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

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

Author: pre-commit-ci[bot]

src/spikeinterface/core/tests/test_waveform_tools.py

@@ -229,9 +229,8 @@ def test_estimate_templates():
 
     # mask with differents sparsity
     sparsity_mask = np.ones((sorting.unit_ids.size, recording.channel_ids.size), dtype=bool)
-    sparsity_mask[:4, :recording.channel_ids.size//2 -1 ] = False
-    sparsity_mask[4:, recording.channel_ids.size//2:] = False
-
+    sparsity_mask[:4, : recording.channel_ids.size // 2 - 1] = False
+    sparsity_mask[4:, recording.channel_ids.size // 2 :] = False
 
     for operator in ("average", "median"):
         templates_array = estimate_templates(
@@ -245,8 +244,15 @@ def test_estimate_templates():
         assert np.any(templates_array != 0)
 
         sparse_templates_array = estimate_templates(
-            recording, spikes, sorting.unit_ids, nbefore, nafter, operator=operator,
-            return_in_uV=True, sparsity_mask=sparsity_mask, **job_kwargs
+            recording,
+            spikes,
+            sorting.unit_ids,
+            nbefore,
+            nafter,
+            operator=operator,
+            return_in_uV=True,
+            sparsity_mask=sparsity_mask,
+            **job_kwargs,
         )
         n_chan = np.max(np.sum(sparsity_mask, axis=1))
         assert n_chan == sparse_templates_array.shape[2]

src/spikeinterface/core/waveform_tools.py

@@ -794,7 +794,15 @@ def estimate_templates(
 
     if operator == "average":
         templates_array = estimate_templates_with_accumulator(
-            recording, spikes, unit_ids, nbefore, nafter, return_in_uV=return_in_uV, sparsity_mask=sparsity_mask, job_name=job_name, **job_kwargs
+            recording,
+            spikes,
+            unit_ids,
+            nbefore,
+            nafter,
+            return_in_uV=return_in_uV,
+            sparsity_mask=sparsity_mask,
+            job_name=job_name,
+            **job_kwargs,
         )
     elif operator == "median":
         all_waveforms, wf_array_info = extract_waveforms_to_single_buffer(
@@ -894,7 +902,7 @@ def estimate_templates_with_accumulator(
     else:
         num_chans = int(max(np.sum(sparsity_mask, axis=1)))  # This is a numpy scalar, so we cast to int
     num_units = len(unit_ids)
-    
+
     shape = (num_worker, num_units, nbefore + nafter, num_chans)
 
     dtype = np.dtype("float32")
@@ -1060,11 +1068,9 @@ def _worker_estimate_templates(segment_index, start_frame, end_frame, worker_dic
                 if waveform_squared_accumulator_per_worker is not None:
                     waveform_squared_accumulator_per_worker[worker_index, unit_index, :, :] += wf**2
 
-
             else:
                 mask = sparsity_mask[unit_index, :]
                 wf = wf[:, mask]
                 waveform_accumulator_per_worker[worker_index, unit_index, :, : wf.shape[1]] += wf
                 if waveform_squared_accumulator_per_worker is not None:
-                    waveform_squared_accumulator_per_worker[worker_index, unit_index, :, :wf.shape[1]] += wf**2
-
+                    waveform_squared_accumulator_per_worker[worker_index, unit_index, :, : wf.shape[1]] += wf**2