example_units_table.py

# 8/31/22
# https://figurl.org/f?v=gs://figurl/spikesortingview-8&d=sha1://3bd6d3c6b77428cca467bc1afb48c5f272d7b4e6&label=Units%20table%20example

from typing import List
import numpy as np
import sortingview.views as vv
import spikeinterface as si
import spikeinterface.extractors as se
import kachery as ka


def main():
    ka.use_sandbox()
    recording, sorting = se.toy_example(num_units=12, duration=300, seed=0, num_segments=1)
    assert isinstance(recording, si.BaseRecording)

    view = example_units_table(recording=recording, sorting=sorting)

    url = view.url(label="Units table example")
    print(url)


def example_units_table(*, recording: si.BaseRecording, sorting: si.BaseSorting, height=600):
    columns: List[vv.UnitsTableColumn] = [
        vv.UnitsTableColumn(key="unitId", label="Unit", dtype="int"),
        vv.UnitsTableColumn(key="numEvents", label="Num. events", dtype="int"),
        vv.UnitsTableColumn(key="firingRateHz", label="Firing rate (Hz)", dtype="float"),
    ]
    rows: List[vv.UnitsTableRow] = []
    for unit_id in sorting.get_unit_ids():
        spike_train = sorting.get_unit_spike_train(unit_id=unit_id)
        rows.append(
            vv.UnitsTableRow(
                unit_id=unit_id,
                values={"unitId": unit_id, "numEvents": len(spike_train), "firingRateHz": len(spike_train) / (recording.get_num_frames() / recording.get_sampling_frequency())},
            )
        )
    dummy_similarity_scores: List[vv.UnitSimilarityScore] = []
    for id1 in sorting.get_unit_ids():
        for id2 in sorting.get_unit_ids():
            if id2 != id1:
                if np.random.random() < 0.5:
                    dummy_similarity_scores.append(vv.UnitSimilarityScore(unit_id1=id1, unit_id2=id2, similarity=np.random.random()))
    view = vv.UnitsTable(columns=columns, rows=rows, similarity_scores=dummy_similarity_scores, height=height)
    return view


if __name__ == "__main__":
    main()