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

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src/spikeinterface/core/job_tools.py

@@ -436,6 +436,7 @@ def function_wrapper(args):
 
 # Here some utils copy/paste from DART (Charlie Windolf)
 
+
 class MockFuture:
     """A non-concurrent class for mocking the concurrent.futures API."""
 

src/spikeinterface/core/tests/test_globals.py

@@ -39,14 +39,18 @@ def test_global_tmp_folder():
 def test_global_job_kwargs():
     job_kwargs = dict(n_jobs=4, chunk_duration="1s", progress_bar=True, mp_context=None, max_threads_per_process=1)
     global_job_kwargs = get_global_job_kwargs()
-    assert global_job_kwargs == dict(n_jobs=1, chunk_duration="1s", progress_bar=True, mp_context=None, max_threads_per_process=1)
+    assert global_job_kwargs == dict(
+        n_jobs=1, chunk_duration="1s", progress_bar=True, mp_context=None, max_threads_per_process=1
+    )
     set_global_job_kwargs(**job_kwargs)
     assert get_global_job_kwargs() == job_kwargs
     # test updating only one field
     partial_job_kwargs = dict(n_jobs=2)
     set_global_job_kwargs(**partial_job_kwargs)
     global_job_kwargs = get_global_job_kwargs()
-    assert global_job_kwargs == dict(n_jobs=2, chunk_duration="1s", progress_bar=True, mp_context=None, max_threads_per_process=1)
+    assert global_job_kwargs == dict(
+        n_jobs=2, chunk_duration="1s", progress_bar=True, mp_context=None, max_threads_per_process=1
+    )
     # test that fix_job_kwargs grabs global kwargs
     new_job_kwargs = dict(n_jobs=10)
     job_kwargs_split = fix_job_kwargs(new_job_kwargs)

src/spikeinterface/core/tests/test_waveform_extractor.py

@@ -558,4 +558,4 @@ def test_non_json_object():
     # test_portability()
     test_recordingless()
     # test_compute_sparsity()
-    # test_non_json_object()
+    # test_non_json_object()

src/spikeinterface/sortingcomponents/clustering/merge.py

@@ -80,45 +80,46 @@ def merge_clusters(
         method_kwargs=method_kwargs,
         **job_kwargs,
     )
-
-
+
     DEBUG = False
     if DEBUG:
         import matplotlib.pyplot as plt
+
         fig, ax = plt.subplots()
         ax.matshow(pair_values)
-        
-        pair_values[~pair_mask]  = 20
-        
+
+        pair_values[~pair_mask] = 20
+
         import hdbscan
+
         fig, ax = plt.subplots()
-        clusterer = hdbscan.HDBSCAN(metric='precomputed', min_cluster_size=2, allow_single_cluster=True)
+        clusterer = hdbscan.HDBSCAN(metric="precomputed", min_cluster_size=2, allow_single_cluster=True)
         clusterer.fit(pair_values)
         print(clusterer.labels_)
-        clusterer.single_linkage_tree_.plot(cmap='viridis', colorbar=True)
-        #~ fig, ax = plt.subplots()
-        #~ clusterer.minimum_spanning_tree_.plot(edge_cmap='viridis',
-                                          #~ edge_alpha=0.6,
-                                          #~ node_size=80,
-                                          #~ edge_linewidth=2)
-        
+        clusterer.single_linkage_tree_.plot(cmap="viridis", colorbar=True)
+        # ~ fig, ax = plt.subplots()
+        # ~ clusterer.minimum_spanning_tree_.plot(edge_cmap='viridis',
+        # ~ edge_alpha=0.6,
+        # ~ node_size=80,
+        # ~ edge_linewidth=2)
+
         graph = clusterer.single_linkage_tree_.to_networkx()
 
         import scipy.cluster
+
         fig, ax = plt.subplots()
         scipy.cluster.hierarchy.dendrogram(clusterer.single_linkage_tree_.to_numpy(), ax=ax)
-        
+
         import networkx as nx
+
         fig = plt.figure()
         nx.draw_networkx(graph)
         plt.show()
 
         plt.show()
-
-
-
+
     merges = agglomerate_pairs(labels_set, pair_mask, pair_values, connection_mode="partial")
-    # merges = agglomerate_pairs(labels_set, pair_mask, pair_values, connection_mode="full")
+    # merges = agglomerate_pairs(labels_set, pair_mask, pair_values, connection_mode="full")
 
     group_shifts = resolve_final_shifts(labels_set, merges, pair_mask, pair_shift)
 
@@ -223,7 +224,7 @@ def agglomerate_pairs(labels_set, pair_mask, pair_values, connection_mode="full"
             else:
                 raise ValueError
 
-            # DEBUG = True
+            # DEBUG = True
             DEBUG = False
             if DEBUG:
                 import matplotlib.pyplot as plt
@@ -232,7 +233,7 @@ def agglomerate_pairs(labels_set, pair_mask, pair_values, connection_mode="full"
                 nx.draw_networkx(sub_graph)
                 plt.show()
 
-    # DEBUG = True
+    # DEBUG = True
     DEBUG = False
     if DEBUG:
         import matplotlib.pyplot as plt
@@ -377,9 +378,10 @@ class ProjectDistribution:
     The idea is :
       * project the waveform (or features) samples on a 1d axis (using  LDA for instance).
       * check that it is the same or not distribution (diptest, distrib_overlap, ...)
-    
+
 
     """
+
     name = "project_distribution"
 
     @staticmethod
@@ -412,13 +414,12 @@ def merge(
         chans1 = np.unique(peaks["channel_index"][inds1])
         target_chans1 = np.flatnonzero(np.all(waveforms_sparse_mask[chans1, :], axis=0))
 
-        if inds0.size <40 or inds1.size <40:
+        if inds0.size < 40 or inds1.size < 40:
             is_merge = False
             merge_value = 0
             final_shift = 0
             return is_merge, label0, label1, final_shift, merge_value
 
-
         target_chans = np.intersect1d(target_chans0, target_chans1)
 
         inds = np.concatenate([inds0, inds1])
@@ -500,20 +501,19 @@ def merge(
         elif criteria == "distrib_overlap":
             lim0 = min(np.min(feat0), np.min(feat1))
             lim1 = max(np.max(feat0), np.max(feat1))
-            bin_size = (lim1 - lim0) / 200.
+            bin_size = (lim1 - lim0) / 200.0
             bins = np.arange(lim0, lim1, bin_size)
-            
+
             pdf0, _ = np.histogram(feat0, bins=bins, density=True)
             pdf1, _ = np.histogram(feat1, bins=bins, density=True)
             pdf0 *= bin_size
-            pdf1 *= bin_size 
+            pdf1 *= bin_size
             overlap = np.sum(np.minimum(pdf0, pdf1))
-            
+
             is_merge = overlap >= threshold_overlap
-            
+
             merge_value = 1 - overlap
-
-
+
         else:
             raise ValueError(f"bad criteria {criteria}")
 
@@ -522,13 +522,13 @@ def merge(
         else:
             final_shift = 0
 
-        # DEBUG = True
+        # DEBUG = True
         DEBUG = False
 
         if DEBUG and is_merge:
-        # if DEBUG and not is_merge:
-        # if DEBUG and (overlap > 0.05 and overlap <0.25):
-        # if label0 == 49 and label1== 65:
+            # if DEBUG and not is_merge:
+            # if DEBUG and (overlap > 0.05 and overlap <0.25):
+            # if label0 == 49 and label1== 65:
             import matplotlib.pyplot as plt
 
             flatten_wfs0 = wfs0.swapaxes(1, 2).reshape(wfs0.shape[0], -1)
@@ -551,7 +551,6 @@ def merge(
             count1, _ = np.histogram(feat1, bins=bins, density=True)
             pdf0 = count0 * bin_size
             pdf1 = count1 * bin_size
-
 
             ax = axs[1]
             ax.plot(bins[:-1], pdf0, color="C0")
@@ -564,13 +563,15 @@ def merge(
                 ax.axvline(l0, color="C0")
                 ax.axvline(l1, color="C1")
             elif criteria == "distrib_overlap":
-                print(lim0, lim1, )
+                print(
+                    lim0,
+                    lim1,
+                )
                 ax.set_title(f"{overlap:.4f} {is_merge}")
-                ax.plot(bins[:-1], np.minimum(pdf0, pdf1), ls='--', color='k')
+                ax.plot(bins[:-1], np.minimum(pdf0, pdf1), ls="--", color="k")
 
             plt.show()
 
-
         return is_merge, label0, label1, final_shift, merge_value
 
 

src/spikeinterface/sortingcomponents/clustering/split.py

@@ -154,13 +154,12 @@ def split_function_wrapper(peak_indices):
     return is_split, local_labels, peak_indices
 
 
-
 class LocalFeatureClustering:
     """
     This method is a refactorized mix  between:
        * old tridesclous code
        * "herding_split()" in DART/spikepsvae by Charlie Windolf
-    
+
     The idea simple :
      * agregate features (svd or even waveforms) with sparse channel.
      * run a local feature reduction (pca or  svd)
@@ -183,7 +182,6 @@ def split(
         min_samples=25,
         n_pca_features=2,
         minimum_common_channels=2,
-
     ):
         local_labels = np.zeros(peak_indices.size, dtype=np.int64)
 
@@ -218,8 +216,12 @@ def split(
         final_features = TruncatedSVD(n_pca_features).fit_transform(flatten_features)
 
         if clusterer == "hdbscan":
-            clust = HDBSCAN(min_cluster_size=min_cluster_size, min_samples=min_samples, allow_single_cluster=True,
-                                cluster_selection_method="leaf")
+            clust = HDBSCAN(
+                min_cluster_size=min_cluster_size,
+                min_samples=min_samples,
+                allow_single_cluster=True,
+                cluster_selection_method="leaf",
+            )
             clust.fit(final_features)
             possible_labels = clust.labels_
             is_split = np.setdiff1d(possible_labels, [-1]).size > 1
@@ -236,9 +238,7 @@ def split(
         else:
             raise ValueError(f"wrong clusterer {clusterer}")
 
-
-
-        # DEBUG = True
+        # DEBUG = True
         DEBUG = False
         if DEBUG:
             import matplotlib.pyplot as plt
@@ -259,7 +259,7 @@ def split(
 
                 ax = axs[1]
                 ax.plot(flatten_wfs[mask][sl].T, color=colors[k], alpha=0.5)
-            
+
             axs[0].set_title(f"{clusterer} {is_split}")
 
             plt.show()

src/spikeinterface/sortingcomponents/clustering/tools.py

@@ -94,7 +94,6 @@ def aggregate_sparse_features(peaks, peak_indices, sparse_feature, sparse_mask,
     return aligned_features, dont_have_channels
 
 
-
 def compute_template_from_sparse(
     peaks, labels, labels_set, sparse_waveforms, sparse_mask, total_channels, peak_shifts=None
 ):

src/spikeinterface/sortingcomponents/tests/test_merge.py

@@ -5,6 +5,7 @@
 
 # no proper test at the moment this is used in tridesclous2
 
+
 def test_merge():
     pass
 

src/spikeinterface/sortingcomponents/tests/test_split.py

@@ -5,6 +5,7 @@
 
 # no proper test at the moment this is used in tridesclous2
 
+
 def test_split():
     pass