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Fix: grid convolution select_peaks #4863

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Merged
alejoe91 merged 2 commits into from
Oct 14, 2026
Merged

Fix: grid convolution select_peaks #4863

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1af3969
fix: grid convolution select_peaks
alejoe91 Oct 14, 2026
7a468b1
fix: pop('n_peaks') should default to None
alejoe91 Oct 14, 2026
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2 changes: 1 addition & 1 deletion src/spikeinterface/postprocessing/tests/test_spike_locations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ class TestSpikeLocationsExtension(AnalyzerExtensionCommonTestSuite):
dict(method="center_of_mass", spike_retriever_kwargs=dict(channel_from_template=True)),
dict(method="center_of_mass", spike_retriever_kwargs=dict(channel_from_template=False)),
dict(method="monopolar_triangulation"),
dict(method="grid_convolution"),
dict(method="grid_convolution", method_kwargs=dict(n_peaks=100)),
],
)
def test_extension(self, params):
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6 changes: 6 additions & 0 deletions src/spikeinterface/sortingcomponents/peak_localization/main.py
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Expand Up @@ -60,6 +60,11 @@ def get_localization_pipeline_nodes(
# How do you get peaks

method_kwargs = method_kwargs.copy()
n_peaks = method_kwargs.pop("n_peaks", None)
if n_peaks is not None:
prototype_extra_kwargs = {"n_peaks": n_peaks}
else:
prototype_extra_kwargs = {}
method_kwargs["prototype"], _, _ = get_prototype_and_waveforms_from_peaks(
recording,
peaks=peaks_or_spikes,
Expand All @@ -68,6 +73,7 @@ def get_localization_pipeline_nodes(
channel_index_array=channel_index_array,
job_kwargs=job_kwargs,
seed=seed,
**prototype_extra_kwargs,
)

localization_nodes = method_class(recording, parents=[peak_source, extract_dense_waveforms], **method_kwargs)
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9 changes: 7 additions & 2 deletions src/spikeinterface/sortingcomponents/tools.py
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Expand Up @@ -130,23 +130,28 @@ def get_prototype_and_waveforms_from_peaks(
nbefore = ms_to_samples(ms_before, recording.sampling_frequency)
nafter = ms_to_samples(ms_after, recording.sampling_frequency)

few_peaks = select_peaks(
few_peaks, few_indices = select_peaks(
peaks,
recording=recording,
method="uniform",
n_peaks=n_peaks,
margin=(nbefore, nafter),
channel_index_array=channel_index_array,
return_indices=True,
seed=seed,
)
if channel_index_array is not None:
few_channel_index_array = channel_index_array[few_indices]
else:
few_channel_index_array = None
waveforms = extract_waveform_at_max_channel(
recording,
few_peaks,
ms_before=ms_before,
ms_after=ms_after,
job_kwargs=job_kwargs,
job_name="waveform prototype",
channel_index_array=channel_index_array,
channel_index_array=few_channel_index_array,
)

with np.errstate(divide="ignore", invalid="ignore"):
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