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, 464.78it/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):  80%|████████  | 8/10 [00:00<00:00, 73.71it/s]
compute_waveforms (workers: 2 processes): 100%|██████████| 10/10 [00:00<00:00, 86.98it/s]

noise_level (no parallelization):   0%|          | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 279.05it/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'): 15.9, np.str_('1'): 17.4, np.str_('2'): 14.3, np.str_('3'): 15.4, np.str_('4'): 15.6, np.str_('5'): 16.3, np.str_('6'): 14.6, np.str_('7'): 15.2, np.str_('8'): 16.3, np.str_('9'): 15.7}
{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(0.8115581261096024), np.str_('1'): np.float64(12.148417738280905), np.str_('2'): np.float64(2.937305291301113), np.str_('3'): np.float64(15.222005999580237), np.str_('4'): np.float64(4.905677941163871), np.str_('5'): np.float64(22.62981036736856), np.str_('6'): np.float64(34.107214182773816), np.str_('7'): np.float64(2.000744910270261), np.str_('8'): np.float64(13.100944551144812), np.str_('9'): np.float64(26.655169806779853)}

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         15.9   0.811558               NaN
1         17.4  12.148418               NaN
2         14.3   2.937305               NaN
3         15.4  15.222006               NaN
4         15.6   4.905678               NaN
5         16.3  22.629810               NaN
6         14.6  34.107214               NaN
7         15.2   2.000745               NaN
8         16.3  13.100945               NaN
9         15.7  26.655170               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, 177.31it/s]

Projecting waveforms:   0%|          | 0/10 [00:00<?, ?it/s]
Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 2388.83it/s]

calculate pc_metrics:   0%|          | 0/10 [00:00<?, ?it/s]
calculate pc_metrics: 100%|██████████| 10/10 [00:00<00:00, 531.37it/s]
   isolation_distance    d_prime  firing_rate  amplitude_cutoff        snr
0            5.732070   1.575294         15.9               NaN   0.811558
1           64.558223   3.444697         17.4               NaN  12.148418
2           13.797184   1.327231         14.3               NaN   2.937305
3          108.959216   5.329499         15.4               NaN  15.222006
4           34.651664   3.085616         15.6               NaN   4.905678
5          332.565139   8.813182         16.3               NaN  22.629810
6          718.273351  12.875703         14.6               NaN  34.107214
7            5.261477   1.371375         15.2               NaN   2.000745
8          168.832398   4.488255         16.3               NaN  13.100945
9         1225.517084  11.425180         15.7               NaN  26.655170