Patch lag similarities

src/spikeinterface/postprocessing/template_similarity.py

@@ -234,10 +234,6 @@ def _compute_similarity_matrix_numpy(
             overlapping_templates = np.flatnonzero(np.sum(local_mask, 1))
             tgt_templates = tgt_sliced_templates[overlapping_templates]
             for gcount, j in enumerate(overlapping_templates):
-                # symmetric values are handled later
-                if same_array and j < i:
-                    # no need exhaustive looping when same template
-                    continue
                 src = src_template[:, local_mask[j]].reshape(1, -1)
                 tgt = (tgt_templates[gcount][:, local_mask[j]]).reshape(1, -1)
 
@@ -259,10 +255,8 @@ def _compute_similarity_matrix_numpy(
                     distances[count, i, j] = 1 - distances[count, i, j]
 
                 if same_array:
-                    distances[count, j, i] = distances[count, i, j]
+                    distances[num_shifts_both_sides - count - 1, j, i] = distances[count, i, j]
 
-        if same_array and num_shifts != 0:
-            distances[num_shifts_both_sides - count - 1] = distances[count].T
     return distances
 
 
@@ -331,10 +325,6 @@ def _compute_similarity_matrix_numba(
                 for gcount in range(len(overlapping_templates)):
 
                     j = overlapping_templates[gcount]
-                    # symmetric values are handled later
-                    if same_array and j < i:
-                        # no need exhaustive looping when same template
-                        continue
                     src = src_template[:, local_mask[j]].flatten()
                     tgt = (tgt_templates[gcount][:, local_mask[j]]).flatten()
 
@@ -370,10 +360,7 @@ def _compute_similarity_matrix_numba(
                         distances[count, i, j] = 1 - distances[count, i, j]
 
                     if same_array:
-                        distances[count, j, i] = distances[count, i, j]
-
-            if same_array and num_shifts != 0:
-                distances[num_shifts_both_sides - count - 1] = distances[count].T
+                        distances[num_shifts_both_sides - count - 1, j, i] = distances[count, i, j]
 
         return distances
 

src/spikeinterface/sorters/internal/lupin.py

@@ -109,7 +109,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         from spikeinterface.sortingcomponents.peak_detection import detect_peaks
         from spikeinterface.sortingcomponents.peak_selection import select_peaks
         from spikeinterface.sortingcomponents.clustering.main import find_clusters_from_peaks, clustering_methods
-        from spikeinterface.sortingcomponents.tools import remove_empty_templates
         from spikeinterface.preprocessing import correct_motion
         from spikeinterface.sortingcomponents.motion import InterpolateMotionRecording
         from spikeinterface.sortingcomponents.tools import clean_templates, compute_sparsity_from_peaks_and_label