mock_save_data,
-                         mock_calculate_delta_minus_plus):
-        instrument = 'swapi'
-        incoming_data_level = 'l2'
+    @patch("imap_l3_processing.swapi.swapi_processor.calculate_delta_minus_plus")
+    @patch("imap_l3_processing.swapi.swapi_processor.save_data")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3BCombinedVDF")
+    @patch("imap_l3_processing.swapi.swapi_processor.chunk_l2_data")
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3BDependencies")
+    @patch("imap_l3_processing.swapi.swapi_processor.calculate_alpha_solar_wind_vdf")
+    @patch("imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_vdf")
+    @patch("imap_l3_processing.swapi.swapi_processor.calculate_pui_solar_wind_vdf")
+    @patch(
+        "imap_l3_processing.swapi.swapi_processor.calculate_combined_solar_wind_differential_flux"
+    )
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_l3b(
+        self,
+        mock_spiceypy,
+        mock_calculate_combined_solar_wind_differential_flux,
+        mock_calculate_pui_solar_wind_vdf,
+        mock_calculate_proton_solar_wind_vdf,
+        mock_calculate_alpha_solar_wind_vdf,
+        mock_swapi_l3b_dependencies_class,
+        mock_chunk_l2_data,
+        mock_combined_vdf_data,
+        mock_save_data,
+        mock_calculate_delta_minus_plus,
+    ):
+        instrument = "swapi"
+        incoming_data_level = "l2"
         end_date = datetime.now()
-        version = 'v001'
+        version = "v001"
         outgoing_data_level = "l3b"
         dependency_start_date = "20250101"
         start_date = datetime.now() - timedelta(days=1)
@@ -483,33 +629,67 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
 
         mock_spiceypy.ktotal.return_value = 0
         mock_calculate_proton_solar_wind_vdf.side_effect = [
-            (sentinel.proton_calculated_velocities1, sentinel.proton_calculated_probabilities1),
-            (sentinel.proton_calculated_velocities2, sentinel.proton_calculated_probabilities2),
+            (
+                sentinel.proton_calculated_velocities1,
+                sentinel.proton_calculated_probabilities1,
+            ),
+            (
+                sentinel.proton_calculated_velocities2,
+                sentinel.proton_calculated_probabilities2,
+            ),
         ]
 
         mock_calculate_alpha_solar_wind_vdf.side_effect = [
-            (sentinel.alpha_calculated_velocities1, sentinel.alpha_calculated_probabilities1),
-            (sentinel.alpha_calculated_velocities2, sentinel.alpha_calculated_probabilities2),
+            (
+                sentinel.alpha_calculated_velocities1,
+                sentinel.alpha_calculated_probabilities1,
+            ),
+            (
+                sentinel.alpha_calculated_velocities2,
+                sentinel.alpha_calculated_probabilities2,
+            ),
         ]
 
         mock_calculate_pui_solar_wind_vdf.side_effect = [
-            (sentinel.pui_calculated_velocities1, sentinel.pui_calculated_probabilities1),
-            (sentinel.pui_calculated_velocities2, sentinel.pui_calculated_probabilities2),
+            (
+                sentinel.pui_calculated_velocities1,
+                sentinel.pui_calculated_probabilities1,
+            ),
+            (
+                sentinel.pui_calculated_velocities2,
+                sentinel.pui_calculated_probabilities2,
+            ),
         ]
 
         mock_calculate_combined_solar_wind_differential_flux.side_effect = [
             sentinel.calculated_diffential_flux1,
-            sentinel.calculated_diffential_flux2
+            sentinel.calculated_diffential_flux2,
         ]
 
         mock_calculate_delta_minus_plus.side_effect = [
-            DeltaMinusPlus(sentinel.proton_velocity_delta_minus1, sentinel.proton_velocity_delta_plus1),
-            DeltaMinusPlus(sentinel.alpha_velocity_delta_minus1, sentinel.alpha_velocity_delta_plus1),
-            DeltaMinusPlus(sentinel.pui_velocity_delta_minus1, sentinel.pui_velocity_delta_plus1),
+            DeltaMinusPlus(
+                sentinel.proton_velocity_delta_minus1,
+                sentinel.proton_velocity_delta_plus1,
+            ),
+            DeltaMinusPlus(
+                sentinel.alpha_velocity_delta_minus1,
+                sentinel.alpha_velocity_delta_plus1,
+            ),
+            DeltaMinusPlus(
+                sentinel.pui_velocity_delta_minus1, sentinel.pui_velocity_delta_plus1
+            ),
             DeltaMinusPlus(sentinel.energy_delta_minus1, sentinel.energy_delta_plus1),
-            DeltaMinusPlus(sentinel.proton_velocity_delta_minus2, sentinel.proton_velocity_delta_plus2),
-            DeltaMinusPlus(sentinel.alpha_velocity_delta_minus2, sentinel.alpha_velocity_delta_plus2),
-            DeltaMinusPlus(sentinel.pui_velocity_delta_minus2, sentinel.pui_velocity_delta_plus2),
+            DeltaMinusPlus(
+                sentinel.proton_velocity_delta_minus2,
+                sentinel.proton_velocity_delta_plus2,
+            ),
+            DeltaMinusPlus(
+                sentinel.alpha_velocity_delta_minus2,
+                sentinel.alpha_velocity_delta_plus2,
+            ),
+            DeltaMinusPlus(
+                sentinel.pui_velocity_delta_minus2, sentinel.pui_velocity_delta_plus2
+            ),
             DeltaMinusPlus(sentinel.energy_delta_minus2, sentinel.energy_delta_plus2),
         ]
 
@@ -528,97 +708,176 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
 
         average_coincident_count_rates = coarse_count_rates
         # σ propagation through Σ/N over n independent sweeps shrinks the per-sweep σ by √n.
-        average_coincident_count_rate_uncertainties = coarse_count_rate_uncertainties / np.sqrt(n_sweeps)
+        average_coincident_count_rate_uncertainties = (
+            coarse_count_rate_uncertainties / np.sqrt(n_sweeps)
+        )
         energy = coarse_energies
 
         first_chunk_initial_epoch = 10
-        first_l2_data_chunk = SwapiL2Data(np.array([first_chunk_initial_epoch, 11, 12, 13]), energy_per_sweep,
-                                          coincidence_count_rate,
-                                          coincidence_count_rate_uncertainty)
+        first_l2_data_chunk = SwapiL2Data(
+            np.array([first_chunk_initial_epoch, 11, 12, 13]),
+            energy_per_sweep,
+            coincidence_count_rate,
+            coincidence_count_rate_uncertainty,
+        )
 
         second_chunk_initial_epoch = 60
-        second_l2_data_chunk = SwapiL2Data(np.array([second_chunk_initial_epoch, 11, 12, 13]), energy_per_sweep,
-                                           coincidence_count_rate,
-                                           coincidence_count_rate_uncertainty)
+        second_l2_data_chunk = SwapiL2Data(
+            np.array([second_chunk_initial_epoch, 11, 12, 13]),
+            energy_per_sweep,
+            coincidence_count_rate,
+            coincidence_count_rate_uncertainty,
+        )
 
         mock_chunk_l2_data.return_value = [first_l2_data_chunk, second_l2_data_chunk]
 
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date,
-                                       outgoing_version)
+        input_metadata = InputMetadata(
+            instrument, outgoing_data_level, start_date, end_date, outgoing_version
+        )
 
         input_file_names = [
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{incoming_data_level}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{incoming_data_level}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f"imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{incoming_data_level}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
+            f"imap_{instrument}_{incoming_data_level}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf",
         ]
 
         science_inputs = [ScienceInput(file_name) for file_name in input_file_names]
 
         dependencies = ProcessingInputCollection(*science_inputs)
 
-        swapi_processor = SwapiProcessor(
-            dependencies, input_metadata)
+        swapi_processor = SwapiProcessor(dependencies, input_metadata)
         product = swapi_processor.process()
 
-        mock_swapi_l3b_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
+        mock_swapi_l3b_dependencies_class.fetch_dependencies.assert_called_once_with(
+            dependencies
+        )
 
         mock_geometric_factor_calibration_table = mock_swapi_l3b_dependencies_class.fetch_dependencies.return_value.geometric_factor_calibration_table
         mock_efficiency_table = mock_swapi_l3b_dependencies_class.fetch_dependencies.return_value.efficiency_calibration_table
 
-        mock_chunk_l2_data.assert_called_with(mock_swapi_l3b_dependencies_class.fetch_dependencies.return_value.data,
-                                              50)
+        mock_chunk_l2_data.assert_called_with(
+            mock_swapi_l3b_dependencies_class.fetch_dependencies.return_value.data, 50
+        )
 
         mock_efficiency_table.relative_efficiency.assert_has_calls(
-            [call(first_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS, Species.PROTON),
-             call(second_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS, Species.PROTON)])
+            [
+                call(
+                    first_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS,
+                    Species.PROTON,
+                ),
+                call(
+                    second_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS,
+                    Species.PROTON,
+                ),
+            ]
+        )
 
-        expected_count_rate_with_uncertainties = uarray(average_coincident_count_rates,
-                                                        average_coincident_count_rate_uncertainties)
+        expected_count_rate_with_uncertainties = uarray(
+            average_coincident_count_rates, average_coincident_count_rate_uncertainties
+        )
 
-        np.testing.assert_array_equal(energy, mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[0])
+        np.testing.assert_array_equal(
+            energy, mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[0]
+        )
         nominal_count_rates = nominal_values(expected_count_rate_with_uncertainties)
         std_devs_count_rates = std_devs(expected_count_rate_with_uncertainties)
 
-        np.testing.assert_array_equal(nominal_count_rates,
-                                      nominal_values(mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[1]))
-        np.testing.assert_array_equal(std_devs_count_rates,
-                                      std_devs(mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[1]))
-        self.assertEqual(mock_efficiency_table.relative_efficiency.return_value,
-                         mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[2])
-        self.assertEqual(mock_geometric_factor_calibration_table,
-                         mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[3])
-
-        np.testing.assert_array_equal(energy, mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[0])
-        np.testing.assert_array_equal(nominal_count_rates,
-                                      nominal_values(mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[1]))
-        np.testing.assert_array_equal(std_devs_count_rates,
-                                      std_devs(mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[1]))
-
-        self.assertEqual(mock_efficiency_table.relative_efficiency.return_value,
-                         mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[2])
-        self.assertEqual(mock_geometric_factor_calibration_table,
-                         mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[3])
-
-        np.testing.assert_array_equal(energy, mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[0])
-        np.testing.assert_array_equal(nominal_count_rates,
-                                      nominal_values(mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[1]))
-        np.testing.assert_array_equal(std_devs_count_rates,
-                                      std_devs(mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[1]))
-        self.assertEqual(mock_efficiency_table.relative_efficiency.return_value,
-                         mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[2])
-        self.assertEqual(mock_geometric_factor_calibration_table,
-                         mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[3])
-
-        np.testing.assert_array_equal(energy,
-                                      mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[0])
-        np.testing.assert_array_equal(nominal_count_rates, nominal_values(
-            mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[1]))
-        np.testing.assert_array_equal(std_devs_count_rates, std_devs(
-            mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[1]))
-        self.assertEqual(mock_efficiency_table.relative_efficiency.return_value,
-                         mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[2])
-        self.assertEqual(mock_geometric_factor_calibration_table,
-                         mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[3])
+        np.testing.assert_array_equal(
+            nominal_count_rates,
+            nominal_values(
+                mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[1]
+            ),
+        )
+        np.testing.assert_array_equal(
+            std_devs_count_rates,
+            std_devs(mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[1]),
+        )
+        self.assertEqual(
+            mock_efficiency_table.relative_efficiency.return_value,
+            mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[2],
+        )
+        self.assertEqual(
+            mock_geometric_factor_calibration_table,
+            mock_calculate_proton_solar_wind_vdf.call_args_list[0].args[3],
+        )
+
+        np.testing.assert_array_equal(
+            energy, mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[0]
+        )
+        np.testing.assert_array_equal(
+            nominal_count_rates,
+            nominal_values(
+                mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[1]
+            ),
+        )
+        np.testing.assert_array_equal(
+            std_devs_count_rates,
+            std_devs(mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[1]),
+        )
+
+        self.assertEqual(
+            mock_efficiency_table.relative_efficiency.return_value,
+            mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[2],
+        )
+        self.assertEqual(
+            mock_geometric_factor_calibration_table,
+            mock_calculate_alpha_solar_wind_vdf.call_args_list[0].args[3],
+        )
+
+        np.testing.assert_array_equal(
+            energy, mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[0]
+        )
+        np.testing.assert_array_equal(
+            nominal_count_rates,
+            nominal_values(mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[1]),
+        )
+        np.testing.assert_array_equal(
+            std_devs_count_rates,
+            std_devs(mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[1]),
+        )
+        self.assertEqual(
+            mock_efficiency_table.relative_efficiency.return_value,
+            mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[2],
+        )
+        self.assertEqual(
+            mock_geometric_factor_calibration_table,
+            mock_calculate_pui_solar_wind_vdf.call_args_list[0].args[3],
+        )
+
+        np.testing.assert_array_equal(
+            energy,
+            mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[
+                0
+            ],
+        )
+        np.testing.assert_array_equal(
+            nominal_count_rates,
+            nominal_values(
+                mock_calculate_combined_solar_wind_differential_flux.call_args_list[
+                    0
+                ].args[1]
+            ),
+        )
+        np.testing.assert_array_equal(
+            std_devs_count_rates,
+            std_devs(
+                mock_calculate_combined_solar_wind_differential_flux.call_args_list[
+                    0
+                ].args[1]
+            ),
+        )
+        self.assertEqual(
+            mock_efficiency_table.relative_efficiency.return_value,
+            mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[
+                2
+            ],
+        )
+        self.assertEqual(
+            mock_geometric_factor_calibration_table,
+            mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[
+                3
+            ],
+        )
         delta_calls = mock_calculate_delta_minus_plus.call_args_list
         self.assertEqual(sentinel.proton_calculated_velocities1, delta_calls[0].args[0])
         self.assertEqual(sentinel.alpha_calculated_velocities1, delta_calls[1].args[0])
@@ -629,84 +888,175 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
         self.assertEqual(sentinel.pui_calculated_velocities2, delta_calls[6].args[0])
         np.testing.assert_array_equal(energy, delta_calls[7].args[0])
 
-        expected_combined_metadata = InputMetadata(descriptor="combined", data_level=outgoing_data_level,
-                                                   start_date=start_date, end_date=end_date, instrument=instrument,
-                                                   version=outgoing_version)
+        expected_combined_metadata = InputMetadata(
+            descriptor="combined",
+            data_level=outgoing_data_level,
+            start_date=start_date,
+            end_date=end_date,
+            instrument=instrument,
+            version=outgoing_version,
+        )
+
+        self.assertEqual(
+            expected_combined_metadata,
+            mock_combined_vdf_data.call_args_list[0].kwargs["input_metadata"],
+        )
+
+        np.testing.assert_array_equal(
+            np.array(
+                [
+                    first_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS,
+                    second_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS,
+                ]
+            ),
+            mock_combined_vdf_data.call_args_list[0].kwargs["epoch"],
+        )
+
+        np.testing.assert_array_equal(
+            [
+                sentinel.proton_calculated_velocities1,
+                sentinel.proton_calculated_velocities2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs["proton_sw_velocities"],
+        )
+        np.testing.assert_array_equal(
+            [
+                sentinel.proton_velocity_delta_minus1,
+                sentinel.proton_velocity_delta_minus2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "proton_sw_velocities_delta_minus"
+            ],
+        )
+        np.testing.assert_array_equal(
+            [
+                sentinel.proton_velocity_delta_plus1,
+                sentinel.proton_velocity_delta_plus2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "proton_sw_velocities_delta_plus"
+            ],
+        )
+        np.testing.assert_array_equal(
+            [
+                sentinel.proton_calculated_probabilities1,
+                sentinel.proton_calculated_probabilities2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs["proton_sw_combined_vdf"],
+        )
 
-        self.assertEqual(expected_combined_metadata, mock_combined_vdf_data.call_args_list[0].kwargs["input_metadata"])
+        np.testing.assert_array_equal(
+            [
+                sentinel.alpha_calculated_velocities1,
+                sentinel.alpha_calculated_velocities2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs["alpha_sw_velocities"],
+        )
+        np.testing.assert_array_equal(
+            [
+                sentinel.alpha_velocity_delta_minus1,
+                sentinel.alpha_velocity_delta_minus2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "alpha_sw_velocities_delta_minus"
+            ],
+        )
+        np.testing.assert_array_equal(
+            [sentinel.alpha_velocity_delta_plus1, sentinel.alpha_velocity_delta_plus2],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "alpha_sw_velocities_delta_plus"
+            ],
+        )
+        np.testing.assert_array_equal(
+            [
+                sentinel.alpha_calculated_probabilities1,
+                sentinel.alpha_calculated_probabilities2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs["alpha_sw_combined_vdf"],
+        )
 
-        np.testing.assert_array_equal(np.array([first_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS,
-                                                second_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS]),
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["epoch"])
+        np.testing.assert_array_equal(
+            [sentinel.pui_calculated_velocities1, sentinel.pui_calculated_velocities2],
+            mock_combined_vdf_data.call_args_list[0].kwargs["pui_sw_velocities"],
+        )
+        np.testing.assert_array_equal(
+            [sentinel.pui_velocity_delta_minus1, sentinel.pui_velocity_delta_minus2],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "pui_sw_velocities_delta_minus"
+            ],
+        )
+        np.testing.assert_array_equal(
+            [sentinel.pui_velocity_delta_plus1, sentinel.pui_velocity_delta_plus2],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "pui_sw_velocities_delta_plus"
+            ],
+        )
+        np.testing.assert_array_equal(
+            [
+                sentinel.pui_calculated_probabilities1,
+                sentinel.pui_calculated_probabilities2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs["pui_sw_combined_vdf"],
+        )
 
-        np.testing.assert_array_equal([sentinel.proton_calculated_velocities1, sentinel.proton_calculated_velocities2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["proton_sw_velocities"])
-        np.testing.assert_array_equal([sentinel.proton_velocity_delta_minus1, sentinel.proton_velocity_delta_minus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs[
-                                          "proton_sw_velocities_delta_minus"])
-        np.testing.assert_array_equal([sentinel.proton_velocity_delta_plus1, sentinel.proton_velocity_delta_plus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs[
-                                          "proton_sw_velocities_delta_plus"])
         np.testing.assert_array_equal(
-            [sentinel.proton_calculated_probabilities1, sentinel.proton_calculated_probabilities2],
-            mock_combined_vdf_data.call_args_list[0].kwargs["proton_sw_combined_vdf"])
-
-        np.testing.assert_array_equal([sentinel.alpha_calculated_velocities1, sentinel.alpha_calculated_velocities2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["alpha_sw_velocities"])
-        np.testing.assert_array_equal([sentinel.alpha_velocity_delta_minus1, sentinel.alpha_velocity_delta_minus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs[
-                                          "alpha_sw_velocities_delta_minus"])
-        np.testing.assert_array_equal([sentinel.alpha_velocity_delta_plus1, sentinel.alpha_velocity_delta_plus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs[
-                                          "alpha_sw_velocities_delta_plus"])
+            [energy, energy],
+            mock_combined_vdf_data.call_args_list[0].kwargs["combined_energy"],
+        )
         np.testing.assert_array_equal(
-            [sentinel.alpha_calculated_probabilities1, sentinel.alpha_calculated_probabilities2],
-            mock_combined_vdf_data.call_args_list[0].kwargs["alpha_sw_combined_vdf"])
-
-        np.testing.assert_array_equal([sentinel.pui_calculated_velocities1, sentinel.pui_calculated_velocities2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["pui_sw_velocities"])
-        np.testing.assert_array_equal([sentinel.pui_velocity_delta_minus1, sentinel.pui_velocity_delta_minus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs[
-                                          "pui_sw_velocities_delta_minus"])
-        np.testing.assert_array_equal([sentinel.pui_velocity_delta_plus1, sentinel.pui_velocity_delta_plus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs[
-                                          "pui_sw_velocities_delta_plus"])
+            [sentinel.energy_delta_minus1, sentinel.energy_delta_minus2],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "combined_energy_delta_minus"
+            ],
+        )
         np.testing.assert_array_equal(
-            [sentinel.pui_calculated_probabilities1, sentinel.pui_calculated_probabilities2],
-            mock_combined_vdf_data.call_args_list[0].kwargs["pui_sw_combined_vdf"])
-
-        np.testing.assert_array_equal([energy, energy],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["combined_energy"])
-        np.testing.assert_array_equal([sentinel.energy_delta_minus1, sentinel.energy_delta_minus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["combined_energy_delta_minus"])
-        np.testing.assert_array_equal([sentinel.energy_delta_plus1, sentinel.energy_delta_plus2],
-                                      mock_combined_vdf_data.call_args_list[0].kwargs["combined_energy_delta_plus"])
+            [sentinel.energy_delta_plus1, sentinel.energy_delta_plus2],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "combined_energy_delta_plus"
+            ],
+        )
         np.testing.assert_array_equal(
-            [sentinel.calculated_diffential_flux1, sentinel.calculated_diffential_flux2],
-            mock_combined_vdf_data.call_args_list[0].kwargs["combined_differential_flux"])
+            [
+                sentinel.calculated_diffential_flux1,
+                sentinel.calculated_diffential_flux2,
+            ],
+            mock_combined_vdf_data.call_args_list[0].kwargs[
+                "combined_differential_flux"
+            ],
+        )
 
-        self.assertEqual(input_file_names, mock_combined_vdf_data.return_value.parent_file_names)
+        self.assertEqual(
+            input_file_names, mock_combined_vdf_data.return_value.parent_file_names
+        )
         mock_save_data.assert_called_once_with(mock_combined_vdf_data.return_value)
         self.assertEqual([mock_save_data.return_value], product)
 
-
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3a_raises_for_unknown_descriptor(self, mock_spicepy,
-                                                       mock_swapi_l3_dependencies_class):
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies")
+    @patch("imap_l3_processing.processor.spiceypy")
+    def test_process_l3a_raises_for_unknown_descriptor(
+        self, mock_spicepy, mock_swapi_l3_dependencies_class
+    ):
         mock_spicepy.ktotal.return_value = 0
-        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = create_swapi_l3a_dependencies_with_mocks()
+        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = (
+            create_swapi_l3a_dependencies_with_mocks()
+        )
 
-        input_metadata = InputMetadata('swapi', 'l3a', datetime(2025, 6, 12), datetime(2025, 6, 13), 'v123')
+        input_metadata = InputMetadata(
+            "swapi", "l3a", datetime(2025, 6, 12), datetime(2025, 6, 13), "v123"
+        )
         input_metadata.descriptor = "not-a-real-descriptor"
 
         swapi_processor = SwapiProcessor(Mock(), input_metadata)
         with self.assertRaises(NotImplementedError) as cm:
             swapi_processor.process()
-        self.assertEqual(("unknown descriptor", "not-a-real-descriptor"), cm.exception.args)
+        self.assertEqual(
+            ("unknown descriptor", "not-a-real-descriptor"), cm.exception.args
+        )
 
     def test_process_l3a_alpha_raises_when_mag_data_missing(self):
-        input_metadata = InputMetadata('swapi', 'l3a', datetime(2025, 8, 28), datetime(2025, 8, 29), 'v123')
+        input_metadata = InputMetadata(
+            "swapi", "l3a", datetime(2025, 8, 28), datetime(2025, 8, 29), "v123"
+        )
         input_metadata.descriptor = "alpha-sw"
 
         dependencies = create_swapi_l3a_dependencies_with_mocks()
diff --git a/tests/swe/l3/science/test_models.py b/tests/swe/l3/science/test_models.py
index 3cdb5fea7..44ccee3af 100644
--- a/tests/swe/l3/science/test_models.py
+++ b/tests/swe/l3/science/test_models.py
@@ -3,50 +3,106 @@
 
 import numpy as np
 
-from imap_l3_processing.swe.l3.models import SweL3Data, EPOCH_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME, \
-    ENERGY_CDF_VAR_NAME, \
-    ENERGY_DELTA_PLUS_CDF_VAR_NAME, ENERGY_DELTA_MINUS_CDF_VAR_NAME, PITCH_ANGLE_CDF_VAR_NAME, \
-    PITCH_ANGLE_DELTA_CDF_VAR_NAME, PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME, \
-    PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME, PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME, \
-    PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME, \
-    SPACECRAFT_POTENTIAL_CDF_VAR_NAME, CORE_HALO_BREAKPOINT_CDF_VAR_NAME, CORE_FIT_NUM_POINTS_CDF_VAR_NAME, \
-    CHISQ_C_CDF_VAR_NAME, CHISQ_H_CDF_VAR_NAME, CORE_DENSITY_FIT_CDF_VAR_NAME, HALO_DENSITY_FIT_CDF_VAR_NAME, \
-    CORE_T_PARALLEL_FIT_CDF_VAR_NAME, HALO_T_PARALLEL_FIT_CDF_VAR_NAME, CORE_T_PERPENDICULAR_FIT_CDF_VAR_NAME, \
-    HALO_T_PERPENDICULAR_FIT_CDF_VAR_NAME, CORE_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME, \
-    HALO_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME, CORE_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME, \
-    HALO_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME, CORE_SPEED_FIT_CDF_VAR_NAME, HALO_SPEED_FIT_CDF_VAR_NAME, \
-    CORE_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME, HALO_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME, \
-    INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME, INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME, \
-    CORE_DENSITY_INTEGRATED_CDF_VAR_NAME, HALO_DENSITY_INTEGRATED_CDF_VAR_NAME, TOTAL_DENSITY_INTEGRATED_CDF_VAR_NAME, \
-    CORE_SPEED_INTEGRATED_CDF_VAR_NAME, HALO_SPEED_INTEGRATED_CDF_VAR_NAME, TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME, \
-    CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME, HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME, \
-    TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME, CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME, \
-    CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME, CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME, \
-    HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME, HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME, \
-    HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME, TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME, \
-    TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME, TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME, \
-    CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME, \
-    CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME, HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME, \
-    HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME, TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME, \
-    TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME, CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME, \
-    CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME, HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME, \
-    HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME, TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME, \
-    TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME, SweL3MomentData, CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME, \
-    CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME, HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME, \
-    HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME, TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME, \
-    TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME, CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME, \
-    HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME, TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME, \
-    PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME, GYROPHASE_DELTA_CDF_VAR_NAME, \
-    GYROPHASE_BINS_CDF_VAR_NAME, INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME, \
-    INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME, ENERGY_LABEL, PITCH_ANGLE_LABEL, GYROPHASE_LABEL, RTN_LABEL, \
-    CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME, \
-    HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME, TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME, \
-    CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME, HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME, \
-    TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME, TENSOR_ID, SWE_FLAGS_VAR_NAME, \
-    CORE_VELOCITY_VECTOR_RTN_FIT_LABEL, HALO_VELOCITY_VECTOR_RTN_FIT_LABEL, CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL, \
-    HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL, TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL, \
-    CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL, HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL, \
-    TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL
+from imap_l3_processing.swe.l3.models import (
+    CHISQ_C_CDF_VAR_NAME,
+    CHISQ_H_CDF_VAR_NAME,
+    CORE_DENSITY_FIT_CDF_VAR_NAME,
+    CORE_DENSITY_INTEGRATED_CDF_VAR_NAME,
+    CORE_FIT_NUM_POINTS_CDF_VAR_NAME,
+    CORE_HALO_BREAKPOINT_CDF_VAR_NAME,
+    CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+    CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+    CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+    CORE_SPEED_FIT_CDF_VAR_NAME,
+    CORE_SPEED_INTEGRATED_CDF_VAR_NAME,
+    CORE_T_PARALLEL_FIT_CDF_VAR_NAME,
+    CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+    CORE_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
+    CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
+    CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+    CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+    CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+    CORE_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
+    CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+    CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+    CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+    CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+    CORE_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
+    CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+    CORE_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
+    CORE_VELOCITY_VECTOR_RTN_FIT_LABEL,
+    CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+    CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+    ENERGY_CDF_VAR_NAME,
+    ENERGY_DELTA_MINUS_CDF_VAR_NAME,
+    ENERGY_DELTA_PLUS_CDF_VAR_NAME,
+    ENERGY_LABEL,
+    EPOCH_CDF_VAR_NAME,
+    EPOCH_DELTA_CDF_VAR_NAME,
+    GYROPHASE_BINS_CDF_VAR_NAME,
+    GYROPHASE_DELTA_CDF_VAR_NAME,
+    GYROPHASE_LABEL,
+    HALO_DENSITY_FIT_CDF_VAR_NAME,
+    HALO_DENSITY_INTEGRATED_CDF_VAR_NAME,
+    HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+    HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+    HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+    HALO_SPEED_FIT_CDF_VAR_NAME,
+    HALO_SPEED_INTEGRATED_CDF_VAR_NAME,
+    HALO_T_PARALLEL_FIT_CDF_VAR_NAME,
+    HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+    HALO_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
+    HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
+    HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+    HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+    HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+    HALO_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
+    HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+    HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+    HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+    HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+    HALO_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
+    HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+    HALO_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
+    HALO_VELOCITY_VECTOR_RTN_FIT_LABEL,
+    HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+    HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+    INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+    INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+    INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+    INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+    PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME,
+    PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+    PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+    PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME,
+    PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME,
+    PITCH_ANGLE_CDF_VAR_NAME,
+    PITCH_ANGLE_DELTA_CDF_VAR_NAME,
+    PITCH_ANGLE_LABEL,
+    RTN_LABEL,
+    SPACECRAFT_POTENTIAL_CDF_VAR_NAME,
+    SWE_FLAGS_VAR_NAME,
+    TENSOR_ID,
+    TOTAL_DENSITY_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+    TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+    TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+    TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+    TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+    TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+    SweL3Data,
+    SweL3MomentData,
+)
 from tests.swapi.cdf_model_test_case import CdfModelTestCase
 
 
@@ -157,112 +213,477 @@ def test_data_to_product_variables(self):
 
         variables = iter(variables)
         # @formatter:off
-        self.assert_variable_attributes(next(variables), sentinel.epoch, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
+        self.assert_variable_attributes(
+            next(variables), sentinel.epoch, EPOCH_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.epoch_delta, EPOCH_DELTA_CDF_VAR_NAME
+        )
         self.assert_variable_attributes(next(variables), energy, ENERGY_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.energy_delta_plus, ENERGY_DELTA_PLUS_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.energy_delta_minus, ENERGY_DELTA_MINUS_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), pitch_angle, PITCH_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.pitch_angle_delta, PITCH_ANGLE_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), gyrophase_bins, GYROPHASE_BINS_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.gyrophase_delta, GYROPHASE_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.psd_by_pitch_angle, PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.psd_by_pitch_angle_and_gyrophase, PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.energy_spectrum, PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.energy_spectrum_inbound, PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.energy_spectrum_outbound, PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.intensity_by_pitch_angle, INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.intensity_by_pitch_angle_and_gyrophase, INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.intensity_uncertainty_by_pitch_angle, INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.intensity_uncertainty_by_pitch_angle_and_gyrophase, INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.spacecraft_potential, SPACECRAFT_POTENTIAL_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_halo_breakpoint, CORE_HALO_BREAKPOINT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_fit_num_points, CORE_FIT_NUM_POINTS_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.chisq_c, CHISQ_C_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.chisq_h, CHISQ_H_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_density_fit, CORE_DENSITY_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_density_fit, HALO_DENSITY_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_t_parallel_fit, CORE_T_PARALLEL_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_t_parallel_fit, HALO_T_PARALLEL_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_t_perpendicular_fit, CORE_T_PERPENDICULAR_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_t_perpendicular_fit, HALO_T_PERPENDICULAR_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_temperature_phi_rtn_fit, CORE_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_temperature_phi_rtn_fit, HALO_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_temperature_theta_rtn_fit, CORE_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_temperature_theta_rtn_fit, HALO_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_speed_fit, CORE_SPEED_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_speed_fit, HALO_SPEED_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_velocity_vector_rtn_fit, CORE_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_velocity_vector_rtn_fit, HALO_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_density_integrated, CORE_DENSITY_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_density_integrated, HALO_DENSITY_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_density_integrated, TOTAL_DENSITY_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_speed_integrated, CORE_SPEED_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_speed_integrated, HALO_SPEED_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_speed_integrated, TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_velocity_vector_rtn_integrated, CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_velocity_vector_rtn_integrated, HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_velocity_vector_rtn_integrated, TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_heat_flux_magnitude_integrated, CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_heat_flux_theta_integrated, CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_heat_flux_phi_integrated, CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_heat_flux_magnitude_integrated, HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_heat_flux_theta_integrated, HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_heat_flux_phi_integrated, HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_heat_flux_magnitude_integrated, TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_heat_flux_theta_integrated, TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_heat_flux_phi_integrated, TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_t_parallel_integrated, CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [1,3], CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [2,4], CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_t_parallel_integrated, HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [5,7], HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [6,8], HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_t_parallel_integrated, TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [9,11], TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [10,12], TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_temperature_theta_rtn_integrated, CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_temperature_phi_rtn_integrated, CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_temperature_theta_rtn_integrated, HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_temperature_phi_rtn_integrated, HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_temperature_theta_rtn_integrated, TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_temperature_phi_rtn_integrated, TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_temperature_parallel_to_mag, CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [11,13], CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [12,14], CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_temperature_parallel_to_mag, HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [15,17], HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [16,18], HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_temperature_parallel_to_mag, TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [19,21], TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), [20,22], TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.core_temperature_tensor_integrated, CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.halo_temperature_tensor_integrated, HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.total_temperature_tensor_integrated, TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.swp_flags, SWE_FLAGS_VAR_NAME)
-        self.assert_variable_attributes(next(variables), sentinel.raw_1d_psd_rebinned, 'raw_1d_psd_rebinned')
-        self.assert_variable_attributes(next(variables), sentinel.raw_psd_by_theta_rebinned, 'raw_psd_by_theta_rebinned')
-        self.assert_variable_attributes(next(variables), sentinel.raw_psd_by_phi_rebinned, 'raw_psd_by_phi_rebinned')
-        self.assert_variable_attributes(next(variables), sentinel.inst_az, 'inst_az')
-        self.assert_variable_attributes(next(variables), sentinel.inst_el, 'inst_el')
-        self.assert_variable_attributes(next(variables), ["10.0 eV", "20.0 eV", "30.0 eV"], ENERGY_LABEL)
-        self.assert_variable_attributes(next(variables), ["PA 11", "PA 12", "PA 13", "PA 14", "PA 15"], PITCH_ANGLE_LABEL)
-        self.assert_variable_attributes(next(variables), ["Gyrophase 20", "Gyrophase 40", "Gyrophase 60", "Gyrophase 80"], GYROPHASE_LABEL)
+        self.assert_variable_attributes(
+            next(variables), sentinel.energy_delta_plus, ENERGY_DELTA_PLUS_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.energy_delta_minus,
+            ENERGY_DELTA_MINUS_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), pitch_angle, PITCH_ANGLE_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.pitch_angle_delta, PITCH_ANGLE_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), gyrophase_bins, GYROPHASE_BINS_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.gyrophase_delta, GYROPHASE_DELTA_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.psd_by_pitch_angle,
+            PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.psd_by_pitch_angle_and_gyrophase,
+            PHASE_SPACE_DENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.energy_spectrum,
+            PHASE_SPACE_DENSITY_1D_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.energy_spectrum_inbound,
+            PHASE_SPACE_DENSITY_INWARD_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.energy_spectrum_outbound,
+            PHASE_SPACE_DENSITY_OUTWARD_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.intensity_by_pitch_angle,
+            INTENSITY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.intensity_by_pitch_angle_and_gyrophase,
+            INTENSITY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.intensity_uncertainty_by_pitch_angle,
+            INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.intensity_uncertainty_by_pitch_angle_and_gyrophase,
+            INTENSITY_UNCERTAINTY_BY_PITCH_ANGLE_AND_GYROPHASE_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.spacecraft_potential,
+            SPACECRAFT_POTENTIAL_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_halo_breakpoint,
+            CORE_HALO_BREAKPOINT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_fit_num_points,
+            CORE_FIT_NUM_POINTS_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.chisq_c, CHISQ_C_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.chisq_h, CHISQ_H_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.core_density_fit, CORE_DENSITY_FIT_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.halo_density_fit, HALO_DENSITY_FIT_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_t_parallel_fit,
+            CORE_T_PARALLEL_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_t_parallel_fit,
+            HALO_T_PARALLEL_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_t_perpendicular_fit,
+            CORE_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_t_perpendicular_fit,
+            HALO_T_PERPENDICULAR_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_temperature_phi_rtn_fit,
+            CORE_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_temperature_phi_rtn_fit,
+            HALO_TEMPERATURE_PHI_RTN_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_temperature_theta_rtn_fit,
+            CORE_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_temperature_theta_rtn_fit,
+            HALO_TEMPERATURE_THETA_RTN_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.core_speed_fit, CORE_SPEED_FIT_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.halo_speed_fit, HALO_SPEED_FIT_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_velocity_vector_rtn_fit,
+            CORE_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_velocity_vector_rtn_fit,
+            HALO_VELOCITY_VECTOR_RTN_FIT_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_density_integrated,
+            CORE_DENSITY_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_density_integrated,
+            HALO_DENSITY_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_density_integrated,
+            TOTAL_DENSITY_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_speed_integrated,
+            CORE_SPEED_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_speed_integrated,
+            HALO_SPEED_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_speed_integrated,
+            TOTAL_SPEED_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_velocity_vector_rtn_integrated,
+            CORE_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_velocity_vector_rtn_integrated,
+            HALO_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_velocity_vector_rtn_integrated,
+            TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_heat_flux_magnitude_integrated,
+            CORE_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_heat_flux_theta_integrated,
+            CORE_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_heat_flux_phi_integrated,
+            CORE_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_heat_flux_magnitude_integrated,
+            HALO_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_heat_flux_theta_integrated,
+            HALO_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_heat_flux_phi_integrated,
+            HALO_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_heat_flux_magnitude_integrated,
+            TOTAL_HEAT_FLUX_MAGNITUDE_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_heat_flux_theta_integrated,
+            TOTAL_HEAT_FLUX_THETA_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_heat_flux_phi_integrated,
+            TOTAL_HEAT_FLUX_PHI_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_t_parallel_integrated,
+            CORE_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), [1, 3], CORE_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), [2, 4], CORE_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_t_parallel_integrated,
+            HALO_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), [5, 7], HALO_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), [6, 8], HALO_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_t_parallel_integrated,
+            TOTAL_T_PARALLEL_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), [9, 11], TOTAL_T_PERPENDICULAR_INTEGRATED_CDF_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [10, 12],
+            TOTAL_T_PERPENDICULAR_RATIO_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_temperature_theta_rtn_integrated,
+            CORE_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_temperature_phi_rtn_integrated,
+            CORE_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_temperature_theta_rtn_integrated,
+            HALO_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_temperature_phi_rtn_integrated,
+            HALO_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_temperature_theta_rtn_integrated,
+            TOTAL_TEMPERATURE_THETA_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_temperature_phi_rtn_integrated,
+            TOTAL_TEMPERATURE_PHI_RTN_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_temperature_parallel_to_mag,
+            CORE_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [11, 13],
+            CORE_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [12, 14],
+            CORE_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_temperature_parallel_to_mag,
+            HALO_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [15, 17],
+            HALO_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [16, 18],
+            HALO_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_temperature_parallel_to_mag,
+            TOTAL_TEMPERATURE_PARALLEL_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [19, 21],
+            TOTAL_TEMPERATURE_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [20, 22],
+            TOTAL_TEMPERATURE_RATIO_PERPENDICULAR_TO_MAG_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.core_temperature_tensor_integrated,
+            CORE_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.halo_temperature_tensor_integrated,
+            HALO_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.total_temperature_tensor_integrated,
+            TOTAL_TEMPERATURE_TENSOR_INTEGRATED_CDF_VAR_NAME,
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.swp_flags, SWE_FLAGS_VAR_NAME
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.raw_1d_psd_rebinned, "raw_1d_psd_rebinned"
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            sentinel.raw_psd_by_theta_rebinned,
+            "raw_psd_by_theta_rebinned",
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.raw_psd_by_phi_rebinned, "raw_psd_by_phi_rebinned"
+        )
+        self.assert_variable_attributes(next(variables), sentinel.inst_az, "inst_az")
+        self.assert_variable_attributes(next(variables), sentinel.inst_el, "inst_el")
+        self.assert_variable_attributes(
+            next(variables), ["10.0 eV", "20.0 eV", "30.0 eV"], ENERGY_LABEL
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            ["PA 11", "PA 12", "PA 13", "PA 14", "PA 15"],
+            PITCH_ANGLE_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            ["Gyrophase 20", "Gyrophase 40", "Gyrophase 60", "Gyrophase 80"],
+            GYROPHASE_LABEL,
+        )
         self.assert_variable_attributes(next(variables), ["R", "T", "N"], RTN_LABEL)
-        self.assert_variable_attributes(next(variables), ["Core v fit R", "Core v fit T", "Core v fit N"], CORE_VELOCITY_VECTOR_RTN_FIT_LABEL)
-        self.assert_variable_attributes(next(variables), ["Halo v fit R", "Halo v fit T", "Halo v fit N"], HALO_VELOCITY_VECTOR_RTN_FIT_LABEL)
-        self.assert_variable_attributes(next(variables), ["Core v int. R", "Core v int. T", "Core v int. N"], CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL)
-        self.assert_variable_attributes(next(variables), ["Halo v int. R", "Halo v int. T", "Halo v int. N"], HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL)
-        self.assert_variable_attributes(next(variables), ["Total v int. R", "Total v int. T", "Total v int. N"], TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL)
-        self.assert_variable_attributes(next(variables), ["Core T_XX", "Core T_XY", "Core T_YY", "Core T_XZ", "Core T_YZ", "Core T_ZZ"], CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL)
-        self.assert_variable_attributes(next(variables), ["Halo T_XX", "Halo T_XY", "Halo T_YY", "Halo T_XZ", "Halo T_YZ", "Halo T_ZZ"], HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL)
-        self.assert_variable_attributes(next(variables), ["Total T_XX", "Total T_XY", "Total T_YY", "Total T_XZ", "Total T_YZ", "Total T_ZZ"], TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL)
-        self.assert_variable_attributes(next(variables), sentinel.inst_az_label, 'inst_az_label')
-        self.assert_variable_attributes(next(variables), sentinel.inst_el_label, 'inst_el_label')
-        self.assert_variable_attributes(next(variables),[1,2,3,4,5, 6], TENSOR_ID)
+        self.assert_variable_attributes(
+            next(variables),
+            ["Core v fit R", "Core v fit T", "Core v fit N"],
+            CORE_VELOCITY_VECTOR_RTN_FIT_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            ["Halo v fit R", "Halo v fit T", "Halo v fit N"],
+            HALO_VELOCITY_VECTOR_RTN_FIT_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            ["Core v int. R", "Core v int. T", "Core v int. N"],
+            CORE_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            ["Halo v int. R", "Halo v int. T", "Halo v int. N"],
+            HALO_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            ["Total v int. R", "Total v int. T", "Total v int. N"],
+            TOTAL_VELOCITY_VECTOR_RTN_INTEGRATED_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [
+                "Core T_XX",
+                "Core T_XY",
+                "Core T_YY",
+                "Core T_XZ",
+                "Core T_YZ",
+                "Core T_ZZ",
+            ],
+            CORE_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [
+                "Halo T_XX",
+                "Halo T_XY",
+                "Halo T_YY",
+                "Halo T_XZ",
+                "Halo T_YZ",
+                "Halo T_ZZ",
+            ],
+            HALO_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables),
+            [
+                "Total T_XX",
+                "Total T_XY",
+                "Total T_YY",
+                "Total T_XZ",
+                "Total T_YZ",
+                "Total T_ZZ",
+            ],
+            TOTAL_TEMPERATURE_TENSOR_INTEGRATED_LABEL,
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.inst_az_label, "inst_az_label"
+        )
+        self.assert_variable_attributes(
+            next(variables), sentinel.inst_el_label, "inst_el_label"
+        )
+        self.assert_variable_attributes(next(variables), [1, 2, 3, 4, 5, 6], TENSOR_ID)
 
         self.assertEqual([], list(variables), "unexpected variable found")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/swe/l3/science/test_moments_calculation.py b/tests/swe/l3/science/test_moments_calculation.py
index a0202c42a..899e0378a 100644
--- a/tests/swe/l3/science/test_moments_calculation.py
+++ b/tests/swe/l3/science/test_moments_calculation.py
@@ -1,185 +1,332 @@
 import dataclasses
+import math
 import unittest
 from datetime import datetime
-from unittest.mock import patch, call, sentinel, create_autospec
+from unittest.mock import call, create_autospec, patch, sentinel
 
-import math
 import numpy as np
 import spiceypy
 from spiceypy import SpiceyError
 
 from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
 from imap_l3_processing.swe.l3.science import moment_calculations
-from imap_l3_processing.swe.l3.science.moment_calculations import compute_maxwellian_weight_factors, \
-    filter_and_flatten_regress_parameters, regress, calculate_fit_temperature_density_velocity, rotate_temperature, \
-    apply_rotation_matrix, Moments, halotrunc, compute_density_scale, core_fit_moments_retrying_on_failure, \
-    halo_fit_moments_retrying_on_failure, scale_halo_density, rotate_vector_to_rtn_spherical_coordinates, \
-    calculate_primary_eigenvector, rotation_matrix_builder, rotate_temperature_tensor_to_mag, MomentFitResults, \
-    rotate_rtn_vectors_to_dps
-from tests.test_helpers import create_dataclass_mock
-from tests.test_helpers import get_test_data_path
-from imap_l3_processing.swe.swe_processor import check_temperature_outlier_flag
+from imap_l3_processing.swe.l3.science.moment_calculations import (
+    MomentFitResults,
+    Moments,
+    apply_rotation_matrix,
+    calculate_fit_temperature_density_velocity,
+    calculate_primary_eigenvector,
+    compute_density_scale,
+    compute_maxwellian_weight_factors,
+    core_fit_moments_retrying_on_failure,
+    filter_and_flatten_regress_parameters,
+    halo_fit_moments_retrying_on_failure,
+    halotrunc,
+    regress,
+    rotate_rtn_vectors_to_dps,
+    rotate_temperature,
+    rotate_temperature_tensor_to_mag,
+    rotate_vector_to_rtn_spherical_coordinates,
+    rotation_matrix_builder,
+    scale_halo_density,
+)
 from imap_l3_processing.swe.quality_flags import SweL3Flags
+from imap_l3_processing.swe.swe_processor import check_temperature_outlier_flag
+from tests.test_helpers import create_dataclass_mock, get_test_data_path
 
-MODULE = 'imap_l3_processing.swe.l3.science.moment_calculations'
+MODULE = "imap_l3_processing.swe.l3.science.moment_calculations"
 
-class TestMomentsCalculation(unittest.TestCase):
 
-    @patch(f'{MODULE}.halotrunc')
-    @patch(f'{MODULE}.regress')
-    @patch(f'{MODULE}.calculate_fit_temperature_density_velocity')
-    @patch(f'{MODULE}.filter_and_flatten_regress_parameters')
-    def test_halo_fit_moments_retrying_on_failure(self, mock_filter_and_flatten_regress_parameters,
-                                                  mock_calculate_fit_temp_dens_velocity, mock_regress, mock_halotrunc):
+class TestMomentsCalculation(unittest.TestCase):
+    @patch(f"{MODULE}.halotrunc")
+    @patch(f"{MODULE}.regress")
+    @patch(f"{MODULE}.calculate_fit_temperature_density_velocity")
+    @patch(f"{MODULE}.filter_and_flatten_regress_parameters")
+    def test_halo_fit_moments_retrying_on_failure(
+        self,
+        mock_filter_and_flatten_regress_parameters,
+        mock_calculate_fit_temp_dens_velocity,
+        mock_regress,
+        mock_halotrunc,
+    ):
 
         density_history = np.array([100, 89, 72])
 
         negative_density_value = -1
-        density_greater_than_rolling_average_of_history = np.average(density_history) * 1.35 + 1
+        density_greater_than_rolling_average_of_history = (
+            np.average(density_history) * 1.35 + 1
+        )
 
         valid_density = 13
         moments_1 = create_dataclass_mock(Moments, density=sentinel.density1)
         moments_2 = create_dataclass_mock(Moments, density=sentinel.density2)
-        moments_3_with_valid_density = create_dataclass_mock(Moments, density=sentinel.density3)
+        moments_3_with_valid_density = create_dataclass_mock(
+            Moments, density=sentinel.density3
+        )
         mock_calculate_fit_temp_dens_velocity.side_effect = [
             moments_1,
             moments_2,
-            moments_3_with_valid_density
+            moments_3_with_valid_density,
         ]
 
         mock_filter_and_flatten_regress_parameters.side_effect = [
-            (sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg),
-            (sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1),
-            (sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1)
+            (
+                sentinel.filtered_velocity_vectors,
+                sentinel.filtered_weights,
+                sentinel.filtered_yreg,
+            ),
+            (
+                sentinel.filtered_velocity_vectors_1,
+                sentinel.filtered_weights_1,
+                sentinel.filtered_yreg_1,
+            ),
+            (
+                sentinel.filtered_velocity_vectors_1,
+                sentinel.filtered_weights_1,
+                sentinel.filtered_yreg_1,
+            ),
         ]
 
         mock_regress.side_effect = [
             (sentinel.fit_function_1, sentinel.chisq_1),
             (sentinel.fit_function_2, sentinel.chisq_2),
-            (sentinel.fit_function_3, sentinel.chisq_3)
+            (sentinel.fit_function_3, sentinel.chisq_3),
         ]
 
         mock_halotrunc.side_effect = [
             negative_density_value,
             density_greater_than_rolling_average_of_history,
-            valid_density
+            valid_density,
         ]
 
-        moment_fit_result = halo_fit_moments_retrying_on_failure(sentinel.corrected_energy_bins,
-                                                                 sentinel.velocity_vectors,
-                                                                 sentinel.phase_space_density,
-                                                                 sentinel.weights,
-                                                                 0,
-                                                                 8,
-                                                                 density_history,
-                                                                 sentinel.spacecraft_potential,
-                                                                 sentinel.halo_core_breakpoint)
-
-        mock_filter_and_flatten_regress_parameters.assert_has_calls([
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 8),
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 7),
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 6)
-        ])
-
-        mock_regress.assert_has_calls([
-            call(sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg),
-            call(sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1),
-            call(sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1)
-        ])
-
-        mock_calculate_fit_temp_dens_velocity.assert_has_calls([
-            call(sentinel.fit_function_1),
-            call(sentinel.fit_function_2),
-            call(sentinel.fit_function_3),
-        ])
-
-        mock_halotrunc.assert_has_calls([
-            call(moments_1, sentinel.halo_core_breakpoint, sentinel.spacecraft_potential, ),
-            call(moments_2, sentinel.halo_core_breakpoint, sentinel.spacecraft_potential, ),
-            call(moments_3_with_valid_density, sentinel.halo_core_breakpoint, sentinel.spacecraft_potential, )
-        ])
+        moment_fit_result = halo_fit_moments_retrying_on_failure(
+            sentinel.corrected_energy_bins,
+            sentinel.velocity_vectors,
+            sentinel.phase_space_density,
+            sentinel.weights,
+            0,
+            8,
+            density_history,
+            sentinel.spacecraft_potential,
+            sentinel.halo_core_breakpoint,
+        )
+
+        mock_filter_and_flatten_regress_parameters.assert_has_calls(
+            [
+                call(
+                    sentinel.corrected_energy_bins,
+                    sentinel.velocity_vectors,
+                    sentinel.phase_space_density,
+                    sentinel.weights,
+                    0,
+                    8,
+                ),
+                call(
+                    sentinel.corrected_energy_bins,
+                    sentinel.velocity_vectors,
+                    sentinel.phase_space_density,
+                    sentinel.weights,
+                    0,
+                    7,
+                ),
+                call(
+                    sentinel.corrected_energy_bins,
+                    sentinel.velocity_vectors,
+                    sentinel.phase_space_density,
+                    sentinel.weights,
+                    0,
+                    6,
+                ),
+            ]
+        )
+
+        mock_regress.assert_has_calls(
+            [
+                call(
+                    sentinel.filtered_velocity_vectors,
+                    sentinel.filtered_weights,
+                    sentinel.filtered_yreg,
+                ),
+                call(
+                    sentinel.filtered_velocity_vectors_1,
+                    sentinel.filtered_weights_1,
+                    sentinel.filtered_yreg_1,
+                ),
+                call(
+                    sentinel.filtered_velocity_vectors_1,
+                    sentinel.filtered_weights_1,
+                    sentinel.filtered_yreg_1,
+                ),
+            ]
+        )
+
+        mock_calculate_fit_temp_dens_velocity.assert_has_calls(
+            [
+                call(sentinel.fit_function_1),
+                call(sentinel.fit_function_2),
+                call(sentinel.fit_function_3),
+            ]
+        )
+
+        mock_halotrunc.assert_has_calls(
+            [
+                call(
+                    moments_1,
+                    sentinel.halo_core_breakpoint,
+                    sentinel.spacecraft_potential,
+                ),
+                call(
+                    moments_2,
+                    sentinel.halo_core_breakpoint,
+                    sentinel.spacecraft_potential,
+                ),
+                call(
+                    moments_3_with_valid_density,
+                    sentinel.halo_core_breakpoint,
+                    sentinel.spacecraft_potential,
+                ),
+            ]
+        )
 
         self.assertIs(moments_3_with_valid_density, moment_fit_result.moments)
         self.assertIs(sentinel.fit_function_3, moment_fit_result.regress_result)
         self.assertEqual(sentinel.chisq_3, moment_fit_result.chisq)
         self.assertEqual(6, moment_fit_result.number_of_points)
 
-    @patch(f'{MODULE}.regress')
-    @patch(f'{MODULE}.calculate_fit_temperature_density_velocity')
-    @patch(f'{MODULE}.filter_and_flatten_regress_parameters')
-    def test_fit_moments_retrying_on_failure(self, mock_filter_and_flatten_regress_parameters,
-                                             mock_calculate_fit_temp_dens_velocity, mock_regress):
+    @patch(f"{MODULE}.regress")
+    @patch(f"{MODULE}.calculate_fit_temperature_density_velocity")
+    @patch(f"{MODULE}.filter_and_flatten_regress_parameters")
+    def test_fit_moments_retrying_on_failure(
+        self,
+        mock_filter_and_flatten_regress_parameters,
+        mock_calculate_fit_temp_dens_velocity,
+        mock_regress,
+    ):
 
         density_history = np.array([100, 89, 72])
 
         negative_density_value = -1
-        density_greater_than_rolling_average_of_history = np.average(density_history) * 1.85 + 1
+        density_greater_than_rolling_average_of_history = (
+            np.average(density_history) * 1.85 + 1
+        )
 
         valid_density = 13
-        moments_with_valid_density = create_dataclass_mock(Moments, density=valid_density)
+        moments_with_valid_density = create_dataclass_mock(
+            Moments, density=valid_density
+        )
         mock_calculate_fit_temp_dens_velocity.side_effect = [
             create_dataclass_mock(Moments, density=negative_density_value),
-            create_dataclass_mock(Moments, density=density_greater_than_rolling_average_of_history),
-            moments_with_valid_density
+            create_dataclass_mock(
+                Moments, density=density_greater_than_rolling_average_of_history
+            ),
+            moments_with_valid_density,
         ]
 
         mock_filter_and_flatten_regress_parameters.side_effect = [
-            (sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg),
-            (sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1),
-            (sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1)
+            (
+                sentinel.filtered_velocity_vectors,
+                sentinel.filtered_weights,
+                sentinel.filtered_yreg,
+            ),
+            (
+                sentinel.filtered_velocity_vectors_1,
+                sentinel.filtered_weights_1,
+                sentinel.filtered_yreg_1,
+            ),
+            (
+                sentinel.filtered_velocity_vectors_1,
+                sentinel.filtered_weights_1,
+                sentinel.filtered_yreg_1,
+            ),
         ]
 
         mock_regress.side_effect = [
             (sentinel.fit_function_1, sentinel.chisq_1),
             (sentinel.fit_function_2, sentinel.chisq_2),
-            (sentinel.fit_function_3, sentinel.chisq_3)
+            (sentinel.fit_function_3, sentinel.chisq_3),
         ]
 
-        moment_fit_result = core_fit_moments_retrying_on_failure(sentinel.corrected_energy_bins,
-                                                                 sentinel.velocity_vectors,
-                                                                 sentinel.phase_space_density,
-                                                                 sentinel.weights,
-                                                                 0,
-                                                                 8,
-                                                                 density_history)
-
-        mock_filter_and_flatten_regress_parameters.assert_has_calls([
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 8),
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 7),
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 6)
-        ])
-
-        mock_regress.assert_has_calls([
-            call(sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg),
-            call(sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1),
-            call(sentinel.filtered_velocity_vectors_1, sentinel.filtered_weights_1, sentinel.filtered_yreg_1)
-        ])
-
-        mock_calculate_fit_temp_dens_velocity.assert_has_calls([
-            call(sentinel.fit_function_1),
-            call(sentinel.fit_function_2),
-            call(sentinel.fit_function_3),
-        ])
+        moment_fit_result = core_fit_moments_retrying_on_failure(
+            sentinel.corrected_energy_bins,
+            sentinel.velocity_vectors,
+            sentinel.phase_space_density,
+            sentinel.weights,
+            0,
+            8,
+            density_history,
+        )
+
+        mock_filter_and_flatten_regress_parameters.assert_has_calls(
+            [
+                call(
+                    sentinel.corrected_energy_bins,
+                    sentinel.velocity_vectors,
+                    sentinel.phase_space_density,
+                    sentinel.weights,
+                    0,
+                    8,
+                ),
+                call(
+                    sentinel.corrected_energy_bins,
+                    sentinel.velocity_vectors,
+                    sentinel.phase_space_density,
+                    sentinel.weights,
+                    0,
+                    7,
+                ),
+                call(
+                    sentinel.corrected_energy_bins,
+                    sentinel.velocity_vectors,
+                    sentinel.phase_space_density,
+                    sentinel.weights,
+                    0,
+                    6,
+                ),
+            ]
+        )
+
+        mock_regress.assert_has_calls(
+            [
+                call(
+                    sentinel.filtered_velocity_vectors,
+                    sentinel.filtered_weights,
+                    sentinel.filtered_yreg,
+                ),
+                call(
+                    sentinel.filtered_velocity_vectors_1,
+                    sentinel.filtered_weights_1,
+                    sentinel.filtered_yreg_1,
+                ),
+                call(
+                    sentinel.filtered_velocity_vectors_1,
+                    sentinel.filtered_weights_1,
+                    sentinel.filtered_yreg_1,
+                ),
+            ]
+        )
+
+        mock_calculate_fit_temp_dens_velocity.assert_has_calls(
+            [
+                call(sentinel.fit_function_1),
+                call(sentinel.fit_function_2),
+                call(sentinel.fit_function_3),
+            ]
+        )
 
         self.assertIs(moments_with_valid_density, moment_fit_result.moments)
         self.assertIs(sentinel.fit_function_3, moment_fit_result.regress_result)
         self.assertEqual(sentinel.chisq_3, moment_fit_result.chisq)
         self.assertEqual(6, moment_fit_result.number_of_points)
 
-    @patch(f'{MODULE}.regress')
-    @patch(f'{MODULE}.calculate_fit_temperature_density_velocity')
-    @patch(f'{MODULE}.filter_and_flatten_regress_parameters')
+    @patch(f"{MODULE}.regress")
+    @patch(f"{MODULE}.calculate_fit_temperature_density_velocity")
+    @patch(f"{MODULE}.filter_and_flatten_regress_parameters")
     def test_fit_moments_retrying_on_failure_should_stop_retrying_with_few_energies_and_return_none_if_negative_density(
-            self,
-            mock_filter_and_flatten_regress_parameters,
-            mock_calculate_fit_temp_dens_velocity,
-            mock_regress):
+        self,
+        mock_filter_and_flatten_regress_parameters,
+        mock_calculate_fit_temp_dens_velocity,
+        mock_regress,
+    ):
         density_history = np.array([100, 89, 72])
 
         negative_density_value = -1
@@ -189,42 +336,69 @@ def test_fit_moments_retrying_on_failure_should_stop_retrying_with_few_energies_
         ]
 
         mock_filter_and_flatten_regress_parameters.side_effect = [
-            (sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg)
+            (
+                sentinel.filtered_velocity_vectors,
+                sentinel.filtered_weights,
+                sentinel.filtered_yreg,
+            )
         ]
 
         mock_regress.side_effect = [
             (sentinel.fit_function_1, sentinel.chisq_1),
         ]
 
-        self.assertIsNone(core_fit_moments_retrying_on_failure(sentinel.corrected_energy_bins,
-                                                               sentinel.velocity_vectors,
-                                                               sentinel.phase_space_density,
-                                                               sentinel.weights,
-                                                               0,
-                                                               3,
-                                                               density_history))
-
-        mock_filter_and_flatten_regress_parameters.assert_has_calls([(
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 3, )
-        )])
-
-        mock_regress.assert_has_calls([
-            call(sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg),
-        ])
-
-        mock_calculate_fit_temp_dens_velocity.assert_has_calls([
-            call(sentinel.fit_function_1),
-        ])
-
-    @patch(f'{MODULE}.regress')
-    @patch(f'{MODULE}.calculate_fit_temperature_density_velocity')
-    @patch(f'{MODULE}.filter_and_flatten_regress_parameters')
+        self.assertIsNone(
+            core_fit_moments_retrying_on_failure(
+                sentinel.corrected_energy_bins,
+                sentinel.velocity_vectors,
+                sentinel.phase_space_density,
+                sentinel.weights,
+                0,
+                3,
+                density_history,
+            )
+        )
+
+        mock_filter_and_flatten_regress_parameters.assert_has_calls(
+            [
+                (
+                    call(
+                        sentinel.corrected_energy_bins,
+                        sentinel.velocity_vectors,
+                        sentinel.phase_space_density,
+                        sentinel.weights,
+                        0,
+                        3,
+                    )
+                )
+            ]
+        )
+
+        mock_regress.assert_has_calls(
+            [
+                call(
+                    sentinel.filtered_velocity_vectors,
+                    sentinel.filtered_weights,
+                    sentinel.filtered_yreg,
+                ),
+            ]
+        )
+
+        mock_calculate_fit_temp_dens_velocity.assert_has_calls(
+            [
+                call(sentinel.fit_function_1),
+            ]
+        )
+
+    @patch(f"{MODULE}.regress")
+    @patch(f"{MODULE}.calculate_fit_temperature_density_velocity")
+    @patch(f"{MODULE}.filter_and_flatten_regress_parameters")
     def test_fit_moments_retrying_on_failure_should_stop_retrying_with_few_energies_but_return_results_if_high_density(
-            self,
-            mock_filter_and_flatten_regress_parameters,
-            mock_calculate_fit_temp_dens_velocity,
-            mock_regress):
+        self,
+        mock_filter_and_flatten_regress_parameters,
+        mock_calculate_fit_temp_dens_velocity,
+        mock_regress,
+    ):
         density_history = np.array([100, 89, 72])
 
         high_density_value = 200
@@ -235,44 +409,69 @@ def test_fit_moments_retrying_on_failure_should_stop_retrying_with_few_energies_
         ]
 
         mock_filter_and_flatten_regress_parameters.side_effect = [
-            (sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg)
+            (
+                sentinel.filtered_velocity_vectors,
+                sentinel.filtered_weights,
+                sentinel.filtered_yreg,
+            )
         ]
 
         mock_regress.side_effect = [
             (sentinel.fit_function_1, sentinel.chisq_1),
         ]
 
-        result = core_fit_moments_retrying_on_failure(sentinel.corrected_energy_bins,
-                                                      sentinel.velocity_vectors,
-                                                      sentinel.phase_space_density,
-                                                      sentinel.weights,
-                                                      0,
-                                                      3,
-                                                      density_history)
+        result = core_fit_moments_retrying_on_failure(
+            sentinel.corrected_energy_bins,
+            sentinel.velocity_vectors,
+            sentinel.phase_space_density,
+            sentinel.weights,
+            0,
+            3,
+            density_history,
+        )
         self.assertIsInstance(result, MomentFitResults)
         self.assertIs(calculated_moments, result.moments)
 
-        mock_filter_and_flatten_regress_parameters.assert_has_calls([(
-            call(sentinel.corrected_energy_bins, sentinel.velocity_vectors, sentinel.phase_space_density,
-                 sentinel.weights, 0, 3, )
-        )])
+        mock_filter_and_flatten_regress_parameters.assert_has_calls(
+            [
+                (
+                    call(
+                        sentinel.corrected_energy_bins,
+                        sentinel.velocity_vectors,
+                        sentinel.phase_space_density,
+                        sentinel.weights,
+                        0,
+                        3,
+                    )
+                )
+            ]
+        )
 
-        mock_regress.assert_has_calls([
-            call(sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg),
-        ])
+        mock_regress.assert_has_calls(
+            [
+                call(
+                    sentinel.filtered_velocity_vectors,
+                    sentinel.filtered_weights,
+                    sentinel.filtered_yreg,
+                ),
+            ]
+        )
 
-        mock_calculate_fit_temp_dens_velocity.assert_has_calls([
-            call(sentinel.fit_function_1),
-        ])
+        mock_calculate_fit_temp_dens_velocity.assert_has_calls(
+            [
+                call(sentinel.fit_function_1),
+            ]
+        )
 
-    @patch(f'{MODULE}.regress')
-    @patch(f'{MODULE}.calculate_fit_temperature_density_velocity')
-    @patch(f'{MODULE}.filter_and_flatten_regress_parameters')
+    @patch(f"{MODULE}.regress")
+    @patch(f"{MODULE}.calculate_fit_temperature_density_velocity")
+    @patch(f"{MODULE}.filter_and_flatten_regress_parameters")
     def test_fit_moments_retrying_on_failure_should_stop_retrying_with_few_energies_and_return_none_if_no_density(
-            self,
-            mock_filter_and_flatten_regress_parameters,
-            mock_calculate_fit_temp_dens_velocity,
-            mock_regress):
+        self,
+        mock_filter_and_flatten_regress_parameters,
+        mock_calculate_fit_temp_dens_velocity,
+        mock_regress,
+    ):
         density_history = np.array([100, 89, 72])
 
         high_density_value = 200
@@ -283,71 +482,107 @@ def test_fit_moments_retrying_on_failure_should_stop_retrying_with_few_energies_
         ]
 
         mock_filter_and_flatten_regress_parameters.side_effect = [
-            (sentinel.filtered_velocity_vectors, sentinel.filtered_weights, sentinel.filtered_yreg)
+            (
+                sentinel.filtered_velocity_vectors,
+                sentinel.filtered_weights,
+                sentinel.filtered_yreg,
+            )
         ]
 
         mock_regress.side_effect = [
             (sentinel.fit_function_1, sentinel.chisq_1),
         ]
 
-        result = core_fit_moments_retrying_on_failure(sentinel.corrected_energy_bins,
-                                                      sentinel.velocity_vectors,
-                                                      sentinel.phase_space_density,
-                                                      sentinel.weights,
-                                                      0,
-                                                      3,
-                                                      density_history)
+        result = core_fit_moments_retrying_on_failure(
+            sentinel.corrected_energy_bins,
+            sentinel.velocity_vectors,
+            sentinel.phase_space_density,
+            sentinel.weights,
+            0,
+            3,
+            density_history,
+        )
         self.assertIsNone(result)
 
     def test_compute_maxwellian_weight_factors_reproduces_heritage_results(self):
         counts = np.array([[[536.0, 20000, 536.0], [1.2, 3072.0000001359296, 1.2]]])
-        acquisition_duration = np.array([[80000., 40000.]])
+        acquisition_duration = np.array([[80000.0, 40000.0]])
         count_rates = counts / acquisition_duration[:, :, np.newaxis]
-        weight_factors = compute_maxwellian_weight_factors(count_rates, acquisition_duration)
+        weight_factors = compute_maxwellian_weight_factors(
+            count_rates, acquisition_duration
+        )
 
         first_weight = np.sqrt(21.25 + 536) / 536
         second_weight = np.sqrt(87381.25 + 20000.0) / 20000
         third_weight = moment_calculations.MINIMUM_WEIGHT
         fourth_weight = np.sqrt(341.25 + 3072) / 3072
 
-        np.testing.assert_array_almost_equal(weight_factors,
-                                             np.array([[[first_weight, second_weight, first_weight],
-                                                        [third_weight, fourth_weight, third_weight]]]))
+        np.testing.assert_array_almost_equal(
+            weight_factors,
+            np.array(
+                [
+                    [
+                        [first_weight, second_weight, first_weight],
+                        [third_weight, fourth_weight, third_weight],
+                    ]
+                ]
+            ),
+        )
 
     def test_regress_reproduces_heritage_results_given_all_test_data(self):
-        velocity_vectors = np.loadtxt(get_test_data_path("swe/fake_velocity_vectors.csv"), delimiter=",",
-                                      dtype=np.float64)
-        weights = np.loadtxt(get_test_data_path("swe/fake_weights.csv"), delimiter=",", dtype=np.float64)
-        yreg = np.loadtxt(get_test_data_path("swe/fake_yreg.csv"), delimiter=",", dtype=np.float64)
+        velocity_vectors = np.loadtxt(
+            get_test_data_path("swe/fake_velocity_vectors.csv"),
+            delimiter=",",
+            dtype=np.float64,
+        )
+        weights = np.loadtxt(
+            get_test_data_path("swe/fake_weights.csv"), delimiter=",", dtype=np.float64
+        )
+        yreg = np.loadtxt(
+            get_test_data_path("swe/fake_yreg.csv"), delimiter=",", dtype=np.float64
+        )
 
         regression_values, chisq = regress(velocity_vectors, weights, yreg)
 
-        np.testing.assert_array_almost_equal(regression_values,
-                                             [59277.743670,
-                                              57999.454287,
-                                              58812.363965,
-                                              1073.651710,
-                                              -185.816917,
-                                              455.279162,
-                                              -1409352.589002,
-                                              2485621.191362,
-                                              -7552247.685865,
-                                              -0.281777])
+        np.testing.assert_array_almost_equal(
+            regression_values,
+            [
+                59277.743670,
+                57999.454287,
+                58812.363965,
+                1073.651710,
+                -185.816917,
+                455.279162,
+                -1409352.589002,
+                2485621.191362,
+                -7552247.685865,
+                -0.281777,
+            ],
+        )
 
         self.assertAlmostEqual(75398.120454, chisq, places=6)
 
-    def test_calculate_fit_temperature_density_velocity_is_consistent_with_heritage_on_full_data(self):
-        regress_output_of_full_fake_data = np.array([59277.743670,
-                                                     57999.454287,
-                                                     58812.363965,
-                                                     1073.651710,
-                                                     -185.816917,
-                                                     455.279162,
-                                                     -1409352.589002,
-                                                     2485621.191362,
-                                                     -7552247.685865,
-                                                     -0.281777], dtype=np.float64)
-        moments = calculate_fit_temperature_density_velocity(regress_output_of_full_fake_data)
+    def test_calculate_fit_temperature_density_velocity_is_consistent_with_heritage_on_full_data(
+        self,
+    ):
+        regress_output_of_full_fake_data = np.array(
+            [
+                59277.743670,
+                57999.454287,
+                58812.363965,
+                1073.651710,
+                -185.816917,
+                455.279162,
+                -1409352.589002,
+                2485621.191362,
+                -7552247.685865,
+                -0.281777,
+            ],
+            dtype=np.float64,
+        )
+        moments = calculate_fit_temperature_density_velocity(
+            regress_output_of_full_fake_data
+        )
 
         self.assertAlmostEqual(1.958299, moments.alpha, places=5)
         self.assertAlmostEqual(1.729684, moments.beta, places=5)
@@ -361,40 +596,54 @@ def test_calculate_fit_temperature_density_velocity_is_consistent_with_heritage_
 
     def test_filter_and_flatten_regress_parameters(self):
         corrected_energy_bins = np.array([-1, 0, 3, 4.5, 5])
-        phase_space_density = np.array([
-            [[1, 2], [2, 3]],
-            [[5, 6], [6, 7]],
-            [[3, 1e-36], [0, 0]],
-            [[10, 11], [0, 12]],
-            [[21, 22], [23, 24]],
-        ])
-
-        weights = np.array([
-            [[1, 2], [2, 3]],
-            [[5, 6], [6, 7]],
-            [[3, 1e-36], [0, 0]],
-            [[10, 11], [0, 12]],
-            [[21, 22], [23, 24]],
-        ])
-
-        velocity_vectors = np.array([
-            [[[1, 0, 0], [1, 0, 0]], [[2, 0, 0], [2, 0, 0]]],
-            [[[5, 0, 0], [5, 0, 0]], [[6, 0, 0], [6, 0, 0]]],
-            [[[3, 0, 0], [4, 0, 0]], [[0, 0, 0], [0, 0, 0]]],
-            [[[10, 0, 0], [10, 0, 0]], [[0, 0, 0], [0, 0, 0]]],
-            [[[20, 0, 0], [8, 0, 0]], [[12, 0, 0], [23, 0, 0]]],
-        ])
+        phase_space_density = np.array(
+            [
+                [[1, 2], [2, 3]],
+                [[5, 6], [6, 7]],
+                [[3, 1e-36], [0, 0]],
+                [[10, 11], [0, 12]],
+                [[21, 22], [23, 24]],
+            ]
+        )
+
+        weights = np.array(
+            [
+                [[1, 2], [2, 3]],
+                [[5, 6], [6, 7]],
+                [[3, 1e-36], [0, 0]],
+                [[10, 11], [0, 12]],
+                [[21, 22], [23, 24]],
+            ]
+        )
+
+        velocity_vectors = np.array(
+            [
+                [[[1, 0, 0], [1, 0, 0]], [[2, 0, 0], [2, 0, 0]]],
+                [[[5, 0, 0], [5, 0, 0]], [[6, 0, 0], [6, 0, 0]]],
+                [[[3, 0, 0], [4, 0, 0]], [[0, 0, 0], [0, 0, 0]]],
+                [[[10, 0, 0], [10, 0, 0]], [[0, 0, 0], [0, 0, 0]]],
+                [[[20, 0, 0], [8, 0, 0]], [[12, 0, 0], [23, 0, 0]]],
+            ]
+        )
 
         core_breakpoint_index = 1
         core_halo_breakpoint_index = 4
-        vectors, actual_weights, yreg = filter_and_flatten_regress_parameters(corrected_energy_bins, velocity_vectors,
-                                                                              phase_space_density, weights,
-                                                                              core_breakpoint_index,
-                                                                              core_halo_breakpoint_index)
+        vectors, actual_weights, yreg = filter_and_flatten_regress_parameters(
+            corrected_energy_bins,
+            velocity_vectors,
+            phase_space_density,
+            weights,
+            core_breakpoint_index,
+            core_halo_breakpoint_index,
+        )
 
-        np.testing.assert_array_equal(vectors, [[3, 0, 0], [4, 0, 0], [10, 0, 0], [10, 0, 0], [0, 0, 0]])
+        np.testing.assert_array_equal(
+            vectors, [[3, 0, 0], [4, 0, 0], [10, 0, 0], [10, 0, 0], [0, 0, 0]]
+        )
         np.testing.assert_array_equal(actual_weights, [3, 1e-36, 10, 11, 12])
-        np.testing.assert_array_equal(yreg, [np.log(3), -80.6, np.log(10), np.log(11), np.log(12)])
+        np.testing.assert_array_equal(
+            yreg, [np.log(3), -80.6, np.log(10), np.log(11), np.log(12)]
+        )
 
     def test_rotate_dps_vector_to_rtn(self):
         dsp_vector = np.array([0, 1, 0])
@@ -410,29 +659,41 @@ def test_rotate_dps_vector_to_rtn(self):
                 rtn_vector = apply_rotation_matrix(input_rotation_matrix, dsp_vector)
                 np.testing.assert_array_equal(rtn_vector, expected_rtn_vector)
 
-    @patch(f'{MODULE}.spiceypy.pxform')
-    @patch(f'{MODULE}.spiceypy.datetime2et')
-    def test_get_dps_to_rtn_rotation_matrix_returns_pxform_result(self, mock_datetime2et, mock_pxform):
+    @patch(f"{MODULE}.spiceypy.pxform")
+    @patch(f"{MODULE}.spiceypy.datetime2et")
+    def test_get_dps_to_rtn_rotation_matrix_returns_pxform_result(
+        self, mock_datetime2et, mock_pxform
+    ):
         epoch = datetime(year=2020, month=3, day=10)
         rotation_matrix = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
         mock_pxform.return_value = rotation_matrix
 
-        actual_rotation_matrix = moment_calculations.get_dps_to_rtn_rotation_matrix(epoch)
+        actual_rotation_matrix = moment_calculations.get_dps_to_rtn_rotation_matrix(
+            epoch
+        )
         mock_datetime2et.assert_called_once_with(epoch)
-        mock_pxform.assert_called_once_with("IMAP_DPS", "IMAP_RTN", mock_datetime2et.return_value)
+        mock_pxform.assert_called_once_with(
+            "IMAP_DPS", "IMAP_RTN", mock_datetime2et.return_value
+        )
         np.testing.assert_array_equal(actual_rotation_matrix, rotation_matrix)
 
-    @patch(f'{MODULE}.spiceypy.pxform')
-    @patch(f'{MODULE}.spiceypy.datetime2et')
-    def test_get_dps_to_rtn_rotation_matrix_propagates_spice_errors(self, mock_datetime2et, mock_pxform):
+    @patch(f"{MODULE}.spiceypy.pxform")
+    @patch(f"{MODULE}.spiceypy.datetime2et")
+    def test_get_dps_to_rtn_rotation_matrix_propagates_spice_errors(
+        self, mock_datetime2et, mock_pxform
+    ):
         epoch = datetime(year=2020, month=3, day=10)
-        mock_pxform.side_effect = spiceypy.utils.exceptions.SpiceyError("Missing coverage for IMAP_DPS")
+        mock_pxform.side_effect = spiceypy.utils.exceptions.SpiceyError(
+            "Missing coverage for IMAP_DPS"
+        )
 
         with self.assertRaises(spiceypy.utils.exceptions.SpiceyError):
             moment_calculations.get_dps_to_rtn_rotation_matrix(epoch)
 
         mock_datetime2et.assert_called_once_with(epoch)
-        mock_pxform.assert_called_once_with("IMAP_DPS", "IMAP_RTN", mock_datetime2et.return_value)
+        mock_pxform.assert_called_once_with(
+            "IMAP_DPS", "IMAP_RTN", mock_datetime2et.return_value
+        )
 
     def test_rotate_temperature(self):
         temperature_alpha = math.pi / 4
@@ -451,17 +712,23 @@ def test_rotate_temperature(self):
         cases = [
             (
                 rotation_matrix,
-                np.array([
-                    np.asin(expected_rtn_temperature[2]),
-                    np.atan2(expected_rtn_temperature[1], expected_rtn_temperature[0]),
-                ]),
+                np.array(
+                    [
+                        np.asin(expected_rtn_temperature[2]),
+                        np.atan2(
+                            expected_rtn_temperature[1], expected_rtn_temperature[0]
+                        ),
+                    ]
+                ),
             ),
             (np.full((3, 3), np.nan), np.full(2, np.nan)),
         ]
 
         for input_rotation_matrix, expected_angles in cases:
             with self.subTest(input_rotation_matrix):
-                theta, phi = rotate_temperature(input_rotation_matrix, temperature_alpha, temperature_beta)
+                theta, phi = rotate_temperature(
+                    input_rotation_matrix, temperature_alpha, temperature_beta
+                )
                 np.testing.assert_array_equal([theta, phi], expected_angles)
 
     def test_momscale(self):
@@ -471,15 +738,26 @@ def test_momscale(self):
         test_cases = [
             ("normal values", 1.92424e4, 1.74908e5, 68.3, 3497.7912, 1e-4),
             ("the sun froze", 12301, 12301, 1500, 7.0965e62, 1e57),
-            ("the sun froze", 12301, 12301, 400.3, 1.99655e+45, 1e40)
+            ("the sun froze", 12301, 12301, 400.3, 1.99655e45, 1e40),
         ]
 
-        for test_name, t_parallel, t_perpendicular, speed, expected_density_scale, allowable_difference in test_cases:
+        for (
+            test_name,
+            t_parallel,
+            t_perpendicular,
+            speed,
+            expected_density_scale,
+            allowable_difference,
+        ) in test_cases:
             with self.subTest(test_name):
                 temperature = (t_parallel + 2 * t_perpendicular) / 3
-                dscale = compute_density_scale(core_halo_break - spacecraft_potential, speed, temperature)
+                dscale = compute_density_scale(
+                    core_halo_break - spacecraft_potential, speed, temperature
+                )
 
-                self.assertAlmostEqual(expected_density_scale, dscale, delta=allowable_difference)
+                self.assertAlmostEqual(
+                    expected_density_scale, dscale, delta=allowable_difference
+                )
 
     def test_halotrunc(self):
         density = 130
@@ -500,20 +778,72 @@ def test_halotrunc(self):
         )
 
         test_cases = [
-            ("t parallel less then 1e4, density should not change", 1e3, 1e7, 15, moments.density),
-            ("t perpendicular less then 1e4, density should not change", 1e7, 1e2, 15, moments.density),
-            ("t perpendicular greater then 1e8, density should be thrown out", 1e9, 1e1, 15, None),
-            ("t parallel greater then 1e8, density should be thrown out", 1e2, 1e9, 15, None),
-            ("core energy range is greater than 5 and temp is greater than 1e7", 1e7, 1.5e7, 15, moments.density),
-            ("core energy range is less than 5 and temperatures greater than 1e4", 1e5, 1.5e6, 10, moments.density),
-            ("core energy range is greater than 5 and temperatures greater than 1e4", 1e5, 1.5e6, 15, 126.487),
+            (
+                "t parallel less then 1e4, density should not change",
+                1e3,
+                1e7,
+                15,
+                moments.density,
+            ),
+            (
+                "t perpendicular less then 1e4, density should not change",
+                1e7,
+                1e2,
+                15,
+                moments.density,
+            ),
+            (
+                "t perpendicular greater then 1e8, density should be thrown out",
+                1e9,
+                1e1,
+                15,
+                None,
+            ),
+            (
+                "t parallel greater then 1e8, density should be thrown out",
+                1e2,
+                1e9,
+                15,
+                None,
+            ),
+            (
+                "core energy range is greater than 5 and temp is greater than 1e7",
+                1e7,
+                1.5e7,
+                15,
+                moments.density,
+            ),
+            (
+                "core energy range is less than 5 and temperatures greater than 1e4",
+                1e5,
+                1.5e6,
+                10,
+                moments.density,
+            ),
+            (
+                "core energy range is greater than 5 and temperatures greater than 1e4",
+                1e5,
+                1.5e6,
+                15,
+                126.487,
+            ),
         ]
 
-        for test_name, t_parallel, t_perpendicular, core_halo_breakpoint, expected_density in test_cases:
+        for (
+            test_name,
+            t_parallel,
+            t_perpendicular,
+            core_halo_breakpoint,
+            expected_density,
+        ) in test_cases:
             with self.subTest(test_name):
-                moments = dataclasses.replace(moments, t_parallel=t_parallel, t_perpendicular=t_perpendicular)
+                moments = dataclasses.replace(
+                    moments, t_parallel=t_parallel, t_perpendicular=t_perpendicular
+                )
 
-                scaled_density = halotrunc(moments, core_halo_breakpoint, spacecraft_potential)
+                scaled_density = halotrunc(
+                    moments, core_halo_breakpoint, spacecraft_potential
+                )
 
                 self.assertAlmostEqual(expected_density, scaled_density, places=3)
 
@@ -521,71 +851,134 @@ def test_integrate(self):
         istart = 1
         iend = 19
 
-        inst_az_data = np.loadtxt(get_test_data_path("swe/instrument_azimuth.csv"), delimiter=",").reshape(20, 7, 30)
+        inst_az_data = np.loadtxt(
+            get_test_data_path("swe/instrument_azimuth.csv"), delimiter=","
+        ).reshape(20, 7, 30)
         inst_az_data_reshaped = inst_az_data[:, 0, :]
 
-        phase_space_density = np.loadtxt(get_test_data_path("swe/phase_space_density.csv"), delimiter=",").reshape(20,
-                                                                                                                   7,
-                                                                                                                   30)
+        phase_space_density = np.loadtxt(
+            get_test_data_path("swe/phase_space_density.csv"), delimiter=","
+        ).reshape(20, 7, 30)
         phase_space_density_expected_shape = np.moveaxis(phase_space_density, 2, 1)
 
-        energy = np.loadtxt(get_test_data_path("swe/energies.csv"), delimiter=",").reshape(20, 7, 30)[:, 0, 0]
-
-        sintheta = np.array([-0.1673557, 0.91652155, -0.83665564, 0., 0.83665564, -0.91652155, 0.1673557])
+        energy = np.loadtxt(
+            get_test_data_path("swe/energies.csv"), delimiter=","
+        ).reshape(20, 7, 30)[:, 0, 0]
+
+        sintheta = np.array(
+            [
+                -0.1673557,
+                0.91652155,
+                -0.83665564,
+                0.0,
+                0.83665564,
+                -0.91652155,
+                0.1673557,
+            ]
+        )
 
-        costheta = np.array([0.9858965815825497, -0.39998531498835127, -0.5477292602242684, 1.0, -0.5477292602242684,
-                             -0.39998531498835127, 0.9858965815825497])
+        costheta = np.array(
+            [
+                0.9858965815825497,
+                -0.39998531498835127,
+                -0.5477292602242684,
+                1.0,
+                -0.5477292602242684,
+                -0.39998531498835127,
+                0.9858965815825497,
+            ]
+        )
 
         deltheta = np.array([0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196])
 
         cdelnv = np.array([0, 0, 0, 0])
         cdelt = np.array([0, 0, 0, 0, 0, 0])
         spacecraft_potential = 12
-        integrate_outputs = moment_calculations.integrate(istart, iend, energy - spacecraft_potential, sintheta,
-                                                          costheta,
-                                                          deltheta, phase_space_density_expected_shape,
-                                                          inst_az_data_reshaped, spacecraft_potential, cdelnv, cdelt)
+        integrate_outputs = moment_calculations.integrate(
+            istart,
+            iend,
+            energy - spacecraft_potential,
+            sintheta,
+            costheta,
+            deltheta,
+            phase_space_density_expected_shape,
+            inst_az_data_reshaped,
+            spacecraft_potential,
+            cdelnv,
+            cdelt,
+        )
 
-        np.testing.assert_allclose(6.71828e+23, integrate_outputs.density, rtol=1e-5)
+        np.testing.assert_allclose(6.71828e23, integrate_outputs.density, rtol=1e-5)
 
-        np.testing.assert_allclose(np.array([-1053.53, 327.55, 1919.34]), integrate_outputs.velocity,
-                                   rtol=1e-4)
-        np.testing.assert_allclose(np.array(-10.7911), integrate_outputs.base_energy,
-                                   rtol=1e-4)
+        np.testing.assert_allclose(
+            np.array([-1053.53, 327.55, 1919.34]), integrate_outputs.velocity, rtol=1e-4
+        )
+        np.testing.assert_allclose(
+            np.array(-10.7911), integrate_outputs.base_energy, rtol=1e-4
+        )
         np.testing.assert_allclose(
             np.array([48.1855, -1.06466, 62.4149, -1.06081, -37.8901, -8.58049]),
-            integrate_outputs.temperature, rtol=2e-4)
+            integrate_outputs.temperature,
+            rtol=2e-4,
+        )
         np.testing.assert_allclose(
-            np.array([9.92303e+24, -2.88985e+25, 9.45357e+24]),
-            integrate_outputs.heat_flux, rtol=1e-4)
+            np.array([9.92303e24, -2.88985e25, 9.45357e24]),
+            integrate_outputs.heat_flux,
+            rtol=1e-4,
+        )
 
     def test_integrate_returns_early_when_density_is_negative(self):
         istart = 1
         iend = 19
 
-        inst_az_data = np.loadtxt(get_test_data_path("swe/instrument_azimuth.csv"), delimiter=",").reshape(20, 7, 30)
+        inst_az_data = np.loadtxt(
+            get_test_data_path("swe/instrument_azimuth.csv"), delimiter=","
+        ).reshape(20, 7, 30)
         inst_az_data_reshaped = inst_az_data[:, 0, :]
 
-        phase_space_density = np.loadtxt(get_test_data_path("swe/phase_space_density.csv"), delimiter=",").reshape(20,
-                                                                                                                   7,
-                                                                                                                   30)
+        phase_space_density = np.loadtxt(
+            get_test_data_path("swe/phase_space_density.csv"), delimiter=","
+        ).reshape(20, 7, 30)
         phase_space_density_expected_shape = np.moveaxis(phase_space_density, 2, 1)
 
-        energy = np.loadtxt(get_test_data_path("swe/energies.csv"), delimiter=",").reshape(20, 7, 30)[:, 0, 0]
-
-        sintheta = np.array([-0.89100652, -0.66913061, -0.35836795, 0., 0.35836795, 0.66913061, 0.89100652])
+        energy = np.loadtxt(
+            get_test_data_path("swe/energies.csv"), delimiter=","
+        ).reshape(20, 7, 30)[:, 0, 0]
+
+        sintheta = np.array(
+            [
+                -0.89100652,
+                -0.66913061,
+                -0.35836795,
+                0.0,
+                0.35836795,
+                0.66913061,
+                0.89100652,
+            ]
+        )
 
-        costheta = np.array([0.4539905, 0.74314483, 0.93358043, 1., 0.93358043, 0.74314483, 0.4539905])
+        costheta = np.array(
+            [0.4539905, 0.74314483, 0.93358043, 1.0, 0.93358043, 0.74314483, 0.4539905]
+        )
 
         deltheta = np.array([0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196])
 
         spacecraft_potential = 12
         cdelnv = np.array([0, 0, 0, 0])
         cdelt = np.array([0, 0, 0, 0, 0, 0])
-        integrate_outputs = moment_calculations.integrate(istart, iend, energy - spacecraft_potential, sintheta,
-                                                          costheta,
-                                                          deltheta, phase_space_density_expected_shape,
-                                                          inst_az_data_reshaped, spacecraft_potential, cdelnv, cdelt)
+        integrate_outputs = moment_calculations.integrate(
+            istart,
+            iend,
+            energy - spacecraft_potential,
+            sintheta,
+            costheta,
+            deltheta,
+            phase_space_density_expected_shape,
+            inst_az_data_reshaped,
+            spacecraft_potential,
+            cdelnv,
+            cdelt,
+        )
 
         self.assertIsNone(integrate_outputs)
 
@@ -593,45 +986,78 @@ def test_integrate_with_nonzero_cdelnv_and_cdelt(self):
         istart = 1
         iend = 19
 
-        inst_az_data = np.loadtxt(get_test_data_path("swe/instrument_azimuth.csv"), delimiter=",").reshape(20, 7, 30)
+        inst_az_data = np.loadtxt(
+            get_test_data_path("swe/instrument_azimuth.csv"), delimiter=","
+        ).reshape(20, 7, 30)
 
-        phase_space_density = np.loadtxt(get_test_data_path("swe/phase_space_density.csv"), delimiter=",").reshape(20,
-                                                                                                                   7,
-                                                                                                                   30)
+        phase_space_density = np.loadtxt(
+            get_test_data_path("swe/phase_space_density.csv"), delimiter=","
+        ).reshape(20, 7, 30)
         inst_az_data_energy_spin = inst_az_data[:, 0, :]
         phase_space_density_energy_spin_cem = np.moveaxis(phase_space_density, 2, 1)
 
-        energy = np.loadtxt(get_test_data_path("swe/energies.csv"), delimiter=",").reshape(20, 7, 30)[:, 0, 0]
-
-        artificial_all_positive_sintheta = np.array([0.89100652, 0.66913061, 0.35836795, 0., 0.35836795, 0.66913061,
-                                                     0.89100652])
+        energy = np.loadtxt(
+            get_test_data_path("swe/energies.csv"), delimiter=","
+        ).reshape(20, 7, 30)[:, 0, 0]
+
+        artificial_all_positive_sintheta = np.array(
+            [
+                0.89100652,
+                0.66913061,
+                0.35836795,
+                0.0,
+                0.35836795,
+                0.66913061,
+                0.89100652,
+            ]
+        )
 
-        costheta = np.array([0.4539905, 0.74314483, 0.93358043, 1., 0.93358043, 0.74314483, 0.4539905])
+        costheta = np.array(
+            [0.4539905, 0.74314483, 0.93358043, 1.0, 0.93358043, 0.74314483, 0.4539905]
+        )
 
         deltheta = np.array([0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196])
 
         spacecraft_potential = 12
         cdelnv = np.array([1e23, 2e23, 3e23, 4e23])
         cdelt = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6]) * 1e40
-        integrate_outputs = moment_calculations.integrate(istart, iend, energy - spacecraft_potential,
-                                                          artificial_all_positive_sintheta,
-                                                          costheta,
-                                                          deltheta, phase_space_density_energy_spin_cem,
-                                                          inst_az_data_energy_spin, spacecraft_potential, cdelnv, cdelt)
+        integrate_outputs = moment_calculations.integrate(
+            istart,
+            iend,
+            energy - spacecraft_potential,
+            artificial_all_positive_sintheta,
+            costheta,
+            deltheta,
+            phase_space_density_energy_spin_cem,
+            inst_az_data_energy_spin,
+            spacecraft_potential,
+            cdelnv,
+            cdelt,
+        )
 
-        np.testing.assert_allclose(1.94762e+25, integrate_outputs.density, rtol=1e-5)
+        np.testing.assert_allclose(1.94762e25, integrate_outputs.density, rtol=1e-5)
 
-        np.testing.assert_allclose(np.array([53.8532, 10.9898, -2355.75]), integrate_outputs.velocity,
-                                   rtol=1e-4)
+        np.testing.assert_allclose(
+            np.array([53.8532, 10.9898, -2355.75]),
+            integrate_outputs.velocity,
+            rtol=1e-4,
+        )
 
-        np.testing.assert_allclose(np.array(-10.7911), integrate_outputs.base_energy,
-                                   rtol=1e-4)
         np.testing.assert_allclose(
-            np.array([5.13448e+13, 1.0269e+14, 1.54034e+14, 2.05379e+14, 2.56724e+14, 3.08069e+14]),
-            integrate_outputs.temperature, rtol=2e-4)
+            np.array(-10.7911), integrate_outputs.base_energy, rtol=1e-4
+        )
         np.testing.assert_allclose(
-            np.array([-5.50653e+24, -1.61473e+24, -3.40728e+26]),
-            integrate_outputs.heat_flux, rtol=2e-4)
+            np.array(
+                [5.13448e13, 1.0269e14, 1.54034e14, 2.05379e14, 2.56724e14, 3.08069e14]
+            ),
+            integrate_outputs.temperature,
+            rtol=2e-4,
+        )
+        np.testing.assert_allclose(
+            np.array([-5.50653e24, -1.61473e24, -3.40728e26]),
+            integrate_outputs.heat_flux,
+            rtol=2e-4,
+        )
 
     def test_scale_core_density(self):
         core_velocity = np.array([300, 400, 500], dtype=float)
@@ -647,50 +1073,135 @@ def test_scale_core_density(self):
             velocity_z=7,
             density=8,
             aoo=9e5,
-            ao=10
+            ao=10,
         )
 
         ifit = 5
         spacecraft_potential = 12
         cosin_p = np.array([0.9034, 0.6947, 0.3730, 0.0, -0.3714, -0.6896, -0.8996])
-        aperture_field_of_view = [0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196]
+        aperture_field_of_view = [
+            0.6178,
+            0.3770,
+            0.3857,
+            0.3805,
+            0.3805,
+            0.3805,
+            0.6196,
+        ]
 
-        regress_outputs = np.array([-1e-9, -9e-10, -8e-10, -7e-10, -6e-10, -5e-10, -4e-10, -3e-10, -2e-10, -1e-10])
+        regress_outputs = np.array(
+            [
+                -1e-9,
+                -9e-10,
+                -8e-10,
+                -7e-10,
+                -6e-10,
+                -5e-10,
+                -4e-10,
+                -3e-10,
+                -2e-10,
+                -1e-10,
+            ]
+        )
         core_density = 1.23456789
         base_energy = 100
 
-        swepam_energies = np.array([2.55714286, 3.65142857, 5.16, 7.30571429,
-                                    10.32857143, 14.34285714, 19.95714286, 27.42857143,
-                                    38.37142857, 52.82857143, 73.32857143, 102.0,
-                                    142.14285714, 196.57142857, 272., 372.71428571,
-                                    519.0, 712.57142857, 987.14285714, 1370.0])
+        swepam_energies = np.array(
+            [
+                2.55714286,
+                3.65142857,
+                5.16,
+                7.30571429,
+                10.32857143,
+                14.34285714,
+                19.95714286,
+                27.42857143,
+                38.37142857,
+                52.82857143,
+                73.32857143,
+                102.0,
+                142.14285714,
+                196.57142857,
+                272.0,
+                372.71428571,
+                519.0,
+                712.57142857,
+                987.14285714,
+                1370.0,
+            ]
+        )
 
         phi = np.broadcast_to((np.arange(0, 30) * 360 / 30)[np.newaxis, :], (20, 30))
 
-        core_density_output = \
-            moment_calculations.scale_core_density(
-                core_density,
-                core_velocity, core_temp,
-                core_moment_fit, ifit, swepam_energies - spacecraft_potential,
-                spacecraft_potential, cosin_p,
-                aperture_field_of_view,
-                phi,
-                regress_outputs,
-                base_energy)
+        core_density_output = moment_calculations.scale_core_density(
+            core_density,
+            core_velocity,
+            core_temp,
+            core_moment_fit,
+            ifit,
+            swepam_energies - spacecraft_potential,
+            spacecraft_potential,
+            cosin_p,
+            aperture_field_of_view,
+            phi,
+            regress_outputs,
+            base_energy,
+        )
 
         np.testing.assert_allclose(
-            np.array([5718524.18228547, -263791830.41031304, 85711056.45722340, -8111566.20922477, ]),
-            core_density_output.cdelnv, rtol=2e-5)
+            np.array(
+                [
+                    5718524.18228547,
+                    -263791830.41031304,
+                    85711056.45722340,
+                    -8111566.20922477,
+                ]
+            ),
+            core_density_output.cdelnv,
+            rtol=2e-5,
+        )
+        np.testing.assert_allclose(
+            np.array(
+                [
+                    9087717.95919351,
+                    395602.46759361,
+                    10176602.48232761,
+                    3589.91254779,
+                    -1123.68989106,
+                    11120511.58411137,
+                ]
+            ),
+            core_density_output.cdelt,
+            rtol=5e-5,
+        )
         np.testing.assert_allclose(
-            np.array([9087717.95919351, 395602.46759361, 10176602.48232761, 3589.91254779, -1123.68989106,
-                      11120511.58411137, ]),
-            core_density_output.cdelt, rtol=5e-5)
-        np.testing.assert_allclose(5718525.41685336, core_density_output.density, rtol=1e-5)
+            5718525.41685336, core_density_output.density, rtol=1e-5
+        )
         np.testing.assert_allclose(
-            np.array([1.58917372, 0.06918342, 1.77959155, 0.00063640, -0.00018571, 1.94465965, ]),
-            core_density_output.temperature, rtol=5e-5)
-        np.testing.assert_allclose(np.array([-46.12927998, 14.98840069, -1.41836371, ]), core_density_output.velocity,
-                                   rtol=1e-5)
+            np.array(
+                [
+                    1.58917372,
+                    0.06918342,
+                    1.77959155,
+                    0.00063640,
+                    -0.00018571,
+                    1.94465965,
+                ]
+            ),
+            core_density_output.temperature,
+            rtol=5e-5,
+        )
+        np.testing.assert_allclose(
+            np.array(
+                [
+                    -46.12927998,
+                    14.98840069,
+                    -1.41836371,
+                ]
+            ),
+            core_density_output.velocity,
+            rtol=1e-5,
+        )
 
     def test_scale_core_density_with_base_less_than_zero(self):
         core_velocity = np.array([300, 400, 500], dtype=float)
@@ -706,49 +1217,126 @@ def test_scale_core_density_with_base_less_than_zero(self):
             velocity_z=7,
             density=8,
             aoo=9,
-            ao=10
+            ao=10,
         )
         ifit = 5
         spacecraft_potential = 14
         cosin_p = np.array([0.9034, 0.6947, 0.3730, 0.0, -0.3714, -0.6896, -0.8996])
-        aperture_field_of_view = [0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196]
+        aperture_field_of_view = [
+            0.6178,
+            0.3770,
+            0.3857,
+            0.3805,
+            0.3805,
+            0.3805,
+            0.6196,
+        ]
 
-        regress_outputs = np.array([-1e-9, -9e-10, -8e-10, -7e-10, -6e-10, -5e-10, -4e-10, -3e-10, -2e-10, -1e-10])
+        regress_outputs = np.array(
+            [
+                -1e-9,
+                -9e-10,
+                -8e-10,
+                -7e-10,
+                -6e-10,
+                -5e-10,
+                -4e-10,
+                -3e-10,
+                -2e-10,
+                -1e-10,
+            ]
+        )
         core_density = 1.23456789
         base_energy = 100
 
-        swepam_energies = np.array([2.55714286, 3.65142857, 5.16, 7.30571429,
-                                    10.32857143, 14.34285714, 19.95714286, 27.42857143,
-                                    38.37142857, 52.82857143, 73.32857143, 102.0,
-                                    142.14285714, 196.57142857, 272., 372.71428571,
-                                    519.0, 712.57142857, 987.14285714, 1370.0])
+        swepam_energies = np.array(
+            [
+                2.55714286,
+                3.65142857,
+                5.16,
+                7.30571429,
+                10.32857143,
+                14.34285714,
+                19.95714286,
+                27.42857143,
+                38.37142857,
+                52.82857143,
+                73.32857143,
+                102.0,
+                142.14285714,
+                196.57142857,
+                272.0,
+                372.71428571,
+                519.0,
+                712.57142857,
+                987.14285714,
+                1370.0,
+            ]
+        )
 
         phi = np.broadcast_to((np.arange(0, 30) * 360 / 30)[np.newaxis, :], (20, 30))
 
         core_density_output = moment_calculations.scale_core_density(
             core_density,
-            core_velocity, core_temp,
-            core_moment_fit, ifit,
+            core_velocity,
+            core_temp,
+            core_moment_fit,
+            ifit,
             swepam_energies - spacecraft_potential,
-            spacecraft_potential, cosin_p,
+            spacecraft_potential,
+            cosin_p,
             aperture_field_of_view,
             phi,
             regress_outputs,
-            base_energy)
+            base_energy,
+        )
 
         np.testing.assert_allclose(
-            np.array([817041.86654199, -14699338.23262659, 4776103.65373425, -451959.71106777, ]),
-            core_density_output.cdelnv, 2e-5)
+            np.array(
+                [
+                    817041.86654199,
+                    -14699338.23262659,
+                    4776103.65373425,
+                    -451959.71106777,
+                ]
+            ),
+            core_density_output.cdelnv,
+            2e-5,
+        )
         np.testing.assert_allclose(
-            np.array([194444.92879664, 8463.82584106, 217743.48011274, 76.19579312, -24.62356212, 237940.02226200, ]),
-            core_density_output.cdelt, 5e-5)
+            np.array(
+                [
+                    194444.92879664,
+                    8463.82584106,
+                    217743.48011274,
+                    76.19579312,
+                    -24.62356212,
+                    237940.02226200,
+                ]
+            ),
+            core_density_output.cdelt,
+            5e-5,
+        )
         np.testing.assert_allclose(817043, core_density_output.density, rtol=1e-5)
         np.testing.assert_allclose(
-            np.array([0.23800124, 0.01038931, 0.26654716, 0.00015370, 0.00004541, 0.29131156, ]),
+            np.array(
+                [
+                    0.23800124,
+                    0.01038931,
+                    0.26654716,
+                    0.00015370,
+                    0.00004541,
+                    0.29131156,
+                ]
+            ),
             core_density_output.temperature,
-            rtol=5e-5)
-        np.testing.assert_allclose(np.array([-17.99044364, 5.84620013, -0.55240957]), core_density_output.velocity,
-                                   rtol=1e-5)
+            rtol=5e-5,
+        )
+        np.testing.assert_allclose(
+            np.array([-17.99044364, 5.84620013, -0.55240957]),
+            core_density_output.velocity,
+            rtol=1e-5,
+        )
 
     def test_scale_halo_density(self):
         halo_velocities = np.array([300, 400, 500], dtype=float)
@@ -764,14 +1352,35 @@ def test_scale_halo_density(self):
             velocity_z=7,
             density=8,
             aoo=9,
-            ao=10
+            ao=10,
         )
         space_craft_potential = 12
 
         cosin_p = np.array([0.9034, 0.6947, 0.3730, 0.0, -0.3714, -0.6896, -0.8996])
-        aperture_field_of_view = [0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196]
+        aperture_field_of_view = [
+            0.6178,
+            0.3770,
+            0.3857,
+            0.3805,
+            0.3805,
+            0.3805,
+            0.6196,
+        ]
 
-        regress_outputs = np.array([-1e-9, -9e-10, -8e-10, -7e-10, -6e-10, -5e-10, -4e-10, -3e-10, -2e-10, -1e-10])
+        regress_outputs = np.array(
+            [
+                -1e-9,
+                -9e-10,
+                -8e-10,
+                -7e-10,
+                -6e-10,
+                -5e-10,
+                -4e-10,
+                -3e-10,
+                -2e-10,
+                -1e-10,
+            ]
+        )
         base_energy = 100
 
         phi = np.broadcast_to((np.arange(0, 30) * 360 / 30)[np.newaxis, :], (20, 30))
@@ -780,20 +1389,47 @@ def test_scale_halo_density(self):
         core_halo_break = 80
 
         scaled_density = scale_halo_density(
-            halo_density, halo_velocities, halo_temps, halo_moment_fits,
-            space_craft_potential, core_halo_break,
+            halo_density,
+            halo_velocities,
+            halo_temps,
+            halo_moment_fits,
+            space_craft_potential,
+            core_halo_break,
             cosin_p,
             aperture_field_of_view,
             phi,
             regress_outputs,
-            base_energy)
+            base_energy,
+        )
 
-        np.testing.assert_allclose(1.8148638e+09, scaled_density.density, rtol=1e-5)
-        np.testing.assert_allclose(np.array([-281.57623, 91.485629, -8.6923434, ]), scaled_density.velocity)
-        np.testing.assert_allclose(np.array([58.311792, 2.5465379, 65.29518, 0.030580338, -9.4024419e-05, 71.348932, ]),
-                                   scaled_density.temperature)
+        np.testing.assert_allclose(1.8148638e09, scaled_density.density, rtol=1e-5)
+        np.testing.assert_allclose(
+            np.array(
+                [
+                    -281.57623,
+                    91.485629,
+                    -8.6923434,
+                ]
+            ),
+            scaled_density.velocity,
+        )
+        np.testing.assert_allclose(
+            np.array(
+                [
+                    58.311792,
+                    2.5465379,
+                    65.29518,
+                    0.030580338,
+                    -9.4024419e-05,
+                    71.348932,
+                ]
+            ),
+            scaled_density.temperature,
+        )
 
-    def test_scale_halo_density_difference_in_breakpoints_is_greater_than_base_energy(self):
+    def test_scale_halo_density_difference_in_breakpoints_is_greater_than_base_energy(
+        self,
+    ):
         halo_velocities = np.array([300, 400, 500], dtype=float)
         halo_temps = np.array([10, 20, 30, 40, 50, 60], dtype=float)
 
@@ -807,14 +1443,35 @@ def test_scale_halo_density_difference_in_breakpoints_is_greater_than_base_energ
             velocity_z=7,
             density=8,
             aoo=9,
-            ao=10
+            ao=10,
         )
         space_craft_potential = 12
 
         cosin_p = np.array([0.9034, 0.6947, 0.3730, 0.0, -0.3714, -0.6896, -0.8996])
-        aperture_field_of_view = [0.6178, 0.3770, 0.3857, 0.3805, 0.3805, 0.3805, 0.6196]
+        aperture_field_of_view = [
+            0.6178,
+            0.3770,
+            0.3857,
+            0.3805,
+            0.3805,
+            0.3805,
+            0.6196,
+        ]
 
-        regress_outputs = np.array([-1e-9, -9e-10, -8e-10, -7e-10, -6e-10, -5e-10, -4e-10, -3e-10, -2e-10, -1e-10])
+        regress_outputs = np.array(
+            [
+                -1e-9,
+                -9e-10,
+                -8e-10,
+                -7e-10,
+                -6e-10,
+                -5e-10,
+                -4e-10,
+                -3e-10,
+                -2e-10,
+                -1e-10,
+            ]
+        )
         base_energy = 60
 
         phi = np.broadcast_to((np.arange(0, 30) * 360 / 30)[np.newaxis, :], (20, 30))
@@ -823,18 +1480,32 @@ def test_scale_halo_density_difference_in_breakpoints_is_greater_than_base_energ
         core_halo_break = 80
 
         scaled_density = scale_halo_density(
-            halo_density, halo_velocities, halo_temps, halo_moment_fits,
-            space_craft_potential, core_halo_break,
+            halo_density,
+            halo_velocities,
+            halo_temps,
+            halo_moment_fits,
+            space_craft_potential,
+            core_halo_break,
             cosin_p,
             aperture_field_of_view,
             phi,
             regress_outputs,
-            base_energy)
+            base_energy,
+        )
 
-        np.testing.assert_allclose(3.0876812e+08, scaled_density.density, rtol=1e-5)
-        np.testing.assert_allclose(np.array([1521.8375, 1116.6945, 1636.3078]), scaled_density.velocity, rtol=1e-5)
-        np.testing.assert_allclose(np.array([-67.071986, 60.433456, -14.211044, 129.49805, 161.93695, 72.622535]),
-                                   scaled_density.temperature, rtol=3e-5)
+        np.testing.assert_allclose(3.0876812e08, scaled_density.density, rtol=1e-5)
+        np.testing.assert_allclose(
+            np.array([1521.8375, 1116.6945, 1636.3078]),
+            scaled_density.velocity,
+            rtol=1e-5,
+        )
+        np.testing.assert_allclose(
+            np.array(
+                [-67.071986, 60.433456, -14.211044, 129.49805, 161.93695, 72.622535]
+            ),
+            scaled_density.temperature,
+            rtol=3e-5,
+        )
 
     def test_rotate_heat_flux(self):
         rotation_matrix = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
@@ -846,13 +1517,24 @@ def test_rotate_heat_flux(self):
             (rotation_matrix, [0, 0, -1], 1, 0, math.pi),
             (nan_rotation_matrix, [2, 0, 0], 2, np.nan, np.nan),
         ]
-        for input_rotation_matrix, input, expected_mag, expected_theta, expected_phi in cases:
+        for (
+            input_rotation_matrix,
+            input,
+            expected_mag,
+            expected_theta,
+            expected_phi,
+        ) in cases:
             with self.subTest(input):
                 dsp_vector = np.array(input)
 
-                magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(input_rotation_matrix, dsp_vector)
+                magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(
+                    input_rotation_matrix, dsp_vector
+                )
 
-                np.testing.assert_array_equal([magnitude, theta, phi], [expected_mag, expected_theta, expected_phi])
+                np.testing.assert_array_equal(
+                    [magnitude, theta, phi],
+                    [expected_mag, expected_theta, expected_phi],
+                )
 
     def test_calculate_primary_eigenvector(self):
         cases = [
@@ -882,23 +1564,46 @@ def test_calculate_primary_eigenvector_error_cases(self):
                 np.testing.assert_equal(temps, [np.nan, np.nan, np.nan])
 
     def test_rotation_matrix_builder(self):
-        cases = [([25, 50, 75], [[0.26726124, 0.53452247, 0.80178374], [0.00000000, 0.83205032, -0.55470020],
-                                 [-0.96362412, 0.14824985, 0.22237480]]),
-                 ([500.123, 999.99, 750.23],
-                  [[0.37143612, 0.74268216, 0.55718797], [0.00000000, 0.60012156, -0.79990882],
-                   [-0.92845851, 0.29711503, 0.22290681, ]]),
-                 ([0, 0, 1], [[0, 0, 1], [0, 1, 0], [-1, 0, 0]])]
+        cases = [
+            (
+                [25, 50, 75],
+                [
+                    [0.26726124, 0.53452247, 0.80178374],
+                    [0.00000000, 0.83205032, -0.55470020],
+                    [-0.96362412, 0.14824985, 0.22237480],
+                ],
+            ),
+            (
+                [500.123, 999.99, 750.23],
+                [
+                    [0.37143612, 0.74268216, 0.55718797],
+                    [0.00000000, 0.60012156, -0.79990882],
+                    [
+                        -0.92845851,
+                        0.29711503,
+                        0.22290681,
+                    ],
+                ],
+            ),
+            ([0, 0, 1], [[0, 0, 1], [0, 1, 0], [-1, 0, 0]]),
+        ]
 
         for b_vector, expected_matrix in cases:
             with self.subTest(b_vector):
-                np.testing.assert_allclose(rotation_matrix_builder(b_vector), expected_matrix, rtol=2e-7)
+                np.testing.assert_allclose(
+                    rotation_matrix_builder(b_vector), expected_matrix, rtol=2e-7
+                )
 
     def test_rotate_temperature_tensor_to_mag(self):
         expected_t_par = 9e4
         expected_t_perp = 3e4
         expected_ratio = 5
 
-        t_parallel, t_perp_average, t_perp_ratio, = rotate_temperature_tensor_to_mag([1, 2, 5, 3, 6, 9], [0, 0, 1])
+        (
+            t_parallel,
+            t_perp_average,
+            t_perp_ratio,
+        ) = rotate_temperature_tensor_to_mag([1, 2, 5, 3, 6, 9], [0, 0, 1])
         self.assertEqual(expected_t_par, t_parallel)
         self.assertEqual(expected_t_perp, t_perp_average)
         self.assertEqual(expected_ratio, t_perp_ratio)
@@ -908,7 +1613,11 @@ def test_rotate_temperature_tensor_to_mag_with_ratio_less_than_one(self):
         expected_t_perp = 6e4
         expected_ratio = 3
 
-        t_parallel, t_perp_average, t_perp_ratio, = rotate_temperature_tensor_to_mag([9, 6, 3, 5, 2, 1], [0, 0, 1])
+        (
+            t_parallel,
+            t_perp_average,
+            t_perp_ratio,
+        ) = rotate_temperature_tensor_to_mag([9, 6, 3, 5, 2, 1], [0, 0, 1])
         self.assertEqual(expected_t_par, t_parallel)
         self.assertEqual(expected_t_perp, t_perp_average)
         self.assertEqual(expected_ratio, t_perp_ratio)
@@ -918,40 +1627,225 @@ def test_rotate_temperature_sensor_to_mag_with_negative_diagonal(self):
         expected_t_perp = 2e4
         expected_ratio = 0
 
-        t_parallel, t_perp_average, t_perp_ratio, = rotate_temperature_tensor_to_mag([9, 1, 1, 8, 1, -5], [0, 1, 0])
+        (
+            t_parallel,
+            t_perp_average,
+            t_perp_ratio,
+        ) = rotate_temperature_tensor_to_mag([9, 1, 1, 8, 1, -5], [0, 1, 0])
         self.assertEqual(expected_t_par, t_parallel)
         self.assertEqual(expected_t_perp, t_perp_average)
         self.assertEqual(expected_ratio, t_perp_ratio)
 
-
     def test_check_temperature_outlier_flag(self):
         # Make sure to replace temperatures with an array of the actual temperatures!!
-        temperatures = np.asarray([115994.66 , 116026.59 , 118674.664,  81440.03 ,  64882.246,
-        81693.66 ,  97943.58 , 106707.7  , 110098.68 , 118224.14 ,
-       114332.87 ,  72112.38 , 112917.73 , 114900.66 , 111031.34 ,
-       101084.9  , 104621.27 , 105482.9  , 111774.54 , 117124.625,
-       115963.13 , 116497.22 , 117109.44 , 116067.71 , 112034.125,
-       115428.125, 112301.64 , 114308.836, 112778.016, 112792.6  ,
-       112106.82 , 113119.766, 113032.69 , 113007.12 , 112749.11 ,
-       114590.734, 155481.47 , 117895.46 , 117952.94 , 115017.12 ,
-       110511.68 , 112724.29 , 111804.49 , 111790.33 , 110265.12 ,
-       111288.8  , 113818.85 , 113016.04 , 112490.38 , 113095.38 ,
-       112651.984, 113292.79 , 114452.2  , 113125.41 , 107491.9  ,
-       116490.92 , 114905.55 , 119953.88 , 123044.64 , 121589.14 ,
-       127217.89 , 124885.76 , 126984.54 , 126696.9  , 132537.52 ,
-       130044.89 , 203212.89 , 126546.88 , 129790.25 , 130226.16 ,
-       128036.91 , 123267.95 , 132878.33 , 133391.45 , 132277.88 ,
-       130638.78 , 131021.56 , 130341.88 , 132855.5  , 133775.25 ,
-       130266.41 , 126470.85 , 124299.48 , 126457.62 , 125728.02 ,
-       132413.53 , 125549.89 , 127563.04 , 133693.   , 125295.82 ,
-       130699.61 , 125014.195, 126216.7  , 131265.03 , 133390.77 ,
-       130349.43 , 130807.32 , 129782.42 , 129749.54 , 128326.375])
-        expected_values = np.asarray([0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,
-       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
-       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
-       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
-       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
-       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
+        temperatures = np.asarray(
+            [
+                115994.66,
+                116026.59,
+                118674.664,
+                81440.03,
+                64882.246,
+                81693.66,
+                97943.58,
+                106707.7,
+                110098.68,
+                118224.14,
+                114332.87,
+                72112.38,
+                112917.73,
+                114900.66,
+                111031.34,
+                101084.9,
+                104621.27,
+                105482.9,
+                111774.54,
+                117124.625,
+                115963.13,
+                116497.22,
+                117109.44,
+                116067.71,
+                112034.125,
+                115428.125,
+                112301.64,
+                114308.836,
+                112778.016,
+                112792.6,
+                112106.82,
+                113119.766,
+                113032.69,
+                113007.12,
+                112749.11,
+                114590.734,
+                155481.47,
+                117895.46,
+                117952.94,
+                115017.12,
+                110511.68,
+                112724.29,
+                111804.49,
+                111790.33,
+                110265.12,
+                111288.8,
+                113818.85,
+                113016.04,
+                112490.38,
+                113095.38,
+                112651.984,
+                113292.79,
+                114452.2,
+                113125.41,
+                107491.9,
+                116490.92,
+                114905.55,
+                119953.88,
+                123044.64,
+                121589.14,
+                127217.89,
+                124885.76,
+                126984.54,
+                126696.9,
+                132537.52,
+                130044.89,
+                203212.89,
+                126546.88,
+                129790.25,
+                130226.16,
+                128036.91,
+                123267.95,
+                132878.33,
+                133391.45,
+                132277.88,
+                130638.78,
+                131021.56,
+                130341.88,
+                132855.5,
+                133775.25,
+                130266.41,
+                126470.85,
+                124299.48,
+                126457.62,
+                125728.02,
+                132413.53,
+                125549.89,
+                127563.04,
+                133693.0,
+                125295.82,
+                130699.61,
+                125014.195,
+                126216.7,
+                131265.03,
+                133390.77,
+                130349.43,
+                130807.32,
+                129782.42,
+                129749.54,
+                128326.375,
+            ]
+        )
+        expected_values = np.asarray(
+            [
+                0.0,
+                0.0,
+                0.0,
+                1.0,
+                1.0,
+                1.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                1.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                1.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                1.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
         expected_values = expected_values.astype(int).astype(SweL3Flags)
         expected_values[expected_values == 0] = SweL3Flags.NONE
         expected_values[expected_values == 1] = SweL3Flags.TEMPERATURE_OUTLIER
@@ -960,10 +1854,12 @@ def test_check_temperature_outlier_flag(self):
         for i in np.arange(len(tof_flags)):
             self.assertEqual(expected_values[i], tof_flags[i])
 
-    @patch(f"spiceypy.datetime2et")
-    @patch(f"spiceypy.pxform")
+    @patch("spiceypy.datetime2et")
+    @patch("spiceypy.pxform")
     @patch(f"{MODULE}.PredictedEphemerisTracker")
-    def test_rotate_rtn_vectors_to_dps(self, mock_predicted_ephemeris_tracker_class, mock_pxform, mock_datetime2et):
+    def test_rotate_rtn_vectors_to_dps(
+        self, mock_predicted_ephemeris_tracker_class, mock_pxform, mock_datetime2et
+    ):
         pred_tracker_1 = create_autospec(PredictedEphemerisTracker, used_predict=False)
         pred_tracker_1.run.return_value = np.eye(3) * 2
 
@@ -979,36 +1875,33 @@ def test_rotate_rtn_vectors_to_dps(self, mock_predicted_ephemeris_tracker_class,
             pred_tracker_3,
         ]
 
-        epochs = np.array([
-            datetime(2020, 1, 1),
-            datetime(2020, 1, 2),
-            datetime(2020, 1, 3)
-        ])
-        rtn_vectors = np.array([
-            [1, 2, 3],
-            [4, 5, 6],
-            [7, 8, 9]
-        ])
+        epochs = np.array(
+            [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 3)]
+        )
+        rtn_vectors = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
         mock_datetime2et.side_effect = (sentinel.et_1, sentinel.et_2, sentinel.et_3)
 
         dps_vectors, used_predict = rotate_rtn_vectors_to_dps(epochs, rtn_vectors)
 
         self.assertEqual(3, mock_predicted_ephemeris_tracker_class.call_count)
 
-
         self.assertEqual(epochs[0], mock_datetime2et.call_args_list[0].args[0])
         self.assertEqual(epochs[1], mock_datetime2et.call_args_list[1].args[0])
         self.assertEqual(epochs[2], mock_datetime2et.call_args_list[2].args[0])
 
-        pred_tracker_1.run.assert_called_once_with(mock_pxform, "IMAP_RTN", "IMAP_DPS", sentinel.et_1)
-        pred_tracker_2.run.assert_called_once_with(mock_pxform, "IMAP_RTN", "IMAP_DPS", sentinel.et_2)
-        pred_tracker_3.run.assert_called_once_with(mock_pxform, "IMAP_RTN", "IMAP_DPS", sentinel.et_3)
+        pred_tracker_1.run.assert_called_once_with(
+            mock_pxform, "IMAP_RTN", "IMAP_DPS", sentinel.et_1
+        )
+        pred_tracker_2.run.assert_called_once_with(
+            mock_pxform, "IMAP_RTN", "IMAP_DPS", sentinel.et_2
+        )
+        pred_tracker_3.run.assert_called_once_with(
+            mock_pxform, "IMAP_RTN", "IMAP_DPS", sentinel.et_3
+        )
 
-        expected_dps_vectors = np.array([
-            [2, 4, 6],
-            [12, 15, 18],
-            [np.nan, np.nan, np.nan]
-        ])
+        expected_dps_vectors = np.array(
+            [[2, 4, 6], [12, 15, 18], [np.nan, np.nan, np.nan]]
+        )
 
         np.testing.assert_array_equal(dps_vectors, expected_dps_vectors)
-        np.testing.assert_array_equal(used_predict, [False, True, False])
\ No newline at end of file
+        np.testing.assert_array_equal(used_predict, [False, True, False])
diff --git a/tests/swe/l3/science/test_pitch_calculations.py b/tests/swe/l3/science/test_pitch_calculations.py
index 6769cb01f..4d1f4a6dc 100644
--- a/tests/swe/l3/science/test_pitch_calculations.py
+++ b/tests/swe/l3/science/test_pitch_calculations.py
@@ -1,114 +1,122 @@
 import unittest
-from unittest.mock import Mock, patch, call, sentinel
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
 
-from imap_l3_processing.swe.l3.science.pitch_calculations import mec_breakpoint_finder, \
-    average_over_look_directions, calculate_velocity_in_dsp_frame_km_s, calculate_look_directions, rebin_by_pitch_angle, \
-    correct_and_rebin, calculate_energy_in_ev_from_velocity_in_km_per_second, integrate_distribution_to_get_1d_spectrum, \
-    integrate_distribution_to_get_inbound_and_outbound_1d_spectrum, \
-    rebin_by_pitch_angle_and_gyrophase, swe_rebin_intensity_by_pitch_angle_and_gyrophase
-from tests.test_helpers import build_swe_configuration, NumpyArrayMatcher
-
+from imap_l3_processing.swe.l3.science.pitch_calculations import (
+    average_over_look_directions,
+    calculate_energy_in_ev_from_velocity_in_km_per_second,
+    calculate_look_directions,
+    calculate_velocity_in_dsp_frame_km_s,
+    correct_and_rebin,
+    integrate_distribution_to_get_1d_spectrum,
+    integrate_distribution_to_get_inbound_and_outbound_1d_spectrum,
+    mec_breakpoint_finder,
+    rebin_by_pitch_angle,
+    rebin_by_pitch_angle_and_gyrophase,
+    swe_rebin_intensity_by_pitch_angle_and_gyrophase,
+)
 from imap_l3_processing.swe.quality_flags import SweL3Flags
+from tests.test_helpers import NumpyArrayMatcher, build_swe_configuration
+
 
 class TestPitchCalculations(unittest.TestCase):
     def test_average_flux(self):
-        flux_data = np.array([
+        flux_data = np.array(
             [
-                [1, 2, 3, 4],
-                [5, 6, 7, 8],
-                [9, 10, 11, 12],
-            ],
-            [
-                [13, 14, 15, 16],
-                [17, 18, 19, 20],
-                [21, 22, 23, 24],
-            ],
-        ])
+                [
+                    [1, 2, 3, 4],
+                    [5, 6, 7, 8],
+                    [9, 10, 11, 12],
+                ],
+                [
+                    [13, 14, 15, 16],
+                    [17, 18, 19, 20],
+                    [21, 22, 23, 24],
+                ],
+            ]
+        )
         geometric_weights = [0.5, 0.25, 0.25, 0]
         result = average_over_look_directions(flux_data, geometric_weights, 1e-32)
 
         expected_result = [
-            ((1 * 0.5 + 2 * 0.25 + 3 * 0.25) + (5 * 0.5 + 6 * 0.25 + 7 * 0.25) + (9 * 0.5 + 10 * 0.25 + 11 * 0.25)) / 3,
-            ((13 * 0.5 + 14 * 0.25 + 15 * 0.25) + (17 * 0.5 + 18 * 0.25 + 19 * 0.25) + (
-                    21 * 0.5 + 22 * 0.25 + 23 * 0.25)) / 3,
-
+            (
+                (1 * 0.5 + 2 * 0.25 + 3 * 0.25)
+                + (5 * 0.5 + 6 * 0.25 + 7 * 0.25)
+                + (9 * 0.5 + 10 * 0.25 + 11 * 0.25)
+            )
+            / 3,
+            (
+                (13 * 0.5 + 14 * 0.25 + 15 * 0.25)
+                + (17 * 0.5 + 18 * 0.25 + 19 * 0.25)
+                + (21 * 0.5 + 22 * 0.25 + 23 * 0.25)
+            )
+            / 3,
         ]
         np.testing.assert_almost_equal(result, expected_result)
 
     def test_average_over_look_directions_with_zeroes(self):
-        flux_data = np.array([
-            [
-                [1, 2, 3, 4],
-            ],
+        flux_data = np.array(
             [
-                [0, 0, 0, 1e-36],
-            ],
-        ])
+                [
+                    [1, 2, 3, 4],
+                ],
+                [
+                    [0, 0, 0, 1e-36],
+                ],
+            ]
+        )
         geometric_weights = [0.25, 0.25, 0.25, 0.25]
         result = average_over_look_directions(flux_data, geometric_weights, 1e-34)
 
-        expected_result = [
-            (1 * 0.25 + 2 * 0.25 + 3 * 0.25 + 4 * 0.25), 1e-34
-        ]
+        expected_result = [(1 * 0.25 + 2 * 0.25 + 3 * 0.25 + 4 * 0.25), 1e-34]
         np.testing.assert_allclose(result, expected_result)
 
     def test_look_direction(self):
         inst_az = np.array([[0, 90], [180, 270]])
         inst_el = np.array([-90, 0, 90])
         shape = (2, 2, 3, 3)
-        expected_look_direction = np.array([
+        expected_look_direction = np.array(
             [
                 [
-                    [0, 0, -1],
-                    [1, 0, 0],
-                    [0, 0, 1]
+                    [[0, 0, -1], [1, 0, 0], [0, 0, 1]],
+                    [[0, 0, -1], [0, 1, 0], [0, 0, 1]],
                 ],
                 [
-                    [0, 0, -1],
-                    [0, 1, 0],
-                    [0, 0, 1]
-                ]
-            ],
-            [
-                [
-                    [0, 0, -1],
-                    [-1, 0, 0],
-                    [0, 0, 1]
+                    [[0, 0, -1], [-1, 0, 0], [0, 0, 1]],
+                    [[0, 0, -1], [0, -1, 0], [0, 0, 1]],
                 ],
-                [
-                    [0, 0, -1],
-                    [0, -1, 0],
-                    [0, 0, 1]
-                ]
             ]
-        ])
+        )
 
         actual_look_direction = calculate_look_directions(inst_el, inst_az)
-        np.testing.assert_array_almost_equal(actual_look_direction, expected_look_direction)
+        np.testing.assert_array_almost_equal(
+            actual_look_direction, expected_look_direction
+        )
 
     def test_compute_velocity(self):
         energy = np.array([1, 2])  # 2 -- energy
         inst_el = np.array([0, 90])
         inst_az = np.array([[0], [90]])  # 2, 1  --  energy, spin sector
 
-        scalar_speeds = np.sqrt(energy * 1.602176634e-19 * 2 /
-                                9.109_383_7139e-31) / 1000
-        expected_velocity = np.array([
-            [  # energy 1
-                [  # spin sector 1
-                    [-scalar_speeds[0], 0, 0],  # CEM 1
-                    [0, 0, -scalar_speeds[0]],
+        scalar_speeds = (
+            np.sqrt(energy * 1.602176634e-19 * 2 / 9.109_383_7139e-31) / 1000
+        )
+        expected_velocity = np.array(
+            [
+                [  # energy 1
+                    [  # spin sector 1
+                        [-scalar_speeds[0], 0, 0],  # CEM 1
+                        [0, 0, -scalar_speeds[0]],
+                    ],
                 ],
-            ],
-            [  # energy 2
-                [
-                    [0, -scalar_speeds[1], 0],
-                    [0, 0, -scalar_speeds[1]],
+                [  # energy 2
+                    [
+                        [0, -scalar_speeds[1], 0],
+                        [0, 0, -scalar_speeds[1]],
+                    ],
                 ],
-            ],
-        ]
+            ]
         )
 
         velocity = calculate_velocity_in_dsp_frame_km_s(energy, inst_el, inst_az)
@@ -121,26 +129,71 @@ def test_compute_velocity_and_confirm_energy_calculation(self):
         inst_az = rng.random((24, 30)) * 360
         velocity = calculate_velocity_in_dsp_frame_km_s(energy, inst_el, inst_az)
         scalar_velocity = np.linalg.norm(velocity, axis=-1)
-        calculated_energy = 0.5 * 9.109_383_7139e-31 * np.square(scalar_velocity * 1000) / 1.602176634e-19
+        calculated_energy = (
+            0.5
+            * 9.109_383_7139e-31
+            * np.square(scalar_velocity * 1000)
+            / 1.602176634e-19
+        )
 
         np.testing.assert_almost_equal(calculated_energy[0], energy[0])
         np.testing.assert_almost_equal(calculated_energy[1], energy[1])
         self.assertEqual((24, 30, 7, 3), velocity.shape)
 
     def test_mec_breakpoint_finder(self):
-        average_psd = np.asarray([3.86611617e-05, 1.75927552e-05, 6.06703495e-06, 1.47614412e-06,
-       2.46270751e-07, 4.97864654e-08, 3.90323281e-08, 4.21037028e-08,
-       4.07355835e-08, 3.45166807e-08, 2.52075419e-08, 1.54987172e-08,
-       7.84932795e-09, 3.32903783e-09, 1.40908421e-09, 8.53206391e-10,
-       7.21564216e-10, 6.31903627e-10, 5.07238502e-10, 3.58827457e-10])
-        energies = np.asarray([  2.66      ,   3.37852249,   4.29113316,   5.45025936,
-         6.92249015,   8.79240175,  11.16741618,  14.18397245,
-        18.01536462,  22.88169719,  29.06252952,  36.91293593,
-        46.88390382,  59.54824189,  75.63348661,  96.06369752,
-       122.01254227, 154.97072104, 196.83160382, 250.        ])
-
-        sc_pot_to_test, ch_break_to_test, quality_flag_to_test = mec_breakpoint_finder(energies, average_psd)
-        expected_values = [np.float64(8.79240175),np.float64(75.63348660999998)]
+        average_psd = np.asarray(
+            [
+                3.86611617e-05,
+                1.75927552e-05,
+                6.06703495e-06,
+                1.47614412e-06,
+                2.46270751e-07,
+                4.97864654e-08,
+                3.90323281e-08,
+                4.21037028e-08,
+                4.07355835e-08,
+                3.45166807e-08,
+                2.52075419e-08,
+                1.54987172e-08,
+                7.84932795e-09,
+                3.32903783e-09,
+                1.40908421e-09,
+                8.53206391e-10,
+                7.21564216e-10,
+                6.31903627e-10,
+                5.07238502e-10,
+                3.58827457e-10,
+            ]
+        )
+        energies = np.asarray(
+            [
+                2.66,
+                3.37852249,
+                4.29113316,
+                5.45025936,
+                6.92249015,
+                8.79240175,
+                11.16741618,
+                14.18397245,
+                18.01536462,
+                22.88169719,
+                29.06252952,
+                36.91293593,
+                46.88390382,
+                59.54824189,
+                75.63348661,
+                96.06369752,
+                122.01254227,
+                154.97072104,
+                196.83160382,
+                250.0,
+            ]
+        )
+
+        sc_pot_to_test, ch_break_to_test, quality_flag_to_test = mec_breakpoint_finder(
+            energies, average_psd
+        )
+        expected_values = [np.float64(8.79240175), np.float64(75.63348660999998)]
         self.assertAlmostEqual(expected_values[0], sc_pot_to_test)
         self.assertAlmostEqual(expected_values[1], ch_break_to_test)
         self.assertEqual(SweL3Flags.NONE, quality_flag_to_test)
@@ -151,51 +204,49 @@ def test_rebin_by_pitch_angle(self):
         energy = np.array([10 * 0.8, 10 / 0.8, 10 * 0.9 * 0.9, 10 / 0.9])
 
         config = build_swe_configuration(
-            pitch_angle_bins=[45, 135],
-            pitch_angle_deltas=[45, 45],
-            energy_bins=[10]
+            pitch_angle_bins=[45, 135], pitch_angle_deltas=[45, 45], energy_bins=[10]
         )
 
         result = rebin_by_pitch_angle(flux, pitch_angle, energy, config)
 
-        expected_result = np.array(
-            [
-                [100, 128]
-            ]
-        )
+        expected_result = np.array([[100, 128]])
         np.testing.assert_almost_equal(result, expected_result)
 
     def test_rebin_by_pitch_angle_and_gyrophase(self):
-        psd = np.array([1000, 10,
-                        32, 256,
-                        3, 9, 81,
-                        5, 25, 125])
+        psd = np.array([1000, 10, 32, 256, 3, 9, 81, 5, 25, 125])
 
         pitch_angle = np.array([25, 60, 25, 60, 120, 170, 165, 120, 170, 165])
         gyrophase = np.array([10, 120, 210, 350, 10, 120, 95, 210, 350, 260])
 
         central_energy_value = 10  # bin range is really 60 - 140
-        energy = np.array([central_energy_value * 0.8, central_energy_value / 0.8,
-                           central_energy_value * 0.8 ** 2, central_energy_value / 0.8,
-                           central_energy_value * 0.8 ** 2, central_energy_value * 0.8, central_energy_value / 0.8,
-                           central_energy_value * 0.8, central_energy_value, central_energy_value / 0.8])
+        energy = np.array(
+            [
+                central_energy_value * 0.8,
+                central_energy_value / 0.8,
+                central_energy_value * 0.8**2,
+                central_energy_value / 0.8,
+                central_energy_value * 0.8**2,
+                central_energy_value * 0.8,
+                central_energy_value / 0.8,
+                central_energy_value * 0.8,
+                central_energy_value,
+                central_energy_value / 0.8,
+            ]
+        )
 
         config = build_swe_configuration(
             pitch_angle_bins=[45, 135],
             pitch_angle_deltas=[45, 45],
             energy_bins=[central_energy_value],
             gyrophase_bins=[90, 270],
-            gyrophase_deltas=[90, 90]
+            gyrophase_deltas=[90, 90],
         )
 
-        rebinned_by_gyro = rebin_by_pitch_angle_and_gyrophase(psd, pitch_angle, gyrophase, energy, config)
+        rebinned_by_gyro = rebin_by_pitch_angle_and_gyrophase(
+            psd, pitch_angle, gyrophase, energy, config
+        )
 
-        expected_gyro = np.array([
-            [
-                [100, 128],
-                [27, 25]
-            ]
-        ])
+        expected_gyro = np.array([[[100, 128], [27, 25]]])
 
         np.testing.assert_almost_equal(expected_gyro, rebinned_by_gyro)
 
@@ -205,19 +256,17 @@ def test_rebin_by_pitch_angle_ignores_zero_measurements(self):
         energy = np.array([10 * 0.8, 10 / 0.8, 10 * 0.9 * 0.9, 10 / 0.9, 10 / 0.9])
 
         config = build_swe_configuration(
-            pitch_angle_bins=[45, 135],
-            pitch_angle_deltas=[45, 45],
-            energy_bins=[10]
+            pitch_angle_bins=[45, 135], pitch_angle_deltas=[45, 45], energy_bins=[10]
         )
 
         result = rebin_by_pitch_angle(flux, pitch_angle, energy, config)
 
-        expected_result = np.array([
-            [100, 128]
-        ])
+        expected_result = np.array([[100, 128]])
         np.testing.assert_almost_equal(result, expected_result)
 
-    def test_rebin_by_pitch_angle_uses_energy_within_configured_percent_of_nominal(self):
+    def test_rebin_by_pitch_angle_uses_energy_within_configured_percent_of_nominal(
+        self,
+    ):
         flux = np.array([9999, 50, 50, 9999])
         pitch_angle = np.array([25, 60, 120, 170])
         energy = np.array([10 * 0.69, 10 * 0.71, 10 * 1.49, 10 * 1.51])
@@ -231,9 +280,7 @@ def test_rebin_by_pitch_angle_uses_energy_within_configured_percent_of_nominal(s
         )
         result = rebin_by_pitch_angle(flux, pitch_angle, energy, config)
 
-        expected_result = np.array([
-            [50]
-        ])
+        expected_result = np.array([[50]])
         np.testing.assert_almost_equal(result, expected_result)
 
     def test_rebin_by_pitch_angle_skips_bins_with_less_than_two_measurements(self):
@@ -251,26 +298,57 @@ def test_rebin_by_pitch_angle_skips_bins_with_less_than_two_measurements(self):
 
         result = rebin_by_pitch_angle(flux, pitch_angle, energy, config)
 
-        expected_result = np.array(
-            [
-                [np.nan, 50],
-                [np.nan, np.nan]
-            ]
-        )
+        expected_result = np.array([[np.nan, 50], [np.nan, np.nan]])
         np.testing.assert_almost_equal(result, expected_result)
 
     def test_rebin_by_pitch_angle_skips_bins_with_invalid_measurements(self):
         test_cases = [
-            ('no energy is close enough', [5.9, 6, 8.2, 11.8, 12.2], np.nan),
-            ('overall max value is in range and an energy is close enough below', [8, 8.21, 11.8, 12, 12.1], 50),
-            ('overall max value is in range and an energy is close enough above', [8, 8.20, 11.79, 12, 12.1], 50),
-            ('min and max are outside window and points on both side of nominal', [5.8, 5.9, 14.1, 9, 11], 50),
-            ('two lowest mins and max are outside window and points only below', [5.8, 5.9, 14.1, 8, 9], np.nan),
-            ('two lowest mins and max are outside window and points only above', [5.8, 5.9, 14.1, 11, 12], np.nan),
-            ('second_lowest in window and an energy close enough', [5.8, 6.1, 14.1, 11, 12], 50),
-            ('second_lowest in window and points on both sides', [5.8, 6.1, 14.1, 8, 12], 50),
-            ('second_lowest in window but all above and not close', [5.8, 11.3, 14.1, 11.3, 12], np.nan),
-            ('second_lowest in window but all below and not close', [5.8, 6.1, 14.1, 8, 8.7], np.nan),
+            ("no energy is close enough", [5.9, 6, 8.2, 11.8, 12.2], np.nan),
+            (
+                "overall max value is in range and an energy is close enough below",
+                [8, 8.21, 11.8, 12, 12.1],
+                50,
+            ),
+            (
+                "overall max value is in range and an energy is close enough above",
+                [8, 8.20, 11.79, 12, 12.1],
+                50,
+            ),
+            (
+                "min and max are outside window and points on both side of nominal",
+                [5.8, 5.9, 14.1, 9, 11],
+                50,
+            ),
+            (
+                "two lowest mins and max are outside window and points only below",
+                [5.8, 5.9, 14.1, 8, 9],
+                np.nan,
+            ),
+            (
+                "two lowest mins and max are outside window and points only above",
+                [5.8, 5.9, 14.1, 11, 12],
+                np.nan,
+            ),
+            (
+                "second_lowest in window and an energy close enough",
+                [5.8, 6.1, 14.1, 11, 12],
+                50,
+            ),
+            (
+                "second_lowest in window and points on both sides",
+                [5.8, 6.1, 14.1, 8, 12],
+                50,
+            ),
+            (
+                "second_lowest in window but all above and not close",
+                [5.8, 11.3, 14.1, 11.3, 12],
+                np.nan,
+            ),
+            (
+                "second_lowest in window but all below and not close",
+                [5.8, 6.1, 14.1, 8, 8.7],
+                np.nan,
+            ),
         ]
         for case, energy, expected_output in test_cases:
             with self.subTest(case):
@@ -281,7 +359,7 @@ def test_rebin_by_pitch_angle_skips_bins_with_invalid_measurements(self):
                     energy_bin_low_multiplier=0.6,
                     energy_bin_high_multiplier=1.4,
                     high_energy_proximity_threshold=0.18,
-                    low_energy_proximity_threshold=0.12
+                    low_energy_proximity_threshold=0.12,
                 )
 
                 pitch_angle = np.array([30, 35, 40, 45, 50])
@@ -300,41 +378,59 @@ def test_rebin_full_size_data(self):
         pitch_angle_bins = np.linspace(0, 180, 20, endpoint=False) + 4.5
         pitch_angle_deltas = np.repeat(4.5, 20)
         energy_bins = np.geomspace(2, 5000, 24)
-        config = build_swe_configuration(pitch_angle_bins=pitch_angle_bins,
-                                         pitch_angle_deltas=pitch_angle_deltas,
-                                         energy_bins=energy_bins)
+        config = build_swe_configuration(
+            pitch_angle_bins=pitch_angle_bins,
+            pitch_angle_deltas=pitch_angle_deltas,
+            energy_bins=energy_bins,
+        )
         result = rebin_by_pitch_angle(flux, pitch_angle, energy, config)
         self.assertEqual((24, 20), result.shape)
 
     def test_calculate_energy(self):
-        velocities = np.array([
-            [400, 0, 0],
-            [0, -25000, 0],
-        ])
+        velocities = np.array(
+            [
+                [400, 0, 0],
+                [0, -25000, 0],
+            ]
+        )
         expected_energies = [0.45485041, 1776.7594]
         result = calculate_energy_in_ev_from_velocity_in_km_per_second(velocities)
         np.testing.assert_allclose(result, expected_energies, rtol=1e-7)
 
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.rebin_by_pitch_angle_and_gyrophase')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_gyrophase')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_energy_in_ev_from_velocity_in_km_per_second')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.rebin_by_pitch_angle')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_pitch_angle')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_velocity_in_sw_frame')
-    def test_correct_and_rebin(self, mock_calculate_velocity_in_sw_frame,
-                               mock_calculate_pitch_angle, mock_rebin_by_pitch_angle, mock_calculate_energy,
-                               mock_calculate_gyrophase, mock_rebin_by_pitch_angle_and_gyrophase):
-
-        mag_vectors = np.array([
+    @patch(
+        "imap_l3_processing.swe.l3.science.pitch_calculations.rebin_by_pitch_angle_and_gyrophase"
+    )
+    @patch("imap_l3_processing.swe.l3.science.pitch_calculations.calculate_gyrophase")
+    @patch(
+        "imap_l3_processing.swe.l3.science.pitch_calculations.calculate_energy_in_ev_from_velocity_in_km_per_second"
+    )
+    @patch("imap_l3_processing.swe.l3.science.pitch_calculations.rebin_by_pitch_angle")
+    @patch("imap_l3_processing.swe.l3.science.pitch_calculations.calculate_pitch_angle")
+    @patch(
+        "imap_l3_processing.swe.l3.science.pitch_calculations.calculate_velocity_in_sw_frame"
+    )
+    def test_correct_and_rebin(
+        self,
+        mock_calculate_velocity_in_sw_frame,
+        mock_calculate_pitch_angle,
+        mock_rebin_by_pitch_angle,
+        mock_calculate_energy,
+        mock_calculate_gyrophase,
+        mock_rebin_by_pitch_angle_and_gyrophase,
+    ):
+
+        mag_vectors = np.array(
             [
-                [1, 0, 0],
-                [0, 1, 0],
-            ],
-            [
-                [0, 0, 1],
-                [1, 1, 1],
-            ],
-        ])
+                [
+                    [1, 0, 0],
+                    [0, 1, 0],
+                ],
+                [
+                    [0, 0, 1],
+                    [1, 1, 1],
+                ],
+            ]
+        )
 
         flux_data = Mock()
         solar_wind_vector = Mock()
@@ -347,27 +443,38 @@ def test_correct_and_rebin(self, mock_calculate_velocity_in_sw_frame,
             config=configuration,
         )
 
-        expected_mag_vectors_with_cem_axis = np.array([
-            [
-                [[1, 0, 0]],
-                [[0, 1, 0]],
-            ],
+        expected_mag_vectors_with_cem_axis = np.array(
             [
-                [[0, 0, 1]],
-                [[1, 1, 1]],
-            ],
-        ])
+                [
+                    [[1, 0, 0]],
+                    [[0, 1, 0]],
+                ],
+                [
+                    [[0, 0, 1]],
+                    [[1, 1, 1]],
+                ],
+            ]
+        )
 
-        mock_calculate_velocity_in_sw_frame.assert_called_once_with(sentinel.dsp_velocities, solar_wind_vector)
+        mock_calculate_velocity_in_sw_frame.assert_called_once_with(
+            sentinel.dsp_velocities, solar_wind_vector
+        )
 
-        mock_calculate_pitch_angle.assert_called_once_with(mock_calculate_velocity_in_sw_frame.return_value,
-                                                           NumpyArrayMatcher(expected_mag_vectors_with_cem_axis))
-        mock_calculate_gyrophase.assert_called_once_with(mock_calculate_velocity_in_sw_frame.return_value,
-                                                         NumpyArrayMatcher(expected_mag_vectors_with_cem_axis))
+        mock_calculate_pitch_angle.assert_called_once_with(
+            mock_calculate_velocity_in_sw_frame.return_value,
+            NumpyArrayMatcher(expected_mag_vectors_with_cem_axis),
+        )
+        mock_calculate_gyrophase.assert_called_once_with(
+            mock_calculate_velocity_in_sw_frame.return_value,
+            NumpyArrayMatcher(expected_mag_vectors_with_cem_axis),
+        )
 
-        mock_calculate_energy.assert_called_with(mock_calculate_velocity_in_sw_frame.return_value)
+        mock_calculate_energy.assert_called_with(
+            mock_calculate_velocity_in_sw_frame.return_value
+        )
         mock_rebin_by_pitch_angle.assert_called_with(
-            flux_data, mock_calculate_pitch_angle.return_value,
+            flux_data,
+            mock_calculate_pitch_angle.return_value,
             mock_calculate_energy.return_value,
             configuration,
         )
@@ -378,75 +485,96 @@ def test_correct_and_rebin(self, mock_calculate_velocity_in_sw_frame,
             mock_calculate_energy.return_value,
             configuration,
         )
-        self.assertEqual((mock_rebin_by_pitch_angle.return_value, mock_rebin_by_pitch_angle_and_gyrophase.return_value),
-                         result)
+        self.assertEqual(
+            (
+                mock_rebin_by_pitch_angle.return_value,
+                mock_rebin_by_pitch_angle_and_gyrophase.return_value,
+            ),
+            result,
+        )
 
     def test_integrate_distribution_to_get_1d_spectrum(self):
         pitch_angle_bins = [30, 90]
         pitch_angle_deltas = [15, 15]
-        configuration = build_swe_configuration(pitch_angle_bins=pitch_angle_bins,
-                                                pitch_angle_deltas=pitch_angle_deltas,
-                                                in_vs_out_energy_index=1,
-                                                )
+        configuration = build_swe_configuration(
+            pitch_angle_bins=pitch_angle_bins,
+            pitch_angle_deltas=pitch_angle_deltas,
+            in_vs_out_energy_index=1,
+        )
 
-        psd_by_pitch_angles = np.array([
-            [10, 20],
-            [5, 10],
-            [7, 15],
-        ])
+        psd_by_pitch_angles = np.array(
+            [
+                [10, 20],
+                [5, 10],
+                [7, 15],
+            ]
+        )
 
-        actual_integrated_spectrum = integrate_distribution_to_get_1d_spectrum(psd_by_pitch_angles, configuration)
+        actual_integrated_spectrum = integrate_distribution_to_get_1d_spectrum(
+            psd_by_pitch_angles, configuration
+        )
 
-        bin1_factor = ((0.5 * np.deg2rad(30)) / 2)
-        bin2_factor = ((1 * np.deg2rad(30)) / 2)
+        bin1_factor = (0.5 * np.deg2rad(30)) / 2
+        bin2_factor = (1 * np.deg2rad(30)) / 2
         expected_integrated_spectrum = [
             10 * bin1_factor + 20 * bin2_factor,
             5 * bin1_factor + 10 * bin2_factor,
-            7 * bin1_factor + 15 * bin2_factor
+            7 * bin1_factor + 15 * bin2_factor,
         ]
-        np.testing.assert_allclose(actual_integrated_spectrum, expected_integrated_spectrum)
+        np.testing.assert_allclose(
+            actual_integrated_spectrum, expected_integrated_spectrum
+        )
 
     def test_integrate_distribution_to_get_1d_spectrum_ignores_nan_values(self):
         pitch_angle_bins = [30, 90]
         pitch_angle_deltas = [15, 15]
-        configuration = build_swe_configuration(pitch_angle_bins=pitch_angle_bins,
-                                                pitch_angle_deltas=pitch_angle_deltas,
-                                                in_vs_out_energy_index=1,
-                                                )
-
-        psd_by_energy_and_pitch_angles = np.array([
-            [10, np.nan],
-            [5, 10],
-            [np.nan, 15],
-        ])
-
-        actual_integrated_spectrum = integrate_distribution_to_get_1d_spectrum(psd_by_energy_and_pitch_angles,
-                                                                               configuration)
-
-        bin1_factor = ((0.5 * np.deg2rad(30)) / 2)
-        bin2_factor = ((1 * np.deg2rad(30)) / 2)
+        configuration = build_swe_configuration(
+            pitch_angle_bins=pitch_angle_bins,
+            pitch_angle_deltas=pitch_angle_deltas,
+            in_vs_out_energy_index=1,
+        )
+
+        psd_by_energy_and_pitch_angles = np.array(
+            [
+                [10, np.nan],
+                [5, 10],
+                [np.nan, 15],
+            ]
+        )
+
+        actual_integrated_spectrum = integrate_distribution_to_get_1d_spectrum(
+            psd_by_energy_and_pitch_angles, configuration
+        )
+
+        bin1_factor = (0.5 * np.deg2rad(30)) / 2
+        bin2_factor = (1 * np.deg2rad(30)) / 2
         expected_integrated_spectrum = [
             10 * bin1_factor,
             5 * bin1_factor + 10 * bin2_factor,
-            15 * bin2_factor
+            15 * bin2_factor,
         ]
-        np.testing.assert_allclose(actual_integrated_spectrum, expected_integrated_spectrum)
+        np.testing.assert_allclose(
+            actual_integrated_spectrum, expected_integrated_spectrum
+        )
 
     def test_integrate_distribution_decides_inbound_and_outbound_based_on_config_energy_index(
-            self):
+        self,
+    ):
         pitch_angle_bins = [10, 85.5, 94.5, 100]
         pitch_angle_deltas = [4.5, 4.5, 4.5, 4.5]
 
-        psd_by_pitch_angles = np.array([
-            [10, 20, 30, 40],
-            [15, 20, 5, 10],
-            [1, 2, 3, 4],
-        ])
+        psd_by_pitch_angles = np.array(
+            [
+                [10, 20, 30, 40],
+                [15, 20, 5, 10],
+                [1, 2, 3, 4],
+            ]
+        )
 
-        bin1_factor = (np.sin(np.deg2rad(10)) * np.deg2rad(4.5 * 2))
-        bin2_factor = (np.sin(np.deg2rad(85.5)) * np.deg2rad(4.5 * 2))
-        bin3_factor = (np.sin(np.deg2rad(94.5)) * np.deg2rad(4.5 * 2))
-        bin4_factor = (np.sin(np.deg2rad(100)) * np.deg2rad(4.5 * 2))
+        bin1_factor = np.sin(np.deg2rad(10)) * np.deg2rad(4.5 * 2)
+        bin2_factor = np.sin(np.deg2rad(85.5)) * np.deg2rad(4.5 * 2)
+        bin3_factor = np.sin(np.deg2rad(94.5)) * np.deg2rad(4.5 * 2)
+        bin4_factor = np.sin(np.deg2rad(100)) * np.deg2rad(4.5 * 2)
 
         expected_A_spectrum = [
             10 * bin1_factor + 20 * bin2_factor,
@@ -465,32 +593,37 @@ def test_integrate_distribution_decides_inbound_and_outbound_based_on_config_ene
         ]
         for index, expected_in, expected_out in cases:
             with self.subTest(index):
-                configuration = build_swe_configuration(pitch_angle_bins=pitch_angle_bins,
-                                                        pitch_angle_deltas=pitch_angle_deltas,
-                                                        in_vs_out_energy_index=index)
+                configuration = build_swe_configuration(
+                    pitch_angle_bins=pitch_angle_bins,
+                    pitch_angle_deltas=pitch_angle_deltas,
+                    in_vs_out_energy_index=index,
+                )
 
-                in_spectrum, out_spectrum = integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
-                    psd_by_pitch_angles,
-                    configuration)
+                in_spectrum, out_spectrum = (
+                    integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
+                        psd_by_pitch_angles, configuration
+                    )
+                )
 
                 np.testing.assert_allclose(in_spectrum, expected_in)
                 np.testing.assert_allclose(out_spectrum, expected_out)
 
-    def test_integrate_distribution_to_get_inbound_and_outbound_ignores_fill(
-            self):
+    def test_integrate_distribution_to_get_inbound_and_outbound_ignores_fill(self):
         pitch_angle_bins = [10, 85.5, 94.5, 100]
         pitch_angle_deltas = [4.5, 4.5, 4.5, 4.5]
 
-        psd_by_pitch_angles = np.array([
-            [10, 20, 30, 40],
-            [np.nan, 20, 5, 10],
-            [1, 2, 3, np.nan],
-        ])
+        psd_by_pitch_angles = np.array(
+            [
+                [10, 20, 30, 40],
+                [np.nan, 20, 5, 10],
+                [1, 2, 3, np.nan],
+            ]
+        )
 
-        bin1_factor = (np.sin(np.deg2rad(10)) * np.deg2rad(4.5 * 2))
-        bin2_factor = (np.sin(np.deg2rad(85.5)) * np.deg2rad(4.5 * 2))
-        bin3_factor = (np.sin(np.deg2rad(94.5)) * np.deg2rad(4.5 * 2))
-        bin4_factor = (np.sin(np.deg2rad(100)) * np.deg2rad(4.5 * 2))
+        bin1_factor = np.sin(np.deg2rad(10)) * np.deg2rad(4.5 * 2)
+        bin2_factor = np.sin(np.deg2rad(85.5)) * np.deg2rad(4.5 * 2)
+        bin3_factor = np.sin(np.deg2rad(94.5)) * np.deg2rad(4.5 * 2)
+        bin4_factor = np.sin(np.deg2rad(100)) * np.deg2rad(4.5 * 2)
 
         expected_A_spectrum = [
             10 * bin1_factor + 20 * bin2_factor,
@@ -503,69 +636,85 @@ def test_integrate_distribution_to_get_inbound_and_outbound_ignores_fill(
             3 * bin3_factor + 0,
         ]
 
-        configuration = build_swe_configuration(pitch_angle_bins=pitch_angle_bins,
-                                                pitch_angle_deltas=pitch_angle_deltas,
-                                                in_vs_out_energy_index=0)
+        configuration = build_swe_configuration(
+            pitch_angle_bins=pitch_angle_bins,
+            pitch_angle_deltas=pitch_angle_deltas,
+            in_vs_out_energy_index=0,
+        )
 
-        in_spectrum, out_spectrum = integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
-            psd_by_pitch_angles,
-            configuration)
+        in_spectrum, out_spectrum = (
+            integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
+                psd_by_pitch_angles, configuration
+            )
+        )
 
         np.testing.assert_allclose(in_spectrum, expected_A_spectrum)
         np.testing.assert_allclose(out_spectrum, expected_B_spectrum)
 
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_unit_vector')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_pitch_angle')
-    @patch('imap_l3_processing.swe.l3.science.pitch_calculations.calculate_gyrophase')
-    def test_rebin_swe_intensity(self, mock_calculate_gyrophases, mock_calculate_pitch_angles,
-                                 mock_calculate_unit_vector):
+    @patch("imap_l3_processing.swe.l3.science.pitch_calculations.calculate_unit_vector")
+    @patch("imap_l3_processing.swe.l3.science.pitch_calculations.calculate_pitch_angle")
+    @patch("imap_l3_processing.swe.l3.science.pitch_calculations.calculate_gyrophase")
+    def test_rebin_swe_intensity(
+        self,
+        mock_calculate_gyrophases,
+        mock_calculate_pitch_angles,
+        mock_calculate_unit_vector,
+    ):
         intensity_data_for_energy_1 = [[0, 1, 2, 3], [4, 5, 6, 7]]
         intensity_data_for_energy_2 = [[8, 9, 10, 11], [12, 13, 14, 15]]
-        intensity = np.array([
-            intensity_data_for_energy_1,
-            intensity_data_for_energy_2
-        ])
+        intensity = np.array([intensity_data_for_energy_1, intensity_data_for_energy_2])
 
         pitch_angles = np.array(
             [
                 [[45, 45, 135, 135], [45, 45, 135, 135]],
                 [[45, 45, 135, 135], [45, 45, 135, 135]],
-            ])
+            ]
+        )
         mock_calculate_pitch_angles.return_value = pitch_angles
 
         gyrophases = np.array(
             [
                 [[135, 225, 225, 135], [45, 315, 315, 45]],
-                [[135, 225, 225, 135], [45, 315, 315, 45]]
-            ])
+                [[135, 225, 225, 135], [45, 315, 315, 45]],
+            ]
+        )
         mock_calculate_gyrophases.return_value = gyrophases
 
-        counts = np.array([
-            [[4, 9, 49, 36], [0, 16, 64, 25]],
-            [[4 * 4, 4 * 9, 4 * 49, 4 * 36], [4 * 1, 4 * 16, 4 * 64, 4 * 25]],
-        ])
-
-        normalized_mag_vectors = np.array([
-            [
-                [1, 0, 0],
-                [0, 1, 0],
-            ],
+        counts = np.array(
             [
-                [0, 0, 1],
-                [1, 1, 1],
-            ],
-        ])
-        expected_mag_vectors_with_cem_axis = np.array([
+                [[4, 9, 49, 36], [0, 16, 64, 25]],
+                [[4 * 4, 4 * 9, 4 * 49, 4 * 36], [4 * 1, 4 * 16, 4 * 64, 4 * 25]],
+            ]
+        )
+
+        normalized_mag_vectors = np.array(
             [
-                [[1, 0, 0]],
-                [[0, 1, 0]],
-            ],
+                [
+                    [1, 0, 0],
+                    [0, 1, 0],
+                ],
+                [
+                    [0, 0, 1],
+                    [1, 1, 1],
+                ],
+            ]
+        )
+        expected_mag_vectors_with_cem_axis = np.array(
             [
-                [[0, 0, 1]],
-                [[1, 1, 1]],
-            ],
-        ])
-        mock_calculate_unit_vector.side_effect = [sentinel.normalized_dsp_velocities, normalized_mag_vectors]
+                [
+                    [[1, 0, 0]],
+                    [[0, 1, 0]],
+                ],
+                [
+                    [[0, 0, 1]],
+                    [[1, 1, 1]],
+                ],
+            ]
+        )
+        mock_calculate_unit_vector.side_effect = [
+            sentinel.normalized_dsp_velocities,
+            normalized_mag_vectors,
+        ]
 
         config = build_swe_configuration(
             pitch_angle_bins=np.array([45, 135]),
@@ -573,37 +722,58 @@ def test_rebin_swe_intensity(self, mock_calculate_gyrophases, mock_calculate_pit
             gyrophase_bins=np.array([45, 135, 225, 315]),
             gyrophase_deltas=np.array([45, 45, 45, 45]),
         )
-        expected_intensity_by_pa_and_gyro = np.array([
-            [[4, 0, 1, 5], [7, 3, 2, 6]],
-            [[12, 8, 9, 13], [15, 11, 10, 14]]
-        ])
+        expected_intensity_by_pa_and_gyro = np.array(
+            [[[4, 0, 1, 5], [7, 3, 2, 6]], [[12, 8, 9, 13], [15, 11, 10, 14]]]
+        )
         expected_intensity_by_pa = np.array([[2.5, 4.5], [10.5, 12.5]])
 
-        expected_uncertainty_by_pa_gyro = expected_intensity_by_pa_and_gyro * np.array([
-            [[np.nan, 1 / 2, 1 / 3, 1 / 4], [1 / 5, 1 / 6, 1 / 7, 1 / 8]],
-            [[1 / 2, 1 / 4, 1 / 6, 1 / 8], [1 / 10, 1 / 12, 1 / 14, 1 / 16]]
-        ])
+        expected_uncertainty_by_pa_gyro = expected_intensity_by_pa_and_gyro * np.array(
+            [
+                [[np.nan, 1 / 2, 1 / 3, 1 / 4], [1 / 5, 1 / 6, 1 / 7, 1 / 8]],
+                [[1 / 2, 1 / 4, 1 / 6, 1 / 8], [1 / 10, 1 / 12, 1 / 14, 1 / 16]],
+            ]
+        )
         expected_uncertainty_by_pa = expected_intensity_by_pa * np.array(
-            [[1 / np.sqrt(4 + 9 + 0 + 16), 1 / np.sqrt(49 + 36 + 64 + 25)],
-             [1 / (2 * np.sqrt(4 + 9 + 1 + 16)),
-              1 / (2 * np.sqrt(49 + 36 + 64 + 25))]])
-
-        rebinned_data = swe_rebin_intensity_by_pitch_angle_and_gyrophase(intensity,
-                                                                         counts,
-                                                                         sentinel.dsp_velocities,
-                                                                         sentinel.mag_vectors, config)
-        mock_calculate_unit_vector.assert_has_calls([call(sentinel.dsp_velocities), call(sentinel.mag_vectors)])
-        mock_calculate_pitch_angles.assert_called_once_with(sentinel.normalized_dsp_velocities,
-                                                            NumpyArrayMatcher(expected_mag_vectors_with_cem_axis))
-        mock_calculate_gyrophases.assert_called_once_with(sentinel.normalized_dsp_velocities,
-                                                          NumpyArrayMatcher(expected_mag_vectors_with_cem_axis))
-        actual_rebinned_by_pa_and_gyro, actual_rebinned_by_pa, actual_intensity_uncertainty_by_pa_and_gyro, actual_intensity_uncertainty_by_pa = rebinned_data
-        np.testing.assert_equal(actual_rebinned_by_pa_and_gyro, expected_intensity_by_pa_and_gyro)
+            [
+                [1 / np.sqrt(4 + 9 + 0 + 16), 1 / np.sqrt(49 + 36 + 64 + 25)],
+                [
+                    1 / (2 * np.sqrt(4 + 9 + 1 + 16)),
+                    1 / (2 * np.sqrt(49 + 36 + 64 + 25)),
+                ],
+            ]
+        )
+
+        rebinned_data = swe_rebin_intensity_by_pitch_angle_and_gyrophase(
+            intensity, counts, sentinel.dsp_velocities, sentinel.mag_vectors, config
+        )
+        mock_calculate_unit_vector.assert_has_calls(
+            [call(sentinel.dsp_velocities), call(sentinel.mag_vectors)]
+        )
+        mock_calculate_pitch_angles.assert_called_once_with(
+            sentinel.normalized_dsp_velocities,
+            NumpyArrayMatcher(expected_mag_vectors_with_cem_axis),
+        )
+        mock_calculate_gyrophases.assert_called_once_with(
+            sentinel.normalized_dsp_velocities,
+            NumpyArrayMatcher(expected_mag_vectors_with_cem_axis),
+        )
+        (
+            actual_rebinned_by_pa_and_gyro,
+            actual_rebinned_by_pa,
+            actual_intensity_uncertainty_by_pa_and_gyro,
+            actual_intensity_uncertainty_by_pa,
+        ) = rebinned_data
+        np.testing.assert_equal(
+            actual_rebinned_by_pa_and_gyro, expected_intensity_by_pa_and_gyro
+        )
         np.testing.assert_equal(actual_rebinned_by_pa, expected_intensity_by_pa)
-        np.testing.assert_array_almost_equal(actual_intensity_uncertainty_by_pa_and_gyro,
-                                             expected_uncertainty_by_pa_gyro)
-        np.testing.assert_array_almost_equal(actual_intensity_uncertainty_by_pa, expected_uncertainty_by_pa)
+        np.testing.assert_array_almost_equal(
+            actual_intensity_uncertainty_by_pa_and_gyro, expected_uncertainty_by_pa_gyro
+        )
+        np.testing.assert_array_almost_equal(
+            actual_intensity_uncertainty_by_pa, expected_uncertainty_by_pa
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/swe/l3/test_swe_l3_dependencies.py b/tests/swe/l3/test_swe_l3_dependencies.py
index 18aa9f3a3..f8e4a46b8 100644
--- a/tests/swe/l3/test_swe_l3_dependencies.py
+++ b/tests/swe/l3/test_swe_l3_dependencies.py
@@ -1,8 +1,12 @@
 import unittest
 from pathlib import Path
-from unittest.mock import patch, Mock, call
+from unittest.mock import Mock, call, patch
 
-from imap_data_access.processing_input import ScienceInput, AncillaryInput, ProcessingInputCollection
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
 from imap_l3_processing.swe.l3.swe_l3_dependencies import SweL3Dependencies
 
@@ -15,15 +19,23 @@ class TestSweL3Dependencies(unittest.TestCase):
     @patch(f"{MODULE}.read_l2_swe_data")
     @patch(f"{MODULE}.read_l3a_swapi_proton_data")
     @patch(f"{MODULE}.read_swe_config")
-    def test_from_file_paths(self, mock_read_swe_config, mock_read_swapi_data, mock_read_swe_data, mock_read_mag_data,
-                             mock_read_l1b_swe_data):
+    def test_from_file_paths(
+        self,
+        mock_read_swe_config,
+        mock_read_swapi_data,
+        mock_read_swe_data,
+        mock_read_mag_data,
+        mock_read_l1b_swe_data,
+    ):
         swe_path = Path("test_swe_cdf.cdf")
         swe_l1b_path = Path("test_swe_l1b_cdf.cdf")
         mag_path = Path("test_mag_cdf.cdf")
         swapi_path = Path("test_swapi_cdf.cdf")
         config_path = Path("test_config.json")
 
-        result = SweL3Dependencies.from_file_paths(swe_path, swe_l1b_path, mag_path, swapi_path, config_path)
+        result = SweL3Dependencies.from_file_paths(
+            swe_path, swe_l1b_path, mag_path, swapi_path, config_path
+        )
 
         mock_read_l1b_swe_data.assert_called_once_with(swe_l1b_path)
         mock_read_swe_data.assert_called_once_with(swe_path)
@@ -34,52 +46,78 @@ def test_from_file_paths(self, mock_read_swe_config, mock_read_swapi_data, mock_
         self.assertEqual(mock_read_l1b_swe_data.return_value, result.swe_l1b_data)
         self.assertEqual(mock_read_swe_data.return_value, result.swe_l2_data)
         self.assertEqual(mock_read_mag_data.return_value, result.mag_data)
-        self.assertEqual(mock_read_swapi_data.return_value, result.swapi_l3a_proton_data)
+        self.assertEqual(
+            mock_read_swapi_data.return_value, result.swapi_l3a_proton_data
+        )
         self.assertEqual(mock_read_swe_config.return_value, result.configuration)
         self.assertFalse(result.mag_is_preliminary)
 
         result_preliminary = SweL3Dependencies.from_file_paths(
-            swe_path, swe_l1b_path, mag_path, swapi_path, config_path, mag_is_preliminary=True
+            swe_path,
+            swe_l1b_path,
+            mag_path,
+            swapi_path,
+            config_path,
+            mag_is_preliminary=True,
         )
         self.assertTrue(result_preliminary.mag_is_preliminary)
 
     @patch("imap_l3_processing.utils.download")
     @patch(f"{MODULE}.download")
     @patch(f"{MODULE}.SweL3Dependencies.from_file_paths")
-    def test_fetch_dependencies(self, mock_from_file_paths, mock_download_dependency, mock_utils_download):
+    def test_fetch_dependencies(
+        self, mock_from_file_paths, mock_download_dependency, mock_utils_download
+    ):
         swe_l2_dependency = ScienceInput("imap_swe_l2_sci_20200101_v000.cdf")
         swe_l1b_dependency = ScienceInput("imap_swe_l1b_sci_20200101_v000.cdf")
         mag_l1d_dependency = ScienceInput("imap_mag_l1d_norm-dsrf_20200101_v000.cdf")
         swapi_l3a_dependency = ScienceInput("imap_swapi_l3_proton-sw_20200101_v000.cdf")
         config_dependency = AncillaryInput("imap_swe_config_20250101_v000.json")
 
-        processing_input_collection = ProcessingInputCollection(swe_l2_dependency, swe_l1b_dependency,
-                                                                mag_l1d_dependency, swapi_l3a_dependency,
-                                                                config_dependency)
+        processing_input_collection = ProcessingInputCollection(
+            swe_l2_dependency,
+            swe_l1b_dependency,
+            mag_l1d_dependency,
+            swapi_l3a_dependency,
+            config_dependency,
+        )
 
         expected_swe_path = Mock()
         expected_swe_l1b_path = Mock()
         expected_mag_path = Mock()
         expected_swapi_path = Mock()
         expected_config_path = Mock()
-        mock_download_dependency.side_effect = [expected_swe_path, expected_swe_l1b_path,
-                                                expected_swapi_path,
-                                                expected_config_path]
+        mock_download_dependency.side_effect = [
+            expected_swe_path,
+            expected_swe_l1b_path,
+            expected_swapi_path,
+            expected_config_path,
+        ]
         mock_utils_download.return_value = expected_mag_path
 
         result = SweL3Dependencies.fetch_dependencies(processing_input_collection)
 
-        mock_download_dependency.assert_has_calls([call(swe_l2_dependency.imap_file_paths[0].construct_path()),
-                                                   call(swe_l1b_dependency.imap_file_paths[0].construct_path()),
-                                                   call(swapi_l3a_dependency.imap_file_paths[0].construct_path()),
-                                                   call(config_dependency.imap_file_paths[0].construct_path())],
-                                                  any_order=False)
-        mock_utils_download.assert_called_once_with(mag_l1d_dependency.imap_file_paths[0].construct_path())
-
-        mock_from_file_paths.assert_called_with(expected_swe_path, expected_swe_l1b_path, expected_mag_path,
-                                                expected_swapi_path,
-                                                expected_config_path,
-                                                mag_is_preliminary=True)
+        mock_download_dependency.assert_has_calls(
+            [
+                call(swe_l2_dependency.imap_file_paths[0].construct_path()),
+                call(swe_l1b_dependency.imap_file_paths[0].construct_path()),
+                call(swapi_l3a_dependency.imap_file_paths[0].construct_path()),
+                call(config_dependency.imap_file_paths[0].construct_path()),
+            ],
+            any_order=False,
+        )
+        mock_utils_download.assert_called_once_with(
+            mag_l1d_dependency.imap_file_paths[0].construct_path()
+        )
+
+        mock_from_file_paths.assert_called_with(
+            expected_swe_path,
+            expected_swe_l1b_path,
+            expected_mag_path,
+            expected_swapi_path,
+            expected_config_path,
+            mag_is_preliminary=True,
+        )
         self.assertEqual(mock_from_file_paths.return_value, result)
 
     @patch(f"{MODULE}.select_mag_path")
@@ -90,7 +128,10 @@ def test_fetch_dependencies_raises_if_no_mag(self, _, mock_select_mag_path):
         swapi_l3a_dependency = ScienceInput("imap_swapi_l3_proton-sw_20200101_v000.cdf")
         config_dependency = AncillaryInput("imap_swe_config_20250101_v000.json")
         processing_input_collection = ProcessingInputCollection(
-            swe_l2_dependency, swe_l1b_dependency, swapi_l3a_dependency, config_dependency,
+            swe_l2_dependency,
+            swe_l1b_dependency,
+            swapi_l3a_dependency,
+            config_dependency,
         )
         mock_select_mag_path.return_value = (None, None)
 
@@ -102,7 +143,9 @@ def test_fetch_dependencies_raises_if_no_mag(self, _, mock_select_mag_path):
     @patch("imap_l3_processing.utils.download")
     @patch(f"{MODULE}.download")
     @patch(f"{MODULE}.SweL3Dependencies.from_file_paths")
-    def test_fetch_dependencies_prioritizes_mag_l2_data(self, mock_from_file_paths, mock_download, mock_utils_download):
+    def test_fetch_dependencies_prioritizes_mag_l2_data(
+        self, mock_from_file_paths, mock_download, mock_utils_download
+    ):
         swe_l2_dependency = ScienceInput("imap_swe_l2_sci_20200101_v000.cdf")
         swe_l1b_dependency = ScienceInput("imap_swe_l1b_sci_20200101_v000.cdf")
         mag_l1d_dependency = ScienceInput("imap_mag_l1d_norm-dsrf_20200101_v000.cdf")
@@ -134,12 +177,18 @@ def test_fetch_dependencies_prioritizes_mag_l2_data(self, mock_from_file_paths,
 
         result = SweL3Dependencies.fetch_dependencies(processing_input_collection)
 
-        mock_download.assert_has_calls([call(swe_l2_dependency.imap_file_paths[0].construct_path()),
-                                        call(swe_l1b_dependency.imap_file_paths[0].construct_path()),
-                                        call(swapi_l3a_dependency.imap_file_paths[0].construct_path()),
-                                        call(config_dependency.imap_file_paths[0].construct_path())],
-                                       any_order=False)
-        mock_utils_download.assert_called_once_with(mag_l2_dependency.imap_file_paths[0].construct_path())
+        mock_download.assert_has_calls(
+            [
+                call(swe_l2_dependency.imap_file_paths[0].construct_path()),
+                call(swe_l1b_dependency.imap_file_paths[0].construct_path()),
+                call(swapi_l3a_dependency.imap_file_paths[0].construct_path()),
+                call(config_dependency.imap_file_paths[0].construct_path()),
+            ],
+            any_order=False,
+        )
+        mock_utils_download.assert_called_once_with(
+            mag_l2_dependency.imap_file_paths[0].construct_path()
+        )
 
         mock_from_file_paths.assert_called_with(
             expected_swe_path,
diff --git a/tests/swe/l3/test_utils.py b/tests/swe/l3/test_utils.py
index 33bc43db4..d870d8e19 100644
--- a/tests/swe/l3/test_utils.py
+++ b/tests/swe/l3/test_utils.py
@@ -9,9 +9,14 @@
 from imap_processing.quality_flags import SweL1bFlags
 from spacepy.pycdf import CDF
 
-from imap_l3_processing.swe.l3.models import SweL2Data, SwapiL3aProtonData, SweL1bData
-from imap_l3_processing.swe.l3.utils import read_swe_config, read_l2_swe_data, read_l3a_swapi_proton_data, \
-    read_l1b_swe_data, compute_epoch_delta_in_ns
+from imap_l3_processing.swe.l3.models import SwapiL3aProtonData, SweL1bData, SweL2Data
+from imap_l3_processing.swe.l3.utils import (
+    compute_epoch_delta_in_ns,
+    read_l1b_swe_data,
+    read_l2_swe_data,
+    read_l3a_swapi_proton_data,
+    read_swe_config,
+)
 from tests.spice_test_case import SpiceTestCase
 from tests.test_helpers import get_test_data_path
 
@@ -23,23 +28,25 @@ def _copy_with_rtn_velocity(source: Path, dest: Path) -> Path:
     copy in a tempdir rather than mutating the checked-in CDF."""
     shutil.copyfile(source, dest)
     with CDF(str(dest), readonly=False) as cdf:
-        if 'proton_sw_velocity_rtn' in cdf:
+        if "proton_sw_velocity_rtn" in cdf:
             return dest
-        speed = cdf['proton_sw_speed'][:].astype(float)
+        speed = cdf["proton_sw_speed"][:].astype(float)
         velocity_rtn = np.stack(
             [-speed, np.zeros_like(speed), np.zeros_like(speed)], axis=-1
         ).astype(np.float32)
-        cdf.new('proton_sw_velocity_rtn', data=velocity_rtn)
-        cdf['proton_sw_velocity_rtn'].attrs['FILLVAL'] = np.float32(-1e31)
-        cdf['proton_sw_velocity_rtn'].attrs['UNITS'] = 'km/s'
+        cdf.new("proton_sw_velocity_rtn", data=velocity_rtn)
+        cdf["proton_sw_velocity_rtn"].attrs["FILLVAL"] = np.float32(-1e31)
+        cdf["proton_sw_velocity_rtn"].attrs["UNITS"] = "km/s"
     return dest
 
 
 class TestUtils(SpiceTestCase):
     def test_read_swe_config(self):
-        result = read_swe_config(get_test_data_path('swe/example_swe_config.json'))
-        self.assertEqual([0.0781351, 0.151448, 0.204686, 0.181759, 0.175125, 0.138312, 0.0697327],
-                         result["geometric_fractions"])
+        result = read_swe_config(get_test_data_path("swe/example_swe_config.json"))
+        self.assertEqual(
+            [0.0781351, 0.151448, 0.204686, 0.181759, 0.175125, 0.138312, 0.0697327],
+            result["geometric_fractions"],
+        )
         self.assertEqual(20, len(result["pitch_angle_bins"]))
         self.assertEqual(13.5, result["pitch_angle_bins"][1])
         self.assertEqual(20, len(result["pitch_angle_deltas"]))
@@ -61,7 +68,8 @@ def test_read_swe_config(self):
 
     def test_read_l1b_swe_data(self):
         result: SweL1bData = read_l1b_swe_data(
-            get_test_data_path('swe/imap_swe_l1b_sci_20240510_v002.cdf'))
+            get_test_data_path("swe/imap_swe_l1b_sci_20240510_v002.cdf")
+        )
 
         self.assertEqual((6,), result.epoch.shape)
         self.assertEqual(datetime(2010, 1, 1, 0, 0), result.epoch[0])
@@ -75,7 +83,9 @@ def test_read_l1b_swe_data(self):
         self.assertEqual(3333, result.settle_duration[0, 0])
 
     def test_read_l2_swe_data(self):
-        result: SweL2Data = read_l2_swe_data(get_test_data_path('swe/imap_swe_l2_sci_20260512_v001-trimmed.cdf'))
+        result: SweL2Data = read_l2_swe_data(
+            get_test_data_path("swe/imap_swe_l2_sci_20260512_v001-trimmed.cdf")
+        )
 
         self.assertEqual(result.epoch[0], datetime(2026, 5, 11, 23, 59, 45, 714250))
         self.assertEqual(len(result.epoch), 6)
@@ -105,11 +115,16 @@ def test_read_l2_swe_data(self):
         self.assertTrue(np.all(np.isnan(result.inst_az_spin_sector[1])))
         spiceypy.sct2e(-43, 0.1)
         self.assertEqual(result.phase_space_density.shape, (6, 24, 30, 7))
-        self.assertEqual(result.phase_space_density[3][11][8][4], 3.1664245168848865e-28)
+        self.assertEqual(
+            result.phase_space_density[3][11][8][4], 3.1664245168848865e-28
+        )
         self.assertTrue(np.all(np.isnan(result.phase_space_density[1])))
 
         self.assertEqual(result.acquisition_time.shape, (6, 24, 30))
-        self.assertEqual(result.acquisition_time[0][0][0], np.datetime64('2026-05-11T23:59:28.899551988'))
+        self.assertEqual(
+            result.acquisition_time[0][0][0],
+            np.datetime64("2026-05-11T23:59:28.899551988"),
+        )
         self.assertTrue(np.all(np.isnat(result.acquisition_time[1])))
 
         self.assertEqual(result.acquisition_duration.shape, (6, 24, 30))
@@ -117,7 +132,9 @@ def test_read_l2_swe_data(self):
         self.assertTrue(np.all(result.acquisition_duration[1] == 0))
 
         self.assertEqual(result.phase_space_density_rebinned.shape, (6, 24, 30, 7))
-        self.assertEqual(result.phase_space_density_rebinned[3][11][8][4], 1.2744421815220178e-27)
+        self.assertEqual(
+            result.phase_space_density_rebinned[3][11][8][4], 1.2744421815220178e-27
+        )
         self.assertTrue(np.isnan(result.phase_space_density_rebinned[0][0][0][0]))
         self.assertTrue(np.all(np.isnan(result.phase_space_density_rebinned[1])))
 
@@ -127,8 +144,8 @@ def test_read_l2_swe_data(self):
     def test_read_l3a_swapi_proton_data(self):
         with tempfile.TemporaryDirectory() as tempdir:
             cdf_path = _copy_with_rtn_velocity(
-                get_test_data_path('swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf'),
-                Path(tempdir, 'swapi_l3a_with_rtn.cdf'),
+                get_test_data_path("swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf"),
+                Path(tempdir, "swapi_l3a_with_rtn.cdf"),
             )
             result = read_l3a_swapi_proton_data(cdf_path)
         self.assertIsInstance(result, SwapiL3aProtonData)
@@ -139,18 +156,20 @@ def test_read_l3a_swapi_proton_data(self):
         self.assertEqual(datetime(2025, 1, 1), result.epoch[0])
         self.assertEqual(timedelta(seconds=30), result.epoch_delta[0])
         np.testing.assert_array_equal(
-            np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.uint16), result.swp_flags, strict=True
+            np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.uint16),
+            result.swp_flags,
+            strict=True,
         )
 
     def test_read_l3a_swapi_proton_data_with_fill_values(self):
         with tempfile.TemporaryDirectory() as tempdir:
             cdf_with_fill_path = _copy_with_rtn_velocity(
-                get_test_data_path('swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf'),
-                Path(tempdir, 'swe_file_with_fill.cdf'),
+                get_test_data_path("swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf"),
+                Path(tempdir, "swe_file_with_fill.cdf"),
             )
             with CDF(str(cdf_with_fill_path), readonly=False) as cdf:
-                velocity_fill_value = cdf['proton_sw_velocity_rtn'].attrs['FILLVAL']
-                cdf['proton_sw_velocity_rtn'][0] = [velocity_fill_value] * 3
+                velocity_fill_value = cdf["proton_sw_velocity_rtn"].attrs["FILLVAL"]
+                cdf["proton_sw_velocity_rtn"][0] = [velocity_fill_value] * 3
 
             swapi_l3a_data = read_l3a_swapi_proton_data(cdf_with_fill_path)
             self.assertTrue(np.all(np.isnan(swapi_l3a_data.proton_sw_velocity_rtn[0])))
@@ -158,10 +177,12 @@ def test_read_l3a_swapi_proton_data_with_fill_values(self):
     def test_compute_epoch_delta_in_ns(self):
         acq_duration_microseconds = np.full((4, 24, 30), 80_000)
         settle_duration_microseconds = np.full((4, 4), 10000 / 3)
-        result = compute_epoch_delta_in_ns(acq_duration_microseconds, settle_duration_microseconds)
+        result = compute_epoch_delta_in_ns(
+            acq_duration_microseconds, settle_duration_microseconds
+        )
         expected = np.full(4, 30 * 1e9)
         np.testing.assert_array_equal(expected, result)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/swe/test_swe_processor.py b/tests/swe/test_swe_processor.py
index d936f5ea2..9582c9ae8 100644
--- a/tests/swe/test_swe_processor.py
+++ b/tests/swe/test_swe_processor.py
@@ -1,46 +1,76 @@
 import unittest
 from dataclasses import replace
 from datetime import datetime, timedelta, timezone
-from unittest.mock import patch, call, Mock, sentinel, create_autospec
+from unittest.mock import Mock, call, create_autospec, patch, sentinel
 
 import numpy as np
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, AncillaryInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 from imap_processing.quality_flags import SweL1bFlags
 from spiceypy import SpiceyError
 
 from imap_l3_processing.data_utils import NearestInterpolator
-from imap_l3_processing.models import MagData, InputMetadata
+from imap_l3_processing.models import InputMetadata, MagData
 from imap_l3_processing.predicted_ephemeris_tracker import PredictedEphemerisTracker
-from imap_l3_processing.swe.l3.models import SweL2Data, SwapiL3aProtonData, SweL1bData
-from imap_l3_processing.swe.l3.models import SweL3MomentData
-from imap_l3_processing.swe.l3.science.moment_calculations import IntegrateOutputs, MomentFitResults, ScaleDensityOutput
-from imap_l3_processing.swe.l3.science.moment_calculations import Moments
+from imap_l3_processing.swe.l3.models import (
+    SwapiL3aProtonData,
+    SweL1bData,
+    SweL2Data,
+    SweL3MomentData,
+)
+from imap_l3_processing.swe.l3.science.moment_calculations import (
+    IntegrateOutputs,
+    MomentFitResults,
+    Moments,
+    ScaleDensityOutput,
+)
 from imap_l3_processing.swe.l3.swe_l3_dependencies import SweL3Dependencies
 from imap_l3_processing.swe.quality_flags import SweL3Flags
-from imap_l3_processing.swe.swe_processor import SweProcessor, check_and_mask_negative_moments, logger
-from tests.test_helpers import NumpyArrayMatcher, build_swe_configuration, create_dataclass_mock, build_moments, \
-    build_moment_fit_results, build_swe_moment_data
+from imap_l3_processing.swe.swe_processor import (
+    SweProcessor,
+    check_and_mask_negative_moments,
+    logger,
+)
+from tests.test_helpers import (
+    NumpyArrayMatcher,
+    build_moment_fit_results,
+    build_moments,
+    build_swe_configuration,
+    build_swe_moment_data,
+    create_dataclass_mock,
+)
 
 MODULE = "imap_l3_processing.swe.swe_processor"
 
 
 class TestSweProcessor(unittest.TestCase):
-    @patch('imap_l3_processing.processor.spiceypy')
-    @patch('imap_l3_processing.swe.swe_processor.save_data')
-    @patch('imap_l3_processing.swe.swe_processor.SweL3Dependencies.fetch_dependencies')
-    @patch('imap_l3_processing.swe.swe_processor.SweProcessor.calculate_products')
-    def test_process(self, mock_calculate_products, mock_fetch_dependencies, mock_save_data, mock_spiceypy):
+    @patch("imap_l3_processing.processor.spiceypy")
+    @patch("imap_l3_processing.swe.swe_processor.save_data")
+    @patch("imap_l3_processing.swe.swe_processor.SweL3Dependencies.fetch_dependencies")
+    @patch("imap_l3_processing.swe.swe_processor.SweProcessor.calculate_products")
+    def test_process(
+        self,
+        mock_calculate_products,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_spiceypy,
+    ):
         input_file_names = [
             "imap_swe_l2_sci_20200101_v000.cdf",
             "imap_swe_l1b_sci_20200101_v000.cdf",
             "imap_mag_l1d_norm-dsrf_20200101_v000.cdf",
             "imap_swapi_l3_proton-sw_20200101_v000.cdf",
-            "imap_swe_config-json-not-cdf_20200101_v000.cdf"
+            "imap_swe_config-json-not-cdf_20200101_v000.cdf",
         ]
 
         mock_spiceypy.ktotal.return_value = 0
 
-        science_inputs = [ScienceInput(file_name) for file_name in input_file_names[0:4]]
+        science_inputs = [
+            ScienceInput(file_name) for file_name in input_file_names[0:4]
+        ]
         config_input = AncillaryInput(input_file_names[4])
         mock_dependencies = ProcessingInputCollection(*science_inputs, config_input)
 
@@ -53,24 +83,45 @@ def test_process(self, mock_calculate_products, mock_fetch_dependencies, mock_sa
         self.assertEqual(calculate_products_return.parent_file_names, input_file_names)
 
         mock_fetch_dependencies.assert_called_once_with(mock_dependencies)
-        mock_calculate_products.assert_called_once_with(mock_fetch_dependencies.return_value)
+        mock_calculate_products.assert_called_once_with(
+            mock_fetch_dependencies.return_value
+        )
         mock_save_data.assert_called_once_with(mock_calculate_products.return_value)
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.swe.swe_processor.check_and_mask_negative_moments')
+    @patch("imap_l3_processing.swe.swe_processor.check_and_mask_negative_moments")
     @patch("imap_l3_processing.swe.swe_processor.compute_epoch_delta_in_ns")
-    @patch('imap_l3_processing.swe.swe_processor.average_over_look_directions')
-    @patch('imap_l3_processing.swe.swe_processor.mec_breakpoint_finder')
-    @patch('imap_l3_processing.swe.swe_processor.SweProcessor.calculate_pitch_angle_products')
-    @patch('imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products')
-    def test_calculate_products(self, mock_calculate_moment_products, mock_calculate_pitch_angle_products,
-                                mock_breakpoint_finder, mock_average_over_look_directions,
-                                mock_compute_epoch_delta_in_ns, mock_check_and_mask_negative_moments):
+    @patch("imap_l3_processing.swe.swe_processor.average_over_look_directions")
+    @patch("imap_l3_processing.swe.swe_processor.mec_breakpoint_finder")
+    @patch(
+        "imap_l3_processing.swe.swe_processor.SweProcessor.calculate_pitch_angle_products"
+    )
+    @patch(
+        "imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products"
+    )
+    def test_calculate_products(
+        self,
+        mock_calculate_moment_products,
+        mock_calculate_pitch_angle_products,
+        mock_breakpoint_finder,
+        mock_average_over_look_directions,
+        mock_compute_epoch_delta_in_ns,
+        mock_check_and_mask_negative_moments,
+    ):
         mock_compute_epoch_delta_in_ns.return_value = [30e9, 40e9]
         epochs = datetime.now() + np.arange(7) * timedelta(minutes=1)
-        swapi_epochs = datetime.now() - timedelta(seconds=15) + np.arange(2) * timedelta(minutes=.5)
+        swapi_epochs = (
+            datetime.now()
+            - timedelta(seconds=15)
+            + np.arange(2) * timedelta(minutes=0.5)
+        )
         mock_breakpoint_finder.side_effect = [
-            (1, 2, SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE | SweL3Flags.POTENTIAL_FIT_UNCONVERGED),
+            (
+                1,
+                2,
+                SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE
+                | SweL3Flags.POTENTIAL_FIT_UNCONVERGED,
+            ),
             (3, 4, SweL3Flags.NONE),
             (5, 6, SweL3Flags.NONE),
             (7, 8, SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE),
@@ -118,15 +169,17 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
             data_quality=l2_data_quality,
         )
 
-        expected_corrected_energy_bins = np.array([
-            [2 - 1, 4 - 1, 6 - 1],
-            [2 - 3, 4 - 3, 6 - 3],
-            [2 - 5, 4 - 5, 6 - 5],
-            [2 - 7, 4 - 7, 6 - 7],
-            [2 - 9, 4 - 9, 6 - 9],
-            [2 - 11, 4 - 11, 6 - 11],
-            [2 - 13, 4 - 13, 6 - 13],
-        ])
+        expected_corrected_energy_bins = np.array(
+            [
+                [2 - 1, 4 - 1, 6 - 1],
+                [2 - 3, 4 - 3, 6 - 3],
+                [2 - 5, 4 - 5, 6 - 5],
+                [2 - 7, 4 - 7, 6 - 7],
+                [2 - 9, 4 - 9, 6 - 9],
+                [2 - 11, 4 - 11, 6 - 11],
+                [2 - 13, 4 - 13, 6 - 13],
+            ]
+        )
 
         mag_l1d_data = Mock()
 
@@ -150,34 +203,47 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
             spacecraft_potential_initial_guess=15,
             core_halo_breakpoint_initial_guess=90,
             gyrophase_bins=[90, 180, 270],
-            gyrophase_delta=[90, 90, 90]
+            gyrophase_delta=[90, 90, 90],
         )
 
         mock_moment_data = build_swe_moment_data(len(epochs))
-        mock_moment_data.quality_flags = np.array([
-            SweL3Flags.NONE,
-            SweL3Flags.NONE,
-            SweL3Flags.PREDICTIVE_EPHEMERIS,
-            SweL3Flags.NONE,
-            SweL3Flags.PREDICTIVE_EPHEMERIS,
-            SweL3Flags.NONE,
-            SweL3Flags.NONE,
-        ], dtype=np.uint16)
+        mock_moment_data.quality_flags = np.array(
+            [
+                SweL3Flags.NONE,
+                SweL3Flags.NONE,
+                SweL3Flags.PREDICTIVE_EPHEMERIS,
+                SweL3Flags.NONE,
+                SweL3Flags.PREDICTIVE_EPHEMERIS,
+                SweL3Flags.NONE,
+                SweL3Flags.NONE,
+            ],
+            dtype=np.uint16,
+        )
 
         mock_calculate_moment_products.return_value = mock_moment_data
 
-        negative_moment_flags_return = np.array([SweL3Flags.NONE] * len(epochs), dtype=np.uint16)
+        negative_moment_flags_return = np.array(
+            [SweL3Flags.NONE] * len(epochs), dtype=np.uint16
+        )
         negative_moment_flags_return[4] = SweL3Flags.NEGATIVE_MOMENT
         mock_check_and_mask_negative_moments.return_value = negative_moment_flags_return
 
-        calculate_pitch_angle_flags = np.array([SweL3Flags.NONE] * len(epochs), dtype=np.uint16)
+        calculate_pitch_angle_flags = np.array(
+            [SweL3Flags.NONE] * len(epochs), dtype=np.uint16
+        )
         calculate_pitch_angle_flags[0] = SweL3Flags.FALLBACK_SWAPI_SPEED
         calculate_pitch_angle_flags[2] = SweL3Flags.FALLBACK_SWAPI_SPEED
 
-        expected_phase_space_density_by_pitch_angle = np.full((7, 3, 3), physical_psd_value)
+        expected_phase_space_density_by_pitch_angle = np.full(
+            (7, 3, 3), physical_psd_value
+        )
         expected_phase_space_density_by_pitch_angle[1, 0, 0] = unphysical_psd_value
-        expected_phase_space_density_by_pitch_angle_and_gyrophase = np.full((7, 3, 3, 3), physical_psd_value)
-        expected_phase_space_density_by_pitch_angle_and_gyrophase[2, 0, 0, 0] = unphysical_psd_value
+        expected_phase_space_density_by_pitch_angle_and_gyrophase = np.full(
+            (7, 3, 3, 3), physical_psd_value
+        )
+        expected_phase_space_density_by_pitch_angle_and_gyrophase[2, 0, 0, 0] = (
+            unphysical_psd_value
+        )
         expected_energy_spectrum = np.full((7, 3), physical_psd_value)
         expected_energy_spectrum[3, 0] = unphysical_psd_value
         expected_energy_spectrum_inbound = np.full((7, 3), physical_psd_value)
@@ -191,92 +257,169 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
             expected_energy_spectrum,
             expected_energy_spectrum_inbound,
             expected_energy_spectrum_outbound,
-            sentinel.intensity_by_pitch_angle_and_gyrophase, sentinel.intensity_by_pitch_angle,
-            sentinel.uncertainty_by_pitch_angle_and_gyrophase, sentinel.uncertainty_by_pitch_angle,
-            calculate_pitch_angle_flags
+            sentinel.intensity_by_pitch_angle_and_gyrophase,
+            sentinel.intensity_by_pitch_angle,
+            sentinel.uncertainty_by_pitch_angle_and_gyrophase,
+            sentinel.uncertainty_by_pitch_angle,
+            calculate_pitch_angle_flags,
         )
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
         swe_l1b_data = Mock()
-        swe_l3_dependency = SweL3Dependencies(swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data,
-                                              swe_config,
-                                              mag_is_preliminary=True)
+        swe_l3_dependency = SweL3Dependencies(
+            swe_l2_data,
+            swe_l1b_data,
+            mag_l1d_data,
+            swapi_l3a_proton_data,
+            swe_config,
+            mag_is_preliminary=True,
+        )
 
         swe_processor = SweProcessor(swe_l3_dependency, input_metadata)
         swe_l3_data = swe_processor.calculate_products(swe_l3_dependency)
 
-        mock_compute_epoch_delta_in_ns.assert_called_once_with(swe_l2_data.acquisition_duration,
-                                                               swe_l1b_data.settle_duration)
+        mock_compute_epoch_delta_in_ns.assert_called_once_with(
+            swe_l2_data.acquisition_duration, swe_l1b_data.settle_duration
+        )
 
         self.assertEqual(7, mock_average_over_look_directions.call_count)
-        first_average_over_look_directions_call_args = mock_average_over_look_directions.call_args_list[0].args
-        np.testing.assert_array_equal(first_average_over_look_directions_call_args[0],
-                                      swe_l2_data.phase_space_density[0])
-        np.testing.assert_array_equal(first_average_over_look_directions_call_args[1],
-                                      swe_config["geometric_fractions"])
-        np.testing.assert_array_equal(first_average_over_look_directions_call_args[2],
-                                      swe_config["minimum_phase_space_density_value"])
-
-        middle_average_over_look_directions_call_args = mock_average_over_look_directions.call_args_list[3].args
-        np.testing.assert_array_equal(middle_average_over_look_directions_call_args[0],
-                                      np.mean([
-                                          swe_l2_data.phase_space_density[0],
-                                          swe_l2_data.phase_space_density[1],
-                                          swe_l2_data.phase_space_density[2],
-                                          swe_l2_data.phase_space_density[3],
-                                          swe_l2_data.phase_space_density[4],
-                                          swe_l2_data.phase_space_density[5],
-                                          swe_l2_data.phase_space_density[6],
-                                      ], axis=0))
-        np.testing.assert_array_equal(middle_average_over_look_directions_call_args[1],
-                                      swe_config["geometric_fractions"])
-        np.testing.assert_array_equal(middle_average_over_look_directions_call_args[2],
-                                      swe_config["minimum_phase_space_density_value"])
-
-        last_average_over_look_directions_call_args = mock_average_over_look_directions.call_args_list[6].args
-        np.testing.assert_array_equal(last_average_over_look_directions_call_args[0],
-                                      np.mean([
-                                          swe_l2_data.phase_space_density[3],
-                                          swe_l2_data.phase_space_density[4],
-                                          swe_l2_data.phase_space_density[5],
-                                          swe_l2_data.phase_space_density[6],
-                                      ], axis=0))
-        np.testing.assert_array_equal(last_average_over_look_directions_call_args[1],
-                                      swe_config["geometric_fractions"])
-        np.testing.assert_array_equal(last_average_over_look_directions_call_args[2],
-                                      swe_config["minimum_phase_space_density_value"])
-
-        mock_breakpoint_finder.assert_has_calls([
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-            call(swe_l2_data.energy, mock_average_over_look_directions.return_value),
-        ])
-
-        mag_l1d_data.rebin_to.assert_called_with(epochs, [timedelta(seconds=30), timedelta(seconds=40)])
+        first_average_over_look_directions_call_args = (
+            mock_average_over_look_directions.call_args_list[0].args
+        )
+        np.testing.assert_array_equal(
+            first_average_over_look_directions_call_args[0],
+            swe_l2_data.phase_space_density[0],
+        )
+        np.testing.assert_array_equal(
+            first_average_over_look_directions_call_args[1],
+            swe_config["geometric_fractions"],
+        )
+        np.testing.assert_array_equal(
+            first_average_over_look_directions_call_args[2],
+            swe_config["minimum_phase_space_density_value"],
+        )
+
+        middle_average_over_look_directions_call_args = (
+            mock_average_over_look_directions.call_args_list[3].args
+        )
+        np.testing.assert_array_equal(
+            middle_average_over_look_directions_call_args[0],
+            np.mean(
+                [
+                    swe_l2_data.phase_space_density[0],
+                    swe_l2_data.phase_space_density[1],
+                    swe_l2_data.phase_space_density[2],
+                    swe_l2_data.phase_space_density[3],
+                    swe_l2_data.phase_space_density[4],
+                    swe_l2_data.phase_space_density[5],
+                    swe_l2_data.phase_space_density[6],
+                ],
+                axis=0,
+            ),
+        )
+        np.testing.assert_array_equal(
+            middle_average_over_look_directions_call_args[1],
+            swe_config["geometric_fractions"],
+        )
+        np.testing.assert_array_equal(
+            middle_average_over_look_directions_call_args[2],
+            swe_config["minimum_phase_space_density_value"],
+        )
+
+        last_average_over_look_directions_call_args = (
+            mock_average_over_look_directions.call_args_list[6].args
+        )
+        np.testing.assert_array_equal(
+            last_average_over_look_directions_call_args[0],
+            np.mean(
+                [
+                    swe_l2_data.phase_space_density[3],
+                    swe_l2_data.phase_space_density[4],
+                    swe_l2_data.phase_space_density[5],
+                    swe_l2_data.phase_space_density[6],
+                ],
+                axis=0,
+            ),
+        )
+        np.testing.assert_array_equal(
+            last_average_over_look_directions_call_args[1],
+            swe_config["geometric_fractions"],
+        )
+        np.testing.assert_array_equal(
+            last_average_over_look_directions_call_args[2],
+            swe_config["minimum_phase_space_density_value"],
+        )
+
+        mock_breakpoint_finder.assert_has_calls(
+            [
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+                call(
+                    swe_l2_data.energy, mock_average_over_look_directions.return_value
+                ),
+            ]
+        )
+
+        mag_l1d_data.rebin_to.assert_called_with(
+            epochs, [timedelta(seconds=30), timedelta(seconds=40)]
+        )
         mock_calculate_moment_products.assert_called_once()
         self.assertEqual(swe_l2_data, mock_calculate_moment_products.call_args[0][0])
         self.assertEqual(swe_l1b_data, mock_calculate_moment_products.call_args[0][1])
-        self.assertEqual(mag_l1d_data.rebin_to.return_value, mock_calculate_moment_products.call_args[0][2])
-        np.testing.assert_array_equal(mock_calculate_moment_products.call_args[0][3], expected_spacecraft_potential)
-        np.testing.assert_array_equal(mock_calculate_moment_products.call_args[0][4], expected_core_halo_breakpoint)
-        np.testing.assert_array_equal(mock_calculate_moment_products.call_args[0][5], expected_corrected_energy_bins)
+        self.assertEqual(
+            mag_l1d_data.rebin_to.return_value,
+            mock_calculate_moment_products.call_args[0][2],
+        )
+        np.testing.assert_array_equal(
+            mock_calculate_moment_products.call_args[0][3],
+            expected_spacecraft_potential,
+        )
+        np.testing.assert_array_equal(
+            mock_calculate_moment_products.call_args[0][4],
+            expected_core_halo_breakpoint,
+        )
+        np.testing.assert_array_equal(
+            mock_calculate_moment_products.call_args[0][5],
+            expected_corrected_energy_bins,
+        )
         self.assertEqual(swe_config, mock_calculate_moment_products.call_args[0][6])
 
         mock_calculate_pitch_angle_products.assert_called_once()
-        self.assertEqual(swe_l3_dependency, mock_calculate_pitch_angle_products.call_args[0][0])
-        np.testing.assert_array_equal(mock_calculate_pitch_angle_products.call_args[0][1],
-                                      expected_corrected_energy_bins)
+        self.assertEqual(
+            swe_l3_dependency, mock_calculate_pitch_angle_products.call_args[0][0]
+        )
+        np.testing.assert_array_equal(
+            mock_calculate_pitch_angle_products.call_args[0][1],
+            expected_corrected_energy_bins,
+        )
 
         # @formatter:off
-        self.assertEqual(swe_l3_data.input_metadata, replace(input_metadata, descriptor="sci"))
+        self.assertEqual(
+            swe_l3_data.input_metadata, replace(input_metadata, descriptor="sci")
+        )
         # pass through from l2
         np.testing.assert_array_equal(swe_l3_data.epoch, swe_l2_data.epoch)
-        np.testing.assert_array_equal(swe_l3_data.epoch_delta, mock_compute_epoch_delta_in_ns.return_value)
+        np.testing.assert_array_equal(
+            swe_l3_data.epoch_delta, mock_compute_epoch_delta_in_ns.return_value
+        )
         np.testing.assert_array_equal(swe_l3_data.inst_el, swe_l2_data.inst_el)
         self.assertEqual(swe_l3_data.inst_el_label, swe_l2_data.inst_el_label)
         self.assertEqual(swe_l3_data.inst_az, swe_l2_data.inst_az)
@@ -284,33 +427,67 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
 
         # coming from the config
         np.testing.assert_array_equal(swe_l3_data.energy, swe_config["energy_bins"])
-        np.testing.assert_array_equal(swe_l3_data.energy_delta_plus, swe_config["energy_delta_plus"])
-        np.testing.assert_array_equal(swe_l3_data.energy_delta_minus, swe_config["energy_delta_minus"])
-        np.testing.assert_array_equal(swe_l3_data.pitch_angle, swe_config["pitch_angle_bins"])
-        np.testing.assert_array_equal(swe_l3_data.pitch_angle_delta, swe_config["pitch_angle_deltas"])
-        np.testing.assert_array_equal(swe_l3_data.gyrophase_bins, swe_config["gyrophase_bins"])
-        np.testing.assert_array_equal(swe_l3_data.gyrophase_delta, swe_config["gyrophase_deltas"])
-        np.testing.assert_array_equal(swe_l3_data.intensity_by_pitch_angle_and_gyrophase,
-                                      sentinel.intensity_by_pitch_angle_and_gyrophase)
-        np.testing.assert_array_equal(swe_l3_data.intensity_by_pitch_angle, sentinel.intensity_by_pitch_angle)
-        np.testing.assert_array_equal(swe_l3_data.intensity_uncertainty_by_pitch_angle_and_gyrophase,
-                                      sentinel.uncertainty_by_pitch_angle_and_gyrophase)
-        np.testing.assert_array_equal(swe_l3_data.intensity_uncertainty_by_pitch_angle,
-                                      sentinel.uncertainty_by_pitch_angle)
+        np.testing.assert_array_equal(
+            swe_l3_data.energy_delta_plus, swe_config["energy_delta_plus"]
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.energy_delta_minus, swe_config["energy_delta_minus"]
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.pitch_angle, swe_config["pitch_angle_bins"]
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.pitch_angle_delta, swe_config["pitch_angle_deltas"]
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.gyrophase_bins, swe_config["gyrophase_bins"]
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.gyrophase_delta, swe_config["gyrophase_deltas"]
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_by_pitch_angle_and_gyrophase,
+            sentinel.intensity_by_pitch_angle_and_gyrophase,
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_by_pitch_angle, sentinel.intensity_by_pitch_angle
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_uncertainty_by_pitch_angle_and_gyrophase,
+            sentinel.uncertainty_by_pitch_angle_and_gyrophase,
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_uncertainty_by_pitch_angle,
+            sentinel.uncertainty_by_pitch_angle,
+        )
 
         # need for both moments and pitch angle
-        np.testing.assert_array_equal(swe_l3_data.spacecraft_potential, expected_spacecraft_potential)
-        np.testing.assert_array_equal(swe_l3_data.core_halo_breakpoint, expected_core_halo_breakpoint)
+        np.testing.assert_array_equal(
+            swe_l3_data.spacecraft_potential, expected_spacecraft_potential
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.core_halo_breakpoint, expected_core_halo_breakpoint
+        )
 
         # pitch angle specific
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_by_pitch_angle,
-                                      expected_phase_space_density_by_pitch_angle)
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_1d, expected_energy_spectrum)
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_inward, expected_energy_spectrum_inbound)
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_outward, expected_energy_spectrum_outbound)
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_by_pitch_angle,
+            expected_phase_space_density_by_pitch_angle,
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_1d, expected_energy_spectrum
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_inward, expected_energy_spectrum_inbound
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_outward, expected_energy_spectrum_outbound
+        )
         self.assertEqual(mock_moment_data, swe_l3_data.moment_data)
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_by_pitch_angle_and_gyrophase,
-                                      expected_phase_space_density_by_pitch_angle_and_gyrophase)
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_by_pitch_angle_and_gyrophase,
+            expected_phase_space_density_by_pitch_angle_and_gyrophase,
+        )
         expected_phi_avg_at_unphysical_cell = (
             unphysical_psd_value * geometric_fractions[0]
             + physical_psd_value * (sum_geometric_fractions - geometric_fractions[0])
@@ -318,38 +495,69 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
 
         expected_psd_by_phi = np.full((7, 3, 5), physical_psd_value)
         expected_psd_by_phi[6, 0, 0] = expected_phi_avg_at_unphysical_cell
-        np.testing.assert_allclose(swe_l3_data.raw_psd_by_phi_rebinned, expected_psd_by_phi)
+        np.testing.assert_allclose(
+            swe_l3_data.raw_psd_by_phi_rebinned, expected_psd_by_phi
+        )
 
         expected_1d_psd = np.full((7, 3), physical_psd_value)
-        expected_1d_psd[6, 0] = (expected_phi_avg_at_unphysical_cell + 4 * physical_psd_value) / 5
+        expected_1d_psd[6, 0] = (
+            expected_phi_avg_at_unphysical_cell + 4 * physical_psd_value
+        ) / 5
         np.testing.assert_allclose(swe_l3_data.raw_1d_psd_rebinned, expected_1d_psd)
 
         expected_psd_by_theta = np.full((7, 3, 7), physical_psd_value)
-        expected_psd_by_theta[6, 0, 0] = (unphysical_psd_value + 4 * physical_psd_value) / 5
-        np.testing.assert_allclose(swe_l3_data.raw_psd_by_theta_rebinned, expected_psd_by_theta)
-
-        expected_quality_flags = np.array([
-            SweL3Flags.FALLBACK_SWAPI_SPEED | SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE | SweL3Flags.POTENTIAL_FIT_UNCONVERGED | SweL3Flags.PRELIMINARY_MAG,
-            SweL3Flags.PRELIMINARY_MAG | SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED | SweL3Flags.UNPHYSICAL_PSD,
-            SweL3Flags.PRELIMINARY_MAG | SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED | SweL3Flags.UNPHYSICAL_PSD | SweL3Flags.PREDICTIVE_EPHEMERIS | SweL3Flags.FALLBACK_SWAPI_SPEED,
-            SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE | SweL3Flags.PRELIMINARY_MAG | SweL3Flags.UNPHYSICAL_PSD,
-            SweL3Flags.PRELIMINARY_MAG | SweL3Flags.NEGATIVE_MOMENT | SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED | SweL3Flags.UNPHYSICAL_PSD | SweL3Flags.PREDICTIVE_EPHEMERIS,
-            SweL3Flags.PRELIMINARY_MAG | SweL3Flags.UNPHYSICAL_PSD,
-            SweL3Flags.PRELIMINARY_MAG | SweL3Flags.UNPHYSICAL_PSD,
-        ])
+        expected_psd_by_theta[6, 0, 0] = (
+            unphysical_psd_value + 4 * physical_psd_value
+        ) / 5
+        np.testing.assert_allclose(
+            swe_l3_data.raw_psd_by_theta_rebinned, expected_psd_by_theta
+        )
+
+        expected_quality_flags = np.array(
+            [
+                SweL3Flags.FALLBACK_SWAPI_SPEED
+                | SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE
+                | SweL3Flags.POTENTIAL_FIT_UNCONVERGED
+                | SweL3Flags.PRELIMINARY_MAG,
+                SweL3Flags.PRELIMINARY_MAG
+                | SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED
+                | SweL3Flags.UNPHYSICAL_PSD,
+                SweL3Flags.PRELIMINARY_MAG
+                | SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED
+                | SweL3Flags.UNPHYSICAL_PSD
+                | SweL3Flags.PREDICTIVE_EPHEMERIS
+                | SweL3Flags.FALLBACK_SWAPI_SPEED,
+                SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE
+                | SweL3Flags.PRELIMINARY_MAG
+                | SweL3Flags.UNPHYSICAL_PSD,
+                SweL3Flags.PRELIMINARY_MAG
+                | SweL3Flags.NEGATIVE_MOMENT
+                | SweL3Flags.FALLBACK_CALIBRATION_EXTRAPOLATED
+                | SweL3Flags.UNPHYSICAL_PSD
+                | SweL3Flags.PREDICTIVE_EPHEMERIS,
+                SweL3Flags.PRELIMINARY_MAG | SweL3Flags.UNPHYSICAL_PSD,
+                SweL3Flags.PRELIMINARY_MAG | SweL3Flags.UNPHYSICAL_PSD,
+            ]
+        )
 
         np.testing.assert_array_equal(swe_l3_data.swe_flags, expected_quality_flags)
 
         mock_check_and_mask_negative_moments.assert_called_once()
-        self.assertIs(mock_moment_data, mock_check_and_mask_negative_moments.call_args[0][0])
+        self.assertIs(
+            mock_moment_data, mock_check_and_mask_negative_moments.call_args[0][0]
+        )
 
-    @patch('imap_l3_processing.swe.swe_processor.check_temperature_outlier_flag')
-    @patch('imap_l3_processing.swe.swe_processor.check_and_mask_negative_moments')
+    @patch("imap_l3_processing.swe.swe_processor.check_temperature_outlier_flag")
+    @patch("imap_l3_processing.swe.swe_processor.check_and_mask_negative_moments")
     @patch("imap_l3_processing.swe.swe_processor.compute_epoch_delta_in_ns")
-    @patch('imap_l3_processing.swe.swe_processor.average_over_look_directions')
-    @patch('imap_l3_processing.swe.swe_processor.mec_breakpoint_finder')
-    @patch('imap_l3_processing.swe.swe_processor.SweProcessor.calculate_pitch_angle_products')
-    @patch('imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products')
+    @patch("imap_l3_processing.swe.swe_processor.average_over_look_directions")
+    @patch("imap_l3_processing.swe.swe_processor.mec_breakpoint_finder")
+    @patch(
+        "imap_l3_processing.swe.swe_processor.SweProcessor.calculate_pitch_angle_products"
+    )
+    @patch(
+        "imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products"
+    )
     def test_negative_moment_mask_runs_before_temperature_outlier_checks(
         self,
         mock_calculate_moment_products,
@@ -400,8 +608,12 @@ def test_negative_moment_mask_runs_before_temperature_outlier_checks(
 
         moment_data = build_swe_moment_data(num_epochs)
         mock_calculate_moment_products.return_value = moment_data
-        mock_check_and_mask_negative_moments.return_value = np.zeros(num_epochs, dtype=np.uint16)
-        mock_check_temperature_outlier_flag.return_value = np.zeros(num_epochs, dtype=np.uint16)
+        mock_check_and_mask_negative_moments.return_value = np.zeros(
+            num_epochs, dtype=np.uint16
+        )
+        mock_check_temperature_outlier_flag.return_value = np.zeros(
+            num_epochs, dtype=np.uint16
+        )
         mock_calculate_pitch_angle_products.return_value = (
             np.zeros((num_epochs, 3, 3)),
             np.zeros((num_epochs, 3, 3, 3)),
@@ -416,45 +628,71 @@ def test_negative_moment_mask_runs_before_temperature_outlier_checks(
         )
 
         manager = Mock()
-        manager.attach_mock(mock_check_and_mask_negative_moments, 'mask')
-        manager.attach_mock(mock_check_temperature_outlier_flag, 'outlier')
+        manager.attach_mock(mock_check_and_mask_negative_moments, "mask")
+        manager.attach_mock(mock_check_temperature_outlier_flag, "outlier")
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001")
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
         swe_l3_dependency = SweL3Dependencies(
-            swe_l2_data, Mock(), Mock(), swapi_l3a_proton_data, swe_config,
+            swe_l2_data,
+            Mock(),
+            Mock(),
+            swapi_l3a_proton_data,
+            swe_config,
             mag_is_preliminary=False,
         )
         swe_processor = SweProcessor(swe_l3_dependency, input_metadata)
         swe_processor.calculate_products(swe_l3_dependency)
 
         method_call_order = [c[0] for c in manager.mock_calls]
-        self.assertEqual('mask', method_call_order[0])
-        self.assertEqual(['outlier'] * 8, method_call_order[1:])
-
-    @patch('imap_l3_processing.swe.swe_processor.swe_rebin_intensity_by_pitch_angle_and_gyrophase')
-    @patch('imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s')
-    @patch('imap_l3_processing.swe.swe_processor.average_over_look_directions')
-    @patch('imap_l3_processing.swe.swe_processor.mec_breakpoint_finder')
-    @patch('imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps')
-    @patch('imap_l3_processing.swe.swe_processor.correct_and_rebin')
-    @patch('imap_l3_processing.swe.swe_processor.integrate_distribution_to_get_1d_spectrum')
-    @patch('imap_l3_processing.swe.swe_processor.integrate_distribution_to_get_inbound_and_outbound_1d_spectrum')
-    @patch('imap_l3_processing.swe.swe_processor.NearestInterpolator')
-    def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpolator,
-                                                          mock_integrate_distribution_to_get_inbound_and_outbound_1d_spectrum,
-                                                          mock_integrate_distribution_to_get_1d_spectrum,
-                                                          mock_correct_and_rebin,
-                                                          mock_rotate_rtn_vectors_to_dps,
-                                                          _,
-                                                          mock_average_over_look_directions,
-                                                          mock_calculate_velocities, mock_swe_rebin_intensity):
+        self.assertEqual("mask", method_call_order[0])
+        self.assertEqual(["outlier"] * 8, method_call_order[1:])
+
+    @patch(
+        "imap_l3_processing.swe.swe_processor.swe_rebin_intensity_by_pitch_angle_and_gyrophase"
+    )
+    @patch("imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s")
+    @patch("imap_l3_processing.swe.swe_processor.average_over_look_directions")
+    @patch("imap_l3_processing.swe.swe_processor.mec_breakpoint_finder")
+    @patch("imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps")
+    @patch("imap_l3_processing.swe.swe_processor.correct_and_rebin")
+    @patch(
+        "imap_l3_processing.swe.swe_processor.integrate_distribution_to_get_1d_spectrum"
+    )
+    @patch(
+        "imap_l3_processing.swe.swe_processor.integrate_distribution_to_get_inbound_and_outbound_1d_spectrum"
+    )
+    @patch("imap_l3_processing.swe.swe_processor.NearestInterpolator")
+    def test_calculate_pitch_angle_and_gyrophase_products(
+        self,
+        mock_nearest_interpolator,
+        mock_integrate_distribution_to_get_inbound_and_outbound_1d_spectrum,
+        mock_integrate_distribution_to_get_1d_spectrum,
+        mock_correct_and_rebin,
+        mock_rotate_rtn_vectors_to_dps,
+        _,
+        mock_average_over_look_directions,
+        mock_calculate_velocities,
+        mock_swe_rebin_intensity,
+    ):
         epochs = np.arange(3) * timedelta(minutes=1) + datetime.now()
-        mag_epochs = np.arange(10) * timedelta(minutes=.5) + datetime.now() - timedelta(seconds=15)
-        swapi_epochs = np.arange(10) * timedelta(minutes=.5) + datetime.now() - timedelta(seconds=15)
+        mag_epochs = (
+            np.arange(10) * timedelta(minutes=0.5)
+            + datetime.now()
+            - timedelta(seconds=15)
+        )
+        swapi_epochs = (
+            np.arange(10) * timedelta(minutes=0.5)
+            + datetime.now()
+            - timedelta(seconds=15)
+        )
         spacecraft_potential = np.array([12, 16, 19])
         energies = np.array([2, 4, 6])
 
-        corrected_energy_bins = energies.reshape(1, -1) - spacecraft_potential.reshape(-1, 1)
+        corrected_energy_bins = energies.reshape(1, -1) - spacecraft_potential.reshape(
+            -1, 1
+        )
 
         pitch_angle_bins = [0, 90, 180]
         gyrophase_bins = [0, 360]
@@ -478,12 +716,11 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpo
         swe_l1b_data = SweL1bData(
             epoch=epochs,
             count_rates=np.arange(27).reshape(3, 3, 3) + 10,
-            settle_duration=np.array([])
+            settle_duration=np.array([]),
         )
 
         mag_l1d_data = MagData(
-            epoch=mag_epochs,
-            mag_data=np.arange(7, 22).reshape(5, 3).repeat(2, axis=0)
+            epoch=mag_epochs, mag_data=np.arange(7, 22).reshape(5, 3).repeat(2, axis=0)
         )
 
         swapi_l3a_proton_data = SwapiL3aProtonData(
@@ -497,33 +734,58 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpo
         rotated_dps_vectors[3, :] = np.nan
         rotated_dps_vectors[4, :] = np.nan
 
-        mock_rotate_rtn_vectors_to_dps.return_value = rotated_dps_vectors, sentinel.solar_wind_uses_pred_ephem_flags
-        counts = swe_l1b_data.count_rates * swe_l2_data.acquisition_duration[:, :, np.newaxis] / 1e6
+        mock_rotate_rtn_vectors_to_dps.return_value = (
+            rotated_dps_vectors,
+            sentinel.solar_wind_uses_pred_ephem_flags,
+        )
+        counts = (
+            swe_l1b_data.count_rates
+            * swe_l2_data.acquisition_duration[:, :, np.newaxis]
+            / 1e6
+        )
         mock_average_over_look_directions.return_value = np.array([5, 10, 15])
         closest_mag_data = np.arange(9).reshape(3, 3)
         closest_swapi_data = np.arange(8, 17).reshape(3, 3)
 
-        mock_interpolator_mag = create_autospec(NearestInterpolator, best_indices=sentinel.mag_best_indices)
-        mock_interpolator_swapi = create_autospec(NearestInterpolator, best_indices=np.array([3,4,5]))
+        mock_interpolator_mag = create_autospec(
+            NearestInterpolator, best_indices=sentinel.mag_best_indices
+        )
+        mock_interpolator_swapi = create_autospec(
+            NearestInterpolator, best_indices=np.array([3, 4, 5])
+        )
 
         mock_interpolator_mag.interpolate_data.return_value = closest_mag_data
         mock_interpolator_swapi.interpolate_data.return_value = closest_swapi_data
 
-        mock_nearest_interpolator.side_effect = [mock_interpolator_mag, mock_interpolator_swapi]
+        mock_nearest_interpolator.side_effect = [
+            mock_interpolator_mag,
+            mock_interpolator_swapi,
+        ]
 
         rebinned_by_pitch_list = [
-            i + np.arange(len(swe_l2_data.energy) * len(pitch_angle_bins)).reshape(len(swe_l2_data.energy),
-                                                                                   len(pitch_angle_bins)) for i in
-            range(len(epochs))]
+            i
+            + np.arange(len(swe_l2_data.energy) * len(pitch_angle_bins)).reshape(
+                len(swe_l2_data.energy), len(pitch_angle_bins)
+            )
+            for i in range(len(epochs))
+        ]
 
         rebinned_by_pitch_and_gyrophase_list = [
-            i + np.arange(len(swe_l2_data.energy) * len(pitch_angle_bins) * len(gyrophase_bins)).reshape(
-                len(swe_l2_data.energy),
-                len(pitch_angle_bins), len(gyrophase_bins)) for i in
-            range(len(epochs))]
+            i
+            + np.arange(
+                len(swe_l2_data.energy) * len(pitch_angle_bins) * len(gyrophase_bins)
+            ).reshape(
+                len(swe_l2_data.energy), len(pitch_angle_bins), len(gyrophase_bins)
+            )
+            for i in range(len(epochs))
+        ]
 
-        rebinned_by_pitch_and_gyrophase = [(pa, pa_and_gyro) for pa, pa_and_gyro in
-                                           zip(rebinned_by_pitch_list, rebinned_by_pitch_and_gyrophase_list)]
+        rebinned_by_pitch_and_gyrophase = [
+            (pa, pa_and_gyro)
+            for pa, pa_and_gyro in zip(
+                rebinned_by_pitch_list, rebinned_by_pitch_and_gyrophase_list
+            )
+        ]
         mock_correct_and_rebin.side_effect = rebinned_by_pitch_and_gyrophase
         integrated_spectrum = np.arange(9).reshape(3, 3) + 11
 
@@ -532,12 +794,25 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpo
         expected_uncertainty_by_pa_and_gyro = np.arange(27).reshape(3, 3, 3) + 27
         expected_uncertainty_by_pa = np.arange(27).reshape(3, 3, 3) + 28
         mock_swe_rebin_intensity.side_effect = [
-            (expected_intensity_by_pa_and_gyro[0], expected_intensity_by_pa[0], expected_uncertainty_by_pa_and_gyro[0],
-             expected_uncertainty_by_pa[0]),
-            (expected_intensity_by_pa_and_gyro[1], expected_intensity_by_pa[1], expected_uncertainty_by_pa_and_gyro[1],
-             expected_uncertainty_by_pa[1]),
-            (expected_intensity_by_pa_and_gyro[2], expected_intensity_by_pa[2], expected_uncertainty_by_pa_and_gyro[2],
-             expected_uncertainty_by_pa[2])]
+            (
+                expected_intensity_by_pa_and_gyro[0],
+                expected_intensity_by_pa[0],
+                expected_uncertainty_by_pa_and_gyro[0],
+                expected_uncertainty_by_pa[0],
+            ),
+            (
+                expected_intensity_by_pa_and_gyro[1],
+                expected_intensity_by_pa[1],
+                expected_uncertainty_by_pa_and_gyro[1],
+                expected_uncertainty_by_pa[1],
+            ),
+            (
+                expected_intensity_by_pa_and_gyro[2],
+                expected_intensity_by_pa[2],
+                expected_uncertainty_by_pa_and_gyro[2],
+                expected_uncertainty_by_pa[2],
+            ),
+        ]
 
         mock_integrate_distribution_to_get_1d_spectrum.side_effect = integrated_spectrum
 
@@ -546,10 +821,18 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpo
         mock_integrate_distribution_to_get_inbound_and_outbound_1d_spectrum.side_effect = [
             (expected_inbound_spectrum[0], expected_outbound_spectrum[0]),
             (expected_inbound_spectrum[1], expected_outbound_spectrum[1]),
-            (expected_inbound_spectrum[2], expected_outbound_spectrum[2])]
+            (expected_inbound_spectrum[2], expected_outbound_spectrum[2]),
+        ]
 
-        geometric_fractions = [0.0697327, 0.138312, 0.175125, 0.181759,
-                               0.204686, 0.151448, 0.0781351]
+        geometric_fractions = [
+            0.0697327,
+            0.138312,
+            0.175125,
+            0.181759,
+            0.204686,
+            0.151448,
+            0.0781351,
+        ]
         swe_config = build_swe_configuration(
             geometric_fractions=geometric_fractions,
             pitch_angle_bins=pitch_angle_bins,
@@ -564,9 +847,12 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpo
             core_halo_breakpoint_initial_guess=90,
         )
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
-        swel3_dependency = SweL3Dependencies(swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, swe_config)
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
+        swel3_dependency = SweL3Dependencies(
+            swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, swe_config
+        )
         swe_processor = SweProcessor(dependencies=[], input_metadata=input_metadata)
 
         mock_interpolator_swapi.interpolate_flags.side_effect = [
@@ -574,126 +860,233 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_nearest_interpo
             np.array([False, True, False]),
         ]
 
-        actual_phase_space_density_by_pitch_angle, actual_phase_space_density_by_pa_and_gyrophase, actual_energy_spectrum, actual_energy_spectrum_inbound, actual_energy_spectrum_outbound, \
-            actual_intensity_by_pa_and_gyro, actual_intensity_by_pa, actual_uncertainty_by_pa_and_gyro, actual_uncertainty_by_pa, actual_swe_flags \
-            = swe_processor.calculate_pitch_angle_products(swel3_dependency, corrected_energy_bins)
+        (
+            actual_phase_space_density_by_pitch_angle,
+            actual_phase_space_density_by_pa_and_gyrophase,
+            actual_energy_spectrum,
+            actual_energy_spectrum_inbound,
+            actual_energy_spectrum_outbound,
+            actual_intensity_by_pa_and_gyro,
+            actual_intensity_by_pa,
+            actual_uncertainty_by_pa_and_gyro,
+            actual_uncertainty_by_pa,
+            actual_swe_flags,
+        ) = swe_processor.calculate_pitch_angle_products(
+            swel3_dependency, corrected_energy_bins
+        )
 
         self.assertEqual(3, mock_correct_and_rebin.call_count)
         self.assertEqual(3, mock_integrate_distribution_to_get_1d_spectrum.call_count)
         mock_rotate_rtn_vectors_to_dps.assert_called_once()
         rotate_args = mock_rotate_rtn_vectors_to_dps.call_args[0]
         np.testing.assert_array_equal(rotate_args[0], swapi_epochs)
-        np.testing.assert_array_equal(rotate_args[1], swapi_l3a_proton_data.proton_sw_velocity_rtn)
+        np.testing.assert_array_equal(
+            rotate_args[1], swapi_l3a_proton_data.proton_sw_velocity_rtn
+        )
         expected_dps_vectors_after_speed_fallback = rotated_dps_vectors.copy()
         expected_dps_vectors_after_speed_fallback[3] = [0.0, 0.0, -400.0]
         expected_dps_vectors_after_speed_fallback[4] = [0.0, 0.0, -400.0]
-        mock_nearest_interpolator.assert_has_calls([
-            call(
-                from_epoch=mag_epochs,
-                from_data=mag_l1d_data.mag_data,
-                to_epoch=swe_l2_data.acquisition_time,
-                maximum_distance=np.timedelta64(1, 'm')
-            ),
-            call(
-                from_epoch=swapi_epochs,
-                from_data=NumpyArrayMatcher(expected_dps_vectors_after_speed_fallback),
-                to_epoch=epochs,
-                maximum_distance=np.timedelta64(5, 'm')
-            )
-        ])
+        mock_nearest_interpolator.assert_has_calls(
+            [
+                call(
+                    from_epoch=mag_epochs,
+                    from_data=mag_l1d_data.mag_data,
+                    to_epoch=swe_l2_data.acquisition_time,
+                    maximum_distance=np.timedelta64(1, "m"),
+                ),
+                call(
+                    from_epoch=swapi_epochs,
+                    from_data=NumpyArrayMatcher(
+                        expected_dps_vectors_after_speed_fallback
+                    ),
+                    to_epoch=epochs,
+                    maximum_distance=np.timedelta64(5, "m"),
+                ),
+            ]
+        )
 
         mock_interpolator_swapi.interpolate_flags.call_args_list[0][0]
 
-        expected_fallback_flags = [False, False, False, True, True, False, False, False, False, False,]
+        expected_fallback_flags = [
+            False,
+            False,
+            False,
+            True,
+            True,
+            False,
+            False,
+            False,
+            False,
+            False,
+        ]
 
         self.assertEqual(2, mock_interpolator_swapi.interpolate_flags.call_count)
-        mock_interpolator_swapi.interpolate_flags.assert_has_calls([call(NumpyArrayMatcher(expected_fallback_flags)), call(sentinel.solar_wind_uses_pred_ephem_flags)], any_order=False)
-
+        mock_interpolator_swapi.interpolate_flags.assert_has_calls(
+            [
+                call(NumpyArrayMatcher(expected_fallback_flags)),
+                call(sentinel.solar_wind_uses_pred_ephem_flags),
+            ],
+            any_order=False,
+        )
 
-        expected_flags = np.array([SweL3Flags.FALLBACK_SWAPI_SPEED, SweL3Flags.FALLBACK_SWAPI_SPEED | SweL3Flags.PREDICTIVE_EPHEMERIS, SweL3Flags.NONE])
+        expected_flags = np.array(
+            [
+                SweL3Flags.FALLBACK_SWAPI_SPEED,
+                SweL3Flags.FALLBACK_SWAPI_SPEED | SweL3Flags.PREDICTIVE_EPHEMERIS,
+                SweL3Flags.NONE,
+            ]
+        )
 
-        np.testing.assert_array_equal(actual_phase_space_density_by_pitch_angle, rebinned_by_pitch_list)
-        np.testing.assert_array_equal(actual_phase_space_density_by_pa_and_gyrophase,
-                                      rebinned_by_pitch_and_gyrophase_list)
+        np.testing.assert_array_equal(
+            actual_phase_space_density_by_pitch_angle, rebinned_by_pitch_list
+        )
+        np.testing.assert_array_equal(
+            actual_phase_space_density_by_pa_and_gyrophase,
+            rebinned_by_pitch_and_gyrophase_list,
+        )
         np.testing.assert_array_equal(actual_energy_spectrum, integrated_spectrum)
-        np.testing.assert_array_equal(actual_energy_spectrum_inbound, expected_inbound_spectrum)
-        np.testing.assert_array_equal(actual_energy_spectrum_outbound, expected_outbound_spectrum)
-        np.testing.assert_array_equal(actual_energy_spectrum_outbound, expected_outbound_spectrum)
-        np.testing.assert_array_equal(actual_intensity_by_pa_and_gyro, expected_intensity_by_pa_and_gyro)
+        np.testing.assert_array_equal(
+            actual_energy_spectrum_inbound, expected_inbound_spectrum
+        )
+        np.testing.assert_array_equal(
+            actual_energy_spectrum_outbound, expected_outbound_spectrum
+        )
+        np.testing.assert_array_equal(
+            actual_energy_spectrum_outbound, expected_outbound_spectrum
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_by_pa_and_gyro, expected_intensity_by_pa_and_gyro
+        )
         np.testing.assert_array_equal(actual_intensity_by_pa, expected_intensity_by_pa)
-        np.testing.assert_array_equal(actual_uncertainty_by_pa_and_gyro, expected_uncertainty_by_pa_and_gyro)
-        np.testing.assert_array_equal(actual_uncertainty_by_pa, expected_uncertainty_by_pa)
+        np.testing.assert_array_equal(
+            actual_uncertainty_by_pa_and_gyro, expected_uncertainty_by_pa_and_gyro
+        )
+        np.testing.assert_array_equal(
+            actual_uncertainty_by_pa, expected_uncertainty_by_pa
+        )
         np.testing.assert_array_equal(actual_swe_flags, expected_flags)
 
-
         def call_with_array_matchers(*args):
             return call(*[NumpyArrayMatcher(x) for x in args])
 
         actual_calc_velocity_calls = mock_calculate_velocities.call_args_list
         expected_calc_velocity_calls = [
-            call_with_array_matchers(swe_l2_data.energy - 12, swe_l2_data.inst_el,
-                                     swe_l2_data.inst_az_spin_sector[0]),
-            call_with_array_matchers(swe_l2_data.energy - 16, swe_l2_data.inst_el,
-                                     swe_l2_data.inst_az_spin_sector[1]),
-            call_with_array_matchers(swe_l2_data.energy - 19, swe_l2_data.inst_el,
-                                     swe_l2_data.inst_az_spin_sector[2])
+            call_with_array_matchers(
+                swe_l2_data.energy - 12,
+                swe_l2_data.inst_el,
+                swe_l2_data.inst_az_spin_sector[0],
+            ),
+            call_with_array_matchers(
+                swe_l2_data.energy - 16,
+                swe_l2_data.inst_el,
+                swe_l2_data.inst_az_spin_sector[1],
+            ),
+            call_with_array_matchers(
+                swe_l2_data.energy - 19,
+                swe_l2_data.inst_el,
+                swe_l2_data.inst_az_spin_sector[2],
+            ),
         ]
         self.assertEqual(expected_calc_velocity_calls, actual_calc_velocity_calls)
 
         correct_and_rebin_actual_calls = mock_correct_and_rebin.call_args_list
 
         correct_and_rebin_expected_calls = [
-            call_with_array_matchers(swe_l2_data.phase_space_density[0], closest_swapi_data[0],
-                                     mock_calculate_velocities.return_value, closest_mag_data[0],
-                                     swe_config),
-            call_with_array_matchers(swe_l2_data.phase_space_density[1], closest_swapi_data[1],
-                                     mock_calculate_velocities.return_value, closest_mag_data[1],
-                                     swe_config),
-            call_with_array_matchers(swe_l2_data.phase_space_density[2], closest_swapi_data[2],
-                                     mock_calculate_velocities.return_value, closest_mag_data[2],
-                                     swe_config)
+            call_with_array_matchers(
+                swe_l2_data.phase_space_density[0],
+                closest_swapi_data[0],
+                mock_calculate_velocities.return_value,
+                closest_mag_data[0],
+                swe_config,
+            ),
+            call_with_array_matchers(
+                swe_l2_data.phase_space_density[1],
+                closest_swapi_data[1],
+                mock_calculate_velocities.return_value,
+                closest_mag_data[1],
+                swe_config,
+            ),
+            call_with_array_matchers(
+                swe_l2_data.phase_space_density[2],
+                closest_swapi_data[2],
+                mock_calculate_velocities.return_value,
+                closest_mag_data[2],
+                swe_config,
+            ),
         ]
-        self.assertEqual(correct_and_rebin_expected_calls, correct_and_rebin_actual_calls)
+        self.assertEqual(
+            correct_and_rebin_expected_calls, correct_and_rebin_actual_calls
+        )
 
         mock_rebin_intensity_expected_calls = [
-            call_with_array_matchers(swe_l2_data.flux[0], counts[0], mock_calculate_velocities.return_value,
-                                     closest_mag_data[0], swe_config),
-            call_with_array_matchers(swe_l2_data.flux[1], counts[1], mock_calculate_velocities.return_value,
-                                     closest_mag_data[1], swe_config),
-            call_with_array_matchers(swe_l2_data.flux[2], counts[2], mock_calculate_velocities.return_value,
-                                     closest_mag_data[2], swe_config)
+            call_with_array_matchers(
+                swe_l2_data.flux[0],
+                counts[0],
+                mock_calculate_velocities.return_value,
+                closest_mag_data[0],
+                swe_config,
+            ),
+            call_with_array_matchers(
+                swe_l2_data.flux[1],
+                counts[1],
+                mock_calculate_velocities.return_value,
+                closest_mag_data[1],
+                swe_config,
+            ),
+            call_with_array_matchers(
+                swe_l2_data.flux[2],
+                counts[2],
+                mock_calculate_velocities.return_value,
+                closest_mag_data[2],
+                swe_config,
+            ),
         ]
-        self.assertEqual(mock_rebin_intensity_expected_calls, mock_swe_rebin_intensity.call_args_list)
-
-        mock_integrate_distribution_to_get_1d_spectrum.assert_has_calls([
-            call(rebinned_by_pitch_list[0], swe_config),
-            call(rebinned_by_pitch_list[1], swe_config),
-            call(rebinned_by_pitch_list[2], swe_config)
-        ])
-        mock_integrate_distribution_to_get_inbound_and_outbound_1d_spectrum.assert_has_calls([
-            call(rebinned_by_pitch_list[0], swe_config),
-            call(rebinned_by_pitch_list[1], swe_config),
-            call(rebinned_by_pitch_list[2], swe_config)
-        ])
+        self.assertEqual(
+            mock_rebin_intensity_expected_calls, mock_swe_rebin_intensity.call_args_list
+        )
+
+        mock_integrate_distribution_to_get_1d_spectrum.assert_has_calls(
+            [
+                call(rebinned_by_pitch_list[0], swe_config),
+                call(rebinned_by_pitch_list[1], swe_config),
+                call(rebinned_by_pitch_list[2], swe_config),
+            ]
+        )
+        mock_integrate_distribution_to_get_inbound_and_outbound_1d_spectrum.assert_has_calls(
+            [
+                call(rebinned_by_pitch_list[0], swe_config),
+                call(rebinned_by_pitch_list[1], swe_config),
+                call(rebinned_by_pitch_list[2], swe_config),
+            ]
+        )
 
     @patch("imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps")
-    @patch("imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products")
-    def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, mock_calculate_moments, mock_rotate_rtn_vectors_to_dps):
+    @patch(
+        "imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products"
+    )
+    def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(
+        self, mock_calculate_moments, mock_rotate_rtn_vectors_to_dps
+    ):
         epochs = np.array([datetime(2025, 3, 6)])
-        mag_epochs = np.array([
-            datetime(2025, 3, 6, 0, 1, 30),
-            datetime(2025, 3, 6, 0, 2, 30),
-            datetime(2025, 3, 6, 0, 3, 30),
-            datetime(2025, 3, 6, 0, 4, 30),
-            datetime(2025, 3, 6, 0, 5, 30),
-            datetime(2025, 3, 6, 0, 6, 30),
-            datetime(2025, 3, 6, 0, 7, 30),
-            datetime(2025, 3, 6, 0, 8, 30),
-            datetime(2025, 3, 6, 0, 9, 30),
-            datetime(2025, 3, 6, 0, 10, 30),
-        ])
+        mag_epochs = np.array(
+            [
+                datetime(2025, 3, 6, 0, 1, 30),
+                datetime(2025, 3, 6, 0, 2, 30),
+                datetime(2025, 3, 6, 0, 3, 30),
+                datetime(2025, 3, 6, 0, 4, 30),
+                datetime(2025, 3, 6, 0, 5, 30),
+                datetime(2025, 3, 6, 0, 6, 30),
+                datetime(2025, 3, 6, 0, 7, 30),
+                datetime(2025, 3, 6, 0, 8, 30),
+                datetime(2025, 3, 6, 0, 9, 30),
+                datetime(2025, 3, 6, 0, 10, 30),
+            ]
+        )
         swapi_epochs = np.array([datetime(2025, 3, 6)])
-        mock_rotate_rtn_vectors_to_dps.return_value = np.tile([0.0, 0.0, -400.0], (len(swapi_epochs), 1)), np.zeros(len(swapi_epochs), dtype=bool)
+        mock_rotate_rtn_vectors_to_dps.return_value = (
+            np.tile([0.0, 0.0, -400.0], (len(swapi_epochs), 1)),
+            np.zeros(len(swapi_epochs), dtype=bool),
+        )
 
         pitch_angle_bins = [70, 100, 130]
 
@@ -705,17 +1098,26 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
 
         swe_l2_data = SweL2Data(
             epoch=epochs,
-            phase_space_density=np.arange(num_epochs * num_energies * 5 * 7).reshape(num_epochs, num_energies, 5,
-                                                                                     7) + 100,
-            flux=np.arange(num_epochs * num_energies * 5 * 7).reshape(num_epochs, num_energies, 5, 7),
+            phase_space_density=np.arange(num_epochs * num_energies * 5 * 7).reshape(
+                num_epochs, num_energies, 5, 7
+            )
+            + 100,
+            flux=np.arange(num_epochs * num_energies * 5 * 7).reshape(
+                num_epochs, num_energies, 5, 7
+            ),
             energy=np.arange(num_energies) + 20,
             inst_el=np.array([-30, -20, -10, 0, 10, 20, 30]),
             inst_el_label=sentinel.inst_el_label,
             inst_az=sentinel.inst_az,
             inst_az_label=sentinel.inst_az_label,
-            inst_az_spin_sector=np.arange(num_epochs * num_energies * 5).reshape(num_epochs, num_energies, 5),
-            acquisition_time=np.linspace(datetime(2025, 3, 6), datetime(2025, 3, 6, 0, 1),
-                                         num_epochs * num_energies * 5).reshape(num_epochs, num_energies, 5),
+            inst_az_spin_sector=np.arange(num_epochs * num_energies * 5).reshape(
+                num_epochs, num_energies, 5
+            ),
+            acquisition_time=np.linspace(
+                datetime(2025, 3, 6),
+                datetime(2025, 3, 6, 0, 1),
+                num_epochs * num_energies * 5,
+            ).reshape(num_epochs, num_energies, 5),
             acquisition_duration=np.full((num_epochs, num_energies, 5), 80000),
             phase_space_density_rebinned=np.zeros((num_epochs, num_energies, 5, 7)),
             data_quality=np.zeros(num_epochs, dtype=np.uint8),
@@ -724,12 +1126,11 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
         swe_l1b_data = SweL1bData(
             epoch=epochs,
             count_rates=np.full((num_epochs, num_energies, 5, 7), 350),
-            settle_duration=np.full((num_epochs, 3), 333)
+            settle_duration=np.full((num_epochs, 3), 333),
         )
 
         mag_l1d_data = MagData(
-            epoch=mag_epochs,
-            mag_data=np.arange(7, 22).reshape(5, 3).repeat(2, axis=0)
+            epoch=mag_epochs, mag_data=np.arange(7, 22).reshape(5, 3).repeat(2, axis=0)
         )
 
         swapi_l3a_proton_data = SwapiL3aProtonData(
@@ -737,10 +1138,17 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
             epoch_delta=np.repeat(timedelta(seconds=30), 10),
             proton_sw_velocity_rtn=np.tile([-400.0, 0.0, 0.0], (len(swapi_epochs), 1)),
             proton_sw_speed=np.full(len(swapi_epochs), 400.0),
-            swp_flags=np.full(len(swapi_epochs), 0)
+            swp_flags=np.full(len(swapi_epochs), 0),
         )
-        geometric_fractions = [0.0697327, 0.138312, 0.175125, 0.181759,
-                               0.204686, 0.151448, 0.0781351]
+        geometric_fractions = [
+            0.0697327,
+            0.138312,
+            0.175125,
+            0.181759,
+            0.204686,
+            0.151448,
+            0.0781351,
+        ]
         energy_bins = [8, 10, 13]
         swe_config = build_swe_configuration(
             geometric_fractions=geometric_fractions,
@@ -755,67 +1163,126 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
             max_mag_offset_in_minutes=1,
             spacecraft_potential_initial_guess=15,
             core_halo_breakpoint_initial_guess=90,
-            in_vs_out_energy_index=len(energy_bins) - 1
+            in_vs_out_energy_index=len(energy_bins) - 1,
         )
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
-        swel3_dependency = SweL3Dependencies(swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, swe_config)
-        swe_processor = SweProcessor(ProcessingInputCollection(), input_metadata=input_metadata)
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
+        swel3_dependency = SweL3Dependencies(
+            swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, swe_config
+        )
+        swe_processor = SweProcessor(
+            ProcessingInputCollection(), input_metadata=input_metadata
+        )
         swe_l3_data = swe_processor.calculate_products(swel3_dependency)
 
-        self.assertEqual(replace(input_metadata, descriptor="sci"), swe_l3_data.input_metadata)
-        self.assertEqual(swe_l3_data.pitch_angle, swel3_dependency.configuration["pitch_angle_bins"])
-        self.assertEqual(swe_l3_data.pitch_angle_delta, swel3_dependency.configuration["pitch_angle_deltas"])
-        self.assertEqual(swe_l3_data.energy, swel3_dependency.configuration["energy_bins"])
-        self.assertEqual(swe_l3_data.energy_delta_plus, swel3_dependency.configuration["energy_delta_plus"])
-        self.assertEqual(swe_l3_data.energy_delta_minus, swel3_dependency.configuration["energy_delta_minus"])
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_by_pitch_angle,
-                                      np.full((len(epochs), len(energy_bins), len(pitch_angle_bins)), np.nan))
+        self.assertEqual(
+            replace(input_metadata, descriptor="sci"), swe_l3_data.input_metadata
+        )
+        self.assertEqual(
+            swe_l3_data.pitch_angle, swel3_dependency.configuration["pitch_angle_bins"]
+        )
+        self.assertEqual(
+            swe_l3_data.pitch_angle_delta,
+            swel3_dependency.configuration["pitch_angle_deltas"],
+        )
+        self.assertEqual(
+            swe_l3_data.energy, swel3_dependency.configuration["energy_bins"]
+        )
+        self.assertEqual(
+            swe_l3_data.energy_delta_plus,
+            swel3_dependency.configuration["energy_delta_plus"],
+        )
+        self.assertEqual(
+            swe_l3_data.energy_delta_minus,
+            swel3_dependency.configuration["energy_delta_minus"],
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_by_pitch_angle,
+            np.full((len(epochs), len(energy_bins), len(pitch_angle_bins)), np.nan),
+        )
         np.testing.assert_array_equal(swe_l3_data.epoch, swe_l2_data.epoch)
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_1d, np.full((len(epochs), len(energy_bins)), np.nan))
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_inward,
-                                      np.full((len(epochs), len(energy_bins)), np.nan))
-        np.testing.assert_array_equal(swe_l3_data.phase_space_density_outward,
-                                      np.full((len(epochs), len(energy_bins)), np.nan))
-        np.testing.assert_array_equal(swe_l3_data.intensity_by_pitch_angle,
-                                      np.full((len(epochs), num_energies, len(pitch_angle_bins)), np.nan))
-        np.testing.assert_array_equal(swe_l3_data.intensity_by_pitch_angle_and_gyrophase,
-                                      np.full((len(epochs), num_energies, len(pitch_angle_bins), len(gyrophase_bins)),
-                                              np.nan))
-        np.testing.assert_array_equal(swe_l3_data.intensity_uncertainty_by_pitch_angle,
-                                      np.full((len(epochs), num_energies, len(pitch_angle_bins)), np.nan))
-        np.testing.assert_array_equal(swe_l3_data.intensity_uncertainty_by_pitch_angle_and_gyrophase,
-                                      np.full((len(epochs), num_energies, len(pitch_angle_bins), len(gyrophase_bins)),
-                                              np.nan))
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_1d,
+            np.full((len(epochs), len(energy_bins)), np.nan),
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_inward,
+            np.full((len(epochs), len(energy_bins)), np.nan),
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.phase_space_density_outward,
+            np.full((len(epochs), len(energy_bins)), np.nan),
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_by_pitch_angle,
+            np.full((len(epochs), num_energies, len(pitch_angle_bins)), np.nan),
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_by_pitch_angle_and_gyrophase,
+            np.full(
+                (len(epochs), num_energies, len(pitch_angle_bins), len(gyrophase_bins)),
+                np.nan,
+            ),
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_uncertainty_by_pitch_angle,
+            np.full((len(epochs), num_energies, len(pitch_angle_bins)), np.nan),
+        )
+        np.testing.assert_array_equal(
+            swe_l3_data.intensity_uncertainty_by_pitch_angle_and_gyrophase,
+            np.full(
+                (len(epochs), num_energies, len(pitch_angle_bins), len(gyrophase_bins)),
+                np.nan,
+            ),
+        )
 
     @patch("imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps")
-    @patch("imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products")
-    def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_moments, mock_rotate_rtn_vectors_to_dps):
+    @patch(
+        "imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products"
+    )
+    def test_calculate_pitch_angle_products_without_mocks(
+        self, mock_calculate_moments, mock_rotate_rtn_vectors_to_dps
+    ):
         epochs = np.array([datetime(2025, 3, 6)])
         mag_start_time = datetime(2025, 3, 6, 0, 1, 0)
-        mag_epochs = np.array([mag_start_time + i * timedelta(seconds=1) for i in range(10)])
+        mag_epochs = np.array(
+            [mag_start_time + i * timedelta(seconds=1) for i in range(10)]
+        )
         swapi_epochs = np.array([datetime(2025, 3, 6), datetime(2025, 3, 10)])
         swp_flags = np.zeros(2)
         mock_calculate_moments.return_value = build_swe_moment_data(len(epochs))
-        mock_rotate_rtn_vectors_to_dps.return_value = np.full((len(swapi_epochs), 3), np.nan), np.zeros(len(swapi_epochs), dtype=bool)
+        mock_rotate_rtn_vectors_to_dps.return_value = (
+            np.full((len(swapi_epochs), 3), np.nan),
+            np.zeros(len(swapi_epochs), dtype=bool),
+        )
         pitch_angle_bins = [70, 100, 130]
 
         num_energies = 9
         num_epochs = 1
         swe_l2_data = SweL2Data(
             epoch=epochs,
-            phase_space_density=np.arange(num_epochs * num_energies * 5 * 7).reshape(num_epochs, num_energies, 5,
-                                                                                     7) + 100,
-            flux=np.arange(num_epochs * num_energies * 5 * 7).reshape(num_epochs, num_energies, 5, 7),
+            phase_space_density=np.arange(num_epochs * num_energies * 5 * 7).reshape(
+                num_epochs, num_energies, 5, 7
+            )
+            + 100,
+            flux=np.arange(num_epochs * num_energies * 5 * 7).reshape(
+                num_epochs, num_energies, 5, 7
+            ),
             energy=np.arange(num_energies) + 5,
             inst_el=np.array([-30, -20, -10, 0, 10, 20, 30]),
             inst_el_label=sentinel.inst_el_label,
             inst_az=sentinel.inst_az,
             inst_az_label=sentinel.inst_az_label,
-            inst_az_spin_sector=np.arange(num_epochs * num_energies * 5).reshape(num_epochs, num_energies, 5),
-            acquisition_time=np.linspace(datetime(2025, 3, 6), datetime(2025, 3, 6, 0, 1),
-                                         num_epochs * num_energies * 5).reshape(num_epochs, num_energies, 5),
+            inst_az_spin_sector=np.arange(num_epochs * num_energies * 5).reshape(
+                num_epochs, num_energies, 5
+            ),
+            acquisition_time=np.linspace(
+                datetime(2025, 3, 6),
+                datetime(2025, 3, 6, 0, 1),
+                num_epochs * num_energies * 5,
+            ).reshape(num_epochs, num_energies, 5),
             acquisition_duration=np.full((num_epochs, num_energies, 5), 80000),
             phase_space_density_rebinned=np.zeros((num_epochs, num_energies, 5, 7)),
             data_quality=np.zeros(num_epochs, dtype=np.uint8),
@@ -823,15 +1290,18 @@ def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_momen
 
         swe_l1b_data = SweL1bData(
             epoch=epochs,
-            count_rates=(np.arange(num_epochs * num_energies * 5 * 7).reshape(num_epochs, num_energies, 5,
-                                                                              7) + 350) * 1e6,
-            settle_duration=np.full((num_epochs, 3), 333)
-
+            count_rates=(
+                np.arange(num_epochs * num_energies * 5 * 7).reshape(
+                    num_epochs, num_energies, 5, 7
+                )
+                + 350
+            )
+            * 1e6,
+            settle_duration=np.full((num_epochs, 3), 333),
         )
 
         mag_l1d_data = MagData(
-            epoch=mag_epochs,
-            mag_data=np.arange(7, 22).reshape(5, 3).repeat(2, axis=0)
+            epoch=mag_epochs, mag_data=np.arange(7, 22).reshape(5, 3).repeat(2, axis=0)
         )
 
         swapi_l3a_proton_data = SwapiL3aProtonData(
@@ -839,10 +1309,17 @@ def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_momen
             epoch_delta=np.repeat(timedelta(seconds=30), 10),
             proton_sw_velocity_rtn=np.full((len(swapi_epochs), 3), np.nan),
             proton_sw_speed=np.full(len(swapi_epochs), 400.0),
-            swp_flags=swp_flags
+            swp_flags=swp_flags,
         )
-        geometric_fractions = [0.0697327, 0.138312, 0.175125, 0.181759,
-                               0.204686, 0.151448, 0.0781351]
+        geometric_fractions = [
+            0.0697327,
+            0.138312,
+            0.175125,
+            0.181759,
+            0.204686,
+            0.151448,
+            0.0781351,
+        ]
         energy_bins = [8, 10, 13]
         swe_config = build_swe_configuration(
             geometric_fractions=geometric_fractions,
@@ -857,80 +1334,140 @@ def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_momen
             core_halo_breakpoint_initial_guess=90,
             in_vs_out_energy_index=len(energy_bins) - 1,
             gyrophase_bins=[90, 180, 270],
-            gyrophase_deltas=[45, 45, 45]
+            gyrophase_deltas=[45, 45, 45],
         )
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
 
-        swel3_dependency = SweL3Dependencies(swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, swe_config)
-        swe_processor = SweProcessor(ProcessingInputCollection(), input_metadata=input_metadata)
+        swel3_dependency = SweL3Dependencies(
+            swe_l2_data, swe_l1b_data, mag_l1d_data, swapi_l3a_proton_data, swe_config
+        )
+        swe_processor = SweProcessor(
+            ProcessingInputCollection(), input_metadata=input_metadata
+        )
         swe_l3_data = swe_processor.calculate_products(swel3_dependency)
 
-        self.assertEqual(replace(input_metadata, descriptor="sci"), swe_l3_data.input_metadata)
-        self.assertEqual(swe_l3_data.pitch_angle, swel3_dependency.configuration["pitch_angle_bins"])
-        self.assertEqual(swe_l3_data.pitch_angle_delta, swel3_dependency.configuration["pitch_angle_deltas"])
-        self.assertEqual(swe_l3_data.energy, swel3_dependency.configuration["energy_bins"])
-        self.assertEqual(swe_l3_data.energy_delta_plus, swel3_dependency.configuration["energy_delta_plus"])
-        self.assertEqual(swe_l3_data.energy_delta_minus, swel3_dependency.configuration["energy_delta_minus"])
-        self.assertEqual(swe_l3_data.gyrophase_bins, swel3_dependency.configuration["gyrophase_bins"])
-        self.assertEqual(swe_l3_data.gyrophase_delta, swel3_dependency.configuration["gyrophase_deltas"])
-        np.testing.assert_allclose(swe_l3_data.phase_space_density_by_pitch_angle,
-                                   np.array([[[np.nan, 249.548992, 310.137124],
-                                              [np.nan, 308.858857, 363.635632],
-                                              [np.nan, 394.907904, 437.401188]]]))
+        self.assertEqual(
+            replace(input_metadata, descriptor="sci"), swe_l3_data.input_metadata
+        )
+        self.assertEqual(
+            swe_l3_data.pitch_angle, swel3_dependency.configuration["pitch_angle_bins"]
+        )
+        self.assertEqual(
+            swe_l3_data.pitch_angle_delta,
+            swel3_dependency.configuration["pitch_angle_deltas"],
+        )
+        self.assertEqual(
+            swe_l3_data.energy, swel3_dependency.configuration["energy_bins"]
+        )
+        self.assertEqual(
+            swe_l3_data.energy_delta_plus,
+            swel3_dependency.configuration["energy_delta_plus"],
+        )
+        self.assertEqual(
+            swe_l3_data.energy_delta_minus,
+            swel3_dependency.configuration["energy_delta_minus"],
+        )
+        self.assertEqual(
+            swe_l3_data.gyrophase_bins, swel3_dependency.configuration["gyrophase_bins"]
+        )
+        self.assertEqual(
+            swe_l3_data.gyrophase_delta,
+            swel3_dependency.configuration["gyrophase_deltas"],
+        )
+        np.testing.assert_allclose(
+            swe_l3_data.phase_space_density_by_pitch_angle,
+            np.array(
+                [
+                    [
+                        [np.nan, 249.548992, 310.137124],
+                        [np.nan, 308.858857, 363.635632],
+                        [np.nan, 394.907904, 437.401188],
+                    ]
+                ]
+            ),
+        )
         np.testing.assert_allclose(swe_l3_data.epoch_delta, np.array([1807492500]))
         np.testing.assert_array_equal(swe_l3_data.epoch, swe_l2_data.epoch)
-        np.testing.assert_allclose(swe_l3_data.phase_space_density_1d, np.array([[126.537227, 152.557743, 189.536765]]))
-        np.testing.assert_allclose(swe_l3_data.phase_space_density_inward, np.array([[0., 0., 0.]]))
-        np.testing.assert_allclose(swe_l3_data.phase_space_density_outward,
-                                   np.array([[253.074453, 305.115485, 379.073531]]))
-        self.assertEqual((1, 9, 3, 3), swe_l3_data.intensity_by_pitch_angle_and_gyrophase.shape)
+        np.testing.assert_allclose(
+            swe_l3_data.phase_space_density_1d,
+            np.array([[126.537227, 152.557743, 189.536765]]),
+        )
+        np.testing.assert_allclose(
+            swe_l3_data.phase_space_density_inward, np.array([[0.0, 0.0, 0.0]])
+        )
+        np.testing.assert_allclose(
+            swe_l3_data.phase_space_density_outward,
+            np.array([[253.074453, 305.115485, 379.073531]]),
+        )
+        self.assertEqual(
+            (1, 9, 3, 3), swe_l3_data.intensity_by_pitch_angle_and_gyrophase.shape
+        )
         self.assertEqual((1, 9, 3), swe_l3_data.intensity_by_pitch_angle.shape)
-        np.testing.assert_allclose(swe_l3_data.intensity_by_pitch_angle[0, 1, 1:3],
-                                   np.array([49.5, 53. ]))
-        np.testing.assert_allclose(swe_l3_data.intensity_by_pitch_angle_and_gyrophase[0, 0, 1, 2],
-                                   np.array([15.]))
-
-        np.testing.assert_allclose(swe_l3_data.intensity_uncertainty_by_pitch_angle[0, 0, 1:3],
-                                   np.array([0.000717, 0.000762]), atol=1e-6)
-        np.testing.assert_allclose(swe_l3_data.intensity_uncertainty_by_pitch_angle_and_gyrophase[0, 0, 1:3, 2],
-                                   np.array([0.000717, 0.000762]), atol=1e-6)
-        expected_swe_flags = np.array([
-            SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE
-            |SweL3Flags.BACKUP_SPLINE_UNRESOLVED
-            |SweL3Flags.BREAKPOINT_FIT_UNCONVERGED
-            |SweL3Flags.FALLBACK_SWAPI_SPEED
-            |SweL3Flags.UNPHYSICAL_PSD
-        ])
+        np.testing.assert_allclose(
+            swe_l3_data.intensity_by_pitch_angle[0, 1, 1:3], np.array([49.5, 53.0])
+        )
+        np.testing.assert_allclose(
+            swe_l3_data.intensity_by_pitch_angle_and_gyrophase[0, 0, 1, 2],
+            np.array([15.0]),
+        )
+
+        np.testing.assert_allclose(
+            swe_l3_data.intensity_uncertainty_by_pitch_angle[0, 0, 1:3],
+            np.array([0.000717, 0.000762]),
+            atol=1e-6,
+        )
+        np.testing.assert_allclose(
+            swe_l3_data.intensity_uncertainty_by_pitch_angle_and_gyrophase[
+                0, 0, 1:3, 2
+            ],
+            np.array([0.000717, 0.000762]),
+            atol=1e-6,
+        )
+        expected_swe_flags = np.array(
+            [
+                SweL3Flags.FALLBACK_POTENTIAL_ESTIMATE
+                | SweL3Flags.BACKUP_SPLINE_UNRESOLVED
+                | SweL3Flags.BREAKPOINT_FIT_UNCONVERGED
+                | SweL3Flags.FALLBACK_SWAPI_SPEED
+                | SweL3Flags.UNPHYSICAL_PSD
+            ]
+        )
         np.testing.assert_array_equal(swe_l3_data.swe_flags, expected_swe_flags)
 
     @patch(f"{MODULE}.get_dps_to_rtn_rotation_matrix")
-    @patch('imap_l3_processing.swe.swe_processor.rotate_temperature_tensor_to_mag')
-    @patch('imap_l3_processing.swe.swe_processor.calculate_primary_eigenvector')
-    @patch('imap_l3_processing.swe.swe_processor.rotate_vector_to_rtn_spherical_coordinates')
-    @patch('imap_l3_processing.swe.swe_processor.scale_halo_density')
-    @patch('imap_l3_processing.swe.swe_processor.scale_core_density')
-    @patch('imap_l3_processing.swe.swe_processor.integrate')
-    @patch('imap_l3_processing.swe.swe_processor.halo_fit_moments_retrying_on_failure')
-    @patch('imap_l3_processing.swe.swe_processor.core_fit_moments_retrying_on_failure')
-    @patch('imap_l3_processing.swe.swe_processor.compute_maxwellian_weight_factors')
-    @patch('imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s')
-    @patch('imap_l3_processing.swe.swe_processor.apply_rotation_matrix')
-    @patch('imap_l3_processing.swe.swe_processor.rotate_temperature')
-    def test_calculate_moment_products(self, mock_rotate_temperature,
-                                       mock_rotate_dps_vector_to_rtn,
-                                       mock_calculate_velocity_in_dsp_frame_km_s,
-                                       mock_compute_maxwellian_weight_factors,
-                                       mock_core_fit_moments_retrying_on_failure: Mock,
-                                       mock_halo_fit_moments_retrying_on_failure: Mock,
-                                       mock_integrate: Mock,
-                                       mock_scale_core_density: Mock,
-                                       mock_scale_halo_density: Mock,
-                                       mock_rotate_vector_to_rtn_spherical_coordinates,
-                                       mock_calculate_primary_eigenvector,
-                                       mock_rotate_temperature_tensor_to_mag,
-                                       mock_get_dps_to_rtn_rotation_matrix):
+    @patch("imap_l3_processing.swe.swe_processor.rotate_temperature_tensor_to_mag")
+    @patch("imap_l3_processing.swe.swe_processor.calculate_primary_eigenvector")
+    @patch(
+        "imap_l3_processing.swe.swe_processor.rotate_vector_to_rtn_spherical_coordinates"
+    )
+    @patch("imap_l3_processing.swe.swe_processor.scale_halo_density")
+    @patch("imap_l3_processing.swe.swe_processor.scale_core_density")
+    @patch("imap_l3_processing.swe.swe_processor.integrate")
+    @patch("imap_l3_processing.swe.swe_processor.halo_fit_moments_retrying_on_failure")
+    @patch("imap_l3_processing.swe.swe_processor.core_fit_moments_retrying_on_failure")
+    @patch("imap_l3_processing.swe.swe_processor.compute_maxwellian_weight_factors")
+    @patch("imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s")
+    @patch("imap_l3_processing.swe.swe_processor.apply_rotation_matrix")
+    @patch("imap_l3_processing.swe.swe_processor.rotate_temperature")
+    def test_calculate_moment_products(
+        self,
+        mock_rotate_temperature,
+        mock_rotate_dps_vector_to_rtn,
+        mock_calculate_velocity_in_dsp_frame_km_s,
+        mock_compute_maxwellian_weight_factors,
+        mock_core_fit_moments_retrying_on_failure: Mock,
+        mock_halo_fit_moments_retrying_on_failure: Mock,
+        mock_integrate: Mock,
+        mock_scale_core_density: Mock,
+        mock_scale_halo_density: Mock,
+        mock_rotate_vector_to_rtn_spherical_coordinates,
+        mock_calculate_primary_eigenvector,
+        mock_rotate_temperature_tensor_to_mag,
+        mock_get_dps_to_rtn_rotation_matrix,
+    ):
         epochs = datetime.now() + np.arange(3) * timedelta(minutes=1)
         dps_to_rtn_matrices = [
             sentinel.dps_to_rtn_1,
@@ -957,54 +1494,118 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
         )
         expected_sin_theta = np.sin(np.deg2rad(90 - instrument_elevation))
         expected_cos_theta = np.cos(np.deg2rad(90 - instrument_elevation))
-        np.testing.assert_allclose(expected_cos_theta,
-                                   [-0.9034, -0.6947, -0.3730, 0.0, 0.3714, 0.6896, 0.8996], atol=0.03)
-        swe_l1_data = SweL1bData(epoch=epochs,
-                                 count_rates=[sentinel.l1b_count_rates_1, sentinel.l1b_count_rates_2,
-                                              sentinel.l1b_count_rates_3],
-                                 settle_duration=Mock())
+        np.testing.assert_allclose(
+            expected_cos_theta,
+            [-0.9034, -0.6947, -0.3730, 0.0, 0.3714, 0.6896, 0.8996],
+            atol=0.03,
+        )
+        swe_l1_data = SweL1bData(
+            epoch=epochs,
+            count_rates=[
+                sentinel.l1b_count_rates_1,
+                sentinel.l1b_count_rates_2,
+                sentinel.l1b_count_rates_3,
+            ],
+            settle_duration=Mock(),
+        )
 
         spacecraft_potential = np.array([12, 14, 16])
         core_halo_breakpoint = np.array([96, 54, 103])
 
-        corrected_energy_bins = swe_l2_data.energy.reshape(1, -1) - spacecraft_potential.reshape(-1, 1)
+        corrected_energy_bins = swe_l2_data.energy.reshape(
+            1, -1
+        ) - spacecraft_potential.reshape(-1, 1)
 
         velocity_in_dsp_frame_km_s = [
             np.full(shape=(24, 30, 7, 3), fill_value=1),
             np.full(shape=(24, 30, 7, 3), fill_value=2),
-            np.full(shape=(24, 30, 7, 3), fill_value=3)
+            np.full(shape=(24, 30, 7, 3), fill_value=3),
         ]
 
-        mock_calculate_velocity_in_dsp_frame_km_s.side_effect = velocity_in_dsp_frame_km_s
+        mock_calculate_velocity_in_dsp_frame_km_s.side_effect = (
+            velocity_in_dsp_frame_km_s
+        )
 
-        maxwellian_weight_factors = [np.full(shape=(24, 30, 7), fill_value=1),
-                                     np.full(shape=(24, 30, 7), fill_value=2),
-                                     np.full(shape=(24, 30, 7), fill_value=3)]
+        maxwellian_weight_factors = [
+            np.full(shape=(24, 30, 7), fill_value=1),
+            np.full(shape=(24, 30, 7), fill_value=2),
+            np.full(shape=(24, 30, 7), fill_value=3),
+        ]
 
         mock_compute_maxwellian_weight_factors.side_effect = maxwellian_weight_factors
 
-        core_moments1 = create_dataclass_mock(Moments, density=15, velocity_x=5, velocity_y=6, velocity_z=7,
-                                              t_parallel=1e5, t_perpendicular=1e5)
-        halo_moments1 = create_dataclass_mock(Moments, density=22, velocity_x=5, velocity_y=6,
-                                              velocity_z=7, t_parallel=1e5, t_perpendicular=1e5)
-        core_moments2 = create_dataclass_mock(Moments, density=12, velocity_x=8, velocity_y=9, velocity_z=10,
-                                              t_parallel=1e5, t_perpendicular=1e5)
-        halo_moments2 = create_dataclass_mock(Moments, density=19, velocity_x=8, velocity_y=9,
-                                              velocity_z=10, t_parallel=1e5, t_perpendicular=1e5)
-
-        core_moment_fit_results_1 = MomentFitResults(moments=core_moments1, chisq=4730912.0, number_of_points=10,
-                                                     regress_result=sentinel.core_regress_result_1)
-        core_moment_fit_results_2 = MomentFitResults(moments=core_moments2, chisq=4705988.0, number_of_points=11,
-                                                     regress_result=sentinel.core_regress_result_2)
-        halo_moment_fit_results_1 = MomentFitResults(moments=halo_moments1, chisq=3412.0, number_of_points=12,
-                                                     regress_result=sentinel.halo_regress_result_1)
-        halo_moment_fit_results_2 = MomentFitResults(moments=halo_moments2, chisq=3214.0, number_of_points=13,
-                                                     regress_result=sentinel.halo_regress_result_2)
-
-        mock_core_fit_moments_retrying_on_failure.side_effect = [core_moment_fit_results_1, core_moment_fit_results_2,
-                                                                 None]
-        mock_halo_fit_moments_retrying_on_failure.side_effect = [halo_moment_fit_results_1, halo_moment_fit_results_2,
-                                                                 None]
+        core_moments1 = create_dataclass_mock(
+            Moments,
+            density=15,
+            velocity_x=5,
+            velocity_y=6,
+            velocity_z=7,
+            t_parallel=1e5,
+            t_perpendicular=1e5,
+        )
+        halo_moments1 = create_dataclass_mock(
+            Moments,
+            density=22,
+            velocity_x=5,
+            velocity_y=6,
+            velocity_z=7,
+            t_parallel=1e5,
+            t_perpendicular=1e5,
+        )
+        core_moments2 = create_dataclass_mock(
+            Moments,
+            density=12,
+            velocity_x=8,
+            velocity_y=9,
+            velocity_z=10,
+            t_parallel=1e5,
+            t_perpendicular=1e5,
+        )
+        halo_moments2 = create_dataclass_mock(
+            Moments,
+            density=19,
+            velocity_x=8,
+            velocity_y=9,
+            velocity_z=10,
+            t_parallel=1e5,
+            t_perpendicular=1e5,
+        )
+
+        core_moment_fit_results_1 = MomentFitResults(
+            moments=core_moments1,
+            chisq=4730912.0,
+            number_of_points=10,
+            regress_result=sentinel.core_regress_result_1,
+        )
+        core_moment_fit_results_2 = MomentFitResults(
+            moments=core_moments2,
+            chisq=4705988.0,
+            number_of_points=11,
+            regress_result=sentinel.core_regress_result_2,
+        )
+        halo_moment_fit_results_1 = MomentFitResults(
+            moments=halo_moments1,
+            chisq=3412.0,
+            number_of_points=12,
+            regress_result=sentinel.halo_regress_result_1,
+        )
+        halo_moment_fit_results_2 = MomentFitResults(
+            moments=halo_moments2,
+            chisq=3214.0,
+            number_of_points=13,
+            regress_result=sentinel.halo_regress_result_2,
+        )
+
+        mock_core_fit_moments_retrying_on_failure.side_effect = [
+            core_moment_fit_results_1,
+            core_moment_fit_results_2,
+            None,
+        ]
+        mock_halo_fit_moments_retrying_on_failure.side_effect = [
+            halo_moment_fit_results_1,
+            halo_moment_fit_results_2,
+            None,
+        ]
 
         core_rtn_theta_1, core_rtn_phi_1 = (62, 190)
         halo_rtn_theta_1, halo_rtn_phi_1 = (54, 222)
@@ -1031,7 +1632,7 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
             halo_fit_velocity_1,
             halo_integrated_velocity_rtn,
             core_fit_velocity_2,
-            halo_fit_velocity_2
+            halo_fit_velocity_2,
         ]
 
         core_integrate_output = Mock()
@@ -1041,25 +1642,39 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
         total_integrate_output.temperature = total_temperature
         halo_integrate_output = Mock()
 
-        mock_integrate.side_effect = [core_integrate_output, total_integrate_output, halo_integrate_output, None, None]
+        mock_integrate.side_effect = [
+            core_integrate_output,
+            total_integrate_output,
+            halo_integrate_output,
+            None,
+            None,
+        ]
 
         scaled_core_velocity = [900, 800, 700]
         scaled_core_temperature = np.array([90000, 80000, 70000, 80000, 90000, 100000])
         core_cdelnv = Mock()
         core_cdelt = Mock()
-        scale_core_density_output = ScaleDensityOutput(density=400, velocity=scaled_core_velocity,
-                                                       temperature=scaled_core_temperature,
-                                                       cdelnv=core_cdelnv,
-                                                       cdelt=core_cdelt)
+        scale_core_density_output = ScaleDensityOutput(
+            density=400,
+            velocity=scaled_core_velocity,
+            temperature=scaled_core_temperature,
+            cdelnv=core_cdelnv,
+            cdelt=core_cdelt,
+        )
 
         mock_scale_core_density.side_effect = [scale_core_density_output]
 
         scaled_halo_velocity = [2000, 1800, 1700]
-        scaled_halo_temperature = np.array([180000, 190000, 200000, 201000, 202000, 203000])
-        scale_halo_density_output = ScaleDensityOutput(density=500, velocity=scaled_halo_velocity,
-                                                       temperature=scaled_halo_temperature,
-                                                       cdelnv=None,
-                                                       cdelt=None)
+        scaled_halo_temperature = np.array(
+            [180000, 190000, 200000, 201000, 202000, 203000]
+        )
+        scale_halo_density_output = ScaleDensityOutput(
+            density=500,
+            velocity=scaled_halo_velocity,
+            temperature=scaled_halo_temperature,
+            cdelnv=None,
+            cdelt=None,
+        )
 
         mock_scale_halo_density.side_effect = [scale_halo_density_output]
 
@@ -1069,7 +1684,8 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
             (101, np.deg2rad(102), np.deg2rad(103)),
             (104, np.deg2rad(105), np.deg2rad(106)),
             (13, np.deg2rad(14), np.deg2rad(15)),
-            (19, np.deg2rad(20), np.deg2rad(21))]
+            (19, np.deg2rad(20), np.deg2rad(21)),
+        ]
 
         core_primary_evec = Mock()
         total_primary_evec = Mock()
@@ -1089,66 +1705,120 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
             (halo_primary_evec, np.array([t_par_2, t_perp_2, gyro_2])),
         ]
 
-        mock_rotate_temperature_tensor_to_mag.side_effect = [(11.4, 12.4, 13.4), (14.4, 15.4, 16.4), (17.4, 18.4, 19.4)]
+        mock_rotate_temperature_tensor_to_mag.side_effect = [
+            (11.4, 12.4, 13.4),
+            (14.4, 15.4, 16.4),
+            (17.4, 18.4, 19.4),
+        ]
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
 
         swe_processor = SweProcessor(dependencies=[], input_metadata=input_metadata)
         config = build_swe_configuration()
 
-        rebinned_mag_data = [sentinel.mag_data_1, sentinel.mag_data_2, sentinel.mag_data_3]
+        rebinned_mag_data = [
+            sentinel.mag_data_1,
+            sentinel.mag_data_2,
+            sentinel.mag_data_3,
+        ]
 
-        swe_moment_data = swe_processor.calculate_moment_products(swe_l2_data, swe_l1_data, rebinned_mag_data,
-                                                                  spacecraft_potential,
-                                                                  core_halo_breakpoint,
-                                                                  corrected_energy_bins,
-                                                                  config)
+        swe_moment_data = swe_processor.calculate_moment_products(
+            swe_l2_data,
+            swe_l1_data,
+            rebinned_mag_data,
+            spacecraft_potential,
+            core_halo_breakpoint,
+            corrected_energy_bins,
+            config,
+        )
 
-        calculate_velocity_call_1 = mock_calculate_velocity_in_dsp_frame_km_s.mock_calls[0]
-        np.testing.assert_array_equal(corrected_energy_bins[0], calculate_velocity_call_1.args[0])
-        np.testing.assert_array_equal(swe_l2_data.inst_el, calculate_velocity_call_1.args[1])
-        np.testing.assert_array_equal(swe_l2_data.inst_az_spin_sector[0], calculate_velocity_call_1.args[2])
+        calculate_velocity_call_1 = (
+            mock_calculate_velocity_in_dsp_frame_km_s.mock_calls[0]
+        )
+        np.testing.assert_array_equal(
+            corrected_energy_bins[0], calculate_velocity_call_1.args[0]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.inst_el, calculate_velocity_call_1.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.inst_az_spin_sector[0], calculate_velocity_call_1.args[2]
+        )
 
-        calculate_velocity_call_2 = mock_calculate_velocity_in_dsp_frame_km_s.mock_calls[1]
-        np.testing.assert_array_equal(corrected_energy_bins[1], calculate_velocity_call_2.args[0])
-        np.testing.assert_array_equal(swe_l2_data.inst_el, calculate_velocity_call_2.args[1])
-        np.testing.assert_array_equal(swe_l2_data.inst_az_spin_sector[1], calculate_velocity_call_2.args[2])
+        calculate_velocity_call_2 = (
+            mock_calculate_velocity_in_dsp_frame_km_s.mock_calls[1]
+        )
+        np.testing.assert_array_equal(
+            corrected_energy_bins[1], calculate_velocity_call_2.args[0]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.inst_el, calculate_velocity_call_2.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.inst_az_spin_sector[1], calculate_velocity_call_2.args[2]
+        )
 
-        calculate_velocity_call_3 = mock_calculate_velocity_in_dsp_frame_km_s.mock_calls[2]
-        np.testing.assert_array_equal(corrected_energy_bins[2], calculate_velocity_call_3.args[0])
-        np.testing.assert_array_equal(swe_l2_data.inst_el, calculate_velocity_call_3.args[1])
-        np.testing.assert_array_equal(swe_l2_data.inst_az_spin_sector[2], calculate_velocity_call_3.args[2])
+        calculate_velocity_call_3 = (
+            mock_calculate_velocity_in_dsp_frame_km_s.mock_calls[2]
+        )
+        np.testing.assert_array_equal(
+            corrected_energy_bins[2], calculate_velocity_call_3.args[0]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.inst_el, calculate_velocity_call_3.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.inst_az_spin_sector[2], calculate_velocity_call_3.args[2]
+        )
 
         self.assertEqual(3, mock_compute_maxwellian_weight_factors.call_count)
         mock_compute_maxwellian_weight_factors.assert_has_calls(
-            [call(sentinel.l1b_count_rates_1, 10.0),
-             call(sentinel.l1b_count_rates_2, 20.0),
-             call(sentinel.l1b_count_rates_3, 30.0),
-             ])
+            [
+                call(sentinel.l1b_count_rates_1, 10.0),
+                call(sentinel.l1b_count_rates_2, 20.0),
+                call(sentinel.l1b_count_rates_3, 30.0),
+            ]
+        )
 
         self.assertEqual(3, mock_core_fit_moments_retrying_on_failure.call_count)
         self.assertEqual(3, mock_halo_fit_moments_retrying_on_failure.call_count)
 
-        core_fit_moments_call_1 = mock_core_fit_moments_retrying_on_failure.mock_calls[0]
-        np.testing.assert_array_equal(core_fit_moments_call_1.args[0], corrected_energy_bins[0])
-        np.testing.assert_array_equal(velocity_in_dsp_frame_km_s[0] * 100000,
-                                      core_fit_moments_call_1.args[1])
-        np.testing.assert_array_equal(swe_l2_data.phase_space_density[0], core_fit_moments_call_1.args[2])
-        np.testing.assert_array_equal(maxwellian_weight_factors[0],
-                                      core_fit_moments_call_1.args[3])
+        core_fit_moments_call_1 = mock_core_fit_moments_retrying_on_failure.mock_calls[
+            0
+        ]
+        np.testing.assert_array_equal(
+            core_fit_moments_call_1.args[0], corrected_energy_bins[0]
+        )
+        np.testing.assert_array_equal(
+            velocity_in_dsp_frame_km_s[0] * 100000, core_fit_moments_call_1.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.phase_space_density[0], core_fit_moments_call_1.args[2]
+        )
+        np.testing.assert_array_equal(
+            maxwellian_weight_factors[0], core_fit_moments_call_1.args[3]
+        )
         self.assertEqual(3, core_fit_moments_call_1.args[4])
         self.assertEqual(7, core_fit_moments_call_1.args[5])
         np.testing.assert_array_equal(core_fit_moments_call_1.args[6], [100, 100, 100])
 
-        halo_fit_moments_call_1 = mock_halo_fit_moments_retrying_on_failure.mock_calls[0]
-        np.testing.assert_array_equal(corrected_energy_bins[0],
-                                      halo_fit_moments_call_1.args[0])
-        np.testing.assert_array_equal(velocity_in_dsp_frame_km_s[0] * 100000,
-                                      halo_fit_moments_call_1.args[1])
-        np.testing.assert_array_equal(swe_l2_data.phase_space_density[0], halo_fit_moments_call_1.args[2])
-        np.testing.assert_array_equal(maxwellian_weight_factors[0],
-                                      halo_fit_moments_call_1.args[3])
+        halo_fit_moments_call_1 = mock_halo_fit_moments_retrying_on_failure.mock_calls[
+            0
+        ]
+        np.testing.assert_array_equal(
+            corrected_energy_bins[0], halo_fit_moments_call_1.args[0]
+        )
+        np.testing.assert_array_equal(
+            velocity_in_dsp_frame_km_s[0] * 100000, halo_fit_moments_call_1.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.phase_space_density[0], halo_fit_moments_call_1.args[2]
+        )
+        np.testing.assert_array_equal(
+            maxwellian_weight_factors[0], halo_fit_moments_call_1.args[3]
+        )
         self.assertEqual(6, halo_fit_moments_call_1.args[4])
         self.assertEqual(11, halo_fit_moments_call_1.args[5])
         np.testing.assert_array_equal(halo_fit_moments_call_1.args[6], [25, 25, 25])
@@ -1156,54 +1826,84 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
         self.assertEqual(core_halo_breakpoint[0], halo_fit_moments_call_1.args[8])
 
         core_fit_moments_2 = mock_core_fit_moments_retrying_on_failure.mock_calls[1]
-        np.testing.assert_array_equal(corrected_energy_bins[1],
-                                      core_fit_moments_2.args[0])
-        np.testing.assert_array_equal(velocity_in_dsp_frame_km_s[1] * 100000,
-                                      core_fit_moments_2.args[1])
-        np.testing.assert_array_equal(swe_l2_data.phase_space_density[1], core_fit_moments_2.args[2])
-        np.testing.assert_array_equal(maxwellian_weight_factors[1],
-                                      core_fit_moments_2.args[3])
+        np.testing.assert_array_equal(
+            corrected_energy_bins[1], core_fit_moments_2.args[0]
+        )
+        np.testing.assert_array_equal(
+            velocity_in_dsp_frame_km_s[1] * 100000, core_fit_moments_2.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.phase_space_density[1], core_fit_moments_2.args[2]
+        )
+        np.testing.assert_array_equal(
+            maxwellian_weight_factors[1], core_fit_moments_2.args[3]
+        )
         self.assertEqual(4, core_fit_moments_2.args[4])
         self.assertEqual(6, core_fit_moments_2.args[5])
-        np.testing.assert_array_equal(core_fit_moments_2.args[6], [100, 100, core_moments1.density])
+        np.testing.assert_array_equal(
+            core_fit_moments_2.args[6], [100, 100, core_moments1.density]
+        )
 
         halo_fit_moments_2 = mock_halo_fit_moments_retrying_on_failure.mock_calls[1]
-        np.testing.assert_array_equal(corrected_energy_bins[1],
-                                      halo_fit_moments_2.args[0])
-        np.testing.assert_array_equal(velocity_in_dsp_frame_km_s[1] * 100000,
-                                      halo_fit_moments_2.args[1])
-        np.testing.assert_array_equal(swe_l2_data.phase_space_density[1], halo_fit_moments_2.args[2])
-        np.testing.assert_array_equal(maxwellian_weight_factors[1],
-                                      halo_fit_moments_2.args[3])
+        np.testing.assert_array_equal(
+            corrected_energy_bins[1], halo_fit_moments_2.args[0]
+        )
+        np.testing.assert_array_equal(
+            velocity_in_dsp_frame_km_s[1] * 100000, halo_fit_moments_2.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.phase_space_density[1], halo_fit_moments_2.args[2]
+        )
+        np.testing.assert_array_equal(
+            maxwellian_weight_factors[1], halo_fit_moments_2.args[3]
+        )
         self.assertEqual(5, halo_fit_moments_2.args[4])
         self.assertEqual(10, halo_fit_moments_2.args[5])
-        np.testing.assert_array_equal(halo_fit_moments_2.args[6], [25, 25, halo_moments1.density])
+        np.testing.assert_array_equal(
+            halo_fit_moments_2.args[6], [25, 25, halo_moments1.density]
+        )
         self.assertEqual(spacecraft_potential[1], halo_fit_moments_2.args[7])
         self.assertEqual(core_halo_breakpoint[1], halo_fit_moments_2.args[8])
 
         core_fit_moments_3 = mock_core_fit_moments_retrying_on_failure.mock_calls[2]
-        np.testing.assert_array_equal(corrected_energy_bins[2],
-                                      core_fit_moments_3.args[0])
-        np.testing.assert_array_equal(velocity_in_dsp_frame_km_s[2] * 100000,
-                                      core_fit_moments_3.args[1])
-        np.testing.assert_array_equal(swe_l2_data.phase_space_density[2], core_fit_moments_3.args[2])
-        np.testing.assert_array_equal(maxwellian_weight_factors[2],
-                                      core_fit_moments_3.args[3])
+        np.testing.assert_array_equal(
+            corrected_energy_bins[2], core_fit_moments_3.args[0]
+        )
+        np.testing.assert_array_equal(
+            velocity_in_dsp_frame_km_s[2] * 100000, core_fit_moments_3.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.phase_space_density[2], core_fit_moments_3.args[2]
+        )
+        np.testing.assert_array_equal(
+            maxwellian_weight_factors[2], core_fit_moments_3.args[3]
+        )
         self.assertEqual(5, core_fit_moments_3.args[4])
         self.assertEqual(9, core_fit_moments_3.args[5])
-        np.testing.assert_array_equal(core_fit_moments_3.args[6], [100, core_moments1.density, core_moments2.density])
+        np.testing.assert_array_equal(
+            core_fit_moments_3.args[6],
+            [100, core_moments1.density, core_moments2.density],
+        )
 
         halo_fit_moments_3 = mock_halo_fit_moments_retrying_on_failure.mock_calls[2]
-        np.testing.assert_array_equal(corrected_energy_bins[2],
-                                      halo_fit_moments_3.args[0])
-        np.testing.assert_array_equal(velocity_in_dsp_frame_km_s[2] * 100000,
-                                      halo_fit_moments_3.args[1])
-        np.testing.assert_array_equal(swe_l2_data.phase_space_density[2], halo_fit_moments_3.args[2])
-        np.testing.assert_array_equal(maxwellian_weight_factors[2],
-                                      halo_fit_moments_3.args[3])
+        np.testing.assert_array_equal(
+            corrected_energy_bins[2], halo_fit_moments_3.args[0]
+        )
+        np.testing.assert_array_equal(
+            velocity_in_dsp_frame_km_s[2] * 100000, halo_fit_moments_3.args[1]
+        )
+        np.testing.assert_array_equal(
+            swe_l2_data.phase_space_density[2], halo_fit_moments_3.args[2]
+        )
+        np.testing.assert_array_equal(
+            maxwellian_weight_factors[2], halo_fit_moments_3.args[3]
+        )
         self.assertEqual(8, halo_fit_moments_3.args[4])
         self.assertEqual(11, halo_fit_moments_3.args[5])
-        np.testing.assert_array_equal(halo_fit_moments_3.args[6], [25, halo_moments1.density, halo_moments2.density])
+        np.testing.assert_array_equal(
+            halo_fit_moments_3.args[6],
+            [25, halo_moments1.density, halo_moments2.density],
+        )
         self.assertEqual(spacecraft_potential[2], halo_fit_moments_3.args[7])
         self.assertEqual(core_halo_breakpoint[2], halo_fit_moments_3.args[8])
 
@@ -1213,285 +1913,554 @@ def test_calculate_moment_products(self, mock_rotate_temperature,
         )
         self.assertEqual(7, mock_rotate_dps_vector_to_rtn.call_count)
 
-        self.assertEqual(dps_to_rtn_matrices[0], mock_rotate_dps_vector_to_rtn.call_args_list[0].args[0])
+        self.assertEqual(
+            dps_to_rtn_matrices[0],
+            mock_rotate_dps_vector_to_rtn.call_args_list[0].args[0],
+        )
         np.testing.assert_array_equal(
-            np.array([core_moments1.velocity_x, core_moments1.velocity_y, core_moments1.velocity_z]),
-            mock_rotate_dps_vector_to_rtn.call_args_list[0].args[1])
-        self.assertEqual(dps_to_rtn_matrices[0], mock_rotate_dps_vector_to_rtn.call_args_list[1].args[0])
+            np.array(
+                [
+                    core_moments1.velocity_x,
+                    core_moments1.velocity_y,
+                    core_moments1.velocity_z,
+                ]
+            ),
+            mock_rotate_dps_vector_to_rtn.call_args_list[0].args[1],
+        )
+        self.assertEqual(
+            dps_to_rtn_matrices[0],
+            mock_rotate_dps_vector_to_rtn.call_args_list[1].args[0],
+        )
         np.testing.assert_array_equal(
             scaled_core_velocity,
-            mock_rotate_dps_vector_to_rtn.call_args_list[1].args[1])
+            mock_rotate_dps_vector_to_rtn.call_args_list[1].args[1],
+        )
 
-        self.assertEqual(dps_to_rtn_matrices[0], mock_rotate_dps_vector_to_rtn.call_args_list[2].args[0])
+        self.assertEqual(
+            dps_to_rtn_matrices[0],
+            mock_rotate_dps_vector_to_rtn.call_args_list[2].args[0],
+        )
         np.testing.assert_array_equal(
             total_integrate_output.velocity,
-            mock_rotate_dps_vector_to_rtn.call_args_list[2].args[1])
+            mock_rotate_dps_vector_to_rtn.call_args_list[2].args[1],
+        )
 
-        self.assertEqual(dps_to_rtn_matrices[0], mock_rotate_dps_vector_to_rtn.call_args_list[3].args[0])
+        self.assertEqual(
+            dps_to_rtn_matrices[0],
+            mock_rotate_dps_vector_to_rtn.call_args_list[3].args[0],
+        )
         np.testing.assert_array_equal(
-            np.array([halo_moments1.velocity_x, halo_moments1.velocity_y, halo_moments1.velocity_z]),
-            mock_rotate_dps_vector_to_rtn.call_args_list[3].args[1])
+            np.array(
+                [
+                    halo_moments1.velocity_x,
+                    halo_moments1.velocity_y,
+                    halo_moments1.velocity_z,
+                ]
+            ),
+            mock_rotate_dps_vector_to_rtn.call_args_list[3].args[1],
+        )
 
-        self.assertEqual(dps_to_rtn_matrices[0], mock_rotate_dps_vector_to_rtn.call_args_list[4].args[0])
+        self.assertEqual(
+            dps_to_rtn_matrices[0],
+            mock_rotate_dps_vector_to_rtn.call_args_list[4].args[0],
+        )
         np.testing.assert_array_equal(
             scaled_halo_velocity,
-            mock_rotate_dps_vector_to_rtn.call_args_list[4].args[1])
+            mock_rotate_dps_vector_to_rtn.call_args_list[4].args[1],
+        )
 
-        self.assertEqual(dps_to_rtn_matrices[1], mock_rotate_dps_vector_to_rtn.call_args_list[5].args[0])
+        self.assertEqual(
+            dps_to_rtn_matrices[1],
+            mock_rotate_dps_vector_to_rtn.call_args_list[5].args[0],
+        )
         np.testing.assert_array_equal(
-            np.array([core_moments2.velocity_x, core_moments2.velocity_y, core_moments2.velocity_z]),
-            mock_rotate_dps_vector_to_rtn.call_args_list[5].args[1])
+            np.array(
+                [
+                    core_moments2.velocity_x,
+                    core_moments2.velocity_y,
+                    core_moments2.velocity_z,
+                ]
+            ),
+            mock_rotate_dps_vector_to_rtn.call_args_list[5].args[1],
+        )
 
-        self.assertEqual(dps_to_rtn_matrices[1], mock_rotate_dps_vector_to_rtn.call_args_list[6].args[0])
+        self.assertEqual(
+            dps_to_rtn_matrices[1],
+            mock_rotate_dps_vector_to_rtn.call_args_list[6].args[0],
+        )
         np.testing.assert_array_equal(
-            np.array([halo_moments2.velocity_x, halo_moments2.velocity_y, halo_moments2.velocity_z]),
-            mock_rotate_dps_vector_to_rtn.call_args_list[6].args[1])
+            np.array(
+                [
+                    halo_moments2.velocity_x,
+                    halo_moments2.velocity_y,
+                    halo_moments2.velocity_z,
+                ]
+            ),
+            mock_rotate_dps_vector_to_rtn.call_args_list[6].args[1],
+        )
 
         self.assertEqual(4, mock_rotate_temperature.call_count)
         mock_rotate_temperature.assert_has_calls(
-            [call(dps_to_rtn_matrices[0], core_moments1.alpha, core_moments1.beta),
-             call(dps_to_rtn_matrices[0], halo_moments1.alpha, halo_moments1.beta),
-             call(dps_to_rtn_matrices[1], core_moments2.alpha, core_moments2.beta),
-             call(dps_to_rtn_matrices[1], halo_moments2.alpha, halo_moments2.beta), ])
+            [
+                call(dps_to_rtn_matrices[0], core_moments1.alpha, core_moments1.beta),
+                call(dps_to_rtn_matrices[0], halo_moments1.alpha, halo_moments1.beta),
+                call(dps_to_rtn_matrices[1], core_moments2.alpha, core_moments2.beta),
+                call(dps_to_rtn_matrices[1], halo_moments2.alpha, halo_moments2.beta),
+            ]
+        )
 
         def call_with_array_matchers(*args):
             return call(*[NumpyArrayMatcher(x) for x in args])
 
         self.assertEqual(5, mock_integrate.call_count)
         mock_integrate.assert_has_calls(
-            [call_with_array_matchers(4, 5,
-                                      corrected_energy_bins[0], expected_sin_theta, expected_cos_theta,
-                                      config["aperture_field_of_view_radians"],
-                                      swe_l2_data.phase_space_density[0],
-                                      swe_l2_data.inst_az_spin_sector[0],
-                                      12, np.array([0, 0, 0, 0]), np.array([0, 0, 0, 0, 0, 0])),
-             call_with_array_matchers(4, 5,
-                                      corrected_energy_bins[0], expected_sin_theta, expected_cos_theta,
-                                      config["aperture_field_of_view_radians"],
-                                      swe_l2_data.phase_space_density[0],
-                                      swe_l2_data.inst_az_spin_sector[0],
-                                      12, core_cdelnv, core_cdelt),
-             call_with_array_matchers(6, 5,
-                                      corrected_energy_bins[0], expected_sin_theta, expected_cos_theta,
-                                      config["aperture_field_of_view_radians"],
-                                      swe_l2_data.phase_space_density[0],
-                                      swe_l2_data.inst_az_spin_sector[0],
-                                      12, np.array([0, 0, 0, 0]), np.array([0, 0, 0, 0, 0, 0])),
-             call_with_array_matchers(5, 4,
-                                      corrected_energy_bins[1], expected_sin_theta, expected_cos_theta,
-                                      config["aperture_field_of_view_radians"],
-                                      swe_l2_data.phase_space_density[1],
-                                      swe_l2_data.inst_az_spin_sector[1],
-                                      14, np.array([0, 0, 0, 0]), np.array([0, 0, 0, 0, 0, 0])),
-             call_with_array_matchers(5, 5,
-                                      corrected_energy_bins[1], expected_sin_theta, expected_cos_theta,
-                                      config["aperture_field_of_view_radians"],
-                                      swe_l2_data.phase_space_density[1],
-                                      swe_l2_data.inst_az_spin_sector[1],
-                                      14, np.array([0, 0, 0, 0]), np.array([0, 0, 0, 0, 0, 0])),
-
-             ]
+            [
+                call_with_array_matchers(
+                    4,
+                    5,
+                    corrected_energy_bins[0],
+                    expected_sin_theta,
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.phase_space_density[0],
+                    swe_l2_data.inst_az_spin_sector[0],
+                    12,
+                    np.array([0, 0, 0, 0]),
+                    np.array([0, 0, 0, 0, 0, 0]),
+                ),
+                call_with_array_matchers(
+                    4,
+                    5,
+                    corrected_energy_bins[0],
+                    expected_sin_theta,
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.phase_space_density[0],
+                    swe_l2_data.inst_az_spin_sector[0],
+                    12,
+                    core_cdelnv,
+                    core_cdelt,
+                ),
+                call_with_array_matchers(
+                    6,
+                    5,
+                    corrected_energy_bins[0],
+                    expected_sin_theta,
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.phase_space_density[0],
+                    swe_l2_data.inst_az_spin_sector[0],
+                    12,
+                    np.array([0, 0, 0, 0]),
+                    np.array([0, 0, 0, 0, 0, 0]),
+                ),
+                call_with_array_matchers(
+                    5,
+                    4,
+                    corrected_energy_bins[1],
+                    expected_sin_theta,
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.phase_space_density[1],
+                    swe_l2_data.inst_az_spin_sector[1],
+                    14,
+                    np.array([0, 0, 0, 0]),
+                    np.array([0, 0, 0, 0, 0, 0]),
+                ),
+                call_with_array_matchers(
+                    5,
+                    5,
+                    corrected_energy_bins[1],
+                    expected_sin_theta,
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.phase_space_density[1],
+                    swe_l2_data.inst_az_spin_sector[1],
+                    14,
+                    np.array([0, 0, 0, 0]),
+                    np.array([0, 0, 0, 0, 0, 0]),
+                ),
+            ]
         )
 
         self.assertEqual(1, mock_scale_core_density.call_count)
-        mock_scale_core_density.assert_has_calls([
-            call_with_array_matchers(core_integrate_output.density, core_integrate_output.velocity,
-                                     core_integrate_output.temperature, core_moments1, 3, corrected_energy_bins[0],
-                                     spacecraft_potential[0], expected_cos_theta,
-                                     config["aperture_field_of_view_radians"],
-                                     swe_l2_data.inst_az_spin_sector[0],
-                                     sentinel.core_regress_result_1,
-                                     core_integrate_output.base_energy)
-        ])
+        mock_scale_core_density.assert_has_calls(
+            [
+                call_with_array_matchers(
+                    core_integrate_output.density,
+                    core_integrate_output.velocity,
+                    core_integrate_output.temperature,
+                    core_moments1,
+                    3,
+                    corrected_energy_bins[0],
+                    spacecraft_potential[0],
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.inst_az_spin_sector[0],
+                    sentinel.core_regress_result_1,
+                    core_integrate_output.base_energy,
+                )
+            ]
+        )
 
         self.assertEqual(1, mock_scale_halo_density.call_count)
 
-        mock_scale_halo_density.assert_has_calls([
-            call_with_array_matchers(halo_integrate_output.density, halo_integrate_output.velocity,
-                                     halo_integrate_output.temperature, halo_moments1, spacecraft_potential[0],
-                                     core_halo_breakpoint[0], expected_cos_theta,
-                                     config["aperture_field_of_view_radians"],
-                                     swe_l2_data.inst_az_spin_sector[0], sentinel.halo_regress_result_1,
-                                     halo_integrate_output.base_energy)
-        ])
+        mock_scale_halo_density.assert_has_calls(
+            [
+                call_with_array_matchers(
+                    halo_integrate_output.density,
+                    halo_integrate_output.velocity,
+                    halo_integrate_output.temperature,
+                    halo_moments1,
+                    spacecraft_potential[0],
+                    core_halo_breakpoint[0],
+                    expected_cos_theta,
+                    config["aperture_field_of_view_radians"],
+                    swe_l2_data.inst_az_spin_sector[0],
+                    sentinel.halo_regress_result_1,
+                    halo_integrate_output.base_energy,
+                )
+            ]
+        )
 
         self.assertEqual(6, mock_rotate_vector_to_rtn_spherical_coordinates.call_count)
 
-        mock_rotate_vector_to_rtn_spherical_coordinates.assert_has_calls([
-            call(dps_to_rtn_matrices[0], core_integrate_output.heat_flux),
-            call(dps_to_rtn_matrices[0], core_primary_evec),
-            call(dps_to_rtn_matrices[0], total_integrate_output.heat_flux),
-            call(dps_to_rtn_matrices[0], total_primary_evec),
-            call(dps_to_rtn_matrices[0], halo_integrate_output.heat_flux),
-            call(dps_to_rtn_matrices[0], halo_primary_evec),
-        ])
+        mock_rotate_vector_to_rtn_spherical_coordinates.assert_has_calls(
+            [
+                call(dps_to_rtn_matrices[0], core_integrate_output.heat_flux),
+                call(dps_to_rtn_matrices[0], core_primary_evec),
+                call(dps_to_rtn_matrices[0], total_integrate_output.heat_flux),
+                call(dps_to_rtn_matrices[0], total_primary_evec),
+                call(dps_to_rtn_matrices[0], halo_integrate_output.heat_flux),
+                call(dps_to_rtn_matrices[0], halo_primary_evec),
+            ]
+        )
 
         self.assertEqual(3, mock_calculate_primary_eigenvector.call_count)
-        mock_calculate_primary_eigenvector.assert_has_calls([
-            call(scaled_core_temperature),
-            call(total_integrate_output.temperature),
-            call(scaled_halo_temperature),
-        ])
+        mock_calculate_primary_eigenvector.assert_has_calls(
+            [
+                call(scaled_core_temperature),
+                call(total_integrate_output.temperature),
+                call(scaled_halo_temperature),
+            ]
+        )
 
         self.assertEqual(3, mock_rotate_temperature_tensor_to_mag.call_count)
 
-        mock_rotate_temperature_tensor_to_mag.assert_has_calls([
-            call(scale_core_density_output.temperature, sentinel.mag_data_1),
-            call(total_integrate_output.temperature, sentinel.mag_data_1),
-            call(scale_halo_density_output.temperature, sentinel.mag_data_1),
-        ])
+        mock_rotate_temperature_tensor_to_mag.assert_has_calls(
+            [
+                call(scale_core_density_output.temperature, sentinel.mag_data_1),
+                call(total_integrate_output.temperature, sentinel.mag_data_1),
+                call(scale_halo_density_output.temperature, sentinel.mag_data_1),
+            ]
+        )
 
         # @formatter:off
         self.assertIsInstance(swe_moment_data, SweL3MomentData)
-        np.testing.assert_array_equal(swe_moment_data.core_fit_num_points, [core_moment_fit_results_1.number_of_points,
-                                                                            core_moment_fit_results_2.number_of_points,
-                                                                            np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_chisq,
-                                      [core_moment_fit_results_1.chisq, core_moment_fit_results_2.chisq, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_chisq,
-                                      [halo_moment_fit_results_1.chisq, halo_moment_fit_results_2.chisq, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_density_fit,
-                                      [core_moments1.density, core_moments2.density, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_density_fit,
-                                      [halo_moments1.density, halo_moments2.density, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_t_parallel_fit,
-                                      [core_moments1.t_parallel, core_moments2.t_parallel, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_t_parallel_fit,
-                                      [halo_moments1.t_parallel, halo_moments2.t_parallel, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_t_perpendicular_fit,
-                                      [core_moments1.t_perpendicular, core_moments2.t_perpendicular, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_t_perpendicular_fit,
-                                      [halo_moments1.t_perpendicular, halo_moments2.t_perpendicular, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_temperature_phi_rtn_fit,
-                                      [core_rtn_phi_1, core_rtn_phi_2, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_temperature_phi_rtn_fit,
-                                      [halo_rtn_phi_1, halo_rtn_phi_2, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_temperature_theta_rtn_fit,
-                                      [core_rtn_theta_1, core_rtn_theta_2, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_temperature_theta_rtn_fit,
-                                      [halo_rtn_theta_1, halo_rtn_theta_2, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_speed_fit,
-                                      [np.linalg.norm(core_fit_velocity_1), np.linalg.norm(core_fit_velocity_2),
-                                       np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_speed_fit,
-                                      [np.linalg.norm(halo_fit_velocity_1), np.linalg.norm(halo_fit_velocity_2),
-                                       np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_velocity_vector_rtn_fit,
-                                      [core_fit_velocity_1, core_fit_velocity_2, [np.nan, np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.halo_velocity_vector_rtn_fit,
-                                      [halo_fit_velocity_1, halo_fit_velocity_2, [np.nan, np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.core_density_integrated,
-                                      [scale_core_density_output.density, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_density_integrated,
-                                      [scale_halo_density_output.density, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.total_density_integrated,
-                                      [total_integrate_output.density, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_speed_integrated,
-                                      [np.linalg.norm(core_integrated_velocity_rtn), np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_speed_integrated,
-                                      [np.linalg.norm(halo_integrated_velocity_rtn), np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.total_speed_integrated,
-                                      [np.linalg.norm(total_integrated_velocity_rtn), np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_velocity_vector_rtn_integrated,
-                                      [core_integrated_velocity_rtn, [np.nan, np.nan, np.nan],
-                                       [np.nan, np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.halo_velocity_vector_rtn_integrated,
-                                      [halo_integrated_velocity_rtn, [np.nan, np.nan, np.nan],
-                                       [np.nan, np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.total_velocity_vector_rtn_integrated,
-                                      [total_integrated_velocity_rtn, [np.nan, np.nan, np.nan],
-                                       [np.nan, np.nan, np.nan]])
-
-        np.testing.assert_allclose(swe_moment_data.core_heat_flux_magnitude_integrated, [10, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.core_heat_flux_theta_integrated, [11, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.core_heat_flux_phi_integrated, [12, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.halo_heat_flux_magnitude_integrated, [13, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.halo_heat_flux_theta_integrated, [14, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.halo_heat_flux_phi_integrated, [15, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.total_heat_flux_magnitude_integrated, [101, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.total_heat_flux_theta_integrated, [102, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.total_heat_flux_phi_integrated, [103, np.nan, np.nan])
-
-        np.testing.assert_array_equal(swe_moment_data.core_t_parallel_integrated, [t_par_1, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_t_perpendicular_integrated,
-                                      [[t_perp_1, gyro_1], [np.nan, np.nan], [np.nan, np.nan]])
-
-        np.testing.assert_array_equal(swe_moment_data.halo_t_parallel_integrated, [t_par_2, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_t_perpendicular_integrated,
-                                      [[t_perp_2, gyro_2], [np.nan, np.nan], [np.nan, np.nan]])
-
-        np.testing.assert_array_equal(swe_moment_data.total_t_parallel_integrated, [t_par_3, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.total_t_perpendicular_integrated,
-                                      [[t_perp_3, gyro_3], [np.nan, np.nan], [np.nan, np.nan]])
-        np.testing.assert_allclose(swe_moment_data.core_temperature_theta_rtn_integrated, [17, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.core_temperature_phi_rtn_integrated, [18, np.nan, np.nan])
-
-        np.testing.assert_allclose(swe_moment_data.halo_temperature_theta_rtn_integrated, [20, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.halo_temperature_phi_rtn_integrated, [21, np.nan, np.nan])
-
-        np.testing.assert_allclose(swe_moment_data.total_temperature_theta_rtn_integrated, [105, np.nan, np.nan])
-        np.testing.assert_allclose(swe_moment_data.total_temperature_phi_rtn_integrated, [106, np.nan, np.nan])
-
-        np.testing.assert_array_equal(swe_moment_data.core_temperature_parallel_to_mag, [11.4, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.core_temperature_perpendicular_to_mag,
-                                      [[12.4, 13.4], [np.nan, np.nan], [np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.halo_temperature_parallel_to_mag, [17.4, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.halo_temperature_perpendicular_to_mag,
-                                      [[18.4, 19.4], [np.nan, np.nan], [np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.total_temperature_parallel_to_mag, [14.4, np.nan, np.nan])
-        np.testing.assert_array_equal(swe_moment_data.total_temperature_perpendicular_to_mag,
-                                      [[15.4, 16.4], [np.nan, np.nan], [np.nan, np.nan]])
-        np.testing.assert_array_equal(swe_moment_data.total_temperature_perpendicular_to_mag,
-                                      [[15.4, 16.4], [np.nan, np.nan], [np.nan, np.nan]])
-
-        np.testing.assert_array_equal(swe_moment_data.core_temperature_tensor_integrated,
-                                      [scaled_core_temperature * 1e4, np.full(6, np.nan), np.full(6, np.nan)])
-        np.testing.assert_array_equal(swe_moment_data.halo_temperature_tensor_integrated,
-                                      [scaled_halo_temperature * 1e4, np.full(6, np.nan), np.full(6, np.nan)])
-        np.testing.assert_array_equal(swe_moment_data.total_temperature_tensor_integrated,
-                                      [total_temperature * 1e4, np.full(6, np.nan), np.full(6, np.nan)])
+        np.testing.assert_array_equal(
+            swe_moment_data.core_fit_num_points,
+            [
+                core_moment_fit_results_1.number_of_points,
+                core_moment_fit_results_2.number_of_points,
+                np.nan,
+            ],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_chisq,
+            [core_moment_fit_results_1.chisq, core_moment_fit_results_2.chisq, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_chisq,
+            [halo_moment_fit_results_1.chisq, halo_moment_fit_results_2.chisq, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_density_fit,
+            [core_moments1.density, core_moments2.density, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_density_fit,
+            [halo_moments1.density, halo_moments2.density, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_t_parallel_fit,
+            [core_moments1.t_parallel, core_moments2.t_parallel, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_t_parallel_fit,
+            [halo_moments1.t_parallel, halo_moments2.t_parallel, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_t_perpendicular_fit,
+            [core_moments1.t_perpendicular, core_moments2.t_perpendicular, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_t_perpendicular_fit,
+            [halo_moments1.t_perpendicular, halo_moments2.t_perpendicular, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_temperature_phi_rtn_fit,
+            [core_rtn_phi_1, core_rtn_phi_2, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_temperature_phi_rtn_fit,
+            [halo_rtn_phi_1, halo_rtn_phi_2, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_temperature_theta_rtn_fit,
+            [core_rtn_theta_1, core_rtn_theta_2, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_temperature_theta_rtn_fit,
+            [halo_rtn_theta_1, halo_rtn_theta_2, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_speed_fit,
+            [
+                np.linalg.norm(core_fit_velocity_1),
+                np.linalg.norm(core_fit_velocity_2),
+                np.nan,
+            ],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_speed_fit,
+            [
+                np.linalg.norm(halo_fit_velocity_1),
+                np.linalg.norm(halo_fit_velocity_2),
+                np.nan,
+            ],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_velocity_vector_rtn_fit,
+            [core_fit_velocity_1, core_fit_velocity_2, [np.nan, np.nan, np.nan]],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_velocity_vector_rtn_fit,
+            [halo_fit_velocity_1, halo_fit_velocity_2, [np.nan, np.nan, np.nan]],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_density_integrated,
+            [scale_core_density_output.density, np.nan, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_density_integrated,
+            [scale_halo_density_output.density, np.nan, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_density_integrated,
+            [total_integrate_output.density, np.nan, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_speed_integrated,
+            [np.linalg.norm(core_integrated_velocity_rtn), np.nan, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_speed_integrated,
+            [np.linalg.norm(halo_integrated_velocity_rtn), np.nan, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_speed_integrated,
+            [np.linalg.norm(total_integrated_velocity_rtn), np.nan, np.nan],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_velocity_vector_rtn_integrated,
+            [
+                core_integrated_velocity_rtn,
+                [np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan],
+            ],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_velocity_vector_rtn_integrated,
+            [
+                halo_integrated_velocity_rtn,
+                [np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan],
+            ],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_velocity_vector_rtn_integrated,
+            [
+                total_integrated_velocity_rtn,
+                [np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan],
+            ],
+        )
+
+        np.testing.assert_allclose(
+            swe_moment_data.core_heat_flux_magnitude_integrated, [10, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.core_heat_flux_theta_integrated, [11, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.core_heat_flux_phi_integrated, [12, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.halo_heat_flux_magnitude_integrated, [13, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.halo_heat_flux_theta_integrated, [14, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.halo_heat_flux_phi_integrated, [15, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.total_heat_flux_magnitude_integrated, [101, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.total_heat_flux_theta_integrated, [102, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.total_heat_flux_phi_integrated, [103, np.nan, np.nan]
+        )
+
+        np.testing.assert_array_equal(
+            swe_moment_data.core_t_parallel_integrated, [t_par_1, np.nan, np.nan]
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_t_perpendicular_integrated,
+            [[t_perp_1, gyro_1], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_t_parallel_integrated, [t_par_2, np.nan, np.nan]
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_t_perpendicular_integrated,
+            [[t_perp_2, gyro_2], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+
+        np.testing.assert_array_equal(
+            swe_moment_data.total_t_parallel_integrated, [t_par_3, np.nan, np.nan]
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_t_perpendicular_integrated,
+            [[t_perp_3, gyro_3], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.core_temperature_theta_rtn_integrated, [17, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.core_temperature_phi_rtn_integrated, [18, np.nan, np.nan]
+        )
+
+        np.testing.assert_allclose(
+            swe_moment_data.halo_temperature_theta_rtn_integrated, [20, np.nan, np.nan]
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.halo_temperature_phi_rtn_integrated, [21, np.nan, np.nan]
+        )
+
+        np.testing.assert_allclose(
+            swe_moment_data.total_temperature_theta_rtn_integrated,
+            [105, np.nan, np.nan],
+        )
+        np.testing.assert_allclose(
+            swe_moment_data.total_temperature_phi_rtn_integrated, [106, np.nan, np.nan]
+        )
+
+        np.testing.assert_array_equal(
+            swe_moment_data.core_temperature_parallel_to_mag, [11.4, np.nan, np.nan]
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.core_temperature_perpendicular_to_mag,
+            [[12.4, 13.4], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_temperature_parallel_to_mag, [17.4, np.nan, np.nan]
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_temperature_perpendicular_to_mag,
+            [[18.4, 19.4], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_temperature_parallel_to_mag, [14.4, np.nan, np.nan]
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_temperature_perpendicular_to_mag,
+            [[15.4, 16.4], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_temperature_perpendicular_to_mag,
+            [[15.4, 16.4], [np.nan, np.nan], [np.nan, np.nan]],
+        )
+
+        np.testing.assert_array_equal(
+            swe_moment_data.core_temperature_tensor_integrated,
+            [scaled_core_temperature * 1e4, np.full(6, np.nan), np.full(6, np.nan)],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.halo_temperature_tensor_integrated,
+            [scaled_halo_temperature * 1e4, np.full(6, np.nan), np.full(6, np.nan)],
+        )
+        np.testing.assert_array_equal(
+            swe_moment_data.total_temperature_tensor_integrated,
+            [total_temperature * 1e4, np.full(6, np.nan), np.full(6, np.nan)],
+        )
 
         # @formatter:on
 
     @patch(f"{MODULE}.get_dps_to_rtn_rotation_matrix")
-    @patch('imap_l3_processing.swe.swe_processor.rotate_vector_to_rtn_spherical_coordinates')
-    @patch('imap_l3_processing.swe.swe_processor.scale_halo_density')
-    @patch('imap_l3_processing.swe.swe_processor.scale_core_density')
-    @patch('imap_l3_processing.swe.swe_processor.rotate_temperature')
-    @patch('imap_l3_processing.swe.swe_processor.apply_rotation_matrix')
-    @patch('imap_l3_processing.swe.swe_processor.compute_maxwellian_weight_factors')
-    @patch('imap_l3_processing.swe.swe_processor.integrate')
-    @patch('imap_l3_processing.swe.swe_processor.halo_fit_moments_retrying_on_failure')
-    @patch('imap_l3_processing.swe.swe_processor.core_fit_moments_retrying_on_failure')
-    def test_calculate_moment_products_only_integrates_temperatures_in_a_range(self,
-                                                                               mock_core_fit_moments_retrying_on_failure,
-                                                                               mock_halo_fit_moments_retrying_on_failure,
-                                                                               mock_integrate,
-                                                                               _, mock_rotate_dps_vector_to_rtn,
-                                                                               mock_rotate_temperature,
-                                                                               mock_scale_core_density,
-                                                                               mock_scale_halo_density,
-                                                                               mock_rotate_vector_to_rtn_spherical_coordinates,
-                                                                               mock_get_dps_to_rtn_rotation_matrix):
+    @patch(
+        "imap_l3_processing.swe.swe_processor.rotate_vector_to_rtn_spherical_coordinates"
+    )
+    @patch("imap_l3_processing.swe.swe_processor.scale_halo_density")
+    @patch("imap_l3_processing.swe.swe_processor.scale_core_density")
+    @patch("imap_l3_processing.swe.swe_processor.rotate_temperature")
+    @patch("imap_l3_processing.swe.swe_processor.apply_rotation_matrix")
+    @patch("imap_l3_processing.swe.swe_processor.compute_maxwellian_weight_factors")
+    @patch("imap_l3_processing.swe.swe_processor.integrate")
+    @patch("imap_l3_processing.swe.swe_processor.halo_fit_moments_retrying_on_failure")
+    @patch("imap_l3_processing.swe.swe_processor.core_fit_moments_retrying_on_failure")
+    def test_calculate_moment_products_only_integrates_temperatures_in_a_range(
+        self,
+        mock_core_fit_moments_retrying_on_failure,
+        mock_halo_fit_moments_retrying_on_failure,
+        mock_integrate,
+        _,
+        mock_rotate_dps_vector_to_rtn,
+        mock_rotate_temperature,
+        mock_scale_core_density,
+        mock_scale_halo_density,
+        mock_rotate_vector_to_rtn_spherical_coordinates,
+        mock_get_dps_to_rtn_rotation_matrix,
+    ):
         mock_core_fit_moments_retrying_on_failure.side_effect = [
-            build_moment_fit_results(moments=build_moments(t_parallel=1e3 - 1, t_perpendicular=1e3 - 1)),
-            build_moment_fit_results(moments=build_moments(t_parallel=1e3 + 1, t_perpendicular=1e3 + 1)),
-            build_moment_fit_results(moments=build_moments(t_parallel=1e7 - 1, t_perpendicular=1e7 - 1)),
-            build_moment_fit_results(moments=build_moments(t_parallel=1e7 + 1, t_perpendicular=1e7 + 1)),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e3 - 1, t_perpendicular=1e3 - 1)
+            ),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e3 + 1, t_perpendicular=1e3 + 1)
+            ),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e7 - 1, t_perpendicular=1e7 - 1)
+            ),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e7 + 1, t_perpendicular=1e7 + 1)
+            ),
         ]
 
         mock_halo_fit_moments_retrying_on_failure.side_effect = [
-            build_moment_fit_results(moments=build_moments(t_parallel=1e4 - 1, t_perpendicular=1e4 - 1)),
-            build_moment_fit_results(moments=build_moments(t_parallel=1e4 + 1, t_perpendicular=1e4 + 1)),
-            build_moment_fit_results(moments=build_moments(t_parallel=1e8 - 1, t_perpendicular=1e8 - 1)),
-            build_moment_fit_results(moments=build_moments(t_parallel=1e8 + 1, t_perpendicular=1e8 + 1)),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e4 - 1, t_perpendicular=1e4 - 1)
+            ),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e4 + 1, t_perpendicular=1e4 + 1)
+            ),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e8 - 1, t_perpendicular=1e8 - 1)
+            ),
+            build_moment_fit_results(
+                moments=build_moments(t_parallel=1e8 + 1, t_perpendicular=1e8 + 1)
+            ),
         ]
 
-        mock_rotate_temperature.return_value = ((4, 5))
-        mock_rotate_vector_to_rtn_spherical_coordinates.return_value = ((1, 2, 3))
+        mock_rotate_temperature.return_value = (4, 5)
+        mock_rotate_vector_to_rtn_spherical_coordinates.return_value = (1, 2, 3)
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
 
         swe_processor = SweProcessor(dependencies=[], input_metadata=input_metadata)
         config = build_swe_configuration()
@@ -1513,14 +2482,18 @@ def test_calculate_moment_products_only_integrates_temperatures_in_a_range(self,
             phase_space_density_rebinned=np.array([]),
             data_quality=np.array([]),
         )
-        swe_l1_data = SweL1bData(epoch=epochs,
-                                 count_rates=[Mock(), Mock(), Mock(), Mock()],
-                                 settle_duration=Mock())
+        swe_l1_data = SweL1bData(
+            epoch=epochs,
+            count_rates=[Mock(), Mock(), Mock(), Mock()],
+            settle_duration=Mock(),
+        )
 
         spacecraft_potential = np.array([12, 14, 16, 18])
         core_halo_breakpoint = np.array([96, 54, 103, 124])
 
-        corrected_energy_bins = swe_l2_data.energy.reshape(1, -1) - spacecraft_potential.reshape(-1, 1)
+        corrected_energy_bins = swe_l2_data.energy.reshape(
+            1, -1
+        ) - spacecraft_potential.reshape(-1, 1)
 
         mock_integrate.return_value.temperature = np.full(6, 0)
 
@@ -1532,22 +2505,36 @@ def test_calculate_moment_products_only_integrates_temperatures_in_a_range(self,
         mock_rotate_dps_vector_to_rtn.return_value = (2, 3, 4)
 
         rebinned_mag_data = np.full((3, 3), 1)
-        swe_moment_data = swe_processor.calculate_moment_products(swe_l2_data, swe_l1_data, rebinned_mag_data,
-                                                                  spacecraft_potential,
-                                                                  core_halo_breakpoint,
-                                                                  corrected_energy_bins,
-                                                                  config)
+        swe_moment_data = swe_processor.calculate_moment_products(
+            swe_l2_data,
+            swe_l1_data,
+            rebinned_mag_data,
+            spacecraft_potential,
+            core_halo_breakpoint,
+            corrected_energy_bins,
+            config,
+        )
 
         self.assertEqual(6, mock_integrate.call_count)
         self.assertEqual(2, mock_scale_core_density.call_count)
         self.assertEqual(2, mock_scale_halo_density.call_count)
 
-        np.testing.assert_array_equal([np.nan, 1, 1, np.nan], swe_moment_data.core_density_integrated)
+        np.testing.assert_array_equal(
+            [np.nan, 1, 1, np.nan], swe_moment_data.core_density_integrated
+        )
         self.assertEqual(4, len(swe_moment_data.core_velocity_vector_rtn_integrated))
-        np.testing.assert_array_equal([np.nan, np.nan, np.nan], swe_moment_data.core_velocity_vector_rtn_integrated[0])
-        np.testing.assert_array_equal([np.nan, np.nan, np.nan], swe_moment_data.core_velocity_vector_rtn_integrated[3])
+        np.testing.assert_array_equal(
+            [np.nan, np.nan, np.nan],
+            swe_moment_data.core_velocity_vector_rtn_integrated[0],
+        )
+        np.testing.assert_array_equal(
+            [np.nan, np.nan, np.nan],
+            swe_moment_data.core_velocity_vector_rtn_integrated[3],
+        )
 
-        np.testing.assert_array_equal([np.nan, 1, 1, np.nan], swe_moment_data.halo_density_integrated)
+        np.testing.assert_array_equal(
+            [np.nan, 1, 1, np.nan], swe_moment_data.halo_density_integrated
+        )
         self.assertEqual(4, len(swe_moment_data.halo_velocity_vector_rtn_integrated))
 
     @patch(f"{MODULE}.PredictedEphemerisTracker")
@@ -1560,34 +2547,55 @@ def test_calculate_moment_products_only_integrates_temperatures_in_a_range(self,
     @patch(f"{MODULE}.calculate_velocity_in_dsp_frame_km_s")
     @patch(f"{MODULE}.get_dps_to_rtn_rotation_matrix")
     def test_calculate_moments_products_flags_data_and_uses_fill_based_on_spice_call_result(
-            self,
-            mock_get_dps_to_rtn_rotation_matrix,
-            mock_calculate_velocity_in_dsp_frame_km_s,
-            mock_compute_maxwellian_weight_factors,
-            mock_core_fit_moments_retrying_on_failure: Mock,
-            mock_halo_fit_moments_retrying_on_failure: Mock,
-            mock_integrate: Mock,
-            mock_scale_core_density: Mock,
-            mock_scale_halo_density: Mock,
-            mock_predicted_ephemeris_tracker: Mock, ):
-        epochs = datetime(2025, 1, 1, tzinfo=timezone.utc) + np.arange(3) * timedelta(minutes=1)
-
-        mock_predicted_ephemeris_tracker_epoch_1 = create_autospec(PredictedEphemerisTracker, used_predict=False)
-        mock_predicted_ephemeris_tracker_epoch_2 = create_autospec(PredictedEphemerisTracker, used_predict=False)
-        mock_predicted_ephemeris_tracker_epoch_3 = create_autospec(PredictedEphemerisTracker, used_predict=True)
-
-        mock_predicted_ephemeris_tracker.side_effect = (mock_predicted_ephemeris_tracker_epoch_1,
-                                                        mock_predicted_ephemeris_tracker_epoch_2,
-                                                        mock_predicted_ephemeris_tracker_epoch_3)
+        self,
+        mock_get_dps_to_rtn_rotation_matrix,
+        mock_calculate_velocity_in_dsp_frame_km_s,
+        mock_compute_maxwellian_weight_factors,
+        mock_core_fit_moments_retrying_on_failure: Mock,
+        mock_halo_fit_moments_retrying_on_failure: Mock,
+        mock_integrate: Mock,
+        mock_scale_core_density: Mock,
+        mock_scale_halo_density: Mock,
+        mock_predicted_ephemeris_tracker: Mock,
+    ):
+        epochs = datetime(2025, 1, 1, tzinfo=timezone.utc) + np.arange(3) * timedelta(
+            minutes=1
+        )
+
+        mock_predicted_ephemeris_tracker_epoch_1 = create_autospec(
+            PredictedEphemerisTracker, used_predict=False
+        )
+        mock_predicted_ephemeris_tracker_epoch_2 = create_autospec(
+            PredictedEphemerisTracker, used_predict=False
+        )
+        mock_predicted_ephemeris_tracker_epoch_3 = create_autospec(
+            PredictedEphemerisTracker, used_predict=True
+        )
+
+        mock_predicted_ephemeris_tracker.side_effect = (
+            mock_predicted_ephemeris_tracker_epoch_1,
+            mock_predicted_ephemeris_tracker_epoch_2,
+            mock_predicted_ephemeris_tracker_epoch_3,
+        )
 
         mock_predicted_ephemeris_tracker_epoch_1.run.return_value = np.eye(3)
-        mock_predicted_ephemeris_tracker_epoch_2.run.side_effect = SpiceyError("Missing coverage for IMAP_DPS")
+        mock_predicted_ephemeris_tracker_epoch_2.run.side_effect = SpiceyError(
+            "Missing coverage for IMAP_DPS"
+        )
         mock_predicted_ephemeris_tracker_epoch_3.run.return_value = np.eye(3)
 
-        mock_calculate_velocity_in_dsp_frame_km_s.return_value = np.full(shape=(24, 30, 7, 3), fill_value=1)
-        mock_compute_maxwellian_weight_factors.return_value = np.full(shape=(24, 30, 7), fill_value=1)
-        mock_core_fit_moments_retrying_on_failure.return_value = build_moment_fit_results()
-        mock_halo_fit_moments_retrying_on_failure.return_value = build_moment_fit_results()
+        mock_calculate_velocity_in_dsp_frame_km_s.return_value = np.full(
+            shape=(24, 30, 7, 3), fill_value=1
+        )
+        mock_compute_maxwellian_weight_factors.return_value = np.full(
+            shape=(24, 30, 7), fill_value=1
+        )
+        mock_core_fit_moments_retrying_on_failure.return_value = (
+            build_moment_fit_results()
+        )
+        mock_halo_fit_moments_retrying_on_failure.return_value = (
+            build_moment_fit_results()
+        )
         mock_integrate.return_value = IntegrateOutputs(
             100,
             np.array([1, 2, 3]),
@@ -1628,12 +2636,18 @@ def test_calculate_moments_products_flags_data_and_uses_fill_based_on_spice_call
         )
         swe_l1_data = SweL1bData(
             epoch=epochs,
-            count_rates=[sentinel.l1b_count_rates_1, sentinel.l1b_count_rates_2, sentinel.l1b_count_rates_3],
+            count_rates=[
+                sentinel.l1b_count_rates_1,
+                sentinel.l1b_count_rates_2,
+                sentinel.l1b_count_rates_3,
+            ],
             settle_duration=Mock(),
         )
         spacecraft_potential = np.array([12, 14, 16])
         core_halo_breakpoint = np.array([96, 54, 103])
-        corrected_energy_bins = swe_l2_data.energy.reshape(1, -1) - spacecraft_potential.reshape(-1, 1)
+        corrected_energy_bins = swe_l2_data.energy.reshape(
+            1, -1
+        ) - spacecraft_potential.reshape(-1, 1)
 
         input_metadata = InputMetadata(
             "swe",
@@ -1656,9 +2670,15 @@ def test_calculate_moments_products_flags_data_and_uses_fill_based_on_spice_call
             config,
         )
 
-        mock_predicted_ephemeris_tracker_epoch_1.run.assert_called_once_with(mock_get_dps_to_rtn_rotation_matrix, epochs[0])
-        mock_predicted_ephemeris_tracker_epoch_2.run.assert_called_once_with(mock_get_dps_to_rtn_rotation_matrix, epochs[1])
-        mock_predicted_ephemeris_tracker_epoch_3.run.assert_called_once_with(mock_get_dps_to_rtn_rotation_matrix, epochs[2])
+        mock_predicted_ephemeris_tracker_epoch_1.run.assert_called_once_with(
+            mock_get_dps_to_rtn_rotation_matrix, epochs[0]
+        )
+        mock_predicted_ephemeris_tracker_epoch_2.run.assert_called_once_with(
+            mock_get_dps_to_rtn_rotation_matrix, epochs[1]
+        )
+        mock_predicted_ephemeris_tracker_epoch_3.run.assert_called_once_with(
+            mock_get_dps_to_rtn_rotation_matrix, epochs[2]
+        )
 
         rotated_field_names = (
             "core_temperature_phi_rtn_fit",
@@ -1713,11 +2733,20 @@ def test_calculate_moments_products_flags_data_and_uses_fill_based_on_spice_call
         )
 
         _assert_field_nan_at_index_and_finite_elsewhere(
-            self, swe_moment_data, rotated_field_names, nan_index=1, finite_indices=(0, 2)
+            self,
+            swe_moment_data,
+            rotated_field_names,
+            nan_index=1,
+            finite_indices=(0, 2),
         )
         _assert_fields_finite_everywhere(self, swe_moment_data, finite_field_names)
 
-        np.testing.assert_array_equal(swe_moment_data.quality_flags, np.array([SweL3Flags.NONE, SweL3Flags.NONE, SweL3Flags.PREDICTIVE_EPHEMERIS]))
+        np.testing.assert_array_equal(
+            swe_moment_data.quality_flags,
+            np.array(
+                [SweL3Flags.NONE, SweL3Flags.NONE, SweL3Flags.PREDICTIVE_EPHEMERIS]
+            ),
+        )
         self.assertEqual(np.uint16, swe_moment_data.quality_flags.dtype)
 
     def test_calculate_moment_products_handles_bad_fit_indices_and_continues(self):
@@ -1739,64 +2768,83 @@ def test_calculate_moment_products_handles_bad_fit_indices_and_continues(self):
             phase_space_density_rebinned=np.array([]),
             data_quality=np.array([]),
         )
-        swe_l1_data = SweL1bData(epoch=epochs,
-                                 count_rates=np.full((1, 11, 3, 7), 10.5),
-                                 settle_duration=Mock())
+        swe_l1_data = SweL1bData(
+            epoch=epochs,
+            count_rates=np.full((1, 11, 3, 7), 10.5),
+            settle_duration=Mock(),
+        )
         spacecraft_potential = np.array([12, 14])
         core_halo_breakpoint = np.array([12, 54])
-        corrected_energy_bins = swe_l2_data.energy.reshape(1, -1) - spacecraft_potential.reshape(-1, 1)
+        corrected_energy_bins = swe_l2_data.energy.reshape(
+            1, -1
+        ) - spacecraft_potential.reshape(-1, 1)
 
-        input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                       datetime(2025, 2, 22), "v001")
+        input_metadata = InputMetadata(
+            "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+        )
 
-        swe_processor = SweProcessor(ProcessingInputCollection(), input_metadata=input_metadata)
+        swe_processor = SweProcessor(
+            ProcessingInputCollection(), input_metadata=input_metadata
+        )
         config = build_swe_configuration()
 
         rebinned_mag_data = [sentinel.mag_data_1]
 
-        with self.assertLogs(logger, level='INFO') as log_context:
-            swe_moment_data = swe_processor.calculate_moment_products(swe_l2_data, swe_l1_data, rebinned_mag_data,
-                                                                      spacecraft_potential,
-                                                                      core_halo_breakpoint,
-                                                                      corrected_energy_bins,
-                                                                      config)
+        with self.assertLogs(logger, level="INFO") as log_context:
+            swe_moment_data = swe_processor.calculate_moment_products(
+                swe_l2_data,
+                swe_l1_data,
+                rebinned_mag_data,
+                spacecraft_potential,
+                core_halo_breakpoint,
+                corrected_energy_bins,
+                config,
+            )
 
             self.assertIsInstance(swe_moment_data, SweL3MomentData)
-            self.assertEqual(log_context.output,
-                             [
-                                 "INFO:imap_l3_processing.swe.swe_processor:Bad core-halo breakpoint value at index 0. Continuing."])
-
-    @patch('imap_l3_processing.swe.swe_processor.rotate_temperature_tensor_to_mag')
-    @patch('imap_l3_processing.swe.swe_processor.calculate_primary_eigenvector')
-    @patch('imap_l3_processing.swe.swe_processor.rotate_vector_to_rtn_spherical_coordinates')
-    @patch('imap_l3_processing.swe.swe_processor.scale_halo_density')
-    @patch('imap_l3_processing.swe.swe_processor.scale_core_density')
-    @patch('imap_l3_processing.swe.swe_processor.integrate')
-    @patch('imap_l3_processing.swe.swe_processor.halo_fit_moments_retrying_on_failure')
-    @patch('imap_l3_processing.swe.swe_processor.core_fit_moments_retrying_on_failure')
-    @patch('imap_l3_processing.swe.swe_processor.compute_maxwellian_weight_factors')
-    @patch('imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s')
-    @patch('imap_l3_processing.swe.swe_processor.apply_rotation_matrix')
-    @patch('imap_l3_processing.swe.swe_processor.rotate_temperature')
-    def test_calculate_moment_products_handles_errors_with_fill(self, mock_rotate_temperature,
-                                                                mock_rotate_dps_vector_to_rtn,
-                                                                mock_calculate_velocity_in_dsp_frame_km_s,
-                                                                mock_compute_maxwellian_weight_factors,
-                                                                mock_core_fit_moments_retrying_on_failure: Mock,
-                                                                mock_halo_fit_moments_retrying_on_failure: Mock,
-                                                                mock_integrate: Mock,
-                                                                mock_scale_core_density: Mock,
-                                                                mock_scale_halo_density: Mock,
-                                                                mock_rotate_vector_to_rtn_spherical_coordinates,
-                                                                mock_calculate_primary_eigenvector,
-                                                                mock_rotate_temperature_tensor_to_mag):
+            self.assertEqual(
+                log_context.output,
+                [
+                    "INFO:imap_l3_processing.swe.swe_processor:Bad core-halo breakpoint value at index 0. Continuing."
+                ],
+            )
+
+    @patch("imap_l3_processing.swe.swe_processor.rotate_temperature_tensor_to_mag")
+    @patch("imap_l3_processing.swe.swe_processor.calculate_primary_eigenvector")
+    @patch(
+        "imap_l3_processing.swe.swe_processor.rotate_vector_to_rtn_spherical_coordinates"
+    )
+    @patch("imap_l3_processing.swe.swe_processor.scale_halo_density")
+    @patch("imap_l3_processing.swe.swe_processor.scale_core_density")
+    @patch("imap_l3_processing.swe.swe_processor.integrate")
+    @patch("imap_l3_processing.swe.swe_processor.halo_fit_moments_retrying_on_failure")
+    @patch("imap_l3_processing.swe.swe_processor.core_fit_moments_retrying_on_failure")
+    @patch("imap_l3_processing.swe.swe_processor.compute_maxwellian_weight_factors")
+    @patch("imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s")
+    @patch("imap_l3_processing.swe.swe_processor.apply_rotation_matrix")
+    @patch("imap_l3_processing.swe.swe_processor.rotate_temperature")
+    def test_calculate_moment_products_handles_errors_with_fill(
+        self,
+        mock_rotate_temperature,
+        mock_rotate_dps_vector_to_rtn,
+        mock_calculate_velocity_in_dsp_frame_km_s,
+        mock_compute_maxwellian_weight_factors,
+        mock_core_fit_moments_retrying_on_failure: Mock,
+        mock_halo_fit_moments_retrying_on_failure: Mock,
+        mock_integrate: Mock,
+        mock_scale_core_density: Mock,
+        mock_scale_halo_density: Mock,
+        mock_rotate_vector_to_rtn_spherical_coordinates,
+        mock_calculate_primary_eigenvector,
+        mock_rotate_temperature_tensor_to_mag,
+    ):
         mock_rotate_dps_vector_to_rtn.return_value = np.full(3, np.nan)
         mock_rotate_temperature.return_value = 1, 2
         mock_core_fit_moments_retrying_on_failure.return_value = MomentFitResults(
             moments=Moments(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
             chisq=2.5,
             number_of_points=100,
-            regress_result=np.array([1, 2, 3, 4])
+            regress_result=np.array([1, 2, 3, 4]),
         )
         cases = [
             mock_rotate_temperature,
@@ -1810,7 +2858,7 @@ def test_calculate_moment_products_handles_errors_with_fill(self, mock_rotate_te
             mock_scale_halo_density,
             mock_rotate_vector_to_rtn_spherical_coordinates,
             mock_calculate_primary_eigenvector,
-            mock_rotate_temperature_tensor_to_mag
+            mock_rotate_temperature_tensor_to_mag,
         ]
 
         for failing_function in cases:
@@ -1835,31 +2883,48 @@ def test_calculate_moment_products_handles_errors_with_fill(self, mock_rotate_te
                     phase_space_density_rebinned=np.array([]),
                     data_quality=np.array([]),
                 )
-                swe_l1_data = SweL1bData(epoch=epochs,
-                                         count_rates=[sentinel.l1b_count_rates_1, sentinel.l1b_count_rates_2,
-                                                      sentinel.l1b_count_rates_3],
-                                         settle_duration=Mock())
+                swe_l1_data = SweL1bData(
+                    epoch=epochs,
+                    count_rates=[
+                        sentinel.l1b_count_rates_1,
+                        sentinel.l1b_count_rates_2,
+                        sentinel.l1b_count_rates_3,
+                    ],
+                    settle_duration=Mock(),
+                )
 
                 spacecraft_potential = np.array([12, 14, 16])
                 core_halo_breakpoint = np.array([96, 54, 103])
 
-                corrected_energy_bins = swe_l2_data.energy.reshape(1, -1) - spacecraft_potential.reshape(-1, 1)
+                corrected_energy_bins = swe_l2_data.energy.reshape(
+                    1, -1
+                ) - spacecraft_potential.reshape(-1, 1)
 
-                input_metadata = InputMetadata("swe", "l3", datetime(2025, 2, 21),
-                                               datetime(2025, 2, 22), "v001")
+                input_metadata = InputMetadata(
+                    "swe", "l3", datetime(2025, 2, 21), datetime(2025, 2, 22), "v001"
+                )
 
-                swe_processor = SweProcessor(ProcessingInputCollection(), input_metadata=input_metadata)
+                swe_processor = SweProcessor(
+                    ProcessingInputCollection(), input_metadata=input_metadata
+                )
                 config = build_swe_configuration()
 
-                rebinned_mag_data = [sentinel.mag_data_1, sentinel.mag_data_2, sentinel.mag_data_3]
+                rebinned_mag_data = [
+                    sentinel.mag_data_1,
+                    sentinel.mag_data_2,
+                    sentinel.mag_data_3,
+                ]
 
                 with self.assertLogs():
-                    swe_moment_data = swe_processor.calculate_moment_products(swe_l2_data, swe_l1_data,
-                                                                              rebinned_mag_data,
-                                                                              spacecraft_potential,
-                                                                              core_halo_breakpoint,
-                                                                              corrected_energy_bins,
-                                                                              config)
+                    swe_moment_data = swe_processor.calculate_moment_products(
+                        swe_l2_data,
+                        swe_l1_data,
+                        rebinned_mag_data,
+                        spacecraft_potential,
+                        core_halo_breakpoint,
+                        corrected_energy_bins,
+                        config,
+                    )
 
                 self.assertIsInstance(swe_moment_data, SweL3MomentData)
 
@@ -2001,24 +3066,35 @@ def _build_finite_moment_data(num_epochs: int) -> SweL3MomentData:
     )
 
 
-def _assert_field_nan_at_index_and_finite_elsewhere(test: unittest.TestCase, moment_data: SweL3MomentData,
-                                                    field_names: tuple[str, ...], nan_index: int,
-                                                    finite_indices: tuple[int, ...]) -> None:
+def _assert_field_nan_at_index_and_finite_elsewhere(
+    test: unittest.TestCase,
+    moment_data: SweL3MomentData,
+    field_names: tuple[str, ...],
+    nan_index: int,
+    finite_indices: tuple[int, ...],
+) -> None:
     for field_name in field_names:
         field = getattr(moment_data, field_name)
-        test.assertTrue(np.all(np.isnan(field[nan_index])),
-                        f"{field_name}[{nan_index}] should be NaN, got {field[nan_index]!r}")
+        test.assertTrue(
+            np.all(np.isnan(field[nan_index])),
+            f"{field_name}[{nan_index}] should be NaN, got {field[nan_index]!r}",
+        )
         for finite_index in finite_indices:
-            test.assertTrue(np.all(np.isfinite(field[finite_index])),
-                            f"{field_name}[{finite_index}] should be finite, got {field[finite_index]!r}")
+            test.assertTrue(
+                np.all(np.isfinite(field[finite_index])),
+                f"{field_name}[{finite_index}] should be finite, got {field[finite_index]!r}",
+            )
 
 
-def _assert_fields_finite_everywhere(test: unittest.TestCase, moment_data: SweL3MomentData,
-                                     field_names: tuple[str, ...]) -> None:
+def _assert_fields_finite_everywhere(
+    test: unittest.TestCase, moment_data: SweL3MomentData, field_names: tuple[str, ...]
+) -> None:
     for field_name in field_names:
         field = getattr(moment_data, field_name)
-        test.assertTrue(np.all(np.isfinite(field)),
-                        f"{field_name} should be finite at every index, got {field!r}")
+        test.assertTrue(
+            np.all(np.isfinite(field)),
+            f"{field_name} should be finite at every index, got {field!r}",
+        )
 
 
 class TestCheckAndMaskNegativeMoments(unittest.TestCase):
@@ -2029,18 +3105,29 @@ def test_negative_core_density_masks_core_and_total_and_flags(self):
         flags = check_and_mask_negative_moments(moment_data)
 
         expected_flags = np.array(
-            [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE], dtype=np.uint16
+            [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE],
+            dtype=np.uint16,
         )
         np.testing.assert_array_equal(flags, expected_flags)
         self.assertEqual(np.uint16, flags.dtype)
 
         _assert_field_nan_at_index_and_finite_elsewhere(
-            self, moment_data, _CORE_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+            self,
+            moment_data,
+            _CORE_INTEGRATED_FIELD_NAMES,
+            nan_index=1,
+            finite_indices=(0, 2),
         )
         _assert_field_nan_at_index_and_finite_elsewhere(
-            self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+            self,
+            moment_data,
+            _TOTAL_INTEGRATED_FIELD_NAMES,
+            nan_index=1,
+            finite_indices=(0, 2),
+        )
+        _assert_fields_finite_everywhere(
+            self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
         )
-        _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
         _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_core_tensor_diagonal_masks_core_and_total_and_flags(self):
@@ -2057,18 +3144,29 @@ def test_negative_core_tensor_diagonal_masks_core_and_total_and_flags(self):
                 flags = check_and_mask_negative_moments(moment_data)
 
                 expected_flags = np.array(
-                    [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE], dtype=np.uint16
+                    [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE],
+                    dtype=np.uint16,
                 )
                 np.testing.assert_array_equal(flags, expected_flags)
                 self.assertEqual(np.uint16, flags.dtype)
 
                 _assert_field_nan_at_index_and_finite_elsewhere(
-                    self, moment_data, _CORE_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+                    self,
+                    moment_data,
+                    _CORE_INTEGRATED_FIELD_NAMES,
+                    nan_index=1,
+                    finite_indices=(0, 2),
                 )
                 _assert_field_nan_at_index_and_finite_elsewhere(
-                    self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+                    self,
+                    moment_data,
+                    _TOTAL_INTEGRATED_FIELD_NAMES,
+                    nan_index=1,
+                    finite_indices=(0, 2),
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
                 )
-                _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
                 _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_core_tensor_off_diagonal_does_not_flag_or_mask(self):
@@ -2090,9 +3188,15 @@ def test_negative_core_tensor_off_diagonal_does_not_flag_or_mask(self):
                 np.testing.assert_array_equal(flags, expected_flags)
                 self.assertEqual(np.uint16, flags.dtype)
 
-                _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES)
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES
+                )
                 _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_halo_density_masks_halo_and_total_and_flags(self):
@@ -2102,18 +3206,29 @@ def test_negative_halo_density_masks_halo_and_total_and_flags(self):
         flags = check_and_mask_negative_moments(moment_data)
 
         expected_flags = np.array(
-            [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE], dtype=np.uint16
+            [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE],
+            dtype=np.uint16,
         )
         np.testing.assert_array_equal(flags, expected_flags)
         self.assertEqual(np.uint16, flags.dtype)
 
         _assert_field_nan_at_index_and_finite_elsewhere(
-            self, moment_data, _HALO_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+            self,
+            moment_data,
+            _HALO_INTEGRATED_FIELD_NAMES,
+            nan_index=1,
+            finite_indices=(0, 2),
         )
         _assert_field_nan_at_index_and_finite_elsewhere(
-            self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+            self,
+            moment_data,
+            _TOTAL_INTEGRATED_FIELD_NAMES,
+            nan_index=1,
+            finite_indices=(0, 2),
+        )
+        _assert_fields_finite_everywhere(
+            self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
         )
-        _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
         _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_halo_tensor_diagonal_masks_halo_and_total_and_flags(self):
@@ -2130,18 +3245,29 @@ def test_negative_halo_tensor_diagonal_masks_halo_and_total_and_flags(self):
                 flags = check_and_mask_negative_moments(moment_data)
 
                 expected_flags = np.array(
-                    [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE], dtype=np.uint16
+                    [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE],
+                    dtype=np.uint16,
                 )
                 np.testing.assert_array_equal(flags, expected_flags)
                 self.assertEqual(np.uint16, flags.dtype)
 
                 _assert_field_nan_at_index_and_finite_elsewhere(
-                    self, moment_data, _HALO_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+                    self,
+                    moment_data,
+                    _HALO_INTEGRATED_FIELD_NAMES,
+                    nan_index=1,
+                    finite_indices=(0, 2),
                 )
                 _assert_field_nan_at_index_and_finite_elsewhere(
-                    self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+                    self,
+                    moment_data,
+                    _TOTAL_INTEGRATED_FIELD_NAMES,
+                    nan_index=1,
+                    finite_indices=(0, 2),
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
                 )
-                _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
                 _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_halo_tensor_off_diagonal_does_not_flag_or_mask(self):
@@ -2163,9 +3289,15 @@ def test_negative_halo_tensor_off_diagonal_does_not_flag_or_mask(self):
                 np.testing.assert_array_equal(flags, expected_flags)
                 self.assertEqual(np.uint16, flags.dtype)
 
-                _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES)
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES
+                )
                 _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_total_density_masks_only_total_and_flags(self):
@@ -2175,16 +3307,25 @@ def test_negative_total_density_masks_only_total_and_flags(self):
         flags = check_and_mask_negative_moments(moment_data)
 
         expected_flags = np.array(
-            [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE], dtype=np.uint16
+            [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE],
+            dtype=np.uint16,
         )
         np.testing.assert_array_equal(flags, expected_flags)
         self.assertEqual(np.uint16, flags.dtype)
 
         _assert_field_nan_at_index_and_finite_elsewhere(
-            self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+            self,
+            moment_data,
+            _TOTAL_INTEGRATED_FIELD_NAMES,
+            nan_index=1,
+            finite_indices=(0, 2),
+        )
+        _assert_fields_finite_everywhere(
+            self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
+        )
+        _assert_fields_finite_everywhere(
+            self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
         )
-        _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
-        _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
         _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_total_tensor_diagonal_masks_only_total_and_flags(self):
@@ -2201,16 +3342,25 @@ def test_negative_total_tensor_diagonal_masks_only_total_and_flags(self):
                 flags = check_and_mask_negative_moments(moment_data)
 
                 expected_flags = np.array(
-                    [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE], dtype=np.uint16
+                    [SweL3Flags.NONE, SweL3Flags.NEGATIVE_MOMENT, SweL3Flags.NONE],
+                    dtype=np.uint16,
                 )
                 np.testing.assert_array_equal(flags, expected_flags)
                 self.assertEqual(np.uint16, flags.dtype)
 
                 _assert_field_nan_at_index_and_finite_elsewhere(
-                    self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES, nan_index=1, finite_indices=(0, 2)
+                    self,
+                    moment_data,
+                    _TOTAL_INTEGRATED_FIELD_NAMES,
+                    nan_index=1,
+                    finite_indices=(0, 2),
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
                 )
-                _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
                 _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_negative_total_tensor_off_diagonal_does_not_flag_or_mask(self):
@@ -2232,9 +3382,15 @@ def test_negative_total_tensor_off_diagonal_does_not_flag_or_mask(self):
                 np.testing.assert_array_equal(flags, expected_flags)
                 self.assertEqual(np.uint16, flags.dtype)
 
-                _assert_fields_finite_everywhere(self, moment_data, _CORE_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _HALO_INTEGRATED_FIELD_NAMES)
-                _assert_fields_finite_everywhere(self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES)
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _CORE_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _HALO_INTEGRATED_FIELD_NAMES
+                )
+                _assert_fields_finite_everywhere(
+                    self, moment_data, _TOTAL_INTEGRATED_FIELD_NAMES
+                )
                 _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
 
     def test_combines_negative_sources_correctly(self):
@@ -2242,8 +3398,22 @@ def test_combines_negative_sources_correctly(self):
 
         moment_data.core_density_integrated[1] = -1.0
         moment_data.halo_density_integrated[2] = -1.0
-        moment_data.core_temperature_tensor_integrated[3] = [-1.0, 0.0, 1.0, 0.0, 0.0, 1.0]
-        moment_data.halo_temperature_tensor_integrated[3] = [-1.0, 0.0, 1.0, 0.0, 0.0, 1.0]
+        moment_data.core_temperature_tensor_integrated[3] = [
+            -1.0,
+            0.0,
+            1.0,
+            0.0,
+            0.0,
+            1.0,
+        ]
+        moment_data.halo_temperature_tensor_integrated[3] = [
+            -1.0,
+            0.0,
+            1.0,
+            0.0,
+            0.0,
+            1.0,
+        ]
         moment_data.total_density_integrated[3] = -1.0
 
         flags = check_and_mask_negative_moments(moment_data)
@@ -2260,8 +3430,9 @@ def test_combines_negative_sources_correctly(self):
         np.testing.assert_array_equal(flags, expected_flags)
         self.assertEqual(np.uint16, flags.dtype)
 
-        def assert_population_at_epoch(epoch: int, field_names: tuple[str, ...],
-                                       should_be_masked: bool) -> None:
+        def assert_population_at_epoch(
+            epoch: int, field_names: tuple[str, ...], should_be_masked: bool
+        ) -> None:
             for field_name in field_names:
                 slice_at_epoch = getattr(moment_data, field_name)[epoch]
                 if should_be_masked:
@@ -2283,8 +3454,14 @@ def assert_population_at_epoch(epoch: int, field_names: tuple[str, ...],
         )
         for epoch, core_masked, halo_masked, total_masked in epoch_masking_matrix:
             with self.subTest(epoch=epoch):
-                assert_population_at_epoch(epoch, _CORE_INTEGRATED_FIELD_NAMES, core_masked)
-                assert_population_at_epoch(epoch, _HALO_INTEGRATED_FIELD_NAMES, halo_masked)
-                assert_population_at_epoch(epoch, _TOTAL_INTEGRATED_FIELD_NAMES, total_masked)
+                assert_population_at_epoch(
+                    epoch, _CORE_INTEGRATED_FIELD_NAMES, core_masked
+                )
+                assert_population_at_epoch(
+                    epoch, _HALO_INTEGRATED_FIELD_NAMES, halo_masked
+                )
+                assert_population_at_epoch(
+                    epoch, _TOTAL_INTEGRATED_FIELD_NAMES, total_masked
+                )
 
         _assert_fields_finite_everywhere(self, moment_data, _FIT_FIELD_NAMES)
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/archive/map_SCFrame.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/archive/map_SCFrame.py
index f734506f8..448e2aa10 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/archive/map_SCFrame.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/archive/map_SCFrame.py
@@ -1,27 +1,24 @@
 # May 18, 2023 - NAS
 # in Version 2 we are adding the TOF3 < 20 ns criterion
 #!/usr/bin/python
-import numpy as np
 import argparse
-from scipy.optimize import curve_fit
-import random
-import datetime
-from spacepy.pycdf import CDF
-import pandas as pd
 import os
 from pathlib import Path
 
+import numpy as np
+import pandas as pd
+
 PI = np.pi
 
 # ESA Steps
 nesa = 7
 nalpha = 40
 nphi = 3600
-dang = 0.1 * PI /180.0
+dang = 0.1 * PI / 180.0
 deg = 6.0
 nra = 60
 ncolat = 30
-nmap = nra*ncolat*nesa
+nmap = nra * ncolat * nesa
 
 
 def argParsing():
@@ -33,28 +30,21 @@ def argParsing():
     )
 
     parser.add_argument(
-        '-de', '--defiledir',
+        "-de",
+        "--defiledir",
         required=True,
-        help='Path to the location of CSV files generated by flight_to_csv.py'
+        help="Path to the location of CSV files generated by flight_to_csv.py",
     )
-    
+
     parser.add_argument(
-        '-se', '--start_esa',
-        required=False,
-        type = int,
-        help='Start ESA Number'
+        "-se", "--start_esa", required=False, type=int, help="Start ESA Number"
     )
-    
+
     parser.add_argument(
-        '-ee', '--end_esa',
-        required=False,
-        type = int,
-        help='End ESA Number'
+        "-ee", "--end_esa", required=False, type=int, help="End ESA Number"
     )
-                           
-                        
-    return parser.parse_args()
 
+    return parser.parse_args()
 
 
 eff_h = 1.0
@@ -67,7 +57,7 @@ def argParsing():
     5: 0.200,
     6: 0.405,
     7: 0.787,
-    8: 1.821
+    8: 1.821,
 }
 
 gf = {
@@ -78,7 +68,7 @@ def argParsing():
     5: 14.0e-5,
     6: 17.7e-5,
     7: 22.5e-5,
-    8: 6.721e-5
+    8: 6.721e-5,
 }
 
 ox_gf = {
@@ -101,7 +91,7 @@ def argParsing():
     4: 0.000197526,
     5: 0.000258304,
     6: 0.000113958,
-    7: 0.000189929
+    7: 0.000189929,
 }
 
 # args = argParsing()
@@ -114,126 +104,125 @@ def argParsing():
 # start_esa = args.start_esa
 # end_esa = args.end_esa
 
-for pp in [75,90,105]:
-    work_dir1 = f'./outdir/pivot_{pp}/daily'
+for pp in [75, 90, 105]:
+    work_dir1 = f"./outdir/pivot_{pp}/daily"
     map_dir = f"./outdir/pivot_{pp}/maps/"
-    
+
     os.makedirs(map_dir, exist_ok=True)
-    
+
     data_dir = Path(work_dir1)
-    
+
     print(f"Making Maps for Pivot {pp}")
-    
-    for esa in range(1,8):
-    
+
+    for esa in range(1, 8):
         print(esa)
-        h_cnts_map = np.zeros((30,60))
-        h_rate_map = np.zeros((30,60))
-        h_flux_map = np.zeros((30,60))
-        h_func_map = np.zeros((30,60))
-        h_fvar_map = np.zeros((30,60))
-        exposure = np.zeros((30,60))
-        background = np.zeros((30,60))
-        back_map = np.zeros((30,60))
-        back_rate_map = np.zeros((30,60))
-        stonoise = np.zeros((30,60))
-        h_rate_unc = np.zeros((30,60))
-        h_rate_var = np.zeros((30,60))
-    
-        for filepath in data_dir.glob(f'*esa{esa}.csv'):
-        # for yd in range(start_day,end_day+1):
-        #     sk = ''
-        #     file = work_dir1 + f"/data_YD_{yd}_esa{esa}.csv"
+        h_cnts_map = np.zeros((30, 60))
+        h_rate_map = np.zeros((30, 60))
+        h_flux_map = np.zeros((30, 60))
+        h_func_map = np.zeros((30, 60))
+        h_fvar_map = np.zeros((30, 60))
+        exposure = np.zeros((30, 60))
+        background = np.zeros((30, 60))
+        back_map = np.zeros((30, 60))
+        back_rate_map = np.zeros((30, 60))
+        stonoise = np.zeros((30, 60))
+        h_rate_unc = np.zeros((30, 60))
+        h_rate_var = np.zeros((30, 60))
+
+        for filepath in data_dir.glob(f"*esa{esa}.csv"):
+            # for yd in range(start_day,end_day+1):
+            #     sk = ''
+            #     file = work_dir1 + f"/data_YD_{yd}_esa{esa}.csv"
             file = str(filepath)
             # if not os.path.exists(file):
             #     print("No file")
             #     continue
             df = pd.read_csv(file)
-            ps_ra = df['ra'].values
-            ps_dec = df['dec'].values
-            counts = df['counts'].values
-            expo = df['expo'].values
-            
-            for ia in range(0, 60):
-                    ra = ps_ra[ia]
-                    dec = ps_dec[ia]
-                    
-                    theta = 90.0 + dec
-                    
-                    imap = int(ra/deg)
-                    if (imap == 60):
-                        imap = 0
-                    jmap = int(theta/deg)
-                    if (jmap == 30):
-                        # jmap = 29
-                        jmap = 0
-                    
-                    h_cnts_map[jmap,imap] += counts[ia]
-                    exposure[jmap,imap] += expo[ia]
-                    
-        for imap in range(0, nra):
-            for jmap in range(0,ncolat):
-                
-                expo = exposure[jmap,imap]
+            ps_ra = df["ra"].values
+            ps_dec = df["dec"].values
+            counts = df["counts"].values
+            expo = df["expo"].values
+
+            for ia in range(60):
+                ra = ps_ra[ia]
+                dec = ps_dec[ia]
+
+                theta = 90.0 + dec
+
+                imap = int(ra / deg)
+                if imap == 60:
+                    imap = 0
+                jmap = int(theta / deg)
+                if jmap == 30:
+                    # jmap = 29
+                    jmap = 0
+
+                h_cnts_map[jmap, imap] += counts[ia]
+                exposure[jmap, imap] += expo[ia]
+
+        for imap in range(nra):
+            for jmap in range(ncolat):
+                expo = exposure[jmap, imap]
                 energy = esa_energy[esa]
                 # if el == 'Hy':
                 #     geo = gf[esa]
                 #     brate = bg_rate[esa]
                 # else:
                 #     geo = ox_gf[esa]
-                
+
                 geo = gf[esa]
                 brate = bg_rate[esa]
-                
-                if (expo > 0.0):
-                    back_rate_map[jmap,imap] = background[jmap,imap]
-                    h_rate_map[jmap,imap] = h_cnts_map[jmap,imap] / expo
-                    h_flux_map[jmap,imap] = h_rate_map[jmap,imap] / (geo * energy)
-                    back_map[jmap,imap] = background[jmap,imap] / (geo * energy)
-                    h_rate_unc[jmap,imap] = np.sqrt( h_cnts_map[jmap,imap] )/expo
-                    h_rate_var[jmap,imap] = h_rate_unc[jmap,imap]**2
-                    if (brate > 0.0):
-                        stonoise[jmap,imap] = h_rate_map[jmap,imap] / ( brate )
-                    if (h_cnts_map[jmap,imap] > 1.0):
-                        h_func_map[jmap,imap] = h_flux_map[jmap,imap] / np.sqrt( h_cnts_map[jmap,imap] )
-                        h_fvar_map[jmap,imap] = h_func_map[jmap,imap]*2
+
+                if expo > 0.0:
+                    back_rate_map[jmap, imap] = background[jmap, imap]
+                    h_rate_map[jmap, imap] = h_cnts_map[jmap, imap] / expo
+                    h_flux_map[jmap, imap] = h_rate_map[jmap, imap] / (geo * energy)
+                    back_map[jmap, imap] = background[jmap, imap] / (geo * energy)
+                    h_rate_unc[jmap, imap] = np.sqrt(h_cnts_map[jmap, imap]) / expo
+                    h_rate_var[jmap, imap] = h_rate_unc[jmap, imap] ** 2
+                    if brate > 0.0:
+                        stonoise[jmap, imap] = h_rate_map[jmap, imap] / (brate)
+                    if h_cnts_map[jmap, imap] > 1.0:
+                        h_func_map[jmap, imap] = h_flux_map[jmap, imap] / np.sqrt(
+                            h_cnts_map[jmap, imap]
+                        )
+                        h_fvar_map[jmap, imap] = h_func_map[jmap, imap] * 2
                     else:
-                        h_func_map[jmap,imap] = 0.0
-                        h_fvar_map[jmap,imap] = 0.0
-                        
-        
+                        h_func_map[jmap, imap] = 0.0
+                        h_fvar_map[jmap, imap] = 0.0
+
         sbg_file = pd.DataFrame(stonoise)
-        sbg_file.to_csv(map_dir+f"/map_stbg_esa{esa}.csv", index=False)
-        
+        sbg_file.to_csv(map_dir + f"/map_stbg_esa{esa}.csv", index=False)
+
         expo_file = pd.DataFrame(exposure)
-        expo_file.to_csv(map_dir+f"/map_expo_esa{esa}.csv", index=False)
-        
+        expo_file.to_csv(map_dir + f"/map_expo_esa{esa}.csv", index=False)
+
         cnts_file = pd.DataFrame(h_cnts_map)
-        cnts_file.to_csv(map_dir+f"/map_cnts_esa{esa}.csv", index=False)
-        
+        cnts_file.to_csv(map_dir + f"/map_cnts_esa{esa}.csv", index=False)
+
         rate_file = pd.DataFrame(h_rate_map)
-        rate_file.to_csv(map_dir+f"/map_rate_esa{esa}.csv", index=False)
-        
+        rate_file.to_csv(map_dir + f"/map_rate_esa{esa}.csv", index=False)
+
         flux_file = pd.DataFrame(h_flux_map)
-        flux_file.to_csv(map_dir+f"/map_flux_esa{esa}.csv", index=False)
-        
+        flux_file.to_csv(map_dir + f"/map_flux_esa{esa}.csv", index=False)
+
         func_file = pd.DataFrame(h_func_map)
-        func_file.to_csv(map_dir+f"/map_func_esa{esa}.csv", index=False)
-        
+        func_file.to_csv(map_dir + f"/map_func_esa{esa}.csv", index=False)
+
         rate_unc_file = pd.DataFrame(h_rate_unc)
-        rate_unc_file.to_csv(map_dir+f"/map_runc_esa{esa}.csv", index=False)
-        
+        rate_unc_file.to_csv(map_dir + f"/map_runc_esa{esa}.csv", index=False)
+
         h_rate_var_file = pd.DataFrame(h_rate_var)
-        h_rate_var_file.to_csv(map_dir+f"/map_rvar_esa{esa}.csv", index=False)
-        
+        h_rate_var_file.to_csv(map_dir + f"/map_rvar_esa{esa}.csv", index=False)
+
         h_fvar_map_file = pd.DataFrame(h_fvar_map)
-        h_fvar_map_file.to_csv(map_dir+f"/map_fvar_esa{esa}.csv", index=False)
-    
+        h_fvar_map_file.to_csv(map_dir + f"/map_fvar_esa{esa}.csv", index=False)
+
         # func_file = pd.DataFrame(h_func_map)
         # func_file.to_csv(f"2009_lo_maps/2009_lo_map_func_{esa}_{el}.csv", index=False)
-        
+
         # back_file = pd.DataFrame(back_map)
         # back_file.to_csv(f"2009_lo_maps/2009_lo_map_back_{esa}_{el}.csv", index=False)
-        
+
         # backrate_file = pd.DataFrame(back_rate_map)
         # backrate_file.to_csv(f"2009_lo_maps/2009_lo_map_backrate_{esa}_{el}.csv", index=False)
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/crap/new_patch_arc2.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/crap/new_patch_arc2.py
index 720ff65a9..db88f10da 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/crap/new_patch_arc2.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/crap/new_patch_arc2.py
@@ -1,16 +1,17 @@
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-from scipy.spatial.transform import Rotation as R
 from matplotlib.colors import LogNorm
+from scipy.spatial.transform import Rotation as R
 
 PI = np.pi
 
-def somewhat_great_circle_centered(rot, lat0, lon0,  angle, npts=400):
+
+def somewhat_great_circle_centered(rot, lat0, lon0, angle, npts=400):
     """
     Somewhat great circle whose pole is (lat0, lon0).
-    angle is the angle from the center 
+    angle is the angle from the center
     Returns coordinates in the ROTATED frame.
     """
 
@@ -28,25 +29,22 @@ def somewhat_great_circle_centered(rot, lat0, lon0,  angle, npts=400):
 
     v = np.cross(p, u)
 
-    t = np.linspace(0, 2*np.pi, npts)
+    t = np.linspace(0, 2 * np.pi, npts)
 
     angle_r = np.radians(angle)
-    pts = (
-        np.cos(angle_r) * p
-        + np.sin(angle_r) * (
-            np.outer(np.cos(t), u) +
-            np.outer(np.sin(t), v)
+    pts = np.cos(angle_r) * p + np.sin(angle_r) * (
+        np.outer(np.cos(t), u) + np.outer(np.sin(t), v)
     )
-)
     # rotate into map frame
     pts_rot = rot.apply(pts)
 
-    lat = np.degrees(np.arcsin(pts_rot[:,2]))
-    lon = np.degrees(np.arctan2(pts_rot[:,1], pts_rot[:,0]))
+    lat = np.degrees(np.arcsin(pts_rot[:, 2]))
+    lon = np.degrees(np.arctan2(pts_rot[:, 1], pts_rot[:, 0]))
     lon = (lon + 180) % 360 - 180
 
     return lat, lon
 
+
 def plot_wrapped_line(ax, lon, lat, **plot_kwargs):
     """
     Plot a line while avoiding wraparound artifacts.
@@ -56,14 +54,16 @@ def plot_wrapped_line(ax, lon, lat, **plot_kwargs):
     lat = np.asarray(lat)
 
     jumps = np.where(np.abs(np.diff(lon)) > 180)[0]
-    split_idx = np.append(jumps, len(lon)-1)
+    split_idx = np.append(jumps, len(lon) - 1)
 
     start = 0
     for idx in split_idx:
-        ax.plot(lon[start:idx+1],
-                lat[start:idx+1],
-                transform=ccrs.PlateCarree(),
-                **plot_kwargs)
+        ax.plot(
+            lon[start : idx + 1],
+            lat[start : idx + 1],
+            transform=ccrs.PlateCarree(),
+            **plot_kwargs,
+        )
         start = idx + 1
 
 
@@ -75,14 +75,16 @@ def sph_to_cart(lat, lon):
     z = np.sin(lat_rad)
     return np.array([x, y, z])
 
+
 def rotation_matrix_to_center(new_colat0, new_lon0):
     # Step 1: rotate around Z axis by -lon0
-    r1 = R.from_euler('z', -new_lon0, degrees=True)
+    r1 = R.from_euler("z", -new_lon0, degrees=True)
     # Step 2: rotate around Y axis by 90-lat0
-    r2 = R.from_euler('y', 90-new_colat0, degrees=True)
+    r2 = R.from_euler("y", 90 - new_colat0, degrees=True)
     # Combined rotation
     return r2 * r1
 
+
 def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     Apply inverse rotation to xyz_rot points.
@@ -92,55 +94,101 @@ def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     return rot.inv().apply(xyz_rot)
 
+
 def make_imap_lo_reference_points(rot, option=1):
-    if option==1:
+    if option == 1:
         npoints = 4
         lons = np.array([259.200622, 255.499300, 289.800000, 79.200622])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296])
-        labels=[ 'Nose', 'V1','V2','Tail']
-    if option==2:
+        lats = np.array([5.116296, 34.999941, -35.600000, -5.116296])
+        labels = ["Nose", "V1", "V2", "Tail"]
+    if option == 2:
         npoints = 7
-        lons = np.array([259.200622, 255.499300, 289.800000, 79.200622, 221,150.,0.0 ])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296, 39, 0.0, 0.0])
-        labels=[ 'Nose', 'V1','V2','Tail', 'Rcen','Stbd', 'Port']
-    if option==3:
+        lons = np.array(
+            [259.200622, 255.499300, 289.800000, 79.200622, 221, 150.0, 0.0]
+        )
+        lats = np.array([5.116296, 34.999941, -35.600000, -5.116296, 39, 0.0, 0.0])
+        labels = ["Nose", "V1", "V2", "Tail", "Rcen", "Stbd", "Port"]
+    if option == 3:
         npoints = 10
-        lons = np.array([259.200622, 255.499300, 289.800000, 79.200622, 221,150.,0.0, 41, 250.624523, 70.624523])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296, 39, 0.0, 0.0, -39, -41.242979, 41.242979])
-        labels=[ 'Nose', 'V1','V2','Tail', 'RibC','Stbd', 'Port','DwnB','ULSR', 'DLSR']
-
+        lons = np.array(
+            [
+                259.200622,
+                255.499300,
+                289.800000,
+                79.200622,
+                221,
+                150.0,
+                0.0,
+                41,
+                250.624523,
+                70.624523,
+            ]
+        )
+        lats = np.array(
+            [
+                5.116296,
+                34.999941,
+                -35.600000,
+                -5.116296,
+                39,
+                0.0,
+                0.0,
+                -39,
+                -41.242979,
+                41.242979,
+            ]
+        )
+        labels = [
+            "Nose",
+            "V1",
+            "V2",
+            "Tail",
+            "RibC",
+            "Stbd",
+            "Port",
+            "DwnB",
+            "ULSR",
+            "DLSR",
+        ]
 
         # --- Reference point in ORIGINAL coordinates ---
-    lat_points = np.linspace(0.0,0.0,npoints)
-    lon_points = np.linspace(0.0,0.0,npoints)
-    for i in range(0,npoints):
-        ref_lat = lats[i]     # degrees
-        ref_lon = lons[i]     # degrees
-
-    # Convert to xyz
-        xyz_ref = np.array([
-            np.cos(np.radians(ref_lat)) * np.cos(np.radians(ref_lon)),
-            np.cos(np.radians(ref_lat)) * np.sin(np.radians(ref_lon)),
-            np.sin(np.radians(ref_lat))
-            ])
+    lat_points = np.linspace(0.0, 0.0, npoints)
+    lon_points = np.linspace(0.0, 0.0, npoints)
+    for i in range(npoints):
+        ref_lat = lats[i]  # degrees
+        ref_lon = lons[i]  # degrees
+
+        # Convert to xyz
+        xyz_ref = np.array(
+            [
+                np.cos(np.radians(ref_lat)) * np.cos(np.radians(ref_lon)),
+                np.cos(np.radians(ref_lat)) * np.sin(np.radians(ref_lon)),
+                np.sin(np.radians(ref_lat)),
+            ]
+        )
 
         # Rotate to map frame
         xyz_ref_rot = rot.apply(xyz_ref)
 
         # Back to lat/lon in rotated frame
         lat_ref_rot = np.degrees(np.arcsin(xyz_ref_rot[2]))
-        lon_ref_rot = -np.degrees(np.arctan2(xyz_ref_rot[1], xyz_ref_rot[0])) # negative sign since we are looking from the inside
-        lon_ref_rot = (lon_ref_rot + 180) % 360 - 180   # wrap
+        lon_ref_rot = -np.degrees(
+            np.arctan2(xyz_ref_rot[1], xyz_ref_rot[0])
+        )  # negative sign since we are looking from the inside
+        lon_ref_rot = (lon_ref_rot + 180) % 360 - 180  # wrap
 
         lat_points[i] = lat_ref_rot
         lon_points[i] = lon_ref_rot
-        
+
     return labels, lon_points, lat_points
 
 
 # --- Load data ---
 
-def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, opt_points=1, opt_ribbon=0):
+
+def make_imap_lo_map(
+    file, lat_center, lon_center, vmin, vmax, output, opt_points=1, opt_ribbon=0
+):
 
     data = pd.read_csv(file, header=0).values
     ny, nx = data.shape
@@ -150,93 +198,110 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, opt_point
     lons = np.linspace(3, 357, nx)
 
     # Build corner grid for pcolormesh
-    lat_edges = np.linspace(-90, 90, ny+1)
-    lon_edges = np.linspace(0, 360, nx+1)
+    lat_edges = np.linspace(-90, 90, ny + 1)
+    lon_edges = np.linspace(0, 360, nx + 1)
     lon_grid, lat_grid = np.meshgrid(lon_edges, lat_edges)
 
     # Flatten grid for rotation
-    xyz = np.column_stack([
-        np.cos(np.radians(lat_grid.ravel())) * np.cos(np.radians(lon_grid.ravel())),
-        np.cos(np.radians(lat_grid.ravel())) * np.sin(np.radians(lon_grid.ravel())),
-        np.sin(np.radians(lat_grid.ravel()))
-    ])
+    xyz = np.column_stack(
+        [
+            np.cos(np.radians(lat_grid.ravel())) * np.cos(np.radians(lon_grid.ravel())),
+            np.cos(np.radians(lat_grid.ravel())) * np.sin(np.radians(lon_grid.ravel())),
+            np.sin(np.radians(lat_grid.ravel())),
+        ]
+    )
 
     # --- Rotation ---
     new_colat0, new_lon0 = 90.0 - lat_center, lon_center
-    rot = R.from_euler('y', 90-new_colat0, degrees=True) * R.from_euler('z', -new_lon0, degrees=True) 
+    rot = R.from_euler("y", 90 - new_colat0, degrees=True) * R.from_euler(
+        "z", -new_lon0, degrees=True
+    )
     xyz_rot = rot.apply(xyz)
 
-    lat_rot = np.degrees(np.arcsin(xyz_rot[:,2])).reshape(ny+1, nx+1)
-    lon_rot = np.degrees(np.arctan2(xyz_rot[:,1], xyz_rot[:,0])).reshape(ny+1, nx+1)
+    lat_rot = np.degrees(np.arcsin(xyz_rot[:, 2])).reshape(ny + 1, nx + 1)
+    lon_rot = np.degrees(np.arctan2(xyz_rot[:, 1], xyz_rot[:, 0])).reshape(
+        ny + 1, nx + 1
+    )
     lon_rot = (lon_rot + 180) % 360 - 180
 
     # For the data
     lon_rot_inside = -lon_rot  # flip longitude
 
-    #lon_edges = compute_edges(lon_rot)
-    #lat_edges = compute_edges(lat_rot)
+    # lon_edges = compute_edges(lon_rot)
+    # lat_edges = compute_edges(lat_rot)
 
     # --- Plot using pcolormesh ---
-    fig, ax = plt.subplots(figsize=(10,5), subplot_kw={'projection': ccrs.Mollweide()})
-    ax.set_facecolor('darkblue')
+    fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": ccrs.Mollweide()})
+    ax.set_facecolor("darkblue")
 
-    vmin= vmin
+    vmin = vmin
     vmax = vmax
     data_clipped = np.clip(data, vmin, vmax)
     data_filled = np.copy(data_clipped)
     data_filled[np.isnan(data_clipped)] = vmin
 
-    pcm = ax.pcolormesh(lon_rot_inside, lat_rot, data_filled,  # no interpolation!
-                        transform=ccrs.PlateCarree(),
-                        cmap='viridis', norm=LogNorm(vmin=vmin, vmax=vmax), shading='auto')
-
+    pcm = ax.pcolormesh(
+        lon_rot_inside,
+        lat_rot,
+        data_filled,  # no interpolation!
+        transform=ccrs.PlateCarree(),
+        cmap="viridis",
+        norm=LogNorm(vmin=vmin, vmax=vmax),
+        shading="auto",
+    )
 
     lat_lines = np.linspace(-90, 90, 7)  # every ~30 deg
     for lat0 in lat_lines:
-        lon_line = np.linspace(0, 360, nx+1)
+        lon_line = np.linspace(0, 360, nx + 1)
         lat_line = np.full_like(lon_line, lat0)
 
         # Convert to xyz and rotate
-        xyz_line = np.column_stack([
-            np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
-            np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
-            np.sin(np.radians(lat_line))
-        ])
+        xyz_line = np.column_stack(
+            [
+                np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
+                np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
+                np.sin(np.radians(lat_line)),
+            ]
+        )
         xyz_line_rot = rot.apply(xyz_line)
 
         # Back to lat/lon
-        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:,2]))
-        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:,1], xyz_line_rot[:,0]))
+        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:, 2]))
+        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:, 1], xyz_line_rot[:, 0]))
         lon_rot_line = (lon_rot_line + 180) % 360 - 180  # wrap to [-180,180]
         # flip for inside view
 
         # --- Split line wherever jump > 180°
         jump_idx = np.where(np.abs(np.diff(lon_rot_line)) > 180)[0]
-        split_idx = np.append(jump_idx, len(lon_rot_line)-1)
+        split_idx = np.append(jump_idx, len(lon_rot_line) - 1)
         start = 0
         for idx in split_idx:
-            seg_lon = lon_rot_line[start:idx+1]
-            seg_lat = lat_rot_line[start:idx+1]
-            ax.plot(seg_lon, seg_lat, transform=ccrs.PlateCarree(),
-                    color='orange', lw=0.8, alpha=0.7)
-            start = idx+1
-
-
-        # pick a tolerance in degrees
-            tol = 100.0   # widen if labels disappear
+            seg_lon = lon_rot_line[start : idx + 1]
+            seg_lat = lat_rot_line[start : idx + 1]
+            ax.plot(
+                seg_lon,
+                seg_lat,
+                transform=ccrs.PlateCarree(),
+                color="orange",
+                lw=0.8,
+                alpha=0.7,
+            )
+            start = idx + 1
 
-            lon_here = ((seg_lon[0] + 180) % 360) - 180   # ensure [-180,180]
+            # pick a tolerance in degrees
+            tol = 100.0  # widen if labels disappear
 
-            if abs(lon_here) < tol:   # only near 0° rotated longitude
+            lon_here = ((seg_lon[0] + 180) % 360) - 180  # ensure [-180,180]
 
+            if abs(lon_here) < tol:  # only near 0° rotated longitude
                 theta = np.radians(90.0 - seg_lat[0])
-                phi = -np.radians(seg_lon[0])   # inside view sign flip
-                phi = np.mod(phi, 2.0*np.pi)
+                phi = -np.radians(seg_lon[0])  # inside view sign flip
+                phi = np.mod(phi, 2.0 * np.pi)
 
                 xyz = [
-                    np.sin(theta)*np.cos(phi),
-                    np.sin(theta)*np.sin(phi),
-                    np.cos(theta)
+                    np.sin(theta) * np.cos(phi),
+                    np.sin(theta) * np.sin(phi),
+                    np.cos(theta),
                 ]
 
                 xyz_inv = rot.inv().apply(xyz)
@@ -249,83 +314,92 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, opt_point
                         seg_lat[0],
                         f"{int(round(lat_orig))}°",
                         transform=ccrs.PlateCarree(),
-                        color='orange',
+                        color="orange",
                         fontsize=6,
-                        verticalalignment='top',
-                        horizontalalignment='right'
+                        verticalalignment="top",
+                        horizontalalignment="right",
                     )
 
-            #theta = np.radians(90.0-seg_lat[0])
-            #phi = -np.radians(seg_lon[0]) # sign inversion looking from inside
-            #phi = np.mod(phi, 2.0*np.pi)         # wrap to [0, 2π)
-            #if (phi > PI):
+            # theta = np.radians(90.0-seg_lat[0])
+            # phi = -np.radians(seg_lon[0]) # sign inversion looking from inside
+            # phi = np.mod(phi, 2.0*np.pi)         # wrap to [0, 2π)
+            # if (phi > PI):
             #    xyz = [np.sin(theta)*np.cos(phi),np.sin(theta)*np.sin(phi), np.cos(theta) ]
             #    xyz_inv = rot.inv().apply(xyz)
             #    lat_orig = np.degrees(np.arcsin(xyz_inv[2]))
             #    lon_orig = np.degrees(np.arctan2(xyz_inv[1], xyz_inv[0]))
             #    if not np.isnan(lat_orig):
             #        lat_lab_1 = round(lat_orig)
-            #    else: 
+            #    else:
             #        lat_lab_1 = ''
             #    lon_lab = ((seg_lon[0]+180) % 360) - 180
             #    if lat_lab_1 != '':
     #      ##         lon_lab_inside = -lon_lab
-            ##           transform=ccrs.PlateCarree(),
-            #          color='orange', fontsize=6,
-            #          verticalalignment='center', horizontalalignment='right')
-
+    ##           transform=ccrs.PlateCarree(),
+    #          color='orange', fontsize=6,
+    #          verticalalignment='center', horizontalalignment='right')
 
     lon_lines = np.linspace(0, 360, 13)  # every 30 deg
     for lon0 in lon_lines:
-        lat_line = np.linspace(-90, 90, ny+1)
+        lat_line = np.linspace(-90, 90, ny + 1)
         lon_line = np.full_like(lat_line, lon0)
 
-        xyz_line = np.column_stack([
-            np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
-            np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
-            np.sin(np.radians(lat_line))
-        ])
+        xyz_line = np.column_stack(
+            [
+                np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
+                np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
+                np.sin(np.radians(lat_line)),
+            ]
+        )
         xyz_line_rot = rot.apply(xyz_line)
 
-        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:,2]))
-        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:,1], xyz_line_rot[:,0]))
+        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:, 2]))
+        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:, 1], xyz_line_rot[:, 0]))
         lon_rot_line = (lon_rot_line + 180) % 360 - 180
         # flip for inside view
 
         jump_idx = np.where(np.abs(np.diff(lon_rot_line)) > 180)[0]
-        split_idx = np.append(jump_idx, len(lon_rot_line)-1)
+        split_idx = np.append(jump_idx, len(lon_rot_line) - 1)
         start = 0
 
         # Find the row closest to 0° rotated latitude
-        lat0_idx = np.argmin(np.abs(lat_rot_line))  # lat_rot_line = your rotated lat array for the line
+        lat0_idx = np.argmin(
+            np.abs(lat_rot_line)
+        )  # lat_rot_line = your rotated lat array for the line
 
         # Get the corresponding longitude and latitude along that row
         lon_line = lon_rot_line[lat0_idx]
         lat_line = lat_rot_line[lat0_idx]
 
         for idx in split_idx:
-            seg_lon = lon_rot_line[start:idx+1]
-            seg_lat = lat_rot_line[start:idx+1]
-            ax.plot(seg_lon, seg_lat, transform=ccrs.PlateCarree(),
-                    color='orange', lw=0.8, alpha=0.7)
-        
-            mid_idx = len(seg_lon) // 2 
+            seg_lon = lon_rot_line[start : idx + 1]
+            seg_lat = lat_rot_line[start : idx + 1]
+            ax.plot(
+                seg_lon,
+                seg_lat,
+                transform=ccrs.PlateCarree(),
+                color="orange",
+                lw=0.8,
+                alpha=0.7,
+            )
+
+            mid_idx = len(seg_lon) // 2
             lon_plot = seg_lon[mid_idx]
             lat_plot = seg_lat[mid_idx]
 
-            start = idx+1
+            start = idx + 1
 
-            xyz_rot = sph_to_cart(lat_plot, lon_plot)   # returns (3,) or (1,3)
+            xyz_rot = sph_to_cart(lat_plot, lon_plot)  # returns (3,) or (1,3)
             # print(xyz_rot)
 
             # ensure shape (1,3) for scipy Rotation
             xyz_rot = np.atleast_2d(xyz_rot)
             #    print(xyz_rot)
 
-        # --- invert rotation ---
+            # --- invert rotation ---
             xyz_orig = rot.inv().apply(xyz_rot)
 
-        # --- back to spherical ---
+            # --- back to spherical ---
             lat_label = np.degrees(np.arcsin(xyz_orig[0, 2]))
             lon_label = np.degrees(np.arctan2(xyz_orig[0, 1], xyz_orig[0, 0]))
 
@@ -333,34 +407,38 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, opt_point
             xyz_rot_label = rot.apply(xyz)
             lat_plot = np.degrees(np.arcsin(xyz_rot_label[2]))
 
-            print('lon_label = ',lon_label)
-    #        lon_label = (-1.0*lon_label + 180) % 360 - 180
-    #        print('lon_label (2) = ',lon_label)
+            print("lon_label = ", lon_label)
+            #        lon_label = (-1.0*lon_label + 180) % 360 - 180
+            #        print('lon_label (2) = ',lon_label)
             lon_plot = -lon_plot
             if np.abs(lat_label) < 30.0:
-                ax.text(lon_plot, lat_plot,
-                            f"{lon_label:.0f}°",
-                            transform=ccrs.PlateCarree(),
-                            color='orange', fontsize=6,
-                            verticalalignment='bottom', horizontalalignment='right')
-
-    if (opt_points > 0):
-
+                ax.text(
+                    lon_plot,
+                    lat_plot,
+                    f"{lon_label:.0f}°",
+                    transform=ccrs.PlateCarree(),
+                    color="orange",
+                    fontsize=6,
+                    verticalalignment="bottom",
+                    horizontalalignment="right",
+                )
+
+    if opt_points > 0:
         labels, lon_points, lat_points = make_imap_lo_reference_points(rot, opt_points)
         npoints = len(labels)
-        for i in range(0,npoints):
+        for i in range(npoints):
             lon_ref_rot = lon_points[i]
             lat_ref_rot = lat_points[i]
             ref_label = labels[i]
-        # --- Plot marker ---
+            # --- Plot marker ---
             ax.plot(
                 lon_ref_rot,
                 lat_ref_rot,
-                marker='o',
+                marker="o",
                 markersize=1,
-                color='white',
+                color="white",
                 transform=ccrs.PlateCarree(),
-                zorder=5
+                zorder=5,
             )
 
             # --- Add text ---
@@ -369,50 +447,51 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, opt_point
                 lat_ref_rot,
                 f" {ref_label}",
                 transform=ccrs.PlateCarree(),
-                color='white',
+                color="white",
                 fontsize=6,
-                ha='left',
-                va='center',
-                zorder=5
+                ha="left",
+                va="center",
+                zorder=5,
             )
 
-# now optionally add the great circle line
-
-    if (opt_ribbon > 0):
+    # now optionally add the great circle line
 
-        lat_line, lon_line = somewhat_great_circle_centered( rot,
-            lat0=39,
-            lon0=221.0,
-            angle=70-15
+    if opt_ribbon > 0:
+        lat_line, lon_line = somewhat_great_circle_centered(
+            rot, lat0=39, lon0=221.0, angle=70 - 15
         )
 
         plot_wrapped_line(
-                ax,
-                -lon_line,     # inside view flip if you use it
-                lat_line,
-                color='white',
-                lw=0.5
+            ax,
+            -lon_line,  # inside view flip if you use it
+            lat_line,
+            color="white",
+            lw=0.5,
         )
 
-
-        lat_line, lon_line = somewhat_great_circle_centered( rot,
-            lat0=39,
-            lon0=221.0,
-            angle=70+15
+        lat_line, lon_line = somewhat_great_circle_centered(
+            rot, lat0=39, lon0=221.0, angle=70 + 15
         )
 
         plot_wrapped_line(
-                ax,
-                -lon_line,     # inside view flip if you use it
-                lat_line,
-                color='white',
-                lw=0.5
+            ax,
+            -lon_line,  # inside view flip if you use it
+            lat_line,
+            color="white",
+            lw=0.5,
         )
 
-
-    plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, label='Flux')
+    plt.colorbar(pcm, ax=ax, orientation="horizontal", pad=0.05, label="Flux")
     ax.set_global()
-   # plt.show()
-    plt.savefig(output, dpi=200, facecolor='w', edgecolor='w', orientation='portrait', format=None, transparent=False, bbox_inches=None, pad_inches=0.1)
-
-
+    # plt.show()
+    plt.savefig(
+        output,
+        dpi=200,
+        facecolor="w",
+        edgecolor="w",
+        orientation="portrait",
+        format=None,
+        transparent=False,
+        bbox_inches=None,
+        pad_inches=0.1,
+    )
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv2yaml.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv2yaml.py
index a22cdf71c..deb657135 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv2yaml.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv2yaml.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Wed Feb 25 17:06:47 2026
 
@@ -10,15 +9,7 @@
 import os
 from datetime import datetime, timedelta, timezone
 
-esa_energy = {
-    1: 16,
-    2: 30,
-    3: 56,
-    4: 106,
-    5: 200,
-    6: 404,
-    7: 787
-}
+esa_energy = {1: 16, 2: 30, 3: 56, 4: 106, 5: 200, 6: 404, 7: 787}
 
 label_map = {
     "cnts": "Counts",
@@ -31,7 +22,7 @@
     "fvar": "Variance Intensity (#/cm4 s2 sr2 keV2)",
     "runc": "Unc Rate",
     "rvar": "Variance Rate (#/s2)",
-    "flux": "Intensity (#/cm2 s sr keV)"
+    "flux": "Intensity (#/cm2 s sr keV)",
 }
 
 
@@ -43,13 +34,14 @@ def latest_monday_tag():
     days_since_monday = today.weekday()
     last_monday = today - timedelta(days=days_since_monday)
 
-    year = last_monday.strftime('%Y')
+    year = last_monday.strftime("%Y")
     year_short = last_monday.strftime("%y")
     doy = last_monday.strftime("%j")
-    
-    end_day = last_monday.strftime('%Y%m%d')
 
-    return year,end_day,f"v{year_short}DOY{doy}"
+    end_day = last_monday.strftime("%Y%m%d")
+
+    return year, end_day, f"v{year_short}DOY{doy}"
+
 
 def csv_to_map_yaml(
     csv_file,
@@ -61,22 +53,20 @@ def csv_to_map_yaml(
     uniqueness_index=0,
     label="Flux",
     pivot="90",
-    metadata="flux"
+    metadata="flux",
 ):
     data_rows = []
 
-
     with open(csv_file, newline="") as f:
         reader = csv.reader(f)
         header = next(reader)  # 10–59, not used
         for row in reader:
             formatted_row = [
-                f"{float(x):.3E}" if float(x) != 0 else "0.000E+00"
-                for x in row
+                f"{float(x):.3E}" if float(x) != 0 else "0.000E+00" for x in row
             ]
             data_rows.append(formatted_row)
-            
-    _,_,tag = latest_monday_tag()
+
+    _, _, tag = latest_monday_tag()
 
     # ----------------------------
     # Write YAML-like output
@@ -107,76 +97,89 @@ def csv_to_map_yaml(
         for row in data_rows:
             f.write("- [" + ", ".join(row) + "]\n")
 
-year,end_day,tag = latest_monday_tag()
-start_day = '20251108'
-sec_layr = f'{start_day}_{end_day}_lo_{year}'
 
-for pp in [75,90,105]:
+year, end_day, tag = latest_monday_tag()
+start_day = "20251108"
+sec_layr = f"{start_day}_{end_day}_lo_{year}"
+
+for pp in [75, 90, 105]:
     if pp == 75:
-        pp_str = 'p075'
+        pp_str = "p075"
     if pp == 90:
-        pp_str = 'p090'
+        pp_str = "p090"
     if pp == 105:
-        pp_str = 'p105'
-    work_dir1 = f'./outdir/pivot_{pp}/maps'
+        pp_str = "p105"
+    work_dir1 = f"./outdir/pivot_{pp}/maps"
     # yaml_dir_flux = f'./outdir/pivot_{pp}/yaml/cava_txt_lo_H_{tag}{pp_str}F/{sec_layr}_flux'
     # yaml_dir_rate = f'./outdir/pivot_{pp}/yaml/cava_txt_lo_H_{tag}{pp_str}R/{sec_layr}_rate'
     # yaml_dir_cnts = f'./outdir/pivot_{pp}/yaml/cava_txt_lo_H_{tag}{pp_str}C/{sec_layr}_cnts'
-    
-    yaml_dir_flux = f'./outdir/cava/cava_txt_lo_H_{tag}{pp_str}F/{sec_layr}_flux'
-    yaml_dir_rate = f'./outdir/cava/cava_txt_lo_H_{tag}{pp_str}R/{sec_layr}_rate'
-    yaml_dir_cnts = f'./outdir/cava/cava_txt_lo_H_{tag}{pp_str}C/{sec_layr}_cnts'
-    
+
+    yaml_dir_flux = f"./outdir/cava/cava_txt_lo_H_{tag}{pp_str}F/{sec_layr}_flux"
+    yaml_dir_rate = f"./outdir/cava/cava_txt_lo_H_{tag}{pp_str}R/{sec_layr}_rate"
+    yaml_dir_cnts = f"./outdir/cava/cava_txt_lo_H_{tag}{pp_str}C/{sec_layr}_cnts"
+
     os.makedirs(yaml_dir_flux, exist_ok=True)
     os.makedirs(yaml_dir_rate, exist_ok=True)
     os.makedirs(yaml_dir_cnts, exist_ok=True)
-    
-    for esa in range(1,8):
-        print(pp,esa)
-        for tt in ["expo","rate","flux","fvar","fser","rvar","cnts"]:#,"stbg","func","runc",,"rvar"]:
-                filename = os.path.join(work_dir1, f"map_{tt}_esa{esa}.csv")
-                energy = esa_energy[esa]
-                label = label_map[tt]
-                base_filename = os.path.splitext(os.path.basename(filename))[0]
-                
-                if tt=='expo':
-                    tt='exposure'
-                    outputs = [ os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt"),
-                                os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ] 
-                if tt=='cnts':
-                    outputs = [ os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-flux.txt"),
-                                os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-exposure.txt"), 
-                                os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-variance.txt")] 
-                
-                if tt=="rvar":
-                    tt='variance'
-                    outputs = [ os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                    
-                if tt=="fvar":
-                    tt='variance'
-                    outputs = [ os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                
-                if tt=="fser":
-                    outputs = [ os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                    
-                if tt=="flux":
-                    tt='flux'
-                    outputs = [ os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                    
-                if tt =='rate':
-                    tt='flux'       
-                    outputs = [ os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                
-                for output in outputs:
-                    name = output
-                    fst_str = name.split('/')[-2]
-                    des = fst_str.split('_')[-1]
-                    csv_to_map_yaml(
-                        csv_file=filename,
-                        output_file=output,
-                        energy_value=energy,
-                        map_year=year,
-                        label=label,
-                        pivot=pp,
-                        metadata=des
-                    )
+
+    for esa in range(1, 8):
+        print(pp, esa)
+        for tt in [
+            "expo",
+            "rate",
+            "flux",
+            "fvar",
+            "fser",
+            "rvar",
+            "cnts",
+        ]:  # ,"stbg","func","runc",,"rvar"]:
+            filename = os.path.join(work_dir1, f"map_{tt}_esa{esa}.csv")
+            energy = esa_energy[esa]
+            label = label_map[tt]
+            base_filename = os.path.splitext(os.path.basename(filename))[0]
+
+            if tt == "expo":
+                tt = "exposure"
+                outputs = [
+                    os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt"),
+                    os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt"),
+                ]
+            if tt == "cnts":
+                outputs = [
+                    os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-flux.txt"),
+                    os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-exposure.txt"),
+                    os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-variance.txt"),
+                ]
+
+            if tt == "rvar":
+                tt = "variance"
+                outputs = [os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "fvar":
+                tt = "variance"
+                outputs = [os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "fser":
+                outputs = [os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "flux":
+                tt = "flux"
+                outputs = [os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "rate":
+                tt = "flux"
+                outputs = [os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            for output in outputs:
+                name = output
+                fst_str = name.split("/")[-2]
+                des = fst_str.split("_")[-1]
+                csv_to_map_yaml(
+                    csv_file=filename,
+                    output_file=output,
+                    energy_value=energy,
+                    map_year=year,
+                    label=label,
+                    pivot=pp,
+                    metadata=des,
+                )
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv_to_soc.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv_to_soc.py
index 38aed9b3c..923367be0 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv_to_soc.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/csv_to_soc.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Sat Jan  3 21:04:46 2026
 
@@ -7,16 +6,17 @@
 """
 
 import os
-import glob
+from datetime import datetime
 from pathlib import Path
-import pandas as pd
+
 import numpy as np
-from datetime import datetime
+import pandas as pd
 
 # -----------------------------
 # Configuration
 # -----------------------------
 
+
 # -----------------------------
 # Helper: infer file type
 # -----------------------------
@@ -27,16 +27,14 @@ def infer_file_type(filename):
             return key
     return "unknown"
 
+
 # -----------------------------
 # Header builder
 # -----------------------------
 def build_header(file_type, hmin, hmax):
     now = datetime.now().strftime("%a %b %d %H:%M:%S %Y")
 
-    meta = TYPE_MAP.get(
-        file_type,
-        {"h_title": "Unknown", "desc": "unknown"}
-    )
+    meta = TYPE_MAP.get(file_type, {"h_title": "Unknown", "desc": "unknown"})
 
     header_lines = [
         "27:30x60:-5.0x-5.0:7x6:0:9",
@@ -59,93 +57,76 @@ def build_header(file_type, hmin, hmax):
         " chat=0 smearspread='0/0/0' calc='0/0/0'",
         " frame_epoch=914561779.587 resp_class=lo_triple "
         "zaxis_ra_deg=+274.476346 zaxis_dec_deg=-22.782473",
-        " ram_ra_deg=+186.7725 ram_dec_deg=-3.2847 "
-        "mtype_list='40' energy_list='21,41'",
-        " check_mt_list='40' check_species='20' rate_factor='1000' "
-        "e_nominal='0.015'",
+        " ram_ra_deg=+186.7725 ram_dec_deg=-3.2847 mtype_list='40' energy_list='21,41'",
+        " check_mt_list='40' check_species='20' rate_factor='1000' e_nominal='0.015'",
+        "",
         "",
         "",
         "",
         "",
         "",
-        ""
     ]
 
     return ["# " + line for line in header_lines[:27]]
 
-for pp in [75,90,105]:
-    
-    INPUT_DIR = f'./outdir/pivot_{pp}/maps'
-    OUTPUT_DIR = f'./outdir/pivot_{pp}/txt_maps/'
+
+for pp in [75, 90, 105]:
+    INPUT_DIR = f"./outdir/pivot_{pp}/maps"
+    OUTPUT_DIR = f"./outdir/pivot_{pp}/txt_maps/"
 
     os.makedirs(OUTPUT_DIR, exist_ok=True)
-    
+
     # Mapping file type → header description
     TYPE_MAP = {
-        "cats": {
-            "h_title": "Total Counts",
-            "desc": "counts"
-        },
-        "expo": {
-            "h_title": "Exposure Time",
-            "desc": "exposure (s)"
-        },
-        "flux": {
-            "h_title": "Flux",
-            "desc": "flux (1 / cm^2 s sr keV)"
-        },
-        "rate": {
-            "h_title": "Rate",
-            "desc": "rate ( 1/s)"
-        }
+        "cats": {"h_title": "Total Counts", "desc": "counts"},
+        "expo": {"h_title": "Exposure Time", "desc": "exposure (s)"},
+        "flux": {"h_title": "Flux", "desc": "flux (1 / cm^2 s sr keV)"},
+        "rate": {"h_title": "Rate", "desc": "rate ( 1/s)"},
     }
-    
-    
+
     # -----------------------------
     # Batch processing
     # -----------------------------
     csv_files = Path(INPUT_DIR).glob("*.csv")
-    
+
     input_dir = Path(INPUT_DIR)
-    
+
     # if not csv_files:
     #     raise RuntimeError(f"No CSV files found in {INPUT_DIR}")
-    
-    for csv_file in input_dir.glob("*.csv"):
 
+    for csv_file in input_dir.glob("*.csv"):
         if "manifest" in csv_file.name:
             continue
 
         fname = os.path.basename(csv_file)
-    
+
         # Infer type from filename
         file_type = infer_file_type(fname)
-    
+
         # Read CSV
         df = pd.read_csv(csv_file)
 
         data = df.to_numpy(dtype=float)
-        #data = df.iloc[:, :60].to_numpy(dtype=float)
-    
+        # data = df.iloc[:, :60].to_numpy(dtype=float)
+
         if data.shape != (30, 60):
             raise Warning(f"{fname}: Expected 30x60, got {data.shape}")
             continue
-    
+
         # Compute h_min / h_max
         h_min = np.nanmin(data)
         h_max = np.nanmax(data)
-    
+
         # Build header
         header = build_header(file_type, h_min, h_max)
-    
+
         # Output filename
         out_name = os.path.splitext(fname)[0] + ".txt"
         out_path = os.path.join(OUTPUT_DIR, out_name)
-    
+
         # with open(f'/Users/hafijulislam/UNH/csv_to_isoc/converted/map_cnts_esa{esa}.txt', "w") as f:
-        with open(out_path, "w") as f:    
+        with open(out_path, "w") as f:
             f.write("\n".join(header) + "\n")
-            for row in data:
-                f.write("\t".join(f"{val:.6e}" for val in row) + "\n")
-    
+            f.writelines("\t".join(f"{val:.6e}" for val in row) + "\n" for row in data)
+
     print(f"Converted Maps for Pivot {pp}")
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/l1b_to_spin.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/l1b_to_spin.py
index 4e75345fe..6d9d108ec 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/l1b_to_spin.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/l1b_to_spin.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Sat Feb  7 11:53:09 2026
 
@@ -7,120 +6,152 @@
 
 This code produce spin angle distribution from the l1b histograms.
 """
-from spacepy.pycdf import CDF
-import numpy as np
+
 import os
+import re
+import shutil
 import sys
-import argparse
 from datetime import datetime
 from pathlib import Path
-import pandas as pd, re, shutil
 
-def radec2cart(ra,th):
+import numpy as np
+import pandas as pd
+from spacepy.pycdf import CDF
+
+
+def radec2cart(ra, th):
 
     x = np.cos(np.radians(ra)) * np.sin(np.radians(th))
     y = np.sin(np.radians(ra)) * np.sin(np.radians(th))
     z = np.cos(np.radians(th))
-    
-    return np.array([x,y,z])
+
+    return np.array([x, y, z])
+
 
 def equatorial_to_ecliptic(alpha, delta):
-    
+
     # Obliquity of ecliptic at J2000.0 epoch
     epsilon_0 = 23.43929111  # degrees
-    
+
     alpha_rad = np.radians(alpha)
     delta_rad = np.radians(delta)
     epsilon_rad = np.radians(epsilon_0)
-    
-    numerator = np.sin(alpha_rad) * np.cos(epsilon_rad) + np.tan(delta_rad) * np.sin(epsilon_rad)
+
+    numerator = np.sin(alpha_rad) * np.cos(epsilon_rad) + np.tan(delta_rad) * np.sin(
+        epsilon_rad
+    )
     denominator = np.cos(alpha_rad)
-    
+
     # Calculate ecliptic longitude
     lambda_ecl_rad = np.arctan2(numerator, denominator)
-    
+
     # Calculate ecliptic latitude
-    beta_ecl_rad = np.arcsin(np.sin(delta_rad) * np.cos(epsilon_rad) - 
-                            np.cos(delta_rad) * np.sin(epsilon_rad) * np.sin(alpha_rad))
-    
+    beta_ecl_rad = np.arcsin(
+        np.sin(delta_rad) * np.cos(epsilon_rad)
+        - np.cos(delta_rad) * np.sin(epsilon_rad) * np.sin(alpha_rad)
+    )
 
     lambda_ecl = np.degrees(lambda_ecl_rad)
     beta_ecl = np.degrees(beta_ecl_rad)
 
     lambda_ecl = lambda_ecl % 360
-    
+
     return lambda_ecl, beta_ecl
 
-def create_ra_dec(s_ra,s_dec,pivot_angle):
-    
-    spin_ra,spin_dec = s_ra,s_dec # Using inertial pointing file give spin axis information
+
+def create_ra_dec(s_ra, s_dec, pivot_angle):
+
+    spin_ra, spin_dec = (
+        s_ra,
+        s_dec,
+    )  # Using inertial pointing file give spin axis information
     spin_th = 90.0 - spin_dec
 
-    nep_ra,nep_dec = 270, 66.56 # Standard NEP direction in J2000
+    nep_ra, nep_dec = 270, 66.56  # Standard NEP direction in J2000
     nep_th = 90.0 - nep_dec
 
     a_spine_deg = pivot_angle
-    a_spine = np.radians(a_spine_deg) 
-    
+    a_spine = np.radians(a_spine_deg)
+
     def norm(v):
         return v / np.linalg.norm(v)
 
-    e_nep      = norm(radec2cart(nep_ra, nep_th))
-    e_av_spin  = norm(radec2cart(spin_ra, spin_th))
-    
+    e_nep = norm(radec2cart(nep_ra, nep_th))
+    e_av_spin = norm(radec2cart(spin_ra, spin_th))
+
     e_perp_pole = e_nep - np.dot(e_nep, e_av_spin) * e_av_spin
     e_perp_pole = norm(e_perp_pole)
-    
-    e_perp_ram  = np.cross(e_av_spin, e_perp_pole)
-    e_perp_ram  = norm(e_perp_ram)
-    
-    bin_edges = np.arange(0,366,6)
-    bin_centers = 0.5 * (bin_edges[1:]+bin_edges[:-1])
-    raf,decf = [],[]
+
+    e_perp_ram = np.cross(e_av_spin, e_perp_pole)
+    e_perp_ram = norm(e_perp_ram)
+
+    bin_edges = np.arange(0, 366, 6)
+    bin_centers = 0.5 * (bin_edges[1:] + bin_edges[:-1])
+    raf, decf = [], []
     for i in bin_centers:
-        
-        x,y,z = np.cos(a_spine)*e_av_spin+np.sin(a_spine)*np.cos(np.radians(i))*e_perp_pole + np.sin(a_spine)*np.sin(np.radians(i))*e_perp_ram
+        x, y, z = (
+            np.cos(a_spine) * e_av_spin
+            + np.sin(a_spine) * np.cos(np.radians(i)) * e_perp_pole
+            + np.sin(a_spine) * np.sin(np.radians(i)) * e_perp_ram
+        )
 
-        eq_ra = np.degrees(np.arctan2(y,x))
-        eq_dec =np.degrees( np.arcsin(z))
-        ra,dec = equatorial_to_ecliptic(eq_ra,eq_dec)
+        eq_ra = np.degrees(np.arctan2(y, x))
+        eq_dec = np.degrees(np.arcsin(z))
+        ra, dec = equatorial_to_ecliptic(eq_ra, eq_dec)
         raf.append(ra)
         decf.append(dec)
-    
-    return raf,decf
-
-def estimate_exposure_time(filename,YD, esa):
-    cols = ["YD","gd_start","gd_end","bin_start","bin_end","Instrument",	"E-Step1",	"E-Step2",	"E-Step3",	"E-Step4",	"E-Step5",	"E-Step6",	"E-Step7",	"Comment"]
-    
-    df1 = pd.read_csv(filename,names=cols)
-    
-    df = df1[df1['YD']==YD]
+
+    return raf, decf
+
+
+def estimate_exposure_time(filename, YD, esa):
+    cols = [
+        "YD",
+        "gd_start",
+        "gd_end",
+        "bin_start",
+        "bin_end",
+        "Instrument",
+        "E-Step1",
+        "E-Step2",
+        "E-Step3",
+        "E-Step4",
+        "E-Step5",
+        "E-Step6",
+        "E-Step7",
+        "Comment",
+    ]
+
+    df1 = pd.read_csv(filename, names=cols)
+
+    df = df1[df1["YD"] == YD]
     if df.empty:
         raise ValueError(f"No matching rows found for {YD} in Goodtime file")
-    
-    result = np.zeros((7,60))
+
+    result = np.zeros((7, 60))
     start_arr = []
     end_arr = []
-    
+
     for _, row in df.iterrows():
         # Ensure numerical types for subtraction; convert if needed
-        gd_end = float(row['gd_end'])
-        gd_start = float(row['gd_start'])
+        gd_end = float(row["gd_end"])
+        gd_start = float(row["gd_start"])
         start_arr.append(gd_start)
         end_arr.append(gd_end)
         dt = (gd_end - gd_start) / (7 * 60)
         for i in range(1, 7 + 1):
-            dist = dt * row[f'E-Step{i}']
+            dist = dt * row[f"E-Step{i}"]
             arr = np.zeros(60)
-            arr[int(row['bin_start']):int(row['bin_end']) + 1] = dist
-            result[i-1] += arr
-    
+            arr[int(row["bin_start"]) : int(row["bin_end"]) + 1] = dist
+            result[i - 1] += arr
+
     # Convert to DataFrame for clarity
     bin_indices = np.arange(60)
-    result_df = pd.DataFrame(result.T, columns=[f'E-Step{i}' for i in range(1, 7+1)])
-    result_df['bin'] = bin_indices
-    
-    return start_arr,end_arr,result_df[f'E-Step{esa}']
+    result_df = pd.DataFrame(result.T, columns=[f"E-Step{i}" for i in range(1, 7 + 1)])
+    result_df["bin"] = bin_indices
+
+    return start_arr, end_arr, result_df[f"E-Step{esa}"]
+
 
 def route_by_pivot(data_dir, pivot_csv):
 
@@ -132,140 +163,147 @@ def route_by_pivot(data_dir, pivot_csv):
         if g and int(g.group(1)) in m:
             shutil.move(str(f), Path(data_dir) / f"pivot_{m[int(g.group(1))]}" / f.name)
 
+
 ## Input files
 
 # file_path = '/Users/hafijulislam/UNH/imap-data-access/l1b_histrates/imap_lo_l1b_histrates_20251214-repoint00078_v001.cdf'
 # file_path = '/Users/hafijulislam/Library/CloudStorage/GoogleDrive-hislam09@gmail.com/My Drive/UNH/imap-data-access/l1b_histrates/imap_lo_l1b_histrates_20260114-repoint00126_v001.cdf'
-data_dir_path = '../input_l1b_histrates'
+data_dir_path = "../input_l1b_histrates"
 data_dir = Path(data_dir_path)
 
 ## Make output directories
-for x in [75,90,105]:
-    os.makedirs(f'./outdir/pivot_{x}/daily', exist_ok=True)
+for x in [75, 90, 105]:
+    os.makedirs(f"./outdir/pivot_{x}/daily", exist_ok=True)
 
 for file in data_dir.glob("*.cdf"):
     try:
         file_path = str(file)
-        
+
         # basename = os.path.basename(file_path)
         basename = file.name
-        
-        pointing_file = './config_files/pointing_file.csv'
-        goodtime_file = './config_files/imap_lo_goodtimes_2.csv'
+
+        pointing_file = "./config_files/pointing_file.csv"
+        goodtime_file = "./config_files/imap_lo_goodtimes_2.csv"
         pivot_csv = "./config_files/share_pivot.csv"
-        
+
         for f in [pointing_file, goodtime_file, pivot_csv]:
             if not os.path.exists(f):
                 print(f"File not found: {f}")
                 sys.exit(1)
-        
-        pointing_cols = ['YD', 'spin_ra','spin_dec']
+
+        pointing_cols = ["YD", "spin_ra", "spin_dec"]
         df_point = pd.read_csv(pointing_file, names=pointing_cols, skiprows=1)
-        
+
         ## Convert yyyymmdd to YYYYDOY
-        
-        yymmdd = basename.split('_')[4].split('-')[0]
+
+        yymmdd = basename.split("_")[4].split("-")[0]
         date = datetime.strptime(yymmdd, "%Y%m%d")
         YD = f"{date.year}{date.timetuple().tm_yday:03d}"
         int_YD = int(YD)
-        
+
         print(f"Processing DOY: {YD}")
-        
+
         ## Grab spin axis information from the pointing file
-        
-        df_p = df_point[df_point['YD']==int_YD]
+
+        df_p = df_point[df_point["YD"] == int_YD]
         if df_p.empty:
             raise ValueError(f"No matching rows found for {YD} in the pointing file")
-        
-        s_ra = df_p['spin_ra'].astype(float).values[0]
-        s_dec = df_p['spin_dec'].astype(float).values[0]
-        
+
+        s_ra = df_p["spin_ra"].astype(float).values[0]
+        s_dec = df_p["spin_dec"].astype(float).values[0]
+
         ### -----------------
         df_pivot = pd.read_csv(pivot_csv)
-        df_pp=df_pivot[df_pivot['DOY']==int_YD]
-        pivot = df_pp['Pivot'].astype(float).values[0]
-        
+        df_pp = df_pivot[df_pivot["DOY"] == int_YD]
+        pivot = df_pp["Pivot"].astype(float).values[0]
+
         PIVOT_ANGLE = pivot
-        
+
         pivot_str = f"pivot_{int(pivot)}"
-        
-        ra,dec = create_ra_dec(s_ra,s_dec,PIVOT_ANGLE)
-        
+
+        ra, dec = create_ra_dec(s_ra, s_dec, PIVOT_ANGLE)
+
         cdf = CDF(file_path)
-        
-        
-        for ESA in range(1,8):
-            
-            nep_cnts = np.zeros((60))
-            nep_expo = np.zeros((60))
-            
+
+        for ESA in range(1, 8):
+            nep_cnts = np.zeros(60)
+            nep_expo = np.zeros(60)
+
             ## Filter through Goodtime, create masking
-            start_arr,end_arr,expo = estimate_exposure_time(goodtime_file ,int_YD, ESA)
-            
+            start_arr, end_arr, expo = estimate_exposure_time(
+                goodtime_file, int_YD, ESA
+            )
+
             met_epoch = datetime(2010, 1, 1, 0, 0, 0)
-            epoch_sec = cdf['epoch'][:]
+            epoch_sec = cdf["epoch"][:]
             met_sec = []
-            
+
             for x in range(len(epoch_sec)):
-                ss = (epoch_sec[x]-met_epoch).total_seconds()
+                ss = (epoch_sec[x] - met_epoch).total_seconds()
                 met_sec.append(ss)
-            
-            met_sec = np.asarray(met_sec, dtype=float)    
-            mask = np.zeros_like(epoch_sec,dtype=bool)
-            
+
+            met_sec = np.asarray(met_sec, dtype=float)
+            mask = np.zeros_like(epoch_sec, dtype=bool)
+
             for start, end in zip(start_arr, end_arr):
                 mask |= (met_sec >= start) & (met_sec <= end)
-            
+
             ### Extract values from cdf
-            esa = ESA-1
-            hcnts = cdf['h_counts'][...][mask,esa,:]
-            exposure = np.sum(cdf['exposure_time_6deg'][...][mask,esa,:].T,axis=1)
-            
+            esa = ESA - 1
+            hcnts = cdf["h_counts"][...][mask, esa, :]
+            exposure = np.sum(cdf["exposure_time_6deg"][...][mask, esa, :].T, axis=1)
+
             ## If No filter is appled then uncomment below
             # hcnts = cdf['h_counts'][...][:,esa,:]
             # exposure = np.sum(cdf['exposure_time_6deg'][...][:,esa,:].T,axis=1)
-            
+
             ## Sum over filtered time blocks
-            hcnts = np.sum(hcnts.T,axis=1)
-            
+            hcnts = np.sum(hcnts.T, axis=1)
+
             ## Convert from Spin Angle to NEP angle
             nep_cnts[0:10] = hcnts[50:60]
             nep_cnts[10:30] = hcnts[0:20]
             nep_cnts[30:60] = hcnts[20:50]
-            
+
             nep_expo[0:10] = exposure[50:60]
             nep_expo[10:30] = exposure[0:20]
             nep_expo[30:60] = exposure[20:50]
-            
-            nep_rates = nep_cnts/nep_expo
-            
-            nep_angles = np.linspace(0,360,61)
+
+            nep_rates = nep_cnts / nep_expo
+
+            nep_angles = np.linspace(0, 360, 61)
             bin_centers = 0.5 * (nep_angles[1:] + nep_angles[:-1])
-            
+
             ## Spin axis into ECLIPJ2000
-            seq_ra,seq_dec = equatorial_to_ecliptic(s_ra,s_dec)
-            
+            seq_ra, seq_dec = equatorial_to_ecliptic(s_ra, s_dec)
+
             df_new = pd.DataFrame()
-            
-            df_new['bins'] = bin_centers
-            df_new['counts'] = nep_cnts
-            df_new['ra'] = ra
-            df_new['dec'] = dec
-            df_new['expo'] = nep_expo
-            df_new['spin_ra'] = seq_ra
-            df_new['spin_dec'] = seq_dec
+
+            df_new["bins"] = bin_centers
+            df_new["counts"] = nep_cnts
+            df_new["ra"] = ra
+            df_new["dec"] = dec
+            df_new["expo"] = nep_expo
+            df_new["spin_ra"] = seq_ra
+            df_new["spin_dec"] = seq_dec
 
             # Provenance columns for map manifest
-            df_new['date_yyyymmdd'] = yymmdd
-            df_new['yd'] = YD
-            df_new['repoint'] = basename.split("-repoint")[1].split("_")[0] if "-repoint" in basename else ""
-            df_new['pivot'] = int(pivot)
-            df_new['l1b_product'] = "histrates"
-            df_new['l1b_filename'] = basename
-            df_new['l1b_path'] = str(Path(file_path).resolve())
-
-            df_new.to_csv(f"./outdir/{pivot_str}/daily/data_YD_{YD}_esa{ESA}.csv", index=False)
-        
+            df_new["date_yyyymmdd"] = yymmdd
+            df_new["yd"] = YD
+            df_new["repoint"] = (
+                basename.split("-repoint")[1].split("_")[0]
+                if "-repoint" in basename
+                else ""
+            )
+            df_new["pivot"] = int(pivot)
+            df_new["l1b_product"] = "histrates"
+            df_new["l1b_filename"] = basename
+            df_new["l1b_path"] = str(Path(file_path).resolve())
+
+            df_new.to_csv(
+                f"./outdir/{pivot_str}/daily/data_YD_{YD}_esa{ESA}.csv", index=False
+            )
+
     except Exception as e:
         print(f"Skipping {file.name}: {e}")
 
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/map_SCFrame.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/map_SCFrame.py
index 91f7f8853..5c9451cc3 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/map_SCFrame.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/map_SCFrame.py
@@ -1,4 +1,3 @@
-
 #!/usr/bin/python
 """
 Created on Wed Mar 18 2026
@@ -6,27 +5,23 @@
 
 """
 
-import numpy as np
-import argparse
-from scipy.optimize import curve_fit
-import random
-import datetime
-from spacepy.pycdf import CDF
-import pandas as pd
 import os
 from pathlib import Path
 
+import numpy as np
+import pandas as pd
+
 PI = np.pi
 
 # ESA Steps
 nesa = 7
 nalpha = 40
 nphi = 3600
-dang = 0.1 * PI /180.0
+dang = 0.1 * PI / 180.0
 deg = 6.0
 nra = 60
 ncolat = 30
-nmap = nra*ncolat*nesa
+nmap = nra * ncolat * nesa
 
 eff_h = 1.0
 
@@ -38,7 +33,7 @@
     5: 0.200,
     6: 0.405,
     7: 0.787,
-    8: 1.821
+    8: 1.821,
 }
 
 gf = {
@@ -49,7 +44,7 @@
     5: 14.0e-5,
     6: 17.7e-5,
     7: 22.5e-5,
-    8: 6.721e-5
+    8: 6.721e-5,
 }
 
 dg = {
@@ -60,7 +55,7 @@
     5: 4.5e-5,
     6: 2.0e-5,
     7: 1.4e-5,
-    8: 6.721e-5
+    8: 6.721e-5,
 }
 
 ox_gf = {
@@ -88,60 +83,80 @@
 
 ## Get background Rate
 
-def get_brate(YD,esa):
-    
-    backfile = './config_files/imap_lo_H_background.csv'
-    
-    df = pd.read_csv(backfile,sep=',', names=['YD','start','end','bin_start','bin_end','Lo','esa1','esa2','esa3','esa4','esa5','esa6','esa7','type'])
-    
-    brate = df[(df['YD']==int(YD)) & (df['type']=='rate')][f'esa{esa}'].values[0]
-    
+
+def get_brate(YD, esa):
+
+    backfile = "./config_files/imap_lo_H_background.csv"
+
+    df = pd.read_csv(
+        backfile,
+        sep=",",
+        names=[
+            "YD",
+            "start",
+            "end",
+            "bin_start",
+            "bin_end",
+            "Lo",
+            "esa1",
+            "esa2",
+            "esa3",
+            "esa4",
+            "esa5",
+            "esa6",
+            "esa7",
+            "type",
+        ],
+    )
+
+    brate = df[(df["YD"] == int(YD)) & (df["type"] == "rate")][f"esa{esa}"].values[0]
+
     return brate
 
-for pp in [75,90,105]:
+
+for pp in [75, 90, 105]:
     pivot_deg = pp + 4.0
     pivot = np.radians(pivot_deg)
 
-    work_dir1 = f'./outdir/pivot_{pp}/daily'
+    work_dir1 = f"./outdir/pivot_{pp}/daily"
     map_dir = f"./outdir/pivot_{pp}/maps/"
-    
+
     os.makedirs(map_dir, exist_ok=True)
-    
+
     data_dir = Path(work_dir1)
-    
+
     print(f"Making Maps for Pivot {pp}")
-    
-    for esa in range(1,8):
+
+    for esa in range(1, 8):
         print(esa)
 
         map_manifest_rows = []
-        
-        h_cnts_map = np.zeros((30,60))        
-        exposure = np.zeros((30,60))
-
-        h_rate_map = np.zeros((30,60))
-        h_rate_var = np.zeros((30,60))
-
-        h_flux_map = np.zeros((30,60))
-        h_fvar_map = np.zeros((30,60))
-        h_fser_map = np.zeros((30,60))
-        h_fvto_map = np.zeros((30,60))
-
-        back_rate_map = np.zeros((30,60))
-        back_rate_var = np.zeros((30,60))
-        back_flux_map = np.zeros((30,60))
-        back_flux_var = np.zeros((30,60))
-        
-        stonoise_map = np.zeros((30,60))
-        stonoise_var_map = np.zeros((30,60))
-
-        cosalpha_map = np.zeros((30,60))
-        
-        for filepath in data_dir.glob(f'*esa{esa}.csv'):
-            
-            file = str(filepath)            
-            filename = file.split('/')[-1]
-            YD = filename.split('_')[2]
+
+        h_cnts_map = np.zeros((30, 60))
+        exposure = np.zeros((30, 60))
+
+        h_rate_map = np.zeros((30, 60))
+        h_rate_var = np.zeros((30, 60))
+
+        h_flux_map = np.zeros((30, 60))
+        h_fvar_map = np.zeros((30, 60))
+        h_fser_map = np.zeros((30, 60))
+        h_fvto_map = np.zeros((30, 60))
+
+        back_rate_map = np.zeros((30, 60))
+        back_rate_var = np.zeros((30, 60))
+        back_flux_map = np.zeros((30, 60))
+        back_flux_var = np.zeros((30, 60))
+
+        stonoise_map = np.zeros((30, 60))
+        stonoise_var_map = np.zeros((30, 60))
+
+        cosalpha_map = np.zeros((30, 60))
+
+        for filepath in data_dir.glob(f"*esa{esa}.csv"):
+            file = str(filepath)
+            filename = file.split("/")[-1]
+            YD = filename.split("_")[2]
 
             df = pd.read_csv(file)
 
@@ -174,78 +189,88 @@ def get_brate(YD,esa):
             prov["daily_csv_path"] = str(Path(file).resolve())
             map_manifest_rows.append(prov)
 
-            ps_ra = df['ra'].values
-            ps_dec = df['dec'].values
-            counts = df['counts'].values
-            expo = df['expo'].values
-            
-            for ia in range(0, 60):
-                    ra = ps_ra[ia]
-                    dec = ps_dec[ia]
-                    
-                    theta = 90.0 + dec
-                    
-                    imap = int(ra/deg)
-                    if (imap == 60):
-                        imap = 0
-                    jmap = int(theta/deg)
-                    if (jmap == 30):
-                        jmap = 0
-                    
-                    # Bin center angle in radians
-                    alpha = np.radians((ia + 0.5) * 6.0)
-    
-                    # coord system with x = NEP, y = RAM, z = Sun
-                    # look_x = np.sin(pivot)*np.cos(alpha)
-                    look_y = np.sin(pivot)*np.sin(alpha)
-                    # look_z = np.cos(pivot)
-                    cosalpha = look_y
-
-                    cosalpha_map[jmap, imap] = cosalpha
-
-                    h_cnts_map[jmap,imap] += counts[ia]
-                    exposure[jmap,imap] += expo[ia]
-                    
-        for imap in range(0, nra):
-            for jmap in range(0,ncolat):
-                
-                expo = exposure[jmap,imap]
+            ps_ra = df["ra"].values
+            ps_dec = df["dec"].values
+            counts = df["counts"].values
+            expo = df["expo"].values
+
+            for ia in range(60):
+                ra = ps_ra[ia]
+                dec = ps_dec[ia]
+
+                theta = 90.0 + dec
+
+                imap = int(ra / deg)
+                if imap == 60:
+                    imap = 0
+                jmap = int(theta / deg)
+                if jmap == 30:
+                    jmap = 0
+
+                # Bin center angle in radians
+                alpha = np.radians((ia + 0.5) * 6.0)
+
+                # coord system with x = NEP, y = RAM, z = Sun
+                # look_x = np.sin(pivot)*np.cos(alpha)
+                look_y = np.sin(pivot) * np.sin(alpha)
+                # look_z = np.cos(pivot)
+                cosalpha = look_y
+
+                cosalpha_map[jmap, imap] = cosalpha
+
+                h_cnts_map[jmap, imap] += counts[ia]
+                exposure[jmap, imap] += expo[ia]
+
+        for imap in range(nra):
+            for jmap in range(ncolat):
+                expo = exposure[jmap, imap]
                 energy = esa_energy[esa]
 
                 geo = gf[esa]
                 dge = dg[esa]
-                
+
                 # Only look up background rate if this bin has exposure,
                 # so we don't depend on YD when there were no input files.
-                if (expo > 0.0):
+                if expo > 0.0:
                     brate = get_brate(YD, esa)
-                    back_rate_map[jmap,imap] = brate
-                    back_rate_var[jmap,imap] = brate/expo
-                    h_rate_map[jmap,imap] = h_cnts_map[jmap,imap] / expo
-                    h_flux_map[jmap,imap] = h_rate_map[jmap,imap] / (geo * energy)
-                    h_fser_map[jmap,imap] = h_rate_map[jmap,imap] * dge / (geo * geo * energy )
-                    back_flux_map[jmap,imap] = brate / (geo * energy)
-                    back_flux_var[jmap,imap] = back_rate_var[jmap,imap] / (geo * energy)**2
+                    back_rate_map[jmap, imap] = brate
+                    back_rate_var[jmap, imap] = brate / expo
+                    h_rate_map[jmap, imap] = h_cnts_map[jmap, imap] / expo
+                    h_flux_map[jmap, imap] = h_rate_map[jmap, imap] / (geo * energy)
+                    h_fser_map[jmap, imap] = (
+                        h_rate_map[jmap, imap] * dge / (geo * geo * energy)
+                    )
+                    back_flux_map[jmap, imap] = brate / (geo * energy)
+                    back_flux_var[jmap, imap] = (
+                        back_rate_var[jmap, imap] / (geo * energy) ** 2
+                    )
                     # represent uncertainty in terms of variance (Poisson counts)
-                    h_rate_var[jmap,imap] = h_rate_map[jmap,imap] / expo
-                    if (brate > 0.0):
-                        stonoise_map[jmap,imap] = h_rate_map[jmap,imap] / ( brate )
+                    h_rate_var[jmap, imap] = h_rate_map[jmap, imap] / expo
+                    if brate > 0.0:
+                        stonoise_map[jmap, imap] = h_rate_map[jmap, imap] / (brate)
                         # Guard against division by zero in stonoise_var components
-                        if (back_rate_map[jmap,imap] > 0.0) and (h_rate_map[jmap,imap] > 0.0):
-                            stonoise_var_map[jmap,imap] = (
-                                h_rate_var[jmap,imap] / back_rate_map[jmap,imap]**2
-                                + back_rate_var[jmap,imap] / h_rate_map[jmap,imap]**2
+                        if (back_rate_map[jmap, imap] > 0.0) and (
+                            h_rate_map[jmap, imap] > 0.0
+                        ):
+                            stonoise_var_map[jmap, imap] = (
+                                h_rate_var[jmap, imap] / back_rate_map[jmap, imap] ** 2
+                                + back_rate_var[jmap, imap]
+                                / h_rate_map[jmap, imap] ** 2
                             )
                         else:
-                            stonoise_var_map[jmap,imap] = 0.0
+                            stonoise_var_map[jmap, imap] = 0.0
                     # represent uncertainty in terms of variance (Poisson counts)
-                    if (h_cnts_map[jmap,imap] > 0.0):
-                        h_fvar_map[jmap,imap] = h_flux_map[jmap,imap]**2 / h_cnts_map[jmap,imap]
-                        h_fvto_map[jmap,imap] = (h_flux_map[jmap,imap]**2 / h_cnts_map[jmap,imap]) + h_fser_map[jmap,imap]**2
+                    if h_cnts_map[jmap, imap] > 0.0:
+                        h_fvar_map[jmap, imap] = (
+                            h_flux_map[jmap, imap] ** 2 / h_cnts_map[jmap, imap]
+                        )
+                        h_fvto_map[jmap, imap] = (
+                            h_flux_map[jmap, imap] ** 2 / h_cnts_map[jmap, imap]
+                        ) + h_fser_map[jmap, imap] ** 2
                     else:
-                        h_fvar_map[jmap,imap] = 0.0
-                        h_fvto_map[jmap,imap] = h_fser_map[jmap,imap]**2
-                        
+                        h_fvar_map[jmap, imap] = 0.0
+                        h_fvto_map[jmap, imap] = h_fser_map[jmap, imap] ** 2
+
         # ------------------------------------------------------------
         # Final ram-side filter
         # Keep only pixels where cosalpha > 0
@@ -271,69 +296,60 @@ def get_brate(YD,esa):
             arr[~ram_mask] = 0.0
 
         sbg_file = pd.DataFrame(stonoise_map)
-        sbg_file.to_csv(map_dir+f"/map_stbg_esa{esa}.csv", index=False)
-        
+        sbg_file.to_csv(map_dir + f"/map_stbg_esa{esa}.csv", index=False)
+
         svar_file = pd.DataFrame(stonoise_var_map)
-        svar_file.to_csv(map_dir+f"/map_svar_esa{esa}.csv", index=False)
-        
+        svar_file.to_csv(map_dir + f"/map_svar_esa{esa}.csv", index=False)
+
         expo_file = pd.DataFrame(exposure)
-        expo_file.to_csv(map_dir+f"/map_expo_esa{esa}.csv", index=False)
-        
+        expo_file.to_csv(map_dir + f"/map_expo_esa{esa}.csv", index=False)
+
         cnts_file = pd.DataFrame(h_cnts_map)
-        cnts_file.to_csv(map_dir+f"/map_cnts_esa{esa}.csv", index=False)
-        
+        cnts_file.to_csv(map_dir + f"/map_cnts_esa{esa}.csv", index=False)
+
         rate_file = pd.DataFrame(h_rate_map)
-        rate_file.to_csv(map_dir+f"/map_rate_esa{esa}.csv", index=False)
-        
+        rate_file.to_csv(map_dir + f"/map_rate_esa{esa}.csv", index=False)
+
         flux_file = pd.DataFrame(h_flux_map)
-        flux_file.to_csv(map_dir+f"/map_flux_esa{esa}.csv", index=False)
+        flux_file.to_csv(map_dir + f"/map_flux_esa{esa}.csv", index=False)
 
         sflx_file = pd.DataFrame(h_fser_map)
-        sflx_file.to_csv(map_dir+f"/map_fser_esa{esa}.csv", index=False)
-        
+        sflx_file.to_csv(map_dir + f"/map_fser_esa{esa}.csv", index=False)
+
         h_rate_var_file = pd.DataFrame(h_rate_var)
-        h_rate_var_file.to_csv(map_dir+f"/map_rvar_esa{esa}.csv", index=False)
-        
+        h_rate_var_file.to_csv(map_dir + f"/map_rvar_esa{esa}.csv", index=False)
+
         h_fvar_map_file = pd.DataFrame(h_fvar_map)
-        h_fvar_map_file.to_csv(map_dir+f"/map_fvar_esa{esa}.csv", index=False)
-    
+        h_fvar_map_file.to_csv(map_dir + f"/map_fvar_esa{esa}.csv", index=False)
+
         h_fvto_map_file = pd.DataFrame(h_fvto_map)
-        h_fvto_map_file.to_csv(map_dir+f"/map_fvto_esa{esa}.csv", index=False)
+        h_fvto_map_file.to_csv(map_dir + f"/map_fvto_esa{esa}.csv", index=False)
 
         # Background rate and flux products from the same brate
         backrate_file = pd.DataFrame(back_rate_map)
-        backrate_file.to_csv(map_dir+f"/map_brate_esa{esa}.csv", index=False)
+        backrate_file.to_csv(map_dir + f"/map_brate_esa{esa}.csv", index=False)
 
         backrate_var_file = pd.DataFrame(back_rate_var)
-        backrate_var_file.to_csv(map_dir+f"/map_bvar_esa{esa}.csv", index=False)
+        backrate_var_file.to_csv(map_dir + f"/map_bvar_esa{esa}.csv", index=False)
 
         bflux_file = pd.DataFrame(back_flux_map)
-        bflux_file.to_csv(map_dir+f"/map_bflux_esa{esa}.csv", index=False)
+        bflux_file.to_csv(map_dir + f"/map_bflux_esa{esa}.csv", index=False)
 
         bflux_var_file = pd.DataFrame(back_flux_var)
-        bflux_var_file.to_csv(map_dir+f"/map_bfvar_esa{esa}.csv", index=False)
+        bflux_var_file.to_csv(map_dir + f"/map_bfvar_esa{esa}.csv", index=False)
 
         cosalpha_file = pd.DataFrame(cosalpha_map)
-        cosalpha_file.to_csv(map_dir+f"/map_cosalpha_esa{esa}.csv", index=False)
+        cosalpha_file.to_csv(map_dir + f"/map_cosalpha_esa{esa}.csv", index=False)
 
         # Write map-level provenance manifest for this pivot/ESA
         if map_manifest_rows:
-
             manifest_df = pd.DataFrame(map_manifest_rows)
 
             manifest_df["date_yyyymmdd"] = manifest_df["date_yyyymmdd"].astype(str)
             manifest_df["repoint"] = manifest_df["repoint"].astype(str)
 
-            manifest_df = (
-                manifest_df
-                .drop_duplicates()
-                .sort_values(
-                    by=["date_yyyymmdd", "repoint"],
-                    ascending=[True, True]
-                )
+            manifest_df = manifest_df.drop_duplicates().sort_values(
+                by=["date_yyyymmdd", "repoint"], ascending=[True, True]
             )
 
-            manifest_df.to_csv(
-                map_dir + f"/map_l1b_manifest_esa{esa}.csv",
-                index=False
-            )
+            manifest_df.to_csv(map_dir + f"/map_l1b_manifest_esa{esa}.csv", index=False)
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2.py
index 917aca684..d9e4a7162 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2.py
@@ -1,16 +1,17 @@
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-from scipy.spatial.transform import Rotation as R
 from matplotlib.colors import LogNorm
+from scipy.spatial.transform import Rotation as R
 
 PI = np.pi
 
-def somewhat_great_circle_centered(rot, lat0, lon0,  angle, npts=400):
+
+def somewhat_great_circle_centered(rot, lat0, lon0, angle, npts=400):
     """
     Somewhat great circle whose pole is (lat0, lon0).
-    angle is the angle from the center 
+    angle is the angle from the center
     Returns coordinates in the ROTATED frame.
     """
 
@@ -28,25 +29,22 @@ def somewhat_great_circle_centered(rot, lat0, lon0,  angle, npts=400):
 
     v = np.cross(p, u)
 
-    t = np.linspace(0, 2*np.pi, npts)
+    t = np.linspace(0, 2 * np.pi, npts)
 
     angle_r = np.radians(angle)
-    pts = (
-        np.cos(angle_r) * p
-        + np.sin(angle_r) * (
-            np.outer(np.cos(t), u) +
-            np.outer(np.sin(t), v)
+    pts = np.cos(angle_r) * p + np.sin(angle_r) * (
+        np.outer(np.cos(t), u) + np.outer(np.sin(t), v)
     )
-)
     # rotate into map frame
     pts_rot = rot.apply(pts)
 
-    lat = np.degrees(np.arcsin(pts_rot[:,2]))
-    lon = np.degrees(np.arctan2(pts_rot[:,1], pts_rot[:,0]))
+    lat = np.degrees(np.arcsin(pts_rot[:, 2]))
+    lon = np.degrees(np.arctan2(pts_rot[:, 1], pts_rot[:, 0]))
     lon = (lon + 180) % 360 - 180
 
     return lat, lon
 
+
 def plot_wrapped_line(ax, lon, lat, **plot_kwargs):
     """
     Plot a line while avoiding wraparound artifacts.
@@ -56,14 +54,16 @@ def plot_wrapped_line(ax, lon, lat, **plot_kwargs):
     lat = np.asarray(lat)
 
     jumps = np.where(np.abs(np.diff(lon)) > 180)[0]
-    split_idx = np.append(jumps, len(lon)-1)
+    split_idx = np.append(jumps, len(lon) - 1)
 
     start = 0
     for idx in split_idx:
-        ax.plot(lon[start:idx+1],
-                lat[start:idx+1],
-                transform=ccrs.PlateCarree(),
-                **plot_kwargs)
+        ax.plot(
+            lon[start : idx + 1],
+            lat[start : idx + 1],
+            transform=ccrs.PlateCarree(),
+            **plot_kwargs,
+        )
         start = idx + 1
 
 
@@ -75,14 +75,16 @@ def sph_to_cart(lat, lon):
     z = np.sin(lat_rad)
     return np.array([x, y, z])
 
+
 def rotation_matrix_to_center(new_colat0, new_lon0):
     # Step 1: rotate around Z axis by -lon0
-    r1 = R.from_euler('z', -new_lon0, degrees=True)
+    r1 = R.from_euler("z", -new_lon0, degrees=True)
     # Step 2: rotate around Y axis by 90-lat0
-    r2 = R.from_euler('y', 90-new_colat0, degrees=True)
+    r2 = R.from_euler("y", 90 - new_colat0, degrees=True)
     # Combined rotation
     return r2 * r1
 
+
 def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     Apply inverse rotation to xyz_rot points.
@@ -92,55 +94,110 @@ def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     return rot.inv().apply(xyz_rot)
 
+
 def make_imap_lo_reference_points(rot, option=1):
-    if option==1:
+    if option == 1:
         npoints = 4
         lons = np.array([259.200622, 255.499300, 289.800000, 79.200622])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296])
-        labels=[ 'Nose', 'V1','V2','Tail']
-    if option==2:
+        lats = np.array([5.116296, 34.999941, -35.600000, -5.116296])
+        labels = ["Nose", "V1", "V2", "Tail"]
+    if option == 2:
         npoints = 7
-        lons = np.array([259.200622, 255.499300, 289.800000, 79.200622, 221,150.,0.0 ])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296, 39, 0.0, 0.0])
-        labels=[ 'Nose', 'V1','V2','Tail', 'Rcen','Stbd', 'Port']
-    if option==3:
+        lons = np.array(
+            [259.200622, 255.499300, 289.800000, 79.200622, 221, 150.0, 0.0]
+        )
+        lats = np.array([5.116296, 34.999941, -35.600000, -5.116296, 39, 0.0, 0.0])
+        labels = ["Nose", "V1", "V2", "Tail", "Rcen", "Stbd", "Port"]
+    if option == 3:
         npoints = 10
-        lons = np.array([259.200622, 255.499300, 289.800000, 79.200622, 221,150.,0.0, 41, 250.624523, 70.624523])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296, 39, 0.0, 0.0, -39, -41.242979, 41.242979])
-        labels=[ 'Nose', 'V1','V2','Tail', 'RibC','Stbd', 'Port','DwnB','ULSR', 'DLSR']
-
+        lons = np.array(
+            [
+                259.200622,
+                255.499300,
+                289.800000,
+                79.200622,
+                221,
+                150.0,
+                0.0,
+                41,
+                250.624523,
+                70.624523,
+            ]
+        )
+        lats = np.array(
+            [
+                5.116296,
+                34.999941,
+                -35.600000,
+                -5.116296,
+                39,
+                0.0,
+                0.0,
+                -39,
+                -41.242979,
+                41.242979,
+            ]
+        )
+        labels = [
+            "Nose",
+            "V1",
+            "V2",
+            "Tail",
+            "RibC",
+            "Stbd",
+            "Port",
+            "DwnB",
+            "ULSR",
+            "DLSR",
+        ]
 
         # --- Reference point in ORIGINAL coordinates ---
-    lat_points = np.linspace(0.0,0.0,npoints)
-    lon_points = np.linspace(0.0,0.0,npoints)
-    for i in range(0,npoints):
-        ref_lat = lats[i]     # degrees
-        ref_lon = lons[i]     # degrees
-
-    # Convert to xyz
-        xyz_ref = np.array([
-            np.cos(np.radians(ref_lat)) * np.cos(np.radians(ref_lon)),
-            np.cos(np.radians(ref_lat)) * np.sin(np.radians(ref_lon)),
-            np.sin(np.radians(ref_lat))
-            ])
+    lat_points = np.linspace(0.0, 0.0, npoints)
+    lon_points = np.linspace(0.0, 0.0, npoints)
+    for i in range(npoints):
+        ref_lat = lats[i]  # degrees
+        ref_lon = lons[i]  # degrees
+
+        # Convert to xyz
+        xyz_ref = np.array(
+            [
+                np.cos(np.radians(ref_lat)) * np.cos(np.radians(ref_lon)),
+                np.cos(np.radians(ref_lat)) * np.sin(np.radians(ref_lon)),
+                np.sin(np.radians(ref_lat)),
+            ]
+        )
 
         # Rotate to map frame
         xyz_ref_rot = rot.apply(xyz_ref)
 
         # Back to lat/lon in rotated frame
         lat_ref_rot = np.degrees(np.arcsin(xyz_ref_rot[2]))
-        lon_ref_rot = -np.degrees(np.arctan2(xyz_ref_rot[1], xyz_ref_rot[0])) # negative sign since we are looking from the inside
-        lon_ref_rot = (lon_ref_rot + 180) % 360 - 180   # wrap
+        lon_ref_rot = -np.degrees(
+            np.arctan2(xyz_ref_rot[1], xyz_ref_rot[0])
+        )  # negative sign since we are looking from the inside
+        lon_ref_rot = (lon_ref_rot + 180) % 360 - 180  # wrap
 
         lat_points[i] = lat_ref_rot
         lon_points[i] = lon_ref_rot
-        
+
     return labels, lon_points, lat_points
 
 
 # --- Load data ---
 
-def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_colorbar='Flux', plot_title='PPM = 90', opt_points=1, opt_ribbon=0):
+
+def make_imap_lo_map(
+    file,
+    lat_center,
+    lon_center,
+    vmin,
+    vmax,
+    output,
+    label_colorbar="Flux",
+    plot_title="PPM = 90",
+    opt_points=1,
+    opt_ribbon=0,
+):
 
     data = pd.read_csv(file, header=0).values
     ny, nx = data.shape
@@ -150,93 +207,110 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
     lons = np.linspace(3, 357, nx)
 
     # Build corner grid for pcolormesh
-    lat_edges = np.linspace(-90, 90, ny+1)
-    lon_edges = np.linspace(0, 360, nx+1)
+    lat_edges = np.linspace(-90, 90, ny + 1)
+    lon_edges = np.linspace(0, 360, nx + 1)
     lon_grid, lat_grid = np.meshgrid(lon_edges, lat_edges)
 
     # Flatten grid for rotation
-    xyz = np.column_stack([
-        np.cos(np.radians(lat_grid.ravel())) * np.cos(np.radians(lon_grid.ravel())),
-        np.cos(np.radians(lat_grid.ravel())) * np.sin(np.radians(lon_grid.ravel())),
-        np.sin(np.radians(lat_grid.ravel()))
-    ])
+    xyz = np.column_stack(
+        [
+            np.cos(np.radians(lat_grid.ravel())) * np.cos(np.radians(lon_grid.ravel())),
+            np.cos(np.radians(lat_grid.ravel())) * np.sin(np.radians(lon_grid.ravel())),
+            np.sin(np.radians(lat_grid.ravel())),
+        ]
+    )
 
     # --- Rotation ---
     new_colat0, new_lon0 = 90.0 - lat_center, lon_center
-    rot = R.from_euler('y', 90-new_colat0, degrees=True) * R.from_euler('z', -new_lon0, degrees=True) 
+    rot = R.from_euler("y", 90 - new_colat0, degrees=True) * R.from_euler(
+        "z", -new_lon0, degrees=True
+    )
     xyz_rot = rot.apply(xyz)
 
-    lat_rot = np.degrees(np.arcsin(xyz_rot[:,2])).reshape(ny+1, nx+1)
-    lon_rot = np.degrees(np.arctan2(xyz_rot[:,1], xyz_rot[:,0])).reshape(ny+1, nx+1)
+    lat_rot = np.degrees(np.arcsin(xyz_rot[:, 2])).reshape(ny + 1, nx + 1)
+    lon_rot = np.degrees(np.arctan2(xyz_rot[:, 1], xyz_rot[:, 0])).reshape(
+        ny + 1, nx + 1
+    )
     lon_rot = (lon_rot + 180) % 360 - 180
 
     # For the data
     lon_rot_inside = -lon_rot  # flip longitude
 
-    #lon_edges = compute_edges(lon_rot)
-    #lat_edges = compute_edges(lat_rot)
+    # lon_edges = compute_edges(lon_rot)
+    # lat_edges = compute_edges(lat_rot)
 
     # --- Plot using pcolormesh ---
-    fig, ax = plt.subplots(figsize=(10,5), subplot_kw={'projection': ccrs.Mollweide()})
-    ax.set_facecolor('darkblue')
+    fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": ccrs.Mollweide()})
+    ax.set_facecolor("darkblue")
 
-    vmin= vmin
+    vmin = vmin
     vmax = vmax
     data_clipped = np.clip(data, vmin, vmax)
     data_filled = np.copy(data_clipped)
     data_filled[np.isnan(data_clipped)] = vmin
 
-    pcm = ax.pcolormesh(lon_rot_inside, lat_rot, data_filled,  # no interpolation!
-                        transform=ccrs.PlateCarree(),
-                        cmap='viridis', norm=LogNorm(vmin=vmin, vmax=vmax), shading='auto')
-
+    pcm = ax.pcolormesh(
+        lon_rot_inside,
+        lat_rot,
+        data_filled,  # no interpolation!
+        transform=ccrs.PlateCarree(),
+        cmap="viridis",
+        norm=LogNorm(vmin=vmin, vmax=vmax),
+        shading="auto",
+    )
 
     lat_lines = np.linspace(-90, 90, 7)  # every ~30 deg
     for lat0 in lat_lines:
-        lon_line = np.linspace(0, 360, nx+1)
+        lon_line = np.linspace(0, 360, nx + 1)
         lat_line = np.full_like(lon_line, lat0)
 
         # Convert to xyz and rotate
-        xyz_line = np.column_stack([
-            np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
-            np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
-            np.sin(np.radians(lat_line))
-        ])
+        xyz_line = np.column_stack(
+            [
+                np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
+                np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
+                np.sin(np.radians(lat_line)),
+            ]
+        )
         xyz_line_rot = rot.apply(xyz_line)
 
         # Back to lat/lon
-        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:,2]))
-        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:,1], xyz_line_rot[:,0]))
+        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:, 2]))
+        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:, 1], xyz_line_rot[:, 0]))
         lon_rot_line = (lon_rot_line + 180) % 360 - 180  # wrap to [-180,180]
         # flip for inside view
 
         # --- Split line wherever jump > 180°
         jump_idx = np.where(np.abs(np.diff(lon_rot_line)) > 180)[0]
-        split_idx = np.append(jump_idx, len(lon_rot_line)-1)
+        split_idx = np.append(jump_idx, len(lon_rot_line) - 1)
         start = 0
         for idx in split_idx:
-            seg_lon = lon_rot_line[start:idx+1]
-            seg_lat = lat_rot_line[start:idx+1]
-            ax.plot(seg_lon, seg_lat, transform=ccrs.PlateCarree(),
-                    color='orange', lw=0.8, alpha=0.7)
-            start = idx+1
-
-
-        # pick a tolerance in degrees
-            tol = 100.0   # widen if labels disappear
+            seg_lon = lon_rot_line[start : idx + 1]
+            seg_lat = lat_rot_line[start : idx + 1]
+            ax.plot(
+                seg_lon,
+                seg_lat,
+                transform=ccrs.PlateCarree(),
+                color="orange",
+                lw=0.8,
+                alpha=0.7,
+            )
+            start = idx + 1
 
-            lon_here = ((seg_lon[0] + 180) % 360) - 180   # ensure [-180,180]
+            # pick a tolerance in degrees
+            tol = 100.0  # widen if labels disappear
 
-            if abs(lon_here) < tol:   # only near 0° rotated longitude
+            lon_here = ((seg_lon[0] + 180) % 360) - 180  # ensure [-180,180]
 
+            if abs(lon_here) < tol:  # only near 0° rotated longitude
                 theta = np.radians(90.0 - seg_lat[0])
-                phi = -np.radians(seg_lon[0])   # inside view sign flip
-                phi = np.mod(phi, 2.0*np.pi)
+                phi = -np.radians(seg_lon[0])  # inside view sign flip
+                phi = np.mod(phi, 2.0 * np.pi)
 
                 xyz = [
-                    np.sin(theta)*np.cos(phi),
-                    np.sin(theta)*np.sin(phi),
-                    np.cos(theta)
+                    np.sin(theta) * np.cos(phi),
+                    np.sin(theta) * np.sin(phi),
+                    np.cos(theta),
                 ]
 
                 xyz_inv = rot.inv().apply(xyz)
@@ -249,83 +323,92 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
                         seg_lat[0],
                         f"{int(round(lat_orig))}°",
                         transform=ccrs.PlateCarree(),
-                        color='orange',
+                        color="orange",
                         fontsize=6,
-                        verticalalignment='top',
-                        horizontalalignment='right'
+                        verticalalignment="top",
+                        horizontalalignment="right",
                     )
 
-            #theta = np.radians(90.0-seg_lat[0])
-            #phi = -np.radians(seg_lon[0]) # sign inversion looking from inside
-            #phi = np.mod(phi, 2.0*np.pi)         # wrap to [0, 2π)
-            #if (phi > PI):
+            # theta = np.radians(90.0-seg_lat[0])
+            # phi = -np.radians(seg_lon[0]) # sign inversion looking from inside
+            # phi = np.mod(phi, 2.0*np.pi)         # wrap to [0, 2π)
+            # if (phi > PI):
             #    xyz = [np.sin(theta)*np.cos(phi),np.sin(theta)*np.sin(phi), np.cos(theta) ]
             #    xyz_inv = rot.inv().apply(xyz)
             #    lat_orig = np.degrees(np.arcsin(xyz_inv[2]))
             #    lon_orig = np.degrees(np.arctan2(xyz_inv[1], xyz_inv[0]))
             #    if not np.isnan(lat_orig):
             #        lat_lab_1 = round(lat_orig)
-            #    else: 
+            #    else:
             #        lat_lab_1 = ''
             #    lon_lab = ((seg_lon[0]+180) % 360) - 180
             #    if lat_lab_1 != '':
     #      ##         lon_lab_inside = -lon_lab
-            ##           transform=ccrs.PlateCarree(),
-            #          color='orange', fontsize=6,
-            #          verticalalignment='center', horizontalalignment='right')
-
+    ##           transform=ccrs.PlateCarree(),
+    #          color='orange', fontsize=6,
+    #          verticalalignment='center', horizontalalignment='right')
 
     lon_lines = np.linspace(0, 360, 13)  # every 30 deg
     for lon0 in lon_lines:
-        lat_line = np.linspace(-90, 90, ny+1)
+        lat_line = np.linspace(-90, 90, ny + 1)
         lon_line = np.full_like(lat_line, lon0)
 
-        xyz_line = np.column_stack([
-            np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
-            np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
-            np.sin(np.radians(lat_line))
-        ])
+        xyz_line = np.column_stack(
+            [
+                np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
+                np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
+                np.sin(np.radians(lat_line)),
+            ]
+        )
         xyz_line_rot = rot.apply(xyz_line)
 
-        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:,2]))
-        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:,1], xyz_line_rot[:,0]))
+        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:, 2]))
+        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:, 1], xyz_line_rot[:, 0]))
         lon_rot_line = (lon_rot_line + 180) % 360 - 180
         # flip for inside view
 
         jump_idx = np.where(np.abs(np.diff(lon_rot_line)) > 180)[0]
-        split_idx = np.append(jump_idx, len(lon_rot_line)-1)
+        split_idx = np.append(jump_idx, len(lon_rot_line) - 1)
         start = 0
 
         # Find the row closest to 0° rotated latitude
-        lat0_idx = np.argmin(np.abs(lat_rot_line))  # lat_rot_line = your rotated lat array for the line
+        lat0_idx = np.argmin(
+            np.abs(lat_rot_line)
+        )  # lat_rot_line = your rotated lat array for the line
 
         # Get the corresponding longitude and latitude along that row
         lon_line = lon_rot_line[lat0_idx]
         lat_line = lat_rot_line[lat0_idx]
 
         for idx in split_idx:
-            seg_lon = lon_rot_line[start:idx+1]
-            seg_lat = lat_rot_line[start:idx+1]
-            ax.plot(seg_lon, seg_lat, transform=ccrs.PlateCarree(),
-                    color='orange', lw=0.8, alpha=0.7)
-        
-            mid_idx = len(seg_lon) // 2 
+            seg_lon = lon_rot_line[start : idx + 1]
+            seg_lat = lat_rot_line[start : idx + 1]
+            ax.plot(
+                seg_lon,
+                seg_lat,
+                transform=ccrs.PlateCarree(),
+                color="orange",
+                lw=0.8,
+                alpha=0.7,
+            )
+
+            mid_idx = len(seg_lon) // 2
             lon_plot = seg_lon[mid_idx]
             lat_plot = seg_lat[mid_idx]
 
-            start = idx+1
+            start = idx + 1
 
-            xyz_rot = sph_to_cart(lat_plot, lon_plot)   # returns (3,) or (1,3)
+            xyz_rot = sph_to_cart(lat_plot, lon_plot)  # returns (3,) or (1,3)
             # print(xyz_rot)
 
             # ensure shape (1,3) for scipy Rotation
             xyz_rot = np.atleast_2d(xyz_rot)
             #    print(xyz_rot)
 
-        # --- invert rotation ---
+            # --- invert rotation ---
             xyz_orig = rot.inv().apply(xyz_rot)
 
-        # --- back to spherical ---
+            # --- back to spherical ---
             lat_label = np.degrees(np.arcsin(xyz_orig[0, 2]))
             lon_label = np.degrees(np.arctan2(xyz_orig[0, 1], xyz_orig[0, 0]))
 
@@ -333,34 +416,38 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
             xyz_rot_label = rot.apply(xyz)
             lat_plot = np.degrees(np.arcsin(xyz_rot_label[2]))
 
-    #        print('lon_label = ',lon_label)
-    #        lon_label = (-1.0*lon_label + 180) % 360 - 180
-    #        print('lon_label (2) = ',lon_label)
+            #        print('lon_label = ',lon_label)
+            #        lon_label = (-1.0*lon_label + 180) % 360 - 180
+            #        print('lon_label (2) = ',lon_label)
             lon_plot = -lon_plot
             if np.abs(lat_label) < 30.0:
-                ax.text(lon_plot, lat_plot,
-                            f"{lon_label:.0f}°",
-                            transform=ccrs.PlateCarree(),
-                            color='orange', fontsize=6,
-                            verticalalignment='bottom', horizontalalignment='right')
-
-    if (opt_points > 0):
-
+                ax.text(
+                    lon_plot,
+                    lat_plot,
+                    f"{lon_label:.0f}°",
+                    transform=ccrs.PlateCarree(),
+                    color="orange",
+                    fontsize=6,
+                    verticalalignment="bottom",
+                    horizontalalignment="right",
+                )
+
+    if opt_points > 0:
         labels, lon_points, lat_points = make_imap_lo_reference_points(rot, opt_points)
         npoints = len(labels)
-        for i in range(0,npoints):
+        for i in range(npoints):
             lon_ref_rot = lon_points[i]
             lat_ref_rot = lat_points[i]
             ref_label = labels[i]
-        # --- Plot marker ---
+            # --- Plot marker ---
             ax.plot(
                 lon_ref_rot,
                 lat_ref_rot,
-                marker='o',
+                marker="o",
                 markersize=1,
-                color='white',
+                color="white",
                 transform=ccrs.PlateCarree(),
-                zorder=5
+                zorder=5,
             )
 
             # --- Add text ---
@@ -369,57 +456,60 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
                 lat_ref_rot,
                 f" {ref_label}",
                 transform=ccrs.PlateCarree(),
-                color='white',
+                color="white",
                 fontsize=6,
-                ha='left',
-                va='center',
-                zorder=5
+                ha="left",
+                va="center",
+                zorder=5,
             )
 
-# now optionally add the great circle line
-
-    if (opt_ribbon > 0):
+    # now optionally add the great circle line
 
-        lat_line, lon_line = somewhat_great_circle_centered( rot,
-            lat0=39,
-            lon0=221.0,
-            angle=70-15
+    if opt_ribbon > 0:
+        lat_line, lon_line = somewhat_great_circle_centered(
+            rot, lat0=39, lon0=221.0, angle=70 - 15
         )
 
         plot_wrapped_line(
-                ax,
-                -lon_line,     # inside view flip if you use it
-                lat_line,
-                color='white',
-                lw=0.5
+            ax,
+            -lon_line,  # inside view flip if you use it
+            lat_line,
+            color="white",
+            lw=0.5,
         )
 
-
-        lat_line, lon_line = somewhat_great_circle_centered( rot,
-            lat0=39,
-            lon0=221.0,
-            angle=70+15
+        lat_line, lon_line = somewhat_great_circle_centered(
+            rot, lat0=39, lon0=221.0, angle=70 + 15
         )
 
         plot_wrapped_line(
-                ax,
-                -lon_line,     # inside view flip if you use it
-                lat_line,
-                color='white',
-                lw=0.5
+            ax,
+            -lon_line,  # inside view flip if you use it
+            lat_line,
+            color="white",
+            lw=0.5,
         )
 
-#    label = label_map[dtype]
+    #    label = label_map[dtype]
     #                       "Intensity (counts cm$^{-2}$ s$^{-1}$ sr$^{-1}$ keV$^{-1}$)")
-#    value = esa_energy[esa] * 1000.0
-#    s = f"{value:.2f}"
-#    cbar.set_label(label)
-#    cbar.set_label(f"{label} [ECLIPJ2000] at ESA (eV) = {s}")
+    #    value = esa_energy[esa] * 1000.0
+    #    s = f"{value:.2f}"
+    #    cbar.set_label(label)
+    #    cbar.set_label(f"{label} [ECLIPJ2000] at ESA (eV) = {s}")
     plt.suptitle(plot_title)
 
-    plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, label=label_colorbar)
+    plt.colorbar(pcm, ax=ax, orientation="horizontal", pad=0.05, label=label_colorbar)
     ax.set_global()
-   # plt.show()
-    plt.savefig(output, dpi=200, facecolor='w', edgecolor='w', orientation='portrait', format=None, transparent=False, bbox_inches=None, pad_inches=0.1)
+    # plt.show()
+    plt.savefig(
+        output,
+        dpi=200,
+        facecolor="w",
+        edgecolor="w",
+        orientation="portrait",
+        format=None,
+        transparent=False,
+        bbox_inches=None,
+        pad_inches=0.1,
+    )
     plt.close(fig)
-
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2_linear.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2_linear.py
index 9f495a89e..9c98ecd69 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2_linear.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/new_patch_arc2_linear.py
@@ -1,16 +1,16 @@
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
 from scipy.spatial.transform import Rotation as R
-from matplotlib.colors import LogNorm
 
 PI = np.pi
 
-def somewhat_great_circle_centered(rot, lat0, lon0,  angle, npts=400):
+
+def somewhat_great_circle_centered(rot, lat0, lon0, angle, npts=400):
     """
     Somewhat great circle whose pole is (lat0, lon0).
-    angle is the angle from the center 
+    angle is the angle from the center
     Returns coordinates in the ROTATED frame.
     """
 
@@ -28,25 +28,22 @@ def somewhat_great_circle_centered(rot, lat0, lon0,  angle, npts=400):
 
     v = np.cross(p, u)
 
-    t = np.linspace(0, 2*np.pi, npts)
+    t = np.linspace(0, 2 * np.pi, npts)
 
     angle_r = np.radians(angle)
-    pts = (
-        np.cos(angle_r) * p
-        + np.sin(angle_r) * (
-            np.outer(np.cos(t), u) +
-            np.outer(np.sin(t), v)
+    pts = np.cos(angle_r) * p + np.sin(angle_r) * (
+        np.outer(np.cos(t), u) + np.outer(np.sin(t), v)
     )
-)
     # rotate into map frame
     pts_rot = rot.apply(pts)
 
-    lat = np.degrees(np.arcsin(pts_rot[:,2]))
-    lon = np.degrees(np.arctan2(pts_rot[:,1], pts_rot[:,0]))
+    lat = np.degrees(np.arcsin(pts_rot[:, 2]))
+    lon = np.degrees(np.arctan2(pts_rot[:, 1], pts_rot[:, 0]))
     lon = (lon + 180) % 360 - 180
 
     return lat, lon
 
+
 def plot_wrapped_line(ax, lon, lat, **plot_kwargs):
     """
     Plot a line while avoiding wraparound artifacts.
@@ -56,14 +53,16 @@ def plot_wrapped_line(ax, lon, lat, **plot_kwargs):
     lat = np.asarray(lat)
 
     jumps = np.where(np.abs(np.diff(lon)) > 180)[0]
-    split_idx = np.append(jumps, len(lon)-1)
+    split_idx = np.append(jumps, len(lon) - 1)
 
     start = 0
     for idx in split_idx:
-        ax.plot(lon[start:idx+1],
-                lat[start:idx+1],
-                transform=ccrs.PlateCarree(),
-                **plot_kwargs)
+        ax.plot(
+            lon[start : idx + 1],
+            lat[start : idx + 1],
+            transform=ccrs.PlateCarree(),
+            **plot_kwargs,
+        )
         start = idx + 1
 
 
@@ -75,14 +74,16 @@ def sph_to_cart(lat, lon):
     z = np.sin(lat_rad)
     return np.array([x, y, z])
 
+
 def rotation_matrix_to_center(new_colat0, new_lon0):
     # Step 1: rotate around Z axis by -lon0
-    r1 = R.from_euler('z', -new_lon0, degrees=True)
+    r1 = R.from_euler("z", -new_lon0, degrees=True)
     # Step 2: rotate around Y axis by 90-lat0
-    r2 = R.from_euler('y', 90-new_colat0, degrees=True)
+    r2 = R.from_euler("y", 90 - new_colat0, degrees=True)
     # Combined rotation
     return r2 * r1
 
+
 def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     Apply inverse rotation to xyz_rot points.
@@ -92,55 +93,110 @@ def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     return rot.inv().apply(xyz_rot)
 
+
 def make_imap_lo_reference_points(rot, option=1):
-    if option==1:
+    if option == 1:
         npoints = 4
         lons = np.array([259.200622, 255.499300, 289.800000, 79.200622])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296])
-        labels=[ 'Nose', 'V1','V2','Tail']
-    if option==2:
+        lats = np.array([5.116296, 34.999941, -35.600000, -5.116296])
+        labels = ["Nose", "V1", "V2", "Tail"]
+    if option == 2:
         npoints = 7
-        lons = np.array([259.200622, 255.499300, 289.800000, 79.200622, 221,150.,0.0 ])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296, 39, 0.0, 0.0])
-        labels=[ 'Nose', 'V1','V2','Tail', 'Rcen','Stbd', 'Port']
-    if option==3:
+        lons = np.array(
+            [259.200622, 255.499300, 289.800000, 79.200622, 221, 150.0, 0.0]
+        )
+        lats = np.array([5.116296, 34.999941, -35.600000, -5.116296, 39, 0.0, 0.0])
+        labels = ["Nose", "V1", "V2", "Tail", "Rcen", "Stbd", "Port"]
+    if option == 3:
         npoints = 10
-        lons = np.array([259.200622, 255.499300, 289.800000, 79.200622, 221,150.,0.0, 41, 250.624523, 70.624523])
-        lats = np.array([5.116296, 34.999941,  -35.600000, -5.116296, 39, 0.0, 0.0, -39, -41.242979, 41.242979])
-        labels=[ 'Nose', 'V1','V2','Tail', 'RibC','Stbd', 'Port','DwnB','ULSR', 'DLSR']
-
+        lons = np.array(
+            [
+                259.200622,
+                255.499300,
+                289.800000,
+                79.200622,
+                221,
+                150.0,
+                0.0,
+                41,
+                250.624523,
+                70.624523,
+            ]
+        )
+        lats = np.array(
+            [
+                5.116296,
+                34.999941,
+                -35.600000,
+                -5.116296,
+                39,
+                0.0,
+                0.0,
+                -39,
+                -41.242979,
+                41.242979,
+            ]
+        )
+        labels = [
+            "Nose",
+            "V1",
+            "V2",
+            "Tail",
+            "RibC",
+            "Stbd",
+            "Port",
+            "DwnB",
+            "ULSR",
+            "DLSR",
+        ]
 
         # --- Reference point in ORIGINAL coordinates ---
-    lat_points = np.linspace(0.0,0.0,npoints)
-    lon_points = np.linspace(0.0,0.0,npoints)
-    for i in range(0,npoints):
-        ref_lat = lats[i]     # degrees
-        ref_lon = lons[i]     # degrees
-
-    # Convert to xyz
-        xyz_ref = np.array([
-            np.cos(np.radians(ref_lat)) * np.cos(np.radians(ref_lon)),
-            np.cos(np.radians(ref_lat)) * np.sin(np.radians(ref_lon)),
-            np.sin(np.radians(ref_lat))
-            ])
+    lat_points = np.linspace(0.0, 0.0, npoints)
+    lon_points = np.linspace(0.0, 0.0, npoints)
+    for i in range(npoints):
+        ref_lat = lats[i]  # degrees
+        ref_lon = lons[i]  # degrees
+
+        # Convert to xyz
+        xyz_ref = np.array(
+            [
+                np.cos(np.radians(ref_lat)) * np.cos(np.radians(ref_lon)),
+                np.cos(np.radians(ref_lat)) * np.sin(np.radians(ref_lon)),
+                np.sin(np.radians(ref_lat)),
+            ]
+        )
 
         # Rotate to map frame
         xyz_ref_rot = rot.apply(xyz_ref)
 
         # Back to lat/lon in rotated frame
         lat_ref_rot = np.degrees(np.arcsin(xyz_ref_rot[2]))
-        lon_ref_rot = -np.degrees(np.arctan2(xyz_ref_rot[1], xyz_ref_rot[0])) # negative sign since we are looking from the inside
-        lon_ref_rot = (lon_ref_rot + 180) % 360 - 180   # wrap
+        lon_ref_rot = -np.degrees(
+            np.arctan2(xyz_ref_rot[1], xyz_ref_rot[0])
+        )  # negative sign since we are looking from the inside
+        lon_ref_rot = (lon_ref_rot + 180) % 360 - 180  # wrap
 
         lat_points[i] = lat_ref_rot
         lon_points[i] = lon_ref_rot
-        
+
     return labels, lon_points, lat_points
 
 
 # --- Load data ---
 
-def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_colorbar='Flux', plot_title='PPM = 90', opt_points=1, opt_ribbon=0):
+
+def make_imap_lo_map(
+    file,
+    lat_center,
+    lon_center,
+    vmin,
+    vmax,
+    output,
+    label_colorbar="Flux",
+    plot_title="PPM = 90",
+    opt_points=1,
+    opt_ribbon=0,
+):
 
     data = pd.read_csv(file, header=0).values
     ny, nx = data.shape
@@ -150,37 +206,43 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
     lons = np.linspace(3, 357, nx)
 
     # Build corner grid for pcolormesh
-    lat_edges = np.linspace(-90, 90, ny+1)
-    lon_edges = np.linspace(0, 360, nx+1)
+    lat_edges = np.linspace(-90, 90, ny + 1)
+    lon_edges = np.linspace(0, 360, nx + 1)
     lon_grid, lat_grid = np.meshgrid(lon_edges, lat_edges)
 
     # Flatten grid for rotation
-    xyz = np.column_stack([
-        np.cos(np.radians(lat_grid.ravel())) * np.cos(np.radians(lon_grid.ravel())),
-        np.cos(np.radians(lat_grid.ravel())) * np.sin(np.radians(lon_grid.ravel())),
-        np.sin(np.radians(lat_grid.ravel()))
-    ])
+    xyz = np.column_stack(
+        [
+            np.cos(np.radians(lat_grid.ravel())) * np.cos(np.radians(lon_grid.ravel())),
+            np.cos(np.radians(lat_grid.ravel())) * np.sin(np.radians(lon_grid.ravel())),
+            np.sin(np.radians(lat_grid.ravel())),
+        ]
+    )
 
     # --- Rotation ---
     new_colat0, new_lon0 = 90.0 - lat_center, lon_center
-    rot = R.from_euler('y', 90-new_colat0, degrees=True) * R.from_euler('z', -new_lon0, degrees=True) 
+    rot = R.from_euler("y", 90 - new_colat0, degrees=True) * R.from_euler(
+        "z", -new_lon0, degrees=True
+    )
     xyz_rot = rot.apply(xyz)
 
-    lat_rot = np.degrees(np.arcsin(xyz_rot[:,2])).reshape(ny+1, nx+1)
-    lon_rot = np.degrees(np.arctan2(xyz_rot[:,1], xyz_rot[:,0])).reshape(ny+1, nx+1)
+    lat_rot = np.degrees(np.arcsin(xyz_rot[:, 2])).reshape(ny + 1, nx + 1)
+    lon_rot = np.degrees(np.arctan2(xyz_rot[:, 1], xyz_rot[:, 0])).reshape(
+        ny + 1, nx + 1
+    )
     lon_rot = (lon_rot + 180) % 360 - 180
 
     # For the data
     lon_rot_inside = -lon_rot  # flip longitude
 
-    #lon_edges = compute_edges(lon_rot)
-    #lat_edges = compute_edges(lat_rot)
+    # lon_edges = compute_edges(lon_rot)
+    # lat_edges = compute_edges(lat_rot)
 
     # --- Plot using pcolormesh ---
-    fig, ax = plt.subplots(figsize=(10,5), subplot_kw={'projection': ccrs.Mollweide()})
-    ax.set_facecolor('darkblue')
+    fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": ccrs.Mollweide()})
+    ax.set_facecolor("darkblue")
 
-    vmin= vmin
+    vmin = vmin
     vmax = vmax
     data_clipped = np.clip(data, vmin, vmax)
     data_filled = np.copy(data_clipped)
@@ -191,58 +253,64 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
         lat_rot,
         data_filled,
         transform=ccrs.PlateCarree(),
-        cmap='viridis',
+        cmap="viridis",
         vmin=vmin,
         vmax=vmax,
-        shading='auto'
+        shading="auto",
     )
 
     lat_lines = np.linspace(-90, 90, 7)  # every ~30 deg
     for lat0 in lat_lines:
-        lon_line = np.linspace(0, 360, nx+1)
+        lon_line = np.linspace(0, 360, nx + 1)
         lat_line = np.full_like(lon_line, lat0)
 
         # Convert to xyz and rotate
-        xyz_line = np.column_stack([
-            np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
-            np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
-            np.sin(np.radians(lat_line))
-        ])
+        xyz_line = np.column_stack(
+            [
+                np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
+                np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
+                np.sin(np.radians(lat_line)),
+            ]
+        )
         xyz_line_rot = rot.apply(xyz_line)
 
         # Back to lat/lon
-        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:,2]))
-        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:,1], xyz_line_rot[:,0]))
+        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:, 2]))
+        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:, 1], xyz_line_rot[:, 0]))
         lon_rot_line = (lon_rot_line + 180) % 360 - 180  # wrap to [-180,180]
         # flip for inside view
 
         # --- Split line wherever jump > 180°
         jump_idx = np.where(np.abs(np.diff(lon_rot_line)) > 180)[0]
-        split_idx = np.append(jump_idx, len(lon_rot_line)-1)
+        split_idx = np.append(jump_idx, len(lon_rot_line) - 1)
         start = 0
         for idx in split_idx:
-            seg_lon = lon_rot_line[start:idx+1]
-            seg_lat = lat_rot_line[start:idx+1]
-            ax.plot(seg_lon, seg_lat, transform=ccrs.PlateCarree(),
-                    color='orange', lw=0.8, alpha=0.7)
-            start = idx+1
-
-
-        # pick a tolerance in degrees
-            tol = 100.0   # widen if labels disappear
+            seg_lon = lon_rot_line[start : idx + 1]
+            seg_lat = lat_rot_line[start : idx + 1]
+            ax.plot(
+                seg_lon,
+                seg_lat,
+                transform=ccrs.PlateCarree(),
+                color="orange",
+                lw=0.8,
+                alpha=0.7,
+            )
+            start = idx + 1
 
-            lon_here = ((seg_lon[0] + 180) % 360) - 180   # ensure [-180,180]
+            # pick a tolerance in degrees
+            tol = 100.0  # widen if labels disappear
 
-            if abs(lon_here) < tol:   # only near 0° rotated longitude
+            lon_here = ((seg_lon[0] + 180) % 360) - 180  # ensure [-180,180]
 
+            if abs(lon_here) < tol:  # only near 0° rotated longitude
                 theta = np.radians(90.0 - seg_lat[0])
-                phi = -np.radians(seg_lon[0])   # inside view sign flip
-                phi = np.mod(phi, 2.0*np.pi)
+                phi = -np.radians(seg_lon[0])  # inside view sign flip
+                phi = np.mod(phi, 2.0 * np.pi)
 
                 xyz = [
-                    np.sin(theta)*np.cos(phi),
-                    np.sin(theta)*np.sin(phi),
-                    np.cos(theta)
+                    np.sin(theta) * np.cos(phi),
+                    np.sin(theta) * np.sin(phi),
+                    np.cos(theta),
                 ]
 
                 xyz_inv = rot.inv().apply(xyz)
@@ -255,83 +323,92 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
                         seg_lat[0],
                         f"{int(round(lat_orig))}°",
                         transform=ccrs.PlateCarree(),
-                        color='orange',
+                        color="orange",
                         fontsize=6,
-                        verticalalignment='top',
-                        horizontalalignment='right'
+                        verticalalignment="top",
+                        horizontalalignment="right",
                     )
 
-            #theta = np.radians(90.0-seg_lat[0])
-            #phi = -np.radians(seg_lon[0]) # sign inversion looking from inside
-            #phi = np.mod(phi, 2.0*np.pi)         # wrap to [0, 2π)
-            #if (phi > PI):
+            # theta = np.radians(90.0-seg_lat[0])
+            # phi = -np.radians(seg_lon[0]) # sign inversion looking from inside
+            # phi = np.mod(phi, 2.0*np.pi)         # wrap to [0, 2π)
+            # if (phi > PI):
             #    xyz = [np.sin(theta)*np.cos(phi),np.sin(theta)*np.sin(phi), np.cos(theta) ]
             #    xyz_inv = rot.inv().apply(xyz)
             #    lat_orig = np.degrees(np.arcsin(xyz_inv[2]))
             #    lon_orig = np.degrees(np.arctan2(xyz_inv[1], xyz_inv[0]))
             #    if not np.isnan(lat_orig):
             #        lat_lab_1 = round(lat_orig)
-            #    else: 
+            #    else:
             #        lat_lab_1 = ''
             #    lon_lab = ((seg_lon[0]+180) % 360) - 180
             #    if lat_lab_1 != '':
     #      ##         lon_lab_inside = -lon_lab
-            ##           transform=ccrs.PlateCarree(),
-            #          color='orange', fontsize=6,
-            #          verticalalignment='center', horizontalalignment='right')
-
+    ##           transform=ccrs.PlateCarree(),
+    #          color='orange', fontsize=6,
+    #          verticalalignment='center', horizontalalignment='right')
 
     lon_lines = np.linspace(0, 360, 13)  # every 30 deg
     for lon0 in lon_lines:
-        lat_line = np.linspace(-90, 90, ny+1)
+        lat_line = np.linspace(-90, 90, ny + 1)
         lon_line = np.full_like(lat_line, lon0)
 
-        xyz_line = np.column_stack([
-            np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
-            np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
-            np.sin(np.radians(lat_line))
-        ])
+        xyz_line = np.column_stack(
+            [
+                np.cos(np.radians(lat_line)) * np.cos(np.radians(lon_line)),
+                np.cos(np.radians(lat_line)) * np.sin(np.radians(lon_line)),
+                np.sin(np.radians(lat_line)),
+            ]
+        )
         xyz_line_rot = rot.apply(xyz_line)
 
-        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:,2]))
-        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:,1], xyz_line_rot[:,0]))
+        lat_rot_line = np.degrees(np.arcsin(xyz_line_rot[:, 2]))
+        lon_rot_line = -np.degrees(np.arctan2(xyz_line_rot[:, 1], xyz_line_rot[:, 0]))
         lon_rot_line = (lon_rot_line + 180) % 360 - 180
         # flip for inside view
 
         jump_idx = np.where(np.abs(np.diff(lon_rot_line)) > 180)[0]
-        split_idx = np.append(jump_idx, len(lon_rot_line)-1)
+        split_idx = np.append(jump_idx, len(lon_rot_line) - 1)
         start = 0
 
         # Find the row closest to 0° rotated latitude
-        lat0_idx = np.argmin(np.abs(lat_rot_line))  # lat_rot_line = your rotated lat array for the line
+        lat0_idx = np.argmin(
+            np.abs(lat_rot_line)
+        )  # lat_rot_line = your rotated lat array for the line
 
         # Get the corresponding longitude and latitude along that row
         lon_line = lon_rot_line[lat0_idx]
         lat_line = lat_rot_line[lat0_idx]
 
         for idx in split_idx:
-            seg_lon = lon_rot_line[start:idx+1]
-            seg_lat = lat_rot_line[start:idx+1]
-            ax.plot(seg_lon, seg_lat, transform=ccrs.PlateCarree(),
-                    color='orange', lw=0.8, alpha=0.7)
-        
-            mid_idx = len(seg_lon) // 2 
+            seg_lon = lon_rot_line[start : idx + 1]
+            seg_lat = lat_rot_line[start : idx + 1]
+            ax.plot(
+                seg_lon,
+                seg_lat,
+                transform=ccrs.PlateCarree(),
+                color="orange",
+                lw=0.8,
+                alpha=0.7,
+            )
+
+            mid_idx = len(seg_lon) // 2
             lon_plot = seg_lon[mid_idx]
             lat_plot = seg_lat[mid_idx]
 
-            start = idx+1
+            start = idx + 1
 
-            xyz_rot = sph_to_cart(lat_plot, lon_plot)   # returns (3,) or (1,3)
+            xyz_rot = sph_to_cart(lat_plot, lon_plot)  # returns (3,) or (1,3)
             # print(xyz_rot)
 
             # ensure shape (1,3) for scipy Rotation
             xyz_rot = np.atleast_2d(xyz_rot)
             #    print(xyz_rot)
 
-        # --- invert rotation ---
+            # --- invert rotation ---
             xyz_orig = rot.inv().apply(xyz_rot)
 
-        # --- back to spherical ---
+            # --- back to spherical ---
             lat_label = np.degrees(np.arcsin(xyz_orig[0, 2]))
             lon_label = np.degrees(np.arctan2(xyz_orig[0, 1], xyz_orig[0, 0]))
 
@@ -339,34 +416,38 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
             xyz_rot_label = rot.apply(xyz)
             lat_plot = np.degrees(np.arcsin(xyz_rot_label[2]))
 
-    #        print('lon_label = ',lon_label)
-    #        lon_label = (-1.0*lon_label + 180) % 360 - 180
-    #        print('lon_label (2) = ',lon_label)
+            #        print('lon_label = ',lon_label)
+            #        lon_label = (-1.0*lon_label + 180) % 360 - 180
+            #        print('lon_label (2) = ',lon_label)
             lon_plot = -lon_plot
             if np.abs(lat_label) < 30.0:
-                ax.text(lon_plot, lat_plot,
-                            f"{lon_label:.0f}°",
-                            transform=ccrs.PlateCarree(),
-                            color='orange', fontsize=6,
-                            verticalalignment='bottom', horizontalalignment='right')
-
-    if (opt_points > 0):
-
+                ax.text(
+                    lon_plot,
+                    lat_plot,
+                    f"{lon_label:.0f}°",
+                    transform=ccrs.PlateCarree(),
+                    color="orange",
+                    fontsize=6,
+                    verticalalignment="bottom",
+                    horizontalalignment="right",
+                )
+
+    if opt_points > 0:
         labels, lon_points, lat_points = make_imap_lo_reference_points(rot, opt_points)
         npoints = len(labels)
-        for i in range(0,npoints):
+        for i in range(npoints):
             lon_ref_rot = lon_points[i]
             lat_ref_rot = lat_points[i]
             ref_label = labels[i]
-        # --- Plot marker ---
+            # --- Plot marker ---
             ax.plot(
                 lon_ref_rot,
                 lat_ref_rot,
-                marker='o',
+                marker="o",
                 markersize=1,
-                color='white',
+                color="white",
                 transform=ccrs.PlateCarree(),
-                zorder=5
+                zorder=5,
             )
 
             # --- Add text ---
@@ -375,57 +456,60 @@ def make_imap_lo_map(file, lat_center, lon_center, vmin, vmax, output, label_col
                 lat_ref_rot,
                 f" {ref_label}",
                 transform=ccrs.PlateCarree(),
-                color='white',
+                color="white",
                 fontsize=6,
-                ha='left',
-                va='center',
-                zorder=5
+                ha="left",
+                va="center",
+                zorder=5,
             )
 
-# now optionally add the great circle line
+    # now optionally add the great circle line
 
-    if (opt_ribbon > 0):
-
-        lat_line, lon_line = somewhat_great_circle_centered( rot,
-            lat0=39,
-            lon0=221.0,
-            angle=70-15
+    if opt_ribbon > 0:
+        lat_line, lon_line = somewhat_great_circle_centered(
+            rot, lat0=39, lon0=221.0, angle=70 - 15
         )
 
         plot_wrapped_line(
-                ax,
-                -lon_line,     # inside view flip if you use it
-                lat_line,
-                color='white',
-                lw=0.5
+            ax,
+            -lon_line,  # inside view flip if you use it
+            lat_line,
+            color="white",
+            lw=0.5,
         )
 
-
-        lat_line, lon_line = somewhat_great_circle_centered( rot,
-            lat0=39,
-            lon0=221.0,
-            angle=70+15
+        lat_line, lon_line = somewhat_great_circle_centered(
+            rot, lat0=39, lon0=221.0, angle=70 + 15
         )
 
         plot_wrapped_line(
-                ax,
-                -lon_line,     # inside view flip if you use it
-                lat_line,
-                color='white',
-                lw=0.5
+            ax,
+            -lon_line,  # inside view flip if you use it
+            lat_line,
+            color="white",
+            lw=0.5,
         )
 
-#    label = label_map[dtype]
+    #    label = label_map[dtype]
     #                       "Intensity (counts cm$^{-2}$ s$^{-1}$ sr$^{-1}$ keV$^{-1}$)")
-#    value = esa_energy[esa] * 1000.0
-#    s = f"{value:.2f}"
-#    cbar.set_label(label)
-#    cbar.set_label(f"{label} [ECLIPJ2000] at ESA (eV) = {s}")
+    #    value = esa_energy[esa] * 1000.0
+    #    s = f"{value:.2f}"
+    #    cbar.set_label(label)
+    #    cbar.set_label(f"{label} [ECLIPJ2000] at ESA (eV) = {s}")
     plt.suptitle(plot_title)
 
-    plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, label=label_colorbar)
+    plt.colorbar(pcm, ax=ax, orientation="horizontal", pad=0.05, label=label_colorbar)
     ax.set_global()
-   # plt.show()
-    plt.savefig(output, dpi=200, facecolor='w', edgecolor='w', orientation='portrait', format=None, transparent=False, bbox_inches=None, pad_inches=0.1)
+    # plt.show()
+    plt.savefig(
+        output,
+        dpi=200,
+        facecolor="w",
+        edgecolor="w",
+        orientation="portrait",
+        format=None,
+        transparent=False,
+        bbox_inches=None,
+        pad_inches=0.1,
+    )
     plt.close(fig)
-
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/old/csv2yaml.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/old/csv2yaml.py
index 91faa4d19..c14c1d653 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/old/csv2yaml.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/old/csv2yaml.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Wed Feb 25 17:06:47 2026
 
@@ -10,15 +9,7 @@
 import os
 from datetime import datetime, timedelta, timezone
 
-esa_energy = {
-    1: 16,
-    2: 30,
-    3: 56,
-    4: 106,
-    5: 200,
-    6: 404,
-    7: 787
-}
+esa_energy = {1: 16, 2: 30, 3: 56, 4: 106, 5: 200, 6: 404, 7: 787}
 
 label_map = {
     "cnts": "Counts",
@@ -30,7 +21,7 @@
     "fvar": "VVariance Intensity (#/cm4 s2 sr2 keV2)",
     "runc": "Unc Rate",
     "rvar": "Variance Rate (#/s2)",
-    "flux": "Intensity (#/cm2 s sr keV)"
+    "flux": "Intensity (#/cm2 s sr keV)",
 }
 
 
@@ -47,6 +38,7 @@ def latest_monday_tag():
 
     return f"maps_v{year_short}DOY{doy}"
 
+
 def csv_to_map_yaml(
     csv_file,
     output_file,
@@ -56,21 +48,19 @@ def csv_to_map_yaml(
     map_year="2026",
     uniqueness_index=0,
     label="Flux",
-    pivot="90"
+    pivot="90",
 ):
     data_rows = []
 
-
     with open(csv_file, newline="") as f:
         reader = csv.reader(f)
         header = next(reader)  # 10–59, not used
         for row in reader:
             formatted_row = [
-                f"{float(x):.3E}" if float(x) != 0 else "0.000E+00"
-                for x in row
+                f"{float(x):.3E}" if float(x) != 0 else "0.000E+00" for x in row
             ]
             data_rows.append(formatted_row)
-            
+
     tag = latest_monday_tag()
 
     # ----------------------------
@@ -102,56 +92,66 @@ def csv_to_map_yaml(
         for row in data_rows:
             f.write("- [" + ", ".join(row) + "]\n")
 
-for pp in [75,90,105]:
-    
-    work_dir1 = f'./outdir/pivot_{pp}/maps'
-    yaml_dir_flux = f'./outdir/pivot_{pp}/yaml/flux'
-    yaml_dir_rate = f'./outdir/pivot_{pp}/yaml/rate'
-    yaml_dir_cnts = f'./outdir/pivot_{pp}/yaml/cnts'
-    
+
+for pp in [75, 90, 105]:
+    work_dir1 = f"./outdir/pivot_{pp}/maps"
+    yaml_dir_flux = f"./outdir/pivot_{pp}/yaml/flux"
+    yaml_dir_rate = f"./outdir/pivot_{pp}/yaml/rate"
+    yaml_dir_cnts = f"./outdir/pivot_{pp}/yaml/cnts"
+
     os.makedirs(yaml_dir_flux, exist_ok=True)
     os.makedirs(yaml_dir_rate, exist_ok=True)
     os.makedirs(yaml_dir_cnts, exist_ok=True)
-    
-    for esa in range(1,8):
-        print(pp,esa)
-        for tt in ["expo","rate","flux","fvar","rvar","cnts"]:#,"stbg","func","runc",,"rvar"]:
-                filename = os.path.join(work_dir1, f"map_{tt}_esa{esa}.csv")
-                energy = esa_energy[esa]
-                label = label_map[tt]
-                base_filename = os.path.splitext(os.path.basename(filename))[0]
-                
-                if tt=='expo':
-                    tt='exposure'
-                    outputs = [ os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt"),
-                                os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ] 
-                if tt=='cnts':
-
-                    outputs = [ os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-flux.txt"),
-                                os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-exposure.txt"), 
-                                os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-variance.txt")] 
-                
-                if tt=="rvar":
-                    tt='variance'
-                    outputs = [ os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                    
-                if tt=="fvar":
-                    tt='variance'
-                    outputs = [ os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                    
-                if tt=="flux":
-                    tt='flux'
-                    outputs = [ os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                    
-                if tt =='rate':
-                    tt='flux'       
-                    outputs = [ os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt") ]
-                
-                for output in outputs:
-                    csv_to_map_yaml(
-                        csv_file=filename,
-                        output_file=output,
-                        energy_value=energy,
-                        label=label,
-                        pivot=pp
-                    )
+
+    for esa in range(1, 8):
+        print(pp, esa)
+        for tt in [
+            "expo",
+            "rate",
+            "flux",
+            "fvar",
+            "rvar",
+            "cnts",
+        ]:  # ,"stbg","func","runc",,"rvar"]:
+            filename = os.path.join(work_dir1, f"map_{tt}_esa{esa}.csv")
+            energy = esa_energy[esa]
+            label = label_map[tt]
+            base_filename = os.path.splitext(os.path.basename(filename))[0]
+
+            if tt == "expo":
+                tt = "exposure"
+                outputs = [
+                    os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt"),
+                    os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt"),
+                ]
+            if tt == "cnts":
+                outputs = [
+                    os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-flux.txt"),
+                    os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-exposure.txt"),
+                    os.path.join(yaml_dir_cnts, f"IMAPLo-{energy}ev-variance.txt"),
+                ]
+
+            if tt == "rvar":
+                tt = "variance"
+                outputs = [os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "fvar":
+                tt = "variance"
+                outputs = [os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "flux":
+                tt = "flux"
+                outputs = [os.path.join(yaml_dir_flux, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            if tt == "rate":
+                tt = "flux"
+                outputs = [os.path.join(yaml_dir_rate, f"IMAPLo-{energy}ev-{tt}.txt")]
+
+            for output in outputs:
+                csv_to_map_yaml(
+                    csv_file=filename,
+                    output_file=output,
+                    energy_value=energy,
+                    label=label,
+                    pivot=pp,
+                )
diff --git a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/plot_map_DN.py b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/plot_map_DN.py
index dacb5f0f6..4f5e648a0 100755
--- a/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/plot_map_DN.py
+++ b/tests/test_data/lo/lo_txt_pipeline/3S5_l1b_ram_maps/plot_map_DN.py
@@ -1,23 +1,20 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Sun Feb 15 18:14:16 2026
 
 @author: hafijulislam
 """
 
-import numpy as np
-import pandas as pd
 import os
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-from scipy.spatial.transform import Rotation as R
-from matplotlib.colors import LogNorm
+
 import new_patch_arc2 as patch
 import new_patch_arc2_linear as pchlin
+import numpy as np
+from scipy.spatial.transform import Rotation as R
 
 PI = np.pi
 
+
 def sph_to_cart(lat, lon):
     lat_rad = np.radians(lat)
     lon_rad = np.radians(lon)
@@ -26,14 +23,16 @@ def sph_to_cart(lat, lon):
     z = np.sin(lat_rad)
     return np.array([x, y, z])
 
+
 def rotation_matrix_to_center(new_colat0, new_lon0):
     # Step 1: rotate around Z axis by -lon0
-    r1 = R.from_euler('z', -new_lon0, degrees=True)
+    r1 = R.from_euler("z", -new_lon0, degrees=True)
     # Step 2: rotate around Y axis by 90-lat0
-    r2 = R.from_euler('y', 90-new_colat0, degrees=True)
+    r2 = R.from_euler("y", 90 - new_colat0, degrees=True)
     # Combined rotation
     return r2 * r1
 
+
 def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     Apply inverse rotation to xyz_rot points.
@@ -43,6 +42,7 @@ def inverse_rotation_matrix_to_center(rot, xyz_rot):
     """
     return rot.inv().apply(xyz_rot)
 
+
 esa_energy = {
     1: 0.016,
     2: 0.030,
@@ -51,97 +51,120 @@ def inverse_rotation_matrix_to_center(rot, xyz_rot):
     5: 0.200,
     6: 0.404,
     7: 0.787,
-    8: 1.821
+    8: 1.821,
 }
 
 label_map = {
-        "cnts": "Counts",
-        "rate": "Rate (counts s$^{-1}$)",
-        "backrate": "Rate (counts s$^{-1}$)",
-        "expo": "Exposure Time (sec)",
-        "stbg": "Signal/Ubiq-Bkgd",
-        "func": "Unc Intensity",
-        "fser": "Flux Sys Uncertainty",
-        "fvar": "Variance Intensity",
-        "fvto": "Variance Total",
-        "runc": "Unc Rate",
-        "rvar": "Variance Rate",
-        "flux": "Intensity (counts cm$^{-2}$ s$^{-1}$ sr$^{-1}$ keV$^{-1}$)",
-        "cosalpha": "cos(alpha), alpha angle between Ram and Boresight"
-    }
-
-# MAIN 
-
-for pp in [75,90,105]:
-    work_dir1 = f'./outdir/pivot_{pp}/maps'
-    plot_dir = f'./outdir/pivot_{pp}/plots'
-    os.makedirs(plot_dir, exist_ok=True)
-    
-    for esa in range(1,8):
-        print(esa)
-        for tt in ["expo","rate","flux","cnts","fser","func","runc","fvar","fvto","rvar","stbg","cosalpha"]:
-                filename = os.path.join(work_dir1, f"map_{tt}_esa{esa}.csv")
-                data = np.loadtxt(filename, delimiter=',', skiprows=1)
+    "cnts": "Counts",
+    "rate": "Rate (counts s$^{-1}$)",
+    "backrate": "Rate (counts s$^{-1}$)",
+    "expo": "Exposure Time (sec)",
+    "stbg": "Signal/Ubiq-Bkgd",
+    "func": "Unc Intensity",
+    "fser": "Flux Sys Uncertainty",
+    "fvar": "Variance Intensity",
+    "fvto": "Variance Total",
+    "runc": "Unc Rate",
+    "rvar": "Variance Rate",
+    "flux": "Intensity (counts cm$^{-2}$ s$^{-1}$ sr$^{-1}$ keV$^{-1}$)",
+    "cosalpha": "cos(alpha), alpha angle between Ram and Boresight",
+}
 
-                base_filename = os.path.splitext(os.path.basename(filename))[0]
-                output = os.path.join(plot_dir, f"map_{tt}_esa{esa}.png")
-                print(tt)
+# MAIN
 
-                if tt =="expo":
-                    vmax = np.max(data)
-                    vmin = 1e-1
+for pp in [75, 90, 105]:
+    work_dir1 = f"./outdir/pivot_{pp}/maps"
+    plot_dir = f"./outdir/pivot_{pp}/plots"
+    os.makedirs(plot_dir, exist_ok=True)
 
-                if tt =="cosalpha":
+    for esa in range(1, 8):
+        print(esa)
+        for tt in [
+            "expo",
+            "rate",
+            "flux",
+            "cnts",
+            "fser",
+            "func",
+            "runc",
+            "fvar",
+            "fvto",
+            "rvar",
+            "stbg",
+            "cosalpha",
+        ]:
+            filename = os.path.join(work_dir1, f"map_{tt}_esa{esa}.csv")
+            data = np.loadtxt(filename, delimiter=",", skiprows=1)
+
+            base_filename = os.path.splitext(os.path.basename(filename))[0]
+            output = os.path.join(plot_dir, f"map_{tt}_esa{esa}.png")
+            print(tt)
+
+            if tt == "expo":
+                vmax = np.max(data)
+                vmin = 1e-1
+
+            if tt == "cosalpha":
+                vmax = np.max(data)
+                vmin = -1.0 * vmax
+
+            if tt in [
+                "cnts",
+                "rate",
+                "flux",
+                "fvar",
+                "fvto",
+                "fser",
+                "stbg",
+                "func",
+                "runc",
+                "rvar",
+            ]:
+                if esa == 5:
                     vmax = np.max(data)
-                    vmin = -1.0*vmax
-
-                if tt in ["cnts", "rate", "flux", "fvar", "fvto", "fser", "stbg", "func", "runc", "rvar"]:
-                    if esa==5:
-                        vmax = np.max(data)
-                        vmin = vmax * 1e-2
-                    elif esa==4 or esa==3:
-                        vmax = np.max(data)
-                        vmin = vmax * 1e-4
-                    elif esa==1 or esa==2:
-                        vmax = np.max(data)
-                        vmin = vmax * 1e-3
-                    elif esa==6:
-                        vmax = np.max(data)
-                        vmin = vmax * 1e-2
-                    else:
-                        vmax = np.max(data)
-                        vmin = vmax * 1e-2
-
-                lat_center = 5.0 
-                lon_center = -105.0
-
-                label = label_map[tt]
-    #                       "Intensity (counts cm$^{-2}$ s$^{-1}$ sr$^{-1}$ keV$^{-1}$)")
-                value = esa_energy[esa] * 1000.0
-                s = f"{value:.2f}"
-                label = label + f'[ECLIPJ2000] at ESA (eV) = {s}'
-#                print(label)
-                title = f'Pivot:{pp}'
-                if tt =="cosalpha":
-                    pchlin.make_imap_lo_map(filename, lat_center, lon_center, vmin, vmax,output,label_colorbar=label,plot_title=title)
+                    vmin = vmax * 1e-2
+                elif esa == 4 or esa == 3:
+                    vmax = np.max(data)
+                    vmin = vmax * 1e-4
+                elif esa == 1 or esa == 2:
+                    vmax = np.max(data)
+                    vmin = vmax * 1e-3
+                elif esa == 6:
+                    vmax = np.max(data)
+                    vmin = vmax * 1e-2
                 else:
-                    patch.make_imap_lo_map(filename, lat_center, lon_center, vmin, vmax,output,label_colorbar=label,plot_title=title)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+                    vmax = np.max(data)
+                    vmin = vmax * 1e-2
+
+            lat_center = 5.0
+            lon_center = -105.0
+
+            label = label_map[tt]
+            #                       "Intensity (counts cm$^{-2}$ s$^{-1}$ sr$^{-1}$ keV$^{-1}$)")
+            value = esa_energy[esa] * 1000.0
+            s = f"{value:.2f}"
+            label = label + f"[ECLIPJ2000] at ESA (eV) = {s}"
+            #                print(label)
+            title = f"Pivot:{pp}"
+            if tt == "cosalpha":
+                pchlin.make_imap_lo_map(
+                    filename,
+                    lat_center,
+                    lon_center,
+                    vmin,
+                    vmax,
+                    output,
+                    label_colorbar=label,
+                    plot_title=title,
+                )
+            else:
+                patch.make_imap_lo_map(
+                    filename,
+                    lat_center,
+                    lon_center,
+                    vmin,
+                    vmax,
+                    output,
+                    label_colorbar=label,
+                    plot_title=title,
+                )
diff --git a/tests/test_data_utils.py b/tests/test_data_utils.py
index 87d2f5316..be37b89b4 100644
--- a/tests/test_data_utils.py
+++ b/tests/test_data_utils.py
@@ -4,156 +4,264 @@
 
 import numpy as np
 
-from imap_l3_processing.data_utils import rebin, NearestInterpolator
+from imap_l3_processing.data_utils import NearestInterpolator, rebin
 
 
 class TestDataUtils(unittest.TestCase):
     def test_1d_rebin(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5), datetime(2020, 4, 4, 0, 15)])
+        hit_data_epoch = np.array(
+            [datetime(2020, 4, 4, 0, 5), datetime(2020, 4, 4, 0, 15)]
+        )
         hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300)])
         extra_data_at_beginning = [0, 0.5]
-        extra_dates_at_beginning = [datetime(2020, 4, 3, 0, 23), datetime(2020, 4, 3, 0, 23, 30)]
-        extra_dates_at_end = [datetime(2020, 4, 4, 1, 0), datetime(2020, 4, 4, 1, 23, 30)]
+        extra_dates_at_beginning = [
+            datetime(2020, 4, 3, 0, 23),
+            datetime(2020, 4, 3, 0, 23, 30),
+        ]
+        extra_dates_at_end = [
+            datetime(2020, 4, 4, 1, 0),
+            datetime(2020, 4, 4, 1, 23, 30),
+        ]
         extra_data_at_end = [1, 1.5]
-        input_data = np.array(extra_data_at_beginning +
-                              [0, 1, 0, 1, 0, 1, 0, 0] + extra_data_at_end)
-        mag_epoch = np.array(extra_dates_at_beginning + [datetime(2020, 4, 4, 0, 1),
-                                                         datetime(2020, 4, 4, 0, 3),
-                                                         datetime(2020, 4, 4, 0, 6),
-                                                         datetime(2020, 4, 4, 0, 9, 55),
-                                                         datetime(2020, 4, 4, 0, 12, 45),
-                                                         datetime(2020, 4, 4, 0, 13),
-                                                         datetime(2020, 4, 4, 0, 15),
-                                                         datetime(2020, 4, 4, 0, 17)] + extra_dates_at_end)
+        input_data = np.array(
+            extra_data_at_beginning + [0, 1, 0, 1, 0, 1, 0, 0] + extra_data_at_end
+        )
+        mag_epoch = np.array(
+            extra_dates_at_beginning
+            + [
+                datetime(2020, 4, 4, 0, 1),
+                datetime(2020, 4, 4, 0, 3),
+                datetime(2020, 4, 4, 0, 6),
+                datetime(2020, 4, 4, 0, 9, 55),
+                datetime(2020, 4, 4, 0, 12, 45),
+                datetime(2020, 4, 4, 0, 13),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 17),
+            ]
+            + extra_dates_at_end
+        )
         expected_average = np.array([1 / 2, 1 / 4])
 
-        actual_average = rebin(from_epoch=mag_epoch, from_data=input_data,
-                               to_epoch=hit_data_epoch,
-                               to_epoch_delta=hit_data_delta)
+        actual_average = rebin(
+            from_epoch=mag_epoch,
+            from_data=input_data,
+            to_epoch=hit_data_epoch,
+            to_epoch_delta=hit_data_delta,
+        )
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_rebin_with_extra_data_at_beginning_and_end(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5), datetime(2020, 4, 4, 0, 15)])
+        hit_data_epoch = np.array(
+            [datetime(2020, 4, 4, 0, 5), datetime(2020, 4, 4, 0, 15)]
+        )
         hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300)])
         extra_data_at_beginning = [[0.5, 0, 1], [0, 0.5, 1]]
-        extra_dates_at_beginning = [datetime(2020, 4, 3, 0, 23), datetime(2020, 4, 3, 0, 23, 30)]
-        extra_dates_at_end = [datetime(2020, 4, 4, 1, 0), datetime(2020, 4, 4, 1, 23, 30)]
+        extra_dates_at_beginning = [
+            datetime(2020, 4, 3, 0, 23),
+            datetime(2020, 4, 3, 0, 23, 30),
+        ]
+        extra_dates_at_end = [
+            datetime(2020, 4, 4, 1, 0),
+            datetime(2020, 4, 4, 1, 23, 30),
+        ]
         extra_data_at_end = [[0.5, 1, 1], [1, 0.5, 0.5]]
-        mag_data = np.array(extra_data_at_beginning +
-                            [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0],
-                             [1, 0, 0]] + extra_data_at_end)
-        mag_epoch = np.array(extra_dates_at_beginning + [datetime(2020, 4, 4, 0, 1),
-                                                         datetime(2020, 4, 4, 0, 3), datetime(2020, 4, 4, 0, 6),
-                                                         datetime(2020, 4, 4, 0, 9, 55),
-                                                         datetime(2020, 4, 4, 0, 12, 45),
-                                                         datetime(2020, 4, 4, 0, 13),
-                                                         datetime(2020, 4, 4, 0, 15),
-                                                         datetime(2020, 4, 4, 0, 17)] + extra_dates_at_end)
+        mag_data = np.array(
+            extra_data_at_beginning
+            + [
+                [0, 0, 1],
+                [0, 1, 0],
+                [1, 0, 0],
+                [0, 1, 0],
+                [0, 0, 1],
+                [0, 1, 0],
+                [1, 0, 0],
+                [1, 0, 0],
+            ]
+            + extra_data_at_end
+        )
+        mag_epoch = np.array(
+            extra_dates_at_beginning
+            + [
+                datetime(2020, 4, 4, 0, 1),
+                datetime(2020, 4, 4, 0, 3),
+                datetime(2020, 4, 4, 0, 6),
+                datetime(2020, 4, 4, 0, 9, 55),
+                datetime(2020, 4, 4, 0, 12, 45),
+                datetime(2020, 4, 4, 0, 13),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 17),
+            ]
+            + extra_dates_at_end
+        )
         expected_average = np.array([[1 / 4, 1 / 2, 1 / 4], [1 / 2, 1 / 4, 1 / 4]])
 
-        actual_average = rebin(from_epoch=mag_epoch, from_data=mag_data,
-                               to_epoch=hit_data_epoch,
-                               to_epoch_delta=hit_data_delta)
+        actual_average = rebin(
+            from_epoch=mag_epoch,
+            from_data=mag_data,
+            to_epoch=hit_data_epoch,
+            to_epoch_delta=hit_data_delta,
+        )
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_rebin_handles_missing_data(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5),
-                                   datetime(2020, 4, 4, 0, 15),
-                                   datetime(2020, 4, 4, 0, 25)])
-        hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)])
+        hit_data_epoch = np.array(
+            [
+                datetime(2020, 4, 4, 0, 5),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 25),
+            ]
+        )
+        hit_data_delta = np.array(
+            [timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)]
+        )
         mag_data = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 0]])
-        mag_epoch = [datetime(2020, 4, 4, 0, 1),
-                     datetime(2020, 4, 4, 0, 3),
-                     datetime(2020, 4, 4, 0, 22),
-                     datetime(2020, 4, 4, 0, 27)]
-        expected_average = np.array([[0, 1 / 2, 1 / 2], [np.nan, np.nan, np.nan], [1 / 2, 1 / 2, 0]])
-
-        actual_average = rebin(from_epoch=mag_epoch, from_data=mag_data,
-                               to_epoch=hit_data_epoch,
-                               to_epoch_delta=hit_data_delta)
+        mag_epoch = [
+            datetime(2020, 4, 4, 0, 1),
+            datetime(2020, 4, 4, 0, 3),
+            datetime(2020, 4, 4, 0, 22),
+            datetime(2020, 4, 4, 0, 27),
+        ]
+        expected_average = np.array(
+            [[0, 1 / 2, 1 / 2], [np.nan, np.nan, np.nan], [1 / 2, 1 / 2, 0]]
+        )
+
+        actual_average = rebin(
+            from_epoch=mag_epoch,
+            from_data=mag_data,
+            to_epoch=hit_data_epoch,
+            to_epoch_delta=hit_data_delta,
+        )
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_rebin_with_missing_data_fills_with_nan(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5),
-                                   datetime(2020, 4, 4, 0, 15),
-                                   datetime(2020, 4, 4, 0, 25)])
-        hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)])
+        hit_data_epoch = np.array(
+            [
+                datetime(2020, 4, 4, 0, 5),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 25),
+            ]
+        )
+        hit_data_delta = np.array(
+            [timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)]
+        )
         mag_data = np.empty((0, 3))
         mag_epoch = []
-        actual_average = rebin(from_epoch=mag_epoch, from_data=mag_data,
-                               to_epoch=hit_data_epoch,
-                               to_epoch_delta=hit_data_delta)
+        actual_average = rebin(
+            from_epoch=mag_epoch,
+            from_data=mag_data,
+            to_epoch=hit_data_epoch,
+            to_epoch_delta=hit_data_delta,
+        )
 
         expected_average = [
             [np.nan, np.nan, np.nan],
             [np.nan, np.nan, np.nan],
-            [np.nan, np.nan, np.nan]
+            [np.nan, np.nan, np.nan],
         ]
 
         np.testing.assert_array_equal(expected_average, actual_average)
 
     def test_rebin_empty_bins_with_nan(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5),
-                                   datetime(2020, 4, 4, 0, 15),
-                                   datetime(2020, 4, 4, 0, 25)])
-        hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)])
+        hit_data_epoch = np.array(
+            [
+                datetime(2020, 4, 4, 0, 5),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 25),
+            ]
+        )
+        hit_data_delta = np.array(
+            [timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)]
+        )
         mag_data = np.array([[0, 0, 1], [0, 1, 0]])
-        mag_epoch = [datetime(2020, 4, 4, 0, 1),
-                     datetime(2020, 4, 4, 0, 3)]
-        actual_average = rebin(from_epoch=mag_epoch, from_data=mag_data,
-                               to_epoch=hit_data_epoch,
-                               to_epoch_delta=hit_data_delta)
+        mag_epoch = [datetime(2020, 4, 4, 0, 1), datetime(2020, 4, 4, 0, 3)]
+        actual_average = rebin(
+            from_epoch=mag_epoch,
+            from_data=mag_data,
+            to_epoch=hit_data_epoch,
+            to_epoch_delta=hit_data_delta,
+        )
         expected_average = [
             [0, 0.5, 0.5],
             [np.nan, np.nan, np.nan],
-            [np.nan, np.nan, np.nan]
+            [np.nan, np.nan, np.nan],
         ]
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_gap_in_to_epoch(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5),
-                                   datetime(2020, 4, 4, 0, 15),
-                                   datetime(2020, 4, 4, 0, 30)])
-        hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)])
+        hit_data_epoch = np.array(
+            [
+                datetime(2020, 4, 4, 0, 5),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 30),
+            ]
+        )
+        hit_data_delta = np.array(
+            [timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)]
+        )
         mag_data = np.array([[0, 0, 1], [0, 1, 0], [0, 0, 1], [1, 0, 0]])
-        mag_epoch = [datetime(2020, 4, 4, 0, 1),
-                     datetime(2020, 4, 4, 0, 7),
-                     datetime(2020, 4, 4, 0, 17),
-                     datetime(2020, 4, 4, 0, 21)
-                     ]
-        actual_average = rebin(from_epoch=mag_epoch, from_data=mag_data,
-                               to_epoch=hit_data_epoch,
-                               to_epoch_delta=hit_data_delta)
-        expected_average = [
-            [0, 0.5, 0.5],
-            [0, 0, 1],
-            [np.nan, np.nan, np.nan]
+        mag_epoch = [
+            datetime(2020, 4, 4, 0, 1),
+            datetime(2020, 4, 4, 0, 7),
+            datetime(2020, 4, 4, 0, 17),
+            datetime(2020, 4, 4, 0, 21),
         ]
+        actual_average = rebin(
+            from_epoch=mag_epoch,
+            from_data=mag_data,
+            to_epoch=hit_data_epoch,
+            to_epoch_delta=hit_data_delta,
+        )
+        expected_average = [[0, 0.5, 0.5], [0, 0, 1], [np.nan, np.nan, np.nan]]
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_nearest_interpolator_interpolate(self):
         test_cases = [
-            ("matching cadence", [datetime(2020, 4, 4),
-                                  datetime(2020, 4, 5),
-                                  datetime(2020, 4, 6),
-                                  datetime(2020, 4, 7)],
-             [[0, 0, 1], [0, 2, 0], [0, 0, 3], [4, 0, 0]], [False, True, False, False]),
-            ("to slower cadence", [datetime(2020, 4, 4),
-                                   datetime(2020, 4, 6),
-                                   datetime(2020, 4, 8)],
-             [[0, 0, 1], [0, 0, 3], [4, 0, 0]], [False, False, False]),
-            ("to faster cadence", [datetime(2020, 4, 4, hour=8),
-                                   datetime(2020, 4, 5),
-                                   datetime(2020, 4, 5, hour=16)],
-             [[0, 0, 1], [0, 2, 0], [0, 0, 3]], [False, True, False]),
-            ("outside range", [datetime(2020, 4, 2, hour=23),
-                               datetime(2020, 4, 5),
-                               datetime(2020, 4, 8, hour=1)],
-             [[np.nan, np.nan, np.nan], [0, 2, 0], [np.nan, np.nan, np.nan]], [False, True, False]),
-
-            ("ties round down", [datetime(2020, 4, 4, hour=12),
-                                 datetime(2020, 4, 5, hour=12)],
-             [[0, 0, 1], [0, 2, 0], ], [False, True]),
+            (
+                "matching cadence",
+                [
+                    datetime(2020, 4, 4),
+                    datetime(2020, 4, 5),
+                    datetime(2020, 4, 6),
+                    datetime(2020, 4, 7),
+                ],
+                [[0, 0, 1], [0, 2, 0], [0, 0, 3], [4, 0, 0]],
+                [False, True, False, False],
+            ),
+            (
+                "to slower cadence",
+                [datetime(2020, 4, 4), datetime(2020, 4, 6), datetime(2020, 4, 8)],
+                [[0, 0, 1], [0, 0, 3], [4, 0, 0]],
+                [False, False, False],
+            ),
+            (
+                "to faster cadence",
+                [
+                    datetime(2020, 4, 4, hour=8),
+                    datetime(2020, 4, 5),
+                    datetime(2020, 4, 5, hour=16),
+                ],
+                [[0, 0, 1], [0, 2, 0], [0, 0, 3]],
+                [False, True, False],
+            ),
+            (
+                "outside range",
+                [
+                    datetime(2020, 4, 2, hour=23),
+                    datetime(2020, 4, 5),
+                    datetime(2020, 4, 8, hour=1),
+                ],
+                [[np.nan, np.nan, np.nan], [0, 2, 0], [np.nan, np.nan, np.nan]],
+                [False, True, False],
+            ),
+            (
+                "ties round down",
+                [datetime(2020, 4, 4, hour=12), datetime(2020, 4, 5, hour=12)],
+                [
+                    [0, 0, 1],
+                    [0, 2, 0],
+                ],
+                [False, True],
+            ),
         ]
 
         for case, to_dates, expected_values, expected_quality_flags in test_cases:
@@ -161,43 +269,69 @@ def test_nearest_interpolator_interpolate(self):
                 to_data_epoch = np.array(to_dates)
                 from_data = np.array([[0, 0, 1], [0, 2, 0], [0, 0, 3], [4, 0, 0]])
                 quality_flags = np.array([False, True, False, False])
-                from_date_epoch = np.array([datetime(2020, 4, 4),
-                                            datetime(2020, 4, 5),
-                                            datetime(2020, 4, 6),
-                                            datetime(2020, 4, 7)
-                                            ])
+                from_date_epoch = np.array(
+                    [
+                        datetime(2020, 4, 4),
+                        datetime(2020, 4, 5),
+                        datetime(2020, 4, 6),
+                        datetime(2020, 4, 7),
+                    ]
+                )
 
-                interpolator = NearestInterpolator(from_date_epoch, from_data, to_data_epoch, timedelta(days=1))
+                interpolator = NearestInterpolator(
+                    from_date_epoch, from_data, to_data_epoch, timedelta(days=1)
+                )
 
-                np.testing.assert_array_equal(interpolator.interpolate_data(), expected_values)
-                np.testing.assert_array_equal(interpolator.interpolate_flags(quality_flags), expected_quality_flags)
+                np.testing.assert_array_equal(
+                    interpolator.interpolate_data(), expected_values
+                )
+                np.testing.assert_array_equal(
+                    interpolator.interpolate_flags(quality_flags),
+                    expected_quality_flags,
+                )
 
     def test_find_closest_neighbor_handles_large_dataset(self):
         to_epoch = np.array(
             [
                 [
                     [
-                        datetime(2020, 4, 4) + timedelta(minutes=i) + timedelta(seconds=j) + timedelta(seconds=k) for k
-                        in range(30)
+                        datetime(2020, 4, 4)
+                        + timedelta(minutes=i)
+                        + timedelta(seconds=j)
+                        + timedelta(seconds=k)
+                        for k in range(30)
                     ]
                     for j in range(24)
                 ]
-                for i in range(1440)])
+                for i in range(1440)
+            ]
+        )
 
         from_data = np.repeat([[1, 0, 0]], 86400 * 2, axis=0)
-        from_epoch = np.array([datetime(2020, 4, 4) + timedelta(seconds=0.5) * i for i in range(86400 * 2)])
+        from_epoch = np.array(
+            [
+                datetime(2020, 4, 4) + timedelta(seconds=0.5) * i
+                for i in range(86400 * 2)
+            ]
+        )
 
         t0 = time.perf_counter()
-        actual_neighbor_values = NearestInterpolator(from_epoch, from_data, to_epoch.astype(np.datetime64), timedelta(days=1)).interpolate_data()
+        actual_neighbor_values = NearestInterpolator(
+            from_epoch, from_data, to_epoch.astype(np.datetime64), timedelta(days=1)
+        ).interpolate_data()
         t1 = time.perf_counter()
         self.assertEqual((1440, 24, 30, 3), actual_neighbor_values.shape)
         self.assertLess(t1 - t0, 10)
 
     def test_nearest_interpolator_handles_nan(self):
         to_epoch = np.array([datetime(2020, 4, 4, minute=1)])
-        from_epoch = np.array([datetime(2020, 4, 4, minute=1), datetime(2020, 4, 4, minute=2)])
+        from_epoch = np.array(
+            [datetime(2020, 4, 4, minute=1), datetime(2020, 4, 4, minute=2)]
+        )
 
         from_data = np.array([np.nan, 2])
 
-        interpolator = NearestInterpolator(from_epoch, from_data, to_epoch, timedelta(minutes=1))
+        interpolator = NearestInterpolator(
+            from_epoch, from_data, to_epoch, timedelta(minutes=1)
+        )
         self.assertEqual(interpolator.interpolate_data(), [2])
diff --git a/tests/test_helpers.py b/tests/test_helpers.py
index d35118b6e..d3a3a96ee 100644
--- a/tests/test_helpers.py
+++ b/tests/test_helpers.py
@@ -1,24 +1,28 @@
 import json
 import os
 import tempfile
-from dataclasses import fields, dataclass
-from datetime import timedelta, datetime
+from collections.abc import Callable
+from dataclasses import dataclass, fields
+from datetime import datetime, timedelta
 from functools import wraps
 from pathlib import Path
-from typing import Type, Optional, Callable, TypeVar
+from typing import TypeVar
 from unittest import SkipTest
 from unittest.mock import Mock
 
 import imap_data_access
 import numpy as np
-from imap_data_access import ScienceFilePath, AncillaryFilePath, SPICEFilePath
-from imap_data_access.file_validation import generate_imap_file_path, Version
+from imap_data_access import AncillaryFilePath, ScienceFilePath, SPICEFilePath
+from imap_data_access.file_validation import Version, generate_imap_file_path
 
 import imap_l3_processing
 import tests
 from imap_l3_processing.models import VersionMap
 from imap_l3_processing.swe.l3.models import SweConfiguration, SweL3MomentData
-from imap_l3_processing.swe.l3.science.moment_calculations import Moments, MomentFitResults
+from imap_l3_processing.swe.l3.science.moment_calculations import (
+    MomentFitResults,
+    Moments,
+)
 
 run_local_data_path = Path(tests.__file__).parent.parent / "run_local_input_data"
 
@@ -36,9 +40,11 @@ def try_get_many_run_local_paths(extensions: list[str]) -> tuple[bool, list[Path
             missing_path = True
     return missing_path, paths
 
+
 def get_imap_data_dir_path() -> Path:
     return Path(imap_l3_processing.__file__).parent.parent / "data" / "imap"
 
+
 def get_test_data_path(filename: str) -> Path:
     return Path(tests.__file__).parent / "test_data" / filename
 
@@ -46,6 +52,7 @@ def get_test_data_path(filename: str) -> Path:
 def get_integration_test_data_path(filename: str) -> Path:
     return Path(tests.__file__).parent / "integration" / "test_data" / filename
 
+
 def get_spice_data_path(filename: str) -> Path:
     return Path(tests.__file__).parent.parent / "spice_kernels" / filename
 
@@ -72,21 +79,40 @@ def build_swe_configuration(**args) -> SweConfiguration:
 
 
 def build_moments(**args) -> Moments:
-    default_moments = dict(alpha=1, beta=2, t_parallel=3e5, t_perpendicular=4e5, velocity_x=500, velocity_y=600,
-                           velocity_z=700, density=80, aoo=9, ao=10)
+    default_moments = dict(
+        alpha=1,
+        beta=2,
+        t_parallel=3e5,
+        t_perpendicular=4e5,
+        velocity_x=500,
+        velocity_y=600,
+        velocity_z=700,
+        density=80,
+        aoo=9,
+        ao=10,
+    )
     default_moments.update(**args)
 
     return Moments(**default_moments)
 
 
-def build_moment_fit_results(moments: Moments = None, chisq: float = 1, number_of_points: int = 10,
-                             regress_result: np.ndarray = None) -> MomentFitResults:
+def build_moment_fit_results(
+    moments: Moments = None,
+    chisq: float = 1,
+    number_of_points: int = 10,
+    regress_result: np.ndarray = None,
+) -> MomentFitResults:
     if moments is None:
         moments = build_moments()
     if regress_result is None:
         regress_result = np.ndarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    return MomentFitResults(moments=moments, chisq=chisq, number_of_points=number_of_points,
-                            regress_result=regress_result)
+    return MomentFitResults(
+        moments=moments,
+        chisq=chisq,
+        number_of_points=number_of_points,
+        regress_result=regress_result,
+    )
+
 
 def build_swe_moment_data(epoch_len):
     integrated_field_shapes: dict[str, tuple[int, ...]] = {
@@ -132,23 +158,31 @@ def build_swe_moment_data(epoch_len):
     }
     mock_moment_data = create_dataclass_mock(SweL3MomentData)
     for name, trailing_shape in integrated_field_shapes.items():
-        setattr(mock_moment_data, name, np.ones((epoch_len, *trailing_shape), dtype=np.float64))
+        setattr(
+            mock_moment_data,
+            name,
+            np.ones((epoch_len, *trailing_shape), dtype=np.float64),
+        )
     mock_moment_data.core_t_parallel_integrated = np.arange(epoch_len, dtype=np.float64)
     mock_moment_data.core_t_parallel_fit = np.arange(epoch_len, dtype=np.float64)
-    mock_moment_data.core_t_perpendicular_integrated = np.arange(epoch_len * 2, dtype=np.float64).reshape((-1, 2))
+    mock_moment_data.core_t_perpendicular_integrated = np.arange(
+        epoch_len * 2, dtype=np.float64
+    ).reshape((-1, 2))
     mock_moment_data.core_t_perpendicular_fit = np.arange(epoch_len, dtype=np.float64)
     mock_moment_data.halo_t_parallel_integrated = np.arange(epoch_len, dtype=np.float64)
     mock_moment_data.halo_t_parallel_fit = np.arange(epoch_len, dtype=np.float64)
-    mock_moment_data.halo_t_perpendicular_integrated = np.arange(epoch_len * 2, dtype=np.float64).reshape((-1, 2))
+    mock_moment_data.halo_t_perpendicular_integrated = np.arange(
+        epoch_len * 2, dtype=np.float64
+    ).reshape((-1, 2))
     mock_moment_data.halo_t_perpendicular_fit = np.arange(epoch_len, dtype=np.float64)
     mock_moment_data.quality_flags = np.zeros(epoch_len, dtype=np.uint16)
     return mock_moment_data
 
 
-T = TypeVar('T')
+T = TypeVar("T")
 
 
-def create_dataclass_mock(obj: Type[T], **kwargs) -> T:
+def create_dataclass_mock(obj: type[T], **kwargs) -> T:
     return Mock(spec=[field.name for field in fields(obj)], **kwargs)
 
 
@@ -176,12 +210,15 @@ def assert_dict_close(x, y, rtol=1e-7, path=None):
         path = []
     path_str = " > ".join(path)
     if isinstance(x, dict) and isinstance(y, dict):
-        assert set(x.keys()) == set(
-            y.keys()), f"keys differ at {path_str}\n expected keys: {x.keys()}\n actual keys: {y.keys()}"
+        assert set(x.keys()) == set(y.keys()), (
+            f"keys differ at {path_str}\n expected keys: {x.keys()}\n actual keys: {y.keys()}"
+        )
         for k in x:
             assert_dict_close(x[k], y[k], rtol, path.copy() + [k])
     elif isinstance(x, (list, np.ndarray, float)):
-        np.testing.assert_allclose(x, y, rtol=rtol, err_msg=f"path to failure: {path_str}")
+        np.testing.assert_allclose(
+            x, y, rtol=rtol, err_msg=f"path to failure: {path_str}"
+        )
     else:
         assert x == y, f"{x} != {y} at path {path_str}"
 
@@ -203,13 +240,17 @@ def environment_variables(env_vars: dict):
     def decorator(func):
 
         def wrapper(*args, **kwargs):
-            old_vars = {k: os.environ.get(v) for k, v in env_vars.items() if os.environ.get(str(v)) is not None}
+            old_vars = {
+                k: os.environ.get(v)
+                for k, v in env_vars.items()
+                if os.environ.get(str(v)) is not None
+            }
             for k, v in env_vars.items():
                 os.environ[k] = str(v)
 
             func_result = func(*args, **kwargs)
 
-            for k in env_vars.keys():
+            for k in env_vars:
                 del os.environ[k]
 
             for k, v in old_vars.items():
@@ -222,8 +263,9 @@ def wrapper(*args, **kwargs):
     return decorator
 
 
-def create_mock_query_results(file_names: list[Path | str], ingestion_dates: Optional[list[datetime]] = None) -> list[
-    dict]:
+def create_mock_query_results(
+    file_names: list[Path | str], ingestion_dates: list[datetime] | None = None
+) -> list[dict]:
     file_paths = []
 
     if ingestion_dates is None:
@@ -239,50 +281,64 @@ def create_mock_query_results(file_names: list[Path | str], ingestion_dates: Opt
 
         match imap_file_path:
             case ScienceFilePath():
-                version_object = Version(imap_file_path.major_version, imap_file_path.minor_version)
-                file_paths.append({
-                    "instrument": imap_file_path.instrument,
-                    "data_level": imap_file_path.data_level,
-                    "descriptor": imap_file_path.descriptor,
-                    "start_date": imap_file_path.start_date,
-                    "ingestion_date": ingestion_date.strftime("%Y%m%d %H:%M:%S"),
-                    "major_version": version_object.major,
-                    "minor_version": version_object.minor,
-                    "cr": imap_file_path.cr,
-                    "file_path": file_path,
-                    "repointing": imap_file_path.repointing
-                })
+                version_object = Version(
+                    imap_file_path.major_version, imap_file_path.minor_version
+                )
+                file_paths.append(
+                    {
+                        "instrument": imap_file_path.instrument,
+                        "data_level": imap_file_path.data_level,
+                        "descriptor": imap_file_path.descriptor,
+                        "start_date": imap_file_path.start_date,
+                        "ingestion_date": ingestion_date.strftime("%Y%m%d %H:%M:%S"),
+                        "major_version": version_object.major,
+                        "minor_version": version_object.minor,
+                        "cr": imap_file_path.cr,
+                        "file_path": file_path,
+                        "repointing": imap_file_path.repointing,
+                    }
+                )
             case AncillaryFilePath():
-                file_paths.append({
-                    "instrument": imap_file_path.instrument,
-                    "descriptor": imap_file_path.descriptor,
-                    "start_date": imap_file_path.start_date,
-                    "end_date": imap_file_path.end_date,
-                    "ingestion_date": ingestion_date.strftime("%Y%m%d %H:%M:%S"),
-                    "version": imap_file_path.version,
-                    "file_path": file_path,
-                })
+                file_paths.append(
+                    {
+                        "instrument": imap_file_path.instrument,
+                        "descriptor": imap_file_path.descriptor,
+                        "start_date": imap_file_path.start_date,
+                        "end_date": imap_file_path.end_date,
+                        "ingestion_date": ingestion_date.strftime("%Y%m%d %H:%M:%S"),
+                        "version": imap_file_path.version,
+                        "file_path": file_path,
+                    }
+                )
             case SPICEFilePath():
                 continue
             case _:
                 raise NotImplementedError(f"Unexpected file path type {imap_file_path}")
     return file_paths
 
-def create_mock_version_map(descriptor: Optional[str] = 'descriptor', major_version: Optional[int] = None, minor_version: Optional[int] = 1):
-    return VersionMap({descriptor:Version(major_version, minor_version)})
+
+def create_mock_version_map(
+    descriptor: str | None = "descriptor",
+    major_version: int | None = None,
+    minor_version: int | None = 1,
+):
+    return VersionMap({descriptor: Version(major_version, minor_version)})
+
 
 @dataclass
 class PeriodicallyRunTest:
     test_name: str
     frequency: str
-    last_run: Optional[str]
+    last_run: str | None
 
 
 def run_periodically(frequency: timedelta):
     def run_periodically_decorator(test_item):
         @wraps(test_item)
         def test_thing(*args):
-            periodically_run_tests_path = Path(__file__).parent / "periodically_run_tests.json"
+            periodically_run_tests_path = (
+                Path(__file__).parent / "periodically_run_tests.json"
+            )
             periodically_run_tests = json.loads(periodically_run_tests_path.read_text())
 
             last_run = periodically_run_tests.get(test_item.__name__)
@@ -290,15 +346,21 @@ def test_thing(*args):
             if last_run is not None:
                 last_run_time = datetime.fromisoformat(last_run) + frequency
                 if datetime.now() < last_run_time:
-                    raise SkipTest(f'Skipping expensive test, {test_item.__name__}, because it passed recently')
+                    raise SkipTest(
+                        f"Skipping expensive test, {test_item.__name__}, because it passed recently"
+                    )
 
             try:
                 test_item(*args)
                 periodically_run_tests[test_item.__name__] = datetime.now().isoformat()
-                periodically_run_tests_path.write_text(json.dumps(periodically_run_tests, indent=4))
+                periodically_run_tests_path.write_text(
+                    json.dumps(periodically_run_tests, indent=4)
+                )
             except Exception as e:
                 periodically_run_tests[test_item.__name__] = None
-                periodically_run_tests_path.write_text(json.dumps(periodically_run_tests, indent=4))
+                periodically_run_tests_path.write_text(
+                    json.dumps(periodically_run_tests, indent=4)
+                )
                 raise e
 
         return test_thing
diff --git a/tests/test_imap_l3_data_processor.py b/tests/test_imap_l3_data_processor.py
index 4b8f08704..3290682e3 100644
--- a/tests/test_imap_l3_data_processor.py
+++ b/tests/test_imap_l3_data_processor.py
@@ -1,13 +1,16 @@
-import itertools
 import json
 from datetime import datetime
 from pathlib import Path
 from unittest import TestCase
-from unittest.mock import patch, call, Mock, sentinel
+from unittest.mock import Mock, call, patch, sentinel
 
 from imap_data_access.file_validation import Version
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, \
-    AncillaryInput, SPICEInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+    SPICEInput,
+)
 
 from imap_l3_data_processor import imap_l3_processor
 from imap_l3_processing.hi.hi_combined_initializer import HI_COMBINED_DESCRIPTORS
@@ -15,28 +18,44 @@
 from imap_l3_processing.lo.l3.lo_sp_initializer import LO_SP_MAP_DESCRIPTORS
 from imap_l3_processing.maps.map_initializer import PossibleMapToProduce
 from imap_l3_processing.models import InputMetadata, VersionMap
-from imap_l3_processing.ultra.ultra_combined_nsp_initializer import ULTRA_COMBINED_NSP_DESCRIPTORS
-from imap_l3_processing.ultra.ultra_sp_initializer import ULTRA_45_DESCRIPTORS, ULTRA_90_DESCRIPTORS, \
-    ULTRA_COMBINED_SP_DESCRIPTORS
+from imap_l3_processing.ultra.ultra_combined_nsp_initializer import (
+    ULTRA_COMBINED_NSP_DESCRIPTORS,
+)
+from imap_l3_processing.ultra.ultra_sp_initializer import (
+    ULTRA_45_DESCRIPTORS,
+    ULTRA_90_DESCRIPTORS,
+    ULTRA_COMBINED_SP_DESCRIPTORS,
+)
 
 
 class TestImapL3DataProcessor(TestCase):
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    @patch('imap_l3_data_processor.SwapiProcessor')
-    @patch('imap_l3_data_processor.SweProcessor')
-    @patch('imap_l3_data_processor.HitProcessor')
-    @patch('imap_l3_data_processor.HiProcessor')
-    @patch('imap_l3_data_processor.LoProcessor')
-    @patch('imap_l3_data_processor.UltraProcessor')
-    @patch('imap_l3_data_processor.GlowsProcessor')
-    @patch('imap_l3_data_processor.CodiceLoProcessor')
-    @patch('imap_l3_data_processor.CodiceHiProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    def test_invokes_correct_processor(self, mock_argparse, mock_codice_hi, mock_codice_lo, mock_glows,
-                                       mock_ultra, mock_lo, mock_hi, mock_hit, mock_swe,
-                                       mock_swapi, mock_upload,
-                                       mock_processing_input):
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    @patch("imap_l3_data_processor.SwapiProcessor")
+    @patch("imap_l3_data_processor.SweProcessor")
+    @patch("imap_l3_data_processor.HitProcessor")
+    @patch("imap_l3_data_processor.HiProcessor")
+    @patch("imap_l3_data_processor.LoProcessor")
+    @patch("imap_l3_data_processor.UltraProcessor")
+    @patch("imap_l3_data_processor.GlowsProcessor")
+    @patch("imap_l3_data_processor.CodiceLoProcessor")
+    @patch("imap_l3_data_processor.CodiceHiProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    def test_invokes_correct_processor(
+        self,
+        mock_argparse,
+        mock_codice_hi,
+        mock_codice_lo,
+        mock_glows,
+        mock_ultra,
+        mock_lo,
+        mock_hi,
+        mock_hit,
+        mock_swe,
+        mock_swapi,
+        mock_upload,
+        mock_processing_input,
+    ):
         cases = [
             ("swapi", "l3a", "proton", mock_swapi),
             ("swapi", "l3b", "combined", mock_swapi),
@@ -57,7 +76,7 @@ def test_invokes_correct_processor(self, mock_argparse, mock_codice_hi, mock_cod
             expected_processor.reset_mock()
             mock_upload.reset_mock()
             mock_processing_input.reset_mock()
-            with self.subTest(f'{instrument}{data_level}'):
+            with self.subTest(f"{instrument}{data_level}"):
                 mock_argument_parser = mock_argparse.ArgumentParser.return_value
                 mock_argument_parser.parse_args.return_value.instrument = instrument
                 mock_argument_parser.parse_args.return_value.data_level = data_level
@@ -68,130 +87,242 @@ def test_invokes_correct_processor(self, mock_argparse, mock_codice_hi, mock_cod
                 mock_argument_parser.parse_args.return_value.version = "v099"
                 mock_argument_parser.parse_args.return_value.descriptor = descriptor
 
-                expected_input_metadata = InputMetadata(instrument, data_level, datetime(2025, 1, 1),
-                                                        datetime(2025, 1, 1),
-                                                        VersionMap({},Version(None, 99)), descriptor=descriptor,
-                                                        repointing=22)
+                expected_input_metadata = InputMetadata(
+                    instrument,
+                    data_level,
+                    datetime(2025, 1, 1),
+                    datetime(2025, 1, 1),
+                    VersionMap({}, Version(None, 99)),
+                    descriptor=descriptor,
+                    repointing=22,
+                )
 
                 expected_processor.return_value.process.return_value = [sentinel.cdf]
 
                 imap_l3_processor()
 
-                mock_argument_parser.add_argument.assert_has_calls([
-                    call("--instrument"),
-                    call("--data-level"),
-                    call("--descriptor"),
-                    call("--start-date"),
-                    call("--end-date", required=False),
-                    call("--repointing", required=False),
-                    call("--version", required=False),
-                    call("--dependency"),
-                    call("--upload-to-sdc", action="store_true", required=False,
-                         help="Upload completed output files to the IMAP SDC.")
-                ])
-
-                mock_processing_input.return_value.deserialize.assert_called_once_with("[]")
-
-                expected_processor.assert_called_once_with(mock_processing_input.return_value, expected_input_metadata)
+                mock_argument_parser.add_argument.assert_has_calls(
+                    [
+                        call("--instrument"),
+                        call("--data-level"),
+                        call("--descriptor"),
+                        call("--start-date"),
+                        call("--end-date", required=False),
+                        call("--repointing", required=False),
+                        call("--version", required=False),
+                        call("--dependency"),
+                        call(
+                            "--upload-to-sdc",
+                            action="store_true",
+                            required=False,
+                            help="Upload completed output files to the IMAP SDC.",
+                        ),
+                    ]
+                )
+
+                mock_processing_input.return_value.deserialize.assert_called_once_with(
+                    "[]"
+                )
+
+                expected_processor.assert_called_once_with(
+                    mock_processing_input.return_value, expected_input_metadata
+                )
                 mock_upload.assert_called_once_with(sentinel.cdf)
 
-    @patch('imap_l3_data_processor.HiCombinedInitializer')
-    @patch('imap_l3_data_processor.UltraCombinedNSPInitializer')
-    @patch('imap_l3_data_processor.UltraSPInitializer')
-    @patch('imap_l3_data_processor.UltraProcessor')
-    @patch('imap_l3_data_processor.HiSPInitializer')
-    @patch('imap_l3_data_processor.HiProcessor')
-    @patch('imap_l3_data_processor.LoSPInitializer')
-    @patch('imap_l3_data_processor.LoProcessor')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    @patch('imap_l3_data_processor.argparse')
-    def test_scheduled_map_jobs_invoke_correct_initializer(self, mock_argparse, mock_upload,
-                                                           mock_lo_processor_class, mock_lo_initializer_class,
-                                                           mock_hi_processor_class, mock_hi_initializer_class,
-                                                           mock_ultra_processor_class, mock_ultra_initializer_class,
-                                                           mock_ultra_combined_nsp_initializer_class,
-                                                           mock_hi_combined_initializer_class):
+    @patch("imap_l3_data_processor.HiCombinedInitializer")
+    @patch("imap_l3_data_processor.UltraCombinedNSPInitializer")
+    @patch("imap_l3_data_processor.UltraSPInitializer")
+    @patch("imap_l3_data_processor.UltraProcessor")
+    @patch("imap_l3_data_processor.HiSPInitializer")
+    @patch("imap_l3_data_processor.HiProcessor")
+    @patch("imap_l3_data_processor.LoSPInitializer")
+    @patch("imap_l3_data_processor.LoProcessor")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    @patch("imap_l3_data_processor.argparse")
+    def test_scheduled_map_jobs_invoke_correct_initializer(
+        self,
+        mock_argparse,
+        mock_upload,
+        mock_lo_processor_class,
+        mock_lo_initializer_class,
+        mock_hi_processor_class,
+        mock_hi_initializer_class,
+        mock_ultra_processor_class,
+        mock_ultra_initializer_class,
+        mock_ultra_combined_nsp_initializer_class,
+        mock_hi_combined_initializer_class,
+    ):
         instrument_cases = [
-            ("hi", "sp-maps", mock_hi_initializer_class, mock_hi_processor_class, HI_SP_MAP_DESCRIPTORS),
-            ("hi", "hic-maps", mock_hi_combined_initializer_class, mock_hi_processor_class, HI_COMBINED_DESCRIPTORS),
-            ("lo", "all-maps", mock_lo_initializer_class, mock_lo_processor_class, LO_SP_MAP_DESCRIPTORS),
-            ("ultra", "u45-maps", mock_ultra_initializer_class, mock_ultra_processor_class, ULTRA_45_DESCRIPTORS),
-            ("ultra", "u90-maps", mock_ultra_initializer_class, mock_ultra_processor_class, ULTRA_90_DESCRIPTORS),
             (
-                "ultra", "ulc-sp-maps", mock_ultra_initializer_class, mock_ultra_processor_class,
-                ULTRA_COMBINED_SP_DESCRIPTORS),
-            ("ultra", "ulc-nsp-maps", mock_ultra_combined_nsp_initializer_class, mock_ultra_processor_class,
-             ULTRA_COMBINED_NSP_DESCRIPTORS),
+                "hi",
+                "sp-maps",
+                mock_hi_initializer_class,
+                mock_hi_processor_class,
+                HI_SP_MAP_DESCRIPTORS,
+            ),
+            (
+                "hi",
+                "hic-maps",
+                mock_hi_combined_initializer_class,
+                mock_hi_processor_class,
+                HI_COMBINED_DESCRIPTORS,
+            ),
+            (
+                "lo",
+                "all-maps",
+                mock_lo_initializer_class,
+                mock_lo_processor_class,
+                LO_SP_MAP_DESCRIPTORS,
+            ),
+            (
+                "ultra",
+                "u45-maps",
+                mock_ultra_initializer_class,
+                mock_ultra_processor_class,
+                ULTRA_45_DESCRIPTORS,
+            ),
+            (
+                "ultra",
+                "u90-maps",
+                mock_ultra_initializer_class,
+                mock_ultra_processor_class,
+                ULTRA_90_DESCRIPTORS,
+            ),
+            (
+                "ultra",
+                "ulc-sp-maps",
+                mock_ultra_initializer_class,
+                mock_ultra_processor_class,
+                ULTRA_COMBINED_SP_DESCRIPTORS,
+            ),
+            (
+                "ultra",
+                "ulc-nsp-maps",
+                mock_ultra_combined_nsp_initializer_class,
+                mock_ultra_processor_class,
+                ULTRA_COMBINED_NSP_DESCRIPTORS,
+            ),
         ]
 
-        for instrument, descriptor, mock_initializer_class, mock_processor_class, expected_descriptors in instrument_cases:
-            version_descriptors = {descriptor: { "major_version": 123,"minor_version": 0} for descriptor in expected_descriptors}
+        for (
+            instrument,
+            descriptor,
+            mock_initializer_class,
+            mock_processor_class,
+            expected_descriptors,
+        ) in instrument_cases:
+            version_descriptors = {
+                descriptor: {"major_version": 123, "minor_version": 0}
+                for descriptor in expected_descriptors
+            }
             dependency_with_version_info = {
                 "version": version_descriptors,
-                "dependency": []
+                "dependency": [],
             }
-            dependency_with_version_info_as_string = json.dumps(dependency_with_version_info)
+            dependency_with_version_info_as_string = json.dumps(
+                dependency_with_version_info
+            )
 
             dependency_cases = [
                 ("old style", "[]", None, VersionMap({descriptor: Version(None, 1)})),
-                ("new style", dependency_with_version_info_as_string, 123, VersionMap({descriptor: Version(123, 1)})),
+                (
+                    "new style",
+                    dependency_with_version_info_as_string,
+                    123,
+                    VersionMap({descriptor: Version(123, 1)}),
+                ),
             ]
-            for source_of_version_info, dependency_contents, expected_major_version, expected_version_map in dependency_cases:
-
+            for (
+                source_of_version_info,
+                dependency_contents,
+                expected_major_version,
+                expected_version_map,
+            ) in dependency_cases:
                 mock_upload.reset_mock()
                 mock_argparse.reset_mock()
                 mock_initializer_class.reset_mock()
                 mock_processor_class.reset_mock()
 
-                with self.subTest(instrument=instrument, descriptor=descriptor, dependency=source_of_version_info):
+                with self.subTest(
+                    instrument=instrument,
+                    descriptor=descriptor,
+                    dependency=source_of_version_info,
+                ):
                     data_level = "l3"
                     mock_argument_parser = mock_argparse.ArgumentParser.return_value
                     mock_argument_parser.parse_args.return_value.instrument = instrument
                     mock_argument_parser.parse_args.return_value.data_level = data_level
-                    mock_argument_parser.parse_args.return_value.dependency = dependency_contents
+                    mock_argument_parser.parse_args.return_value.dependency = (
+                        dependency_contents
+                    )
                     mock_argument_parser.parse_args.return_value.start_date = "20250101"
                     mock_argument_parser.parse_args.return_value.end_date = None
-                    mock_argument_parser.parse_args.return_value.repointing = "repoint00022"
+                    mock_argument_parser.parse_args.return_value.repointing = (
+                        "repoint00022"
+                    )
                     mock_argument_parser.parse_args.return_value.version = "v001"
                     mock_argument_parser.parse_args.return_value.descriptor = descriptor
 
-                    expected_input_metadata = InputMetadata(instrument, data_level, datetime(2025, 1, 1),
-                                                            datetime(2025, 1, 1),
-                                                            expected_version_map, descriptor=descriptor,
-                                                            )
+                    expected_input_metadata = InputMetadata(
+                        instrument,
+                        data_level,
+                        datetime(2025, 1, 1),
+                        datetime(2025, 1, 1),
+                        expected_version_map,
+                        descriptor=descriptor,
+                    )
 
                     mock_initializer = mock_initializer_class.return_value
                     mock_processor = mock_processor_class.return_value
 
-                    possible_map_to_produce = PossibleMapToProduce(set(), expected_input_metadata)
-                    mock_initializer.get_maps_that_should_be_produced.return_value = [possible_map_to_produce]
+                    possible_map_to_produce = PossibleMapToProduce(
+                        set(), expected_input_metadata
+                    )
+                    mock_initializer.get_maps_that_should_be_produced.return_value = [
+                        possible_map_to_produce
+                    ]
 
                     mock_processor.process.return_value = [sentinel.cdf]
 
                     imap_l3_processor()
 
-                    mock_initializer.get_maps_that_should_be_produced.assert_has_calls([
-                        call(descriptor, expected_major_version) for descriptor in expected_descriptors
-                    ])
-
-                    self.assertEqual(len(expected_descriptors), mock_processor_class.call_count)
-
-                    self.assertEqual(len(expected_descriptors), mock_initializer.furnish_spice_dependencies.call_count)
-                    mock_initializer.furnish_spice_dependencies.assert_called_with(possible_map_to_produce)
-
-                    mock_processor_class.assert_called_with(possible_map_to_produce.processing_input_collection,
-                                                            expected_input_metadata)
-                    self.assertEqual(len(expected_descriptors), mock_processor.process.call_count)
+                    mock_initializer.get_maps_that_should_be_produced.assert_has_calls(
+                        [
+                            call(descriptor, expected_major_version)
+                            for descriptor in expected_descriptors
+                        ]
+                    )
+
+                    self.assertEqual(
+                        len(expected_descriptors), mock_processor_class.call_count
+                    )
+
+                    self.assertEqual(
+                        len(expected_descriptors),
+                        mock_initializer.furnish_spice_dependencies.call_count,
+                    )
+                    mock_initializer.furnish_spice_dependencies.assert_called_with(
+                        possible_map_to_produce
+                    )
+
+                    mock_processor_class.assert_called_with(
+                        possible_map_to_produce.processing_input_collection,
+                        expected_input_metadata,
+                    )
+                    self.assertEqual(
+                        len(expected_descriptors), mock_processor.process.call_count
+                    )
 
                     self.assertEqual(len(expected_descriptors), mock_upload.call_count)
                     mock_upload.assert_called_with(sentinel.cdf)
 
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    @patch('imap_l3_data_processor.HiSPInitializer')
-    @patch('imap_l3_data_processor.HiProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    def test_failing_to_produce_an_sp_map_continues(self, mock_argparse, mock_hi_processor, mock_hi_initializer_class,
-                                                    mock_upload):
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    @patch("imap_l3_data_processor.HiSPInitializer")
+    @patch("imap_l3_data_processor.HiProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    def test_failing_to_produce_an_sp_map_continues(
+        self, mock_argparse, mock_hi_processor, mock_hi_initializer_class, mock_upload
+    ):
         mock_hi_initializer = mock_hi_initializer_class.return_value
 
         instrument = "hi"
@@ -208,13 +339,14 @@ def test_failing_to_produce_an_sp_map_continues(self, mock_argparse, mock_hi_pro
         mock_argument_parser.parse_args.return_value.descriptor = descriptor
 
         mock_hi_initializer.get_maps_that_should_be_produced.side_effect = [
-            [PossibleMapToProduce(set(), Mock())] for _ in range(len(HI_SP_MAP_DESCRIPTORS))
+            [PossibleMapToProduce(set(), Mock())]
+            for _ in range(len(HI_SP_MAP_DESCRIPTORS))
         ]
 
         mock_hi_processor.return_value.process.side_effect = [
             [Path("hi_sp_map_1.cdf")],
             ValueError("something went wrong!"),
-            [Path("hi_sp_map_2.cdf")]
+            [Path("hi_sp_map_2.cdf")],
         ]
 
         with self.assertLogs():
@@ -222,23 +354,34 @@ def test_failing_to_produce_an_sp_map_continues(self, mock_argparse, mock_hi_pro
 
         self.assertEqual(len(HI_SP_MAP_DESCRIPTORS), mock_hi_processor.call_count)
 
-        mock_upload.assert_has_calls([
-            call(Path("hi_sp_map_1.cdf")),
-            call(Path("hi_sp_map_2.cdf")),
-        ])
-
-    @patch('imap_l3_data_processor.spiceypy')
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.imap_data_access.download')
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    @patch('imap_l3_data_processor.SwapiProcessor')
-    def test_get_spice_kernels_based_on_input_collection(self, _, __, mock_processing_input_collection, mock_download,
-                                                         mock_arg_parser_class, mock_spicepy):
+        mock_upload.assert_has_calls(
+            [
+                call(Path("hi_sp_map_1.cdf")),
+                call(Path("hi_sp_map_2.cdf")),
+            ]
+        )
+
+    @patch("imap_l3_data_processor.spiceypy")
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.imap_data_access.download")
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    @patch("imap_l3_data_processor.SwapiProcessor")
+    def test_get_spice_kernels_based_on_input_collection(
+        self,
+        _,
+        __,
+        mock_processing_input_collection,
+        mock_download,
+        mock_arg_parser_class,
+        mock_spicepy,
+    ):
         ancillary_input = AncillaryInput("imap_swe_ancillary_20250101_v112.cdf")
         spice_input_1 = SPICEInput("naif0012.tls")
         spice_input_2 = SPICEInput("imap_sclk_0012.tls")
-        imap_data_access_dependency = ProcessingInputCollection(spice_input_1, ancillary_input, spice_input_2)
+        imap_data_access_dependency = ProcessingInputCollection(
+            spice_input_1, ancillary_input, spice_input_2
+        )
         imap_data_access_dependency.deserialize = Mock()
         mock_processing_input_collection.return_value = imap_data_access_dependency
 
@@ -253,30 +396,31 @@ def test_get_spice_kernels_based_on_input_collection(self, _, __, mock_processin
         mock_argument_parser.parse_args.return_value.dependency = "[]"
         mock_argument_parser.parse_args.return_value.repointing = None
 
-        mock_download.side_effect = [
-            Path("naif0012.tls"),
-            Path("imap_sclk_0012.tls")
-        ]
+        mock_download.side_effect = [Path("naif0012.tls"), Path("imap_sclk_0012.tls")]
 
         imap_l3_processor()
-        expected_spice_paths = imap_data_access_dependency.get_file_paths(data_type='spice')
+        expected_spice_paths = imap_data_access_dependency.get_file_paths(
+            data_type="spice"
+        )
         self.assertEqual(2, mock_download.call_count)
-        mock_download.assert_has_calls([
-            call(expected_spice_paths[0]),
-            call(expected_spice_paths[1])
-        ])
+        mock_download.assert_has_calls(
+            [call(expected_spice_paths[0]), call(expected_spice_paths[1])]
+        )
         mock_spicepy.furnsh.assert_has_calls(
-            [
-                call("naif0012.tls"),
-                call("imap_sclk_0012.tls")
-            ])
-
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    @patch('imap_l3_data_processor.SweProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    def test_sets_end_date_to_start_date_if_not_specified(self, mock_argparse, mock_processor_class, mock_upload,
-                                                         mock_processing_input_collection):
+            [call("naif0012.tls"), call("imap_sclk_0012.tls")]
+        )
+
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    @patch("imap_l3_data_processor.SweProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    def test_sets_end_date_to_start_date_if_not_specified(
+        self,
+        mock_argparse,
+        mock_processor_class,
+        mock_upload,
+        mock_processing_input_collection,
+    ):
         cases = [("20170630", datetime(2017, 6, 30)), (None, datetime(2016, 6, 30))]
 
         instrument_argument = "swe"
@@ -284,10 +428,14 @@ def test_sets_end_date_to_start_date_if_not_specified(self, mock_argparse, mock_
         start_date_argument = "20160630"
         version_argument = "v092"
         descriptor_argument = "pitch-angle"
-        science_input_1 = ScienceInput("imap_swe_l1_sci_20250101_v112.cdf", "imap_swe_l1_sci_20250102_v112.cdf")
+        science_input_1 = ScienceInput(
+            "imap_swe_l1_sci_20250101_v112.cdf", "imap_swe_l1_sci_20250102_v112.cdf"
+        )
         science_input_2 = ScienceInput("imap_mag_l1d_norm-dsrf_20250101_v112.cdf")
         ancillary_input = AncillaryInput("imap_swe_ancillary_20250101_v112.cdf")
-        imap_data_access_dependency = ProcessingInputCollection(science_input_1, science_input_2, ancillary_input)
+        imap_data_access_dependency = ProcessingInputCollection(
+            science_input_1, science_input_2, ancillary_input
+        )
 
         mock_processing_input_collection.return_value = imap_data_access_dependency
         mock_processing_input_collection.deserialize = Mock()
@@ -297,7 +445,9 @@ def test_sets_end_date_to_start_date_if_not_specified(self, mock_argparse, mock_
 
         mock_argument_parser.parse_args.return_value.instrument = instrument_argument
         mock_argument_parser.parse_args.return_value.data_level = data_level_argument
-        mock_argument_parser.parse_args.return_value.dependency = imap_data_access_dependency.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            imap_data_access_dependency.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = start_date_argument
         mock_argument_parser.parse_args.return_value.version = version_argument
         mock_argument_parser.parse_args.return_value.descriptor = descriptor_argument
@@ -316,33 +466,54 @@ def test_sets_end_date_to_start_date_if_not_specified(self, mock_argparse, mock_
                 imap_l3_processor()
 
                 parser = mock_argparse.ArgumentParser()
-                parser.add_argument.assert_has_calls([
-                    call("--instrument"),
-                    call("--data-level"),
-                    call("--descriptor"),
-                    call("--start-date"),
-                    call("--end-date", required=False),
-                    call("--repointing", required=False),
-                    call("--version", required=False),
-                    call("--dependency"),
-                    call("--upload-to-sdc", action="store_true", required=False,
-                         help="Upload completed output files to the IMAP SDC.")
-                ])
-
-                expected_input_metadata = InputMetadata("swe", "l3", datetime(year=2016, month=6, day=30),
-                                                        expected_end_date, VersionMap({}, Version(None, 92)), "pitch-angle")
-
-                mock_processor_class.assert_called_with(imap_data_access_dependency, expected_input_metadata)
+                parser.add_argument.assert_has_calls(
+                    [
+                        call("--instrument"),
+                        call("--data-level"),
+                        call("--descriptor"),
+                        call("--start-date"),
+                        call("--end-date", required=False),
+                        call("--repointing", required=False),
+                        call("--version", required=False),
+                        call("--dependency"),
+                        call(
+                            "--upload-to-sdc",
+                            action="store_true",
+                            required=False,
+                            help="Upload completed output files to the IMAP SDC.",
+                        ),
+                    ]
+                )
+
+                expected_input_metadata = InputMetadata(
+                    "swe",
+                    "l3",
+                    datetime(year=2016, month=6, day=30),
+                    expected_end_date,
+                    VersionMap({}, Version(None, 92)),
+                    "pitch-angle",
+                )
+
+                mock_processor_class.assert_called_with(
+                    imap_data_access_dependency, expected_input_metadata
+                )
 
                 mock_processor.process.assert_called()
-                mock_upload.assert_called_once_with(mock_processor_class.return_value.process.return_value[0])
-
-    @patch('imap_l3_data_processor.GlowsProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    def test_uploads_multiple_files(self, mock_upload, mock_processing_input_collection, mock_argparse,
-                                    mock_glows_processor):
+                mock_upload.assert_called_once_with(
+                    mock_processor_class.return_value.process.return_value[0]
+                )
+
+    @patch("imap_l3_data_processor.GlowsProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    def test_uploads_multiple_files(
+        self,
+        mock_upload,
+        mock_processing_input_collection,
+        mock_argparse,
+        mock_glows_processor,
+    ):
         instrument_arg = "glows"
         start_date_arg = "20250101"
         version_arg = "v001"
@@ -356,7 +527,9 @@ def test_uploads_multiple_files(self, mock_upload, mock_processing_input_collect
 
         mock_argument_parser = mock_argparse.ArgumentParser.return_value
         mock_argument_parser.parse_args.return_value.instrument = instrument_arg
-        mock_argument_parser.parse_args.return_value.dependency = processing_input_collection.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            processing_input_collection.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = start_date_arg
         mock_argument_parser.parse_args.return_value.version = version_arg
         mock_argument_parser.parse_args.return_value.descriptor = descriptor_arg
@@ -365,28 +538,37 @@ def test_uploads_multiple_files(self, mock_upload, mock_processing_input_collect
         mock_argument_parser.parse_args.return_value.data_level = "l3b"
         mock_argument_parser.parse_args.return_value.end_date = None
 
-        mock_glows_processor.return_value.process.return_value = [sentinel.one, sentinel.two, sentinel.three]
+        mock_glows_processor.return_value.process.return_value = [
+            sentinel.one,
+            sentinel.two,
+            sentinel.three,
+        ]
 
         imap_l3_processor()
 
-        mock_upload.assert_has_calls([
-            call(sentinel.one),
-            call(sentinel.two),
-            call(sentinel.three)
-        ])
-
-    @patch('imap_l3_data_processor.GlowsProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    def test_does_not_upload_files_if_upload_to_sdc_flag_is_set_to_false(self, mock_upload,
-                                                                         mock_processing_input_collection,
-                                                                         mock_argparse,
-                                                                         mock_glows_processor):
-        processing_input_collection = ProcessingInputCollection(ScienceInput("imap_glows_l3_science_20250101_v001.cdf"))
+        mock_upload.assert_has_calls(
+            [call(sentinel.one), call(sentinel.two), call(sentinel.three)]
+        )
+
+    @patch("imap_l3_data_processor.GlowsProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    def test_does_not_upload_files_if_upload_to_sdc_flag_is_set_to_false(
+        self,
+        mock_upload,
+        mock_processing_input_collection,
+        mock_argparse,
+        mock_glows_processor,
+    ):
+        processing_input_collection = ProcessingInputCollection(
+            ScienceInput("imap_glows_l3_science_20250101_v001.cdf")
+        )
         mock_argument_parser = mock_argparse.ArgumentParser.return_value
         mock_argument_parser.parse_args.return_value.instrument = "glows"
-        mock_argument_parser.parse_args.return_value.dependency = processing_input_collection.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            processing_input_collection.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = "20250101"
         mock_argument_parser.parse_args.return_value.version = "v001"
         mock_argument_parser.parse_args.return_value.descriptor = "desc"
@@ -406,18 +588,25 @@ def test_does_not_upload_files_if_upload_to_sdc_flag_is_set_to_false(self, mock_
         mock_glows_processor.return_value.process.assert_called()
         mock_upload.assert_not_called()
 
-    @patch('imap_l3_data_processor.GlowsProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    def test_upload_fails_tries_all_uploads_and_raises_exception(self, mock_upload,
-                                                                 mock_processing_input_collection,
-                                                                 mock_argparse,
-                                                                 mock_glows_processor):
-        processing_input_collection = ProcessingInputCollection(ScienceInput("imap_glows_l3_science_20250101_v001.cdf"))
+    @patch("imap_l3_data_processor.GlowsProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    def test_upload_fails_tries_all_uploads_and_raises_exception(
+        self,
+        mock_upload,
+        mock_processing_input_collection,
+        mock_argparse,
+        mock_glows_processor,
+    ):
+        processing_input_collection = ProcessingInputCollection(
+            ScienceInput("imap_glows_l3_science_20250101_v001.cdf")
+        )
         mock_argument_parser = mock_argparse.ArgumentParser.return_value
         mock_argument_parser.parse_args.return_value.instrument = "glows"
-        mock_argument_parser.parse_args.return_value.dependency = processing_input_collection.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            processing_input_collection.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = "20250101"
         mock_argument_parser.parse_args.return_value.version = "v001"
         mock_argument_parser.parse_args.return_value.descriptor = "desc"
@@ -430,8 +619,12 @@ def test_upload_fails_tries_all_uploads_and_raises_exception(self, mock_upload,
         mock_processing_input_collection.deserialize = Mock()
         mock_processing_input_collection.get_science_inputs = Mock(return_value=[])
 
-        mock_glows_processor.return_value.process.return_value = ["data_file_1.cdf", "data_file_2.cdf",
-                                                                  "data_file_3.cdf", "data_file_4.cdf"]
+        mock_glows_processor.return_value.process.return_value = [
+            "data_file_1.cdf",
+            "data_file_2.cdf",
+            "data_file_3.cdf",
+            "data_file_4.cdf",
+        ]
 
         error1 = ValueError("Failure uploading!")
         error2 = ValueError("Failure uploading 2")
@@ -440,20 +633,27 @@ def test_upload_fails_tries_all_uploads_and_raises_exception(self, mock_upload,
         with self.assertRaises(IOError) as exception_ctx:
             imap_l3_processor()
 
-        self.assertEqual((f"Failed to upload some files: {[error1, error2]}",), exception_ctx.exception.args)
+        self.assertEqual(
+            (f"Failed to upload some files: {[error1, error2]}",),
+            exception_ctx.exception.args,
+        )
 
         mock_glows_processor.return_value.process.assert_called()
 
-        mock_upload.assert_has_calls([
-            call("data_file_1.cdf"),
-            call("data_file_2.cdf"),
-            call("data_file_3.cdf"),
-            call("data_file_4.cdf"),
-        ])
+        mock_upload.assert_has_calls(
+            [
+                call("data_file_1.cdf"),
+                call("data_file_2.cdf"),
+                call("data_file_3.cdf"),
+                call("data_file_4.cdf"),
+            ]
+        )
 
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    def test_throws_exception_for_unimplemented_instrument(self, mock_upload, mock_argparse):
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    def test_throws_exception_for_unimplemented_instrument(
+        self, mock_upload, mock_argparse
+    ):
         instrument_argument = "new_instrument"
         data_level_argument = "l3a"
         start_date_argument = "20160630"
@@ -466,7 +666,9 @@ def test_throws_exception_for_unimplemented_instrument(self, mock_upload, mock_a
 
         mock_argument_parser.parse_args.return_value.instrument = instrument_argument
         mock_argument_parser.parse_args.return_value.data_level = data_level_argument
-        mock_argument_parser.parse_args.return_value.dependency = imap_data_access_dependency.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            imap_data_access_dependency.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = start_date_argument
         mock_argument_parser.parse_args.return_value.end_date = end_date_argument
         mock_argument_parser.parse_args.return_value.version = version_argument
@@ -474,13 +676,17 @@ def test_throws_exception_for_unimplemented_instrument(self, mock_upload, mock_a
 
         with self.assertRaises(NotImplementedError) as exception_manager:
             imap_l3_processor()
-        self.assertEqual(str(exception_manager.exception),
-                         "Level l3a data processing has not yet been implemented for new_instrument")
+        self.assertEqual(
+            str(exception_manager.exception),
+            "Level l3a data processing has not yet been implemented for new_instrument",
+        )
         mock_upload.assert_not_called()
 
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    def test_throws_exception_for_codice_descriptor_not_matching_hi_or_lo(self, mock_upload, mock_argparse):
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    def test_throws_exception_for_codice_descriptor_not_matching_hi_or_lo(
+        self, mock_upload, mock_argparse
+    ):
         instrument_argument = "codice"
         data_level_argument = "l3a"
         start_date_argument = "20160630"
@@ -494,7 +700,9 @@ def test_throws_exception_for_codice_descriptor_not_matching_hi_or_lo(self, mock
 
         mock_argument_parser.parse_args.return_value.instrument = instrument_argument
         mock_argument_parser.parse_args.return_value.data_level = data_level_argument
-        mock_argument_parser.parse_args.return_value.dependency = imap_data_access_dependency.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            imap_data_access_dependency.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = start_date_argument
         mock_argument_parser.parse_args.return_value.end_date = end_date_argument
         mock_argument_parser.parse_args.return_value.version = version_argument
@@ -503,13 +711,17 @@ def test_throws_exception_for_codice_descriptor_not_matching_hi_or_lo(self, mock
 
         with self.assertRaises(NotImplementedError) as exception_manager:
             imap_l3_processor()
-        self.assertEqual(str(exception_manager.exception),
-                         "Unknown descriptor 'bad' for codice instrument")
+        self.assertEqual(
+            str(exception_manager.exception),
+            "Unknown descriptor 'bad' for codice instrument",
+        )
         mock_upload.assert_not_called()
 
-    @patch('imap_l3_data_processor.argparse')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    def test_throws_exception_when_attempting_to_process_non_l3_data_levels(self, mock_upload, mock_argparse):
+    @patch("imap_l3_data_processor.argparse")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    def test_throws_exception_when_attempting_to_process_non_l3_data_levels(
+        self, mock_upload, mock_argparse
+    ):
         instrument_argument = "swapi"
         data_level_argument = "l4"
         start_date_argument = "20160630"
@@ -522,7 +734,9 @@ def test_throws_exception_when_attempting_to_process_non_l3_data_levels(self, mo
 
         mock_argument_parser.parse_args.return_value.instrument = instrument_argument
         mock_argument_parser.parse_args.return_value.data_level = data_level_argument
-        mock_argument_parser.parse_args.return_value.dependency = imap_data_access_dependency.serialize()
+        mock_argument_parser.parse_args.return_value.dependency = (
+            imap_data_access_dependency.serialize()
+        )
         mock_argument_parser.parse_args.return_value.start_date = start_date_argument
         mock_argument_parser.parse_args.return_value.end_date = end_date_argument
         mock_argument_parser.parse_args.return_value.version = version_argument
@@ -530,32 +744,43 @@ def test_throws_exception_when_attempting_to_process_non_l3_data_levels(self, mo
 
         with self.assertRaises(NotImplementedError) as exception_manager:
             imap_l3_processor()
-        self.assertEqual(str(exception_manager.exception),
-                         "Level l4 data processing has not yet been implemented for swapi")
+        self.assertEqual(
+            str(exception_manager.exception),
+            "Level l4 data processing has not yet been implemented for swapi",
+        )
         mock_upload.assert_not_called()
 
-    @patch('imap_l3_data_processor.ProcessingInputCollection')
-    @patch('imap_l3_data_processor.imap_data_access.upload')
-    @patch('imap_l3_data_processor.SweProcessor')
-    @patch('imap_l3_data_processor.argparse')
-    def test_version_comes_from_dependency(self, mock_argparse, mock_processor_class, mock_upload,
-                                                         mock_processing_input_collection):
+    @patch("imap_l3_data_processor.ProcessingInputCollection")
+    @patch("imap_l3_data_processor.imap_data_access.upload")
+    @patch("imap_l3_data_processor.SweProcessor")
+    @patch("imap_l3_data_processor.argparse")
+    def test_version_comes_from_dependency(
+        self,
+        mock_argparse,
+        mock_processor_class,
+        mock_upload,
+        mock_processing_input_collection,
+    ):
         instrument_argument = "swe"
         data_level_argument = "l3"
         start_date_argument = "20160630"
         version_argument = "v092"
         descriptor_argument = "pitch-angle"
-        science_input_1 = ScienceInput("imap_swe_l1_sci_20250101_v112.cdf", "imap_swe_l1_sci_20250102_v112.cdf")
+        science_input_1 = ScienceInput(
+            "imap_swe_l1_sci_20250101_v112.cdf", "imap_swe_l1_sci_20250102_v112.cdf"
+        )
         science_input_2 = ScienceInput("imap_mag_l1d_norm-dsrf_20250101_v112.cdf")
         ancillary_input = AncillaryInput("imap_swe_ancillary_20250101_v112.cdf")
-        imap_data_access_dependency = ProcessingInputCollection(science_input_1, science_input_2, ancillary_input)
+        imap_data_access_dependency = ProcessingInputCollection(
+            science_input_1, science_input_2, ancillary_input
+        )
 
         serialized_dependency_paths = imap_data_access_dependency.serialize()
         dependency_without_version_info = json.loads(serialized_dependency_paths)
         dependency_with_version_info = {
             "version": {
                 "sci": {"major_version": 2, "minor_version": 1},
-                "var": {"major_version": 3, "minor_version": 2}
+                "var": {"major_version": 3, "minor_version": 2},
             },
             "dependency": dependency_without_version_info,
         }
@@ -574,12 +799,24 @@ def test_version_comes_from_dependency(self, mock_argparse, mock_processor_class
         mock_argument_parser.parse_args.return_value.repointing = None
 
         mock_processor = mock_processor_class.return_value
-        cases = [("version in dependency", dependency_with_version_info, VersionMap({'sci':Version(2,1), 'var':Version(3,2)})),
-                 ("version on command line", dependency_without_version_info, VersionMap({}, Version(None,92)))]
+        cases = [
+            (
+                "version in dependency",
+                dependency_with_version_info,
+                VersionMap({"sci": Version(2, 1), "var": Version(3, 2)}),
+            ),
+            (
+                "version on command line",
+                dependency_without_version_info,
+                VersionMap({}, Version(None, 92)),
+            ),
+        ]
         input_end_date, expected_end_date = "20170630", datetime(2017, 6, 30)
         for name, dependency, expected_version_map in cases:
             with self.subTest(name):
-                mock_argument_parser.parse_args.return_value.dependency = json.dumps(dependency)
+                mock_argument_parser.parse_args.return_value.dependency = json.dumps(
+                    dependency
+                )
                 mock_upload.reset_mock()
                 imap_data_access_dependency.deserialize.reset_mock()
 
@@ -590,24 +827,42 @@ def test_version_comes_from_dependency(self, mock_argparse, mock_processor_class
                 imap_l3_processor()
 
                 parser = mock_argparse.ArgumentParser()
-                parser.add_argument.assert_has_calls([
-                    call("--instrument"),
-                    call("--data-level"),
-                    call("--descriptor"),
-                    call("--start-date"),
-                    call("--end-date", required=False),
-                    call("--repointing", required=False),
-                    call("--version", required=False),
-                    call("--dependency"),
-                    call("--upload-to-sdc", action="store_true", required=False,
-                         help="Upload completed output files to the IMAP SDC.")
-                ])
-
-                expected_input_metadata = InputMetadata("swe", "l3", datetime(year=2016, month=6, day=30),
-                                                        expected_end_date, expected_version_map, "pitch-angle")
-
-                mock_processor_class.assert_called_with(imap_data_access_dependency, expected_input_metadata)
-
-                imap_data_access_dependency.deserialize.assert_called_once_with(serialized_dependency_paths)
+                parser.add_argument.assert_has_calls(
+                    [
+                        call("--instrument"),
+                        call("--data-level"),
+                        call("--descriptor"),
+                        call("--start-date"),
+                        call("--end-date", required=False),
+                        call("--repointing", required=False),
+                        call("--version", required=False),
+                        call("--dependency"),
+                        call(
+                            "--upload-to-sdc",
+                            action="store_true",
+                            required=False,
+                            help="Upload completed output files to the IMAP SDC.",
+                        ),
+                    ]
+                )
+
+                expected_input_metadata = InputMetadata(
+                    "swe",
+                    "l3",
+                    datetime(year=2016, month=6, day=30),
+                    expected_end_date,
+                    expected_version_map,
+                    "pitch-angle",
+                )
+
+                mock_processor_class.assert_called_with(
+                    imap_data_access_dependency, expected_input_metadata
+                )
+
+                imap_data_access_dependency.deserialize.assert_called_once_with(
+                    serialized_dependency_paths
+                )
                 mock_processor.process.assert_called()
-                mock_upload.assert_called_once_with(mock_processor_class.return_value.process.return_value[0])
+                mock_upload.assert_called_once_with(
+                    mock_processor_class.return_value.process.return_value[0]
+                )
diff --git a/tests/test_models.py b/tests/test_models.py
index 216687320..1f91bf58c 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -8,24 +8,49 @@
 
 
 class TestModels(unittest.TestCase):
-
     def test_calculate_average_mag_data_with_extra_data_at_beginning_and_end(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5), datetime(2020, 4, 4, 0, 15)])
+        hit_data_epoch = np.array(
+            [datetime(2020, 4, 4, 0, 5), datetime(2020, 4, 4, 0, 15)]
+        )
         hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300)])
         extra_data_at_beginning = [[0.5, 0, 1], [0, 0.5, 1]]
-        extra_dates_at_beginning = [datetime(2020, 4, 3, 0, 23), datetime(2020, 4, 3, 0, 23, 30)]
-        extra_dates_at_end = [datetime(2020, 4, 4, 1, 0), datetime(2020, 4, 4, 1, 23, 30)]
+        extra_dates_at_beginning = [
+            datetime(2020, 4, 3, 0, 23),
+            datetime(2020, 4, 3, 0, 23, 30),
+        ]
+        extra_dates_at_end = [
+            datetime(2020, 4, 4, 1, 0),
+            datetime(2020, 4, 4, 1, 23, 30),
+        ]
         extra_data_at_end = [[0.5, 1, 1], [1, 0.5, 0.5]]
-        mag_data = np.array(extra_data_at_beginning +
-                            [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0],
-                             [1, 0, 0]] + extra_data_at_end)
-        mag_epoch = np.array(extra_dates_at_beginning + [datetime(2020, 4, 4, 0, 1),
-                                                         datetime(2020, 4, 4, 0, 3), datetime(2020, 4, 4, 0, 6),
-                                                         datetime(2020, 4, 4, 0, 9, 55),
-                                                         datetime(2020, 4, 4, 0, 12, 45),
-                                                         datetime(2020, 4, 4, 0, 13),
-                                                         datetime(2020, 4, 4, 0, 15),
-                                                         datetime(2020, 4, 4, 0, 17)] + extra_dates_at_end)
+        mag_data = np.array(
+            extra_data_at_beginning
+            + [
+                [0, 0, 1],
+                [0, 1, 0],
+                [1, 0, 0],
+                [0, 1, 0],
+                [0, 0, 1],
+                [0, 1, 0],
+                [1, 0, 0],
+                [1, 0, 0],
+            ]
+            + extra_data_at_end
+        )
+        mag_epoch = np.array(
+            extra_dates_at_beginning
+            + [
+                datetime(2020, 4, 4, 0, 1),
+                datetime(2020, 4, 4, 0, 3),
+                datetime(2020, 4, 4, 0, 6),
+                datetime(2020, 4, 4, 0, 9, 55),
+                datetime(2020, 4, 4, 0, 12, 45),
+                datetime(2020, 4, 4, 0, 13),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 17),
+            ]
+            + extra_dates_at_end
+        )
         expected_average = np.array([[1 / 4, 1 / 2, 1 / 4], [1 / 2, 1 / 4, 1 / 4]])
 
         mag_data_model = MagData(mag_epoch, mag_data)
@@ -34,20 +59,31 @@ def test_calculate_average_mag_data_with_extra_data_at_beginning_and_end(self):
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_calculate_average_mag_data_handles_missing_mag_data(self):
-        hit_data_epoch = np.array([datetime(2020, 4, 4, 0, 5),
-                                   datetime(2020, 4, 4, 0, 15),
-                                   datetime(2020, 4, 4, 0, 25)])
-        hit_data_delta = np.array([timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)])
+        hit_data_epoch = np.array(
+            [
+                datetime(2020, 4, 4, 0, 5),
+                datetime(2020, 4, 4, 0, 15),
+                datetime(2020, 4, 4, 0, 25),
+            ]
+        )
+        hit_data_delta = np.array(
+            [timedelta(seconds=300), timedelta(seconds=300), timedelta(seconds=300)]
+        )
         mag_data = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 0]])
-        mag_epoch = [datetime(2020, 4, 4, 0, 1),
-                     datetime(2020, 4, 4, 0, 3),
-                     datetime(2020, 4, 4, 0, 22),
-                     datetime(2020, 4, 4, 0, 27)]
-        expected_average = np.array([[0, 1 / 2, 1 / 2], [np.nan, np.nan, np.nan], [1 / 2, 1 / 2, 0]])
+        mag_epoch = [
+            datetime(2020, 4, 4, 0, 1),
+            datetime(2020, 4, 4, 0, 3),
+            datetime(2020, 4, 4, 0, 22),
+            datetime(2020, 4, 4, 0, 27),
+        ]
+        expected_average = np.array(
+            [[0, 1 / 2, 1 / 2], [np.nan, np.nan, np.nan], [1 / 2, 1 / 2, 0]]
+        )
 
         mag_data_model = MagData(mag_epoch, mag_data)
-        actual_average = mag_data_model.rebin_to(epoch=hit_data_epoch,
-                                                 epoch_delta=hit_data_delta)
+        actual_average = mag_data_model.rebin_to(
+            epoch=hit_data_epoch, epoch_delta=hit_data_delta
+        )
         np.testing.assert_array_equal(actual_average, expected_average)
 
     def test_version_map(self):
@@ -70,15 +106,12 @@ def test_version_map(self):
 
     def test_version_map_throws_error_if_no_fallback(self):
         version_map = VersionMap(
-            {
-                "sci": Version(1, 5)
-            },
+            {"sci": Version(1, 5)},
         )
 
         with self.assertRaises(KeyError):
             version_map.lookup("other")
 
-
     def test_version_map_equality(self):
         version_map = VersionMap(
             {
@@ -111,5 +144,6 @@ def test_version_map_equality(self):
         self.assertNotEqual(version_map, different_map)
         self.assertNotEqual(version_map, different_fallback)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_pitch_angles.py b/tests/test_pitch_angles.py
index bd2ebe957..480b7d345 100644
--- a/tests/test_pitch_angles.py
+++ b/tests/test_pitch_angles.py
@@ -2,13 +2,17 @@
 
 import numpy as np
 
-from imap_l3_processing.pitch_angles import calculate_pitch_angle, calculate_unit_vector, calculate_gyrophase, \
-    rotate_from_imap_despun_to_hit_despun, rotate_particle_vectors_from_hit_despun_to_imap_despun, \
-    rebin_by_pitch_angle_and_gyrophase
+from imap_l3_processing.pitch_angles import (
+    calculate_gyrophase,
+    calculate_pitch_angle,
+    calculate_unit_vector,
+    rebin_by_pitch_angle_and_gyrophase,
+    rotate_from_imap_despun_to_hit_despun,
+    rotate_particle_vectors_from_hit_despun_to_imap_despun,
+)
 
 
 class TestPitchAngles(unittest.TestCase):
-
     def test_calculate_pitch_angle(self):
         hit_unit_vector = np.array([[-0.09362045, 0.8466484, 0.5238528]])
         mag_unit_vector = np.array([-0.42566603, 0.7890057, 0.44303328])
@@ -36,11 +40,15 @@ def test_calculate_unit_vector(self):
 
         unit_vector = calculate_unit_vector(vector)
 
-        np.testing.assert_array_almost_equal(np.array([0.38103832, 0.47982603, 0.7903017]), unit_vector)
+        np.testing.assert_array_almost_equal(
+            np.array([0.38103832, 0.47982603, 0.7903017]), unit_vector
+        )
 
         many_vectors = np.array([[0.1, 0, 0], [30, 40, 0]])
         unit_vectors = calculate_unit_vector(many_vectors)
-        np.testing.assert_array_almost_equal([[1, 0, 0], [3 / 5, 4 / 5, 0]], unit_vectors)
+        np.testing.assert_array_almost_equal(
+            [[1, 0, 0], [3 / 5, 4 / 5, 0]], unit_vectors
+        )
 
     def test_rotate_from_imap_despun_to_hit_despun(self):
         vector = np.array([1.0, 1.0, 1.0])
@@ -52,35 +60,46 @@ def test_rotate_from_imap_despun_to_hit_despun(self):
         np.testing.assert_array_almost_equal(rotated_vector, expected_vector)
 
     def test_rotate_particle_vectors_from_hit_despun_to_imap_despun(self):
-        particle_vectors = np.array([
-            [[0, 0, 1], [1, 0, 0]],
-            [[1, 1, 0], [0, 1, 1]],
-            [[0, 1, 0], [1, 0, 1]],
-            [[0, 0, 0], [1, 1, 1]]
-        ], dtype=float)
+        particle_vectors = np.array(
+            [
+                [[0, 0, 1], [1, 0, 0]],
+                [[1, 1, 0], [0, 1, 1]],
+                [[0, 1, 0], [1, 0, 1]],
+                [[0, 0, 0], [1, 1, 1]],
+            ],
+            dtype=float,
+        )
 
-        rotated_particle_vectors = rotate_particle_vectors_from_hit_despun_to_imap_despun(particle_vectors)
+        rotated_particle_vectors = (
+            rotate_particle_vectors_from_hit_despun_to_imap_despun(particle_vectors)
+        )
 
-        expected_particle_vectors = np.array([
-            [[0, 0, 1], [0.866025, 0.5, 0.]],
-            [[0.366025, 1.366025, 0.], [-0.5, 0.866025, 1.]],
-            [[- 0.5, 0.866025, 0.], [0.866025, 0.5, 1.]],
-            [[0, 0, 0], [0.366025, 1.366025, 1.]]
-        ])
+        expected_particle_vectors = np.array(
+            [
+                [[0, 0, 1], [0.866025, 0.5, 0.0]],
+                [[0.366025, 1.366025, 0.0], [-0.5, 0.866025, 1.0]],
+                [[-0.5, 0.866025, 0.0], [0.866025, 0.5, 1.0]],
+                [[0, 0, 0], [0.366025, 1.366025, 1.0]],
+            ]
+        )
 
-        np.testing.assert_array_almost_equal(rotated_particle_vectors, expected_particle_vectors)
+        np.testing.assert_array_almost_equal(
+            rotated_particle_vectors, expected_particle_vectors
+        )
 
     def test_calculate_gyrophase(self):
-        particle_vector = np.array([
-            [0, 1, 0],
-            [1, 0, 0],
-            [0, -1, 0],
-            [-1, 0, 0],
-            [.5, .5, 0],
-            [.5, -.5, 0],
-            [0, 0, -1],
-            [0, 0, 1],
-        ])
+        particle_vector = np.array(
+            [
+                [0, 1, 0],
+                [1, 0, 0],
+                [0, -1, 0],
+                [-1, 0, 0],
+                [0.5, 0.5, 0],
+                [0.5, -0.5, 0],
+                [0, 0, -1],
+                [0, 0, 1],
+            ]
+        )
         mag_field_vector = np.array([0, 0, 1])
 
         gyrophases = calculate_gyrophase(particle_vector, mag_field_vector)
@@ -90,20 +109,17 @@ def test_calculate_gyrophase(self):
         np.testing.assert_array_equal(gyrophases, expected_gyrophases)
 
     def test_calculate_gyrophase_fany_input_arrays(self):
-        particle_vector = np.array([
+        particle_vector = np.array(
             [
-                [0, 1, 0],
-                [1, 0, 0],
-                [0, -1, 0],
-                [-1, 0, 0]
-            ],
-            [
-                [.5, .5, 0],
-                [.5, -.5, 0],
-                [0, 0, -1],
-                [0, 0, 1],
+                [[0, 1, 0], [1, 0, 0], [0, -1, 0], [-1, 0, 0]],
+                [
+                    [0.5, 0.5, 0],
+                    [0.5, -0.5, 0],
+                    [0, 0, -1],
+                    [0, 0, 1],
+                ],
             ]
-        ])
+        )
         mag_field_vector = np.array([[0, 0, 1], [0, 0, 1]])[..., np.newaxis, :]
 
         gyrophases = calculate_gyrophase(particle_vector, mag_field_vector)
@@ -113,9 +129,11 @@ def test_calculate_gyrophase_fany_input_arrays(self):
         np.testing.assert_array_equal(gyrophases, expected_gyrophases)
 
     def test_gyrophase_degenerate_particle_and_magnetic_field_returns_zero(self):
-        particle_vector = np.array([
-            [0, 0, 1],
-        ])
+        particle_vector = np.array(
+            [
+                [0, 0, 1],
+            ]
+        )
         mag_field_vector = np.array([0, 0, 1])
 
         gyrophases = calculate_gyrophase(particle_vector, mag_field_vector)
@@ -124,11 +142,17 @@ def test_gyrophase_degenerate_particle_and_magnetic_field_returns_zero(self):
 
         np.testing.assert_array_equal(gyrophases, expected_gyrophases)
 
-    def test_gyrophase_degenerate_dps_frame_y_axis_and_magnetic_field_returns_all_nans(self):
-        particle_vector = np.array([[
-            [1, 0, 0],
-            [-1, 0, 0],
-        ]])
+    def test_gyrophase_degenerate_dps_frame_y_axis_and_magnetic_field_returns_all_nans(
+        self,
+    ):
+        particle_vector = np.array(
+            [
+                [
+                    [1, 0, 0],
+                    [-1, 0, 0],
+                ]
+            ]
+        )
 
         for mag_field_vector in [np.array([0, 1, 0]), np.array([0, -1, 0])]:
             with self.subTest(mag_field_vector):
@@ -142,93 +166,167 @@ def test_rebin_intensity_by_pitch_angle_and_gyrophase(self):
         default_gyrophases = np.array([[45, 135, 225, 315], [45, 135, 225, 315]])
         intensity_data_for_energy_1 = [[0, 1, 2, 3], [4, 5, 6, 7]]
         intensity_data_for_energy_2 = [[8, 9, 10, 11], [12, 13, 14, 15]]
-        intensity = np.array([
-            intensity_data_for_energy_1,
-            intensity_data_for_energy_2
-        ])
+        intensity = np.array([intensity_data_for_energy_1, intensity_data_for_energy_2])
         intensity_delta_plus = intensity * 0.09
         intensity_delta_minus = intensity * 0.11
 
         test_cases = [
-            ('One bin', default_pitch_angles, default_gyrophases, 1, 1, [[[3.5]], [[11.5]]], [[3.5], [11.5]]),
-            ('Rebinned to same bins', default_pitch_angles, default_gyrophases, 2, 4, intensity,
-             np.array([[1.5, 5.5], [9.5, 13.5]])),
-            ('Gyrophase rotated by 90', default_pitch_angles, (default_gyrophases + 90) % 360, 2, 4,
-             [
-                 [[3, 0, 1, 2], [7, 4, 5, 6]],
-                 [[11, 8, 9, 10], [15, 12, 13, 14]]
-             ],
-             np.array([[1.5, 5.5], [9.5, 13.5]])),
-            ('Includes empty bins', default_pitch_angles, default_gyrophases, 2, 6,
-             [
-                 [[0, np.nan, 1, 2, np.nan, 3], [4, np.nan, 5, 6, np.nan, 7]],
-                 [[8, np.nan, 9, 10, np.nan, 11], [12, np.nan, 13, 14, np.nan, 15]]
-             ],
-             np.array([[1.5, 5.5], [9.5, 13.5]])),
-            ('Varying Pitch Angles', np.array([[45, 45, 135, 135], [45, 45, 135, 135]]),
-             np.array([[135, 225, 225, 135], [45, 315, 315, 45]]), 2, 4,
-             [
-                 [[4, 0, 1, 5], [7, 3, 2, 6]],
-                 [[12, 8, 9, 13], [15, 11, 10, 14]]
-             ],
-             [[2.5, 4.5], [10.5, 12.5]]),
-            ('Nan in pitch angles', np.array([[45, 45, 135, 135], [np.nan, 45, 135, 135]]),
-             np.array([[135, 225, 225, 135], [45, 315, 315, 45]]), 2, 4,
-             [
-                 [[np.nan, 0, 1, 5], [7, 3, 2, 6]],
-                 [[np.nan, 8, 9, 13], [15, 11, 10, 14]]
-             ],
-             [[2, 4.5], [10, 12.5]]),
             (
-                'Nan for all pitch angles in a bin', np.array([[np.nan, np.nan, 135, 135], [np.nan, np.nan, 135, 135]]),
-                np.array([[135, 225, 225, 135], [45, 315, 315, 45]]), 2, 4,
+                "One bin",
+                default_pitch_angles,
+                default_gyrophases,
+                1,
+                1,
+                [[[3.5]], [[11.5]]],
+                [[3.5], [11.5]],
+            ),
+            (
+                "Rebinned to same bins",
+                default_pitch_angles,
+                default_gyrophases,
+                2,
+                4,
+                intensity,
+                np.array([[1.5, 5.5], [9.5, 13.5]]),
+            ),
+            (
+                "Gyrophase rotated by 90",
+                default_pitch_angles,
+                (default_gyrophases + 90) % 360,
+                2,
+                4,
+                [[[3, 0, 1, 2], [7, 4, 5, 6]], [[11, 8, 9, 10], [15, 12, 13, 14]]],
+                np.array([[1.5, 5.5], [9.5, 13.5]]),
+            ),
+            (
+                "Includes empty bins",
+                default_pitch_angles,
+                default_gyrophases,
+                2,
+                6,
+                [
+                    [[0, np.nan, 1, 2, np.nan, 3], [4, np.nan, 5, 6, np.nan, 7]],
+                    [[8, np.nan, 9, 10, np.nan, 11], [12, np.nan, 13, 14, np.nan, 15]],
+                ],
+                np.array([[1.5, 5.5], [9.5, 13.5]]),
+            ),
+            (
+                "Varying Pitch Angles",
+                np.array([[45, 45, 135, 135], [45, 45, 135, 135]]),
+                np.array([[135, 225, 225, 135], [45, 315, 315, 45]]),
+                2,
+                4,
+                [[[4, 0, 1, 5], [7, 3, 2, 6]], [[12, 8, 9, 13], [15, 11, 10, 14]]],
+                [[2.5, 4.5], [10.5, 12.5]],
+            ),
+            (
+                "Nan in pitch angles",
+                np.array([[45, 45, 135, 135], [np.nan, 45, 135, 135]]),
+                np.array([[135, 225, 225, 135], [45, 315, 315, 45]]),
+                2,
+                4,
+                [
+                    [[np.nan, 0, 1, 5], [7, 3, 2, 6]],
+                    [[np.nan, 8, 9, 13], [15, 11, 10, 14]],
+                ],
+                [[2, 4.5], [10, 12.5]],
+            ),
+            (
+                "Nan for all pitch angles in a bin",
+                np.array([[np.nan, np.nan, 135, 135], [np.nan, np.nan, 135, 135]]),
+                np.array([[135, 225, 225, 135], [45, 315, 315, 45]]),
+                2,
+                4,
                 [
                     [[np.nan, np.nan, np.nan, np.nan], [7, 3, 2, 6]],
-                    [[np.nan, np.nan, np.nan, np.nan], [15, 11, 10, 14]]
+                    [[np.nan, np.nan, np.nan, np.nan], [15, 11, 10, 14]],
+                ],
+                [[np.nan, 4.5], [np.nan, 12.5]],
+            ),
+            (
+                "Nan in gyrophase",
+                np.array([[45, 45, 135, 135], [45, 45, 135, 135]]),
+                np.array([[np.nan, 225, 225, 135], [45, 315, 315, 45]]),
+                2,
+                4,
+                [
+                    [[4, np.nan, 1, 5], [7, 3, 2, 6]],
+                    [[12, np.nan, 9, 13], [15, 11, 10, 14]],
                 ],
-                [[np.nan, 4.5], [np.nan, 12.5]]),
-            ('Nan in gyrophase', np.array([[45, 45, 135, 135], [45, 45, 135, 135]]),
-             np.array([[np.nan, 225, 225, 135], [45, 315, 315, 45]]), 2, 4,
-             [
-                 [[4, np.nan, 1, 5], [7, 3, 2, 6]],
-                 [[12, np.nan, 9, 13], [15, 11, 10, 14]]
-             ],
-             [[2.5, 4.5], [10.5, 12.5]]),
-
-            ('Gyrophase bin includes 360 bin', default_pitch_angles,
-             np.array([[360, 90, 180, 270], [360, 90, 180, 270]]),
-             2, 4, intensity,
-             np.array([[1.5, 5.5], [9.5, 13.5]]))
+                [[2.5, 4.5], [10.5, 12.5]],
+            ),
+            (
+                "Gyrophase bin includes 360 bin",
+                default_pitch_angles,
+                np.array([[360, 90, 180, 270], [360, 90, 180, 270]]),
+                2,
+                4,
+                intensity,
+                np.array([[1.5, 5.5], [9.5, 13.5]]),
+            ),
         ]
 
-        for case, pitch_angles, gyrophases, number_of_pitch_angle_bins, number_of_gyrophase_bins, \
-                expected_intensity_by_pa_gyrophase, expected_intensity_by_pa in test_cases:
-            with (((self.subTest(case)))):
-                rebinned_data = rebin_by_pitch_angle_and_gyrophase(intensity, intensity_delta_plus,
-                                                                   intensity_delta_minus, pitch_angles, gyrophases,
-                                                                   number_of_pitch_angle_bins, number_of_gyrophase_bins)
+        for (
+            case,
+            pitch_angles,
+            gyrophases,
+            number_of_pitch_angle_bins,
+            number_of_gyrophase_bins,
+            expected_intensity_by_pa_gyrophase,
+            expected_intensity_by_pa,
+        ) in test_cases:
+            with self.subTest(case):
+                rebinned_data = rebin_by_pitch_angle_and_gyrophase(
+                    intensity,
+                    intensity_delta_plus,
+                    intensity_delta_minus,
+                    pitch_angles,
+                    gyrophases,
+                    number_of_pitch_angle_bins,
+                    number_of_gyrophase_bins,
+                )
                 intensity_pa_and_gyro, _, _ = rebinned_data[0:3]
                 intensity_pa_only, _, _ = rebinned_data[3:6]
 
-                np.testing.assert_array_equal(intensity_pa_and_gyro, expected_intensity_by_pa_gyrophase)
-                np.testing.assert_array_equal(intensity_pa_only, expected_intensity_by_pa)
+                np.testing.assert_array_equal(
+                    intensity_pa_and_gyro, expected_intensity_by_pa_gyrophase
+                )
+                np.testing.assert_array_equal(
+                    intensity_pa_only, expected_intensity_by_pa
+                )
 
-    def test_rebin_by_pitch_angle_and_gyrophase_pa_product_only_correctly_weights_when_averaging(self):
+    def test_rebin_by_pitch_angle_and_gyrophase_pa_product_only_correctly_weights_when_averaging(
+        self,
+    ):
         intensity = np.array([[[0, 1, 2], [3, 4, 5]]])
-        rebinned_data = rebin_by_pitch_angle_and_gyrophase(intensity, intensity * 0.01, intensity * 0.01,
-                                                           np.array([[45, 45, 45], [135, 135, 135]]),
-                                                           np.array([[45, 135, 225], [45, 135, 225]]), 1, 2)
+        rebinned_data = rebin_by_pitch_angle_and_gyrophase(
+            intensity,
+            intensity * 0.01,
+            intensity * 0.01,
+            np.array([[45, 45, 45], [135, 135, 135]]),
+            np.array([[45, 135, 225], [45, 135, 225]]),
+            1,
+            2,
+        )
         intensity_pa_and_gyro, _, _ = rebinned_data[0:3]
         intensity_pa_only, _, _ = rebinned_data[3:6]
 
         np.testing.assert_equal(intensity_pa_and_gyro, [[[2, 3.5]]])
         np.testing.assert_equal(intensity_pa_only, [[2.5]])
 
-    def test_rebin_by_pitch_angle_and_gyrophase_pa_product_only_handles_nan_intensity(self):
+    def test_rebin_by_pitch_angle_and_gyrophase_pa_product_only_handles_nan_intensity(
+        self,
+    ):
         intensity = np.array([[[0, np.nan, 2, 7], [3, 4, np.nan, 8]]])
-        rebinned_data = rebin_by_pitch_angle_and_gyrophase(intensity, intensity * 0.01, intensity * 0.01,
-                                                           np.array([[45, 45, 45, 45], [135, 135, 135, 135]]),
-                                                           np.array([[45, 135, 225, 300], [45, 135, 225, 300]]), 2, 2)
+        rebinned_data = rebin_by_pitch_angle_and_gyrophase(
+            intensity,
+            intensity * 0.01,
+            intensity * 0.01,
+            np.array([[45, 45, 45, 45], [135, 135, 135, 135]]),
+            np.array([[45, 135, 225, 300], [45, 135, 225, 300]]),
+            2,
+            2,
+        )
         intensity_pa_and_gyro, _, _ = rebinned_data[0:3]
         intensity_pa_only, _, _ = rebinned_data[3:6]
 
@@ -240,48 +338,57 @@ def test_rebin_by_pitch_angle_and_gyrophase_includes_uncertainties(self):
         default_gyrophases = np.array([[45, 135, 225, 315], [45, 135, 225, 315]])
         intensity_data_for_energy_1 = [[0, 1, 2, 3], [4, 5, 6, 7]]
         intensity_data_for_energy_2 = [[8, 9, 10, 11], [12, 13, 14, 15]]
-        intensity = np.array([
-            intensity_data_for_energy_1,
-            intensity_data_for_energy_2
-        ])
+        intensity = np.array([intensity_data_for_energy_1, intensity_data_for_energy_2])
         intensity_delta_plus = intensity * 0.09
         intensity_delta_minus = intensity * 0.11
 
         number_of_pitch_angle_bins = 2
         number_of_gyrophase_bins = 4
 
-        expected_delta_plus_pa_gyro = np.array([
-            [[0, 0.09, 0.18, 0.27], [0.36, 0.45, 0.54, 0.63]],
-            [[0.72, 0.81, 0.9, 0.99], [1.08, 1.17, 1.26, 1.35]]
-        ])
-
-        expected_delta_minus_pa_gyro = np.array([
-            [[0, 0.11, 0.22, 0.33], [0.44, 0.55, 0.66, 0.77]],
-            [[0.88, 0.99, 1.1, 1.21], [1.32, 1.43, 1.54, 1.65]]
-        ])
-
-        expected_delta_plus_pa_only = np.array([
-            [0.084187, 0.252562],
-            [0.43045, 0.60958]
-        ])
-
-        expected_delta_minus_pa_only = np.array([
-            [0.102896, 0.308687],
-            [0.526106, 0.745042]
-        ])
+        expected_delta_plus_pa_gyro = np.array(
+            [
+                [[0, 0.09, 0.18, 0.27], [0.36, 0.45, 0.54, 0.63]],
+                [[0.72, 0.81, 0.9, 0.99], [1.08, 1.17, 1.26, 1.35]],
+            ]
+        )
 
-        rebinned_data = rebin_by_pitch_angle_and_gyrophase(intensity, intensity_delta_plus, intensity_delta_minus,
-                                                           default_pitch_angles, default_gyrophases,
-                                                           number_of_pitch_angle_bins, number_of_gyrophase_bins)
+        expected_delta_minus_pa_gyro = np.array(
+            [
+                [[0, 0.11, 0.22, 0.33], [0.44, 0.55, 0.66, 0.77]],
+                [[0.88, 0.99, 1.1, 1.21], [1.32, 1.43, 1.54, 1.65]],
+            ]
+        )
+
+        expected_delta_plus_pa_only = np.array(
+            [[0.084187, 0.252562], [0.43045, 0.60958]]
+        )
+
+        expected_delta_minus_pa_only = np.array(
+            [[0.102896, 0.308687], [0.526106, 0.745042]]
+        )
+
+        rebinned_data = rebin_by_pitch_angle_and_gyrophase(
+            intensity,
+            intensity_delta_plus,
+            intensity_delta_minus,
+            default_pitch_angles,
+            default_gyrophases,
+            number_of_pitch_angle_bins,
+            number_of_gyrophase_bins,
+        )
 
         _, delta_plus_pa_gyro, delta_minus_pa_gyro = rebinned_data[0:3]
         _, delta_plus_pa_only, delta_minus_pa_only = rebinned_data[3:6]
 
         np.testing.assert_allclose(delta_plus_pa_gyro, expected_delta_plus_pa_gyro)
         np.testing.assert_allclose(delta_minus_pa_gyro, expected_delta_minus_pa_gyro)
-        np.testing.assert_allclose(delta_plus_pa_only, expected_delta_plus_pa_only, rtol=1e-5)
-        np.testing.assert_allclose(delta_minus_pa_only, expected_delta_minus_pa_only, rtol=1e-5)
+        np.testing.assert_allclose(
+            delta_plus_pa_only, expected_delta_plus_pa_only, rtol=1e-5
+        )
+        np.testing.assert_allclose(
+            delta_minus_pa_only, expected_delta_minus_pa_only, rtol=1e-5
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_predicted_ephemeris_tracker.py b/tests/test_predicted_ephemeris_tracker.py
index e073c716c..7cbad336b 100644
--- a/tests/test_predicted_ephemeris_tracker.py
+++ b/tests/test_predicted_ephemeris_tracker.py
@@ -89,7 +89,6 @@ def requires_predict():
             self.assertEqual(2, mock_spice_function_1.call_count)
             self.assertEqual(1, mock_spice_function_2.call_count)
 
-
     def test_predicted_ephemeris_tracker_skips_retry_if_no_predict_kernel(self):
         other_kernel = get_integration_test_spice_data_path(
             "imap_2025_105_2026_105_01.ah.bc"
@@ -106,4 +105,11 @@ def test_predicted_ephemeris_tracker_allows_unknown_kernel_names(self):
 
         with KernelPool(kernels):
             tracker = PredictedEphemerisTracker()
-            tracker.run(spiceypy.spkezr, "IMAP", spiceypy.datetime2et(datetime(2025,10, 10)), "ECLIPJ2000", "NONE", "SUN")
\ No newline at end of file
+            tracker.run(
+                spiceypy.spkezr,
+                "IMAP",
+                spiceypy.datetime2et(datetime(2025, 10, 10)),
+                "ECLIPJ2000",
+                "NONE",
+                "SUN",
+            )
diff --git a/tests/test_processor.py b/tests/test_processor.py
index 6db65ae7e..887a1337a 100644
--- a/tests/test_processor.py
+++ b/tests/test_processor.py
@@ -2,7 +2,11 @@
 from pathlib import Path
 from unittest.mock import Mock, patch
 
-from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 
 from imap_l3_processing.swapi.swapi_processor import SwapiProcessor
 
@@ -10,9 +14,11 @@
 class TestProcessor(unittest.TestCase):
     @patch("imap_l3_processing.processor.get_spice_parent_file_names")
     def test_get_parent_file_names(self, mock_get_spice_parent_file_names):
-        input_collection = ProcessingInputCollection(ScienceInput("imap_swapi_l2_science_20250417_v001.cdf"),
-                                                     ScienceInput("imap_swapi_l2_science_20250418_v001.cdf"),
-                                                     AncillaryInput("imap_swapi_lookup_20250417_v001.cdf"))
+        input_collection = ProcessingInputCollection(
+            ScienceInput("imap_swapi_l2_science_20250417_v001.cdf"),
+            ScienceInput("imap_swapi_l2_science_20250418_v001.cdf"),
+            AncillaryInput("imap_swapi_lookup_20250417_v001.cdf"),
+        )
 
         mock_get_spice_parent_file_names.return_value = ["kernel_1", "kernel_2"]
 
@@ -29,16 +35,16 @@ def test_get_parent_file_names(self, mock_get_spice_parent_file_names):
 
         self.assertEqual(expected_parent_file_names, actual_parent_file_names)
 
-    @patch('imap_l3_processing.processor.spiceypy')
+    @patch("imap_l3_processing.processor.spiceypy")
     def test_get_parent_files_takes_optional_parameter(self, mock_spiceypy):
-        file_paths = [Path('path')]
+        file_paths = [Path("path")]
         mock_spiceypy.ktotal.return_value = 0
 
         processor = SwapiProcessor(Mock(), Mock())
         actual_parent_file_names = processor.get_parent_file_names(file_paths)
 
-        self.assertEqual(['path'], actual_parent_file_names)
+        self.assertEqual(["path"], actual_parent_file_names)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_utils.py b/tests/test_utils.py
index dfbed6812..6bf0d7b92 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,90 +1,110 @@
 import os
 import tempfile
-from datetime import datetime, date
+from datetime import date, datetime
 from pathlib import Path
 from unittest import TestCase
-from unittest.mock import patch, call, Mock, sentinel
+from unittest.mock import Mock, call, patch, sentinel
 
 import imap_data_access
 import numpy as np
 from imap_data_access import config
 from imap_data_access.file_validation import Version
-from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection
+from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput
 from imap_processing.spice.geometry import SpiceFrame
 from requests import RequestException
 from spacepy.pycdf import CDF
 
 from imap_l3_processing.constants import TEMP_CDF_FOLDER_PATH, TT2000_EPOCH
-from imap_l3_processing.maps.map_models import RectangularSpectralIndexDataProduct, RectangularIntensityDataProduct
+from imap_l3_processing.maps.map_models import (
+    RectangularIntensityDataProduct,
+    RectangularSpectralIndexDataProduct,
+)
 from imap_l3_processing.models import InputMetadata, VersionMap
 from imap_l3_processing.swapi.l3a.models import SwapiL3AlphaSolarWindData
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.utils import (
-    format_time,
-    read_mag_data,
-    save_data,
+    FurnishMetakernelOutput,
+    SpiceKernelTypes,
     download_external_dependency,
+    format_time,
     furnish_local_spice,
-    get_spice_parent_file_names,
     furnish_spice_metakernel,
-    SpiceKernelTypes,
-    FurnishMetakernelOutput,
-    read_cdf_parents,
     get_dependency_paths_by_descriptor,
-    get_version_from_query_result,
+    get_spice_parent_file_names,
     get_temp_cache_dir,
+    get_version_from_query_result,
+    read_cdf_parents,
+    read_mag_data,
+    save_data,
 )
 from imap_l3_processing.version import VERSION
 from tests.cdf.test_cdf_utils import TestDataProduct
 from tests.maps.test_builders import (
-    create_rectangular_spectral_index_map_data,
     create_rectangular_intensity_map_data,
+    create_rectangular_spectral_index_map_data,
 )
 from tests.test_helpers import (
+    create_mock_version_map,
     get_spice_data_path,
     with_tempdir,
-    create_mock_version_map,
 )
 
 
 class TestUtils(TestCase):
     def setUp(self) -> None:
-        if os.path.exists('test_cdf.cdf'):
-            os.remove('test_cdf.cdf')
+        if os.path.exists("test_cdf.cdf"):
+            os.remove("test_cdf.cdf")
 
     def tearDown(self) -> None:
-        if os.path.exists('test_cdf.cdf'):
-            os.remove('test_cdf.cdf')
+        if os.path.exists("test_cdf.cdf"):
+            os.remove("test_cdf.cdf")
 
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
     @patch("imap_l3_processing.utils.write_cdf")
     @patch("imap_l3_processing.utils.ScienceFilePath")
-    def test_save_data(self, mock_science_file_path_class, mock_write_cdf, mock_today, mock_attribute_manager):
+    def test_save_data(
+        self,
+        mock_science_file_path_class,
+        mock_write_cdf,
+        mock_today,
+        mock_attribute_manager,
+    ):
         mock_today.today.return_value = date(2024, 9, 16)
 
-        input_metadata = InputMetadata("swapi", "l2", datetime(2024, 9, 17), datetime(2024, 9, 18), VersionMap({"descriptor":Version(1,2)}),
-                                       "descriptor", repointing=None)
+        input_metadata = InputMetadata(
+            "swapi",
+            "l2",
+            datetime(2024, 9, 17),
+            datetime(2024, 9, 18),
+            VersionMap({"descriptor": Version(1, 2)}),
+            "descriptor",
+            repointing=None,
+        )
         epoch = np.array([1, 2, 3])
 
-        data_product = SwapiL3AlphaSolarWindData(input_metadata=input_metadata, epoch=epoch,
-                                                 alpha_sw_speed=np.array([450.0, 460.0, 470.0]),
-                                                 alpha_sw_speed_uncert=np.array([1.0, 1.0, 1.0]),
-                                                 alpha_sw_speed_sun=np.array([470.0, 480.0, 490.0]),
-                                                 alpha_sw_speed_sun_uncert=np.array([1.3, 1.4, 1.5]),
-                                                 alpha_sw_density=np.array([5, 5, 5]),
-                                                 alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
-                                                 alpha_sw_temperature=np.array([4, 3, 5]),
-                                                 alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
-                                                 alpha_sw_velocity_rtn_sun=np.zeros((3, 3)),
-                                                 alpha_sw_velocity_rtn=np.zeros((3, 3)),
-                                                 alpha_sw_velocity_rtn_covariance=np.zeros((3, 3, 3)),
-                                                 parent_file_names=sentinel.parent_files,
-                                                 quality_flags=sentinel.quality_flags,
-                                                 )
+        data_product = SwapiL3AlphaSolarWindData(
+            input_metadata=input_metadata,
+            epoch=epoch,
+            alpha_sw_speed=np.array([450.0, 460.0, 470.0]),
+            alpha_sw_speed_uncert=np.array([1.0, 1.0, 1.0]),
+            alpha_sw_speed_sun=np.array([470.0, 480.0, 490.0]),
+            alpha_sw_speed_sun_uncert=np.array([1.3, 1.4, 1.5]),
+            alpha_sw_density=np.array([5, 5, 5]),
+            alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
+            alpha_sw_temperature=np.array([4, 3, 5]),
+            alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
+            alpha_sw_velocity_rtn_sun=np.zeros((3, 3)),
+            alpha_sw_velocity_rtn=np.zeros((3, 3)),
+            alpha_sw_velocity_rtn_covariance=np.zeros((3, 3, 3)),
+            parent_file_names=sentinel.parent_files,
+            quality_flags=sentinel.quality_flags,
+        )
 
         mock_science_file_path = Mock()
-        mock_science_file_path_class.generate_from_inputs.return_value = mock_science_file_path
+        mock_science_file_path_class.generate_from_inputs.return_value = (
+            mock_science_file_path
+        )
 
         expected_file_path = mock_science_file_path.construct_path.return_value
 
@@ -103,17 +123,20 @@ def test_save_data(self, mock_science_file_path_class, mock_write_cdf, mock_toda
 
         mock_science_file_path.construct_path.assert_called_once()
 
-        mock_write_cdf.assert_called_once_with(str(expected_file_path), data_product,
-                                               mock_attribute_manager.return_value)
+        mock_write_cdf.assert_called_once_with(
+            str(expected_file_path), data_product, mock_attribute_manager.return_value
+        )
 
-        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls([
-            call("Data_version", "001.0002"),
-            call("Generation_date", "20240916"),
-            call("Logical_source", "imap_swapi_l2_descriptor"),
-            call("Logical_file_id", expected_file_path.stem),
-            call("ground_software_version", VERSION),
-            call("Parents", sentinel.parent_files),
-        ])
+        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", "001.0002"),
+                call("Generation_date", "20240916"),
+                call("Logical_source", "imap_swapi_l2_descriptor"),
+                call("Logical_file_id", expected_file_path.stem),
+                call("ground_software_version", VERSION),
+                call("Parents", sentinel.parent_files),
+            ]
+        )
 
         mock_attribute_manager.return_value.add_instrument_attrs.assert_called_with(
             "swapi", "l2", input_metadata.descriptor
@@ -121,11 +144,12 @@ def test_save_data(self, mock_science_file_path_class, mock_write_cdf, mock_toda
 
         self.assertEqual(expected_file_path, returned_file_path)
 
-
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
     @patch("imap_l3_processing.utils.write_cdf")
-    def test_save_data_adds_global_attrs_if_present(self, _, mock_today, mock_attribute_manager):
+    def test_save_data_adds_global_attrs_if_present(
+        self, _, mock_today, mock_attribute_manager
+    ):
         mock_today.today.return_value = date(2024, 9, 16)
         data_product = TestDataProduct()
         data_product.global_metadata_attrs = {
@@ -134,21 +158,24 @@ def test_save_data_adds_global_attrs_if_present(self, _, mock_today, mock_attrib
         }
         _ = save_data(data_product)
 
-        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls([
-            call("Data_version", "003"),
-            call("Generation_date", "20240916"),
-            call("Logical_source", "imap_glows_l3_descriptor"),
-            call("Logical_file_id",
-                 f"imap_glows_l3_descriptor_20250510_v003"),
-            call("ground_software_version", VERSION),
-            call("flight_software_version", 131848),
-            call("ground_software_version", "0.3"),
-        ])
+        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", "003"),
+                call("Generation_date", "20240916"),
+                call("Logical_source", "imap_glows_l3_descriptor"),
+                call("Logical_file_id", "imap_glows_l3_descriptor_20250510_v003"),
+                call("ground_software_version", VERSION),
+                call("flight_software_version", 131848),
+                call("ground_software_version", "0.3"),
+            ]
+        )
 
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
     @patch("imap_l3_processing.utils.write_cdf")
-    def test_save_data_adds_repointing_if_present(self, mock_write_cdf, mock_today, mock_attribute_manager):
+    def test_save_data_adds_repointing_if_present(
+        self, mock_write_cdf, mock_today, mock_attribute_manager
+    ):
         mock_today.today.return_value = date(2024, 9, 16)
 
         expected_repointing = 2
@@ -156,25 +183,39 @@ def test_save_data_adds_repointing_if_present(self, mock_write_cdf, mock_today,
         data_product.input_metadata.repointing = expected_repointing
         returned_file_path = save_data(data_product)
 
-        mock_write_cdf.assert_called_once_with(str(returned_file_path), data_product,
-                                               mock_attribute_manager.return_value)
+        mock_write_cdf.assert_called_once_with(
+            str(returned_file_path), data_product, mock_attribute_manager.return_value
+        )
 
-        expected_file_path = (config["DATA_DIR"] / "imap" / "glows" / "l3" / "2025" / "05" /
-                              f"imap_glows_l3_descriptor_20250510-repoint0000{expected_repointing}_v003.cdf")
+        expected_file_path = (
+            config["DATA_DIR"]
+            / "imap"
+            / "glows"
+            / "l3"
+            / "2025"
+            / "05"
+            / f"imap_glows_l3_descriptor_20250510-repoint0000{expected_repointing}_v003.cdf"
+        )
         self.assertEqual(expected_file_path, returned_file_path)
 
-        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls([
-            call("Data_version", "003"),
-            call("Generation_date", "20240916"),
-            call("Logical_source", "imap_glows_l3_descriptor"),
-            call("Logical_file_id",
-                 f"imap_glows_l3_descriptor_20250510-repoint0000{expected_repointing}_v003")
-        ])
+        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", "003"),
+                call("Generation_date", "20240916"),
+                call("Logical_source", "imap_glows_l3_descriptor"),
+                call(
+                    "Logical_file_id",
+                    f"imap_glows_l3_descriptor_20250510-repoint0000{expected_repointing}_v003",
+                ),
+            ]
+        )
 
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
     @patch("imap_l3_processing.utils.write_cdf")
-    def test_save_data_adds_CR_if_present(self, mock_write_cdf, mock_today, mock_attribute_manager):
+    def test_save_data_adds_CR_if_present(
+        self, mock_write_cdf, mock_today, mock_attribute_manager
+    ):
         mock_today.today.return_value = date(2024, 9, 16)
 
         expected_cr = 2
@@ -182,17 +223,28 @@ def test_save_data_adds_CR_if_present(self, mock_write_cdf, mock_today, mock_att
         data_product.input_metadata.repointing = None
         returned_file_path = save_data(data_product, cr_number=expected_cr)
 
-        expected_file_path = (config["DATA_DIR"] / "imap" / "glows" / "l3" / "2025" / "05" /
-                              f"imap_glows_l3_descriptor_20250510-cr0000{expected_cr}_v003.cdf")
+        expected_file_path = (
+            config["DATA_DIR"]
+            / "imap"
+            / "glows"
+            / "l3"
+            / "2025"
+            / "05"
+            / f"imap_glows_l3_descriptor_20250510-cr0000{expected_cr}_v003.cdf"
+        )
         self.assertEqual(expected_file_path, returned_file_path)
 
-        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls([
-            call("Data_version", "003"),
-            call("Generation_date", "20240916"),
-            call("Logical_source", "imap_glows_l3_descriptor"),
-            call("Logical_file_id",
-                 f"imap_glows_l3_descriptor_20250510-cr0000{expected_cr}_v003")
-        ])
+        mock_attribute_manager.return_value.add_global_attribute.assert_has_calls(
+            [
+                call("Data_version", "003"),
+                call("Generation_date", "20240916"),
+                call("Logical_source", "imap_glows_l3_descriptor"),
+                call(
+                    "Logical_file_id",
+                    f"imap_glows_l3_descriptor_20250510-cr0000{expected_cr}_v003",
+                ),
+            ]
+        )
 
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
@@ -206,48 +258,62 @@ def test_save_throws_exception_if_given_cr_and_repointing(self, _, mock_today, _
         with self.assertRaises(AssertionError) as exception_manager:
             save_data(data_product, cr_number=cr)
 
-        self.assertEqual(str(exception_manager.exception),
-                         "You cannot call save_data with both a repointing in the metadata while passing in a CR number")
+        self.assertEqual(
+            str(exception_manager.exception),
+            "You cannot call save_data with both a repointing in the metadata while passing in a CR number",
+        )
 
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
     @patch("imap_l3_processing.utils.write_cdf")
-    def test_save_data_does_not_add_parent_attribute_if_empty(self, mock_write_cdf, mock_today, _):
+    def test_save_data_does_not_add_parent_attribute_if_empty(
+        self, mock_write_cdf, mock_today, _
+    ):
         mock_today.today.return_value = date(2024, 9, 16)
 
         descriptor = "descriptor"
-        input_metadata = InputMetadata("swapi", "l2", datetime(2024, 9, 17), datetime(2024, 9, 18), create_mock_version_map(descriptor=descriptor, minor_version=2),
-                                       descriptor)
+        input_metadata = InputMetadata(
+            "swapi",
+            "l2",
+            datetime(2024, 9, 17),
+            datetime(2024, 9, 18),
+            create_mock_version_map(descriptor=descriptor, minor_version=2),
+            descriptor,
+        )
         epoch = np.array([1, 2, 3])
         quality_flags = np.repeat([SwapiL3Flags.NONE], 3)
 
-        data_product = SwapiL3AlphaSolarWindData(input_metadata=input_metadata,
-                                                 epoch=epoch,
-                                                 alpha_sw_speed=np.array([450.0, 460.0, 470.0]),
-                                                 alpha_sw_speed_uncert=np.array([1.0, 1.0, 1.0]),
-                                                 alpha_sw_speed_sun=np.array([470.0, 480.0, 490.0]),
-                                                 alpha_sw_speed_sun_uncert=np.array([1.3, 1.4, 1.5]),
-                                                 alpha_sw_density=np.array([5, 5, 5]),
-                                                 alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
-                                                 alpha_sw_temperature=np.array([4, 3, 5]),
-                                                 alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
-                                                 alpha_sw_velocity_rtn_sun=np.zeros((3, 3)),
-                                                 alpha_sw_velocity_rtn=np.zeros((3, 3)),
-                                                 alpha_sw_velocity_rtn_covariance=np.zeros((3, 3, 3)),
-                                                 quality_flags=quality_flags,
-                                                 )
+        data_product = SwapiL3AlphaSolarWindData(
+            input_metadata=input_metadata,
+            epoch=epoch,
+            alpha_sw_speed=np.array([450.0, 460.0, 470.0]),
+            alpha_sw_speed_uncert=np.array([1.0, 1.0, 1.0]),
+            alpha_sw_speed_sun=np.array([470.0, 480.0, 490.0]),
+            alpha_sw_speed_sun_uncert=np.array([1.3, 1.4, 1.5]),
+            alpha_sw_density=np.array([5, 5, 5]),
+            alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
+            alpha_sw_temperature=np.array([4, 3, 5]),
+            alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
+            alpha_sw_velocity_rtn_sun=np.zeros((3, 3)),
+            alpha_sw_velocity_rtn=np.zeros((3, 3)),
+            alpha_sw_velocity_rtn_covariance=np.zeros((3, 3, 3)),
+            quality_flags=quality_flags,
+        )
         save_data(data_product)
 
         mock_write_cdf.assert_called_once()
         actual_attribute_manager = mock_write_cdf.call_args.args[2]
 
-        self.assertEqual([
-            call("Data_version", "002"),
-            call("Generation_date", "20240916"),
-            call("Logical_source", "imap_swapi_l2_descriptor"),
-            call("Logical_file_id", "imap_swapi_l2_descriptor_20240917_v002"),
-            call("ground_software_version", VERSION)
-        ], actual_attribute_manager.add_global_attribute.call_args_list)
+        self.assertEqual(
+            [
+                call("Data_version", "002"),
+                call("Generation_date", "20240916"),
+                call("Logical_source", "imap_swapi_l2_descriptor"),
+                call("Logical_file_id", "imap_swapi_l2_descriptor_20240917_v002"),
+                call("ground_software_version", VERSION),
+            ],
+            actual_attribute_manager.add_global_attribute.call_args_list,
+        )
 
     @patch("imap_l3_processing.utils.ImapAttributeManager")
     @patch("imap_l3_processing.utils.date")
@@ -256,26 +322,33 @@ def test_save_data_custom_path(self, mock_write_cdf, mock_today, _):
         mock_today.today.return_value = date(2024, 9, 16)
 
         descriptor = "descriptor"
-        input_metadata = InputMetadata("swapi", "l2", datetime(2024, 9, 17), datetime(2024, 9, 18), create_mock_version_map(descriptor=descriptor, minor_version=2),
-                                       descriptor)
+        input_metadata = InputMetadata(
+            "swapi",
+            "l2",
+            datetime(2024, 9, 17),
+            datetime(2024, 9, 18),
+            create_mock_version_map(descriptor=descriptor, minor_version=2),
+            descriptor,
+        )
         epoch = np.array([1, 2, 3])
         quality_flags = np.repeat([SwapiL3Flags.NONE], 3)
 
-        data_product = SwapiL3AlphaSolarWindData(input_metadata=input_metadata,
-                                                 epoch=epoch,
-                                                 alpha_sw_speed=np.array([450.0, 460.0, 470.0]),
-                                                 alpha_sw_speed_uncert=np.array([1.0, 1.0, 1.0]),
-                                                 alpha_sw_speed_sun=np.array([470.0, 480.0, 490.0]),
-                                                 alpha_sw_speed_sun_uncert=np.array([1.3, 1.4, 1.5]),
-                                                 alpha_sw_density=np.array([5, 5, 5]),
-                                                 alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
-                                                 alpha_sw_temperature=np.array([4, 3, 5]),
-                                                 alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
-                                                 alpha_sw_velocity_rtn_sun=np.zeros((3, 3)),
-                                                 alpha_sw_velocity_rtn=np.zeros((3, 3)),
-                                                 alpha_sw_velocity_rtn_covariance=np.zeros((3, 3, 3)),
-                                                 quality_flags=quality_flags,
-                                                 )
+        data_product = SwapiL3AlphaSolarWindData(
+            input_metadata=input_metadata,
+            epoch=epoch,
+            alpha_sw_speed=np.array([450.0, 460.0, 470.0]),
+            alpha_sw_speed_uncert=np.array([1.0, 1.0, 1.0]),
+            alpha_sw_speed_sun=np.array([470.0, 480.0, 490.0]),
+            alpha_sw_speed_sun_uncert=np.array([1.3, 1.4, 1.5]),
+            alpha_sw_density=np.array([5, 5, 5]),
+            alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
+            alpha_sw_temperature=np.array([4, 3, 5]),
+            alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
+            alpha_sw_velocity_rtn_sun=np.zeros((3, 3)),
+            alpha_sw_velocity_rtn=np.zeros((3, 3)),
+            alpha_sw_velocity_rtn_covariance=np.zeros((3, 3, 3)),
+            quality_flags=quality_flags,
+        )
 
         custom_path = TEMP_CDF_FOLDER_PATH / "fancy_path"
         returned_file_path = save_data(data_product, folder_path=custom_path)
@@ -290,13 +363,23 @@ def test_save_data_custom_path(self, mock_write_cdf, mock_today, _):
 
     @patch("imap_l3_processing.utils.write_cdf")
     @patch("imap_l3_processing.utils.ImapAttributeManager.add_global_attribute")
-    def test_save_data_procedurally_generates_map_global_metadata(self, mock_add_global_attr, _):
+    def test_save_data_procedurally_generates_map_global_metadata(
+        self, mock_add_global_attr, _
+    ):
         descriptor = "descriptor"
-        input_metadata = InputMetadata("ultra", "l3", datetime(2025, 1, 1), datetime(2025, 1, 1), create_mock_version_map(descriptor=descriptor, minor_version=1),
-                                       descriptor)
+        input_metadata = InputMetadata(
+            "ultra",
+            "l3",
+            datetime(2025, 1, 1),
+            datetime(2025, 1, 1),
+            create_mock_version_map(descriptor=descriptor, minor_version=1),
+            descriptor,
+        )
 
         epoch_as_datetime = np.array([datetime(2025, 1, 1)])
-        epoch_as_int = np.array([(datetime(2025, 1, 1) - TT2000_EPOCH).total_seconds() * 1e9])
+        epoch_as_int = np.array(
+            [(datetime(2025, 1, 1) - TT2000_EPOCH).total_seconds() * 1e9]
+        )
         epoch_delta = np.array([86400 * 1e9])
 
         cases = [
@@ -307,72 +390,140 @@ def test_save_data_procedurally_generates_map_global_metadata(self, mock_add_glo
         for case, epoch in cases:
             mock_add_global_attr.reset_mock()
             with self.subTest(case=case):
-                rectangular_spectral_index_map_data = create_rectangular_spectral_index_map_data(epoch=epoch,
-                                                                                                 epoch_delta=epoch_delta)
-                data_product = RectangularSpectralIndexDataProduct(input_metadata=input_metadata,
-                                                                   data=rectangular_spectral_index_map_data,
-                                                                   spice_frame_name=SpiceFrame.ECLIPJ2000)
+                rectangular_spectral_index_map_data = (
+                    create_rectangular_spectral_index_map_data(
+                        epoch=epoch, epoch_delta=epoch_delta
+                    )
+                )
+                data_product = RectangularSpectralIndexDataProduct(
+                    input_metadata=input_metadata,
+                    data=rectangular_spectral_index_map_data,
+                    spice_frame_name=SpiceFrame.ECLIPJ2000,
+                )
 
                 save_data(data_product)
 
-                mock_add_global_attr.assert_has_calls([
-                    call("start_date", "2025-01-01T00:00:00"),
-                    call("end_date", "2025-01-02T00:00:00"),
-                    call("Spice_reference_frame", 'ECLIPJ2000')
-                ], any_order=True)
+                mock_add_global_attr.assert_has_calls(
+                    [
+                        call("start_date", "2025-01-01T00:00:00"),
+                        call("end_date", "2025-01-02T00:00:00"),
+                        call("Spice_reference_frame", "ECLIPJ2000"),
+                    ],
+                    any_order=True,
+                )
 
-    @patch("imap_l3_processing.utils.ImapAttributeManager.add_instrument_attrs", autospec=True)
+    @patch(
+        "imap_l3_processing.utils.ImapAttributeManager.add_instrument_attrs",
+        autospec=True,
+    )
     @patch("imap_l3_processing.utils.write_cdf")
-    def test_save_data_procedurally_generates_all_map_global_metadata_if_absent(self, mock_write_cdf,
-                                                                                mock_add_instrument_attrs):
+    def test_save_data_procedurally_generates_all_map_global_metadata_if_absent(
+        self, mock_write_cdf, mock_add_instrument_attrs
+    ):
         descriptor = "descriptor"
-        non_map_input_metadata = InputMetadata("swapi", "l3", datetime(2024, 9, 17), datetime(2024, 9, 18), create_mock_version_map(descriptor=descriptor, minor_version=2) ,
-                                               descriptor)
+        non_map_input_metadata = InputMetadata(
+            "swapi",
+            "l3",
+            datetime(2024, 9, 17),
+            datetime(2024, 9, 18),
+            create_mock_version_map(descriptor=descriptor, minor_version=2),
+            descriptor,
+        )
 
         def add_swapi_attrs_from_file(attr_manager, instrument, level, descriptor):
-            for logical_source in ["imap_swapi_l3_descriptor", "imap_ultra_l3_ena-map-descriptor"]:
-                attr_manager.add_global_attribute(logical_source, {
-                    "Data_level": "Data_level from file",
-                    "Data_type": "Data_type from file",
-                    "Logical_source_description": "Logical_source_description from file"
-                })
+            for logical_source in [
+                "imap_swapi_l3_descriptor",
+                "imap_ultra_l3_ena-map-descriptor",
+            ]:
+                attr_manager.add_global_attribute(
+                    logical_source,
+                    {
+                        "Data_level": "Data_level from file",
+                        "Data_type": "Data_type from file",
+                        "Logical_source_description": "Logical_source_description from file",
+                    },
+                )
 
         mock_add_instrument_attrs.side_effect = add_swapi_attrs_from_file
 
-        map_input_metadata = InputMetadata("hi", "l3b", datetime(2024, 9, 17), datetime(2024, 9, 18), create_mock_version_map(descriptor="spx-map-descriptor", minor_version=2),
-                                           "spx-map-descriptor")
+        map_input_metadata = InputMetadata(
+            "hi",
+            "l3b",
+            datetime(2024, 9, 17),
+            datetime(2024, 9, 18),
+            create_mock_version_map(descriptor="spx-map-descriptor", minor_version=2),
+            "spx-map-descriptor",
+        )
 
-        map_input_metadata_with_existing_global_attrs = InputMetadata("ultra", "l3", datetime(2024, 9, 17),
-                                                                      datetime(2024, 9, 18), create_mock_version_map(descriptor="ena-map-descriptor", minor_version=2),
-                                                                      "ena-map-descriptor")
+        map_input_metadata_with_existing_global_attrs = InputMetadata(
+            "ultra",
+            "l3",
+            datetime(2024, 9, 17),
+            datetime(2024, 9, 18),
+            create_mock_version_map(descriptor="ena-map-descriptor", minor_version=2),
+            "ena-map-descriptor",
+        )
 
         cases = [
-            (non_map_input_metadata, [("Data_level", "Data_level from file"), ("Data_type", "Data_type from file"),
-                                      ("Logical_source_description", "Logical_source_description from file")]),
-            (map_input_metadata,
-             [("Data_level", "3b"), ("Data_type", "L3b_spx-map-descriptor>Level-3b Spectral Fit Index Map"),
-              ("Logical_source_description", "IMAP-hi Level-3b Spectral Fit Index Map"),
-              ("Spice_reference_frame", "ECLIPJ2000")]),
-            (map_input_metadata_with_existing_global_attrs,
-             [("Data_level", "Data_level from file"), ("Data_type", "Data_type from file"),
-              ("Logical_source_description", "Logical_source_description from file"),
-              ("Spice_reference_frame", "ECLIPJ2000")]
-             )
+            (
+                non_map_input_metadata,
+                [
+                    ("Data_level", "Data_level from file"),
+                    ("Data_type", "Data_type from file"),
+                    (
+                        "Logical_source_description",
+                        "Logical_source_description from file",
+                    ),
+                ],
+            ),
+            (
+                map_input_metadata,
+                [
+                    ("Data_level", "3b"),
+                    (
+                        "Data_type",
+                        "L3b_spx-map-descriptor>Level-3b Spectral Fit Index Map",
+                    ),
+                    (
+                        "Logical_source_description",
+                        "IMAP-hi Level-3b Spectral Fit Index Map",
+                    ),
+                    ("Spice_reference_frame", "ECLIPJ2000"),
+                ],
+            ),
+            (
+                map_input_metadata_with_existing_global_attrs,
+                [
+                    ("Data_level", "Data_level from file"),
+                    ("Data_type", "Data_type from file"),
+                    (
+                        "Logical_source_description",
+                        "Logical_source_description from file",
+                    ),
+                    ("Spice_reference_frame", "ECLIPJ2000"),
+                ],
+            ),
         ]
         for input_metadata, expected_global_metadata in cases:
             with self.subTest(name=input_metadata.logical_source):
                 mock_write_cdf.reset_mock()
 
-                data_product = RectangularIntensityDataProduct(input_metadata=input_metadata,
-                                                               data=create_rectangular_intensity_map_data(),
-                                                               spice_frame_name=SpiceFrame.ECLIPJ2000)
+                data_product = RectangularIntensityDataProduct(
+                    input_metadata=input_metadata,
+                    data=create_rectangular_intensity_map_data(),
+                    spice_frame_name=SpiceFrame.ECLIPJ2000,
+                )
                 save_data(data_product)
 
                 mock_write_cdf.assert_called_once()
 
-                [_file_path, _data, attribute_manager] = mock_write_cdf.call_args_list[0].args
+                [_file_path, _data, attribute_manager] = mock_write_cdf.call_args_list[
+                    0
+                ].args
 
-                global_metadata = attribute_manager.try_load_global_metadata(input_metadata.logical_source)
+                global_metadata = attribute_manager.try_load_global_metadata(
+                    input_metadata.logical_source
+                )
                 self.assertIsNotNone(global_metadata)
 
                 for metadata_field, metadata_value in expected_global_metadata:
@@ -387,7 +538,7 @@ def test_format_time(self):
         self.assertEqual(None, actual_time)
 
     @patch("imap_l3_processing.utils.requests")
-    @patch('builtins.open')
+    @patch("builtins.open")
     def test_download_external_dependency(self, mock_open_file, mock_requests):
         expected_url = "https://www.spaceweather.gc.ca/solar_flux_data/daily_flux_values/fluxtable.txt"
         expected_filename = "f107_fluxtable.txt"
@@ -421,16 +572,18 @@ def test_read_l1d_mag_data(self):
         file_name_as_path = Path(file_name_as_str)
 
         epoch = np.array([datetime(2010, 1, 1, 0, 0, 46)])
-        vectors_with_magnitudes = np.array([[0, 1, 2, 0], [255, 255, 255, 255], [6, 7, 8, 0]], dtype=np.float64)
+        vectors_with_magnitudes = np.array(
+            [[0, 1, 2, 0], [255, 255, 255, 255], [6, 7, 8, 0]], dtype=np.float64
+        )
         trimmed_vectors = np.array([[0, 1, 2], [np.nan, np.nan, np.nan], [6, 7, 8]])
         with CDF(file_name_as_str, "") as mag_cdf:
             mag_cdf["epoch"] = epoch
             mag_cdf["b_dsrf"] = vectors_with_magnitudes
-            mag_cdf["b_dsrf"].attrs['FILLVAL'] = 255.0
+            mag_cdf["b_dsrf"].attrs["FILLVAL"] = 255.0
 
         cases = [
             ("file name as str", file_name_as_str),
-            ("file name as Path", file_name_as_path)
+            ("file name as Path", file_name_as_path),
         ]
         for name, path in cases:
             with self.subTest(name):
@@ -442,60 +595,78 @@ def test_read_l1d_mag_data(self):
     @patch("imap_l3_processing.utils.spiceypy")
     def test_furnish_local_spice(self, mock_spiceypy):
         mock_spiceypy.kdata.side_effect = [
-            ("/Users/harrison/Development/imap_L3_processing/spice_kernels/naif0012.tls", "TEXT", '', 0)
+            (
+                "/Users/harrison/Development/imap_L3_processing/spice_kernels/naif0012.tls",
+                "TEXT",
+                "",
+                0,
+            )
         ]
         mock_spiceypy.ktotal.return_value = 1
 
         furnish_local_spice()
 
-        mock_spiceypy.ktotal.assert_called_once_with('ALL')
-        mock_spiceypy.kdata.assert_called_once_with(0, 'ALL')
+        mock_spiceypy.ktotal.assert_called_once_with("ALL")
+        mock_spiceypy.kdata.assert_called_once_with(0, "ALL")
 
         self.assertEqual(10, mock_spiceypy.furnsh.call_count)
-        mock_spiceypy.furnsh.assert_has_calls([
-            call(str(get_spice_data_path("de440s.bsp"))),
-            call(str(get_spice_data_path("imap_science_0001.tf"))),
-            call(str(get_spice_data_path("imap_science_draft.tf"))),
-            call(str(get_spice_data_path("imap_sclk_0000.tsc"))),
-            call(str(get_spice_data_path("imap_sim_ck_2hr_2secsampling_with_nutation.bc"))),
-            call(str(get_spice_data_path("imap_spk_demo.bsp"))),
-            call(str(get_spice_data_path("imap_wkcp.tf"))),
-            call(str(get_spice_data_path("pck00011.tpc"))),
-            call(str(get_spice_data_path("sim_1yr_imap_attitude.bc"))),
-            call(str(get_spice_data_path("sim_1yr_imap_pointing_frame.bc"))),
-        ], any_order=True)
+        mock_spiceypy.furnsh.assert_has_calls(
+            [
+                call(str(get_spice_data_path("de440s.bsp"))),
+                call(str(get_spice_data_path("imap_science_0001.tf"))),
+                call(str(get_spice_data_path("imap_science_draft.tf"))),
+                call(str(get_spice_data_path("imap_sclk_0000.tsc"))),
+                call(
+                    str(
+                        get_spice_data_path(
+                            "imap_sim_ck_2hr_2secsampling_with_nutation.bc"
+                        )
+                    )
+                ),
+                call(str(get_spice_data_path("imap_spk_demo.bsp"))),
+                call(str(get_spice_data_path("imap_wkcp.tf"))),
+                call(str(get_spice_data_path("pck00011.tpc"))),
+                call(str(get_spice_data_path("sim_1yr_imap_attitude.bc"))),
+                call(str(get_spice_data_path("sim_1yr_imap_pointing_frame.bc"))),
+            ],
+            any_order=True,
+        )
 
     @patch("imap_l3_processing.utils.spiceypy")
     def test_get_spice_parent_file_names(self, mock_spicepy):
         mock_spicepy.ktotal.return_value = 2
         mock_spicepy.kdata.side_effect = [
-            [Path("some_spice_1.tf"), 'type', 'source', 'handle'],
-            [Path("some_spice_2.kf"), 'type', 'source', 'handle'],
+            [Path("some_spice_1.tf"), "type", "source", "handle"],
+            [Path("some_spice_2.kf"), "type", "source", "handle"],
         ]
 
         actual_spice_parents = get_spice_parent_file_names()
 
-        mock_spicepy.ktotal.assert_called_once_with('ALL')
-        mock_spicepy.kdata.assert_has_calls([
-            call(0, 'ALL'),
-            call(1, 'ALL')
-        ])
+        mock_spicepy.ktotal.assert_called_once_with("ALL")
+        mock_spicepy.kdata.assert_has_calls([call(0, "ALL"), call(1, "ALL")])
 
         self.assertEqual(["some_spice_1.tf", "some_spice_2.kf"], actual_spice_parents)
 
     @patch("imap_l3_processing.utils.imap_data_access.download")
     @patch("imap_l3_processing.utils.spiceypy")
     @patch("imap_l3_processing.utils.requests")
-    def test_furnish_spice_metakernel(self, mock_requests, mock_spiceypy, mock_download):
+    def test_furnish_spice_metakernel(
+        self, mock_requests, mock_spiceypy, mock_download
+    ):
 
-        metakernel_bytes = b"\n\\begintext\n\nThis is the most up to date Metakernel as of ..."
+        metakernel_bytes = (
+            b"\n\\begintext\n\nThis is the most up to date Metakernel as of ..."
+        )
 
         mock_requests.get.side_effect = [
             Mock(content=metakernel_bytes),
-            Mock(text='["naif0012.tls", "imap_001.tf"]')
+            Mock(text='["naif0012.tls", "imap_001.tf"]'),
         ]
 
-        mock_download.side_effect = [sentinel.naif_downloaded_path, sentinel.imap_downloaded_path]
+        mock_download.side_effect = [
+            sentinel.naif_downloaded_path,
+            sentinel.imap_downloaded_path,
+        ]
 
         start_date = datetime(2010, 1, 1)
         end_date = datetime(2010, 3, 1)
@@ -511,9 +682,19 @@ def test_furnish_spice_metakernel(self, mock_requests, mock_spiceypy, mock_downl
             mock_data_dir = tmp_dir / "some_data_directory"
             mock_data_access_url = "https://imap-mission.com/api-key"
 
-            fake_imap_data_access_config = {"DATA_DIR": mock_data_dir, "DATA_ACCESS_URL": mock_data_access_url}
-            with patch.object(imap_data_access, "config", new=fake_imap_data_access_config):
-                actual_output = furnish_spice_metakernel(start_date, end_date, kernel_types, metakernel_file_name="metakernel_file.txt")
+            fake_imap_data_access_config = {
+                "DATA_DIR": mock_data_dir,
+                "DATA_ACCESS_URL": mock_data_access_url,
+            }
+            with patch.object(
+                imap_data_access, "config", new=fake_imap_data_access_config
+            ):
+                actual_output = furnish_spice_metakernel(
+                    start_date,
+                    end_date,
+                    kernel_types,
+                    metakernel_file_name="metakernel_file.txt",
+                )
 
                 expected_request_params = {
                     "file_types": ["leapseconds", "imap_frames"],
@@ -521,26 +702,43 @@ def test_furnish_spice_metakernel(self, mock_requests, mock_spiceypy, mock_downl
                     "end_time": "320673600",
                 }
 
-                mock_requests.get.assert_has_calls([
-                    call("https://imap-mission.com/metakernel",
-                         params={**expected_request_params, "spice_path": mock_data_dir / "imap" / "spice"}),
-                    call("https://imap-mission.com/metakernel",
-                         params={**expected_request_params, "list_files": "true"})
-                ])
+                mock_requests.get.assert_has_calls(
+                    [
+                        call(
+                            "https://imap-mission.com/metakernel",
+                            params={
+                                **expected_request_params,
+                                "spice_path": mock_data_dir / "imap" / "spice",
+                            },
+                        ),
+                        call(
+                            "https://imap-mission.com/metakernel",
+                            params={**expected_request_params, "list_files": "true"},
+                        ),
+                    ]
+                )
 
-                mock_download.assert_has_calls([
-                    call('naif0012.tls'),
-                    call('imap_001.tf')
-                ])
+                mock_download.assert_has_calls(
+                    [call("naif0012.tls"), call("imap_001.tf")]
+                )
 
-                expected_metakernel_path = mock_data_dir / "metakernel" / "metakernel_file.txt"
-                self.assertEqual(metakernel_bytes, expected_metakernel_path.read_bytes())
+                expected_metakernel_path = (
+                    mock_data_dir / "metakernel" / "metakernel_file.txt"
+                )
+                self.assertEqual(
+                    metakernel_bytes, expected_metakernel_path.read_bytes()
+                )
 
-                mock_spiceypy.furnsh.assert_called_once_with(str(expected_metakernel_path))
+                mock_spiceypy.furnsh.assert_called_once_with(
+                    str(expected_metakernel_path)
+                )
 
                 expected_output = FurnishMetakernelOutput(
                     metakernel_path=expected_metakernel_path,
-                    spice_kernel_paths=[sentinel.naif_downloaded_path, sentinel.imap_downloaded_path]
+                    spice_kernel_paths=[
+                        sentinel.naif_downloaded_path,
+                        sentinel.imap_downloaded_path,
+                    ],
                 )
 
                 self.assertEqual(expected_output, actual_output)
@@ -554,33 +752,48 @@ def test_get_dependency_paths_by_descriptor(self):
         cases = [
             (
                 "base case",
-                ProcessingInputCollection(ScienceInput(filename1), ScienceInput(filename2), ScienceInput(filename3)),
+                ProcessingInputCollection(
+                    ScienceInput(filename1),
+                    ScienceInput(filename2),
+                    ScienceInput(filename3),
+                ),
                 ["u90", "u45"],
-                {"u45": [filename2], "u90": [filename1, filename3]}
+                {"u45": [filename2], "u90": [filename1, filename3]},
             ),
             (
                 "no matching filenames for a descriptor",
-                ProcessingInputCollection(ScienceInput(filename1), ScienceInput(filename3)),
+                ProcessingInputCollection(
+                    ScienceInput(filename1), ScienceInput(filename3)
+                ),
                 ["u90", "u45"],
-                {"u45": [], "u90": [filename1, filename3]}
+                {"u45": [], "u90": [filename1, filename3]},
             ),
             (
                 "extra science file",
-                ProcessingInputCollection(ScienceInput(filename1), ScienceInput(filename4)),
+                ProcessingInputCollection(
+                    ScienceInput(filename1), ScienceInput(filename4)
+                ),
                 ["u90", "u45"],
-                {"u45": [], "u90": [filename1]}
+                {"u45": [], "u90": [filename1]},
             ),
         ]
-        for (case_name, input_collection, descriptors, expected_output) in cases:
+        for case_name, input_collection, descriptors, expected_output in cases:
             with self.subTest(case_name):
-                actual_output = get_dependency_paths_by_descriptor(input_collection, descriptors)
+                actual_output = get_dependency_paths_by_descriptor(
+                    input_collection, descriptors
+                )
 
-                self.assertEqual(sorted(expected_output.keys()), sorted(actual_output.keys()))
+                self.assertEqual(
+                    sorted(expected_output.keys()), sorted(actual_output.keys())
+                )
 
                 for i, output_key in enumerate(expected_output.keys()):
-                    self.assertEqual(sorted(expected_output[output_key]),
-                                     sorted(actual_output[output_key]), f"output not equal for key {output_key}" + \
-                                     f", expected: {sorted(expected_output[output_key])} actual {sorted(actual_output[output_key])}")
+                    self.assertEqual(
+                        sorted(expected_output[output_key]),
+                        sorted(actual_output[output_key]),
+                        f"output not equal for key {output_key}"
+                        + f", expected: {sorted(expected_output[output_key])} actual {sorted(actual_output[output_key])}",
+                    )
 
     @patch("imap_l3_processing.glows.l3bc.utils.imap_data_access.download")
     @with_tempdir
@@ -623,7 +836,9 @@ def test_get_version_from_query_result(self):
         }
 
         science_file_version = get_version_from_query_result(science_file_query_result)
-        ancillary_file_version = get_version_from_query_result(ancillary_file_query_result)
+        ancillary_file_version = get_version_from_query_result(
+            ancillary_file_query_result
+        )
 
         self.assertIsInstance(science_file_version, Version)
         self.assertIsInstance(ancillary_file_version, Version)
@@ -641,4 +856,3 @@ def test_get_cache_directory(self):
         self.assertEqual(cache_temp_dir, second_cache_temp_dir)
 
         self.assertTrue(cache_temp_dir.is_relative_to(Path(tempfile.gettempdir())))
-
diff --git a/tests/ultra/test_models.py b/tests/ultra/test_models.py
index 0d8785f36..dbb03b222 100644
--- a/tests/ultra/test_models.py
+++ b/tests/ultra/test_models.py
@@ -1,4 +1,3 @@
-import traceback
 from datetime import datetime
 
 import numpy as np
@@ -7,27 +6,29 @@
 
 from imap_l3_processing.glows.l3e.glows_l3e_ultra_model import (
     ENERGY_VAR_NAME,
-    PROBABILITY_OF_SURVIVAL_VAR_NAME,
-    HEALPIX_INDEX_VAR_NAME,
     EPOCH_CDF_VAR_NAME,
     GLOWS_FLAGS_VAR_NAME,
+    HEALPIX_INDEX_VAR_NAME,
+    PROBABILITY_OF_SURVIVAL_VAR_NAME,
 )
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
 from imap_l3_processing.ultra.models import UltraGlowsL3eData, UltraL1CPSet
 from tests.spice_test_case import SpiceTestCase
 from tests.test_helpers import (
-    get_test_data_folder,
-    with_tempdir,
     get_integration_test_data_path,
+    get_test_data_folder,
     get_test_data_path,
+    with_tempdir,
 )
 
 
 class TestModels(SpiceTestCase):
-
     @with_tempdir
     def test_glows_l3e_read_from_file(self, temp_dir):
-        path_to_cdf = temp_dir / 'imap_glows_l3e_survival-probability-ul_20250415-repoint01000_v001.cdf'
+        path_to_cdf = (
+            temp_dir
+            / "imap_glows_l3e_survival-probability-ul_20250415-repoint01000_v001.cdf"
+        )
 
         rng = np.random.RandomState(42)
         expected_epoch = datetime(2025, 4, 15, 12, 0, 0, 1)
@@ -36,7 +37,7 @@ def test_glows_l3e_read_from_file(self, temp_dir):
         expected_probability_of_survival = rng.random((1, 16, 3072))
         expected_flags = np.array([GlowsL3Flags.PREDICTIVE_EPHEMERIS], dtype=np.uint16)
 
-        with CDF(str(path_to_cdf), masterpath='') as cdf:
+        with CDF(str(path_to_cdf), masterpath="") as cdf:
             cdf[EPOCH_CDF_VAR_NAME] = [expected_epoch]
             cdf[EPOCH_CDF_VAR_NAME].attrs["FILLVAL"] = -38470932875435
 
@@ -51,7 +52,6 @@ def test_glows_l3e_read_from_file(self, temp_dir):
 
             cdf[GLOWS_FLAGS_VAR_NAME] = expected_flags
 
-
         actual = UltraGlowsL3eData.read_from_path(path_to_cdf)
 
         self.assertEqual(expected_epoch, actual.epoch)
@@ -66,16 +66,26 @@ def test_glows_l3e_read_from_file(self, temp_dir):
     def test_ultra_l1c_read_from_file_and_can_convert_to_xarray(self):
         expected_epoch = datetime(2026, 5, 18, 10, 3, 11, 602839)
 
-        run_local_path = get_test_data_folder() / 'ultra' / 'imap_ultra_l1c_90sensor-spacecraftpset_20260518-repoint00252_v002.cdf'
+        run_local_path = (
+            get_test_data_folder()
+            / "ultra"
+            / "imap_ultra_l1c_90sensor-spacecraftpset_20260518-repoint00252_v002.cdf"
+        )
 
         actual = UltraL1CPSet.read_from_path(run_local_path)
 
         with CDF(str(run_local_path)) as expected:
             self.assertEqual(expected_epoch, actual.epoch)
             self.assertEqual(252, actual.repointing)
-            np.testing.assert_array_equal(expected[CoordNames.ENERGY_ULTRA_L1C.value][...], actual.energy)
-            np.testing.assert_array_equal(expected["exposure_factor"][...], actual.exposure)
-            np.testing.assert_array_equal(expected["epoch_delta"][...], actual.epoch_delta)
+            np.testing.assert_array_equal(
+                expected[CoordNames.ENERGY_ULTRA_L1C.value][...], actual.energy
+            )
+            np.testing.assert_array_equal(
+                expected["exposure_factor"][...], actual.exposure
+            )
+            np.testing.assert_array_equal(
+                expected["epoch_delta"][...], actual.epoch_delta
+            )
             np.testing.assert_array_equal(
                 expected[CoordNames.HEALPIX_INDEX.value][...], actual.healpix_index
             )
@@ -90,27 +100,38 @@ def test_ultra_l1c_read_from_file_and_can_convert_to_xarray(self):
             )
 
     def test_ultra_l1c_convert_to_xarray(self):
-        l1c_path = get_test_data_path("ultra/imap_ultra_l1c_90sensor-spacecraftpset_20260518-repoint00252_v002.cdf")
+        l1c_path = get_test_data_path(
+            "ultra/imap_ultra_l1c_90sensor-spacecraftpset_20260518-repoint00252_v002.cdf"
+        )
         actual = UltraL1CPSet.read_from_path(l1c_path)
 
         dataset = actual.to_xarray()
 
-        self.assertEqual([
-            "exposure_factor",
-            "sensitivity",
-            CoordNames.AZIMUTH_L1C.value,
-            CoordNames.ELEVATION_L1C.value,
-        ], list(dataset.data_vars.keys()))
+        self.assertEqual(
+            [
+                "exposure_factor",
+                "sensitivity",
+                CoordNames.AZIMUTH_L1C.value,
+                CoordNames.ELEVATION_L1C.value,
+            ],
+            list(dataset.data_vars.keys()),
+        )
 
-        self.assertEqual([
-            CoordNames.TIME.value,
-            CoordNames.ENERGY_ULTRA_L1C.value,
-            CoordNames.HEALPIX_INDEX.value,
-        ], list(dataset.coords.keys()))
+        self.assertEqual(
+            [
+                CoordNames.TIME.value,
+                CoordNames.ENERGY_ULTRA_L1C.value,
+                CoordNames.HEALPIX_INDEX.value,
+            ],
+            list(dataset.coords.keys()),
+        )
 
-    def test_ultra_l1c_read_from_file_handles_longitude_latitude_with_time_dimension(self):
+    def test_ultra_l1c_read_from_file_handles_longitude_latitude_with_time_dimension(
+        self,
+    ):
         l1c_path = get_integration_test_data_path(
-            "ultra/imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00000_v000.cdf")
+            "ultra/imap_ultra_l1c_45sensor-spacecraftpset_20250416-repoint00000_v000.cdf"
+        )
 
         actual = UltraL1CPSet.read_from_path(l1c_path)
         self.assertEqual(1, actual.longitude.ndim)
diff --git a/tests/ultra/test_ultra_combined_nsp_initializer.py b/tests/ultra/test_ultra_combined_nsp_initializer.py
index 729fa0c70..ba0945ef5 100644
--- a/tests/ultra/test_ultra_combined_nsp_initializer.py
+++ b/tests/ultra/test_ultra_combined_nsp_initializer.py
@@ -1,19 +1,22 @@
 import unittest
 from datetime import datetime
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
 from imap_data_access.file_validation import Version
 
 from imap_l3_processing.maps.map_initializer import MapInitializer, PossibleMapToProduce
 from imap_l3_processing.models import InputMetadata, VersionMap
-from imap_l3_processing.ultra.ultra_combined_nsp_initializer import UltraCombinedNSPInitializer
+from imap_l3_processing.ultra.ultra_combined_nsp_initializer import (
+    UltraCombinedNSPInitializer,
+)
 from tests.integration.integration_test_helpers import ImapQueryPatcher
 
 
 class TestUltraCombinedNSPInitializer(unittest.TestCase):
     def setUp(self):
         self.mock_query_patcher = patch(
-            'imap_l3_processing.ultra.ultra_combined_nsp_initializer.imap_data_access.query')
+            "imap_l3_processing.ultra.ultra_combined_nsp_initializer.imap_data_access.query"
+        )
         self.mock_query = self.mock_query_patcher.start()
 
     def tearDown(self):
@@ -22,26 +25,27 @@ def tearDown(self):
     def test_is_map_initializer(self):
         self.assertIsInstance(UltraCombinedNSPInitializer(), MapInitializer)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
     def test_get_maps_that_can_be_produced(self, mock_read_cdf_parents):
-        self.mock_query.side_effect = ImapQueryPatcher([
-            'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-            'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-        ])
+        self.mock_query.side_effect = ImapQueryPatcher(
+            [
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+            ]
+        )
 
         initializer = UltraCombinedNSPInitializer()
-        descriptor = 'ulc-ena-h-sf-nsp-full-hae-4deg-3mo'
+        descriptor = "ulc-ena-h-sf-nsp-full-hae-4deg-3mo"
         mock_read_cdf_parents.side_effect = [
             [
                 "imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
                 "imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
                 "imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-
             ],
             [
                 "imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
                 "imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf"
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
             ],
         ]
         actual_possible_maps = initializer.get_maps_that_can_be_produced(descriptor)
@@ -49,8 +53,8 @@ def test_get_maps_that_can_be_produced(self, mock_read_cdf_parents):
         expected_possible_maps = [
             PossibleMapToProduce(
                 input_files={
-                    'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                    'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
+                    "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                    "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
                     "imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
                     "imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
                     "imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
@@ -59,120 +63,142 @@ def test_get_maps_that_can_be_produced(self, mock_read_cdf_parents):
                     "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
                 },
                 input_metadata=InputMetadata(
-                    instrument='ultra',
-                    data_level='l3',
+                    instrument="ultra",
+                    data_level="l3",
                     start_date=datetime(2010, 1, 1),
                     end_date=datetime(2010, 4, 2, 7, 30),
-                    version=VersionMap({},Version(None,1)),
+                    version=VersionMap({}, Version(None, 1)),
                     descriptor=descriptor,
-                )
+                ),
             )
         ]
 
         self.assertEqual(expected_possible_maps, actual_possible_maps)
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
     def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
         cases = [
-            (None, Version(None,2)),
-            (4, Version(4,2)),
+            (None, Version(None, 2)),
+            (4, Version(4, 2)),
         ]
         for major_version, expected_version in cases:
             with self.subTest(major_version=major_version):
-                self.mock_query.side_effect = ImapQueryPatcher([
-                    f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                    f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-
-                    f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                    f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                    f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v002.cdf',
-
-                    f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-                    f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-
-                    f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf',
-
-                    f'imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_{Version(major_version, 1)}.cdf',
-                    f'imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_{Version(major_version, 1)}.cdf',
-                    'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-                ])
+                self.mock_query.side_effect = ImapQueryPatcher(
+                    [
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v002.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf",
+                        f"imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_{Version(major_version, 1)}.cdf",
+                        f"imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_{Version(major_version, 1)}.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    ]
+                )
 
                 mock_read_cdf_parents.side_effect = [
                     [
-                        f"imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                        f"imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                        f"imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
                     ],
                     [
-                        f"imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                        f"imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                        f"imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf"
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
                     ],
                     [
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf'
-                    ], [
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v002.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v002.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v002.cdf'
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
                     ],
                     [
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100701-repoint00201_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100702-repoint00202_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100703-repoint00203_v001.cdf'
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v002.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v002.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v002.cdf",
                     ],
                     [
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100701-repoint00201_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100702-repoint00202_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100703-repoint00203_v001.cdf'
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
                     ],
                     [
-                        f"imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                        f"imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                        f"imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-                        f"imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                        f"imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                        f"imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-                        f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                        f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                        'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
                     ],
                     [
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                        f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                        f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                        'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
-                    ]
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    ],
+                    [
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    ],
                 ]
                 initializer = UltraCombinedNSPInitializer()
-                actual_maps_to_produce = initializer.get_maps_that_should_be_produced(f"ulc-ena-h-sf-nsp-full-hae-4deg-3mo",
-                                                                                      major_version)
-
-                self.mock_query.assert_has_calls([
-                    call(instrument='ultra', data_level='l2'),
-                    call(instrument="ultra", table='ancillary', descriptor='l2-energy-bin-group-sizes',
-                         version='latest'),
-                    call(instrument='ultra', data_level='l3')
-                ])
-
-                mock_read_cdf_parents.assert_has_calls([
-                    call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-                    call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-                    call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf'),
-                    call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v002.cdf'),
-                    call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-                    call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-
-                    call(f'imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_{Version(major_version, 1)}.cdf'),
-                    call(f'imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_{Version(major_version, 1)}.cdf'),
-                ])
+                actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+                    "ulc-ena-h-sf-nsp-full-hae-4deg-3mo", major_version
+                )
+
+                self.mock_query.assert_has_calls(
+                    [
+                        call(instrument="ultra", data_level="l2"),
+                        call(
+                            instrument="ultra",
+                            table="ancillary",
+                            descriptor="l2-energy-bin-group-sizes",
+                            version="latest",
+                        ),
+                        call(instrument="ultra", data_level="l3"),
+                    ]
+                )
+
+                mock_read_cdf_parents.assert_has_calls(
+                    [
+                        call(
+                            "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                        call(
+                            "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                        call(
+                            "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf"
+                        ),
+                        call(
+                            "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v002.cdf"
+                        ),
+                        call(
+                            "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                        ),
+                        call(
+                            "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_{Version(major_version, 1)}.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l3_ulc-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_{Version(major_version, 1)}.cdf"
+                        ),
+                    ]
+                )
 
                 descriptor = "ulc-ena-h-sf-nsp-full-hae-4deg-3mo"
 
@@ -182,20 +208,20 @@ def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
                         data_level="l3",
                         start_date=datetime(2010, 4, 1),
                         end_date=datetime(2010, 7, 1, 7, 30),
-                        version=VersionMap({descriptor:expected_version}),
-                        descriptor=descriptor
+                        version=VersionMap({descriptor: expected_version}),
+                        descriptor=descriptor,
                     ),
                     input_files={
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v002.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v002.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v002.cdf',
-                        f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                        f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v002.cdf',
-                        f'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-                    }
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v002.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v002.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v002.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v002.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    },
                 )
                 expected_possible_map_to_produce_with_no_previous_version = PossibleMapToProduce(
                     input_metadata=InputMetadata(
@@ -204,26 +230,28 @@ def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
                         start_date=datetime(2010, 7, 1),
                         end_date=datetime(2010, 9, 30, 7, 30),
                         version=VersionMap({descriptor: Version(major_version, 1)}),
-                        descriptor=descriptor
+                        descriptor=descriptor,
                     ),
                     input_files={
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100701-repoint00201_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100702-repoint00202_v001.cdf',
-                        f'imap_ultra_l1c_45sensor-spacecraftpset_20100703-repoint00203_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100701-repoint00201_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100702-repoint00202_v001.cdf',
-                        f'imap_ultra_l1c_90sensor-spacecraftpset_20100703-repoint00203_v001.cdf',
-                        f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-                        f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-                        f'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-                    }
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
+                        "imap_ultra_l1c_45sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
+                        "imap_ultra_l1c_90sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
+                        "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                        "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    },
                 )
-                expected_maps_to_produce = [expected_possible_map_to_produce_with_previous_version,
-                            expected_possible_map_to_produce_with_no_previous_version]
+                expected_maps_to_produce = [
+                    expected_possible_map_to_produce_with_previous_version,
+                    expected_possible_map_to_produce_with_no_previous_version,
+                ]
                 self.assertEqual(expected_maps_to_produce, actual_maps_to_produce)
 
     def test_initializer_raises_when_given_invalid_map_descriptor(self):
         initializer = UltraCombinedNSPInitializer()
         with self.assertRaises(ValueError) as e:
-            initializer.get_maps_that_should_be_produced(f"ulc-invalid", None)
-        self.assertEqual(e.exception.args, ("Invalid map descriptor: ulc-invalid",))
\ No newline at end of file
+            initializer.get_maps_that_should_be_produced("ulc-invalid", None)
+        self.assertEqual(e.exception.args, ("Invalid map descriptor: ulc-invalid",))
diff --git a/tests/ultra/test_ultra_l3_dependencies.py b/tests/ultra/test_ultra_l3_dependencies.py
index 48ff731ae..a0b3e6326 100644
--- a/tests/ultra/test_ultra_l3_dependencies.py
+++ b/tests/ultra/test_ultra_l3_dependencies.py
@@ -2,126 +2,197 @@
 import unittest
 from hashlib import sha256
 from pathlib import Path
-from unittest.mock import patch, sentinel, call, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
-from imap_data_access import ScienceInput, AncillaryInput, ProcessingInputCollection
+from imap_data_access import AncillaryInput, ProcessingInputCollection, ScienceInput
 
 from imap_l3_processing.ultra.ultra_l3_dependencies import (
+    UltraL3CombinedDependencies,
     UltraL3Dependencies,
     UltraL3SpectralIndexDependencies,
-    UltraL3CombinedDependencies,
     load_or_create_healpix_l2,
 )
 from imap_l3_processing.utils import clear_temp_cache, get_temp_cache_dir
 from tests.test_helpers import get_test_data_path
 
+
 class TestUltraL3Dependencies(unittest.TestCase):
     def setUp(self):
         clear_temp_cache()
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.HealPixIntensityMapData.read_from_xarray')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.ScienceInput')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.ultra_l2')
-    def test_fetch_dependencies(self, mock_ultra_l2, mock_science_input_constructor, mock_download, mock_read_xarray_healpix, mock_read_from_path_rectangular, mock_read_ultra_l1c,
-                                mock_read_glows):
-        l1c_input_paths = [Path("imap_ultra_l1c_45sensor-pset_20251010_v001.cdf"),
-                           Path("imap_ultra_l1c_45sensor-pset_20251011_v001.cdf"),
-                           Path("imap_ultra_l1c_45sensor-pset_20251012_v001.cdf")]
+
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.HealPixIntensityMapData.read_from_xarray"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.ScienceInput")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.ultra_l2")
+    def test_fetch_dependencies(
+        self,
+        mock_ultra_l2,
+        mock_science_input_constructor,
+        mock_download,
+        mock_read_xarray_healpix,
+        mock_read_from_path_rectangular,
+        mock_read_ultra_l1c,
+        mock_read_glows,
+    ):
+        l1c_input_paths = [
+            Path("imap_ultra_l1c_45sensor-pset_20251010_v001.cdf"),
+            Path("imap_ultra_l1c_45sensor-pset_20251011_v001.cdf"),
+            Path("imap_ultra_l1c_45sensor-pset_20251012_v001.cdf"),
+        ]
 
         glows_file_paths = [
             "imap_glows_l3e_survival-probability-ul-sf_20201001_v001.cdf",
             "imap_glows_l3e_survival-probability-ul-sf_20201002_v002.cdf",
-            "imap_glows_l3e_survival-probability-ul-sf_20201003_v003.cdf"]
+            "imap_glows_l3e_survival-probability-ul-sf_20201003_v003.cdf",
+        ]
 
-        l2_energy_bin_group_sizes = 'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
+        l2_energy_bin_group_sizes = (
+            "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+        )
         l2_energy_bin_group_sizes_path = get_test_data_path(
-            'ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv')
+            "ultra/imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+        )
 
         input_collection = Mock()
         input_collection.get_file_paths.side_effect = [
             [sentinel.l2_server_path],
             l1c_input_paths,
             glows_file_paths,
-            [l2_energy_bin_group_sizes]
+            [l2_energy_bin_group_sizes],
         ]
 
-        returned_download_paths = [sentinel.l2_map_path, l1c_input_paths[0], l1c_input_paths[1], l1c_input_paths[2],
-                                   sentinel.glows_path_1, sentinel.glows_path_2, sentinel.glows_path_3,
-                                   l2_energy_bin_group_sizes_path]
+        returned_download_paths = [
+            sentinel.l2_map_path,
+            l1c_input_paths[0],
+            l1c_input_paths[1],
+            l1c_input_paths[2],
+            sentinel.glows_path_1,
+            sentinel.glows_path_2,
+            sentinel.glows_path_3,
+            l2_energy_bin_group_sizes_path,
+        ]
 
         mock_download.side_effect = returned_download_paths
-        mock_science_input_constructor.return_value = Mock(descriptor="ultra-nsp-descriptor-6deg")
+        mock_science_input_constructor.return_value = Mock(
+            descriptor="ultra-nsp-descriptor-6deg"
+        )
 
-        l1c_data = [sentinel.ultra_l1c_data_1, sentinel.ultra_l1c_data_2,
-                    sentinel.ultra_l1c_data_3]
+        l1c_data = [
+            sentinel.ultra_l1c_data_1,
+            sentinel.ultra_l1c_data_2,
+            sentinel.ultra_l1c_data_3,
+        ]
         mock_read_ultra_l1c.side_effect = l1c_data
 
-        glows_l3e_data = [sentinel.glows_data_1, sentinel.glows_data_2, sentinel.glows_data_3]
+        glows_l3e_data = [
+            sentinel.glows_data_1,
+            sentinel.glows_data_2,
+            sentinel.glows_data_3,
+        ]
         mock_read_glows.side_effect = glows_l3e_data
 
         mock_ultra_l2.return_value = [sentinel.ultra_l2_healpix_map_dataset]
         mock_read_xarray_healpix.return_value = sentinel.ultra_l2_healpix_data
-        mock_read_from_path_rectangular.return_value = sentinel.ultra_l2_rectangular_data
+        mock_read_from_path_rectangular.return_value = (
+            sentinel.ultra_l2_rectangular_data
+        )
 
         dependencies = UltraL3Dependencies.fetch_dependencies(input_collection)
 
         mock_science_input_constructor.assert_called_with(sentinel.l2_map_path.name)
 
-        input_collection.get_file_paths.assert_has_calls([
-            call("ultra", data_type="l2"),
-            call("ultra", data_type="l1c"),
-            call("glows"),
-            call(data_type="ancillary", descriptor="l2-energy-bin-group-sizes")
-        ])
-        expected_data_dictionary = {"imap_ultra_l1c_45sensor-pset_20251010_v001": l1c_input_paths[0],
-                                    "imap_ultra_l1c_45sensor-pset_20251011_v001": l1c_input_paths[1],
-                                    "imap_ultra_l1c_45sensor-pset_20251012_v001": l1c_input_paths[2]}
-        mock_ultra_l2.assert_has_calls([call(expected_data_dictionary, descriptor="ultra-nsp-descriptor-nside32")])
-
+        input_collection.get_file_paths.assert_has_calls(
+            [
+                call("ultra", data_type="l2"),
+                call("ultra", data_type="l1c"),
+                call("glows"),
+                call(data_type="ancillary", descriptor="l2-energy-bin-group-sizes"),
+            ]
+        )
+        expected_data_dictionary = {
+            "imap_ultra_l1c_45sensor-pset_20251010_v001": l1c_input_paths[0],
+            "imap_ultra_l1c_45sensor-pset_20251011_v001": l1c_input_paths[1],
+            "imap_ultra_l1c_45sensor-pset_20251012_v001": l1c_input_paths[2],
+        }
+        mock_ultra_l2.assert_has_calls(
+            [call(expected_data_dictionary, descriptor="ultra-nsp-descriptor-nside32")]
+        )
 
-        expected_parent_file_paths = [sentinel.l2_server_path, *l1c_input_paths, *glows_file_paths,
-                                      l2_energy_bin_group_sizes]
-        mock_download.assert_has_calls([call(file_path) for file_path in
-                                        expected_parent_file_paths])
+        expected_parent_file_paths = [
+            sentinel.l2_server_path,
+            *l1c_input_paths,
+            *glows_file_paths,
+            l2_energy_bin_group_sizes,
+        ]
+        mock_download.assert_has_calls(
+            [call(file_path) for file_path in expected_parent_file_paths]
+        )
 
-        mock_read_xarray_healpix.assert_called_once_with(sentinel.ultra_l2_healpix_map_dataset)
+        mock_read_xarray_healpix.assert_called_once_with(
+            sentinel.ultra_l2_healpix_map_dataset
+        )
         mock_read_from_path_rectangular.assert_called_once_with(sentinel.l2_map_path)
 
         self.assertEqual(l1c_data, dependencies.ultra_l1c_pset)
         self.assertEqual(glows_l3e_data, dependencies.glows_l3e_sp)
-        self.assertEqual(sentinel.ultra_l2_healpix_data, dependencies.ultra_l2_healpix_map)
-        self.assertEqual(sentinel.ultra_l2_rectangular_data, dependencies.ultra_l2_rectangular_map)
+        self.assertEqual(
+            sentinel.ultra_l2_healpix_data, dependencies.ultra_l2_healpix_map
+        )
+        self.assertEqual(
+            sentinel.ultra_l2_rectangular_data, dependencies.ultra_l2_rectangular_map
+        )
         self.assertEqual(returned_download_paths, dependencies.dependency_file_paths)
-        np.testing.assert_array_equal(np.array([0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 46], dtype=np.uint8),
-                                      dependencies.energy_bin_group_sizes, strict=True)
-
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download')
-    def test_fetch_dependencies_without_energy_ancillary_file(self,  mock_download,
-                                                              mock_read_from_path, mock_read_ultra_l1c,
-                                                              mock_read_glows,
-                                                              mock_load_or_create_healpix_l2):
-        l1c_input_paths = ["imap_ultra_l1c_pset_20251010_v001.cdf", "imap_ultra_l1c_pset_20251011_v001.cdf",
-                           "imap_ultra_l1c_pset_20251012_v001.cdf"]
+        np.testing.assert_array_equal(
+            np.array([0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 46], dtype=np.uint8),
+            dependencies.energy_bin_group_sizes,
+            strict=True,
+        )
+
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download")
+    def test_fetch_dependencies_without_energy_ancillary_file(
+        self,
+        mock_download,
+        mock_read_from_path,
+        mock_read_ultra_l1c,
+        mock_read_glows,
+        mock_load_or_create_healpix_l2,
+    ):
+        l1c_input_paths = [
+            "imap_ultra_l1c_pset_20251010_v001.cdf",
+            "imap_ultra_l1c_pset_20251011_v001.cdf",
+            "imap_ultra_l1c_pset_20251012_v001.cdf",
+        ]
 
         glows_file_paths = [
             "imap_glows_l3e_survival-probability-ul-sf_20201001_v001.cdf",
             "imap_glows_l3e_survival-probability-ul-sf_20201002_v002.cdf",
-            "imap_glows_l3e_survival-probability-ul-sf_20201003_v003.cdf"]
+            "imap_glows_l3e_survival-probability-ul-sf_20201003_v003.cdf",
+        ]
 
         input_collection = Mock()
         input_collection.get_file_paths.side_effect = [
             [sentinel.l2_server_path],
             l1c_input_paths,
             glows_file_paths,
-            []
+            [],
         ]
         mock_download.return_value = Path("downloaded_file")
         dependencies = UltraL3Dependencies.fetch_dependencies(input_collection)
@@ -130,17 +201,24 @@ def test_fetch_dependencies_without_energy_ancillary_file(self,  mock_download,
 
     def test_raise_error_for_more_than_one_input_files_paths(self):
         input_collection = Mock()
-        input_collection.get_file_paths.return_value = [sentinel.imap_l2_map_path_1, sentinel.imap_l2_map_path_2]
+        input_collection.get_file_paths.return_value = [
+            sentinel.imap_l2_map_path_1,
+            sentinel.imap_l2_map_path_2,
+        ]
 
         with self.assertRaises(AssertionError) as e:
             UltraL3Dependencies.fetch_dependencies(input_collection)
 
         self.assertEqual("Incorrect number of map dependencies: 2", str(e.exception))
 
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path')
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path")
     def test_from_file_paths(
         self,
         mock_read_l1c: Mock,
@@ -149,31 +227,53 @@ def test_from_file_paths(
         mock_load_or_create_healpix_l2: Mock,
     ):
         ultra_l2_input_path = Path("um_path")
-        ultra_l1c_input_paths = [Path("imap/u_path_3"), Path("imap/u_path_1"), Path("imap/u_path_2")]
+        ultra_l1c_input_paths = [
+            Path("imap/u_path_3"),
+            Path("imap/u_path_1"),
+            Path("imap/u_path_2"),
+        ]
         glows_input_paths = [Path("g_path_1"), Path("g_path_2")]
-        mock_read_l1c.side_effect = [sentinel.ultra_data3, sentinel.ultra_data1, sentinel.ultra_data2]
+        mock_read_l1c.side_effect = [
+            sentinel.ultra_data3,
+            sentinel.ultra_data1,
+            sentinel.ultra_data2,
+        ]
         mock_read_glows.side_effect = [sentinel.glows_data1, sentinel.glows_data2]
         mock_read_l2_from_path.return_value = sentinel.ultra_l2_rectangular_data
         mock_load_or_create_healpix_l2.return_value = sentinel.ultra_l2_healpix_data
 
-        result = UltraL3Dependencies.from_file_paths(ultra_l2_input_path, ultra_l1c_input_paths, glows_input_paths,
-                                                     None)
+        result = UltraL3Dependencies.from_file_paths(
+            ultra_l2_input_path, ultra_l1c_input_paths, glows_input_paths, None
+        )
 
         mock_read_l2_from_path.assert_called_with(ultra_l2_input_path)
         mock_read_l1c.assert_has_calls(
             [call(file_path) for file_path in ultra_l1c_input_paths]
         )
-        mock_read_glows.assert_has_calls([call(file_path) for file_path in glows_input_paths])
-        mock_load_or_create_healpix_l2.assert_called_with(ultra_l2_input_path, ultra_l1c_input_paths)
+        mock_read_glows.assert_has_calls(
+            [call(file_path) for file_path in glows_input_paths]
+        )
+        mock_load_or_create_healpix_l2.assert_called_with(
+            ultra_l2_input_path, ultra_l1c_input_paths
+        )
 
-        self.assertEqual(result.ultra_l1c_pset, [sentinel.ultra_data3, sentinel.ultra_data1, sentinel.ultra_data2])
-        self.assertEqual(result.glows_l3e_sp, [sentinel.glows_data1, sentinel.glows_data2])
+        self.assertEqual(
+            result.ultra_l1c_pset,
+            [sentinel.ultra_data3, sentinel.ultra_data1, sentinel.ultra_data2],
+        )
+        self.assertEqual(
+            result.glows_l3e_sp, [sentinel.glows_data1, sentinel.glows_data2]
+        )
         self.assertEqual(result.ultra_l2_healpix_map, sentinel.ultra_l2_healpix_data)
-        self.assertEqual(result.ultra_l2_rectangular_map, sentinel.ultra_l2_rectangular_data)
+        self.assertEqual(
+            result.ultra_l2_rectangular_map, sentinel.ultra_l2_rectangular_data
+        )
 
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.HealPixIntensityMapData.read_from_xarray')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.ultra_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.ScienceInput')
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.HealPixIntensityMapData.read_from_xarray"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.ultra_l2")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.ScienceInput")
     def test_load_or_create_healpix_l2_saves_to_cache(
         self,
         mock_science_input_constructor,
@@ -181,10 +281,16 @@ def test_load_or_create_healpix_l2_saves_to_cache(
         mock_read_l2_xarray: Mock,
     ):
         ultra_l2_input_path = Path("um_path")
-        ultra_l1c_input_paths = [Path("imap/u_path_3"), Path("imap/u_path_1"), Path("imap/u_path_2")]
+        ultra_l1c_input_paths = [
+            Path("imap/u_path_3"),
+            Path("imap/u_path_1"),
+            Path("imap/u_path_2"),
+        ]
         mock_read_l2_xarray.return_value = sentinel.ultra_l2_healpix_data
         l2_ultra_descriptor = "u45-ena-h-hf-nsp-full-hae-6deg"
-        mock_science_input_constructor.return_value = Mock(descriptor=l2_ultra_descriptor)
+        mock_science_input_constructor.return_value = Mock(
+            descriptor=l2_ultra_descriptor
+        )
         mock_ultra_l2.return_value = [sentinel.ultra_l2_healpix_map]
 
         result = load_or_create_healpix_l2(ultra_l2_input_path, ultra_l1c_input_paths)
@@ -198,7 +304,10 @@ def test_load_or_create_healpix_l2_saves_to_cache(
             },
             descriptor="u45-ena-h-hf-nsp-full-hae-nside32",
         )
-        self.assertEqual(["u_path_1", "u_path_2", "u_path_3"], list(mock_ultra_l2.call_args.args[0].keys()))
+        self.assertEqual(
+            ["u_path_1", "u_path_2", "u_path_3"],
+            list(mock_ultra_l2.call_args.args[0].keys()),
+        )
 
         self.assertEqual(result, sentinel.ultra_l2_healpix_data)
 
@@ -211,9 +320,11 @@ def test_load_or_create_healpix_l2_saves_to_cache(
             cached_healpix = pickle.load(f)
         self.assertEqual(sentinel.ultra_l2_healpix_data, cached_healpix)
 
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.HealPixIntensityMapData.read_from_xarray')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.ultra_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.ScienceInput')
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.HealPixIntensityMapData.read_from_xarray"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.ultra_l2")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.ScienceInput")
     def test_load_or_create_healpix_l2_loads_from_cache(
         self,
         mock_science_input_constructor,
@@ -221,14 +332,21 @@ def test_load_or_create_healpix_l2_loads_from_cache(
         mock_read_l2_xarray: Mock,
     ):
         temp_cache_dir = get_temp_cache_dir()
-        raw_cache_key = "['l1c_path_1.cdf', 'l1c_path_2.cdf']u45-ena-h-hf-nsp-full-hae-nside32"
+        raw_cache_key = (
+            "['l1c_path_1.cdf', 'l1c_path_2.cdf']u45-ena-h-hf-nsp-full-hae-nside32"
+        )
         cached_healpix_filename = sha256(raw_cache_key.encode("utf-8")).hexdigest()
         with open(temp_cache_dir / cached_healpix_filename, "wb") as f:
             pickle.dump(sentinel.ultra_l2_healpix_data, f)
         ultra_l2_input_path = Path("um_path")
-        ultra_l1c_input_paths = [Path("imap/l1c_path_1.cdf"), Path("imap/l1c_path_2.cdf")]
+        ultra_l1c_input_paths = [
+            Path("imap/l1c_path_1.cdf"),
+            Path("imap/l1c_path_2.cdf"),
+        ]
         l2_ultra_descriptor = "u45-ena-h-hf-nsp-full-hae-6deg"
-        mock_science_input_constructor.return_value = Mock(descriptor=l2_ultra_descriptor)
+        mock_science_input_constructor.return_value = Mock(
+            descriptor=l2_ultra_descriptor
+        )
 
         result = load_or_create_healpix_l2(ultra_l2_input_path, ultra_l1c_input_paths)
 
@@ -237,99 +355,164 @@ def test_load_or_create_healpix_l2_loads_from_cache(
 
         self.assertEqual(result, sentinel.ultra_l2_healpix_data)
 
-
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download')
-    def test_spectral_index_fetch_dependencies(self, mock_download, mock_rectangular_read_from_path):
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download")
+    def test_spectral_index_fetch_dependencies(
+        self, mock_download, mock_rectangular_read_from_path
+    ):
 
         for input_data_type in ["l2", "l3"]:
             mock_rectangular_read_from_path.reset_mock()
             with self.subTest(input_data_type):
-                map_file_name = f'imap_ultra_{input_data_type}_ultra-cool-descriptor_20250601_v000.cdf'
-                ancillary_file_name = 'imap_ultra_spx-energy-ranges_20250601_v000.cdf'
-                pointing_set_file_name = 'imap_ultra_l1c_u90spacecraftpset_20250601-repoint00001_v000.cdf'
-                glows_file_name = 'imap_glows_l3e_survival-probability-ul-hf_20250601-repoint00001_v000.cdf'
+                map_file_name = f"imap_ultra_{input_data_type}_ultra-cool-descriptor_20250601_v000.cdf"
+                ancillary_file_name = "imap_ultra_spx-energy-ranges_20250601_v000.cdf"
+                pointing_set_file_name = (
+                    "imap_ultra_l1c_u90spacecraftpset_20250601-repoint00001_v000.cdf"
+                )
+                glows_file_name = "imap_glows_l3e_survival-probability-ul-hf_20250601-repoint00001_v000.cdf"
                 expected_energy_ranges = [[5, 15], [15, 50]]
                 map_input = ScienceInput(map_file_name)
                 ancillary_input = AncillaryInput(ancillary_file_name)
-                extra_inputs_from_mapper = [ScienceInput(pointing_set_file_name), ScienceInput(glows_file_name)]
-                processing_input_collection = ProcessingInputCollection(map_input, ancillary_input, *extra_inputs_from_mapper)
+                extra_inputs_from_mapper = [
+                    ScienceInput(pointing_set_file_name),
+                    ScienceInput(glows_file_name),
+                ]
+                processing_input_collection = ProcessingInputCollection(
+                    map_input, ancillary_input, *extra_inputs_from_mapper
+                )
 
                 mock_download.side_effect = [
                     "map_file",
-                    get_test_data_path('ultra/imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat')
+                    get_test_data_path(
+                        "ultra/imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"
+                    ),
                 ]
 
-                ultra_l3_dependencies = UltraL3SpectralIndexDependencies.fetch_dependencies(processing_input_collection)
+                ultra_l3_dependencies = (
+                    UltraL3SpectralIndexDependencies.fetch_dependencies(
+                        processing_input_collection
+                    )
+                )
 
-                mock_download.assert_has_calls([
-                    call(map_file_name),
-                    call(ancillary_file_name)
-                ])
+                mock_download.assert_has_calls(
+                    [call(map_file_name), call(ancillary_file_name)]
+                )
                 mock_rectangular_read_from_path.assert_called_once_with("map_file")
-                self.assertEqual(ultra_l3_dependencies.map_data, mock_rectangular_read_from_path.return_value)
-                np.testing.assert_array_equal(ultra_l3_dependencies.fit_energy_ranges, expected_energy_ranges)
+                self.assertEqual(
+                    ultra_l3_dependencies.map_data,
+                    mock_rectangular_read_from_path.return_value,
+                )
+                np.testing.assert_array_equal(
+                    ultra_l3_dependencies.fit_energy_ranges, expected_energy_ranges
+                )
 
     def test_spectral_index_fetch_dependencies_raises_exception_on_missing_map(self):
-        ancillary_input = AncillaryInput('imap_ultra_spx-energy-ranges_20250601_v000.dat')
+        ancillary_input = AncillaryInput(
+            "imap_ultra_spx-energy-ranges_20250601_v000.dat"
+        )
         with self.assertRaises(ValueError) as context:
-            UltraL3SpectralIndexDependencies.fetch_dependencies(ProcessingInputCollection(ancillary_input))
+            UltraL3SpectralIndexDependencies.fetch_dependencies(
+                ProcessingInputCollection(ancillary_input)
+            )
         self.assertEqual("Expected 1 input map, got 0: []", str(context.exception))
 
     def test_spectral_index_fetch_dependencies_raises_exception_on_multiple_maps(self):
-        ancillary_input = AncillaryInput('imap_ultra_spx-energy-ranges_20250601_v000.dat')
-        science_input = ScienceInput('imap_ultra_l3_map_20250601_v000.cdf')
+        ancillary_input = AncillaryInput(
+            "imap_ultra_spx-energy-ranges_20250601_v000.dat"
+        )
+        science_input = ScienceInput("imap_ultra_l3_map_20250601_v000.cdf")
 
         with self.assertRaises(ValueError) as context:
-            UltraL3SpectralIndexDependencies.fetch_dependencies(ProcessingInputCollection(ancillary_input, science_input, science_input))
+            UltraL3SpectralIndexDependencies.fetch_dependencies(
+                ProcessingInputCollection(ancillary_input, science_input, science_input)
+            )
         expected_msg = "Expected 1 input map, got 2: [imap_ultra_l3_map_20250601_v000.cdf, imap_ultra_l3_map_20250601_v000.cdf]"
         self.assertEqual(expected_msg, str(context.exception))
 
-    def test_spectral_index_fetch_dependencies_raises_exception_on_missing_ancillary_file(self):
-        science_input = ScienceInput('imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf')
+    def test_spectral_index_fetch_dependencies_raises_exception_on_missing_ancillary_file(
+        self,
+    ):
+        science_input = ScienceInput(
+            "imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf"
+        )
 
         with self.assertRaises(ValueError) as context:
-            UltraL3SpectralIndexDependencies.fetch_dependencies(ProcessingInputCollection(science_input))
-        self.assertEqual("Missing fit energy ranges ancillary file", str(context.exception))
+            UltraL3SpectralIndexDependencies.fetch_dependencies(
+                ProcessingInputCollection(science_input)
+            )
+        self.assertEqual(
+            "Missing fit energy ranges ancillary file", str(context.exception)
+        )
 
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
     def test_spectral_index_from_file_paths(self, mock_read_from_path):
         map_file_path = Mock()
-        ancillary_file_path = get_test_data_path('ultra') / 'imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat'
+        ancillary_file_path = (
+            get_test_data_path("ultra")
+            / "imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"
+        )
         expected_energy_range_values = np.loadtxt(ancillary_file_path)
 
-        actual_dependencies = UltraL3SpectralIndexDependencies.from_file_paths(map_file_path, ancillary_file_path)
+        actual_dependencies = UltraL3SpectralIndexDependencies.from_file_paths(
+            map_file_path, ancillary_file_path
+        )
         self.assertEqual(mock_read_from_path.return_value, actual_dependencies.map_data)
-        np.testing.assert_array_equal(actual_dependencies.fit_energy_ranges, expected_energy_range_values)
+        np.testing.assert_array_equal(
+            actual_dependencies.fit_energy_ranges, expected_energy_range_values
+        )
 
     def test_spectral_index_get_fit_energy_ranges(self):
         expected_energy_range_values = np.array([[5, 10], [15, 20]])
 
-        dependencies = UltraL3SpectralIndexDependencies(map_data=Mock(),
-                                                        fit_energy_ranges=expected_energy_range_values)
+        dependencies = UltraL3SpectralIndexDependencies(
+            map_data=Mock(), fit_energy_ranges=expected_energy_range_values
+        )
         actual_fit_energy_ranges = dependencies.get_fit_energy_ranges()
 
-        np.testing.assert_array_equal(actual_fit_energy_ranges, expected_energy_range_values)
+        np.testing.assert_array_equal(
+            actual_fit_energy_ranges, expected_energy_range_values
+        )
+
 
 class TestUltraL3CombinedDependencies(unittest.TestCase):
     def setUp(self):
         clear_temp_cache()
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download')
-    def test_fetch_dependencies_no_survival_corrected(self, mock_download,
-                                                      mock_l2_read_from_path, mock_l1c_read_from_path,
-                                                      mock_load_or_create_healpix_l2,
-                                                      ):
-        u45_map_file_name = 'imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf'
-        u90_map_file_name = 'imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf'
-        u45_pset_file_name = ["imap_ultra_l1c_45sensor-spacecraftpset_20251010_v001.cdf", "imap_ultra_l1c_45sensor-spacecraftpset_20251011_v001.cdf",
-                           "imap_ultra_l1c_45sensor-spacecraftpset_20251012_v001.cdf"]
-        u90_pset_file_name = ["imap_ultra_l1c_90sensor-spacecraftpset_20251010_v001.cdf", "imap_ultra_l1c_90sensor-spacecraftpset_20251011_v001.cdf",
-                           "imap_ultra_l1c_90sensor-spacecraftpset_20251012_v001.cdf"]
-        l2_energy_bin_group_sizes_file_name = 'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-        l2_energy_bin_group_sizes_file_path = get_test_data_path("ultra/" + l2_energy_bin_group_sizes_file_name)
+
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download")
+    def test_fetch_dependencies_no_survival_corrected(
+        self,
+        mock_download,
+        mock_l2_read_from_path,
+        mock_l1c_read_from_path,
+        mock_load_or_create_healpix_l2,
+    ):
+        u45_map_file_name = "imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf"
+        u90_map_file_name = "imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf"
+        u45_pset_file_name = [
+            "imap_ultra_l1c_45sensor-spacecraftpset_20251010_v001.cdf",
+            "imap_ultra_l1c_45sensor-spacecraftpset_20251011_v001.cdf",
+            "imap_ultra_l1c_45sensor-spacecraftpset_20251012_v001.cdf",
+        ]
+        u90_pset_file_name = [
+            "imap_ultra_l1c_90sensor-spacecraftpset_20251010_v001.cdf",
+            "imap_ultra_l1c_90sensor-spacecraftpset_20251011_v001.cdf",
+            "imap_ultra_l1c_90sensor-spacecraftpset_20251012_v001.cdf",
+        ]
+        l2_energy_bin_group_sizes_file_name = (
+            "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv"
+        )
+        l2_energy_bin_group_sizes_file_path = get_test_data_path(
+            "ultra/" + l2_energy_bin_group_sizes_file_name
+        )
 
         u45_map_input = ScienceInput(u45_map_file_name)
         u90_map_input = ScienceInput(u90_map_file_name)
@@ -337,8 +520,13 @@ def test_fetch_dependencies_no_survival_corrected(self, mock_download,
         u90_pset_inputs = [ScienceInput(pset) for pset in u90_pset_file_name]
         ancillary_inputs = AncillaryInput(l2_energy_bin_group_sizes_file_name)
 
-        processing_input_collection = ProcessingInputCollection(u45_map_input, u90_map_input, *u45_pset_inputs,
-                                                                *u90_pset_inputs, ancillary_inputs)
+        processing_input_collection = ProcessingInputCollection(
+            u45_map_input,
+            u90_map_input,
+            *u45_pset_inputs,
+            *u90_pset_inputs,
+            ancillary_inputs,
+        )
 
         u45_l2_path = Path("imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf")
         u90_l2_path = Path("imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf")
@@ -352,32 +540,43 @@ def test_fetch_dependencies_no_survival_corrected(self, mock_download,
             Path("imap_ultra_l1c_u90-pset_20251012_v001.cdf"),
             u45_l2_path,
             u90_l2_path,
-            l2_energy_bin_group_sizes_file_path
+            l2_energy_bin_group_sizes_file_path,
         ]
 
         mock_download.side_effect = expected_file_paths
 
         mock_l1c_read_from_path.side_effect = [
-            sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3,
-            sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3,
+            sentinel.u45_l1c_1,
+            sentinel.u45_l1c_2,
+            sentinel.u45_l1c_3,
+            sentinel.u90_l1c_1,
+            sentinel.u90_l1c_2,
+            sentinel.u90_l1c_3,
         ]
 
-        mock_l2_read_from_path.side_effect = [sentinel.u45_rectangular_l2, sentinel.u90_rectangular_l2]
+        mock_l2_read_from_path.side_effect = [
+            sentinel.u45_rectangular_l2,
+            sentinel.u90_rectangular_l2,
+        ]
 
         mock_load_or_create_healpix_l2.side_effect = [
             sentinel.u45_healpix_dataset,
-            sentinel.u90_healpix_dataset
+            sentinel.u90_healpix_dataset,
         ]
 
-        combined_dependencies = UltraL3CombinedDependencies.fetch_dependencies(processing_input_collection)
+        combined_dependencies = UltraL3CombinedDependencies.fetch_dependencies(
+            processing_input_collection
+        )
 
-        mock_download.assert_has_calls([
-            *[call(filename) for filename in u45_pset_file_name],
-            *[call(filename) for filename in u90_pset_file_name],
-            call(u45_map_file_name),
-            call(u90_map_file_name),
-            call(l2_energy_bin_group_sizes_file_name)
-        ])
+        mock_download.assert_has_calls(
+            [
+                *[call(filename) for filename in u45_pset_file_name],
+                *[call(filename) for filename in u90_pset_file_name],
+                call(u45_map_file_name),
+                call(u90_map_file_name),
+                call(l2_energy_bin_group_sizes_file_name),
+            ]
+        )
 
         mock_l1c_read_from_path.assert_has_calls(
             [
@@ -408,56 +607,79 @@ def test_fetch_dependencies_no_survival_corrected(self, mock_download,
         self.assertEqual(u90_deps.ultra_l2_healpix_map, sentinel.u90_healpix_dataset)
         self.assertEqual(u45_deps.ultra_l2_rectangular_map, sentinel.u45_rectangular_l2)
         self.assertEqual(u90_deps.ultra_l2_rectangular_map, sentinel.u90_rectangular_l2)
-        self.assertEqual(u45_deps.ultra_l1c_pset, [sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3])
-        self.assertEqual(u90_deps.ultra_l1c_pset, [sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3])
+        self.assertEqual(
+            u45_deps.ultra_l1c_pset,
+            [sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3],
+        )
+        self.assertEqual(
+            u90_deps.ultra_l1c_pset,
+            [sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3],
+        )
         self.assertEqual(u45_deps.glows_l3e_sp, [])
         self.assertEqual(u90_deps.glows_l3e_sp, [])
 
-        np.testing.assert_array_equal(np.array([0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 46], dtype=np.uint8),
-                                      u45_deps.energy_bin_group_sizes, strict=True)
-        np.testing.assert_array_equal(np.array([0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 46], dtype=np.uint8),
-                                      u90_deps.energy_bin_group_sizes, strict=True)
+        np.testing.assert_array_equal(
+            np.array([0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 46], dtype=np.uint8),
+            u45_deps.energy_bin_group_sizes,
+            strict=True,
+        )
+        np.testing.assert_array_equal(
+            np.array([0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 46], dtype=np.uint8),
+            u90_deps.energy_bin_group_sizes,
+            strict=True,
+        )
 
         expected_45_file_paths = [
             u45_l2_path,
             Path("imap_ultra_l1c_u45-pset_20251010_v001.cdf"),
             Path("imap_ultra_l1c_u45-pset_20251011_v001.cdf"),
             Path("imap_ultra_l1c_u45-pset_20251012_v001.cdf"),
-            l2_energy_bin_group_sizes_file_path
+            l2_energy_bin_group_sizes_file_path,
         ]
         expected_90_file_paths = [
             u90_l2_path,
             Path("imap_ultra_l1c_u90-pset_20251010_v001.cdf"),
             Path("imap_ultra_l1c_u90-pset_20251011_v001.cdf"),
             Path("imap_ultra_l1c_u90-pset_20251012_v001.cdf"),
-            l2_energy_bin_group_sizes_file_path
+            l2_energy_bin_group_sizes_file_path,
         ]
         self.assertEqual(u45_deps.dependency_file_paths, expected_45_file_paths)
         self.assertEqual(u90_deps.dependency_file_paths, expected_90_file_paths)
 
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download')
-    def test_fetch_dependencies_without_ancillary_file(self,  mock_download,
-                                                       mock_l1c_read_from_path, mock_l2_read_from_path,
-                                                       mock_load_or_create_healpix_l2):
-        u45_map_file_name = 'imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf'
-        u90_map_file_name = 'imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf'
-        u45_pset_file_name = ["imap_ultra_l1c_45sensor-spacecraftpset_20251010_v001.cdf",
-                              "imap_ultra_l1c_45sensor-spacecraftpset_20251011_v001.cdf",
-                              "imap_ultra_l1c_45sensor-spacecraftpset_20251012_v001.cdf"]
-        u90_pset_file_name = ["imap_ultra_l1c_90sensor-spacecraftpset_20251010_v001.cdf",
-                              "imap_ultra_l1c_90sensor-spacecraftpset_20251011_v001.cdf",
-                              "imap_ultra_l1c_90sensor-spacecraftpset_20251012_v001.cdf"]
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download")
+    def test_fetch_dependencies_without_ancillary_file(
+        self,
+        mock_download,
+        mock_l1c_read_from_path,
+        mock_l2_read_from_path,
+        mock_load_or_create_healpix_l2,
+    ):
+        u45_map_file_name = "imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf"
+        u90_map_file_name = "imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf"
+        u45_pset_file_name = [
+            "imap_ultra_l1c_45sensor-spacecraftpset_20251010_v001.cdf",
+            "imap_ultra_l1c_45sensor-spacecraftpset_20251011_v001.cdf",
+            "imap_ultra_l1c_45sensor-spacecraftpset_20251012_v001.cdf",
+        ]
+        u90_pset_file_name = [
+            "imap_ultra_l1c_90sensor-spacecraftpset_20251010_v001.cdf",
+            "imap_ultra_l1c_90sensor-spacecraftpset_20251011_v001.cdf",
+            "imap_ultra_l1c_90sensor-spacecraftpset_20251012_v001.cdf",
+        ]
 
         u45_map_input = ScienceInput(u45_map_file_name)
         u90_map_input = ScienceInput(u90_map_file_name)
         u45_pset_inputs = [ScienceInput(pset) for pset in u45_pset_file_name]
         u90_pset_inputs = [ScienceInput(pset) for pset in u90_pset_file_name]
 
-        processing_input_collection = ProcessingInputCollection(u45_map_input, u90_map_input, *u45_pset_inputs,
-                                                                *u90_pset_inputs)
+        processing_input_collection = ProcessingInputCollection(
+            u45_map_input, u90_map_input, *u45_pset_inputs, *u90_pset_inputs
+        )
 
         expected_file_paths = [
             Path("imap_ultra_l1c_u45-pset_20251010_v001.cdf"),
@@ -472,34 +694,54 @@ def test_fetch_dependencies_without_ancillary_file(self,  mock_download,
 
         mock_download.side_effect = expected_file_paths
 
-        combined_dependencies = UltraL3CombinedDependencies.fetch_dependencies(processing_input_collection)
+        combined_dependencies = UltraL3CombinedDependencies.fetch_dependencies(
+            processing_input_collection
+        )
 
         self.assertIsNone(combined_dependencies.u45_dependencies.energy_bin_group_sizes)
         self.assertIsNone(combined_dependencies.u90_dependencies.energy_bin_group_sizes)
 
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download')
-    @patch('imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path')
-    def test_fetch_dependencies_with_glows_files(self, mock_l2_read_from_paths, mock_download,
-                                                 mock_l1c_read_from_path, mock_l3e_read_from_path,
-                                                 mock_load_or_create_healpix_l2,
-                                                 ):
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.load_or_create_healpix_l2")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.UltraGlowsL3eData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.UltraL1CPSet.read_from_path")
+    @patch("imap_l3_processing.ultra.ultra_l3_dependencies.imap_data_access.download")
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_dependencies.RectangularIntensityMapData.read_from_path"
+    )
+    def test_fetch_dependencies_with_glows_files(
+        self,
+        mock_l2_read_from_paths,
+        mock_download,
+        mock_l1c_read_from_path,
+        mock_l3e_read_from_path,
+        mock_load_or_create_healpix_l2,
+    ):
         cases = [["sf", "spacecraftpset"], ["hf", "heliopset"]]
         for frame, pset in cases:
-            with (self.subTest(frame)):
-                u45_map_file_name = 'imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf'
-                u90_map_file_name = 'imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf'
-                u45_pset_file_names = [f"imap_ultra_l1c_45sensor-{pset}_20251010_v001.cdf",
-                                       f"imap_ultra_l1c_45sensor-{pset}_20251011_v001.cdf",
-                                       f"imap_ultra_l1c_45sensor-{pset}_20251012_v001.cdf"]
-                u90_pset_file_names = [f"imap_ultra_l1c_90sensor-{pset}_20251010_v001.cdf",
-                                       f"imap_ultra_l1c_90sensor-{pset}_20251011_v001.cdf",
-                                       f"imap_ultra_l1c_90sensor-{pset}_20251012_v001.cdf"]
-                glows_file_names = [f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf",
-                                    f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf",
-                                    f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"]
+            with self.subTest(frame):
+                u45_map_file_name = (
+                    "imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf"
+                )
+                u90_map_file_name = (
+                    "imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf"
+                )
+                u45_pset_file_names = [
+                    f"imap_ultra_l1c_45sensor-{pset}_20251010_v001.cdf",
+                    f"imap_ultra_l1c_45sensor-{pset}_20251011_v001.cdf",
+                    f"imap_ultra_l1c_45sensor-{pset}_20251012_v001.cdf",
+                ]
+                u90_pset_file_names = [
+                    f"imap_ultra_l1c_90sensor-{pset}_20251010_v001.cdf",
+                    f"imap_ultra_l1c_90sensor-{pset}_20251011_v001.cdf",
+                    f"imap_ultra_l1c_90sensor-{pset}_20251012_v001.cdf",
+                ]
+                glows_file_names = [
+                    f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf",
+                    f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf",
+                ]
 
                 u45_map_input = ScienceInput(u45_map_file_name)
                 u90_map_input = ScienceInput(u90_map_file_name)
@@ -524,14 +766,24 @@ def test_fetch_dependencies_with_glows_files(self, mock_l2_read_from_paths, mock
                     Path("imap_ultra_l1c_u90-pset_20251011_v001.cdf"),
                     Path("imap_ultra_l1c_u90-pset_20251012_v001.cdf"),
                 ]
-                u45_l2_path = Path("imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf")
-                u90_l2_path = Path("imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf")
+                u45_l2_path = Path(
+                    "imap_ultra_l2_u45-cool-descriptor_20250601_v000.cdf"
+                )
+                u90_l2_path = Path(
+                    "imap_ultra_l2_u90-cool-descriptor_20250601_v000.cdf"
+                )
                 expected_file_paths = [
                     *u45_l1c_paths,
                     *u90_l1c_paths,
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"),
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"),
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"
+                    ),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"
+                    ),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"
+                    ),
                     u45_l2_path,
                     u90_l2_path,
                 ]
@@ -539,86 +791,164 @@ def test_fetch_dependencies_with_glows_files(self, mock_l2_read_from_paths, mock
                 mock_download.side_effect = expected_file_paths
 
                 mock_l1c_read_from_path.side_effect = [
-                    sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3,
-                    sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3,
+                    sentinel.u45_l1c_1,
+                    sentinel.u45_l1c_2,
+                    sentinel.u45_l1c_3,
+                    sentinel.u90_l1c_1,
+                    sentinel.u90_l1c_2,
+                    sentinel.u90_l1c_3,
                 ]
 
                 mock_load_or_create_healpix_l2.side_effect = [
                     sentinel.u45_healpix_dataset,
-                    sentinel.u90_healpix_dataset
+                    sentinel.u90_healpix_dataset,
+                ]
+                mock_l2_read_from_paths.side_effect = [
+                    sentinel.u45_rectangular_l2,
+                    sentinel.u90_rectangular_l2,
                 ]
-                mock_l2_read_from_paths.side_effect = [sentinel.u45_rectangular_l2, sentinel.u90_rectangular_l2]
 
                 mock_l3e_read_from_path.side_effect = [
-                    sentinel.glows_1, sentinel.glows_2, sentinel.glows_3,
-                    sentinel.glows_1, sentinel.glows_2, sentinel.glows_3,
+                    sentinel.glows_1,
+                    sentinel.glows_2,
+                    sentinel.glows_3,
+                    sentinel.glows_1,
+                    sentinel.glows_2,
+                    sentinel.glows_3,
                 ]
 
-                combined_dependencies = UltraL3CombinedDependencies.fetch_dependencies(processing_input_collection)
-
-                mock_download.assert_has_calls([
-                    *[call(filename) for filename in u45_pset_file_names],
-                    *[call(filename) for filename in u90_pset_file_names],
-                    *[call(filename) for filename in glows_file_names],
-                    call(u45_map_file_name),
-                    call(u90_map_file_name)
-                ])
-
-                mock_l1c_read_from_path.assert_has_calls([
-                    call(Path("imap_ultra_l1c_u45-pset_20251010_v001.cdf")),
-                    call(Path("imap_ultra_l1c_u45-pset_20251011_v001.cdf")),
-                    call(Path("imap_ultra_l1c_u45-pset_20251012_v001.cdf")),
-                    call(Path("imap_ultra_l1c_u90-pset_20251010_v001.cdf")),
-                    call(Path("imap_ultra_l1c_u90-pset_20251011_v001.cdf")),
-                    call(Path("imap_ultra_l1c_u90-pset_20251012_v001.cdf")),
-                ])
-
-                mock_l3e_read_from_path.assert_has_calls([
-                    call(Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf")),
-                    call(Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf")),
-                    call(Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf")),
-                    call(Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf")),
-                    call(Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf")),
-                    call(Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf")),
-                ])
-
-                mock_l2_read_from_paths.assert_has_calls([
-                    call(u45_l2_path),
-                    call(u90_l2_path),
-                ])
-
-                mock_load_or_create_healpix_l2.assert_has_calls([
-                    call(u45_l2_path, u45_l1c_paths),
-                    call(u90_l2_path, u90_l1c_paths),
-                ])
+                combined_dependencies = UltraL3CombinedDependencies.fetch_dependencies(
+                    processing_input_collection
+                )
+
+                mock_download.assert_has_calls(
+                    [
+                        *[call(filename) for filename in u45_pset_file_names],
+                        *[call(filename) for filename in u90_pset_file_names],
+                        *[call(filename) for filename in glows_file_names],
+                        call(u45_map_file_name),
+                        call(u90_map_file_name),
+                    ]
+                )
+
+                mock_l1c_read_from_path.assert_has_calls(
+                    [
+                        call(Path("imap_ultra_l1c_u45-pset_20251010_v001.cdf")),
+                        call(Path("imap_ultra_l1c_u45-pset_20251011_v001.cdf")),
+                        call(Path("imap_ultra_l1c_u45-pset_20251012_v001.cdf")),
+                        call(Path("imap_ultra_l1c_u90-pset_20251010_v001.cdf")),
+                        call(Path("imap_ultra_l1c_u90-pset_20251011_v001.cdf")),
+                        call(Path("imap_ultra_l1c_u90-pset_20251012_v001.cdf")),
+                    ]
+                )
+
+                mock_l3e_read_from_path.assert_has_calls(
+                    [
+                        call(
+                            Path(
+                                f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"
+                            )
+                        ),
+                        call(
+                            Path(
+                                f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"
+                            )
+                        ),
+                        call(
+                            Path(
+                                f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"
+                            )
+                        ),
+                        call(
+                            Path(
+                                f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"
+                            )
+                        ),
+                        call(
+                            Path(
+                                f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"
+                            )
+                        ),
+                        call(
+                            Path(
+                                f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"
+                            )
+                        ),
+                    ]
+                )
+
+                mock_l2_read_from_paths.assert_has_calls(
+                    [
+                        call(u45_l2_path),
+                        call(u90_l2_path),
+                    ]
+                )
+
+                mock_load_or_create_healpix_l2.assert_has_calls(
+                    [
+                        call(u45_l2_path, u45_l1c_paths),
+                        call(u90_l2_path, u90_l1c_paths),
+                    ]
+                )
                 u45_deps = combined_dependencies.u45_dependencies
                 u90_deps = combined_dependencies.u90_dependencies
-                self.assertEqual(u45_deps.ultra_l2_healpix_map, sentinel.u45_healpix_dataset)
-                self.assertEqual(u90_deps.ultra_l2_healpix_map, sentinel.u90_healpix_dataset)
-                self.assertEqual(u45_deps.ultra_l2_rectangular_map, sentinel.u45_rectangular_l2)
-                self.assertEqual(u90_deps.ultra_l2_rectangular_map, sentinel.u90_rectangular_l2)
-                self.assertEqual(u45_deps.ultra_l1c_pset,
-                                 [sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3])
-                self.assertEqual(u90_deps.ultra_l1c_pset,
-                                 [sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3])
-                self.assertEqual(u45_deps.glows_l3e_sp,
-                                 [sentinel.glows_1, sentinel.glows_2, sentinel.glows_3])
-                self.assertEqual(u90_deps.glows_l3e_sp,
-                                 [sentinel.glows_1, sentinel.glows_2, sentinel.glows_3])
+                self.assertEqual(
+                    u45_deps.ultra_l2_healpix_map, sentinel.u45_healpix_dataset
+                )
+                self.assertEqual(
+                    u90_deps.ultra_l2_healpix_map, sentinel.u90_healpix_dataset
+                )
+                self.assertEqual(
+                    u45_deps.ultra_l2_rectangular_map, sentinel.u45_rectangular_l2
+                )
+                self.assertEqual(
+                    u90_deps.ultra_l2_rectangular_map, sentinel.u90_rectangular_l2
+                )
+                self.assertEqual(
+                    u45_deps.ultra_l1c_pset,
+                    [sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3],
+                )
+                self.assertEqual(
+                    u90_deps.ultra_l1c_pset,
+                    [sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3],
+                )
+                self.assertEqual(
+                    u45_deps.glows_l3e_sp,
+                    [sentinel.glows_1, sentinel.glows_2, sentinel.glows_3],
+                )
+                self.assertEqual(
+                    u90_deps.glows_l3e_sp,
+                    [sentinel.glows_1, sentinel.glows_2, sentinel.glows_3],
+                )
                 expected_u45_file_paths = [
                     u45_l2_path,
                     *u45_l1c_paths,
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"),
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"),
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"
+                    ),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"
+                    ),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"
+                    ),
                 ]
                 expected_u90_file_paths = [
                     u90_l2_path,
                     *u90_l1c_paths,
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"),
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"),
-                    Path(f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251010_v001.cdf"
+                    ),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251011_v001.cdf"
+                    ),
+                    Path(
+                        f"imap_glows_l3e_survival-probability-ul-{frame}_20251012_v001.cdf"
+                    ),
                 ]
-                self.assertEqual(u45_deps.dependency_file_paths, expected_u45_file_paths)
-                self.assertEqual(u90_deps.dependency_file_paths, expected_u90_file_paths)
-
+                self.assertEqual(
+                    u45_deps.dependency_file_paths, expected_u45_file_paths
+                )
+                self.assertEqual(
+                    u90_deps.dependency_file_paths, expected_u90_file_paths
+                )
diff --git a/tests/ultra/test_ultra_l3_survival_probabillity.py b/tests/ultra/test_ultra_l3_survival_probabillity.py
index 128ea5ba5..a0e8d2ce6 100644
--- a/tests/ultra/test_ultra_l3_survival_probabillity.py
+++ b/tests/ultra/test_ultra_l3_survival_probabillity.py
@@ -4,29 +4,34 @@
 
 import numpy as np
 from astropy_healpix import HEALPix
-from imap_processing.ena_maps.ena_maps import UltraPointingSet, HealpixSkyMap
-from imap_processing.ena_maps.utils.coordinates import CoordNames
+from imap_processing.ena_maps.ena_maps import HealpixSkyMap, UltraPointingSet
 from imap_processing.spice import geometry
 from imap_processing.ultra.constants import UltraConstants
 
 from imap_l3_processing.constants import (
-    TT2000_EPOCH,
     ONE_SECOND_IN_NANOSECONDS,
     SECONDS_PER_DAY,
+    TT2000_EPOCH,
 )
 from imap_l3_processing.glows.quality_flags import GlowsL3Flags
-from imap_l3_processing.maps.quality_flags import MapL3Flags
-from imap_l3_processing.ultra.models import UltraL1CPSet, UltraGlowsL3eData
-from imap_l3_processing.ultra.science.ultra_survival_probability import UltraSurvivalProbability, \
-    UltraSurvivalProbabilitySkyMap
+from imap_l3_processing.ultra.models import UltraGlowsL3eData, UltraL1CPSet
+from imap_l3_processing.ultra.science.ultra_survival_probability import (
+    UltraSurvivalProbability,
+    UltraSurvivalProbabilitySkyMap,
+)
 from tests.spice_test_case import SpiceTestCase
 
 
 class TestUltraSurvivalProbability(unittest.TestCase):
-
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    def test_ultra_survival_probability_pset_calls_super(self, _, mock_frame_transform_az_el):
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    def test_ultra_survival_probability_pset_calls_super(
+        self, _, mock_frame_transform_az_el
+    ):
         input_l1c_pset = _create_ultra_l1c_pset(np.array([2]), np.full((1, 1, 12), 1))
         glows = _build_glows_l3e_ultra()
 
@@ -38,80 +43,178 @@ def test_ultra_survival_probability_pset_calls_super(self, _, mock_frame_transfo
         self.assertEqual(input_l1c_pset.to_xarray(), prod.data)
         self.assertEqual(geometry.SpiceFrame.IMAP_DPS, prod.spice_reference_frame)
 
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    @patch.object(UltraConstants, 'PSET_ENERGY_BIN_EDGES', new=[0.5, 1.5, 4])
-    def test_ultra_survival_probability_rotates_to_glows_frame(self, mock_unitim, mock_frame_transform_az_el):
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    @patch.object(UltraConstants, "PSET_ENERGY_BIN_EDGES", new=[0.5, 1.5, 4])
+    def test_ultra_survival_probability_rotates_to_glows_frame(
+        self, mock_unitim, mock_frame_transform_az_el
+    ):
         glows_energies = np.array([1, 2])
         input_l1c_pset = _create_ultra_l1c_pset(glows_energies, np.full((1, 2, 12), 1))
         glows_surv_prob = np.array([[[1] * 6 + [0] * 6, [2] * 6 + [3] * 6]])
 
-        glows = _build_glows_l3e_ultra(nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies)
+        glows = _build_glows_l3e_ultra(
+            nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies
+        )
 
         rotate_el_by_180 = np.array([[1, -1]] * 12)
-        mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: az_el * rotate_el_by_180
+        mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: (
+            az_el * rotate_el_by_180
+        )
 
         prod = UltraSurvivalProbability(input_l1c_pset, glows, bin_groups=[0, 1, 2])
 
         expected_tt = (input_l1c_pset.epoch - TT2000_EPOCH).total_seconds() + 43200
         mock_unitim.assert_called_once_with(expected_tt, "TT", "ET")
 
-        mock_frame_transform_az_el.assert_called_once_with(mock_unitim.return_value, prod.az_el_points,
-                                                           geometry.SpiceFrame.IMAP_DPS,
-                                                           geometry.SpiceFrame.ECLIPJ2000)
-        expected_survival_probabilities_values = np.array([[[0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1],
-                                                            [3, 3, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2]]])
-        survival_probability_times_exposure = prod.data['survival_probability_times_exposure']
-        np.testing.assert_array_equal(survival_probability_times_exposure.values,
-                                      expected_survival_probabilities_values)
-
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    @patch.object(UltraConstants, 'PSET_ENERGY_BIN_EDGES', new=[.1, 10])
-    def test_ultra_survival_probability_is_multiplied_by_exposure(self, mock_frame_transform_az_el, _):
+        mock_frame_transform_az_el.assert_called_once_with(
+            mock_unitim.return_value,
+            prod.az_el_points,
+            geometry.SpiceFrame.IMAP_DPS,
+            geometry.SpiceFrame.ECLIPJ2000,
+        )
+        expected_survival_probabilities_values = np.array(
+            [
+                [
+                    [0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1],
+                    [3, 3, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2],
+                ]
+            ]
+        )
+        survival_probability_times_exposure = prod.data[
+            "survival_probability_times_exposure"
+        ]
+        np.testing.assert_array_equal(
+            survival_probability_times_exposure.values,
+            expected_survival_probabilities_values,
+        )
+
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    @patch.object(UltraConstants, "PSET_ENERGY_BIN_EDGES", new=[0.1, 10])
+    def test_ultra_survival_probability_is_multiplied_by_exposure(
+        self, mock_frame_transform_az_el, _
+    ):
         glows_energies = np.array([1])
         input_l1c_pset = _create_ultra_l1c_pset(glows_energies, np.full((1, 1, 12), 2))
         glows_surv_prob = np.array([[[1] * 6 + [0] * 6]])
 
-        glows = _build_glows_l3e_ultra(nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies)
+        glows = _build_glows_l3e_ultra(
+            nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies
+        )
 
         mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: az_el
 
         prod = UltraSurvivalProbability(input_l1c_pset, glows, bin_groups=[0, 1])
 
-        expected_survival_probabilities_values = np.array([[[2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0]]])
-        survival_probability_times_exposure = prod.data['survival_probability_times_exposure']
-        np.testing.assert_array_equal(survival_probability_times_exposure.values,
-                                      expected_survival_probabilities_values)
-
+        expected_survival_probabilities_values = np.array(
+            [[[2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0]]]
+        )
+        survival_probability_times_exposure = prod.data[
+            "survival_probability_times_exposure"
+        ]
+        np.testing.assert_array_equal(
+            survival_probability_times_exposure.values,
+            expected_survival_probabilities_values,
+        )
 
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    @patch.object(UltraConstants, 'PSET_ENERGY_BIN_EDGES', new=[.1, 10])
-    def test_ultra_survival_probability_pset_includes_quality_flags(self, mock_frame_transform_az_el, _):
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    @patch.object(UltraConstants, "PSET_ENERGY_BIN_EDGES", new=[0.1, 10])
+    def test_ultra_survival_probability_pset_includes_quality_flags(
+        self, mock_frame_transform_az_el, _
+    ):
         exposure = np.array([[[2] * 6 + [0] * 6]])
 
         expected_unflagged = np.array([[[0] * 12]])
         expected_flagged = exposure.copy()
         cases = [
-            (GlowsL3Flags.NONE, expected_unflagged, expected_unflagged, expected_unflagged),
-            (GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO, expected_unflagged, expected_flagged, expected_unflagged),
-            (GlowsL3Flags.PREDICTIVE_EPHEMERIS, expected_flagged, expected_unflagged, expected_unflagged),
-            (GlowsL3Flags.PERSISTED_LAST_POINT, expected_unflagged, expected_unflagged, expected_flagged),
-            (GlowsL3Flags.PREDICTIVE_EPHEMERIS | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO, expected_flagged, expected_flagged, expected_unflagged),
-            (GlowsL3Flags.PREDICTIVE_EPHEMERIS | GlowsL3Flags.PERSISTED_LAST_POINT, expected_flagged, expected_unflagged, expected_flagged),
-            (GlowsL3Flags.PERSISTED_LAST_POINT | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO, expected_unflagged, expected_flagged, expected_flagged),
-            (GlowsL3Flags.PERSISTED_LAST_POINT | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PREDICTIVE_EPHEMERIS, expected_flagged, expected_flagged, expected_flagged),
+            (
+                GlowsL3Flags.NONE,
+                expected_unflagged,
+                expected_unflagged,
+                expected_unflagged,
+            ),
+            (
+                GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                expected_unflagged,
+                expected_flagged,
+                expected_unflagged,
+            ),
+            (
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                expected_flagged,
+                expected_unflagged,
+                expected_unflagged,
+            ),
+            (
+                GlowsL3Flags.PERSISTED_LAST_POINT,
+                expected_unflagged,
+                expected_unflagged,
+                expected_flagged,
+            ),
+            (
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS
+                | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                expected_flagged,
+                expected_flagged,
+                expected_unflagged,
+            ),
+            (
+                GlowsL3Flags.PREDICTIVE_EPHEMERIS | GlowsL3Flags.PERSISTED_LAST_POINT,
+                expected_flagged,
+                expected_unflagged,
+                expected_flagged,
+            ),
+            (
+                GlowsL3Flags.PERSISTED_LAST_POINT
+                | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                expected_unflagged,
+                expected_flagged,
+                expected_flagged,
+            ),
+            (
+                GlowsL3Flags.PERSISTED_LAST_POINT
+                | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                | GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+                expected_flagged,
+                expected_flagged,
+                expected_flagged,
+            ),
         ]
-        for flag, expected_predicted_ephem_flag, expected_nominal_alpha_proton_ratio_flag, expected_persisted_last_point_flag in cases:
+        for (
+            flag,
+            expected_predicted_ephem_flag,
+            expected_nominal_alpha_proton_ratio_flag,
+            expected_persisted_last_point_flag,
+        ) in cases:
             with self.subTest(flag):
                 glows_energies = np.array([1])
                 input_l1c_pset = _create_ultra_l1c_pset(glows_energies, exposure)
                 glows_surv_prob = np.full((1, 1, 12), 2)
 
-                glows = _build_glows_l3e_ultra(nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies, flags=flag)
+                glows = _build_glows_l3e_ultra(
+                    nside=1,
+                    survival_probabilities=glows_surv_prob,
+                    energies=glows_energies,
+                    flags=flag,
+                )
 
-                mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: az_el
+                mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: (
+                    az_el
+                )
 
                 prod = UltraSurvivalProbability(
                     input_l1c_pset, glows, bin_groups=[0, 1]
@@ -125,154 +228,296 @@ def test_ultra_survival_probability_pset_includes_quality_flags(self, mock_frame
                     prod.data["survival_probability_times_exposure"].dims,
                 )
                 np.testing.assert_array_equal(
-                    prod.data["nominal_alpha_proton_ratio_flag"], expected_nominal_alpha_proton_ratio_flag
+                    prod.data["nominal_alpha_proton_ratio_flag"],
+                    expected_nominal_alpha_proton_ratio_flag,
                 )
                 np.testing.assert_array_equal(
-                    prod.data["persisted_last_point_flag"], expected_persisted_last_point_flag
+                    prod.data["persisted_last_point_flag"],
+                    expected_persisted_last_point_flag,
                 )
                 self.assertEqual(
                     prod.data["nominal_alpha_proton_ratio_flag"].dims,
                     prod.data["survival_probability_times_exposure"].dims,
                 )
 
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    @patch.object(UltraConstants, 'PSET_ENERGY_BIN_EDGES', new=[1, 100, 10000])
-    def test_ultra_survival_probability_interpolates_over_energy(self, mock_frame_transform_az_el, _):
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    @patch.object(UltraConstants, "PSET_ENERGY_BIN_EDGES", new=[1, 100, 10000])
+    def test_ultra_survival_probability_interpolates_over_energy(
+        self, mock_frame_transform_az_el, _
+    ):
         glows_energies = np.array([1, 100, 10000])
         ultra_energies = np.array([10, 1000])
         input_l1c_pset = _create_ultra_l1c_pset(ultra_energies, np.full((1, 2, 12), 1))
         glows_surv_prob = np.array([[[1] * 12, [3] * 12, [5] * 12]])
 
-        glows = _build_glows_l3e_ultra(nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies)
+        glows = _build_glows_l3e_ultra(
+            nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies
+        )
 
         mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: az_el
 
         prod = UltraSurvivalProbability(input_l1c_pset, glows, bin_groups=[0, 1, 2])
 
         expected_survival_probabilities_values = np.array([[[2] * 12, [4] * 12]])
-        survival_probability_times_exposure = prod.data['survival_probability_times_exposure']
-        np.testing.assert_array_equal(survival_probability_times_exposure.values,
-                                      expected_survival_probabilities_values)
-
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    @patch.object(UltraConstants, 'PSET_ENERGY_BIN_EDGES', new=[1, 1, 1, 1, 100, 100, 100, 100, 10000])
-    def test_ultra_survival_probability_combines_fine_energy_bins(self, mock_frame_transform_az_el, _):
+        survival_probability_times_exposure = prod.data[
+            "survival_probability_times_exposure"
+        ]
+        np.testing.assert_array_equal(
+            survival_probability_times_exposure.values,
+            expected_survival_probabilities_values,
+        )
+
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    @patch.object(
+        UltraConstants,
+        "PSET_ENERGY_BIN_EDGES",
+        new=[1, 1, 1, 1, 100, 100, 100, 100, 10000],
+    )
+    def test_ultra_survival_probability_combines_fine_energy_bins(
+        self, mock_frame_transform_az_el, _
+    ):
         glows_energies = np.array([1, 100, 10000])
         ultra_energies = np.array([10, 10, 10, 10, 1000, 1000, 1000, 1000])
-        input_l1c_pset = _create_ultra_l1c_pset(ultra_energies, np.full((1, 2 * 4, 12), 1))
+        input_l1c_pset = _create_ultra_l1c_pset(
+            ultra_energies, np.full((1, 2 * 4, 12), 1)
+        )
         glows_surv_prob = np.array([[[1] * 12, [3] * 12, [5] * 12]])
 
-        glows = _build_glows_l3e_ultra(nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies)
+        glows = _build_glows_l3e_ultra(
+            nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies
+        )
 
         mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: az_el
 
         prod = UltraSurvivalProbability(input_l1c_pset, glows)
 
         expected_survival_probabilities_values = np.array([[[2] * 12, [4] * 12]])
-        survival_probability_times_exposure = prod.data['survival_probability_times_exposure']
-        np.testing.assert_array_equal(survival_probability_times_exposure.values,
-                                      expected_survival_probabilities_values)
+        survival_probability_times_exposure = prod.data[
+            "survival_probability_times_exposure"
+        ]
+        np.testing.assert_array_equal(
+            survival_probability_times_exposure.values,
+            expected_survival_probabilities_values,
+        )
 
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim')
-    @patch('imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el')
-    def test_ultra_survival_probability_handles_different_resolutions(self, mock_frame_transform_az_el, _):
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.spiceypy.unitim"
+    )
+    @patch(
+        "imap_l3_processing.ultra.science.ultra_survival_probability.geometry.frame_transform_az_el"
+    )
+    def test_ultra_survival_probability_handles_different_resolutions(
+        self, mock_frame_transform_az_el, _
+    ):
         glows_energies = np.array([1])
         ultra_energies = np.array([10])
         input_l1c_pset = _create_ultra_l1c_pset(ultra_energies, np.full((1, 1, 48), 1))
         glows_surv_prob = np.array([[np.arange(12)]])
 
-        glows = _build_glows_l3e_ultra(nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies)
+        glows = _build_glows_l3e_ultra(
+            nside=1, survival_probabilities=glows_surv_prob, energies=glows_energies
+        )
 
         mock_frame_transform_az_el.side_effect = lambda et, az_el, to, _from: az_el
 
         prod = UltraSurvivalProbability(input_l1c_pset, glows, bin_groups=[0, 1])
 
-        expected_survival_probabilities_values = np.array([[[0.7667581, 1.25558603, 1.74441397, 2.2332419, 0.75, 0.25,
-                                                             0.75, 1.25, 1.75, 2.25, 2.75, 2.25,
-                                                             2.83574061, 2.4043331, 2.9043331, 3.4043331, 3.9043331,
-                                                             4.4043331,
-                                                             4.9043331, 4.33574061, 4.25, 4.75, 5.25, 5.75,
-                                                             6.25, 6.75, 6.25, 4.75, 6.56137065, 6.12996314,
-                                                             6.62996314, 7.12996314, 7.62996314, 8.12996314, 8.62996314,
-                                                             8.06137065,
-                                                             8.75, 8.25, 8.75, 9.25, 9.75, 10.25,
-                                                             10.75, 10.25, 8.7667581, 9.25558603, 9.74441397,
-                                                             10.2332419]]])
-        survival_probability_times_exposure = prod.data['survival_probability_times_exposure']
-        np.testing.assert_array_almost_equal(survival_probability_times_exposure.values,
-                                             expected_survival_probabilities_values)
+        expected_survival_probabilities_values = np.array(
+            [
+                [
+                    [
+                        0.7667581,
+                        1.25558603,
+                        1.74441397,
+                        2.2332419,
+                        0.75,
+                        0.25,
+                        0.75,
+                        1.25,
+                        1.75,
+                        2.25,
+                        2.75,
+                        2.25,
+                        2.83574061,
+                        2.4043331,
+                        2.9043331,
+                        3.4043331,
+                        3.9043331,
+                        4.4043331,
+                        4.9043331,
+                        4.33574061,
+                        4.25,
+                        4.75,
+                        5.25,
+                        5.75,
+                        6.25,
+                        6.75,
+                        6.25,
+                        4.75,
+                        6.56137065,
+                        6.12996314,
+                        6.62996314,
+                        7.12996314,
+                        7.62996314,
+                        8.12996314,
+                        8.62996314,
+                        8.06137065,
+                        8.75,
+                        8.25,
+                        8.75,
+                        9.25,
+                        9.75,
+                        10.25,
+                        10.75,
+                        10.25,
+                        8.7667581,
+                        9.25558603,
+                        9.74441397,
+                        10.2332419,
+                    ]
+                ]
+            ]
+        )
+        survival_probability_times_exposure = prod.data[
+            "survival_probability_times_exposure"
+        ]
+        np.testing.assert_array_almost_equal(
+            survival_probability_times_exposure.values,
+            expected_survival_probabilities_values,
+        )
 
 
 class TestUltraSurvivalProbabilitySkyMap(SpiceTestCase):
     def test_ultra_survival_probability_skymap(self):
         pointing_set_nside = 2
-        pointing_set_pixels = 12 * pointing_set_nside ** 2
+        pointing_set_pixels = 12 * pointing_set_nside**2
         l1c_exposure_1 = np.full((1, 1, pointing_set_pixels), 0.5)
         l1c_exposure_2 = np.full((1, 1, pointing_set_pixels), 1)
         l3e_sp_1 = np.full((1, 1, pointing_set_pixels), 0.5)
         l3e_sp_2 = np.full((1, 1, pointing_set_pixels), 0.25)
         glows_energies = np.array([1])
-        l1c_1 = _create_ultra_l1c_pset(energy=np.array([1]), exposure_factor=l1c_exposure_1)
-        l3e_glows_1 = _build_glows_l3e_ultra(survival_probabilities=l3e_sp_1, energies=glows_energies,
-                                             nside=pointing_set_nside, flags=GlowsL3Flags.PREDICTIVE_EPHEMERIS)
-        l1c_2 = _create_ultra_l1c_pset(energy=np.array([1]), exposure_factor=l1c_exposure_2)
-        l3e_glows_2 = _build_glows_l3e_ultra(survival_probabilities=l3e_sp_2, energies=glows_energies,
-                                             nside=pointing_set_nside, flags=GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PERSISTED_LAST_POINT)
+        l1c_1 = _create_ultra_l1c_pset(
+            energy=np.array([1]), exposure_factor=l1c_exposure_1
+        )
+        l3e_glows_1 = _build_glows_l3e_ultra(
+            survival_probabilities=l3e_sp_1,
+            energies=glows_energies,
+            nside=pointing_set_nside,
+            flags=GlowsL3Flags.PREDICTIVE_EPHEMERIS,
+        )
+        l1c_2 = _create_ultra_l1c_pset(
+            energy=np.array([1]), exposure_factor=l1c_exposure_2
+        )
+        l3e_glows_2 = _build_glows_l3e_ultra(
+            survival_probabilities=l3e_sp_2,
+            energies=glows_energies,
+            nside=pointing_set_nside,
+            flags=GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+            | GlowsL3Flags.PERSISTED_LAST_POINT,
+        )
 
         pset_1 = UltraSurvivalProbability(l1c_1, l3e_glows_1, bin_groups=[0, 1])
         pset_2 = UltraSurvivalProbability(l1c_2, l3e_glows_2, bin_groups=[0, 1])
 
         output_nside = 1
-        prod = UltraSurvivalProbabilitySkyMap([pset_1, pset_2], geometry.SpiceFrame.IMAP_DPS, output_nside)
+        prod = UltraSurvivalProbabilitySkyMap(
+            [pset_1, pset_2], geometry.SpiceFrame.IMAP_DPS, output_nside
+        )
 
         self.assertIsInstance(prod, HealpixSkyMap)
 
-        exposure_weighted_survival_probabilities = prod.data_1d["exposure_weighted_survival_probabilities"].values
-        expected_output_pixels = 12 * output_nside ** 2
-        expected_exposure_weighted_survival_probabilities = np.full((1, 1, expected_output_pixels), 1 / 3)
-        np.testing.assert_array_almost_equal(exposure_weighted_survival_probabilities,
-                                             expected_exposure_weighted_survival_probabilities)
+        exposure_weighted_survival_probabilities = prod.data_1d[
+            "exposure_weighted_survival_probabilities"
+        ].values
+        expected_output_pixels = 12 * output_nside**2
+        expected_exposure_weighted_survival_probabilities = np.full(
+            (1, 1, expected_output_pixels), 1 / 3
+        )
+        np.testing.assert_array_almost_equal(
+            exposure_weighted_survival_probabilities,
+            expected_exposure_weighted_survival_probabilities,
+        )
         actual_quality_flags = prod.data_1d["quality_flags"].values
-        expected_quality_flag = GlowsL3Flags.PREDICTIVE_EPHEMERIS | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO | GlowsL3Flags.PERSISTED_LAST_POINT
-        np.testing.assert_equal(actual_quality_flags, np.full((1, 1, expected_output_pixels), expected_quality_flag))
-
+        expected_quality_flag = (
+            GlowsL3Flags.PREDICTIVE_EPHEMERIS
+            | GlowsL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+            | GlowsL3Flags.PERSISTED_LAST_POINT
+        )
+        np.testing.assert_equal(
+            actual_quality_flags,
+            np.full((1, 1, expected_output_pixels), expected_quality_flag),
+        )
 
     def test_ultra_survival_probability_skymap_ignores_nan_survival_probabilities(self):
         pointing_set_nside = 2
-        pointing_set_pixels = 12 * pointing_set_nside ** 2
+        pointing_set_pixels = 12 * pointing_set_nside**2
         l1c_exposure_1 = np.full((1, 1, pointing_set_pixels), 0.5)
         l1c_exposure_2 = np.full((1, 1, pointing_set_pixels), 1)
         l3e_sp_1 = np.full((1, 1, pointing_set_pixels), 0.5)
         l3e_sp_2 = np.full((1, 1, pointing_set_pixels), np.nan)
         glows_energies = np.array([1])
-        l1c_1 = _create_ultra_l1c_pset(energy=np.array([1]), exposure_factor=l1c_exposure_1)
-        l3e_glows_1 = _build_glows_l3e_ultra(survival_probabilities=l3e_sp_1, energies=glows_energies,
-                                             nside=pointing_set_nside)
-        l1c_2 = _create_ultra_l1c_pset(energy=np.array([1]), exposure_factor=l1c_exposure_2)
-        l3e_glows_2 = _build_glows_l3e_ultra(survival_probabilities=l3e_sp_2, energies=glows_energies,
-                                             nside=pointing_set_nside)
+        l1c_1 = _create_ultra_l1c_pset(
+            energy=np.array([1]), exposure_factor=l1c_exposure_1
+        )
+        l3e_glows_1 = _build_glows_l3e_ultra(
+            survival_probabilities=l3e_sp_1,
+            energies=glows_energies,
+            nside=pointing_set_nside,
+        )
+        l1c_2 = _create_ultra_l1c_pset(
+            energy=np.array([1]), exposure_factor=l1c_exposure_2
+        )
+        l3e_glows_2 = _build_glows_l3e_ultra(
+            survival_probabilities=l3e_sp_2,
+            energies=glows_energies,
+            nside=pointing_set_nside,
+        )
 
         pset_1 = UltraSurvivalProbability(l1c_1, l3e_glows_1, bin_groups=[0, 1])
         pset_2 = UltraSurvivalProbability(l1c_2, l3e_glows_2, bin_groups=[0, 1])
 
         output_nside = 1
-        prod = UltraSurvivalProbabilitySkyMap([pset_1, pset_2], geometry.SpiceFrame.IMAP_DPS, output_nside)
+        prod = UltraSurvivalProbabilitySkyMap(
+            [pset_1, pset_2], geometry.SpiceFrame.IMAP_DPS, output_nside
+        )
 
         self.assertIsInstance(prod, HealpixSkyMap)
-        exposure_weighted_survival_probabilities = prod.data_1d["exposure_weighted_survival_probabilities"].values
-        expected_output_pixels = 12 * output_nside ** 2
-        expected_exposure_weighted_survival_probabilities = np.full((1, 1, expected_output_pixels), 0.5)
-        np.testing.assert_array_almost_equal(exposure_weighted_survival_probabilities,
-                                             expected_exposure_weighted_survival_probabilities)
-
-
-def _build_glows_l3e_ultra(survival_probabilities: np.ndarray = None, energies: np.ndarray = None, nside: int = 16, flags: GlowsL3Flags = GlowsL3Flags.NONE):
+        exposure_weighted_survival_probabilities = prod.data_1d[
+            "exposure_weighted_survival_probabilities"
+        ].values
+        expected_output_pixels = 12 * output_nside**2
+        expected_exposure_weighted_survival_probabilities = np.full(
+            (1, 1, expected_output_pixels), 0.5
+        )
+        np.testing.assert_array_almost_equal(
+            exposure_weighted_survival_probabilities,
+            expected_exposure_weighted_survival_probabilities,
+        )
+
+
+def _build_glows_l3e_ultra(
+    survival_probabilities: np.ndarray = None,
+    energies: np.ndarray = None,
+    nside: int = 16,
+    flags: GlowsL3Flags = GlowsL3Flags.NONE,
+):
     energies = np.arange(1, 21) if energies is None else energies
-    num_pixels = 12 * (nside ** 2)
-    survival_probabilities = np.random.rand(1, len(energies),
-                                            num_pixels) if survival_probabilities is None else survival_probabilities
+    num_pixels = 12 * (nside**2)
+    survival_probabilities = (
+        np.random.rand(1, len(energies), num_pixels)
+        if survival_probabilities is None
+        else survival_probabilities
+    )
     healpix_pixels = np.arange(num_pixels)
 
     healpix_grid = HEALPix(nside)
@@ -284,18 +529,20 @@ def _build_glows_l3e_ultra(survival_probabilities: np.ndarray = None, energies:
         energy=energies,
         healpix_index=healpix_pixels,
         survival_probability=survival_probabilities,
-        flags=np.array([flags], dtype=np.uint16)
+        flags=np.array([flags], dtype=np.uint16),
     )
 
 
-def _create_ultra_l1c_pset(energy: np.ndarray,
-                           exposure_factor: np.ndarray,
-                           sensitivity: np.ndarray = None,
-                           counts: np.ndarray = None,
-                           latitude: np.ndarray = None,
-                           longitude: np.ndarray = None,
-                           epoch: datetime = None,
-                           epoch_delta: int = ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY):
+def _create_ultra_l1c_pset(
+    energy: np.ndarray,
+    exposure_factor: np.ndarray,
+    sensitivity: np.ndarray = None,
+    counts: np.ndarray = None,
+    latitude: np.ndarray = None,
+    longitude: np.ndarray = None,
+    epoch: datetime = None,
+    epoch_delta: int = ONE_SECOND_IN_NANOSECONDS * SECONDS_PER_DAY,
+):
     epoch = datetime(2025, 10, 1, 0) if epoch is None else epoch
     sensitivity = sensitivity or np.full_like(exposure_factor[0], 1)
     healpix_index = np.arange(exposure_factor.shape[-1])
@@ -315,5 +562,5 @@ def _create_ultra_l1c_pset(energy: np.ndarray,
     return input_l1c_pset
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/tests/ultra/test_ultra_l3_to_rectangular_dependencies.py b/tests/ultra/test_ultra_l3_to_rectangular_dependencies.py
index 05110d260..cc2d82205 100644
--- a/tests/ultra/test_ultra_l3_to_rectangular_dependencies.py
+++ b/tests/ultra/test_ultra_l3_to_rectangular_dependencies.py
@@ -1,17 +1,20 @@
 import unittest
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
-from imap_data_access import ScienceInput, ProcessingInputCollection
+from imap_data_access import ProcessingInputCollection, ScienceInput
 
-from imap_l3_processing.ultra.ultra_l3_to_rectangular_dependencies import UltraL3ToRectangularDependencies
+from imap_l3_processing.ultra.ultra_l3_to_rectangular_dependencies import (
+    UltraL3ToRectangularDependencies,
+)
 
 
 class TestUltraL3ToRectangularDependencies(unittest.TestCase):
-
-    @patch('imap_l3_processing.ultra.ultra_l3_to_rectangular_dependencies.HealPixIntensityMapData.read_from_path')
-    @patch('imap_l3_processing.ultra.ultra_l3_to_rectangular_dependencies.download')
+    @patch(
+        "imap_l3_processing.ultra.ultra_l3_to_rectangular_dependencies.HealPixIntensityMapData.read_from_path"
+    )
+    @patch("imap_l3_processing.ultra.ultra_l3_to_rectangular_dependencies.download")
     def test_fetch_dependencies(self, mock_download, mock_healpix_read_from_path):
-        map_file_name = 'imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf'
+        map_file_name = "imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf"
         map_input = ScienceInput(map_file_name)
         processing_input_collection = ProcessingInputCollection(map_input)
 
@@ -19,14 +22,21 @@ def test_fetch_dependencies(self, mock_download, mock_healpix_read_from_path):
             "map_file",
         ]
 
-        ultra_l3_to_rectangular_dependencies = UltraL3ToRectangularDependencies.fetch_dependencies(
-            processing_input_collection)
+        ultra_l3_to_rectangular_dependencies = (
+            UltraL3ToRectangularDependencies.fetch_dependencies(
+                processing_input_collection
+            )
+        )
 
-        mock_download.assert_has_calls([
-            call(map_file_name),
-        ])
+        mock_download.assert_has_calls(
+            [
+                call(map_file_name),
+            ]
+        )
 
         mock_healpix_read_from_path.assert_called_once_with("map_file")
 
-        self.assertEqual(ultra_l3_to_rectangular_dependencies.healpix_map_data,
-                         mock_healpix_read_from_path.return_value)
+        self.assertEqual(
+            ultra_l3_to_rectangular_dependencies.healpix_map_data,
+            mock_healpix_read_from_path.return_value,
+        )
diff --git a/tests/ultra/test_ultra_processor.py b/tests/ultra/test_ultra_processor.py
index 876830cda..0d2a89dd0 100644
--- a/tests/ultra/test_ultra_processor.py
+++ b/tests/ultra/test_ultra_processor.py
@@ -3,37 +3,41 @@
 from datetime import datetime, timedelta
 from hashlib import sha256
 from pathlib import Path
-from unittest.mock import patch, sentinel, call, Mock
+from unittest.mock import Mock, call, patch, sentinel
 
 import numpy as np
 import xarray as xr
-from imap_data_access.processing_input import AncillaryInput, ScienceInput, ProcessingInputCollection
+from imap_data_access.processing_input import (
+    AncillaryInput,
+    ProcessingInputCollection,
+    ScienceInput,
+)
 from imap_processing.ena_maps.ena_maps import RectangularSkyMap
 from imap_processing.ena_maps.utils.coordinates import CoordNames
 from imap_processing.spice.geometry import SpiceFrame
 
 from imap_l3_processing.maps.map_models import (
+    HealPixCoords,
     HealPixIntensityMapData,
     IntensityMapData,
-    HealPixCoords,
-    SpectralIndexMapData,
     RectangularIntensityDataProduct,
+    RectangularIntensityMapData,
     RectangularSpectralIndexDataProduct,
     RectangularSpectralIndexMapData,
-    RectangularIntensityMapData,
+    SpectralIndexMapData,
 )
 from imap_l3_processing.maps.quality_flags import MapL3Flags
 from imap_l3_processing.models import InputMetadata
 from imap_l3_processing.ultra.ultra_l3_dependencies import (
+    UltraL3CombinedDependencies,
     UltraL3Dependencies,
     UltraL3SpectralIndexDependencies,
-    UltraL3CombinedDependencies,
 )
 from imap_l3_processing.ultra.ultra_processor import (
     UltraProcessor,
     correct_healpix_data_for_survival_probability,
 )
-from imap_l3_processing.utils import get_temp_cache_dir, clear_temp_cache
+from imap_l3_processing.utils import clear_temp_cache, get_temp_cache_dir
 from tests.maps.test_builders import create_rectangular_intensity_map_data
 from tests.test_helpers import get_test_data_path
 
@@ -78,7 +82,9 @@ def test_correct_healpix_data_for_survival_probability(
 
         input_map_flux = rng.random((1, 9, 12))
         epoch = datetime.now()
-        input_l2_healpix_map = _create_ultra_l2_healpix_data(epoch=[epoch], flux=input_map_flux, healpix_indices=healpix_indices)
+        input_l2_healpix_map = _create_ultra_l2_healpix_data(
+            epoch=[epoch], flux=input_map_flux, healpix_indices=healpix_indices
+        )
         input_l2_healpix_map.intensity_map_data.energy = sentinel.ultra_l2_energies
         input_l2_rectangular_map = create_rectangular_intensity_map_data()
 
@@ -110,10 +116,18 @@ def test_correct_healpix_data_for_survival_probability(
             sentinel.pset_3,
         ]
         computed_survival_probabilities = rng.random((1, 9, healpix_indices.shape[0]))
-        expected_quality_flags = np.full((1, 9, healpix_indices.shape[0]), MapL3Flags.NONE)
-        expected_quality_flags[rng.random((1, 9, healpix_indices.shape[0])) > 0.8] = MapL3Flags.PREDICTIVE_EPHEMERIS
-        expected_quality_flags[rng.random((1, 9, healpix_indices.shape[0])) > 0.7] = MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
-        expected_quality_flags[rng.random((1, 9, healpix_indices.shape[0])) > 0.75] = MapL3Flags.PERSISTED_LAST_POINT
+        expected_quality_flags = np.full(
+            (1, 9, healpix_indices.shape[0]), MapL3Flags.NONE
+        )
+        expected_quality_flags[rng.random((1, 9, healpix_indices.shape[0])) > 0.8] = (
+            MapL3Flags.PREDICTIVE_EPHEMERIS
+        )
+        expected_quality_flags[rng.random((1, 9, healpix_indices.shape[0])) > 0.7] = (
+            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+        )
+        expected_quality_flags[rng.random((1, 9, healpix_indices.shape[0])) > 0.75] = (
+            MapL3Flags.PERSISTED_LAST_POINT
+        )
 
         mock_survival_skymap.return_value.to_dataset.return_value = xr.Dataset(
             {
@@ -141,16 +155,37 @@ def test_correct_healpix_data_for_survival_probability(
             },
         )
 
-        healpix_intensity_map_data = correct_healpix_data_for_survival_probability(dependencies, SpiceFrame.ECLIPJ2000)
+        healpix_intensity_map_data = correct_healpix_data_for_survival_probability(
+            dependencies, SpiceFrame.ECLIPJ2000
+        )
 
-        mock_combine_glows_l3e_with_l1c_pointing.assert_called_once_with(sentinel.glows_l3e_sp, sentinel.ultra_l1c_pset)
-        mock_survival_probability_pointing_set.assert_has_calls([
-            call(sentinel.ultra_l1c_1, sentinel.glows_l3e_1, bin_groups=sentinel.bin_groups),
-            call(sentinel.ultra_l1c_2, sentinel.glows_l3e_2, bin_groups=sentinel.bin_groups),
-            call(sentinel.ultra_l1c_3, sentinel.glows_l3e_3, bin_groups=sentinel.bin_groups)
-        ])
-        mock_survival_skymap.assert_called_once_with([sentinel.pset_1, sentinel.pset_2, sentinel.pset_3],
-                                                     SpiceFrame.ECLIPJ2000, input_l2_healpix_map.coords.nside)
+        mock_combine_glows_l3e_with_l1c_pointing.assert_called_once_with(
+            sentinel.glows_l3e_sp, sentinel.ultra_l1c_pset
+        )
+        mock_survival_probability_pointing_set.assert_has_calls(
+            [
+                call(
+                    sentinel.ultra_l1c_1,
+                    sentinel.glows_l3e_1,
+                    bin_groups=sentinel.bin_groups,
+                ),
+                call(
+                    sentinel.ultra_l1c_2,
+                    sentinel.glows_l3e_2,
+                    bin_groups=sentinel.bin_groups,
+                ),
+                call(
+                    sentinel.ultra_l1c_3,
+                    sentinel.glows_l3e_3,
+                    bin_groups=sentinel.bin_groups,
+                ),
+            ]
+        )
+        mock_survival_skymap.assert_called_once_with(
+            [sentinel.pset_1, sentinel.pset_2, sentinel.pset_3],
+            SpiceFrame.ECLIPJ2000,
+            input_l2_healpix_map.coords.nside,
+        )
         mock_survival_skymap.return_value.to_dataset.assert_called_once_with()
 
         actual_intensity_map_data = healpix_intensity_map_data.intensity_map_data
@@ -174,20 +209,47 @@ def test_correct_healpix_data_for_survival_probability(
             computed_survival_probabilities,
         )
 
-        np.testing.assert_array_equal(actual_intensity_map_data.epoch, intensity_data.epoch)
-        np.testing.assert_array_equal(actual_intensity_map_data.epoch_delta, intensity_data.epoch_delta)
-        np.testing.assert_array_equal(actual_intensity_map_data.energy, intensity_data.energy)
-        np.testing.assert_array_equal(actual_intensity_map_data.energy_delta_plus, intensity_data.energy_delta_plus)
-        np.testing.assert_array_equal(actual_intensity_map_data.energy_delta_minus,
-                                      intensity_data.energy_delta_minus)
-        np.testing.assert_array_equal(actual_intensity_map_data.energy_label, intensity_data.energy_label)
-        np.testing.assert_array_equal(actual_intensity_map_data.latitude, intensity_data.latitude)
-        np.testing.assert_array_equal(actual_intensity_map_data.longitude, intensity_data.longitude)
-        np.testing.assert_array_equal(actual_intensity_map_data.exposure_factor, intensity_data.exposure_factor)
-        np.testing.assert_array_equal(actual_intensity_map_data.obs_date, intensity_data.obs_date)
-        np.testing.assert_array_equal(actual_intensity_map_data.obs_date_range, intensity_data.obs_date_range)
-        np.testing.assert_array_equal(actual_intensity_map_data.solid_angle, intensity_data.solid_angle)
-        np.testing.assert_array_equal(actual_intensity_map_data.quality_flags, expected_quality_flags)
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.epoch, intensity_data.epoch
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.epoch_delta, intensity_data.epoch_delta
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.energy, intensity_data.energy
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.energy_delta_plus,
+            intensity_data.energy_delta_plus,
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.energy_delta_minus,
+            intensity_data.energy_delta_minus,
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.energy_label, intensity_data.energy_label
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.latitude, intensity_data.latitude
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.longitude, intensity_data.longitude
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.exposure_factor, intensity_data.exposure_factor
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.obs_date, intensity_data.obs_date
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.obs_date_range, intensity_data.obs_date_range
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.solid_angle, intensity_data.solid_angle
+        )
+        np.testing.assert_array_equal(
+            actual_intensity_map_data.quality_flags, expected_quality_flags
+        )
         coords = healpix_intensity_map_data.coords
         np.testing.assert_array_equal(
             coords.pixel_index, input_l2_healpix_map.coords.pixel_index
@@ -209,7 +271,9 @@ def test_correct_healpix_data_for_survival_probability_caches_results(
         mock_survival_skymap,
         mock_healpix_intensity_map_data,
     ):
-        mock_healpix_intensity_map_data.return_value = sentinel.healpix_intensity_map_data
+        mock_healpix_intensity_map_data.return_value = (
+            sentinel.healpix_intensity_map_data
+        )
         rng = np.random.default_rng()
         healpix_indices = np.arange(12)
 
@@ -271,20 +335,25 @@ def test_correct_healpix_data_for_survival_probability_caches_results(
             },
         )
 
-        healpix_intensity_map_data = correct_healpix_data_for_survival_probability(dependencies, SpiceFrame.ECLIPJ2000)
+        healpix_intensity_map_data = correct_healpix_data_for_survival_probability(
+            dependencies, SpiceFrame.ECLIPJ2000
+        )
 
         cache_dir = get_temp_cache_dir()
-        expected_cache_key = (f"['{input_l1c_pset_name_1}', '{input_l1c_pset_name_2}']"
-        f"['{input_glows_l3e_name_1}', '{input_glows_l3e_name_2}']"
-        "a-map-descriptor-nside32")
-        expected_cache_path = cache_dir / sha256(expected_cache_key.encode("utf-8")).hexdigest()
+        expected_cache_key = (
+            f"['{input_l1c_pset_name_1}', '{input_l1c_pset_name_2}']"
+            f"['{input_glows_l3e_name_1}', '{input_glows_l3e_name_2}']"
+            "a-map-descriptor-nside32"
+        )
+        expected_cache_path = (
+            cache_dir / sha256(expected_cache_key.encode("utf-8")).hexdigest()
+        )
 
         with open(expected_cache_path, "rb") as f:
             cached_data = pickle.load(f)
 
         self.assertEqual(healpix_intensity_map_data, cached_data)
 
-
     @patch("imap_l3_processing.ultra.ultra_processor.HealPixIntensityMapData")
     @patch("imap_l3_processing.ultra.ultra_processor.UltraSurvivalProbabilitySkyMap")
     @patch("imap_l3_processing.ultra.ultra_processor.UltraSurvivalProbability")
@@ -305,10 +374,14 @@ def test_correct_healpix_data_for_survival_probability_uses_cached_result(
         input_glows_l3e_name_2 = "imap_glows_l3e_a-glows-descriptor_20250602_v000.cdf"
 
         cache_dir = get_temp_cache_dir()
-        expected_cache_key = (f"['{input_l1c_pset_name_1}', '{input_l1c_pset_name_2}']"
-        f"['{input_glows_l3e_name_1}', '{input_glows_l3e_name_2}']"
-        "a-map-descriptor-nside32")
-        expected_cache_path = cache_dir / sha256(expected_cache_key.encode("utf-8")).hexdigest()
+        expected_cache_key = (
+            f"['{input_l1c_pset_name_1}', '{input_l1c_pset_name_2}']"
+            f"['{input_glows_l3e_name_1}', '{input_glows_l3e_name_2}']"
+            "a-map-descriptor-nside32"
+        )
+        expected_cache_path = (
+            cache_dir / sha256(expected_cache_key.encode("utf-8")).hexdigest()
+        )
         cached_result = sentinel.healpix_intensity_map_data
 
         with open(expected_cache_path, "wb") as f:
@@ -341,7 +414,9 @@ def test_correct_healpix_data_for_survival_probability_uses_cached_result(
             energy_bin_group_sizes=sentinel.bin_groups,
         )
 
-        healpix_intensity_map_data = correct_healpix_data_for_survival_probability(dependencies, SpiceFrame.ECLIPJ2000)
+        healpix_intensity_map_data = correct_healpix_data_for_survival_probability(
+            dependencies, SpiceFrame.ECLIPJ2000
+        )
 
         self.assertEqual(cached_result, healpix_intensity_map_data)
         mock_combine_glows_l3e_with_l1c_pointing.assert_not_called()
@@ -349,13 +424,22 @@ def test_correct_healpix_data_for_survival_probability_uses_cached_result(
         mock_survival_skymap.assert_not_called()
         mock_healpix_intensity_map_data.assert_not_called()
 
-    @patch('imap_l3_processing.ultra.ultra_processor.correct_healpix_data_for_survival_probability')
-    @patch('imap_l3_processing.utils.spiceypy')
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3Dependencies.fetch_dependencies')
-    def _test_process_survival_probability(self, degree_spacing, mock_fetch_dependencies,
-                                           mock_save_data, mock_spiceypy,
-                                           mock_correct_healpix_data_for_survival_probability):
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.correct_healpix_data_for_survival_probability"
+    )
+    @patch("imap_l3_processing.utils.spiceypy")
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3Dependencies.fetch_dependencies"
+    )
+    def _test_process_survival_probability(
+        self,
+        degree_spacing,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_spiceypy,
+        mock_correct_healpix_data_for_survival_probability,
+    ):
         rng = np.random.default_rng()
         healpix_indices = np.arange(12)
         mock_spiceypy.ktotal.return_value = 1
@@ -364,7 +448,9 @@ def _test_process_survival_probability(self, degree_spacing, mock_fetch_dependen
 
         input_map_flux = rng.random((1, 9, 12))
         epoch = datetime.now()
-        input_l2_healpix_map = _create_ultra_l2_healpix_data(epoch=[epoch], flux=input_map_flux, healpix_indices=healpix_indices)
+        input_l2_healpix_map = _create_ultra_l2_healpix_data(
+            epoch=[epoch], flux=input_map_flux, healpix_indices=healpix_indices
+        )
         input_l2_healpix_map.intensity_map_data.energy = sentinel.ultra_l2_energies
         input_l2_rectangular_map = create_rectangular_intensity_map_data()
 
@@ -372,12 +458,14 @@ def _test_process_survival_probability(self, degree_spacing, mock_fetch_dependen
         input_l1c_pset_name = "imap_ultra_l1c_a-pset-descriptor_20250601_v000.cdf"
         input_glows_l3e_name = "imap_glows_l3e_a-glows-descriptor_20250601_v000.cdf"
         input_deps = ProcessingInputCollection(ScienceInput(input_l2_map_name))
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor=f"u90-ena-h-sf-sp-full-hae-{degree_spacing}deg-6mo")
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor=f"u90-ena-h-sf-sp-full-hae-{degree_spacing}deg-6mo",
+        )
 
         mock_fetch_dependencies.return_value = UltraL3Dependencies(
             ultra_l2_healpix_map=input_l2_healpix_map,
@@ -393,19 +481,27 @@ def _test_process_survival_probability(self, degree_spacing, mock_fetch_dependen
         )
 
         rectangular_predicted_ephemeris_data = np.array([0.73, 0.24, 0, 0.99, 1, 0])
-        nominal_alpha_proton_ratio_data = np.full_like(rectangular_predicted_ephemeris_data, 0.0)
-        persisted_last_point_data = np.full_like(rectangular_predicted_ephemeris_data, 0.0)
-
-        expected_quality_flags = np.array([
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NONE
-        ])
-
-        mock_healpix_map_data = mock_correct_healpix_data_for_survival_probability.return_value
+        nominal_alpha_proton_ratio_data = np.full_like(
+            rectangular_predicted_ephemeris_data, 0.0
+        )
+        persisted_last_point_data = np.full_like(
+            rectangular_predicted_ephemeris_data, 0.0
+        )
+
+        expected_quality_flags = np.array(
+            [
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NONE,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NONE,
+            ]
+        )
+
+        mock_healpix_map_data = (
+            mock_correct_healpix_data_for_survival_probability.return_value
+        )
         mock_healpix_skymap = Mock()
         mock_healpix_map_data.to_healpix_skymap = Mock(return_value=mock_healpix_skymap)
 
@@ -423,13 +519,18 @@ def _test_process_survival_probability(self, degree_spacing, mock_fetch_dependen
             "predicted_ephemeris_flag": Mock(
                 values=rectangular_predicted_ephemeris_data
             ),
-            "nominal_alpha_proton_ratio_flag": Mock(values=nominal_alpha_proton_ratio_data),
-            "persisted_last_point_flag": Mock(values=persisted_last_point_data)
+            "nominal_alpha_proton_ratio_flag": Mock(
+                values=nominal_alpha_proton_ratio_data
+            ),
+            "persisted_last_point_flag": Mock(values=persisted_last_point_data),
         }
 
         mock_rectangular_sky_map = Mock(spec=RectangularSkyMap)
         mock_rectangular_sky_map.to_dataset.return_value = mock_rectangular_map_dataset
-        mock_healpix_skymap.to_rectangular_skymap.return_value = mock_rectangular_sky_map, 0
+        mock_healpix_skymap.to_rectangular_skymap.return_value = (
+            mock_rectangular_sky_map,
+            0,
+        )
 
         processor = UltraProcessor(input_deps, input_metadata)
         product = processor.process(SpiceFrame.IMAP_GCS)
@@ -439,69 +540,159 @@ def _test_process_survival_probability(self, degree_spacing, mock_fetch_dependen
             mock_fetch_dependencies.return_value, SpiceFrame.IMAP_GCS
         )
         mock_healpix_map_data.to_healpix_skymap.assert_called_once()
-        mock_healpix_skymap.to_rectangular_skymap.assert_called_once_with(degree_spacing, [
-            "ena_intensity",
-            "ena_intensity_stat_uncert",
-            "ena_intensity_sys_err",
-            "predicted_ephemeris_flag",
-            "nominal_alpha_proton_ratio_flag",
-            "persisted_last_point_flag",
-            "survival_probability",
-        ])
+        mock_healpix_skymap.to_rectangular_skymap.assert_called_once_with(
+            degree_spacing,
+            [
+                "ena_intensity",
+                "ena_intensity_stat_uncert",
+                "ena_intensity_sys_err",
+                "predicted_ephemeris_flag",
+                "nominal_alpha_proton_ratio_flag",
+                "persisted_last_point_flag",
+                "survival_probability",
+            ],
+        )
 
         mock_save_data.assert_called_once()
         actual_rectangular_data_product = mock_save_data.call_args_list[0].args[0]
-        self.assertIsInstance(actual_rectangular_data_product, RectangularIntensityDataProduct)
+        self.assertIsInstance(
+            actual_rectangular_data_product, RectangularIntensityDataProduct
+        )
         self.assertEqual(4, len(actual_rectangular_data_product.parent_file_names))
-        self.assertEqual({input_l2_map_name, fake_spice.name, input_l1c_pset_name, input_glows_l3e_name},
-                         set(actual_rectangular_data_product.parent_file_names))
-        self.assertEqual(SpiceFrame.IMAP_GCS, actual_rectangular_data_product.spice_frame_name)
+        self.assertEqual(
+            {
+                input_l2_map_name,
+                fake_spice.name,
+                input_l1c_pset_name,
+                input_glows_l3e_name,
+            },
+            set(actual_rectangular_data_product.parent_file_names),
+        )
+        self.assertEqual(
+            SpiceFrame.IMAP_GCS, actual_rectangular_data_product.spice_frame_name
+        )
         actual_rectangular_data = actual_rectangular_data_product.data
-        self.assertIsInstance(actual_rectangular_data.intensity_map_data, IntensityMapData)
+        self.assertIsInstance(
+            actual_rectangular_data.intensity_map_data, IntensityMapData
+        )
 
         # @formatter:off
-        self.assertEqual(sentinel.rectangular_ena_intensity, actual_rectangular_data.intensity_map_data.ena_intensity)
-        self.assertEqual(sentinel.rectangular_ena_intensity_stat_uncert, actual_rectangular_data.intensity_map_data.ena_intensity_stat_uncert)
-        self.assertEqual(sentinel.rectangular_ena_intensity_sys_err, actual_rectangular_data.intensity_map_data.ena_intensity_sys_err)
-        self.assertEqual(sentinel.rectangular_survival_probability, actual_rectangular_data.intensity_map_data.survival_probability)
-        np.testing.assert_array_equal(actual_rectangular_data.intensity_map_data.quality_flags, expected_quality_flags, strict=True)
+        self.assertEqual(
+            sentinel.rectangular_ena_intensity,
+            actual_rectangular_data.intensity_map_data.ena_intensity,
+        )
+        self.assertEqual(
+            sentinel.rectangular_ena_intensity_stat_uncert,
+            actual_rectangular_data.intensity_map_data.ena_intensity_stat_uncert,
+        )
+        self.assertEqual(
+            sentinel.rectangular_ena_intensity_sys_err,
+            actual_rectangular_data.intensity_map_data.ena_intensity_sys_err,
+        )
+        self.assertEqual(
+            sentinel.rectangular_survival_probability,
+            actual_rectangular_data.intensity_map_data.survival_probability,
+        )
+        np.testing.assert_array_equal(
+            actual_rectangular_data.intensity_map_data.quality_flags,
+            expected_quality_flags,
+            strict=True,
+        )
 
         expected_passthrough = input_l2_rectangular_map.intensity_map_data
-        self.assertIs(expected_passthrough.epoch, actual_rectangular_data.intensity_map_data.epoch)
-        self.assertIs(expected_passthrough.epoch_delta, actual_rectangular_data.intensity_map_data.epoch_delta)
-        self.assertIs(expected_passthrough.energy, actual_rectangular_data.intensity_map_data.energy)
-        self.assertIs(expected_passthrough.energy_delta_plus, actual_rectangular_data.intensity_map_data.energy_delta_plus)
-        self.assertIs(expected_passthrough.energy_delta_minus, actual_rectangular_data.intensity_map_data.energy_delta_minus)
-        self.assertIs(expected_passthrough.energy_label, actual_rectangular_data.intensity_map_data.energy_label)
-        self.assertIs(expected_passthrough.latitude, actual_rectangular_data.intensity_map_data.latitude)
-        self.assertIs(expected_passthrough.longitude, actual_rectangular_data.intensity_map_data.longitude)
-        self.assertIs(expected_passthrough.exposure_factor, actual_rectangular_data.intensity_map_data.exposure_factor)
-        self.assertIs(expected_passthrough.obs_date, actual_rectangular_data.intensity_map_data.obs_date)
-        self.assertIs(expected_passthrough.obs_date_range, actual_rectangular_data.intensity_map_data.obs_date_range)
-        self.assertIs(expected_passthrough.solid_angle, actual_rectangular_data.intensity_map_data.solid_angle)
+        self.assertIs(
+            expected_passthrough.epoch, actual_rectangular_data.intensity_map_data.epoch
+        )
+        self.assertIs(
+            expected_passthrough.epoch_delta,
+            actual_rectangular_data.intensity_map_data.epoch_delta,
+        )
+        self.assertIs(
+            expected_passthrough.energy,
+            actual_rectangular_data.intensity_map_data.energy,
+        )
+        self.assertIs(
+            expected_passthrough.energy_delta_plus,
+            actual_rectangular_data.intensity_map_data.energy_delta_plus,
+        )
+        self.assertIs(
+            expected_passthrough.energy_delta_minus,
+            actual_rectangular_data.intensity_map_data.energy_delta_minus,
+        )
+        self.assertIs(
+            expected_passthrough.energy_label,
+            actual_rectangular_data.intensity_map_data.energy_label,
+        )
+        self.assertIs(
+            expected_passthrough.latitude,
+            actual_rectangular_data.intensity_map_data.latitude,
+        )
+        self.assertIs(
+            expected_passthrough.longitude,
+            actual_rectangular_data.intensity_map_data.longitude,
+        )
+        self.assertIs(
+            expected_passthrough.exposure_factor,
+            actual_rectangular_data.intensity_map_data.exposure_factor,
+        )
+        self.assertIs(
+            expected_passthrough.obs_date,
+            actual_rectangular_data.intensity_map_data.obs_date,
+        )
+        self.assertIs(
+            expected_passthrough.obs_date_range,
+            actual_rectangular_data.intensity_map_data.obs_date_range,
+        )
+        self.assertIs(
+            expected_passthrough.solid_angle,
+            actual_rectangular_data.intensity_map_data.solid_angle,
+        )
 
         expected_coords = input_l2_rectangular_map.coords
-        self.assertIs(expected_coords.latitude_delta, actual_rectangular_data.coords.latitude_delta)
-        self.assertIs(expected_coords.latitude_label, actual_rectangular_data.coords.latitude_label)
-        self.assertIs(expected_coords.longitude_delta, actual_rectangular_data.coords.longitude_delta)
-        self.assertIs(expected_coords.longitude_label, actual_rectangular_data.coords.longitude_label)
+        self.assertIs(
+            expected_coords.latitude_delta,
+            actual_rectangular_data.coords.latitude_delta,
+        )
+        self.assertIs(
+            expected_coords.latitude_label,
+            actual_rectangular_data.coords.latitude_label,
+        )
+        self.assertIs(
+            expected_coords.longitude_delta,
+            actual_rectangular_data.coords.longitude_delta,
+        )
+        self.assertIs(
+            expected_coords.longitude_label,
+            actual_rectangular_data.coords.longitude_label,
+        )
         # @formatter:on
 
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.ultra.ultra_processor.MapProcessor.get_parent_file_names')
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.MapProcessor.get_parent_file_names"
+    )
     @patch("imap_l3_processing.ultra.ultra_processor.ExposureWeightedCombination")
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3CombinedDependencies.fetch_dependencies')
-    def _test_process_combined_sensor(self, degree_spacing, mock_fetch_dependencies, mock_save_data,
-                                      mock_exposure_weighted_combination, _):
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor=f"ulc-ena-h-sf-nsp-full-hae-{degree_spacing}deg-6mo",
-                                       )
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3CombinedDependencies.fetch_dependencies"
+    )
+    def _test_process_combined_sensor(
+        self,
+        degree_spacing,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_exposure_weighted_combination,
+        _,
+    ):
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor=f"ulc-ena-h-sf-nsp-full-hae-{degree_spacing}deg-6mo",
+        )
 
         mock_fetch_dependencies.return_value = UltraL3CombinedDependencies(
             u45_dependencies=UltraL3Dependencies(
@@ -540,24 +731,40 @@ def _test_process_combined_sensor(self, degree_spacing, mock_fetch_dependencies,
         )
 
         predicted_ephemeris_rectangular_values = np.array([0.243, 0, 1, 0, 0.993, 0])
-        nominal_alpha_proton_ratio_rectangular_values = np.array([0, 0.34, 0.8, 0, 0, 0])
+        nominal_alpha_proton_ratio_rectangular_values = np.array(
+            [0, 0.34, 0.8, 0, 0, 0]
+        )
         persisted_last_point_rectangular_values = np.array([0, 0, 0.4, 0, 0, 1])
 
-        expected_quality_flags = np.array([
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
-            MapL3Flags.PREDICTIVE_EPHEMERIS | MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO | MapL3Flags.PERSISTED_LAST_POINT,
-            MapL3Flags.NONE,
-            MapL3Flags.PREDICTIVE_EPHEMERIS,
-            MapL3Flags.PERSISTED_LAST_POINT
-        ])
+        expected_quality_flags = np.array(
+            [
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO,
+                MapL3Flags.PREDICTIVE_EPHEMERIS
+                | MapL3Flags.NOMINAL_ALPHA_PROTON_RATIO
+                | MapL3Flags.PERSISTED_LAST_POINT,
+                MapL3Flags.NONE,
+                MapL3Flags.PREDICTIVE_EPHEMERIS,
+                MapL3Flags.PERSISTED_LAST_POINT,
+            ]
+        )
         converted_rectangular_skymap.to_dataset.return_value = {
             "ena_intensity": Mock(values=sentinel.rectangular_ena_intensity),
-            "ena_intensity_stat_uncert": Mock(values=sentinel.rectangular_ena_intensity_stat_uncert),
-            "ena_intensity_sys_err": Mock(values=sentinel.rectangular_ena_intensity_sys_err),
-            "predicted_ephemeris_flag": Mock(values=predicted_ephemeris_rectangular_values),
-            "nominal_alpha_proton_ratio_flag": Mock(values=nominal_alpha_proton_ratio_rectangular_values),
-            "persisted_last_point_flag": Mock(values=persisted_last_point_rectangular_values),
+            "ena_intensity_stat_uncert": Mock(
+                values=sentinel.rectangular_ena_intensity_stat_uncert
+            ),
+            "ena_intensity_sys_err": Mock(
+                values=sentinel.rectangular_ena_intensity_sys_err
+            ),
+            "predicted_ephemeris_flag": Mock(
+                values=predicted_ephemeris_rectangular_values
+            ),
+            "nominal_alpha_proton_ratio_flag": Mock(
+                values=nominal_alpha_proton_ratio_rectangular_values
+            ),
+            "persisted_last_point_flag": Mock(
+                values=persisted_last_point_rectangular_values
+            ),
         }
 
         processor = UltraProcessor(sentinel.dependencies, input_metadata)
@@ -566,7 +773,8 @@ def _test_process_combined_sensor(self, degree_spacing, mock_fetch_dependencies,
         mock_fetch_dependencies.assert_called_once_with(sentinel.dependencies)
 
         mock_exposure_weighted_combination.return_value.combine_healpix_intensity_map_data.assert_called_once_with(
-            [sentinel.u45_l2_healpix_map, sentinel.u90_l2_healpix_map])
+            [sentinel.u45_l2_healpix_map, sentinel.u90_l2_healpix_map]
+        )
 
         mock_exposure_weighted_combination.return_value.combine_rectangular_intensity_map_data.assert_called_once_with(
             [sentinel.u45_l2_rectangular_map, sentinel.u90_l2_rectangular_map]
@@ -583,13 +791,14 @@ def _test_process_combined_sensor(self, degree_spacing, mock_fetch_dependencies,
                 "predicted_ephemeris_flag",
                 "nominal_alpha_proton_ratio_flag",
                 "persisted_last_point_flag",
-            ]
+            ],
         )
 
         actual_data_product = mock_save_data.call_args_list[0].args[0]
 
         self.assertEqual(
-            sentinel.rectangular_ena_intensity, actual_data_product.data.intensity_map_data.ena_intensity
+            sentinel.rectangular_ena_intensity,
+            actual_data_product.data.intensity_map_data.ena_intensity,
         )
         self.assertEqual(
             sentinel.rectangular_ena_intensity_stat_uncert,
@@ -599,41 +808,88 @@ def _test_process_combined_sensor(self, degree_spacing, mock_fetch_dependencies,
             sentinel.rectangular_ena_intensity_sys_err,
             actual_data_product.data.intensity_map_data.ena_intensity_sys_err,
         )
-        np.testing.assert_array_equal(actual_data_product.data.intensity_map_data.quality_flags, expected_quality_flags)
-        self.assertEqual(rectangular_combination_return_value.intensity_map_data.obs_date, actual_data_product.data.intensity_map_data.obs_date)
-        self.assertEqual(rectangular_combination_return_value.intensity_map_data.obs_date_range, actual_data_product.data.intensity_map_data.obs_date_range)
-        self.assertEqual(rectangular_combination_return_value.intensity_map_data.solid_angle, actual_data_product.data.intensity_map_data.solid_angle)
-        self.assertEqual(rectangular_combination_return_value.intensity_map_data.exposure_factor, actual_data_product.data.intensity_map_data.exposure_factor)
-        self.assertEqual(rectangular_combination_return_value.intensity_map_data.longitude, actual_data_product.data.intensity_map_data.longitude)
-        self.assertEqual(rectangular_combination_return_value.intensity_map_data.latitude, actual_data_product.data.intensity_map_data.latitude)
+        np.testing.assert_array_equal(
+            actual_data_product.data.intensity_map_data.quality_flags,
+            expected_quality_flags,
+        )
+        self.assertEqual(
+            rectangular_combination_return_value.intensity_map_data.obs_date,
+            actual_data_product.data.intensity_map_data.obs_date,
+        )
+        self.assertEqual(
+            rectangular_combination_return_value.intensity_map_data.obs_date_range,
+            actual_data_product.data.intensity_map_data.obs_date_range,
+        )
+        self.assertEqual(
+            rectangular_combination_return_value.intensity_map_data.solid_angle,
+            actual_data_product.data.intensity_map_data.solid_angle,
+        )
+        self.assertEqual(
+            rectangular_combination_return_value.intensity_map_data.exposure_factor,
+            actual_data_product.data.intensity_map_data.exposure_factor,
+        )
+        self.assertEqual(
+            rectangular_combination_return_value.intensity_map_data.longitude,
+            actual_data_product.data.intensity_map_data.longitude,
+        )
+        self.assertEqual(
+            rectangular_combination_return_value.intensity_map_data.latitude,
+            actual_data_product.data.intensity_map_data.latitude,
+        )
 
         self.assertEqual([mock_save_data.return_value], product)
-        self.assertEqual(['u45_map', 'u45_l1c', 'u90_map', 'u90_l1c'], actual_data_product.parent_file_names)
+        self.assertEqual(
+            ["u45_map", "u45_l1c", "u90_map", "u90_l1c"],
+            actual_data_product.parent_file_names,
+        )
 
-    @patch('imap_l3_processing.ultra.ultra_processor.correct_healpix_data_for_survival_probability')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraProcessor._process_healpix_intensity_to_rectangular')
-    @patch('imap_l3_processing.ultra.ultra_processor.MapProcessor.get_parent_file_names')
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.correct_healpix_data_for_survival_probability"
+    )
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraProcessor._process_healpix_intensity_to_rectangular"
+    )
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.MapProcessor.get_parent_file_names"
+    )
     @patch("imap_l3_processing.ultra.ultra_processor.UncertaintyWeightedCombination")
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3CombinedDependencies.fetch_dependencies')
-    def _test_process_combined_sensor_survival_probability(self, degree_spacing, mock_fetch_dependencies,
-                                                           mock_save_data, mock_uncertainty_weighted_combination,
-                                                           mock_get_parent_file_names, mock_healpix_to_rectangular,
-                                                           mock_correct_healpix_data):
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3CombinedDependencies.fetch_dependencies"
+    )
+    def _test_process_combined_sensor_survival_probability(
+        self,
+        degree_spacing,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_uncertainty_weighted_combination,
+        mock_get_parent_file_names,
+        mock_healpix_to_rectangular,
+        mock_correct_healpix_data,
+    ):
         mock_get_parent_file_names.return_value = ["ram_map", "antiram_map"]
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor=f"ulc-ena-h-sf-sp-full-hae-{degree_spacing}deg-6mo",
-                                       )
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor=f"ulc-ena-h-sf-sp-full-hae-{degree_spacing}deg-6mo",
+        )
         mock_fetch_dependencies.return_value = UltraL3CombinedDependencies(
             u45_dependencies=UltraL3Dependencies(
                 ultra_l2_healpix_map=sentinel.u45_l2_healpix_map,
                 ultra_l2_rectangular_map=sentinel.u45_l2_rectangular_map,
-                ultra_l1c_pset=[sentinel.u45_l1c_1, sentinel.u45_l1c_2, sentinel.u45_l1c_3],
-                glows_l3e_sp=[sentinel.glows_pset_1, sentinel.glows_pset_2, sentinel.glows_pset_3],
+                ultra_l1c_pset=[
+                    sentinel.u45_l1c_1,
+                    sentinel.u45_l1c_2,
+                    sentinel.u45_l1c_3,
+                ],
+                glows_l3e_sp=[
+                    sentinel.glows_pset_1,
+                    sentinel.glows_pset_2,
+                    sentinel.glows_pset_3,
+                ],
                 energy_bin_group_sizes=sentinel.energy_bin_sizes,
                 dependency_file_paths=[
                     Path("folder/u45_map"),
@@ -643,8 +899,16 @@ def _test_process_combined_sensor_survival_probability(self, degree_spacing, moc
             u90_dependencies=UltraL3Dependencies(
                 ultra_l2_healpix_map=sentinel.u90_l2_healpix_map,
                 ultra_l2_rectangular_map=sentinel.u90_l2_rectangular_map,
-                ultra_l1c_pset=[sentinel.u90_l1c_1, sentinel.u90_l1c_2, sentinel.u90_l1c_3],
-                glows_l3e_sp=[sentinel.glows_pset_1, sentinel.glows_pset_2, sentinel.glows_pset_3],
+                ultra_l1c_pset=[
+                    sentinel.u90_l1c_1,
+                    sentinel.u90_l1c_2,
+                    sentinel.u90_l1c_3,
+                ],
+                glows_l3e_sp=[
+                    sentinel.glows_pset_1,
+                    sentinel.glows_pset_2,
+                    sentinel.glows_pset_3,
+                ],
                 energy_bin_group_sizes=sentinel.energy_bin_sizes,
                 dependency_file_paths=[
                     Path("folder/u90_map"),
@@ -656,8 +920,10 @@ def _test_process_combined_sensor_survival_probability(self, degree_spacing, moc
         mock_combination_strategy = Mock()
         mock_uncertainty_weighted_combination.return_value = mock_combination_strategy
 
-        mock_correct_healpix_data.side_effect = [sentinel.u45_l2_survival_corrected_map,
-                                                 sentinel.u90_l2_survival_corrected_map]
+        mock_correct_healpix_data.side_effect = [
+            sentinel.u45_l2_survival_corrected_map,
+            sentinel.u90_l2_survival_corrected_map,
+        ]
 
         processor = UltraProcessor(sentinel.dependencies, input_metadata)
         product = processor.process(spice_frame_name=sentinel.spice_frame)
@@ -665,41 +931,69 @@ def _test_process_combined_sensor_survival_probability(self, degree_spacing, moc
         mock_fetch_dependencies.assert_called_once_with(sentinel.dependencies)
 
         mock_combination_strategy.combine_healpix_intensity_map_data.assert_called_once_with(
-            [sentinel.u45_l2_survival_corrected_map, sentinel.u90_l2_survival_corrected_map])
+            [
+                sentinel.u45_l2_survival_corrected_map,
+                sentinel.u90_l2_survival_corrected_map,
+            ]
+        )
         mock_combination_strategy.combine_rectangular_intensity_map_data.assert_called_once_with(
-            [sentinel.u45_l2_rectangular_map, sentinel.u90_l2_rectangular_map])
+            [sentinel.u45_l2_rectangular_map, sentinel.u90_l2_rectangular_map]
+        )
 
         mock_healpix_to_rectangular.assert_called_once_with(
             mock_combination_strategy.combine_healpix_intensity_map_data.return_value,
             mock_combination_strategy.combine_rectangular_intensity_map_data.return_value,
             degree_spacing,
-            spice_frame_name=sentinel.spice_frame)
+            spice_frame_name=sentinel.spice_frame,
+        )
 
         mock_save_data.assert_called_once_with(mock_healpix_to_rectangular.return_value)
         self.assertEqual([mock_save_data.return_value], product)
 
-        mock_correct_healpix_data.assert_has_calls([
-            call(mock_fetch_dependencies.return_value.u45_dependencies, sentinel.spice_frame),
-            call(mock_fetch_dependencies.return_value.u90_dependencies, sentinel.spice_frame)
-        ])
-        mock_healpix_to_rectangular.return_value.add_paths_to_parents.assert_has_calls([
-            call([
-                Path("folder/u45_map"),
-                Path("folder/u45_l1c"),
-            ]),
-            call([
-                Path("folder/u90_map"),
-                Path("folder/u90_l1c"),
-            ]),
-        ])
-
-    @patch('imap_l3_processing.ultra.ultra_processor.correct_healpix_data_for_survival_probability')
-    @patch('imap_l3_processing.processor.spiceypy')
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3Dependencies.fetch_dependencies')
-    def test_defaults_to_ECLIPJ2000_spice_frame(self, mock_fetch_dependencies,
-                                                mock_save_data, mock_spiceypy,
-                                                mock_correct_for_survival_probability):
+        mock_correct_healpix_data.assert_has_calls(
+            [
+                call(
+                    mock_fetch_dependencies.return_value.u45_dependencies,
+                    sentinel.spice_frame,
+                ),
+                call(
+                    mock_fetch_dependencies.return_value.u90_dependencies,
+                    sentinel.spice_frame,
+                ),
+            ]
+        )
+        mock_healpix_to_rectangular.return_value.add_paths_to_parents.assert_has_calls(
+            [
+                call(
+                    [
+                        Path("folder/u45_map"),
+                        Path("folder/u45_l1c"),
+                    ]
+                ),
+                call(
+                    [
+                        Path("folder/u90_map"),
+                        Path("folder/u90_l1c"),
+                    ]
+                ),
+            ]
+        )
+
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.correct_healpix_data_for_survival_probability"
+    )
+    @patch("imap_l3_processing.processor.spiceypy")
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3Dependencies.fetch_dependencies"
+    )
+    def test_defaults_to_ECLIPJ2000_spice_frame(
+        self,
+        mock_fetch_dependencies,
+        mock_save_data,
+        mock_spiceypy,
+        mock_correct_for_survival_probability,
+    ):
         rng = np.random.default_rng()
         healpix_indices = np.arange(12)
         input_map_flux = rng.random((1, 9, 12))
@@ -710,7 +1004,9 @@ def test_defaults_to_ECLIPJ2000_spice_frame(self, mock_fetch_dependencies,
         fake_spice = Path("path/to/fake/spice.tls")
         mock_spiceypy.kdata.return_value = [fake_spice]
 
-        input_l2_map = _create_ultra_l2_healpix_data(epoch=[epoch], flux=input_map_flux, healpix_indices=healpix_indices)
+        input_l2_map = _create_ultra_l2_healpix_data(
+            epoch=[epoch], flux=input_map_flux, healpix_indices=healpix_indices
+        )
         input_l2_rectangular_map = create_rectangular_intensity_map_data()
 
         input_l2_map.intensity_map_data.energy = sentinel.ultra_l2_energies
@@ -726,25 +1022,38 @@ def test_defaults_to_ECLIPJ2000_spice_frame(self, mock_fetch_dependencies,
             ultra_l2_rectangular_map=input_l2_rectangular_map,
             ultra_l1c_pset=sentinel.ultra_l1c_pset,
             glows_l3e_sp=sentinel.glows_l3e_sp,
-            dependency_file_paths=[Path(input_l2_map_name), Path(input_l1c_pset_name), Path(input_glows_l3e_name)],
-            energy_bin_group_sizes=None
+            dependency_file_paths=[
+                Path(input_l2_map_name),
+                Path(input_l1c_pset_name),
+                Path(input_glows_l3e_name),
+            ],
+            energy_bin_group_sizes=None,
+        )
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="",
+            descriptor="u90-ena-h-sf-sp-full-hae-2deg-6mo",
         )
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="",
-                                       descriptor=f"u90-ena-h-sf-sp-full-hae-2deg-6mo"
-                                       )
 
         mock_healpix_skymap = Mock()
-        mock_correct_for_survival_probability.return_value.to_healpix_skymap = Mock(return_value=mock_healpix_skymap)
+        mock_correct_for_survival_probability.return_value.to_healpix_skymap = Mock(
+            return_value=mock_healpix_skymap
+        )
 
         mock_rectangular_map_dataset = {
             "ena_intensity": Mock(values=sentinel.rectangular_ena_intensity),
-            "ena_intensity_stat_uncert": Mock(values=sentinel.rectangular_ena_intensity_stat_unc),
-            "ena_intensity_sys_err": Mock(values=sentinel.rectangular_ena_intensity_sys_err),
-            "survival_probability": Mock(values=sentinel.rectangular_survival_probability),
+            "ena_intensity_stat_uncert": Mock(
+                values=sentinel.rectangular_ena_intensity_stat_unc
+            ),
+            "ena_intensity_sys_err": Mock(
+                values=sentinel.rectangular_ena_intensity_sys_err
+            ),
+            "survival_probability": Mock(
+                values=sentinel.rectangular_survival_probability
+            ),
             "predicted_ephemeris_flag": Mock(values=np.array([])),
             "nominal_alpha_proton_ratio_flag": Mock(values=np.array([])),
             "persisted_last_point_flag": Mock(values=np.array([])),
@@ -752,39 +1061,55 @@ def test_defaults_to_ECLIPJ2000_spice_frame(self, mock_fetch_dependencies,
 
         mock_rectangular_sky_map = Mock(spec=RectangularSkyMap)
         mock_rectangular_sky_map.to_dataset.return_value = mock_rectangular_map_dataset
-        mock_healpix_skymap.to_rectangular_skymap.return_value = mock_rectangular_sky_map, 0
+        mock_healpix_skymap.to_rectangular_skymap.return_value = (
+            mock_rectangular_sky_map,
+            0,
+        )
 
         processor = UltraProcessor(input_deps, input_metadata)
         processor.process()
 
         mock_correct_for_survival_probability.assert_called_once_with(
-            mock_fetch_dependencies.return_value,
-            SpiceFrame.ECLIPJ2000
+            mock_fetch_dependencies.return_value, SpiceFrame.ECLIPJ2000
         )
 
-    @patch('imap_l3_processing.processor.spiceypy')
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.ultra.ultra_processor.calculate_spectral_index_for_multiple_ranges')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3SpectralIndexDependencies.fetch_dependencies')
-    def _test_process_spectral_index(self, degree_spacing,
-                                     mock_fetch_dependencies,
-                                     mock_calculate_spectral_index, mock_save_data,
-                                     mock_spiceypy):
+    @patch("imap_l3_processing.processor.spiceypy")
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.calculate_spectral_index_for_multiple_ranges"
+    )
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3SpectralIndexDependencies.fetch_dependencies"
+    )
+    def _test_process_spectral_index(
+        self,
+        degree_spacing,
+        mock_fetch_dependencies,
+        mock_calculate_spectral_index,
+        mock_save_data,
+        mock_spiceypy,
+    ):
 
         mock_spiceypy.ktotal.return_value = 0
 
-        map_file_name = 'imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf'
-        energy_range_file_name = 'imap_ultra_energy-range-descriptor_20250601_v000.dat'
-        input_deps = ProcessingInputCollection(ScienceInput(map_file_name), AncillaryInput(energy_range_file_name))
+        map_file_name = "imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf"
+        energy_range_file_name = "imap_ultra_energy-range-descriptor_20250601_v000.dat"
+        input_deps = ProcessingInputCollection(
+            ScienceInput(map_file_name), AncillaryInput(energy_range_file_name)
+        )
 
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="v000",
-                                       descriptor=f"u90-spx-h-sf-sp-full-hae-{degree_spacing}deg-6mo")
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="v000",
+            descriptor=f"u90-spx-h-sf-sp-full-hae-{degree_spacing}deg-6mo",
+        )
         input_map_data = RectangularIntensityMapData(Mock(), Mock())
-        dependencies = UltraL3SpectralIndexDependencies(input_map_data, sentinel.energy_ranges)
+        dependencies = UltraL3SpectralIndexDependencies(
+            input_map_data, sentinel.energy_ranges
+        )
         mock_fetch_dependencies.return_value = dependencies
 
         mock_spectral_index_map_data = Mock(spec=SpectralIndexMapData)
@@ -797,112 +1122,188 @@ def _test_process_spectral_index(self, degree_spacing,
 
         mock_save_data.assert_called_once()
         actual_rectangular_data_product = mock_save_data.call_args_list[0].args[0]
-        self.assertIsInstance(actual_rectangular_data_product, RectangularSpectralIndexDataProduct)
-        self.assertEqual(expected_parent_file_names, actual_rectangular_data_product.parent_file_names)
-        self.assertEqual(processor.input_metadata, actual_rectangular_data_product.input_metadata)
-        self.assertEqual(SpiceFrame.ECLIPJ2000, actual_rectangular_data_product.spice_frame_name)
+        self.assertIsInstance(
+            actual_rectangular_data_product, RectangularSpectralIndexDataProduct
+        )
+        self.assertEqual(
+            expected_parent_file_names,
+            actual_rectangular_data_product.parent_file_names,
+        )
+        self.assertEqual(
+            processor.input_metadata, actual_rectangular_data_product.input_metadata
+        )
+        self.assertEqual(
+            SpiceFrame.ECLIPJ2000, actual_rectangular_data_product.spice_frame_name
+        )
 
-        self.assertIsInstance(actual_rectangular_data_product, RectangularSpectralIndexDataProduct)
+        self.assertIsInstance(
+            actual_rectangular_data_product, RectangularSpectralIndexDataProduct
+        )
 
-        actual_rectangular_data: RectangularSpectralIndexMapData = actual_rectangular_data_product.data
-        self.assertIsInstance(actual_rectangular_data_product.data, RectangularSpectralIndexMapData)
-        self.assertIs(mock_spectral_index_map_data, actual_rectangular_data.spectral_index_map_data)
+        actual_rectangular_data: RectangularSpectralIndexMapData = (
+            actual_rectangular_data_product.data
+        )
+        self.assertIsInstance(
+            actual_rectangular_data_product.data, RectangularSpectralIndexMapData
+        )
+        self.assertIs(
+            mock_spectral_index_map_data,
+            actual_rectangular_data.spectral_index_map_data,
+        )
         self.assertIs(input_map_data.coords, actual_rectangular_data.coords)
 
         mock_fetch_dependencies.assert_called_once_with(input_deps)
-        mock_calculate_spectral_index.assert_called_once_with(dependencies.map_data.intensity_map_data,
-                                                              sentinel.energy_ranges)
+        mock_calculate_spectral_index.assert_called_once_with(
+            dependencies.map_data.intensity_map_data, sentinel.energy_ranges
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.processor.spiceypy')
-    @patch('imap_l3_processing.ultra.ultra_processor.calculate_spectral_index_for_multiple_ranges')
-    @patch('imap_l3_processing.ultra.ultra_processor.fit_spectral_index_map')
-    @patch('imap_l3_processing.ultra.ultra_processor.slice_energy_range_by_bin')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3SpectralIndexDependencies.fetch_dependencies')
-    def test_process_spectral_index_with_custom_energy_bin_range(self,
-                                     mock_fetch_dependencies, mock_slice_energy_range_by_bin, mock_fit_spectral_index_map,
-                                     mock_calculate_spectral_index_for_multiple_ranges, mock_spiceypy, mock_save_data
-                                     ):
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch("imap_l3_processing.processor.spiceypy")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.calculate_spectral_index_for_multiple_ranges"
+    )
+    @patch("imap_l3_processing.ultra.ultra_processor.fit_spectral_index_map")
+    @patch("imap_l3_processing.ultra.ultra_processor.slice_energy_range_by_bin")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3SpectralIndexDependencies.fetch_dependencies"
+    )
+    def test_process_spectral_index_with_custom_energy_bin_range(
+        self,
+        mock_fetch_dependencies,
+        mock_slice_energy_range_by_bin,
+        mock_fit_spectral_index_map,
+        mock_calculate_spectral_index_for_multiple_ranges,
+        mock_spiceypy,
+        mock_save_data,
+    ):
 
         mock_spiceypy.ktotal.return_value = 0
 
-        map_file_name = 'imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf'
-        energy_range_file_name = 'imap_ultra_energy-range-descriptor_20250601_v000.dat'
-        input_deps = ProcessingInputCollection(ScienceInput(map_file_name), AncillaryInput(energy_range_file_name))
+        map_file_name = "imap_ultra_l3_ultra-cool-descriptor_20250601_v000.cdf"
+        energy_range_file_name = "imap_ultra_energy-range-descriptor_20250601_v000.dat"
+        input_deps = ProcessingInputCollection(
+            ScienceInput(map_file_name), AncillaryInput(energy_range_file_name)
+        )
 
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="v000",
-                                       descriptor=f"u90-spx0510-h-sf-sp-full-hae-4deg-6mo")
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="v000",
+            descriptor="u90-spx0510-h-sf-sp-full-hae-4deg-6mo",
+        )
         dependencies = mock_fetch_dependencies.return_value
 
         processor = UltraProcessor(input_deps, input_metadata)
         result = processor.process()
 
-        mock_slice_energy_range_by_bin.assert_called_once_with(dependencies.map_data.intensity_map_data, 5, 10)
-        mock_fit_spectral_index_map.assert_called_once_with(mock_slice_energy_range_by_bin.return_value)
+        mock_slice_energy_range_by_bin.assert_called_once_with(
+            dependencies.map_data.intensity_map_data, 5, 10
+        )
+        mock_fit_spectral_index_map.assert_called_once_with(
+            mock_slice_energy_range_by_bin.return_value
+        )
 
         mock_calculate_spectral_index_for_multiple_ranges.assert_not_called()
         mock_save_data.assert_called_once()
         [product] = mock_save_data.call_args.args
         self.assertIsInstance(product, RectangularSpectralIndexDataProduct)
 
-        self.assertEqual(mock_fit_spectral_index_map.return_value, product.data.spectral_index_map_data)
+        self.assertEqual(
+            mock_fit_spectral_index_map.return_value,
+            product.data.spectral_index_map_data,
+        )
 
     def test_process_raises_exception_when_generating_a_healpix_map(self):
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="v000",
-                                       descriptor=f"u90-spx-h-sf-sp-full-hae-nside8-6mo")
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="v000",
+            descriptor="u90-spx-h-sf-sp-full-hae-nside8-6mo",
+        )
 
         processor = UltraProcessor(ProcessingInputCollection(), input_metadata)
 
         with self.assertRaises(NotImplementedError):
             processor.process()
 
-    @patch('imap_l3_processing.ultra.ultra_processor.save_data')
-    @patch('imap_l3_processing.ultra.ultra_processor.UltraL3SpectralIndexDependencies.fetch_dependencies')
-    def test_process_spectral_index_validating_output_values(self, mock_fetch_dependencies, mock_save_data):
-        input_metadata = InputMetadata(instrument="ultra",
-                                       data_level="l3",
-                                       start_date=datetime.now(),
-                                       end_date=datetime.now() + timedelta(days=1),
-                                       version="v000",
-                                       descriptor=f"u90-spx-h-sf-sp-full-hae-6deg-6mo")
-        input_map_path = get_test_data_path('ultra/fake_ultra_map_data_with_breakpoint_at_15keV.cdf')
-        fit_energy_ranges_path = get_test_data_path('ultra/imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat')
-        dependencies = UltraL3SpectralIndexDependencies.from_file_paths(input_map_path, fit_energy_ranges_path)
+    @patch("imap_l3_processing.ultra.ultra_processor.save_data")
+    @patch(
+        "imap_l3_processing.ultra.ultra_processor.UltraL3SpectralIndexDependencies.fetch_dependencies"
+    )
+    def test_process_spectral_index_validating_output_values(
+        self, mock_fetch_dependencies, mock_save_data
+    ):
+        input_metadata = InputMetadata(
+            instrument="ultra",
+            data_level="l3",
+            start_date=datetime.now(),
+            end_date=datetime.now() + timedelta(days=1),
+            version="v000",
+            descriptor="u90-spx-h-sf-sp-full-hae-6deg-6mo",
+        )
+        input_map_path = get_test_data_path(
+            "ultra/fake_ultra_map_data_with_breakpoint_at_15keV.cdf"
+        )
+        fit_energy_ranges_path = get_test_data_path(
+            "ultra/imap_ultra_ulc-spx-energy-ranges_20250407_v000.dat"
+        )
+        dependencies = UltraL3SpectralIndexDependencies.from_file_paths(
+            input_map_path, fit_energy_ranges_path
+        )
         mock_fetch_dependencies.return_value = dependencies
 
-        expected_ena_spectral_index = np.array([2] * (60 * 30) + [3.5] * (60 * 30)).reshape(1, 2, 60, 30)
+        expected_ena_spectral_index = np.array(
+            [2] * (60 * 30) + [3.5] * (60 * 30)
+        ).reshape(1, 2, 60, 30)
 
         processing_input_collection = ProcessingInputCollection()
         processor = UltraProcessor(processing_input_collection, input_metadata)
         product = processor.process()
 
-        actual_data_product: RectangularSpectralIndexDataProduct = mock_save_data.call_args[0][0]
+        actual_data_product: RectangularSpectralIndexDataProduct = (
+            mock_save_data.call_args[0][0]
+        )
 
-        np.testing.assert_array_almost_equal(actual_data_product.data.spectral_index_map_data.ena_spectral_index,
-                                             expected_ena_spectral_index)
+        np.testing.assert_array_almost_equal(
+            actual_data_product.data.spectral_index_map_data.ena_spectral_index,
+            expected_ena_spectral_index,
+        )
         self.assertEqual([mock_save_data.return_value], product)
 
-def _create_ultra_l2_healpix_data(epoch=None, lon=None, lat=None, energy=None, energy_delta=None, flux=None,
-                                  intensity_stat_unc=None, healpix_indices=None) -> HealPixIntensityMapData:
+
+def _create_ultra_l2_healpix_data(
+    epoch=None,
+    lon=None,
+    lat=None,
+    energy=None,
+    energy_delta=None,
+    flux=None,
+    intensity_stat_unc=None,
+    healpix_indices=None,
+) -> HealPixIntensityMapData:
     epoch = epoch if epoch is not None else np.array([datetime.now()])
     lon = lon if lon is not None else np.array([1.0])
     lat = lat if lat is not None else np.array([1.0])
     healpix_indices = healpix_indices if healpix_indices is not None else np.arange(12)
     energy = energy if energy is not None else np.array([1.0])
-    energy_delta = energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
-    flux = flux if flux is not None else np.full((len(epoch), len(energy), len(healpix_indices)), fill_value=1)
-    intensity_stat_uncert = intensity_stat_unc if intensity_stat_unc is not None else np.full(
-        flux.shape,
-        fill_value=1)
+    energy_delta = (
+        energy_delta if energy_delta is not None else np.full((len(energy), 2), 1)
+    )
+    flux = (
+        flux
+        if flux is not None
+        else np.full((len(epoch), len(energy), len(healpix_indices)), fill_value=1)
+    )
+    intensity_stat_uncert = (
+        intensity_stat_unc
+        if intensity_stat_unc is not None
+        else np.full(flux.shape, fill_value=1)
+    )
 
     if isinstance(flux, np.ndarray):
         more_real_flux = flux
@@ -926,10 +1327,10 @@ def _create_ultra_l2_healpix_data(epoch=None, lon=None, lat=None, energy=None, e
             ena_intensity=flux,
             ena_intensity_stat_uncert=intensity_stat_uncert,
             ena_intensity_sys_err=np.full_like(flux, 0),
-            quality_flags=np.full_like(flux, MapL3Flags.NONE)
+            quality_flags=np.full_like(flux, MapL3Flags.NONE),
         ),
         HealPixCoords(
             pixel_index=healpix_indices,
-            pixel_index_label=np.full(healpix_indices.shape, "healpix index label")
-        )
-    )
\ No newline at end of file
+            pixel_index_label=np.full(healpix_indices.shape, "healpix index label"),
+        ),
+    )
diff --git a/tests/ultra/test_ultra_sp_initializer.py b/tests/ultra/test_ultra_sp_initializer.py
index 1f7e5e69e..eba2564bb 100644
--- a/tests/ultra/test_ultra_sp_initializer.py
+++ b/tests/ultra/test_ultra_sp_initializer.py
@@ -1,14 +1,20 @@
 import unittest
 from datetime import datetime
-from unittest.mock import patch, call
+from unittest.mock import call, patch
 
 from imap_data_access.file_validation import Version
 
-from imap_l3_processing.glows.descriptors import GLOWS_L3E_ULTRA_SF_DESCRIPTOR, GLOWS_L3E_ULTRA_HF_DESCRIPTOR
+from imap_l3_processing.glows.descriptors import (
+    GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+    GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+)
 from imap_l3_processing.maps.map_initializer import PossibleMapToProduce
 from imap_l3_processing.maps.sp_map_initializer import SPMapInitializer
 from imap_l3_processing.models import InputMetadata, VersionMap
-from imap_l3_processing.ultra.ultra_sp_initializer import UltraSPInitializer, ULTRA_SP_SPICE_KERNELS
+from imap_l3_processing.ultra.ultra_sp_initializer import (
+    ULTRA_SP_SPICE_KERNELS,
+    UltraSPInitializer,
+)
 from tests.integration.integration_test_helpers import ImapQueryPatcher
 
 
@@ -17,116 +23,146 @@ def test_is_map_initializer(self):
         self.assertIsInstance(UltraSPInitializer(), SPMapInitializer)
 
     def setUp(self):
-        self.mock_query_patcher = patch('imap_l3_processing.ultra.ultra_sp_initializer.imap_data_access.query')
+        self.mock_query_patcher = patch(
+            "imap_l3_processing.ultra.ultra_sp_initializer.imap_data_access.query"
+        )
         self.mock_query = self.mock_query_patcher.start()
 
     def tearDown(self):
         self.mock_query_patcher.stop()
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
     def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
-        sensors = [
-            "90",
-            "45"
-        ]
+        sensors = ["90", "45"]
 
         for sensor in sensors:
             self.mock_query.reset_mock()
             with self.subTest(sensor=sensor):
-                self.mock_query.side_effect = ImapQueryPatcher([
-                    'imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf',
-                    'imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf',
-                    'imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf',
-
-                    'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf',
-                    'imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf',
-                    'imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf',
-                    'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf',
-
-                    'imap_glows_l3e_survival-probability-ul-sf_20100703-repoint00201_v001.cdf',
-
-                    f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                    f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                    f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v000.cdf',
-                    f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-                    f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf',
-
-                    f'imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf',
-                    f'imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf',
-                    'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-                ])
+                self.mock_query.side_effect = ImapQueryPatcher(
+                    [
+                        "imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100703-repoint00201_v001.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v000.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf",
+                        f"imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        f"imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    ]
+                )
 
                 mock_read_cdf_parents.side_effect = [
                     [
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100103-repoint00003_v001.cdf"
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
                     ],
                     [
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100403-repoint00103_v001.cdf'
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
                     ],
                     [
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
-                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100703-repoint00203_v001.cdf"
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
                     ],
                     [
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20101001-repoint00301_v001.cdf",
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20101002-repoint00302_v001.cdf",
-                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20101003-repoint00303_v001.cdf"
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20101003-repoint00303_v001.cdf",
                     ],
                     [
-                        'imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf',
-                        'imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf',
-                        'imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf',
+                        "imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf",
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
                         f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-                        f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                        'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
                     ],
                     [
-                        'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf',
-                        'imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v001.cdf',
-                        'imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                        f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                        'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
-                    ]
+                        "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v001.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    ],
                 ]
 
                 initializer = UltraSPInitializer()
                 actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
-                    f"u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo", None)
+                    f"u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo", None
+                )
 
                 with self.assertRaises(NotImplementedError) as e:
-                    initializer.get_maps_that_should_be_produced(f"u{sensor}-ena-h-hk-sp-full-hae-4deg-3mo", None)
-                self.assertEqual(e.exception.args, ("Reference frame should be either Spacecraft or Heliospheric",))
-
-                self.mock_query.assert_has_calls([
-                    call(instrument='glows', data_level='l3e', descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
-                         version="latest"),
-                    call(instrument='glows', data_level='l3e', descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
-                         version="latest"),
-                    call(instrument='ultra', data_level='l2'),
-                    call(instrument="ultra", table='ancillary', descriptor='l2-energy-bin-group-sizes',
-                         version='latest'),
-                    call(instrument='ultra', data_level='l3')
-                ])
+                    initializer.get_maps_that_should_be_produced(
+                        f"u{sensor}-ena-h-hk-sp-full-hae-4deg-3mo", None
+                    )
+                self.assertEqual(
+                    e.exception.args,
+                    ("Reference frame should be either Spacecraft or Heliospheric",),
+                )
 
-                mock_read_cdf_parents.assert_has_calls([
-                    call(f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-                    call(f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf'),
-                    call(f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-                    call(f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf'),
+                self.mock_query.assert_has_calls(
+                    [
+                        call(
+                            instrument="glows",
+                            data_level="l3e",
+                            descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                            version="latest",
+                        ),
+                        call(
+                            instrument="glows",
+                            data_level="l3e",
+                            descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                            version="latest",
+                        ),
+                        call(instrument="ultra", data_level="l2"),
+                        call(
+                            instrument="ultra",
+                            table="ancillary",
+                            descriptor="l2-energy-bin-group-sizes",
+                            version="latest",
+                        ),
+                        call(instrument="ultra", data_level="l3"),
+                    ]
+                )
 
-                    call(f'imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf'),
-                    call(f'imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf'),
-                ])
+                mock_read_cdf_parents.assert_has_calls(
+                    [
+                        call(
+                            f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf"
+                        ),
+                        call(
+                            f"imap_ultra_l3_u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf"
+                        ),
+                    ]
+                )
                 descriptor = f"u{sensor}-ena-h-sf-sp-full-hae-4deg-3mo"
                 expected_possible_map_to_produce = PossibleMapToProduce(
                     input_metadata=InputMetadata(
@@ -135,151 +171,188 @@ def test_get_maps_that_should_be_produced(self, mock_read_cdf_parents):
                         start_date=datetime(2010, 4, 1),
                         end_date=datetime(2010, 7, 1, 7, 30),
                         version=VersionMap({descriptor: Version(None, 2)}),
-                        descriptor=descriptor
+                        descriptor=descriptor,
                     ),
                     input_files={
-                        'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf',
-                        'imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf',
-                        'imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                        f'imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                        f'imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                        f'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-                    }
+                        "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf",
+                        "imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                        f"imap_ultra_l1c_{sensor}sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                        f"imap_ultra_l2_u{sensor}-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                        "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+                    },
                 )
                 self.assertIn(expected_possible_map_to_produce, actual_maps_to_produce)
                 self.assertEqual(2, len(actual_maps_to_produce))
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_lists_combined_maps(self, mock_read_cdf_parents):
-        self.mock_query.side_effect = ImapQueryPatcher([
-            'imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf',
-            'imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf',
-            'imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf',
-
-            'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf',
-            'imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf',
-            'imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf',
-            'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf',
-
-            'imap_glows_l3e_survival-probability-ul-sf_20100703-repoint00201_v001.cdf',
-
-            f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-            f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-            f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v000.cdf',
-            f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-            f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf',
-
-            f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-            f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-            f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v000.cdf',
-            f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-            f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf',
-
-            f'imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf',
-            f'imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf',
-            'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-        ])
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_lists_combined_maps(
+        self, mock_read_cdf_parents
+    ):
+        self.mock_query.side_effect = ImapQueryPatcher(
+            [
+                "imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100703-repoint00201_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v000.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v000.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf",
+                "imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+            ]
+        )
 
         mock_read_cdf_parents.side_effect = [
             [
-                f"imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf"
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
             ],
             [
-                f"imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf"
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf'
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf'
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100701-repoint00201_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100702-repoint00202_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100703-repoint00203_v001.cdf'
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100701-repoint00201_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100702-repoint00202_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100703-repoint00203_v001.cdf'
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100701-repoint00201_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100702-repoint00202_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100703-repoint00203_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20101001-repoint00301_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20101002-repoint00302_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20101003-repoint00303_v001.cdf'
+                "imap_ultra_l1c_45sensor-spacecraftpset_20101001-repoint00301_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20101002-repoint00302_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20101003-repoint00303_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20101001-repoint00301_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20101002-repoint00302_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20101003-repoint00303_v001.cdf'
+                "imap_ultra_l1c_90sensor-spacecraftpset_20101001-repoint00301_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20101002-repoint00302_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20101003-repoint00303_v001.cdf",
             ],
             [
-                'imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf',
-                f"imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-                f"imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
-                f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
+                "imap_glows_l3e_survival-probability-ul-sf_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100102-repoint00002_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100103-repoint00003_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100103-repoint00003_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
             ],
             [
-                'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
-            ]
+                "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+            ],
         ]
 
         initializer = UltraSPInitializer()
-        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(f"ulc-ena-h-sf-sp-full-hae-4deg-3mo",
-                                                                              None)
-
-        self.mock_query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR, version="latest"),
-            call(instrument='glows', data_level='l3e', descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR, version="latest"),
-            call(instrument='ultra', data_level='l2'),
-            call(instrument="ultra", table='ancillary', descriptor='l2-energy-bin-group-sizes',
-                 version='latest'),
-            call(instrument='ultra', data_level='l3'),
-        ])
+        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+            "ulc-ena-h-sf-sp-full-hae-4deg-3mo", None
+        )
 
-        mock_read_cdf_parents.assert_has_calls([
-            call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf'),
-            call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-            call(f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf'),
+        self.mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                    version="latest",
+                ),
+                call(instrument="ultra", data_level="l2"),
+                call(
+                    instrument="ultra",
+                    table="ancillary",
+                    descriptor="l2-energy-bin-group-sizes",
+                    version="latest",
+                ),
+                call(instrument="ultra", data_level="l3"),
+            ]
+        )
 
-            call(f'imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf'),
-        ])
-        descriptor = f"ulc-ena-h-sf-sp-full-hae-4deg-3mo"
+        mock_read_cdf_parents.assert_has_calls(
+            [
+                call(
+                    "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20101001_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l3_ulc-ena-h-sf-sp-full-hae-4deg-3mo_20100401_v001.cdf"
+                ),
+            ]
+        )
+        descriptor = "ulc-ena-h-sf-sp-full-hae-4deg-3mo"
 
         expected_possible_map_to_produce = PossibleMapToProduce(
             input_metadata=InputMetadata(
@@ -288,156 +361,193 @@ def test_get_maps_that_should_be_produced_lists_combined_maps(self, mock_read_cd
                 start_date=datetime(2010, 4, 1),
                 end_date=datetime(2010, 7, 1, 7, 30),
                 version=VersionMap({descriptor: Version(None, 2)}),
-                descriptor=descriptor
+                descriptor=descriptor,
             ),
             input_files={
-                'imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf',
-                'imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf',
-                'imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                f'imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                f'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-            }
+                "imap_glows_l3e_survival-probability-ul-sf_20100401-repoint00101_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100402-repoint00102_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-sf_20100403-repoint00103_v002.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_45sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_90sensor-spacecraftpset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-sf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+            },
         )
         self.assertIn(expected_possible_map_to_produce, actual_maps_to_produce)
         self.assertEqual(2, len(actual_maps_to_produce))
 
-    @patch('imap_l3_processing.maps.map_initializer.read_cdf_parents')
-    def test_get_maps_that_should_be_produced_lists_cg_corrected_maps(self, mock_read_cdf_parents):
-        self.mock_query.side_effect = ImapQueryPatcher([
-            'imap_glows_l3e_survival-probability-ul-hf_20100101-repoint00001_v001.cdf',
-            'imap_glows_l3e_survival-probability-ul-hf_20100102-repoint00002_v001.cdf',
-            'imap_glows_l3e_survival-probability-ul-hf_20100103-repoint00003_v001.cdf',
-
-            'imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v002.cdf',
-            'imap_glows_l3e_survival-probability-ul-hf_20100402-repoint00102_v002.cdf',
-            'imap_glows_l3e_survival-probability-ul-hf_20100403-repoint00103_v002.cdf',
-            'imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v001.cdf',
-
-            'imap_glows_l3e_survival-probability-ul-hf_20100703-repoint00201_v001.cdf',
-
-            f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-            f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-            f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v000.cdf',
-            f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-            f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf',
-
-            f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-            f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-            f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v000.cdf',
-            f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf',
-            f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf',
-
-            f'imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100101_v001.cdf',
-            f'imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100401_v001.cdf',
-            'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-        ])
+    @patch("imap_l3_processing.maps.map_initializer.read_cdf_parents")
+    def test_get_maps_that_should_be_produced_lists_cg_corrected_maps(
+        self, mock_read_cdf_parents
+    ):
+        self.mock_query.side_effect = ImapQueryPatcher(
+            [
+                "imap_glows_l3e_survival-probability-ul-hf_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100102-repoint00002_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100103-repoint00003_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100402-repoint00102_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100403-repoint00103_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100703-repoint00201_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v000.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v000.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf",
+                "imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+            ]
+        )
 
         mock_read_cdf_parents.side_effect = [
             [
-                f"imap_ultra_l1c_45sensor-heliopset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_45sensor-heliopset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_45sensor-heliopset_20100103-repoint00003_v001.cdf"
+                "imap_ultra_l1c_45sensor-heliopset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100103-repoint00003_v001.cdf",
             ],
             [
-                f"imap_ultra_l1c_90sensor-heliopset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_90sensor-heliopset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_90sensor-heliopset_20100103-repoint00003_v001.cdf"
+                "imap_ultra_l1c_90sensor-heliopset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100103-repoint00003_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_45sensor-heliopset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100403-repoint00103_v001.cdf'
+                "imap_ultra_l1c_45sensor-heliopset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100403-repoint00103_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_90sensor-heliopset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100403-repoint00103_v001.cdf'
+                "imap_ultra_l1c_90sensor-heliopset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100403-repoint00103_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_45sensor-heliopset_20100701-repoint00201_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100702-repoint00202_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100703-repoint00203_v001.cdf'
+                "imap_ultra_l1c_45sensor-heliopset_20100701-repoint00201_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100702-repoint00202_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100703-repoint00203_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_90sensor-heliopset_20100701-repoint00201_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100702-repoint00202_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100703-repoint00203_v001.cdf'
+                "imap_ultra_l1c_90sensor-heliopset_20100701-repoint00201_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100702-repoint00202_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100703-repoint00203_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_45sensor-heliopset_20101001-repoint00301_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20101002-repoint00302_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20101003-repoint00303_v001.cdf'
+                "imap_ultra_l1c_45sensor-heliopset_20101001-repoint00301_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20101002-repoint00302_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20101003-repoint00303_v001.cdf",
             ],
             [
-                f'imap_ultra_l1c_90sensor-heliopset_20101001-repoint00301_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20101002-repoint00302_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20101003-repoint00303_v001.cdf'
+                "imap_ultra_l1c_90sensor-heliopset_20101001-repoint00301_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20101002-repoint00302_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20101003-repoint00303_v001.cdf",
             ],
             [
-                'imap_glows_l3e_survival-probability-ul-hf_20100101-repoint00001_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-hf_20100102-repoint00002_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-hf_20100103-repoint00003_v001.cdf',
-                f"imap_ultra_l1c_45sensor-heliopset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_45sensor-heliopset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_45sensor-heliopset_20100103-repoint00003_v001.cdf",
-                f"imap_ultra_l1c_90sensor-heliopset_20100101-repoint00001_v001.cdf",
-                f"imap_ultra_l1c_90sensor-heliopset_20100102-repoint00002_v001.cdf",
-                f"imap_ultra_l1c_90sensor-heliopset_20100103-repoint00003_v001.cdf",
-                f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf',
-                'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
+                "imap_glows_l3e_survival-probability-ul-hf_20100101-repoint00001_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100102-repoint00002_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100103-repoint00003_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100103-repoint00003_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100101-repoint00001_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100102-repoint00002_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100103-repoint00003_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
             ],
             [
-                'imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-hf_20100402-repoint00102_v001.cdf',
-                'imap_glows_l3e_survival-probability-ul-hf_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv',
-            ]
+                "imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100402-repoint00102_v001.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+            ],
         ]
 
         initializer = UltraSPInitializer()
-        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(f"ulc-ena-h-hf-sp-full-hae-4deg-3mo",
-                                                                              None)
-
-        self.mock_query.assert_has_calls([
-            call(instrument='glows', data_level='l3e', descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR, version="latest"),
-            call(instrument='glows', data_level='l3e', descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR, version="latest"),
-            call(instrument='ultra', data_level='l2'),
-            call(instrument="ultra", table='ancillary', descriptor='l2-energy-bin-group-sizes',
-                 version='latest'),
-            call(instrument='ultra', data_level='l3'),
-        ])
+        actual_maps_to_produce = initializer.get_maps_that_should_be_produced(
+            "ulc-ena-h-hf-sp-full-hae-4deg-3mo", None
+        )
 
-        mock_read_cdf_parents.assert_has_calls([
-            call(f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf'),
-            call(f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf'),
-            call(f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf'),
-            call(f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf'),
+        self.mock_query.assert_has_calls(
+            [
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor=GLOWS_L3E_ULTRA_SF_DESCRIPTOR,
+                    version="latest",
+                ),
+                call(
+                    instrument="glows",
+                    data_level="l3e",
+                    descriptor=GLOWS_L3E_ULTRA_HF_DESCRIPTOR,
+                    version="latest",
+                ),
+                call(instrument="ultra", data_level="l2"),
+                call(
+                    instrument="ultra",
+                    table="ancillary",
+                    descriptor="l2-energy-bin-group-sizes",
+                    version="latest",
+                ),
+                call(instrument="ultra", data_level="l3"),
+            ]
+        )
 
-            call(f'imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100101_v001.cdf'),
-            call(f'imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100401_v001.cdf'),
-        ])
+        mock_read_cdf_parents.assert_has_calls(
+            [
+                call(
+                    "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100701_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20101001_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100101_v001.cdf"
+                ),
+                call(
+                    "imap_ultra_l3_ulc-ena-h-hf-sp-full-hae-4deg-3mo_20100401_v001.cdf"
+                ),
+            ]
+        )
 
-        descriptor = f"ulc-ena-h-hf-sp-full-hae-4deg-3mo"
+        descriptor = "ulc-ena-h-hf-sp-full-hae-4deg-3mo"
         expected_possible_map_to_produce = PossibleMapToProduce(
             input_metadata=InputMetadata(
                 instrument="ultra",
@@ -448,24 +558,24 @@ def test_get_maps_that_should_be_produced_lists_cg_corrected_maps(self, mock_rea
                 descriptor=descriptor,
             ),
             input_files={
-                'imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v002.cdf',
-                'imap_glows_l3e_survival-probability-ul-hf_20100402-repoint00102_v002.cdf',
-                'imap_glows_l3e_survival-probability-ul-hf_20100403-repoint00103_v002.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_45sensor-heliopset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100401-repoint00101_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100402-repoint00102_v001.cdf',
-                f'imap_ultra_l1c_90sensor-heliopset_20100403-repoint00103_v001.cdf',
-                f'imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                f'imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf',
-                f'imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv'
-            }
+                "imap_glows_l3e_survival-probability-ul-hf_20100401-repoint00101_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100402-repoint00102_v002.cdf",
+                "imap_glows_l3e_survival-probability-ul-hf_20100403-repoint00103_v002.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_45sensor-heliopset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100401-repoint00101_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100402-repoint00102_v001.cdf",
+                "imap_ultra_l1c_90sensor-heliopset_20100403-repoint00103_v001.cdf",
+                "imap_ultra_l2_u45-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2_u90-ena-h-hf-nsp-full-hae-4deg-3mo_20100401_v001.cdf",
+                "imap_ultra_l2-energy-bin-group-sizes_20250101_v000.csv",
+            },
         )
         self.assertIn(expected_possible_map_to_produce, actual_maps_to_produce)
         self.assertEqual(2, len(actual_maps_to_produce))
 
-    @patch('imap_l3_processing.ultra.ultra_sp_initializer.furnish_spice_metakernel')
+    @patch("imap_l3_processing.ultra.ultra_sp_initializer.furnish_spice_metakernel")
     def test_furnish_spice_dependencies(self, mock_furnish_metakernel):
         start_date = datetime(2025, 4, 15)
         end_date = datetime(2025, 7, 15)
@@ -477,12 +587,15 @@ def test_furnish_spice_dependencies(self, mock_furnish_metakernel):
             start_date=start_date,
             end_date=end_date,
             version=VersionMap({descriptor: Version(None, 0)}),
-            descriptor=descriptor
+            descriptor=descriptor,
         )
         map_to_produce = PossibleMapToProduce(set(), input_metadata)
 
         ultra_initializer = UltraSPInitializer()
         ultra_initializer.furnish_spice_dependencies(map_to_produce)
 
-        mock_furnish_metakernel.assert_called_once_with(start_date=start_date, end_date=end_date,
-                                                        kernel_types=ULTRA_SP_SPICE_KERNELS)
+        mock_furnish_metakernel.assert_called_once_with(
+            start_date=start_date,
+            end_date=end_date,
+            kernel_types=ULTRA_SP_SPICE_KERNELS,
+        )