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

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

src/spikeinterface/benchmark/benchmark_merging.py

@@ -155,4 +155,4 @@ def plot_potential_merges(self, case_key, min_snr=None, backend="ipywidgets"):
 
         from spikeinterface.widgets import plot_potential_merges
 
-        plot_potential_merges(analyzer, mylist, backend=backend)
+        plot_potential_merges(analyzer, mylist, backend=backend)

src/spikeinterface/benchmark/tests/test_benchmark_merging.py

@@ -5,12 +5,11 @@
 import shutil
 
 from spikeinterface.benchmark.benchmark_merging import MergingStudy
-from spikeinterface.benchmark.tests.common_benchmark_testing import (
-    make_dataset
-)
+from spikeinterface.benchmark.tests.common_benchmark_testing import make_dataset
 
 from spikeinterface.generation.splitting_tools import split_sorting_by_amplitudes, split_sorting_by_times
 
+
 @pytest.mark.skip()
 def test_benchmark_merging(create_cache_folder):
     cache_folder = create_cache_folder
@@ -72,4 +71,4 @@ def test_benchmark_merging(create_cache_folder):
 
 
 if __name__ == "__main__":
-    test_benchmark_merging()
+    test_benchmark_merging()

src/spikeinterface/curation/auto_merge.py

@@ -223,7 +223,9 @@ def auto_merges(
                             sorting_analyzer.compute(["random_spikes", "templates"], **job_kwargs)
                     res_ext = sorting_analyzer.get_extension(step)
                     if res_ext is None:
-                        print(f"Extension {ext} is computed with default params. Precompute it with custom params if needed")
+                        print(
+                            f"Extension {ext} is computed with default params. Precompute it with custom params if needed"
+                        )
                         sorting_analyzer.compute(ext, **job_kwargs)
                 elif not compute_needed_extensions and not sorting_analyzer.has_extension(ext):
                     raise ValueError(f"{step} requires {ext} extension")
@@ -558,9 +560,9 @@ def iterative_merges(
 ):
     """
     Wrapper to conveniently be able to launch several presets for auto_merges in a row, as a list. Merges
-    are applied sequentially or until no more merges are done, one preset at a time, and extensions are 
+    are applied sequentially or until no more merges are done, one preset at a time, and extensions are
     not recomputed thanks to the merging units.
-    
+
     Parameters
     ----------
     sorting_analyzer : SortingAnalyzer

src/spikeinterface/generation/splitting_tools.py

@@ -63,12 +63,14 @@ def split_sorting_by_times(
         new_unit_ids += [max_index + 1]
         splitted_pairs += [(unit_id, new_unit_ids[-1])]
         max_index += 1
-    
+
     new_sorting = NumpySorting(new_spikes, sampling_frequency=sa.sampling_frequency, unit_ids=new_unit_ids)
     return new_sorting, splitted_pairs
 
 
-def split_sorting_by_amplitudes(sorting_analyzer, splitting_probability=0.5, partial_split_prob=0.95, unit_ids=None, min_snr=None, seed=None):
+def split_sorting_by_amplitudes(
+    sorting_analyzer, splitting_probability=0.5, partial_split_prob=0.95, unit_ids=None, min_snr=None, seed=None
+):
     """
     Fonction used to split a sorting based on the amplitudes of the units. This
     might be used for benchmarking meta merging step (see components)
@@ -134,4 +136,4 @@ def split_sorting_by_amplitudes(sorting_analyzer, splitting_probability=0.5, par
         max_index += 1
 
     new_sorting = NumpySorting(new_spikes, sampling_frequency=sa.sampling_frequency, unit_ids=new_unit_ids)
-    return new_sorting, splitted_pairs
+    return new_sorting, splitted_pairs

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -39,7 +39,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter):
         "apply_motion_correction": True,
         "motion_correction": {"preset": "dredge_fast"},
         "merging": {
-            "similarity_kwargs": {"method": "l2", "support": "union", "max_lag_ms": 0.1},            
+            "similarity_kwargs": {"method": "l2", "support": "union", "max_lag_ms": 0.1},
         },
         "clustering": {"legacy": True},
         "matching": {"method": "circus-omp-svd"},
@@ -90,6 +90,7 @@ def get_sorter_version(cls):
     def _run_from_folder(cls, sorter_output_folder, params, verbose):
         try:
             import hdbscan
+
             HAVE_HDBSCAN = True
         except:
             HAVE_HDBSCAN = False
@@ -374,10 +375,11 @@ def final_cleaning_circus(recording, sorting, templates, **merging_kwargs):
     sa.compute("template_similarity", **similarity_kwargs)
     correlograms_kwargs = merging_kwargs.pop("correlograms_kwargs", {})
     sa.compute("correlograms", **correlograms_kwargs)
-    
+
     from spikeinterface.curation.auto_merge import iterative_merges
+
     template_diff_thresh = np.arange(0.05, 0.25, 0.05)
-    presets_params = [{'template_similarity' : {'template_diff_thresh' : i}} for i in template_diff_thresh]
-    presets = ['x_contaminations'] * len(template_diff_thresh)
+    presets_params = [{"template_similarity": {"template_diff_thresh": i}} for i in template_diff_thresh]
+    presets = ["x_contaminations"] * len(template_diff_thresh)
     final_sa = iterative_merges(sa, presets=presets, presets_params=presets_params)
     return final_sa.sorting

src/spikeinterface/sortingcomponents/merging/__init__.py

@@ -1 +1 @@
-from .main import merge_spikes
+from .main import merge_spikes

src/spikeinterface/sortingcomponents/merging/main.py

@@ -8,6 +8,7 @@
 
 merging_methods = ["circus", "auto_merges"]
 
+
 def create_sorting_analyzer_with_templates(sorting, recording, templates, remove_empty=True):
     sparsity = templates.sparsity
     templates_array = templates.get_dense_templates().copy()
@@ -29,19 +30,27 @@ def create_sorting_analyzer_with_templates(sorting, recording, templates, remove
     return sa
 
 
-def merging_circus(sorting_analyzer, similarity_kwargs={"method": "l2", "support": "union", "max_lag_ms": 0.1}, extra_outputs=False, **job_kwargs):
+def merging_circus(
+    sorting_analyzer,
+    similarity_kwargs={"method": "l2", "support": "union", "max_lag_ms": 0.1},
+    extra_outputs=False,
+    **job_kwargs,
+):
 
-    if sorting_analyzer.get_extension('templates') is None:
+    if sorting_analyzer.get_extension("templates") is None:
         sorting_analyzer.compute(["random_spikes", "templates"], **job_kwargs)
     sorting_analyzer.compute("unit_locations", method="monopolar_triangulation")
     sorting_analyzer.compute("template_similarity", **similarity_kwargs)
     sorting_analyzer.compute("correlograms")
-    
+
     from spikeinterface.curation.auto_merge import iterative_merges
+
     template_diff_thresh = np.arange(0.05, 0.25, 0.05)
-    presets_params = [{'template_similarity' : {'template_diff_thresh' : i}} for i in template_diff_thresh]
-    presets = ['x_contaminations'] * len(template_diff_thresh)
-    return iterative_merges(sorting_analyzer, presets=presets, presets_params=presets_params, extra_outputs=extra_outputs, **job_kwargs)
+    presets_params = [{"template_similarity": {"template_diff_thresh": i}} for i in template_diff_thresh]
+    presets = ["x_contaminations"] * len(template_diff_thresh)
+    return iterative_merges(
+        sorting_analyzer, presets=presets, presets_params=presets_params, extra_outputs=extra_outputs, **job_kwargs
+    )
 
 
 def merge_spikes(
@@ -89,11 +98,12 @@ def merge_spikes(
         return merging_circus(sorting_analyzer, extra_outputs=extra_outputs, **method_kwargs)
     elif method == "auto_merges":
         from spikeinterface.curation.auto_merge import get_potential_auto_merge
+
         merges = get_potential_auto_merge(sorting_analyzer, **method_kwargs, resolve_graph=True)
         new_sa = sorting_analyzer.copy()
         new_sa = new_sa.merge_units(merges, merging_mode="soft", sparsity_overlap=0.5, censor_ms=3, **job_kwargs)
         sorting = new_sa.sorting
-        if extra_outputs:   
+        if extra_outputs:
             return sorting, merges, []
         else:
-            return sorting
+            return sorting