Merge pull request #2447 from SpikeInterface/pre-commit-ci-update-config

[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 24.1.1
     hooks:
     -   id: black
         files: ^src/

src/spikeinterface/__init__.py

@@ -2,6 +2,7 @@
 
 
 """
+
 import importlib.metadata
 
 __version__ = importlib.metadata.version("spikeinterface")

src/spikeinterface/core/datasets.py

@@ -1,6 +1,7 @@
 """
 Some simple function to retrieve public datasets with datalad
 """
+
 from __future__ import annotations
 
 from pathlib import Path

src/spikeinterface/core/job_tools.py

@@ -1,6 +1,7 @@
 """
 Some utils to handle parallel jobs on top of job and/or loky
 """
+
 from __future__ import annotations
 from pathlib import Path
 import numpy as np

src/spikeinterface/core/node_pipeline.py

@@ -18,6 +18,7 @@
   * when peaks are already detected and reduced with `select_peaks()`
   * on a sorting object
 """
+
 from __future__ import annotations
 from typing import Optional, Type
 

src/spikeinterface/core/tests/test_base.py

@@ -2,6 +2,7 @@
 test for BaseRecording are done with BinaryRecordingExtractor.
 but check only for BaseRecording general methods.
 """
+
 from typing import Sequence
 from spikeinterface.core.base import BaseExtractor
 from spikeinterface.core import generate_recording, concatenate_recordings

src/spikeinterface/core/tests/test_baserecording.py

@@ -2,6 +2,7 @@
 test for BaseRecording are done with BinaryRecordingExtractor.
 but check only for BaseRecording general methods.
 """
+
 import json
 import pickle
 from pathlib import Path

src/spikeinterface/core/tests/test_basesnippets.py

@@ -2,6 +2,7 @@
 test for BaseSnippets are done with NumpySnippets.
 but check only for BaseRecording general methods.
 """
+
 from pathlib import Path
 import pytest
 import numpy as np

src/spikeinterface/core/tests/test_basesorting.py

@@ -2,6 +2,7 @@
 test for BaseSorting are done with NpzSortingExtractor.
 but check only for BaseRecording general methods.
 """
+
 import shutil
 from pathlib import Path
 

src/spikeinterface/core/waveform_tools.py

@@ -8,6 +8,7 @@
   2. extract and distribute snippets into buffers (optionally in parallel)
 
 """
+
 from __future__ import annotations
 from pathlib import Path
 

src/spikeinterface/full.py

@@ -9,6 +9,7 @@
 # this imports everything in a flat module
 import spieinterface.full as si
 """
+
 import importlib.metadata
 
 __version__ = importlib.metadata.version("spikeinterface")

src/spikeinterface/postprocessing/template_metrics.py

@@ -3,6 +3,7 @@
 https://github.com/AllenInstitute/ecephys_spike_sorting/blob/master/ecephys_spike_sorting/modules/mean_waveforms/waveform_metrics.py
 22/04/2020
 """
+
 from __future__ import annotations
 
 import numpy as np

src/spikeinterface/preprocessing/tests/test_align_snippets.py

@@ -2,6 +2,7 @@
 test for BaseSnippets are done with NumpySnippets.
 but check only for BaseRecording general methods.
 """
+
 from pathlib import Path
 import pytest
 import numpy as np

src/spikeinterface/qualitymetrics/quality_metric_calculator.py

@@ -1,4 +1,5 @@
 """Classes and functions for computing multiple quality metrics."""
+
 import warnings
 from copy import deepcopy
 

src/spikeinterface/sorters/basesorter.py

@@ -1,6 +1,7 @@
 """
 base class for sorters implementation.
 """
+
 import time
 import copy
 from pathlib import Path

src/spikeinterface/sorters/external/hdsort.py

@@ -144,7 +144,7 @@ def _generate_configs_file(sorter_output_folder, params, file_name, file_format)
         P["featureExtraction"] = {"nDims": float(params["n_pc_dims"])}  # 6
         P["clustering"] = {
             "maxSpikes": 50000.0,  # dont align spikes you dont cluster..
-            "meanShiftBandWidthFactor": 1.8
+            "meanShiftBandWidthFactor": 1.8,
             # 'meanShiftBandWidth': sqrt(1.8*6)  # todo: check this!
         }
 

src/spikeinterface/sorters/external/kilosort.py

@@ -161,13 +161,13 @@ def _get_specific_options(cls, ops, params):
         ops["nNeigh"] = 16.0  # visualization only (Phy): number of neighboring templates to retain projections of (16)
 
         # options for channel whitening
-        ops[
-            "whitening"
-        ] = "full"  # type of whitening (default 'full', for 'noSpikes' set options for spike detection below)
+        ops["whitening"] = (
+            "full"  # type of whitening (default 'full', for 'noSpikes' set options for spike detection below)
+        )
         ops["nSkipCov"] = 1.0  # compute whitening matrix from every N-th batch (1)
-        ops[
-            "whiteningRange"
-        ] = 32.0  # how many channels to whiten together (Inf for whole probe whitening, should be fine if Nchan<=32)
+        ops["whiteningRange"] = (
+            32.0  # how many channels to whiten together (Inf for whole probe whitening, should be fine if Nchan<=32)
+        )
 
         # ops['criterionNoiseChannels'] = 0.2  # fraction of "noise" templates allowed to span all channel groups (see createChannelMapFile for more info).
 

src/spikeinterface/sorters/launcher.py

@@ -1,6 +1,7 @@
 """
 Utils functions to launch several sorter on several recording in parallel or not.
 """
+
 from pathlib import Path
 import shutil
 import numpy as np

src/spikeinterface/sortingcomponents/clustering/merge.py

@@ -255,7 +255,7 @@ def find_merge_pairs(
     radius_um=70,
     method="project_distribution",
     method_kwargs={},
-    **job_kwargs
+    **job_kwargs,
     # n_jobs=1,
     # mp_context="fork",
     # max_threads_per_process=1,

src/spikeinterface/sortingcomponents/clustering/sliding_nn.py

@@ -313,13 +313,13 @@ def main_function(cls, recording, peaks, params):
                         peaks_in_chunk_idx <= end_last
                     ]
                     # repeat for cluster probabilities
-                    cluster_probabilities[
-                        peaks_in_chunk_idx[peaks_in_chunk_idx > end_last], 0
-                    ] = clusterer.probabilities_[peaks_in_chunk_idx > end_last]
+                    cluster_probabilities[peaks_in_chunk_idx[peaks_in_chunk_idx > end_last], 0] = (
+                        clusterer.probabilities_[peaks_in_chunk_idx > end_last]
+                    )
                     # put overlapping neighbors from previous in second row
-                    cluster_probabilities[
-                        peaks_in_chunk_idx[peaks_in_chunk_idx <= end_last], 1
-                    ] = clusterer.probabilities_[peaks_in_chunk_idx <= end_last]
+                    cluster_probabilities[peaks_in_chunk_idx[peaks_in_chunk_idx <= end_last], 1] = (
+                        clusterer.probabilities_[peaks_in_chunk_idx <= end_last]
+                    )
 
                     # TODO retrieve templates for each cluster
 

src/spikeinterface/sortingcomponents/features_from_peaks.py

@@ -1,4 +1,5 @@
 """Sorting components: peak waveform features."""
+
 import numpy as np
 
 from spikeinterface.core.job_tools import fix_job_kwargs

src/spikeinterface/sortingcomponents/matching/circus.py

@@ -887,7 +887,6 @@ def main_function(cls, traces, d):
 
 
 class CircusPeeler(BaseTemplateMatchingEngine):
-
     """
     Greedy Template-matching ported from the Spyking Circus sorter
 

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -1,4 +1,5 @@
 """Sorting components: peak detection."""
+
 import copy
 from typing import Tuple, Union, List, Dict, Any, Optional, Callable
 

src/spikeinterface/sortingcomponents/peak_localization.py

@@ -1,4 +1,5 @@
 """Sorting components: peak localization."""
+
 import numpy as np
 from spikeinterface.core.job_tools import _shared_job_kwargs_doc, split_job_kwargs, fix_job_kwargs
 

src/spikeinterface/widgets/utils_ipywidgets.py

@@ -75,7 +75,7 @@ def __init__(self, durations, sampling_frequency, time_range=(0, 1.0), **kwargs)
             min=0.01,
             max=30.0,
             description="win (s)",
-            layout=W.Layout(width="auto")
+            layout=W.Layout(width="auto"),
             # layout=W.Layout(width=f'10%')
         )
         self.window_sizer.observe(self.win_size_changed, names="value", type="change")