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Add neuropixels-analysis skill for extracellular electrophysiology
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>
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Robert
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#!/usr/bin/env python
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"""
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Neuropixels Analysis Template
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Complete analysis workflow from raw data to curated units.
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Copy and customize this template for your analysis.
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Usage:
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1. Copy this file to your analysis directory
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2. Update the PARAMETERS section
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3. Run: python analysis_template.py
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"""
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# =============================================================================
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# PARAMETERS - Customize these for your analysis
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# =============================================================================
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# Input/Output paths
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DATA_PATH = '/path/to/your/spikeglx/data/'
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OUTPUT_DIR = 'analysis_output/'
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DATA_FORMAT = 'spikeglx' # 'spikeglx', 'openephys', or 'nwb'
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STREAM_ID = 'imec0.ap' # For multi-probe recordings
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# Preprocessing parameters
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FREQ_MIN = 300 # Highpass filter (Hz)
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FREQ_MAX = 6000 # Lowpass filter (Hz)
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APPLY_PHASE_SHIFT = True
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APPLY_CMR = True
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DETECT_BAD_CHANNELS = True
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# Motion correction
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CORRECT_MOTION = True
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MOTION_PRESET = 'nonrigid_accurate' # 'kilosort_like', 'nonrigid_fast_and_accurate'
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# Spike sorting
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SORTER = 'kilosort4' # 'kilosort4', 'spykingcircus2', 'mountainsort5'
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SORTER_PARAMS = {
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'batch_size': 30000,
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'nblocks': 1, # Increase for long recordings with drift
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}
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# Quality metrics and curation
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CURATION_METHOD = 'allen' # 'allen', 'ibl', 'strict'
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# Processing
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N_JOBS = -1 # -1 = all cores
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# =============================================================================
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# ANALYSIS PIPELINE - Usually no need to modify below
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# =============================================================================
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from pathlib import Path
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import json
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import spikeinterface.full as si
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from spikeinterface.exporters import export_to_phy
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def main():
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"""Run the full analysis pipeline."""
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output_path = Path(OUTPUT_DIR)
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output_path.mkdir(parents=True, exist_ok=True)
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# =========================================================================
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# 1. LOAD DATA
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# =========================================================================
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print("=" * 60)
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print("1. LOADING DATA")
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print("=" * 60)
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if DATA_FORMAT == 'spikeglx':
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recording = si.read_spikeglx(DATA_PATH, stream_id=STREAM_ID)
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elif DATA_FORMAT == 'openephys':
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recording = si.read_openephys(DATA_PATH)
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elif DATA_FORMAT == 'nwb':
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recording = si.read_nwb(DATA_PATH)
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else:
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raise ValueError(f"Unknown format: {DATA_FORMAT}")
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print(f"Recording: {recording.get_num_channels()} channels")
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print(f"Duration: {recording.get_total_duration():.1f} seconds")
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print(f"Sampling rate: {recording.get_sampling_frequency()} Hz")
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# =========================================================================
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# 2. PREPROCESSING
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# =========================================================================
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print("\n" + "=" * 60)
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print("2. PREPROCESSING")
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print("=" * 60)
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rec = recording
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# Bandpass filter
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print(f"Applying bandpass filter ({FREQ_MIN}-{FREQ_MAX} Hz)...")
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rec = si.bandpass_filter(rec, freq_min=FREQ_MIN, freq_max=FREQ_MAX)
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# Phase shift correction
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if APPLY_PHASE_SHIFT:
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print("Applying phase shift correction...")
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rec = si.phase_shift(rec)
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# Bad channel detection
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if DETECT_BAD_CHANNELS:
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print("Detecting bad channels...")
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bad_ids, _ = si.detect_bad_channels(rec)
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if len(bad_ids) > 0:
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print(f" Removing {len(bad_ids)} bad channels")
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rec = rec.remove_channels(bad_ids)
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# Common median reference
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if APPLY_CMR:
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print("Applying common median reference...")
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rec = si.common_reference(rec, operator='median', reference='global')
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# Save preprocessed
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print("Saving preprocessed recording...")
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rec.save(folder=output_path / 'preprocessed', n_jobs=N_JOBS)
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# =========================================================================
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# 3. MOTION CORRECTION
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# =========================================================================
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if CORRECT_MOTION:
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print("\n" + "=" * 60)
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print("3. MOTION CORRECTION")
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print("=" * 60)
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print(f"Estimating and correcting motion (preset: {MOTION_PRESET})...")
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rec = si.correct_motion(
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rec,
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preset=MOTION_PRESET,
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folder=output_path / 'motion',
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)
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# =========================================================================
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# 4. SPIKE SORTING
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# =========================================================================
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print("\n" + "=" * 60)
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print("4. SPIKE SORTING")
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print("=" * 60)
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print(f"Running {SORTER}...")
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sorting = si.run_sorter(
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SORTER,
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rec,
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output_folder=output_path / f'{SORTER}_output',
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verbose=True,
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**SORTER_PARAMS,
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)
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print(f"Found {len(sorting.unit_ids)} units")
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# =========================================================================
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# 5. POSTPROCESSING
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# =========================================================================
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print("\n" + "=" * 60)
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print("5. POSTPROCESSING")
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print("=" * 60)
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print("Creating SortingAnalyzer...")
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analyzer = si.create_sorting_analyzer(
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sorting,
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rec,
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format='binary_folder',
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folder=output_path / 'analyzer',
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sparse=True,
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)
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print("Computing extensions...")
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analyzer.compute('random_spikes', max_spikes_per_unit=500)
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analyzer.compute('waveforms', ms_before=1.0, ms_after=2.0)
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analyzer.compute('templates', operators=['average', 'std'])
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analyzer.compute('noise_levels')
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analyzer.compute('spike_amplitudes')
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analyzer.compute('correlograms', window_ms=50.0, bin_ms=1.0)
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analyzer.compute('unit_locations', method='monopolar_triangulation')
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# =========================================================================
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# 6. QUALITY METRICS
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# =========================================================================
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print("\n" + "=" * 60)
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print("6. QUALITY METRICS")
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print("=" * 60)
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print("Computing quality metrics...")
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metrics = si.compute_quality_metrics(
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analyzer,
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metric_names=[
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'snr', 'isi_violations_ratio', 'presence_ratio',
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'amplitude_cutoff', 'firing_rate', 'amplitude_cv',
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],
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n_jobs=N_JOBS,
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)
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metrics.to_csv(output_path / 'quality_metrics.csv')
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print(f"Saved metrics to: {output_path / 'quality_metrics.csv'}")
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# Print summary
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print("\nMetrics summary:")
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for col in ['snr', 'isi_violations_ratio', 'presence_ratio', 'firing_rate']:
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if col in metrics.columns:
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print(f" {col}: {metrics[col].median():.4f} (median)")
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# =========================================================================
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# 7. CURATION
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# =========================================================================
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print("\n" + "=" * 60)
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print("7. CURATION")
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print("=" * 60)
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# Curation criteria
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criteria = {
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'allen': {'snr': 3.0, 'isi_violations_ratio': 0.1, 'presence_ratio': 0.9},
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'ibl': {'snr': 4.0, 'isi_violations_ratio': 0.5, 'presence_ratio': 0.5},
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'strict': {'snr': 5.0, 'isi_violations_ratio': 0.01, 'presence_ratio': 0.95},
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}[CURATION_METHOD]
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print(f"Applying {CURATION_METHOD} criteria: {criteria}")
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labels = {}
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for unit_id in metrics.index:
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row = metrics.loc[unit_id]
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is_good = (
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row.get('snr', 0) >= criteria['snr'] and
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row.get('isi_violations_ratio', 1) <= criteria['isi_violations_ratio'] and
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row.get('presence_ratio', 0) >= criteria['presence_ratio']
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)
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if is_good:
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labels[int(unit_id)] = 'good'
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elif row.get('snr', 0) < 2:
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labels[int(unit_id)] = 'noise'
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else:
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labels[int(unit_id)] = 'mua'
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# Save labels
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with open(output_path / 'curation_labels.json', 'w') as f:
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json.dump(labels, f, indent=2)
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# Count
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good_count = sum(1 for v in labels.values() if v == 'good')
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mua_count = sum(1 for v in labels.values() if v == 'mua')
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noise_count = sum(1 for v in labels.values() if v == 'noise')
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print(f"\nCuration results:")
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print(f" Good: {good_count}")
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print(f" MUA: {mua_count}")
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print(f" Noise: {noise_count}")
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print(f" Total: {len(labels)}")
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# =========================================================================
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# 8. EXPORT
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# =========================================================================
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print("\n" + "=" * 60)
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print("8. EXPORT")
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print("=" * 60)
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print("Exporting to Phy...")
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export_to_phy(
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analyzer,
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output_folder=output_path / 'phy_export',
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copy_binary=True,
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)
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print(f"\nAnalysis complete!")
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print(f"Results saved to: {output_path}")
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print(f"\nTo open in Phy:")
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print(f" phy template-gui {output_path / 'phy_export' / 'params.py'}")
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if __name__ == '__main__':
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main()
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