Fix Python 3.14 CI mypy breakage in masked-array call sites

cloudnetpy/categorize/containers.py

@@ -163,6 +163,6 @@ def _find_clutter(
     """
     is_clutter = np.zeros(v.shape, dtype=bool)
     filled = False
-    tiny_velocity = (np.abs(v[:, :n_gates]) < v_lim).filled(filled)
+    tiny_velocity = ma.less(ma.abs(v[:, :n_gates]), v_lim).filled(filled)
     is_clutter[:, :n_gates] = tiny_velocity * utils.transpose(~is_rain)
     return is_clutter

cloudnetpy/cloudnetarray.py

@@ -111,7 +111,7 @@ def filter_vertical_stripes(self) -> None:
 
     def _filter(self, fun: Callable[[npt.NDArray], npt.NDArray]) -> None:
         if not isinstance(self.data, ma.MaskedArray):
-            self.data = ma.masked_array(self.data)
+            self.data = ma.array(self.data)
         is_data = (~self.data.mask).astype(int)
         is_data_filtered = fun(is_data)
         self.data[is_data_filtered == 0] = ma.masked

cloudnetpy/instruments/ceilometer.py

@@ -273,7 +273,7 @@ def _remove_noise(
             ind = self._get_altitude_ind()
             snr[:, ind] *= snr_scale_factor
         if ma.isMaskedArray(array) is False:
-            array = ma.masked_array(array)
+            array = ma.array(array)
         if keep_negative is True:
             array[np.abs(snr) < snr_limit] = ma.masked
         else:

cloudnetpy/instruments/lufft.py

@@ -43,7 +43,7 @@ def _fetch_beta_raw(self, calibration_factor: float | None = None) -> None:
             logging.warning("Using default calibration factor")
             calibration_factor = 3e-12
         beta_raw = self._getvar("beta_raw", "beta_att")
-        beta_raw = ma.masked_array(beta_raw)
+        beta_raw = ma.array(beta_raw)
         old_version = self._get_old_software_version()
         if old_version is not None:
             self.is_old_version = True
@@ -53,7 +53,7 @@ def _fetch_beta_raw(self, calibration_factor: float | None = None) -> None:
             )
             data_std = self._getvar("stddev")
             normalised_apd = self._get_nn()
-            beta_raw *= utils.transpose(ma.masked_array(data_std / normalised_apd))
+            beta_raw *= utils.transpose(ma.array(data_std / normalised_apd))
             beta_raw *= self.data["range"] ** 2
         beta_raw *= calibration_factor
         self.data["calibration_factor"] = float(calibration_factor)

cloudnetpy/model_evaluation/tests/unit/test_tools.py

@@ -37,7 +37,7 @@ def test_calculate_advection_time_hour(model_file) -> None:
     expected = resolution * 1000 / wind / 60**2
     expected[expected > 1 / sampling] = 1 / sampling
     expected = np.asarray([[timedelta(hours=float(t)) for t in tt] for tt in expected])
-    result = tools.calculate_advection_time(resolution, ma.masked_array(wind), sampling)
+    result = tools.calculate_advection_time(resolution, ma.array(wind), sampling)
     assert result.all() == expected.all()
 
 
@@ -49,14 +49,14 @@ def test_calculate_advection_time_10min(model_file) -> None:
     expected = resolution * 1000 / wind / 60**2
     expected[expected > 1 / sampling] = 1 / sampling
     expected = np.asarray([[timedelta(hours=float(t)) for t in tt] for tt in expected])
-    result = tools.calculate_advection_time(resolution, ma.masked_array(wind), sampling)
+    result = tools.calculate_advection_time(resolution, ma.array(wind), sampling)
     assert result.all() == expected.all()
 
 
 def test_calculate_advection_time_fractional_resolution() -> None:
     # A sub-kilometre / fractional resolution must not be truncated to int.
     resolution = 0.5
-    wind = ma.masked_array([[2.0]])
+    wind = ma.array([[2.0]])
     sampling = 6
     result = tools.calculate_advection_time(resolution, wind, sampling)
     expected = timedelta(hours=resolution * 1000 / 2.0 / 60**2)

cloudnetpy/plotting/plotting.py

@@ -671,7 +671,7 @@ def _indicate_rainy_profiles(self, figure_data: FigureData) -> None:
         if "rain_detected" not in figure_data.file.variables:
             return
         rain = figure_data.file.variables["rain_detected"][:]
-        is_rain: ma.MaskedArray = ma.masked_array(np.zeros_like(rain), mask=(rain == 0))
+        is_rain: ma.MaskedArray = ma.array(np.zeros_like(rain), mask=(rain == 0))
         if is_rain.mask.all():
             return
         self._ax.plot(

cloudnetpy/products/lwc.py

@@ -344,7 +344,7 @@ def _calc_lwc_relative_error(self) -> npt.NDArray:
 
     def _calc_lwc_gradient(self) -> npt.NDArray:
         if not isinstance(self.lwc, ma.MaskedArray):
-            self.lwc = ma.masked_array(self.lwc)
+            self.lwc = ma.array(self.lwc)
         gradient_elements = np.gradient(self.lwc.filled(0))
         return utils.l2norm(*gradient_elements)
 

cloudnetpy/utils.py

@@ -146,7 +146,7 @@ def rebin_2d(
     stat_fn = REBIN_STAT_FN[statistic]
 
     shape = array.shape if keepdim else (n_bins, array.shape[1])
-    result: ma.MaskedArray = ma.masked_array(np.ones(shape, dtype="float32"), mask=True)
+    result: ma.MaskedArray = ma.array(np.ones(shape, dtype="float32"), mask=True)
 
     for bin_ind in range(n_bins):
         if counts[bin_ind] < n_min:

tests/unit/test_concat_lib.py

@@ -210,24 +210,24 @@ def test_consistent_masked_arrays(self):
         self._write_array(
             self.file1,
             "kissa",
-            ma.masked_array([1, 2, 3], mask=[True, False, True]),
+            ma.array([1, 2, 3], mask=[True, False, True]),
         )
         self._write_array(
             self.file2,
             "kissa",
-            ma.masked_array([3, 2, 1], mask=[True, False, True]),
+            ma.array([3, 2, 1], mask=[True, False, True]),
         )
 
         with concat_lib._Concat(self.files, str(self.output)) as concat:
             concat.concat_data()
             assert ma.allequal(
                 concat.concatenated_file["kissa"][:],
-                ma.masked_array([1, 2, 3], mask=[True, False, True]),
+                ma.array([1, 2, 3], mask=[True, False, True]),
             )
 
     def test_inconsistent_masked_arrays(self):
-        arr1: npt.NDArray = ma.masked_array([1, 2, 3], mask=[True, False, True])
-        arr2: npt.NDArray = ma.masked_array([3, 2, 1], mask=[True, False, True])
+        arr1: npt.NDArray = ma.array([1, 2, 3], mask=[True, False, True])
+        arr2: npt.NDArray = ma.array([3, 2, 1], mask=[True, False, True])
         self._write_array(
             self.file1,
             "kissa",

tests/unit/test_droplet.py

@@ -8,7 +8,7 @@
 
 class TestIndBase:
     x = ma.array([0, 0.5, 1, -99, 4, 8, 5])
-    mx: ma.MaskedArray = ma.masked_array(
+    mx: ma.MaskedArray = ma.array(
         x, mask=[False, False, False, True, False, False, False]
     )
     diffu = np.diff(mx)
@@ -61,7 +61,7 @@ def test_ind_base_3(self):
         )
 
     def test_ind_base_4(self):
-        mx: ma.MaskedArray = ma.masked_array(
+        mx: ma.MaskedArray = ma.array(
             self.x, mask=[True, False, True, True, True, False, False]
         )
         diffu = ma.diff(mx)
@@ -73,7 +73,7 @@ def test_ind_base_4(self):
 
 class TestIndTop:
     x = np.array([1, 3, 8, 4, -99, 1, 0.5, 0])
-    mx: ma.MaskedArray = ma.masked_array(
+    mx: ma.MaskedArray = ma.array(
         x, mask=[False, False, False, False, True, False, False, False]
     )
     diffu = np.diff(mx)
@@ -136,7 +136,7 @@ def test_ind_top_3(self):
         )
 
     def test_ind_top_4(self):
-        mx: ma.MaskedArray = ma.masked_array(
+        mx: ma.MaskedArray = ma.array(
             self.x, mask=[True, False, True, False, True, False, True, False]
         )
         diffu = ma.diff(mx)
@@ -203,7 +203,7 @@ def test_correct_liquid_top():
     class Obs:
         def __init__(self):
             self.height = np.arange(11)
-            self.z: ma.MaskedArray = ma.masked_array(
+            self.z: ma.MaskedArray = ma.array(
                 np.random.random((3, 10)),
                 mask=[
                     [

tests/unit/test_falling.py

@@ -55,11 +55,11 @@ def test_find_cold_aerosols():
 @pytest.mark.parametrize(
     "z, ind_top, result",
     [
-        (ma.masked_array([1, 1, 1, 1], mask=[False, False, False, False]), 2, False),
-        (ma.masked_array([1, 1, 1, 1], mask=[False, False, False, False]), 2, False),
-        (ma.masked_array([1, 1, 1, 1], mask=[False, False, False, False]), 3, False),
-        (ma.masked_array([1, 1, 1, 1], mask=[True, True, True, True]), 3, False),
-        (ma.masked_array([1, 1, 1, 1], mask=[False, False, False, True]), 2, True),
+        (ma.array([1, 1, 1, 1], mask=[False, False, False, False]), 2, False),
+        (ma.array([1, 1, 1, 1], mask=[False, False, False, False]), 2, False),
+        (ma.array([1, 1, 1, 1], mask=[False, False, False, False]), 3, False),
+        (ma.array([1, 1, 1, 1], mask=[True, True, True, True]), 3, False),
+        (ma.array([1, 1, 1, 1], mask=[False, False, False, True]), 2, True),
     ],
 )
 def test_is_z_missing_above_liquid(z, ind_top, result):
@@ -69,11 +69,11 @@ def test_is_z_missing_above_liquid(z, ind_top, result):
 @pytest.mark.parametrize(
     "z, ind_base, ind_top, result",
     [
-        (ma.masked_array([1, 1, 1, 1], mask=[False, False, False, False]), 1, 2, False),
-        (ma.masked_array([1, 1, 2, 1], mask=[False, False, False, False]), 1, 2, True),
-        (ma.masked_array([1, 2, 1, 1], mask=[False, False, False, False]), 1, 2, False),
-        (ma.masked_array([1, 1, 2, 3], mask=[False, False, True, False]), 1, 3, True),
-        (ma.masked_array([1, 2, 3, 4], mask=[False, True, True, True]), 1, 3, False),
+        (ma.array([1, 1, 1, 1], mask=[False, False, False, False]), 1, 2, False),
+        (ma.array([1, 1, 2, 1], mask=[False, False, False, False]), 1, 2, True),
+        (ma.array([1, 2, 1, 1], mask=[False, False, False, False]), 1, 2, False),
+        (ma.array([1, 1, 2, 3], mask=[False, False, True, False]), 1, 3, True),
+        (ma.array([1, 2, 3, 4], mask=[False, True, True, True]), 1, 3, False),
     ],
 )
 def test_is_z_increasing(z, ind_top, ind_base, result):