Merge branch 'main' into plot_bench

Author: samuelgarcia

README.md

@@ -134,4 +134,4 @@ If you find SpikeInterface useful in your research, please cite:
 ```
 
 Please also cite other relevant papers for the specific components you use.
-For a ful list of references, please check the [references](https://spikeinterface.readthedocs.io/en/latest/references.html) page.
+For a full list of references, please check the [references](https://spikeinterface.readthedocs.io/en/latest/references.html) page.

doc/api.rst

@@ -215,6 +215,7 @@ spikeinterface.postprocessing
     .. autofunction:: compute_spike_locations
     .. autofunction:: compute_template_similarity
     .. autofunction:: compute_correlograms
+    .. autofunction:: compute_acgs_3d
     .. autofunction:: compute_isi_histograms
     .. autofunction:: get_template_metric_names
     .. autofunction:: align_sorting

doc/how_to/process_by_channel_group.rst

@@ -97,12 +97,13 @@ to any preprocessing function.
     shifted_recordings = spre.phase_shift(split_recording_dict)
     filtered_recording = spre.bandpass_filter(shifted_recording)
     referenced_recording = spre.common_reference(filtered_recording)
+    good_channels_recording = spre.detect_and_remove_bad_channels(filtered_recording)
 
 We can then aggregate the recordings back together using the ``aggregate_channels`` function
 
 .. code-block:: python
 
-    combined_preprocessed_recording = aggregate_channels(referenced_recording)
+    combined_preprocessed_recording = aggregate_channels(good_channels_recording)
 
 Now, when ``combined_preprocessed_recording`` is used in sorting, plotting, or whenever
 calling its :py:func:`~get_traces` method, the data will have been

doc/modules/postprocessing.rst

@@ -358,6 +358,26 @@ with shape (num_units, num_units, num_bins) with all correlograms for each pair
 For more information, see :py:func:`~spikeinterface.postprocessing.compute_correlograms`
 
 
+acgs_3d
+^^^^^^^
+
+This extension computes the 3D Autocorrelograms (3D-ACG) from units' spike times to analyze how a neuron's temporal
+firing pattern varies with its firing rate. The 3D-ACG, described in [Beau]_ et al., 2025, provides rich
+representations of a unit's spike train statistics while accounting for firing rate modulations.
+
+.. code-block:: python
+
+    acg3d = sorting_analyzer.compute(
+        input="acgs_3d",
+        window_ms=50.0,
+        bin_ms=1.0,
+        num_firing_rate_quantiles=10,
+        smoothing_factor=250,
+    )
+
+For more information, see :py:func:`~spikeinterface.postprocessing.compute_acgs_3d`
+
+
 isi_histograms
 ^^^^^^^^^^^^^^
 

doc/modules/preprocessing.rst

@@ -247,6 +247,18 @@ interpolated with the :code:`interpolate_bad_channels()` function (channels labe
     # Case 2 : interpolate then
     rec_clean = interpolate_bad_channels(recording=rec, bad_channel_ids=bad_channel_ids)
 
+Once you have tested these functions and decided on your workflow, you can use the `detect_and_*`
+functions to do everything at once. These return a Preprocessor class, so are consistent with
+the "chain" concept for this module. For example:
+
+.. code-block:: python
+
+    # detect and remove bad channels
+    rec_only_good_channels = detect_and_remove_bad_channels(recording=rec)
+
+    # detect and interpolate the bad channels
+    rec_interpolated_channels = detect_and_interpolate_bad_channels(recording=rec)
+
 
 * :py:func:`~spikeinterface.preprocessing.detect_bad_channels()`
 * :py:func:`~spikeinterface.preprocessing.interpolate_bad_channels()`

doc/references.rst

@@ -44,6 +44,11 @@ please include the appropriate citation for the :code:`sorter_name` parameter yo
 - :code:`wavclus` [Chaure]_
 - :code:`yass` [Lee]_
 
+Postprocessing Module
+---------------------
+
+If you use the :code:`acgs_3d` extensions, (i.e. :code:`postprocessing.compute_acgs_3d`, :code:`postprocessing.ComputeACG3D`) please cite [Beau]_
+
 Qualitymetrics Module
 ---------------------
 If you use the :code:`qualitymetrics` module, i.e. you use the :code:`analyzer.compute()`
@@ -76,6 +81,8 @@ If you use the :code:`get_potential_auto_merge` method from the curation module,
 References
 ----------
 
+.. [Beau] `A deep learning strategy to identify cell types across species from high-density extracellular recordings. 2025. <https://doi.org/10.1016/j.cell.2025.01.041>`_
+
 .. [Buccino] `SpikeInterface, a unified framework for spike sorting. 2020. <https://pubmed.ncbi.nlm.nih.gov/33170122/>`_
 
 .. [Buzsáki] `The Log-Dynamic Brain: How Skewed Distributions Affect Network Operations. 2014. <https://pubmed.ncbi.nlm.nih.gov/24569488/>`_
@@ -112,6 +119,8 @@ References
 
 .. [Niediek] `Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours. 2016. <https://pubmed.ncbi.nlm.nih.gov/27930664/>`_
 
+.. [npyx] `NeuroPyxels: loading, processing and plotting Neuropixels data in python. 2021. <https://doi.org/10.5281/zenodo.5509776>_`
+
 .. [Pachitariu] `Spike sorting with Kilosort4. 2024. <https://pubmed.ncbi.nlm.nih.gov/38589517/>`_
 
 .. [Pouzat] `Using noise signature to optimize spike-sorting and to assess neuronal classification quality. 2002. <https://pubmed.ncbi.nlm.nih.gov/12535763/>`_

pyproject.toml

@@ -128,6 +128,9 @@ test_core = [
     # for release we need pypi, so this need to be commented
     "probeinterface @ git+https://github.com/SpikeInterface/probeinterface.git",
     "neo @ git+https://github.com/NeuralEnsemble/python-neo.git",
+
+    # for slurm jobs,
+    "pytest-mock"
 ]
 
 test_extractors = [
@@ -176,6 +179,9 @@ test = [
     # for release we need pypi, so this need to be commented
     "probeinterface @ git+https://github.com/SpikeInterface/probeinterface.git",
     "neo @ git+https://github.com/NeuralEnsemble/python-neo.git",
+
+    # for slurm jobs
+    "pytest-mock",
 ]
 
 docs = [

src/spikeinterface/benchmark/benchmark_matching.py

@@ -38,9 +38,11 @@ def run(self, **job_kwargs):
         self.result = {"sorting": sorting, "spikes": spikes}
         self.result["templates"] = self.templates
 
-    def compute_result(self, with_collision=False, **result_params):
+    def compute_result(self, with_collision=False, match_score=0.5, exhaustive_gt=True):
         sorting = self.result["sorting"]
-        comp = compare_sorter_to_ground_truth(self.gt_sorting, sorting, exhaustive_gt=True)
+        comp = compare_sorter_to_ground_truth(
+            self.gt_sorting, sorting, exhaustive_gt=exhaustive_gt, match_score=match_score
+        )
         self.result["gt_comparison"] = comp
         if with_collision:
             self.result["gt_collision"] = CollisionGTComparison(self.gt_sorting, sorting, exhaustive_gt=True)

src/spikeinterface/benchmark/benchmark_merging.py

@@ -35,9 +35,11 @@ def run(self, **job_kwargs):
 
         self.result["sorting"] = merged_analyzer.sorting
 
-    def compute_result(self, **result_params):
+    def compute_result(self, match_score=0.5, exhaustive_gt=True):
         sorting = self.result["sorting"]
-        comp = compare_sorter_to_ground_truth(self.gt_sorting, sorting, exhaustive_gt=True)
+        comp = compare_sorter_to_ground_truth(
+            self.gt_sorting, sorting, exhaustive_gt=exhaustive_gt, match_score=match_score
+        )
         self.result["gt_comparison"] = comp
 
     _run_key_saved = [("sorting", "sorting"), ("merges", "pickle"), ("merged_pairs", "pickle"), ("outs", "pickle")]

src/spikeinterface/benchmark/benchmark_sorter.py

@@ -26,10 +26,12 @@ def run(self):
         sorting = NumpySorting.from_sorting(raw_sorting)
         self.result = {"sorting": sorting}
 
-    def compute_result(self, exhaustive_gt=True):
+    def compute_result(self, match_score=0.5, exhaustive_gt=True):
         # run becnhmark result
         sorting = self.result["sorting"]
-        comp = compare_sorter_to_ground_truth(self.gt_sorting, sorting, exhaustive_gt=exhaustive_gt)
+        comp = compare_sorter_to_ground_truth(
+            self.gt_sorting, sorting, exhaustive_gt=exhaustive_gt, match_score=match_score
+        )
         self.result["gt_comparison"] = comp
 
     _run_key_saved = [