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Add load_templates_clusters parameter (kilosort/phy output) #4376

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Add load_templates_clusters parameter (kilosort/phy output) #4376

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Add load_templates_clusters parameter (kilosort output // phy/manual …
pas-calc Feb 8, 2026
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[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 8, 2026
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17 changes: 15 additions & 2 deletions src/spikeinterface/extractors/phykilosortextractors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,9 @@ class BasePhyKilosortSortingExtractor(BaseSorting):
If True, empty units are removed from the sorting extractor.
load_all_cluster_properties : bool, default: True
If True, all cluster properties are loaded from the tsv/csv files.
load_templates_clusters : str, templates|clusters|auto , default: "auto"
Defines whether to load templates (kilosort output) or clusters (after manual refinement)
If "auto", try to load clusters, fallback to templates if not existing

Notes
-----
Expand Down Expand Up @@ -68,6 +71,7 @@ def __init__(
keep_good_only: bool = False,
remove_empty_units: bool = False,
load_all_cluster_properties: bool = True,
load_templates_clusters="auto",
):
try:
import pandas as pd
Expand All @@ -77,10 +81,19 @@ def __init__(
phy_folder = Path(folder_path)
spike_times = np.load(phy_folder / "spike_times.npy").astype(int)

if (phy_folder / "spike_clusters.npy").is_file():
if load_templates_clusters == "auto":
if (phy_folder / "spike_clusters.npy").is_file():
spike_clusters = np.load(phy_folder / "spike_clusters.npy")
else:
spike_clusters = np.load(phy_folder / "spike_templates.npy")
elif load_templates_clusters == "templates":
spike_clusters = np.load(phy_folder / "spike_templates.npy")
elif load_templates_clusters == "clusters":
spike_clusters = np.load(phy_folder / "spike_clusters.npy")
else:
spike_clusters = np.load(phy_folder / "spike_templates.npy")
raise ValueError(
"Invalid value provided for load_templates_clusters: '{}'.".format(load_templates_clusters)
)

# spike_times and spike_clusters can be 2d sometimes --> convert to 1d.
spike_times = np.atleast_1d(spike_times.squeeze())
Expand Down
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