davila7/claude-code-templates/neuropixels-analysis

import spikeinterface.full as si
import neuropixels_analysis as npa
# Configure parallel processing
job_kwargs = dict(n_jobs=-1, chunk_duration='1s', progress_bar=True)

# SpikeGLX (most common)
recording = si.read_spikeglx('/path/to/data', stream_id='imec0.ap')
# Open Ephys (common for many labs)
recording = si.read_openephys('/path/to/Record_Node_101/')
# Check available streams
streams, ids = si.get_neo_streams('spikeglx', '/path/to/data')
print(streams)  # ['imec0.ap', 'imec0.lf', 'nidq']
# For testing with subset of data
recording = recording.frame_slice(0, int(60 * recording.get_sampling_frequency()))

# Run full analysis pipeline
results = npa.run_pipeline(
    recording,
    output_dir='output/',
    sorter='kilosort4',
    curation_method='allen',
)
# Access results
sorting = results['sorting']
metrics = results['metrics']
labels = results['labels']

# Recommended preprocessing chain
rec = si.highpass_filter(recording, freq_min=400)
rec = si.phase_shift(rec)  # Required for Neuropixels 1.0
bad_ids, _ = si.detect_bad_channels(rec)
rec = rec.remove_channels(bad_ids)
rec = si.common_reference(rec, operator='median')
# Or use our wrapper
rec = npa.preprocess(recording)

# Check for drift (always do this!)
motion_info = npa.estimate_motion(rec, preset='kilosort_like')
npa.plot_drift(rec, motion_info, output='drift_map.png')
# Apply correction if needed
if motion_info['motion'].max() > 10:  # microns
    rec = npa.correct_motion(rec, preset='nonrigid_accurate')

# Kilosort4 (recommended, requires GPU)
sorting = si.run_sorter('kilosort4', rec, folder='ks4_output')
# CPU alternatives
sorting = si.run_sorter('tridesclous2', rec, folder='tdc2_output')
sorting = si.run_sorter('spykingcircus2', rec, folder='sc2_output')
sorting = si.run_sorter('mountainsort5', rec, folder='ms5_output')
# Check available sorters
print(si.installed_sorters())

# Create analyzer and compute all extensions
analyzer = si.create_sorting_analyzer(sorting, rec, sparse=True)
analyzer.compute('random_spikes', max_spikes_per_unit=500)
analyzer.compute('waveforms', ms_before=1.0, ms_after=2.0)
analyzer.compute('templates', operators=['average', 'std'])
analyzer.compute('spike_amplitudes')
analyzer.compute('correlograms', window_ms=50.0, bin_ms=1.0)
analyzer.compute('unit_locations', method='monopolar_triangulation')
analyzer.compute('quality_metrics')
metrics = analyzer.get_extension('quality_metrics').get_data()

# Allen Institute criteria (conservative)
good_units = metrics.query("""
    presence_ratio > 0.9 and
    isi_violations_ratio < 0.5 and
    amplitude_cutoff < 0.1
""").index.tolist()
# Or use automated curation
labels = npa.curate(metrics, method='allen')  # 'allen', 'ibl', 'strict'

from anthropic import Anthropic
# Setup API client
client = Anthropic()
# Analyze uncertain units visually
uncertain = metrics.query('snr > 3 and snr < 8').index.tolist()
for unit_id in uncertain:
    result = npa.analyze_unit_visually(analyzer, unit_id, api_client=client)
    print(f"Unit {unit_id}: {result['classification']}")
    print(f"  Reasoning: {result['reasoning'][:100]}...")

# Generate comprehensive HTML report with visualizations
report_dir = npa.generate_analysis_report(results, 'output/')
# Opens report.html with summary stats, figures, and unit table
# Print formatted summary to console
npa.print_analysis_summary(results)

# Export to Phy for manual review
si.export_to_phy(analyzer, output_folder='phy_export/',
                 compute_pc_features=True, compute_amplitudes=True)
# Export to NWB
from spikeinterface.exporters import export_to_nwb
export_to_nwb(rec, sorting, 'output.nwb')
# Save quality metrics
metrics.to_csv('quality_metrics.csv')

python scripts/preprocess_recording.py /path/to/data --output preprocessed/

python scripts/run_sorting.py preprocessed/ --sorter kilosort4 --output sorting/

python scripts/compute_metrics.py sorting/ preprocessed/ --output metrics/ --curation allen

python scripts/export_to_phy.py metrics/analyzer --output phy_export/

cp assets/analysis_template.py my_analysis.py
# Edit parameters and run
python my_analysis.py

# Core packages
pip install spikeinterface[full] probeinterface neo
 
# Spike sorters
pip install kilosort          # Kilosort4 (GPU required)
pip install spykingcircus     # SpykingCircus2 (CPU)
pip install mountainsort5     # Mountainsort5 (CPU)
 
# Our toolkit
pip install neuropixels-analysis
 
# Optional: AI curation
pip install anthropic
 
# Optional: IBL tools
pip install ibl-neuropixel ibllib

project/
├── raw_data/
│   └── recording_g0/
│       └── recording_g0_imec0/
│           ├── recording_g0_t0.imec0.ap.bin
│           └── recording_g0_t0.imec0.ap.meta
├── preprocessed/           # Saved preprocessed recording
├── motion/                 # Motion estimation results
├── sorting_output/         # Spike sorter output
├── analyzer/               # SortingAnalyzer (waveforms, metrics)
├── phy_export/             # For manual curation
├── ai_curation/            # AI analysis reports
└── results/
    ├── quality_metrics.csv
    ├── curation_labels.json
    └── output.nwb