Note
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Curation Tutorial
After spike sorting and computing quality metrics, you can automatically curate the spike sorting output using the quality metrics that you have calculated.
Import the modules and/or functions necessary from spikeinterface
import spikeinterface.core as si
Let’s generate a simulated dataset, and imagine that the ground-truth sorting is in fact the output of a sorter.
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 this example, we will need a SortingAnalyzer and some extensions
to be computed first
analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording, format="memory")
analyzer.compute(["random_spikes", "waveforms", "templates", "noise_levels"])
analyzer.compute("principal_components", n_components=3, mode="by_channel_local")
print(analyzer)
estimate_sparsity (no parallelization): 0%| | 0/10 [00:00<?, ?it/s]
estimate_sparsity (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 424.08it/s]
compute_waveforms (no parallelization): 0%| | 0/10 [00:00<?, ?it/s]
compute_waveforms (no parallelization): 100%|██████████| 10/10 [00:00<00:00, 279.61it/s]
noise_level (no parallelization): 0%| | 0/20 [00:00<?, ?it/s]
noise_level (no parallelization): 100%|██████████| 20/20 [00:00<00:00, 267.46it/s]
Fitting PCA: 0%| | 0/10 [00:00<?, ?it/s]
Fitting PCA: 100%|██████████| 10/10 [00:00<00:00, 138.87it/s]
Projecting waveforms: 0%| | 0/10 [00:00<?, ?it/s]
Projecting waveforms: 100%|██████████| 10/10 [00:00<00:00, 1352.70it/s]
SortingAnalyzer: 4 channels - 10 units - 1 segments - memory - sparse - has recording
Loaded 5 extensions: random_spikes, waveforms, templates, noise_levels, principal_components
Then we compute some quality metrics:
metrics_ext = analyzer.compute("quality_metrics", metric_names=["snr", "isi_violation", "nearest_neighbor"])
metrics = metrics_ext.get_data()
print(metrics)
snr isi_violations_ratio ... nn_hit_rate nn_miss_rate
0 25.668774 0.0 ... 0.901935 0.005306
1 7.958497 0.0 ... 0.761963 0.028021
2 22.444027 0.0 ... 0.865385 0.015339
3 29.291918 0.0 ... 0.885294 0.007267
4 17.477671 0.0 ... 0.871724 0.007023
5 11.764989 0.0 ... 0.710811 0.031232
6 13.484159 0.0 ... 0.795652 0.032751
7 19.166367 0.0 ... 0.820134 0.023184
8 9.034860 0.0 ... 0.765680 0.034698
9 13.361816 0.0 ... 0.790850 0.020309
[10 rows x 5 columns]
We can now threshold each quality metric and select units based on some rules.
The easiest and most intuitive way is to use boolean masking with a dataframe.
Then create a list of unit ids that we want to keep
keep_mask = (metrics["snr"] > 7.5) & (metrics["isi_violations_ratio"] < 0.2) & (metrics["nn_hit_rate"] > 0.80)
print(keep_mask)
keep_unit_ids = keep_mask[keep_mask].index.values
keep_unit_ids = [unit_id for unit_id in keep_unit_ids]
print(keep_unit_ids)
0 True
1 False
2 True
3 True
4 True
5 False
6 False
7 True
8 False
9 False
dtype: bool
['0', '2', '3', '4', '7']
And now let’s create a sorting that contains only curated units and save it.
curated_sorting = sorting.select_units(keep_unit_ids)
print(curated_sorting)
curated_sorting.save(folder="curated_sorting", overwrite=True)
GroundTruthSorting (UnitsSelectionSorting): 5 units - 1 segments - 25.0kHz
We can also save the analyzer with only theses units
clean_analyzer = analyzer.select_units(unit_ids=keep_unit_ids, format="zarr", folder="clean_analyzer")
print(clean_analyzer)
SortingAnalyzer: 4 channels - 5 units - 1 segments - zarr - sparse - has recording
Loaded 6 extensions: random_spikes, waveforms, templates, noise_levels, principal_components, quality_metrics
Total running time of the script: (0 minutes 0.614 seconds)