Skip to content

Patch lag similarities #4345

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
samuelgarcia merged 10 commits into from
Jan 29, 2026
+2 −16
Merged

Patch lag similarities #4345

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
30075c0
WIP
yger Jan 28, 2026
fe5f30d
WIP
yger Jan 28, 2026
8fdf96d
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 28, 2026
3a6e954
WIP
yger Jan 29, 2026
a8a72c1
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 29, 2026
5e8e7fc
Clean
yger Jan 29, 2026
63e7919
clean
yger Jan 29, 2026
3d2d6b1
WIP
yger Jan 29, 2026
4311a26
WIP
yger Jan 29, 2026
a894c26
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 29, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
55 changes: 38 additions & 17 deletions src/spikeinterface/postprocessing/template_similarity.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -71,7 +71,8 @@ def _select_extension_data(self, unit_ids):
# filter metrics dataframe
unit_indices = self.sorting_analyzer.sorting.ids_to_indices(unit_ids)
new_similarity = self.data["similarity"][unit_indices][:, unit_indices]
return dict(similarity=new_similarity)
new_lags = self.data["lags"][unit_indices][:, unit_indices]
return dict(similarity=new_similarity, lags=new_lags)

def _merge_extension_data(
self, merge_unit_groups, new_unit_ids, new_sorting_analyzer, keep_mask=None, verbose=False, **job_kwargs
Expand All @@ -90,7 +91,7 @@ def _merge_extension_data(
new_sorting_analyzer.sparsity.mask[keep, :], new_unit_ids, new_sorting_analyzer.channel_ids
)

new_similarity, _ = compute_similarity_with_templates_array(
new_similarity, new_lags = compute_similarity_with_templates_array(
new_templates_array,
all_templates_array,
method=self.params["method"],
Expand All @@ -101,10 +102,12 @@ def _merge_extension_data(
)

old_similarity = self.data["similarity"]
old_lags = self.data["lags"]

all_new_unit_ids = new_sorting_analyzer.unit_ids
n = all_new_unit_ids.size
similarity = np.zeros((n, n), dtype=old_similarity.dtype)
lags = np.zeros((n, n), dtype=old_lags.dtype)

local_mask = ~np.isin(all_new_unit_ids, new_unit_ids)
sub_units_ids = all_new_unit_ids[local_mask]
Expand All @@ -117,13 +120,20 @@ def _merge_extension_data(
similarity[unit_ind1, sub_units_inds] = s
similarity[sub_units_inds, unit_ind1] = s

l = self.data["lags"][old_ind1, old_units_inds]
lags[unit_ind1, sub_units_inds] = l
lags[sub_units_inds, unit_ind1] = l

# insert new similarity both way
for unit_ind, unit_id in enumerate(all_new_unit_ids):
if unit_id in new_unit_ids:
new_index = list(new_unit_ids).index(unit_id)
similarity[unit_ind, :] = new_similarity[new_index, :]
similarity[:, unit_ind] = new_similarity[new_index, :]

lags[unit_ind, :] = new_lags[new_index, :]
lags[:, unit_ind] = new_lags[new_index, :]

return dict(similarity=similarity)

def _split_extension_data(self, split_units, new_unit_ids, new_sorting_analyzer, verbose=False, **job_kwargs):
Expand All @@ -142,7 +152,7 @@ def _split_extension_data(self, split_units, new_unit_ids, new_sorting_analyzer,
new_sorting_analyzer.sparsity.mask[keep, :], new_unit_ids_f, new_sorting_analyzer.channel_ids
)

new_similarity, _ = compute_similarity_with_templates_array(
new_similarity, new_lags = compute_similarity_with_templates_array(
new_templates_array,
all_templates_array,
method=self.params["method"],
Expand All @@ -153,10 +163,12 @@ def _split_extension_data(self, split_units, new_unit_ids, new_sorting_analyzer,
)

old_similarity = self.data["similarity"]
old_lags = self.data["lags"]

all_new_unit_ids = new_sorting_analyzer.unit_ids
n = all_new_unit_ids.size
similarity = np.zeros((n, n), dtype=old_similarity.dtype)
lags = np.zeros((n, n), dtype=old_lags.dtype)

local_mask = ~np.isin(all_new_unit_ids, new_unit_ids_f)
sub_units_ids = all_new_unit_ids[local_mask]
Expand All @@ -169,13 +181,20 @@ def _split_extension_data(self, split_units, new_unit_ids, new_sorting_analyzer,
similarity[unit_ind1, sub_units_inds] = s
similarity[sub_units_inds, unit_ind1] = s

l = self.data["lags"][old_ind1, old_units_inds]
lags[unit_ind1, sub_units_inds] = l
lags[sub_units_inds, unit_ind1] = l

# insert new similarity both way
for unit_ind, unit_id in enumerate(all_new_unit_ids):
if unit_id in new_unit_ids_f:
new_index = list(new_unit_ids_f).index(unit_id)
similarity[unit_ind, :] = new_similarity[new_index, :]
similarity[:, unit_ind] = new_similarity[new_index, :]

lags[unit_ind, :] = new_lags[new_index, :]
lags[:, unit_ind] = new_lags[new_index, :]

return dict(similarity=similarity)

def _run(self, verbose=False):
Expand All @@ -184,7 +203,7 @@ def _run(self, verbose=False):
self.sorting_analyzer, return_in_uV=self.sorting_analyzer.return_in_uV
)
sparsity = self.sorting_analyzer.sparsity
similarity, _ = compute_similarity_with_templates_array(
similarity, lags = compute_similarity_with_templates_array(
templates_array,
templates_array,
method=self.params["method"],
Expand All @@ -194,10 +213,14 @@ def _run(self, verbose=False):
other_sparsity=sparsity,
)
self.data["similarity"] = similarity
self.data["lags"] = lags

def _get_data(self):
return self.data["similarity"]

def get_lags(self):
return self.data["lags"]


# @alessio: compute_template_similarity() is now one inner SortingAnalyzer only
register_result_extension(ComputeTemplateSimilarity)
Expand Down Expand Up @@ -235,9 +258,9 @@ def _compute_similarity_matrix_numpy(
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
# 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)

Expand All @@ -259,10 +282,8 @@ 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


Expand Down Expand Up @@ -332,9 +353,9 @@ def _compute_similarity_matrix_numba(

j = overlapping_templates[gcount]
# symmetric values are handled later
if same_array and j < i:
# no need exhaustive looping when same template
continue
# 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()

Expand Down Expand Up @@ -370,10 +391,10 @@ 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]
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
# if same_array and num_shifts != 0:
# distances[num_shifts_both_sides - count - 1] = distances[count].T

return distances

Expand Down Expand Up @@ -447,7 +468,7 @@ def compute_similarity_with_templates_array(
distances = np.min(distances, axis=0)
similarity = 1 - distances

return similarity, lags
return similarity, lags.astype(np.int32)


def compute_template_similarity_by_pair(
Expand Down
1 change: 0 additions & 1 deletion src/spikeinterface/sorters/internal/lupin.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down