diff --git a/README.md b/README.md index 9b88030..731e8fb 100644 --- a/README.md +++ b/README.md @@ -155,8 +155,6 @@ If you use PyGPLA in your research, please cite the original GPLA method paper: } ``` -A dedicated software citation (JOSS) will be added here once the paper is published. - ## License BSD 2-Clause License. See `LICENSE`. diff --git a/docs/api.md b/docs/api.md index b3eeb8b..c31de35 100644 --- a/docs/api.md +++ b/docs/api.md @@ -49,8 +49,6 @@ pygpla.stats.jitter.interval_jitter pygpla.stats.jitter.isi_preserved_jitter pygpla.stats.jitter.group_preserved_jitter pygpla.stats.jitter.population_jitter -pygpla.stats.summaries.summarize_gplv_results -pygpla.stats.summaries.summarize_plv_matrix ``` ## Simulations diff --git a/docs/conf.py b/docs/conf.py index abbadf0..75d4a65 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ try: release = version("pygpla") except PackageNotFoundError: - release = "0.0.1" + release = "0.0.2" version = release extensions = [ diff --git a/docs/software_architecture.md b/docs/software_architecture.md index f0caa36..28509a4 100644 --- a/docs/software_architecture.md +++ b/docs/software_architecture.md @@ -141,11 +141,11 @@ important QA step. ## Version Management -PyGPLA currently uses an explicit package version: +PyGPLA keeps its version in a single source: -- `project.version = "0.0.1"` in `pyproject.toml` -- `__version__ = "0.0.1"` in `src/pygpla/_version.py` +- `__version__` in `src/pygpla/_version.py` -The Hatch setting `tool.hatch.version.path = "src/pygpla/_version.py"` keeps the package's -runtime version source in one place. Version bumps are therefore currently a deliberate, -manual release action rather than git-tag-driven automation. +`pyproject.toml` declares `dynamic = ["version"]` and points Hatch at that file via +`tool.hatch.version.path = "src/pygpla/_version.py"`, so the build metadata and the runtime +`pygpla.__version__` are read from the same place. Version bumps are a deliberate, manual +release action (edit `_version.py`) rather than git-tag-driven automation. diff --git a/paper/figures/figure2_python.eps b/paper/figures/figure2_python.eps index 04acadf..84b16ef 100644 --- a/paper/figures/figure2_python.eps +++ b/paper/figures/figure2_python.eps @@ -1,11 +1,11 @@ %!PS-Adobe-3.0 EPSF-3.0 %%LanguageLevel: 3 %%Title: figure2_python.eps -%%Creator: Matplotlib v3.11.0, https://matplotlib.org/ -%%CreationDate: Thu Jul 2 19:57:02 2026 +%%Creator: Matplotlib v3.10.5, https://matplotlib.org/ +%%CreationDate: Fri Jul 17 15:15:40 2026 %%Orientation: portrait %%BoundingBox: 0 0 499 560 -%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.701055 +%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.448125 %%EndComments %%BeginProlog /mpldict 10 dict def @@ -23,7 +23,7 @@ mpldict begin 10 dict begin /FontType 42 def /FontMatrix [1 0 0 1 0 0] def - /FontName /ArialMT-0 def + /FontName /ArialMT def /FontInfo 7 dict dup begin /FullName (Arial) 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+ - 1 + 1 - LFP Amplitude + LFP Amplitude @@ -4886,7 +4841,7 @@ L 312.039758 275.111877 L 312.741503 274.743885 L 313.443247 278.820137 L 313.443247 278.820137 -" clip-path="url(#p60bdbbde27)" style="fill: none; stroke: #daa520; stroke-linecap: square"/> +" clip-path="url(#p89ac6024bc)" style="fill: none; stroke: #daa520; stroke-linecap: square"/> - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + @@ -4968,80 +4923,80 @@ z +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0° + 0° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 45° + 45° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 90° + 90° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 135° + 135° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 180° + 180° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 225° + 225° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 270° + 270° +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 315° + 315° @@ -5065,10 +5020,10 @@ C 443.477603 270.2 445.226406 269.852141 446.857955 269.176332 C 448.489503 268.500523 449.972067 267.509905 451.2208 266.261172 C 452.469533 265.012439 453.46015 263.529875 454.13596 261.898327 C 454.811769 260.266778 455.159628 258.517975 455.159628 256.752 -" clip-path="url(#pdce01a9b4e)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0.2 + 0.2 @@ -5090,10 +5045,10 @@ C 445.243578 283.648 448.741185 282.952283 452.004282 281.600664 C 455.267378 280.249045 458.232506 278.26781 460.729972 275.770344 C 463.227438 273.272878 465.208673 270.30775 466.560292 267.044654 C 467.911911 263.781557 468.607628 260.28395 468.607628 256.752 -" clip-path="url(#pdce01a9b4e)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> +" clip-path="url(#pcbb0965ac9)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0.5 + 0.5 @@ -5118,7 +5073,7 @@ C 467.911911 263.781557 468.607628 260.28395 468.607628 256.752 " style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/> - Spike vector + Spike vector @@ -5134,79 +5089,79 @@ C 83.329012 349.339664 83.782441 350.434339 84.589437 351.241335 C 85.396433 352.048331 86.491108 352.50176 87.632372 352.50176 L 87.632372 352.50176 z -" clip-path="url(#pffd3b14bac)" style="fill: none; stroke: #000000; stroke-width: 1.2; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #000000; stroke-width: 1.2; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: #ff0000; stroke: #ff0000; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: #ff0000; stroke: #ff0000; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: #008000; stroke: #008000; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: #008000; stroke: #008000; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: #0000ff; stroke: #0000ff; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: #0000ff; stroke: #0000ff; stroke-linejoin: miter"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #ff0000; stroke-width: 1.1; stroke-linecap: square"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #ff0000; stroke-width: 1.1; stroke-linecap: square"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #008000; stroke-width: 1.1; stroke-linecap: square"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #008000; stroke-width: 1.1; stroke-linecap: square"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #0000ff; stroke-width: 1.1; stroke-linecap: square"/> +" clip-path="url(#p196fa701fc)" style="fill: none; stroke: #0000ff; stroke-width: 1.1; stroke-linecap: square"/> Model 3 @@ -5222,219 +5177,234 @@ z " style="fill: #ffffff"/> - - - - - + + + + + - - - - - - - - - - - - - - - - - - - +" clip-path="url(#paf7ae07132)" style="fill: #e41a1c; stroke: #e41a1c; stroke-width: 0.7"/> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - +" clip-path="url(#paf7ae07132)" style="fill: #ffff33; stroke: #ffff33; stroke-width: 0.7"/> - - - - - - - - - - - - - - - - - - - +" clip-path="url(#paf7ae07132)" style="fill: #ffff33; stroke: #ffff33; stroke-width: 0.7"/> + + + + + + + + + + + + +" clip-path="url(#paf7ae07132)" style="fill: #999999; stroke: #999999; stroke-width: 0.7"/> + - + - 4.25 + 4.25 - + - 4.30 + 4.30 - + - 4.35 + 4.35 - Time (s) + Time (s) - + - −1 + −1 - + - 0 + 0 - + - 1 + 1 - LFP Amplitude + LFP Amplitude @@ -5578,7 +5548,7 @@ L 312.039758 358.669672 L 312.741503 352.959806 L 313.443247 348.84672 L 313.443247 348.84672 -" clip-path="url(#p82f721fc0b)" style="fill: none; stroke: #cd853f; stroke-linecap: square"/> +" clip-path="url(#paf7ae07132)" style="fill: none; stroke: #cd853f; stroke-linecap: square"/> - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + @@ -5660,80 +5630,80 @@ z +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0° + 0° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 45° + 45° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 90° + 90° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 135° + 135° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 180° + 180° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 225° + 225° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 270° + 270° +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 315° + 315° @@ -5757,10 +5727,10 @@ C 443.477603 377.784 445.226406 377.436141 446.857955 376.760332 C 448.489503 376.084523 449.972067 375.093905 451.2208 373.845172 C 452.469533 372.596439 453.46015 371.113875 454.13596 369.482327 C 454.811769 367.850778 455.159628 366.101975 455.159628 364.336 -" clip-path="url(#p676dc49676)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0.2 + 0.2 @@ -5782,10 +5752,10 @@ C 445.243578 391.232 448.741185 390.536283 452.004282 389.184664 C 455.267378 387.833045 458.232506 385.85181 460.729972 383.354344 C 463.227438 380.856878 465.208673 377.89175 466.560292 374.628654 C 467.911911 371.365557 468.607628 367.86795 468.607628 364.336 -" clip-path="url(#p676dc49676)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> +" clip-path="url(#p341bf7a4f3)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0.5 + 0.5 @@ -5810,7 +5780,7 @@ C 467.911911 371.365557 468.607628 367.86795 468.607628 364.336 " style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/> - Spike vector + Spike vector @@ -5826,49 +5796,49 @@ C 83.329012 456.923664 83.782441 458.018339 84.589437 458.825335 C 85.396433 459.632331 86.491108 460.08576 87.632372 460.08576 L 87.632372 460.08576 z -" clip-path="url(#p17b974d0e1)" style="fill: none; stroke: #000000; stroke-width: 1.2; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: none; stroke: #000000; stroke-width: 1.2; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: #808080; stroke: #808080; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: #808080; stroke: #808080; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: #808080; stroke: #808080; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: #808080; stroke: #808080; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: #808080; stroke: #808080; stroke-linejoin: miter"/> +" clip-path="url(#p7cfd7ae0f5)" style="fill: #808080; stroke: #808080; stroke-linejoin: miter"/> Model 4 @@ -5884,207 +5854,231 @@ z " style="fill: #ffffff"/> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - 2.00 + 2.00 - + - 2.05 + 2.05 - + - 2.10 + 2.10 - Time (s) + Time (s) - + - −1 + −1 - + - 0 + 0 - + - 1 + 1 - LFP Amplitude + LFP Amplitude @@ -6221,7 +6215,7 @@ L 312.039758 457.637688 L 312.741503 459.357446 L 313.443247 458.105342 L 313.443247 458.105342 -" clip-path="url(#pfcbe4a8958)" style="fill: none; stroke: #708090; stroke-linecap: square"/> +" clip-path="url(#p117aa74642)" style="fill: none; stroke: #708090; stroke-linecap: square"/> - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + @@ -6303,80 +6297,80 @@ z +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0° + 0° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 45° + 45° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 90° + 90° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 135° + 135° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 180° + 180° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 225° + 225° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 270° + 270° +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 315° + 315° @@ -6400,10 +6394,10 @@ C 443.477603 485.368 445.226406 485.020141 446.857955 484.344332 C 448.489503 483.668523 449.972067 482.677905 451.2208 481.429172 C 452.469533 480.180439 453.46015 478.697875 454.13596 477.066327 C 454.811769 475.434778 455.159628 473.685975 455.159628 471.92 -" clip-path="url(#pb86ecf3458)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0.2 + 0.2 @@ -6425,10 +6419,10 @@ C 445.243578 498.816 448.741185 498.120283 452.004282 496.768664 C 455.267378 495.417045 458.232506 493.43581 460.729972 490.938344 C 463.227438 488.440878 465.208673 485.47575 466.560292 482.212654 C 467.911911 478.949557 468.607628 475.45195 468.607628 471.92 -" clip-path="url(#pb86ecf3458)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> +" clip-path="url(#p8112219eba)" style="fill: none; stroke: #d1d1d1; stroke-width: 0.45; stroke-linecap: square"/> - 0.5 + 0.5 @@ -6453,7 +6447,7 @@ C 467.911911 478.949557 468.607628 475.45195 468.607628 471.92 " style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/> - Spike vector + Spike vector @@ -6466,40 +6460,40 @@ z " style="fill: #ffffff"/> 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id="image21de675f9a" transform="scale(1 -1) translate(0 -11.76)" x="70.32" y="-522.36" width="155.52" height="11.76"/> - + - 0 + 0 - + - 180 + 180 - + - 360 + 360 - Phase lag [deg] + Phase lag [deg] @@ -6517,38 +6511,38 @@ z - A + A - B + B - C + C - D + D - E + E - F + F - + - + - + - + - + - + - + - + - + - + - + - + - + - + Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Generate phase-locked Poisson spike trains using von Mises modulation. Mirrors the MATLAB/PyGPLA_dev implementation. + + Pass ``rng`` (a ``numpy.random.Generator``) for reproducible output; when it + is None a fresh, unseeded generator is used. """ a = np.asarray(spike_params.get("lockingPhase", 0.0)) @@ -180,5 +184,7 @@ def generate_phase_locked_spikes( phase = 2 * np.pi * F[u, tr] * t - A[u, tr] FRmodulators[u, :, tr] = R[u, tr] * np.exp(K[u, tr] * np.cos(phase)) / _i0(K[u, tr]) - spikes = generate_inhomogeneous_poisson(FRmodulators, duration_s, sf, n_tr=n_tr, n_unit=n_unit) + spikes = generate_inhomogeneous_poisson( + FRmodulators, duration_s, sf, n_tr=n_tr, n_unit=n_unit, rng=rng + ) return spikes, theoPLV, FRmodulators diff --git a/src/pygpla/simulations/poisson.py b/src/pygpla/simulations/poisson.py index 0115567..a4205d3 100644 --- a/src/pygpla/simulations/poisson.py +++ b/src/pygpla/simulations/poisson.py @@ -16,6 +16,7 @@ def generate_homogeneous_poisson( n_tr: Optional[int] = None, n_unit: Optional[int] = None, method: str = "based_spikingProbality", + rng: Optional[np.random.Generator] = None, ) -> np.ndarray: """ Generate homogeneous Poisson spike trains. @@ -23,8 +24,14 @@ def generate_homogeneous_poisson( Parameters replicate the MATLAB/Python port: firing_rates can be scalar, per-unit, or per-trial. Returns an array of shape (units, n_bins, trials) with 0/1 spikes. + + Pass ``rng`` (a ``numpy.random.Generator``) for reproducible output; when it + is None a fresh, unseeded generator is used. """ + if rng is None: + rng = np.random.default_rng() + n_bins = int(round(duration_s * sf)) fr = np.asarray(firing_rates) @@ -40,7 +47,7 @@ def generate_homogeneous_poisson( raise ValueError("Only 'based_spikingProbality' method is supported.") if fr.size == 1: - prob = np.full((n_unit, n_bins, n_tr), float(fr) / sf) + prob = np.full((n_unit, n_bins, n_tr), float(fr.item()) / sf) elif fr.shape[1] == 1: prob = np.repeat(fr, n_bins, axis=1)[:, :, None] / sf prob = np.repeat(prob, n_tr, axis=2) @@ -49,7 +56,7 @@ def generate_homogeneous_poisson( tmp[:, 0, :] = fr prob = np.repeat(tmp, n_bins, axis=1) / sf - spikes = (np.random.rand(n_unit, n_bins, n_tr) < prob).astype(int) + spikes = (rng.random((n_unit, n_bins, n_tr)) < prob).astype(int) return spikes @@ -59,13 +66,20 @@ def generate_inhomogeneous_poisson( sf: float, n_tr: Optional[int] = None, n_unit: Optional[int] = None, + rng: Optional[np.random.Generator] = None, ) -> np.ndarray: """ Generate inhomogeneous Poisson spike trains via thinning. `fr_modulators` has shape (units, n_bins[, trials]) containing rate profiles (Hz). + + Pass ``rng`` (a ``numpy.random.Generator``) for reproducible output; when it + is None a fresh, unseeded generator is used. """ + if rng is None: + rng = np.random.default_rng() + fr = np.asarray(fr_modulators) if fr.ndim == 2: fr = fr[:, :, None] @@ -79,12 +93,14 @@ def generate_inhomogeneous_poisson( r_max = fr.max(axis=1, keepdims=True) base_fr = np.squeeze(r_max, axis=1) - hp = generate_homogeneous_poisson(base_fr, duration_s, sf, n_tr=n_tr, n_unit=n_unit) + hp = generate_homogeneous_poisson( + base_fr, duration_s, sf, n_tr=n_tr, n_unit=n_unit, rng=rng + ) with np.errstate(divide="ignore", invalid="ignore"): ratio = fr / r_max ratio = np.nan_to_num(ratio, nan=0.0, posinf=0.0, neginf=0.0) - reject = ratio > np.random.rand(*fr.shape) + reject = ratio > rng.random(fr.shape) spikes = (reject & (hp == 1)).astype(int) return spikes diff --git a/src/pygpla/simulations/transient.py b/src/pygpla/simulations/transient.py index 3645e4e..9786940 100644 --- a/src/pygpla/simulations/transient.py +++ b/src/pygpla/simulations/transient.py @@ -132,7 +132,9 @@ def simulate_transient_locked( num = np.exp(kappa_spk[:, tr][:, None] * np.cos(phase)) FR[:, start:stop, tr] = avFR[:, tr][:, None] * (num / I0_k[:, tr][:, None]) - spikes_3d = generate_inhomogeneous_poisson(FR, duration_s, sf, n_tr=n_tr, n_unit=n_unit) + spikes_3d = generate_inhomogeneous_poisson( + FR, duration_s, sf, n_tr=n_tr, n_unit=n_unit, rng=rng + ) spikes: List[np.ndarray] = [spikes_3d[:, :, tr] for tr in range(n_tr)] if return_analytic: diff --git a/src/pygpla/stats/summaries.py b/src/pygpla/stats/summaries.py deleted file mode 100644 index 23a156c..0000000 --- a/src/pygpla/stats/summaries.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Helpers to summarize GPLA statistical outputs.""" - -from __future__ import annotations - -from typing import Dict, Iterable - -import numpy as np - -__all__ = ["summarize_gplv_results", "summarize_plv_matrix"] - - -def summarize_gplv_results(gplv_values: Iterable[float]) -> Dict[str, float]: - """Compute summary statistics over GPLA values.""" - - values = np.asarray(list(gplv_values), dtype=float) - if values.size == 0: - return {"mean": float("nan"), "std": float("nan")} - return {"mean": float(values.mean()), "std": float(values.std())} - - -def summarize_plv_matrix(plv_matrix: np.ndarray) -> Dict[str, float]: - """Summaries for PLV matrices (placeholder).""" - - if plv_matrix.size == 0: - return {"max": float("nan"), "min": float("nan")} - return { - "max": float(np.nanmax(plv_matrix)), - "min": float(np.nanmin(plv_matrix)), - } diff --git a/tests/test_simulation_rng.py b/tests/test_simulation_rng.py new file mode 100644 index 0000000..9112f0d --- /dev/null +++ b/tests/test_simulation_rng.py @@ -0,0 +1,104 @@ +"""Reproducibility tests for the simulation generators (pygpla.simulations). + +The generators must draw exclusively from the ``rng`` they are handed, so that a +seeded call is reproducible and a library call never disturbs the caller's own +global NumPy random state. +""" + +import numpy as np +import pytest + +from pygpla.simulations import ( + generate_homogeneous_poisson, + generate_inhomogeneous_poisson, + generate_phase_locked_spikes, + simulate_transient_locked, +) + +SIGNAL_PARAMS = dict(nCh=3, nUnit=6, SF=400.0, nTr=2, signalLength=2.0) +GLOBAL_PARAMS = dict( + oscFreq=20.0, + nCycl=10, + syncSigProportion=0.7, + lfpPhaseNoise_kappa=8.0, + whiteNoise_sigma=0.05, +) + + +def _transient(seed): + return simulate_transient_locked( + GLOBAL_PARAMS, + dict(avefiringRate=18.0), + dict(lockingStrength_kappa=10.0, lockingPhase=0.0), + SIGNAL_PARAMS, + return_analytic=True, + rng=np.random.default_rng(seed), + ) + + +def _phase_locked(seed): + spikes, _, _ = generate_phase_locked_spikes( + dict(lockingPhase=0.0, kappa=5.0, avefiringRate=18.0, lockingFreq=20.0), + SIGNAL_PARAMS, + rng=np.random.default_rng(seed), + ) + return spikes + + +def _homogeneous(seed): + return generate_homogeneous_poisson( + 20.0, 1.0, 200.0, n_tr=2, n_unit=4, rng=np.random.default_rng(seed) + ) + + +def _inhomogeneous(seed): + fr = np.full((4, 200, 2), 20.0) + return generate_inhomogeneous_poisson( + fr, 1.0, 200.0, n_tr=2, n_unit=4, rng=np.random.default_rng(seed) + ) + + +SPIKE_GENERATORS = [_phase_locked, _homogeneous, _inhomogeneous] + + +@pytest.mark.parametrize("generator", SPIKE_GENERATORS) +def test_same_seed_reproduces_spikes(generator): + np.testing.assert_array_equal(generator(0), generator(0)) + + +@pytest.mark.parametrize("generator", SPIKE_GENERATORS) +def test_different_seeds_give_different_spikes(generator): + assert not np.array_equal(generator(0), generator(1)) + + +def test_transient_simulation_is_reproducible_including_spikes(): + _, lfp_a, spikes_a, _ = _transient(42) + _, lfp_b, spikes_b, _ = _transient(42) + + np.testing.assert_array_equal(lfp_a, lfp_b) + assert len(spikes_a) == len(spikes_b) + for tr_a, tr_b in zip(spikes_a, spikes_b, strict=True): + np.testing.assert_array_equal(tr_a, tr_b) + + +def test_transient_simulation_varies_with_seed(): + *_, spikes_a, _ = _transient(42) + *_, spikes_b, _ = _transient(43) + + assert not all( + np.array_equal(tr_a, tr_b) for tr_a, tr_b in zip(spikes_a, spikes_b, strict=True) + ) + + +@pytest.mark.parametrize("generator", SPIKE_GENERATORS) +def test_generators_do_not_disturb_global_random_state(generator): + """A seeded caller must get the same draw whether or not pygpla ran.""" + + np.random.seed(0) + expected = np.random.rand() + + np.random.seed(0) + generator(0) + actual = np.random.rand() + + assert actual == expected