[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/spikeinterface/sorters/internal/tridesclous2.py

@@ -23,7 +23,6 @@
 import numpy as np
 
 
-
 class Tridesclous2Sorter(ComponentsBasedSorter):
     sorter_name = "tridesclous2"
 
@@ -96,7 +95,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         job_kwargs = fix_job_kwargs(job_kwargs)
         job_kwargs["progress_bar"] = verbose
 
-
         recording_raw = cls.load_recording_from_folder(sorter_output_folder.parent, with_warnings=False)
 
         num_chans = recording_raw.get_num_channels()
@@ -111,25 +109,37 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
                     rec_for_motion = common_reference(rec_for_motion)
                     if verbose:
                         print("Start correct_motion()")
-                    _, motion_info = correct_motion(rec_for_motion, folder=sorter_output_folder / "motion", output_motion_info=True,
-                                                    **params["motion_correction"])
+                    _, motion_info = correct_motion(
+                        rec_for_motion,
+                        folder=sorter_output_folder / "motion",
+                        output_motion_info=True,
+                        **params["motion_correction"],
+                    )
                     if verbose:
                         print("Done correct_motion()")
 
-            recording = bandpass_filter(recording_raw, **params["filtering"],  dtype="float32")
+            recording = bandpass_filter(recording_raw, **params["filtering"], dtype="float32")
             recording = common_reference(recording)
 
             if params["apply_motion_correction"]:
                 interpolate_motion_kwargs = dict(
-                    direction=1, border_mode="force_extrapolate", spatial_interpolation_method="kriging", sigma_um=20.0, p=2
+                    direction=1,
+                    border_mode="force_extrapolate",
+                    spatial_interpolation_method="kriging",
+                    sigma_um=20.0,
+                    p=2,
                 )
 
                 recording = InterpolateMotionRecording(
-                    recording, motion_info["motion"], motion_info["temporal_bins"], motion_info["spatial_bins"], **interpolate_motion_kwargs
-                )                
+                    recording,
+                    motion_info["motion"],
+                    motion_info["temporal_bins"],
+                    motion_info["spatial_bins"],
+                    **interpolate_motion_kwargs,
+                )
 
             recording = zscore(recording, dtype="float32")
-            recording = whiten(recording, dtype="float32", mode="local", radius_um=100.)
+            recording = whiten(recording, dtype="float32", mode="local", radius_um=100.0)
 
             # used only if "folder" or "zarr"
             cache_folder = sorter_output_folder / "cache_preprocessing"
@@ -186,7 +196,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
 
         nbefore = int(params["templates"]["ms_before"] * sampling_frequency / 1000.0)
         nafter = int(params["templates"]["ms_after"] * sampling_frequency / 1000.0)
-        
+
         templates_array = estimate_templates_with_accumulator(
             recording_for_peeler,
             sorting_pre_peeler.to_spike_vector(),
@@ -239,4 +249,3 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         sorting = sorting.save(folder=sorter_output_folder / "sorting")
 
         return sorting
-

src/spikeinterface/sortingcomponents/benchmark/benchmark_clustering.py

@@ -187,7 +187,7 @@ def plot_agreements(self, case_keys=None, figsize=(15, 15)):
             ax = axs[0, count]
             ax.set_title(self.cases[key]["label"])
             plot_agreement_matrix(self.get_result(key)["gt_comparison"], ax=ax)
-        
+
         return fig
 
     def plot_performances_vs_snr(self, case_keys=None, figsize=(15, 15)):
@@ -247,7 +247,7 @@ def plot_error_metrics(self, metric="cosine", case_keys=None, figsize=(15, 5)):
             fig.colorbar(im, ax=axs[0, count])
             label = self.cases[key]["label"]
             axs[0, count].set_title(label)
-        
+
         return fig
 
     def plot_metrics_vs_snr(self, metric="agreement", case_keys=None, figsize=(15, 5)):
@@ -301,7 +301,7 @@ def plot_metrics_vs_snr(self, metric="agreement", case_keys=None, figsize=(15, 5
             label = self.cases[key]["label"]
             axs[0, count].set_title(label)
             axs[0, count].legend()
-        
+
         return fig
 
     def plot_metrics_vs_depth_and_snr(self, metric="agreement", case_keys=None, figsize=(15, 5)):
@@ -361,7 +361,7 @@ def plot_metrics_vs_depth_and_snr(self, metric="agreement", case_keys=None, figs
             label = self.cases[key]["label"]
             axs[0, count].set_title(label)
             # axs[0, count].legend()
-        
+
         return fig
 
     def plot_unit_losses(self, case_before, case_after, metric="agreement", figsize=None):
@@ -492,7 +492,7 @@ def plot_some_over_merged(self, case_keys=None, overmerged_score=0.05, max_units
                 figs.append(fig)
             else:
                 print(key, "no overmerged")
-        
+
         return figs
 
     def plot_some_over_splited(self, case_keys=None, oversplit_score=0.05, max_units=5, figsize=None):
@@ -531,4 +531,4 @@ def plot_some_over_splited(self, case_keys=None, oversplit_score=0.05, max_units
             else:
                 print(key, "no over splited")
 
-            return figs
+            return figs

src/spikeinterface/sortingcomponents/benchmark/benchmark_matching.py

@@ -73,7 +73,7 @@ def plot_agreements(self, case_keys=None, figsize=None):
             ax = axs[0, count]
             ax.set_title(self.cases[key]["label"])
             plot_agreement_matrix(self.get_result(key)["gt_comparison"], ax=ax)
-        
+
         return fig
 
     def plot_performances_vs_snr(self, case_keys=None, figsize=None):
@@ -97,7 +97,7 @@ def plot_performances_vs_snr(self, case_keys=None, figsize=None):
 
             if count == 2:
                 ax.legend()
-        
+
         return fig
 
     def plot_collisions(self, case_keys=None, figsize=None):
@@ -116,7 +116,7 @@ def plot_collisions(self, case_keys=None, figsize=None):
                 mode="lines",
                 good_only=False,
             )
-        
+
         return fig
 
     def plot_comparison_matching(

src/spikeinterface/sortingcomponents/benchmark/benchmark_tools.py

@@ -228,12 +228,13 @@ def run(self, case_keys=None, keep=True, verbose=False, **job_kwargs):
             benchmark.save_run(bench_folder)
             benchmark.result["run_time"] = float(t1 - t0)
             benchmark.save_main(bench_folder)
-    
+
     def set_colors(self, colors=None, map_name="tab20"):
         if colors is None:
             case_keys = list(self.cases.keys())
-            self.colors = get_some_colors(case_keys, map_name=map_name, 
-                                          color_engine = "matplotlib", shuffle=False, margin=0)
+            self.colors = get_some_colors(
+                case_keys, map_name=map_name, color_engine="matplotlib", shuffle=False, margin=0
+            )
         else:
             self.colors = colors
 
@@ -270,7 +271,7 @@ def plot_run_times(self, case_keys=None):
             rt = run_times.at[key, "run_times"]
             ax.bar(i, rt, width=0.8, color=colors[key])
         ax.set_xticks(np.arange(len(case_keys)))
-        ax.set_xticklabels(labels, rotation=45.)
+        ax.set_xticklabels(labels, rotation=45.0)
         return fig
 
         # ax = run_times.plot(kind="bar")

src/spikeinterface/sortingcomponents/clustering/tdc.py

@@ -157,19 +157,19 @@ def main_function(cls, recording, peaks, params):
             features_folder,
             method="local_feature_clustering",
             method_kwargs=dict(
-
                 clusterer="hdbscan",
-                clusterer_kwargs={"min_cluster_size": min_cluster_size, "allow_single_cluster": True, "cluster_selection_method": "eom"},
-
+                clusterer_kwargs={
+                    "min_cluster_size": min_cluster_size,
+                    "allow_single_cluster": True,
+                    "cluster_selection_method": "eom",
+                },
                 # clusterer="isocut5",
                 # clusterer_kwargs={"min_cluster_size": min_cluster_size},
-
                 feature_name="sparse_tsvd",
                 # feature_name="sparse_wfs",
                 neighbours_mask=neighbours_mask,
                 waveforms_sparse_mask=sparse_mask,
                 min_size_split=min_cluster_size,
-
                 n_pca_features=3,
                 scale_n_pca_by_depth=True,
             ),

src/spikeinterface/widgets/utils.py

@@ -20,7 +20,9 @@
     HAVE_MPL = False
 
 
-def get_some_colors(keys, color_engine="auto", map_name="gist_ncar", format="RGBA", shuffle=None, seed=None, margin=None):
+def get_some_colors(
+    keys, color_engine="auto", map_name="gist_ncar", format="RGBA", shuffle=None, seed=None, margin=None
+):
     """
     Return a dict of colors for given keys