Try to remove get_template_extremum_channel() and peak_sign in many place

src/spikeinterface/benchmark/benchmark_clustering.py

@@ -13,7 +13,6 @@
 from spikeinterface.core.job_tools import fix_job_kwargs, split_job_kwargs
 from .benchmark_base import Benchmark, BenchmarkStudy, MixinStudyUnitCount
 from spikeinterface.core.sortinganalyzer import create_sorting_analyzer
-from spikeinterface.core.template_tools import get_template_extremum_channel
 
 
 class ClusteringBenchmark(Benchmark):

src/spikeinterface/benchmark/tests/test_benchmark_clustering.py

@@ -5,8 +5,6 @@
 
 from spikeinterface.benchmark.tests.common_benchmark_testing import make_dataset
 from spikeinterface.benchmark.benchmark_clustering import ClusteringStudy
-from spikeinterface.core.sortinganalyzer import create_sorting_analyzer
-from spikeinterface.core.template_tools import get_template_extremum_channel
 
 from pathlib import Path
 
@@ -33,7 +31,8 @@ def test_benchmark_clustering(create_cache_folder):
 
         # sorting_analyzer = create_sorting_analyzer(gt_sorting, recording, format="memory", sparse=False)
         # sorting_analyzer.compute(["random_spikes", "templates"])
-        extremum_channel_inds = get_template_extremum_channel(gt_analyzer, outputs="index")
+        extremum_channel_inds = gt_analyzer.get_main_channels(outputs="index", with_dict=True)
+
         spikes = gt_analyzer.sorting.to_spike_vector(extremum_channel_inds=extremum_channel_inds)
         peaks[dataset] = spikes
 

src/spikeinterface/benchmark/tests/test_benchmark_peak_detection.py

@@ -6,7 +6,6 @@
 from spikeinterface.benchmark.tests.common_benchmark_testing import make_dataset
 from spikeinterface.benchmark.benchmark_peak_detection import PeakDetectionStudy
 from spikeinterface.core.sortinganalyzer import create_sorting_analyzer
-from spikeinterface.core.template_tools import get_template_extremum_channel
 
 
 @pytest.mark.skip()
@@ -30,7 +29,7 @@ def test_benchmark_peak_detection(create_cache_folder):
         sorting_analyzer = create_sorting_analyzer(gt_sorting, recording, format="memory", sparse=False, **job_kwargs)
         sorting_analyzer.compute("random_spikes")
         sorting_analyzer.compute("templates", **job_kwargs)
-        extremum_channel_inds = get_template_extremum_channel(sorting_analyzer, outputs="index")
+        extremum_channel_inds = sorting_analyzer.get_main_channels(outputs="index", with_dict=True)
         spikes = gt_sorting.to_spike_vector(extremum_channel_inds=extremum_channel_inds)
         peaks[dataset] = spikes
 

src/spikeinterface/core/basesorting.py

@@ -15,6 +15,9 @@ class BaseSorting(BaseExtractor):
     """
     Abstract class representing several segment several units and relative spiketrains.
     """
+    _main_properties = [
+        "main_channel_index",
+    ]
 
     def __init__(self, sampling_frequency: float, unit_ids: list):
         BaseExtractor.__init__(self, unit_ids)
@@ -786,6 +789,7 @@ def _compute_and_cache_spike_vector(self) -> None:
         self._cached_spike_vector = spikes
         self._cached_spike_vector_segment_slices = segment_slices
 
+    # TODO sam : change extremum_channel_inds to main_channel_index with vector
     def to_spike_vector(
         self,
         concatenated=True,
@@ -806,7 +810,8 @@ def to_spike_vector(
         extremum_channel_inds : None or dict, default: None
             If a dictionnary of unit_id to channel_ind is given then an extra field "channel_index".
             This can be convinient for computing spikes postion after sorter.
-            This dict can be computed with `get_template_extremum_channel(we, outputs="index")`
+            This dict can be given by analyzer.get_main_channels(outputs="index", with_dict=True)
+
         use_cache : bool, default: True
             When True the spikes vector is cached as an attribute of the object (`_cached_spike_vector`).
             This caching only occurs when extremum_channel_inds=None.

src/spikeinterface/core/generate.py

@@ -2444,6 +2444,10 @@ def generate_ground_truth_recording(
             **generate_templates_kwargs,
         )
         sorting.set_property("gt_unit_locations", unit_locations)
+        distances = np.linalg.norm(unit_locations[:, np.newaxis, :2] - channel_locations[np.newaxis, :, :], axis=2)
+        main_channel_index = np.argmin(distances, axis=1)
+        sorting.set_property("main_channel_index", main_channel_index)
+
     else:
         assert templates.shape[0] == num_units
 

src/spikeinterface/core/node_pipeline.py

@@ -132,6 +132,8 @@ def compute(self, traces, start_frame, end_frame, segment_index, max_margin, pea
         return (local_peaks,)
 
 
+# TODO sam replace extremum_channels_indices by main_channel_index
+
 # this is not implemented yet this will be done in separted PR
 class SpikeRetriever(PeakSource):
     """