Rename spike_labels -> spike_unit_indices

Author: JoeZiminski

src/spikeinterface/postprocessing/correlograms.py

@@ -262,16 +262,16 @@ def _compute_correlograms_numpy(sorting, window_size, bin_size):
 
     for seg_index in range(num_seg):
         spike_times = spikes[seg_index]["sample_index"]
-        spike_labels = spikes[seg_index]["unit_index"]
+        spike_unit_indices = spikes[seg_index]["unit_index"]
 
-        c0 = correlogram_for_one_segment(spike_times, spike_labels, window_size, bin_size)
+        c0 = correlogram_for_one_segment(spike_times, spike_unit_indices, window_size, bin_size)
 
         correlograms += c0
 
     return correlograms
 
 
-def correlogram_for_one_segment(spike_times, spike_labels, window_size, bin_size):
+def correlogram_for_one_segment(spike_times, spike_unit_indices, window_size, bin_size):
     """
     A very well optimized algorithm for the cross-correlation of
     spike trains, copied from the Phy package, written by Cyrille Rossant.
@@ -281,7 +281,7 @@ def correlogram_for_one_segment(spike_times, spike_labels, window_size, bin_size
     spike_times : np.ndarray
         An array of spike times (in samples, not seconds).
         This contains spikes from all units.
-    spike_labels : np.ndarray
+    spike_unit_indices : np.ndarray
         An array of labels indicating the unit of the corresponding
         spike in `spike_times`.
     window_size : int
@@ -315,7 +315,7 @@ def correlogram_for_one_segment(spike_times, spike_labels, window_size, bin_size
     match within the window size.
     """
     num_bins, num_half_bins = _compute_num_bins(window_size, bin_size)
-    num_units = len(np.unique(spike_labels))
+    num_units = len(np.unique(spike_unit_indices))
 
     correlograms = np.zeros((num_units, num_units, num_bins), dtype="int64")
 
@@ -347,12 +347,12 @@ def correlogram_for_one_segment(spike_times, spike_labels, window_size, bin_size
             # to be incremented, taking into account the spike unit labels.
             if sign == 1:
                 indices = np.ravel_multi_index(
-                    (spike_labels[+shift:][m], spike_labels[:-shift][m], spike_diff_b[m] + num_half_bins),
+                    (spike_unit_indices[+shift:][m], spike_unit_indices[:-shift][m], spike_diff_b[m] + num_half_bins),
                     correlograms.shape,
                 )
             else:
                 indices = np.ravel_multi_index(
-                    (spike_labels[:-shift][m], spike_labels[+shift:][m], spike_diff_b[m] + num_half_bins),
+                    (spike_unit_indices[:-shift][m], spike_unit_indices[+shift:][m], spike_diff_b[m] + num_half_bins),
                     correlograms.shape,
                 )
 
@@ -411,12 +411,12 @@ def _compute_correlograms_numba(sorting, window_size, bin_size):
 
     for seg_index in range(sorting.get_num_segments()):
         spike_times = spikes[seg_index]["sample_index"]
-        spike_labels = spikes[seg_index]["unit_index"]
+        spike_unit_indices = spikes[seg_index]["unit_index"]
 
         _compute_correlograms_one_segment_numba(
             correlograms,
             spike_times.astype(np.int64, copy=False),
-            spike_labels.astype(np.int32, copy=False),
+            spike_unit_indices.astype(np.int32, copy=False),
             window_size,
             bin_size,
             num_half_bins,
@@ -433,7 +433,7 @@ def _compute_correlograms_numba(sorting, window_size, bin_size):
         cache=False,
     )
     def _compute_correlograms_one_segment_numba(
-        correlograms, spike_times, spike_labels, window_size, bin_size, num_half_bins
+        correlograms, spike_times, spike_unit_indices, window_size, bin_size, num_half_bins
     ):
         """
         Compute the correlograms using `numba` for speed.
@@ -455,7 +455,7 @@ def _compute_correlograms_one_segment_numba(
         spike_times : np.ndarray
             An array of spike times (in samples, not seconds).
             This contains spikes from all units.
-        spike_labels : np.ndarray
+        spike_unit_indices : np.ndarray
             An array of labels indicating the unit of the corresponding
             spike in `spike_times`.
         window_size : int
@@ -494,4 +494,4 @@ def _compute_correlograms_one_segment_numba(
 
                 bin = diff // bin_size
 
-                correlograms[spike_labels[i], spike_labels[j], num_half_bins + bin] += 1
+                correlograms[spike_unit_indices[i], spike_unit_indices[j], num_half_bins + bin] += 1

src/spikeinterface/postprocessing/tests/test_correlograms.py

@@ -189,18 +189,18 @@ def test_compute_correlograms(fill_all_bins, on_time_bin, multi_segment):
     counts should double when two segments with identical spike times / labels are used.
     """
     sampling_frequency = 30000
-    window_ms, bin_ms, spike_times, spike_labels, expected_bins, expected_result_auto, expected_result_corr = (
+    window_ms, bin_ms, spike_times, spike_unit_indices, expected_bins, expected_result_auto, expected_result_corr = (
         generate_correlogram_test_dataset(sampling_frequency, fill_all_bins, on_time_bin)
     )
 
     if multi_segment:
         sorting = NumpySorting.from_times_labels(
-            times_list=[spike_times], labels_list=[spike_labels], sampling_frequency=sampling_frequency
+            times_list=[spike_times], labels_list=[spike_unit_indices], sampling_frequency=sampling_frequency
         )
     else:
         sorting = NumpySorting.from_times_labels(
             times_list=[spike_times, spike_times],
-            labels_list=[spike_labels, spike_labels],
+            labels_list=[spike_unit_indices, spike_unit_indices],
             sampling_frequency=sampling_frequency,
         )
         expected_result_auto *= 2
@@ -235,13 +235,13 @@ def test_compute_correlograms_different_units(method):
     spike_times = np.array([0, 4, 8, 16]) / 1000 * sampling_frequency
     spike_times.astype(int)
 
-    spike_labels = np.array([0, 1, 0, 1])
+    spike_unit_indices = np.array([0, 1, 0, 1])
 
     window_ms = 40
     bin_ms = 5
 
     sorting = NumpySorting.from_times_labels(
-        times_list=[spike_times], labels_list=[spike_labels], sampling_frequency=sampling_frequency
+        times_list=[spike_times], labels_list=[spike_unit_indices], sampling_frequency=sampling_frequency
     )
 
     result, bins = compute_correlograms(sorting, window_ms=window_ms, bin_ms=bin_ms, method=method)
@@ -323,7 +323,7 @@ def generate_correlogram_test_dataset(sampling_frequency, fill_all_bins, hit_bin
     # Now, make a set of times that increase by `base_diff_time` e.g.
     # if base_diff_time=0.0051 then our spike times are [`0.0051, 0.0102, ...]`
     spike_times = np.repeat(np.arange(num_filled_bins), num_units) * base_diff_time
-    spike_labels = np.tile(np.arange(num_units), int(spike_times.size / num_units))
+    spike_unit_indices = np.tile(np.arange(num_units), int(spike_times.size / num_units))
 
     spike_times *= sampling_frequency
     spike_times = spike_times.astype(int)
@@ -368,4 +368,4 @@ def generate_correlogram_test_dataset(sampling_frequency, fill_all_bins, hit_bin
     expected_result_corr = expected_result_auto.copy()
     expected_result_corr[int(num_bins / 2)] = num_filled_bins
 
-    return window_ms, bin_ms, spike_times, spike_labels, expected_bins, expected_result_auto, expected_result_corr
+    return window_ms, bin_ms, spike_times, spike_unit_indices, expected_bins, expected_result_auto, expected_result_corr