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Merge pull request #3138 from chrishalcrow/fix-nn-calculations
Fix nn pca_metric computation and update tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,37 @@ | ||
| import pytest | ||
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| from spikeinterface.core import ( | ||
| generate_ground_truth_recording, | ||
| create_sorting_analyzer, | ||
| ) | ||
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| def _small_sorting_analyzer(): | ||
| recording, sorting = generate_ground_truth_recording( | ||
| durations=[2.0], | ||
| num_units=10, | ||
| seed=1205, | ||
| ) | ||
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| sorting = sorting.select_units([2, 7, 0], ["#3", "#9", "#4"]) | ||
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| sorting_analyzer = create_sorting_analyzer(recording=recording, sorting=sorting, format="memory") | ||
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| extensions_to_compute = { | ||
| "random_spikes": {"seed": 1205}, | ||
| "noise_levels": {"seed": 1205}, | ||
| "waveforms": {}, | ||
| "templates": {"operators": ["average", "median"]}, | ||
| "spike_amplitudes": {}, | ||
| "spike_locations": {}, | ||
| "principal_components": {}, | ||
| } | ||
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| sorting_analyzer.compute(extensions_to_compute) | ||
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| return sorting_analyzer | ||
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| @pytest.fixture(scope="module") | ||
| def small_sorting_analyzer(): | ||
| return _small_sorting_analyzer() |
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76 changes: 9 additions & 67 deletions
76
src/spikeinterface/qualitymetrics/tests/test_pca_metrics.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,82 +1,24 @@ | ||
| import pytest | ||
| from pathlib import Path | ||
| import numpy as np | ||
| from spikeinterface.core import ( | ||
| generate_ground_truth_recording, | ||
| create_sorting_analyzer, | ||
| ) | ||
|
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| from spikeinterface.qualitymetrics import ( | ||
| compute_pc_metrics, | ||
| nearest_neighbors_isolation, | ||
| nearest_neighbors_noise_overlap, | ||
| ) | ||
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| job_kwargs = dict(n_jobs=2, progress_bar=True, chunk_duration="1s") | ||
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| def _sorting_analyzer_simple(): | ||
| recording, sorting = generate_ground_truth_recording( | ||
| durations=[ | ||
| 50.0, | ||
| ], | ||
| sampling_frequency=30_000.0, | ||
| num_channels=6, | ||
| num_units=10, | ||
| generate_sorting_kwargs=dict(firing_rates=6.0, refractory_period_ms=4.0), | ||
| noise_kwargs=dict(noise_levels=5.0, strategy="tile_pregenerated"), | ||
| seed=2205, | ||
| ) | ||
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| sorting_analyzer = create_sorting_analyzer(sorting, recording, format="memory", sparse=True) | ||
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| sorting_analyzer.compute("random_spikes", max_spikes_per_unit=300, seed=2205) | ||
| sorting_analyzer.compute("noise_levels") | ||
| sorting_analyzer.compute("waveforms", **job_kwargs) | ||
| sorting_analyzer.compute("templates", operators=["average", "std", "median"]) | ||
| sorting_analyzer.compute("principal_components", n_components=5, mode="by_channel_local", **job_kwargs) | ||
| sorting_analyzer.compute("spike_amplitudes", **job_kwargs) | ||
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| return sorting_analyzer | ||
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| @pytest.fixture(scope="module") | ||
| def sorting_analyzer_simple(): | ||
| return _sorting_analyzer_simple() | ||
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| def test_calculate_pc_metrics(sorting_analyzer_simple): | ||
| def test_calculate_pc_metrics(small_sorting_analyzer): | ||
| import pandas as pd | ||
|
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| sorting_analyzer = sorting_analyzer_simple | ||
| res1 = compute_pc_metrics(sorting_analyzer, n_jobs=1, progress_bar=True) | ||
| sorting_analyzer = small_sorting_analyzer | ||
| res1 = compute_pc_metrics(sorting_analyzer, n_jobs=1, progress_bar=True, seed=1205) | ||
| res1 = pd.DataFrame(res1) | ||
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| res2 = compute_pc_metrics(sorting_analyzer, n_jobs=2, progress_bar=True) | ||
| res2 = compute_pc_metrics(sorting_analyzer, n_jobs=2, progress_bar=True, seed=1205) | ||
| res2 = pd.DataFrame(res2) | ||
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| for k in res1.columns: | ||
| mask = ~np.isnan(res1[k].values) | ||
| if np.any(mask): | ||
| assert np.array_equal(res1[k].values[mask], res2[k].values[mask]) | ||
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| def test_nearest_neighbors_isolation(sorting_analyzer_simple): | ||
| sorting_analyzer = sorting_analyzer_simple | ||
| this_unit_id = sorting_analyzer.unit_ids[0] | ||
| nearest_neighbors_isolation(sorting_analyzer, this_unit_id) | ||
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| def test_nearest_neighbors_noise_overlap(sorting_analyzer_simple): | ||
| sorting_analyzer = sorting_analyzer_simple | ||
| this_unit_id = sorting_analyzer.unit_ids[0] | ||
| nearest_neighbors_noise_overlap(sorting_analyzer, this_unit_id) | ||
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| for metric_name in res1.columns: | ||
| if metric_name != "nn_unit_id": | ||
| assert not np.all(np.isnan(res1[metric_name].values)) | ||
| assert not np.all(np.isnan(res2[metric_name].values)) | ||
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| if __name__ == "__main__": | ||
| sorting_analyzer = _sorting_analyzer_simple() | ||
| test_calculate_pc_metrics(sorting_analyzer) | ||
| test_nearest_neighbors_isolation(sorting_analyzer) | ||
| test_nearest_neighbors_noise_overlap(sorting_analyzer) | ||
| assert np.array_equal(res1[metric_name].values, res2[metric_name].values) |