Quality Metrics Tutorial

After spike sorting, you might want to validate the ‘goodness’ of the sorted units. This can be done using the qualitymetrics submodule, which computes several quality metrics of the sorted units.

import spikeinterface.core as si
from spikeinterface.qualitymetrics import (
    compute_snrs,
    compute_firing_rates,
    compute_isi_violations,
    compute_quality_metrics,
)

First, let’s generate a simulated recording and sorting

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

Create SortingAnalyzer

For quality metrics we need first to create a SortingAnalyzer.

analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording, format="memory")
print(analyzer)

estimate_sparsity (no parallelization):   0%|          | 0/10 [00:00<?, ?it/s]
estimate_sparsity (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 398.78it/s]
SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
Loaded 0 extensions

Depending on which metrics we want to compute we will need first to compute some necessary extensions. (if not computed an error message will be raised)

analyzer.compute("random_spikes", method="uniform", max_spikes_per_unit=600, seed=2205)
analyzer.compute("waveforms", ms_before=1.3, ms_after=2.6, n_jobs=2)
analyzer.compute("templates", operators=["average", "median", "std"])
analyzer.compute("noise_levels")

print(analyzer)

compute_waveforms (workers: 2 processes):   0%|          | 0/10 [00:00<?, ?it/s]
compute_waveforms (workers: 2 processes):  70%|███████   | 7/10 [00:00<00:00, 61.57it/s]
compute_waveforms (workers: 2 processes): 100%|██████████| 10/10 [00:00<00:00, 78.86it/s]

noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 284.55it/s]
SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
Loaded 4 extensions: random_spikes, waveforms, templates, noise_levels

The spikeinterface.qualitymetrics submodule has a set of functions that allow users to compute metrics in a compact and easy way. To compute a single metric, one can simply run one of the quality metric functions as shown below. Each function has a variety of adjustable parameters that can be tuned.

firing_rates = compute_firing_rates(analyzer)
print(firing_rates)
isi_violation_ratio, isi_violations_count = compute_isi_violations(analyzer)
print(isi_violation_ratio)
snrs = compute_snrs(analyzer)
print(snrs)

{np.str_('0'): 14.2, np.str_('1'): 15.8, np.str_('2'): 14.1, np.str_('3'): 15.3, np.str_('4'): 14.4, np.str_('5'): 16.4, np.str_('6'): 14.3, np.str_('7'): 12.5, np.str_('8'): 14.3, np.str_('9'): 14.2}
{np.str_('0'): np.float64(0.0), np.str_('1'): np.float64(0.0), np.str_('2'): np.float64(0.0), np.str_('3'): np.float64(0.0), np.str_('4'): np.float64(0.0), np.str_('5'): np.float64(0.0), np.str_('6'): np.float64(0.0), np.str_('7'): np.float64(0.0), np.str_('8'): np.float64(0.0), np.str_('9'): np.float64(0.0)}
{np.str_('0'): np.float64(7.057331210324123), np.str_('1'): np.float64(7.952639139858965), np.str_('2'): np.float64(24.39620373538077), np.str_('3'): np.float64(19.797953778743743), np.str_('4'): np.float64(11.141984909365796), np.str_('5'): np.float64(15.625148982867573), np.str_('6'): np.float64(27.66876462754578), np.str_('7'): np.float64(6.80275519893571), np.str_('8'): np.float64(19.18407739605691), np.str_('9'): np.float64(42.27177014828746)}

To compute more than one metric at once, we can use the compute_quality_metrics function and indicate which metrics we want to compute. This will return a pandas dataframe:

metrics = compute_quality_metrics(analyzer, metric_names=["firing_rate", "snr", "amplitude_cutoff"])
print(metrics)

   firing_rate        snr  amplitude_cutoff
0         14.2   7.057331               NaN
1         15.8   7.952639               NaN
2         14.1  24.396204               NaN
3         15.3  19.797954               NaN
4         14.4  11.141985               NaN
5         16.4  15.625149               NaN
6         14.3  27.668765               NaN
7         12.5   6.802755               NaN
8         14.3  19.184077               NaN
9         14.2  42.271770               NaN

Some metrics are based on the principal component scores, so the exwtension must be computed before. For instance:

analyzer.compute("principal_components", n_components=3, mode="by_channel_global", whiten=True)

metrics = compute_quality_metrics(
    analyzer,
    metric_names=[
        "isolation_distance",
        "d_prime",
    ],
)
print(metrics)

Fitting PCA:   0%|          | 0/10 [00:00<?, ?it/s]
Fitting PCA: 100%|██████████| 10/10 [00:00<00:00, 189.22it/s]

Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 2405.96it/s]

calculate pc_metrics:   0%|          | 0/10 [00:00<?, ?it/s]
calculate pc_metrics: 100%|██████████| 10/10 [00:00<00:00, 540.02it/s]
   isolation_distance   d_prime        snr  firing_rate  amplitude_cutoff
0           19.110960  2.775378   7.057331         14.2               NaN
1           23.528726  2.396316   7.952639         15.8               NaN
2          121.887451  3.447779  24.396204         14.1               NaN
3          184.337724  7.040257  19.797954         15.3               NaN
4           34.127789  1.342211  11.141985         14.4               NaN
5           97.350919  3.405871  15.625149         16.4               NaN
6          156.053961  5.489057  27.668765         14.3               NaN
7           24.314433  2.502411   6.802755         12.5               NaN
8           45.907389  1.810996  19.184077         14.3               NaN
9          114.298171  7.891842  42.271770         14.2               NaN