Waveforms Widgets Gallery

Here is a gallery of all the available widgets using a pair of RecordingExtractor-SortingExtractor objects.

import matplotlib.pyplot as plt

import spikeinterface as si
import spikeinterface.extractors as se
import spikeinterface.widgets as sw

First, let’s generate a simulated dataset

recording, sorting = si.generate_ground_truth_recording()
print(recording)
print(sorting)

GroundTruthRecording (InjectTemplatesRecording): 4 channels - 25.0kHz - 1 segments
                      250,000 samples - 10.00s - float32 dtype - 3.81 MiB
GroundTruthSorting (NumpySorting): 10 units - 1 segments - 25.0kHz

Extract spike waveforms

For convenience, metrics are computed on the SortingAnalyzer object that gathers recording/sorting and the extracted waveforms in a single object

analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording, format="memory")
# core extensions
analyzer.compute(["random_spikes", "waveforms", "templates", "noise_levels"])

# more extensions
analyzer.compute(["spike_amplitudes", "unit_locations", "spike_locations", "template_metrics"])

estimate_sparsity (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
estimate_sparsity (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 443.29it/s]

compute_waveforms (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
compute_waveforms (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 320.70it/s]

noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 270.99it/s]

Compute : spike_amplitudes + spike_locations (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
Compute : spike_amplitudes + spike_locations (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 202.44it/s]

plot_unit_waveforms()

unit_ids = sorting.unit_ids[:4]

sw.plot_unit_waveforms(analyzer, unit_ids=unit_ids, figsize=(16, 4))

<spikeinterface.widgets.unit_waveforms.UnitWaveformsWidget object at 0x73e4c0f1fcd0>

plot_unit_templates()

unit_ids = sorting.unit_ids

sw.plot_unit_templates(analyzer, unit_ids=unit_ids, ncols=5, figsize=(16, 8))

<spikeinterface.widgets.unit_templates.UnitTemplatesWidget object at 0x73e4c0f1d810>

plot_amplitudes()

sw.plot_amplitudes(analyzer, plot_histograms=True, figsize=(12, 8))

<spikeinterface.widgets.amplitudes.AmplitudesWidget object at 0x73e4c0f1fe50>

plot_unit_locations()

sw.plot_unit_locations(analyzer, figsize=(4, 8))

<spikeinterface.widgets.unit_locations.UnitLocationsWidget object at 0x73e4c0f1f6d0>

plot_unit_waveform_density_map()

This is your best friend to check for overmerge

unit_ids = sorting.unit_ids[:4]
sw.plot_unit_waveforms_density_map(analyzer, unit_ids=unit_ids, figsize=(14, 8))

<spikeinterface.widgets.unit_waveforms_density_map.UnitWaveformDensityMapWidget object at 0x73e4c15974f0>

plot_amplitudes_distribution()

sw.plot_all_amplitudes_distributions(analyzer, figsize=(10, 10))

<spikeinterface.widgets.all_amplitudes_distributions.AllAmplitudesDistributionsWidget object at 0x73e4c0f1fca0>

plot_units_depths()

sw.plot_unit_depths(analyzer, figsize=(10, 10))

<spikeinterface.widgets.unit_depths.UnitDepthsWidget object at 0x73e4c44b6650>

plot_unit_probe_map()

unit_ids = sorting.unit_ids[:4]
sw.plot_unit_probe_map(analyzer, unit_ids=unit_ids, figsize=(20, 8))


plt.show()