Merge branch 'dev' of github.com:SpikeInterface/spikeinterface into dev

SpikeInterface · Mar 4, 2024 · 4379fe3 · 4379fe3
2 parents e9418e9 + 6bcadd8
commit 4379fe3
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Showing 2 changed files with 15 additions and 8 deletions.
diff --git a/src/spikeinterface/sortingcomponents/clustering/position_and_features.py b/src/spikeinterface/sortingcomponents/clustering/position_and_features.py
@@ -48,7 +48,7 @@ class PositionAndFeaturesClustering:
     @classmethod
     def main_function(cls, recording, peaks, params):
         from sklearn.preprocessing import QuantileTransformer
-        
+
         assert HAVE_HDBSCAN, "twisted clustering needs hdbscan to be installed"
 
         if "n_jobs" in params["job_kwargs"]:
@@ -70,7 +70,10 @@ def main_function(cls, recording, peaks, params):
 
         position_method = d["peak_localization_kwargs"]["method"]
 
-        features_list = [position_method, "ptp",]
+        features_list = [
+            position_method,
+            "ptp",
+        ]
         features_params = {
             position_method: {"radius_um": params["radius_um"]},
             "ptp": {"all_channels": False, "radius_um": params["radius_um"]},
@@ -85,8 +88,6 @@ def main_function(cls, recording, peaks, params):
         hdbscan_data[:, 1] = features_data[0]["y"]
         hdbscan_data[:, 2] = features_data[1]
 
-
-
         preprocessing = QuantileTransformer(output_distribution="uniform")
         hdbscan_data = preprocessing.fit_transform(hdbscan_data)
 

diff --git a/src/spikeinterface/sortingcomponents/tests/test_features_from_peaks.py b/src/spikeinterface/sortingcomponents/tests/test_features_from_peaks.py
@@ -26,7 +26,11 @@ def test_features_from_peaks():
         **job_kwargs,
     )
 
-    feature_list = ["amplitude", "ptp", "center_of_mass",]
+    feature_list = [
+        "amplitude",
+        "ptp",
+        "center_of_mass",
+    ]
     feature_params = {
         "amplitude": {"all_channels": False, "peak_sign": "neg"},
         "ptp": {"all_channels": False},
@@ -44,9 +48,11 @@ def test_features_from_peaks():
 
     # split feature variable
     job_kwargs["n_jobs"] = 2
-    amplitude, ptp, com, = compute_features_from_peaks(
-        recording, peaks, feature_list, feature_params=feature_params, **job_kwargs
-    )
+    (
+        amplitude,
+        ptp,
+        com,
+    ) = compute_features_from_peaks(recording, peaks, feature_list, feature_params=feature_params, **job_kwargs)
     assert amplitude.ndim == 1  # because all_channels=False
     assert ptp.ndim == 1  # because all_channels=False
     assert com.ndim == 1