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, 363.54it/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):  20%|██        | 2/10 [00:00<00:00, 18.22it/s]
compute_waveforms (workers: 2 processes): 100%|██████████| 10/10 [00:00<00:00, 51.21it/s]

noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 254.85it/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'): 13.4, np.str_('1'): 13.0, np.str_('2'): 16.6, np.str_('3'): 14.0, np.str_('4'): 13.9, np.str_('5'): 13.8, np.str_('6'): 15.3, np.str_('7'): 13.9, np.str_('8'): 15.8, np.str_('9'): 14.3}
{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(8.813046455682326), np.str_('1'): np.float64(31.178950114650185), np.str_('2'): np.float64(12.086973259300537), np.str_('3'): np.float64(44.20333888874252), np.str_('4'): np.float64(12.114543850527564), np.str_('5'): np.float64(17.52691772918083), np.str_('6'): np.float64(28.054482398352924), np.str_('7'): np.float64(23.13445074364992), np.str_('8'): np.float64(26.02667401944571), np.str_('9'): np.float64(54.13461155620695)}

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         13.4   8.813046               NaN
1         13.0  31.178950               NaN
2         16.6  12.086973               NaN
3         14.0  44.203339               NaN
4         13.9  12.114544               NaN
5         13.8  17.526918               NaN
6         15.3  28.054482               NaN
7         13.9  23.134451               NaN
8         15.8  26.026674               NaN
9         14.3  54.134612               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, 218.08it/s]

Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 1259.51it/s]

calculate pc_metrics:   0%|          | 0/10 [00:00<?, ?it/s]
calculate pc_metrics: 100%|██████████| 10/10 [00:00<00:00, 352.21it/s]
   isolation_distance    d_prime        snr  amplitude_cutoff  firing_rate
0           25.419220   2.004480   8.813046               NaN         13.4
1          114.651753   3.681103  31.178950               NaN         13.0
2           60.410283   1.669974  12.086973               NaN         16.6
3          611.918300   7.289233  44.203339               NaN         14.0
4           20.047994   1.537571  12.114544               NaN         13.9
5           35.354602   1.521533  17.526918               NaN         13.8
6          234.493479   4.381197  28.054482               NaN         15.3
7          457.358844   5.704074  23.134451               NaN         13.9
8          170.826441   4.279759  26.026674               NaN         15.8
9          947.211467  10.774341  54.134612               NaN         14.3