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https://github.com/K-Dense-AI/claude-scientific-skills.git
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312f18ae60
Adds comprehensive toolkit for analyzing Neuropixels high-density neural recordings using SpikeInterface, Allen Institute, and IBL best practices. Features: - Data loading from SpikeGLX, Open Ephys, and NWB formats - Preprocessing pipelines (filtering, phase shift, CAR, bad channel detection) - Motion/drift estimation and correction - Spike sorting integration (Kilosort4, SpykingCircus2, Mountainsort5) - Quality metrics computation (SNR, ISI violations, presence ratio) - Automated curation using Allen/IBL criteria - AI-assisted visual curation for uncertain units - Export to Phy and NWB formats Supports Neuropixels 1.0 and 2.0 probes. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
99 lines
2.5 KiB
Python
99 lines
2.5 KiB
Python
#!/usr/bin/env python
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"""
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Run spike sorting on preprocessed recording.
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Usage:
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python run_sorting.py preprocessed/ --sorter kilosort4 --output sorting/
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"""
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import argparse
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from pathlib import Path
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import spikeinterface.full as si
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# Default parameters for each sorter
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SORTER_DEFAULTS = {
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'kilosort4': {
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'batch_size': 30000,
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'nblocks': 1,
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'Th_learned': 8,
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'Th_universal': 9,
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},
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'kilosort3': {
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'do_CAR': False, # Already done in preprocessing
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},
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'spykingcircus2': {
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'apply_preprocessing': False,
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},
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'mountainsort5': {
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'filter': False,
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'whiten': False,
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},
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}
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def run_sorting(
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input_path: str,
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output_dir: str,
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sorter: str = 'kilosort4',
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sorter_params: dict = None,
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n_jobs: int = -1,
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):
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"""Run spike sorting."""
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print(f"Loading preprocessed recording from: {input_path}")
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recording = si.load_extractor(Path(input_path) / 'preprocessed')
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print(f"Recording: {recording.get_num_channels()} channels, {recording.get_total_duration():.1f}s")
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# Get sorter parameters
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params = SORTER_DEFAULTS.get(sorter, {}).copy()
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if sorter_params:
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params.update(sorter_params)
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print(f"Running {sorter} with params: {params}")
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output_path = Path(output_dir)
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# Run sorter (note: parameter is 'folder' not 'output_folder' in newer SpikeInterface)
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sorting = si.run_sorter(
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sorter,
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recording,
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folder=output_path / f'{sorter}_output',
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verbose=True,
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**params,
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)
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print(f"\nSorting complete!")
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print(f" Units found: {len(sorting.unit_ids)}")
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print(f" Total spikes: {sum(len(sorting.get_unit_spike_train(uid)) for uid in sorting.unit_ids)}")
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# Save sorting
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sorting.save(folder=output_path / 'sorting')
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print(f" Saved to: {output_path / 'sorting'}")
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return sorting
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def main():
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parser = argparse.ArgumentParser(description='Run spike sorting')
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parser.add_argument('input', help='Path to preprocessed recording')
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parser.add_argument('--output', '-o', default='sorting/', help='Output directory')
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parser.add_argument('--sorter', '-s', default='kilosort4',
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choices=['kilosort4', 'kilosort3', 'spykingcircus2', 'mountainsort5'])
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parser.add_argument('--n-jobs', type=int, default=-1, help='Number of parallel jobs')
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args = parser.parse_args()
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run_sorting(
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args.input,
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args.output,
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sorter=args.sorter,
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n_jobs=args.n_jobs,
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)
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if __name__ == '__main__':
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main()
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