Merge branch 'main' into fix-concatenate-mode

Author: chrishalcrow

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 24.10.0
+    rev: 25.1.0
     hooks:
     -   id: black
         files: ^src/

doc/modules/core.rst

@@ -310,12 +310,13 @@ Since these core extensions are important for all other extensions it is importa
   and the time after (:code:`ms_after`) to ensure that you have a full waveform. Because waveforms occur on multiple channels with multiple
   samples this can be a big data structure.
 * :code:`templates` are calculated from the raw waveform data and are used for downstream analyses
-  (e.g. :code:`spike_amplitudes` are calculated based on the templates). This raises the question: if the :code:`templates` are what are used,
-  then why save the :code:`waveforms`? Well, there are two ways to obtain :code:`templates` data: 1) directly from the raw data (based on the
-  :code:`random_spikes`) or 2) from the :code:`waveforms`. When getting :code:`templates` from the raw data we are limited to obtaining averages
-  and standard deviations. If we calculate the templates from the waveforms, however, we can also calculate the templates as medians or percentiles
+  (e.g. :code:`spike_amplitudes` are calculated based on the templates). This raises the question: if the :code:`templates` are all we need,
+  then why compute and save the :code:`waveforms`? Well, there are two ways to obtain :code:`templates` data: 1) directly from the raw data (based on the
+  :code:`random_spikes`) or 2) from the :code:`waveforms` extension. When getting :code:`templates` from the raw data we are limited to obtaining averages
+  and standard deviations. If we calculate the templates from the waveforms, however, we can also calculate the template medians or percentiles
   in addition to the average or standard deviations of the :code:`waveforms`. So it is important to think about the type of downstream analyses that
-  you may want to do in deciding whether to calculate :code:`templates` with :code:`random_spikes` or using :code:`waveforms`.
+  you may want to do in deciding whether to calculate :code:`templates` with :code:`random_spikes` or using :code:`waveforms`. Note that if the :code:`waveforms`
+  extension is available, :code:`templates` will be calculated from it (the :code:`ms_before` and :code:`ms_after` parameters are also inherited from the :code:`waveforms`).
 * :code:`noise_levels` compute noise-levels in a channel-wise fashion. This provides important information about the specific recording session
   and is important for some downstream quality analyses.
 

examples/tutorials/curation/plot_1_automated_curation.py

@@ -209,8 +209,8 @@ def calculate_moving_avg(label_df, confidence_label, window_size):
 # trained on real data.
 #
 # For example, the following classifiers are trained on Neuropixels data from 11 mice recorded in
-# V1,SC and ALM: https://huggingface.co/AnoushkaJain3/noise_neural_classifier/ and
-# https://huggingface.co/AnoushkaJain3/sua_mua_classifier/ . One will classify units into
+# V1,SC and ALM: https://huggingface.co/SpikeInterface/UnitRefine_noise_neural_classifier/ and
+# https://huggingface.co/SpikeInterface/UnitRefine_sua_mua_classifier/. One will classify units into
 # `noise` or `not-noise` and the other will classify the `not-noise` units into single
 # unit activity (sua) units and multi-unit activity (mua) units.
 #
@@ -221,8 +221,8 @@ def calculate_moving_avg(label_df, confidence_label, window_size):
 
 # Apply the noise/not-noise model
 noise_neuron_labels = sc.auto_label_units(
-    sorting_analyzer = sorting_analyzer,
-    repo_id = "AnoushkaJain3/noise_neural_classifier",
+    sorting_analyzer=sorting_analyzer,
+    repo_id="SpikeInterface/UnitRefine_noise_neural_classifier",
     trust_model=True,
 )
 
@@ -231,8 +231,8 @@ def calculate_moving_avg(label_df, confidence_label, window_size):
 
 # Apply the sua/mua model
 sua_mua_labels = sc.auto_label_units(
-    sorting_analyzer = analyzer_neural,
-    repo_id = "AnoushkaJain3/sua_mua_classifier",
+    sorting_analyzer=analyzer_neural,
+    repo_id="SpikeInterface/UnitRefine_sua_mua_classifier",
     trust_model=True,
 )
 

src/spikeinterface/__init__.py

@@ -1,7 +1,4 @@
-"""
-
-
-"""
+""" """
 
 import importlib.metadata
 

src/spikeinterface/comparison/collision.py

@@ -15,11 +15,11 @@ class CollisionGTComparison(GroundTruthComparison):
 
     Parameters
     ----------
-    gt_sorting : SortingExtractor
+    gt_sorting : BaseSorting
         The first sorting for the comparison
     collision_lag : float, default 2.0
         Collision lag in ms.
-    tested_sorting : SortingExtractor
+    tested_sorting : BaseSorting
         The second sorting for the comparison
     nbins : int, default : 11
         Number of collision bins