Note
Go to the end to download the full example code
Curation Tutorial¶
After spike sorting and computing quality metrics, you can automatically curate the spike sorting output using the quality metrics.
import spikeinterface as si
import spikeinterface.extractors as se
from spikeinterface.postprocessing import compute_principal_components
from spikeinterface.qualitymetrics import compute_quality_metrics
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/checkouts/0.98.0/examples/modules_gallery/qualitymetrics/plot_4_curation.py", line 11, in <module>
import spikeinterface.extractors as se
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/__init__.py", line 1, in <module>
from .extractorlist import *
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/extractorlist.py", line 15, in <module>
from .neoextractors import *
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/__init__.py", line 1, in <module>
from .alphaomega import AlphaOmegaRecordingExtractor, AlphaOmegaEventExtractor, read_alphaomega, read_alphaomega_event
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/alphaomega.py", line 3, in <module>
from .neobaseextractor import NeoBaseRecordingExtractor, NeoBaseEventExtractor
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/neobaseextractor.py", line 332, in <module>
class NeoBaseSortingExtractor(_NeoBaseExtractor, BaseSorting):
File "/home/docs/checkouts/readthedocs.org/user_builds/spikeinterface/conda/0.98.0/lib/python3.9/site-packages/spikeinterface/extractors/neoextractors/neobaseextractor.py", line 480, in NeoBaseSortingExtractor
def _infer_t_start_from_signal_stream(self, segment_index: int, stream_id: Optional[str] = None) -> float | None:
TypeError: unsupported operand type(s) for |: 'type' and 'NoneType'
- First, let’s download a simulated dataset
from the repo ‘https://gin.g-node.org/NeuralEnsemble/ephy_testing_data’
Let’s imagine that the ground-truth sorting is in fact the output of a sorter.
local_path = si.download_dataset(remote_path='mearec/mearec_test_10s.h5')
recording, sorting = se.read_mearec(local_path)
print(recording)
print(sorting)
First, we extract waveforms and compute their PC scores:
folder = 'wfs_mearec'
we = si.extract_waveforms(recording, sorting, folder,
ms_before=1, ms_after=2., max_spikes_per_unit=500,
n_jobs=1, chunk_size=30000)
print(we)
pc = compute_principal_components(we, load_if_exists=True, n_components=3, mode='by_channel_local')
Then we compute some quality metrics:
metrics = compute_quality_metrics(we, metric_names=['snr', 'isi_violation', 'nearest_neighbor'])
print(metrics)
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 dataframe:
keep_mask = (metrics['snr'] > 7.5) & (metrics['isi_violations_ratio'] < 0.2) & (metrics['nn_hit_rate'] > 0.90)
print(keep_mask)
keep_unit_ids = keep_mask[keep_mask].index.values
print(keep_unit_ids)
And now let’s create a sorting that contains only curated units and save it, for example to an NPZ file.
curated_sorting = sorting.select_units(keep_unit_ids)
print(curated_sorting)
se.NpzSortingExtractor.write_sorting(curated_sorting, 'curated_sorting.pnz')
Total running time of the script: ( 0 minutes 0.002 seconds)