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Merged
ttngu207 merged 2 commits into from
Feb 9, 2026
Merged

Pull from main #21

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32578f6
Fix sparse template handling in kilosort reader for SI-exported Phy data
Jan 20, 2026
cde6aad
Merge pull request #20 from dj-sciops/fix/sparse-templates-spike-sites
kushalbakshi Feb 9, 2026
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46 changes: 43 additions & 3 deletions element_array_ephys/readers/kilosort.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,28 @@ def get_best_channel(self, unit):
]
channel_templates = self.data["templates"][template_idx, :, :]
max_channel_idx = np.abs(channel_templates).max(axis=0).argmax()
max_channel = self.data["channel_map"][max_channel_idx]

# Handle sparse templates (SpikeInterface export_to_phy format)
# templates_ind maps (template_idx, sparse_channel_idx) -> actual channel_idx
# Value of -1 indicates padding (no channel at that position)
if "templates_ind" in self.data:
# Use templates_ind to get actual channel index for this template
actual_channel_idx = self.data["templates_ind"][template_idx, max_channel_idx]
if actual_channel_idx >= 0:
max_channel = self.data["channel_map"][actual_channel_idx]
else:
# Fallback if the max is in a padded position (shouldn't happen normally)
log.warning(
f"Unit {unit}: max amplitude in padded channel position, "
"falling back to first valid channel"
)
valid_channels = self.data["templates_ind"][template_idx]
valid_idx = valid_channels[valid_channels >= 0][0]
max_channel = self.data["channel_map"][valid_idx]
max_channel_idx = valid_idx
else:
# Dense templates (native Kilosort format) - use channel_map directly
max_channel = self.data["channel_map"][max_channel_idx]

return max_channel, max_channel_idx

Expand Down Expand Up @@ -179,9 +200,28 @@ def extract_spike_depths(self):
self._data["spike_depths"] = None

# ---- extract spike sites ----
# For each template, find the channel with maximum amplitude
max_site_ind = np.argmax(np.abs(self.data["templates"]).max(axis=1), axis=1)
spike_site_ind = max_site_ind[self.data["spike_templates"]]
self._data["spike_sites"] = self.data["channel_map"][spike_site_ind]

# Handle sparse templates (SpikeInterface export_to_phy format)
# templates_ind maps (template_idx, sparse_channel_idx) -> actual channel_idx
if "templates_ind" in self.data:
# Map sparse indices to actual channel indices using templates_ind
# templates_ind shape: (n_templates, max_sparse_channels)
templates_ind = self.data["templates_ind"]
# For each template, get the actual channel index at the max position
actual_channel_indices = templates_ind[
np.arange(len(max_site_ind)), max_site_ind
]
# Map spike templates to their actual channel indices
spike_actual_channel_ind = actual_channel_indices[
self.data["spike_templates"]
]
self._data["spike_sites"] = self.data["channel_map"][spike_actual_channel_ind]
else:
# Dense templates (native Kilosort format) - use channel_map directly
spike_site_ind = max_site_ind[self.data["spike_templates"]]
self._data["spike_sites"] = self.data["channel_map"][spike_site_ind]


def extract_clustering_info(cluster_output_dir):
Expand Down
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