Merge pull request #2277 from yger/sparsity_inconsistencies

Strict inegality for sparsity with radius_um

src/spikeinterface/core/node_pipeline.py

@@ -175,7 +175,7 @@ def __init__(
 
         if not channel_from_template:
             channel_distance = get_channel_distances(recording)
-            self.neighbours_mask = channel_distance < radius_um
+            self.neighbours_mask = channel_distance <= radius_um
             self.peak_sign = peak_sign
 
         # precompute segment slice
@@ -367,7 +367,7 @@ def __init__(
         self.radius_um = radius_um
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
         self.max_num_chans = np.max(np.sum(self.neighbours_mask, axis=1))
 
     def get_trace_margin(self):

src/spikeinterface/postprocessing/unit_localization.py

@@ -597,7 +597,7 @@ def get_grid_convolution_templates_and_weights(
 
     # mask to get nearest template given a channel
     dist = sklearn.metrics.pairwise_distances(contact_locations, template_positions)
-    nearest_template_mask = dist < radius_um
+    nearest_template_mask = dist <= radius_um
 
     weights = np.zeros((len(sigma_um), len(contact_locations), nb_templates), dtype=np.float32)
     for count, sigma in enumerate(sigma_um):

src/spikeinterface/preprocessing/whiten.py

@@ -197,7 +197,7 @@ def compute_whitening_matrix(recording, mode, random_chunk_kwargs, apply_mean, r
         distances = get_channel_distances(recording)
         W = np.zeros((n, n), dtype="float64")
         for c in range(n):
-            (inds,) = np.nonzero(distances[c, :] < radius_um)
+            (inds,) = np.nonzero(distances[c, :] <= radius_um)
             cov_local = cov[inds, :][:, inds]
             U, S, Ut = np.linalg.svd(cov_local, full_matrices=True)
             W_local = (U @ np.diag(1 / np.sqrt(S + eps))) @ Ut

src/spikeinterface/sortingcomponents/clustering/merge.py

@@ -291,7 +291,7 @@ def find_merge_pairs(
     template_locs = channel_locs[max_chans, :]
     template_dist = scipy.spatial.distance.cdist(template_locs, template_locs, metric="euclidean")
 
-    pair_mask = pair_mask & (template_dist < radius_um)
+    pair_mask = pair_mask & (template_dist <= radius_um)
     indices0, indices1 = np.nonzero(pair_mask)
 
     n_jobs = job_kwargs["n_jobs"]
@@ -337,7 +337,7 @@ def find_merge_pairs(
                 pair_shift[ind0, ind1] = shift
                 pair_values[ind0, ind1] = merge_value
 
-    pair_mask = pair_mask & (template_dist < radius_um)
+    pair_mask = pair_mask & (template_dist <= radius_um)
     indices0, indices1 = np.nonzero(pair_mask)
 
     return labels_set, pair_mask, pair_shift, pair_values

src/spikeinterface/sortingcomponents/features_from_peaks.py

@@ -119,7 +119,7 @@ def __init__(
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
         self.all_channels = all_channels
         self._kwargs.update(dict(radius_um=radius_um, all_channels=all_channels))
         self._dtype = recording.get_dtype()
@@ -157,7 +157,7 @@ def __init__(
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
 
         self._kwargs.update(dict(radius_um=radius_um, all_channels=all_channels))
         self._dtype = recording.get_dtype()
@@ -202,7 +202,7 @@ def __init__(
         self.sigmoid = sigmoid
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
         self.radius_um = radius_um
         self.sparse = sparse
         self._kwargs.update(dict(projections=projections, sigmoid=sigmoid, radius_um=radius_um, sparse=sparse))
@@ -253,7 +253,7 @@ def __init__(
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
 
         self.projections = projections
         self.min_values = min_values
@@ -288,7 +288,7 @@ def __init__(self, recording, name="std_ptp_feature", return_output=True, parent
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
 
         self._kwargs.update(dict(radius_um=radius_um))
 
@@ -313,7 +313,7 @@ def __init__(self, recording, name="global_ptp_feature", return_output=True, par
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
 
         self._kwargs.update(dict(radius_um=radius_um))
 
@@ -338,7 +338,7 @@ def __init__(self, recording, name="kurtosis_ptp_feature", return_output=True, p
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
 
         self._kwargs.update(dict(radius_um=radius_um))
 
@@ -365,7 +365,7 @@ def __init__(self, recording, name="energy_feature", return_output=True, parents
 
         self.contact_locations = recording.get_channel_locations()
         self.channel_distance = get_channel_distances(recording)
-        self.neighbours_mask = self.channel_distance < radius_um
+        self.neighbours_mask = self.channel_distance <= radius_um
 
         self._kwargs.update(dict(radius_um=radius_um))
 

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -542,7 +542,7 @@ def check_params(
         )
 
         channel_distance = get_channel_distances(recording)
-        neighbours_mask = channel_distance < radius_um
+        neighbours_mask = channel_distance <= radius_um
         return args + (neighbours_mask,)
 
     @classmethod
@@ -624,7 +624,7 @@ def check_params(
         neighbour_indices_by_chan = []
         num_channels = recording.get_num_channels()
         for chan in range(num_channels):
-            neighbour_indices_by_chan.append(np.nonzero(channel_distance[chan] < radius_um)[0])
+            neighbour_indices_by_chan.append(np.nonzero(channel_distance[chan] <= radius_um)[0])
         max_neighbs = np.max([len(neigh) for neigh in neighbour_indices_by_chan])
         neighbours_idxs = num_channels * np.ones((num_channels, max_neighbs), dtype=int)
         for i, neigh in enumerate(neighbour_indices_by_chan):
@@ -856,7 +856,7 @@ def check_params(
         abs_threholds = noise_levels * detect_threshold
         exclude_sweep_size = int(exclude_sweep_ms * recording.get_sampling_frequency() / 1000.0)
         channel_distance = get_channel_distances(recording)
-        neighbours_mask = channel_distance < radius_um
+        neighbours_mask = channel_distance <= radius_um
 
         executor = OpenCLDetectPeakExecutor(abs_threholds, exclude_sweep_size, neighbours_mask, peak_sign)