[JoeZiminski]

This PR updates the raster widgets which was not handling t_start, and also patches the associated get_segment_durations function similarly.

The previous implementation calculated spike times using the sample index and sampling frequency, but not incorporating t_start. Similarly created durations for each segment, and used these in raster plots for things like drawing vertical lines betwen segments.

This PR fixes by using the newer time-getting machinery for spike times, and replacing durations with a dictionary of start-stop times (tuples) per segment called segment_start_stop_times. The reason it's a dictionary not a list is because the segments to display can be sub-selected, and if these are not referred to explicitly it gets complicated as the segments are re-indexed in BaseRasterWidget. durations is just removed, I assume that's okay as was only used internally.

Now get_segment_durations also returns this segment_start_stop_times. Two things are a bit weird with this fuction, one the name is now not great because it returns two things. This has now been updated to check to see if the sorting has a recording, and if so get the start/stop times directly.

In the plots below you can see the original issue / fix - in this example the recording has three segments each 10 seconds long starting at [5.0, 25, 50]. The raster plot however all start at t=0 and the segment boundaries are not correct (note motion plot only takes 1 segment).

Before

After (images updated after change to axticklabels)

Code to produce plots below, note that the amplitude plots will not generate as show, as needs #4796

Test code

"""
Generate a multi-segment recording whose segments start at non-zero times,
then plot a raster and motion-correction diagnostics.
"""

import matplotlib.pyplot as plt

import spikeinterface.widgets as sw
from spikeinterface.core import (
    append_recordings,
    append_sortings,
    create_sorting_analyzer,
    generate_ground_truth_recording,
)
from spikeinterface.preprocessing import correct_motion

# When False,  the times are inferred from the spike vector for amplitudes 
# and raster widget (motion info plot requires the recording)
WITH_RECORDING = True


def make_multi_segment_recording(num_segments=3, seed=2205):
    """Build a multi-segment recording and sorting with non-zero segment start times."""

    common_kwargs = dict(
        durations=[10.0],
        sampling_frequency=30000.0,
        num_channels=64,
        num_units=60,
    )

    recordings = []
    sortings = []
    # Offsets so segments do NOT start at time zero.
    start_time_offsets = [5.0, 25, 50]

    for seg in range(num_segments):
        recording, sorting = generate_ground_truth_recording(seed=seed + seg, **common_kwargs)
        # Give this segment a non-zero start time.
        recording.shift_times(shift=start_time_offsets[seg % len(start_time_offsets)])
        recordings.append(recording)
        sortings.append(sorting)

    multi_segment_recording = append_recordings(recordings)
    multi_segment_sorting = append_sortings(sortings)
    return multi_segment_recording, multi_segment_sorting, recordings[0]


def main():
    recording, sorting, motion_recording = make_multi_segment_recording()
    if WITH_RECORDING:
        sorting.register_recording(recording)

    if WITH_RECORDING:
        for seg in range(recording.get_num_segments()):
            print(f"  segment {seg}: start={recording.get_start_time(seg):.1f}s end={recording.get_end_time(seg):.1f}s")

    # Plot a raster of all segments.
    segment_indices = list(range(sorting.get_num_segments()))
    sw.plot_rasters(sorting, segment_indices=segment_indices)

    sorting_analyzer = create_sorting_analyzer(sorting, recording, format="memory")
    sorting_analyzer.compute("random_spikes")
    sorting_analyzer.compute("waveforms")
    sorting_analyzer.compute("templates")
    sorting_analyzer.compute("spike_amplitudes")
    sw.plot_amplitudes(
        sorting_analyzer,
        segment_indices=segment_indices,
        plot_histograms=True,
        plot_legend=False,
    )

    _, motion_info = correct_motion(
        motion_recording,
        preset="kilosort_like",
        output_motion_info=True,
        n_jobs=1,
    )
    sw.plot_motion_info(motion_info, recording=motion_recording, motion_lim=1.0)

    plt.show()


if __name__ == "__main__":
    main()

[JoeZiminski]

This is unused in main, but I guess this is public facing and will need a deprecation warning? (although maybe not if its never used anyway)

[JoeZiminski]

Thanks @alejoe91 this is now updated!

implemented changes

[alejoe91]

Can we move this to core.sorting_tools?

[alejoe91]

Hi @JoeZiminski

This looks great! One final request:

Can you move the get_segment_durations and estimate_segment_durations_from_spike_vector to sorting_tools? I think they should be core functions

[alejoe91]

Hi @JoeZiminski

This looks great! One final request:

Can you move the get_segment_durations and estimate_segment_durations_from_spike_vector to sorting_tools? I think they should be core functions

Done in last commit :)

fixed

[JoeZiminski]

apologies for delay, cheers @alejoe91!