Initial commit for neuropixels-analysis
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dfty
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references/ANALYSIS.md
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references/ANALYSIS.md
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# Post-Processing & Analysis Reference
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Comprehensive guide to quality metrics, visualization, and analysis of sorted Neuropixels data.
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## Sorting Analyzer
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The `SortingAnalyzer` is the central object for post-processing.
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### Create Analyzer
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```python
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import spikeinterface.full as si
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# Create analyzer
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analyzer = si.create_sorting_analyzer(
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sorting,
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recording,
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sparse=True, # Use sparse representation
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format='binary_folder', # Storage format
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folder='analyzer_output' # Save location
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)
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```
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### Compute Extensions
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```python
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# Compute all standard extensions
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analyzer.compute('random_spikes') # Random spike selection
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analyzer.compute('waveforms') # Extract waveforms
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analyzer.compute('templates') # Compute templates
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analyzer.compute('noise_levels') # Noise estimation
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analyzer.compute('principal_components') # PCA
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analyzer.compute('spike_amplitudes') # Amplitude per spike
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analyzer.compute('correlograms') # Auto/cross correlograms
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analyzer.compute('unit_locations') # Unit locations
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analyzer.compute('spike_locations') # Per-spike locations
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analyzer.compute('template_similarity') # Template similarity matrix
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analyzer.compute('quality_metrics') # Quality metrics
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# Or compute multiple at once
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analyzer.compute([
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'random_spikes', 'waveforms', 'templates', 'noise_levels',
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'principal_components', 'spike_amplitudes', 'correlograms',
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'unit_locations', 'quality_metrics'
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])
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```
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### Save and Load
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```python
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# Save
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analyzer.save_as(folder='analyzer_saved', format='binary_folder')
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# Load
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analyzer = si.load_sorting_analyzer('analyzer_saved')
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```
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## Quality Metrics
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### Compute Metrics
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```python
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analyzer.compute('quality_metrics')
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qm = analyzer.get_extension('quality_metrics').get_data()
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print(qm)
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```
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### Available Metrics
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| Metric | Description | Good Values |
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|--------|-------------|-------------|
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| `snr` | Signal-to-noise ratio | > 5 |
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| `isi_violations_ratio` | ISI violation ratio | < 0.01 (1%) |
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| `isi_violations_count` | ISI violation count | Low |
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| `presence_ratio` | Fraction of recording with spikes | > 0.9 |
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| `firing_rate` | Spikes per second | 0.1-50 Hz |
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| `amplitude_cutoff` | Estimated missed spikes | < 0.1 |
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| `amplitude_median` | Median spike amplitude | - |
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| `amplitude_cv` | Coefficient of variation | < 0.5 |
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| `drift_ptp` | Peak-to-peak drift (um) | < 40 |
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| `drift_std` | Standard deviation of drift | < 10 |
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| `drift_mad` | Median absolute deviation | < 10 |
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| `sliding_rp_violation` | Sliding refractory period | < 0.05 |
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| `sync_spike_2` | Synchrony with other units | < 0.5 |
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| `isolation_distance` | Mahalanobis distance | > 20 |
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| `l_ratio` | L-ratio (isolation) | < 0.1 |
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| `d_prime` | Discriminability | > 5 |
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| `nn_hit_rate` | Nearest neighbor hit rate | > 0.9 |
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| `nn_miss_rate` | Nearest neighbor miss rate | < 0.1 |
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| `silhouette_score` | Cluster silhouette | > 0.5 |
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### Compute Specific Metrics
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```python
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analyzer.compute(
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'quality_metrics',
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metric_names=['snr', 'isi_violations_ratio', 'presence_ratio', 'firing_rate']
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)
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```
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### Custom Quality Thresholds
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```python
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qm = analyzer.get_extension('quality_metrics').get_data()
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# Define quality criteria
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quality_criteria = {
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'snr': ('>', 5),
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'isi_violations_ratio': ('<', 0.01),
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'presence_ratio': ('>', 0.9),
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'firing_rate': ('>', 0.1),
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'amplitude_cutoff': ('<', 0.1),
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}
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# Filter good units
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good_units = qm.query(
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"(snr > 5) & (isi_violations_ratio < 0.01) & (presence_ratio > 0.9)"
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).index.tolist()
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print(f"Good units: {len(good_units)}/{len(qm)}")
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```
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## Waveforms & Templates
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### Extract Waveforms
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```python
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analyzer.compute('waveforms', ms_before=1.5, ms_after=2.5, max_spikes_per_unit=500)
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# Get waveforms for a unit
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waveforms = analyzer.get_extension('waveforms').get_waveforms(unit_id=0)
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print(f"Shape: {waveforms.shape}") # (n_spikes, n_samples, n_channels)
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```
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### Compute Templates
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```python
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analyzer.compute('templates', operators=['average', 'std', 'median'])
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# Get template
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templates_ext = analyzer.get_extension('templates')
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template = templates_ext.get_unit_template(unit_id=0, operator='average')
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```
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### Template Similarity
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```python
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analyzer.compute('template_similarity')
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sim = analyzer.get_extension('template_similarity').get_data()
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# Matrix of cosine similarities between templates
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```
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## Unit Locations
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### Compute Locations
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```python
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analyzer.compute('unit_locations', method='monopolar_triangulation')
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locations = analyzer.get_extension('unit_locations').get_data()
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print(locations) # x, y coordinates per unit
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```
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### Spike Locations
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```python
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analyzer.compute('spike_locations', method='center_of_mass')
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spike_locs = analyzer.get_extension('spike_locations').get_data()
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```
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### Location Methods
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- `'center_of_mass'` - Fast, less accurate
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- `'monopolar_triangulation'` - More accurate, slower
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- `'grid_convolution'` - Good balance
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## Correlograms
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### Auto-correlograms
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```python
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analyzer.compute('correlograms', window_ms=50, bin_ms=1)
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correlograms, bins = analyzer.get_extension('correlograms').get_data()
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# correlograms shape: (n_units, n_units, n_bins)
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# Auto-correlogram for unit i: correlograms[i, i, :]
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# Cross-correlogram units i,j: correlograms[i, j, :]
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```
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## Visualization
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### Probe Map
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```python
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si.plot_probe_map(recording, with_channel_ids=True)
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```
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### Unit Templates
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```python
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# All units
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si.plot_unit_templates(analyzer)
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# Specific units
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si.plot_unit_templates(analyzer, unit_ids=[0, 1, 2])
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```
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### Waveforms
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```python
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# Plot waveforms with template
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si.plot_unit_waveforms(analyzer, unit_ids=[0])
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# Waveform density
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si.plot_unit_waveforms_density_map(analyzer, unit_id=0)
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```
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### Raster Plot
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```python
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si.plot_rasters(sorting, time_range=(0, 10)) # First 10 seconds
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```
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### Amplitudes
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```python
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analyzer.compute('spike_amplitudes')
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si.plot_amplitudes(analyzer)
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# Distribution
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si.plot_all_amplitudes_distributions(analyzer)
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```
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### Correlograms
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```python
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# Auto-correlograms
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si.plot_autocorrelograms(analyzer, unit_ids=[0, 1, 2])
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# Cross-correlograms
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si.plot_crosscorrelograms(analyzer, unit_ids=[0, 1])
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```
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### Quality Metrics
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```python
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# Summary plot
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si.plot_quality_metrics(analyzer)
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# Specific metric distribution
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import matplotlib.pyplot as plt
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qm = analyzer.get_extension('quality_metrics').get_data()
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plt.hist(qm['snr'], bins=50)
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plt.xlabel('SNR')
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plt.ylabel('Count')
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```
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### Unit Locations on Probe
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```python
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si.plot_unit_locations(analyzer)
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```
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### Drift Map
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```python
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si.plot_drift_raster(sorting, recording)
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```
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### Summary Plot
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```python
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# Comprehensive unit summary
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si.plot_unit_summary(analyzer, unit_id=0)
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```
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## LFP Analysis
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### Load LFP Data
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```python
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lfp = si.read_spikeglx('/path/to/data', stream_id='imec0.lf')
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print(f"LFP: {lfp.get_sampling_frequency()} Hz")
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```
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### Basic LFP Processing
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```python
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# Downsample if needed
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lfp_ds = si.resample(lfp, resample_rate=1000)
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# Common average reference
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lfp_car = si.common_reference(lfp_ds, reference='global', operator='median')
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```
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### Extract LFP Traces
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```python
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import numpy as np
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# Get traces (channels x samples)
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traces = lfp.get_traces(start_frame=0, end_frame=30000)
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# Specific channels
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traces = lfp.get_traces(channel_ids=[0, 1, 2])
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```
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### Spectral Analysis
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```python
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from scipy import signal
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import matplotlib.pyplot as plt
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# Get single channel
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trace = lfp.get_traces(channel_ids=[0]).flatten()
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fs = lfp.get_sampling_frequency()
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# Power spectrum
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freqs, psd = signal.welch(trace, fs, nperseg=4096)
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plt.semilogy(freqs, psd)
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plt.xlabel('Frequency (Hz)')
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plt.ylabel('Power')
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plt.xlim(0, 100)
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```
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### Spectrogram
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```python
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f, t, Sxx = signal.spectrogram(trace, fs, nperseg=2048, noverlap=1024)
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plt.pcolormesh(t, f, 10*np.log10(Sxx), shading='gouraud')
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plt.ylabel('Frequency (Hz)')
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plt.xlabel('Time (s)')
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plt.ylim(0, 100)
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plt.colorbar(label='Power (dB)')
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```
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## Export Formats
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### Export to Phy
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```python
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si.export_to_phy(
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analyzer,
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output_folder='phy_export',
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compute_pc_features=True,
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compute_amplitudes=True,
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copy_binary=True
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)
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# Then: phy template-gui phy_export/params.py
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```
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### Export to NWB
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```python
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from spikeinterface.exporters import export_to_nwb
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export_to_nwb(
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recording,
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sorting,
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'output.nwb',
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metadata=dict(
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session_description='Neuropixels recording',
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experimenter='Name',
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lab='Lab name',
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institution='Institution'
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)
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)
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```
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### Export Report
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```python
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si.export_report(
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analyzer,
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output_folder='report',
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remove_if_exists=True,
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format='html'
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)
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```
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## Complete Analysis Pipeline
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```python
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import spikeinterface.full as si
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def analyze_sorting(recording, sorting, output_dir):
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"""Complete post-processing pipeline."""
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# Create analyzer
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analyzer = si.create_sorting_analyzer(
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sorting, recording,
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sparse=True,
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folder=f'{output_dir}/analyzer'
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)
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# Compute all extensions
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print("Computing extensions...")
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analyzer.compute(['random_spikes', 'waveforms', 'templates', 'noise_levels'])
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analyzer.compute(['principal_components', 'spike_amplitudes'])
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analyzer.compute(['correlograms', 'unit_locations', 'template_similarity'])
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analyzer.compute('quality_metrics')
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# Get quality metrics
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qm = analyzer.get_extension('quality_metrics').get_data()
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# Filter good units
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good_units = qm.query(
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"(snr > 5) & (isi_violations_ratio < 0.01) & (presence_ratio > 0.9)"
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).index.tolist()
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print(f"Quality filtering: {len(good_units)}/{len(qm)} units passed")
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# Export
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si.export_to_phy(analyzer, f'{output_dir}/phy')
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si.export_report(analyzer, f'{output_dir}/report')
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# Save metrics
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qm.to_csv(f'{output_dir}/quality_metrics.csv')
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return analyzer, qm, good_units
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# Usage
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analyzer, qm, good_units = analyze_sorting(recording, sorting, 'output/')
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```
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