Imports

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -631,7 +631,7 @@ def __init__(
         weight_method={},
     ):
         PeakDetector.__init__(self, recording, return_output=True)
-        import scipy
+        from scipy.sparse import csr_matrix
 
         if not HAVE_NUMBA:
             raise ModuleNotFoundError('matched_filtering" needs numba which is not installed')
@@ -665,7 +665,7 @@ def __init__(
             self.num_templates *= 2
 
         self.weights = self.weights.reshape(self.num_templates * self.num_z_factors, -1)
-        self.weights = scipy.sparse.csr_matrix(self.weights)
+        self.weights = csr_matrix(self.weights)
         random_data = get_random_data_chunks(recording, return_scaled=False, **random_chunk_kwargs)
         conv_random_data = self.get_convolved_traces(random_data)
         medians = np.median(conv_random_data, axis=1)
@@ -735,9 +735,8 @@ def compute(self, traces, start_frame, end_frame, segment_index, max_margin):
         return (local_peaks,)
 
     def get_convolved_traces(self, traces):
-        import scipy.signal
-
-        tmp = scipy.signal.oaconvolve(self.prototype[None, :], traces.T, axes=1, mode="valid")
+        from scipy.signal import oaconvolve
+        tmp = oaconvolve(self.prototype[None, :], traces.T, axes=1, mode="valid")
         scalar_products = self.weights.dot(tmp)
         return scalar_products