Bombcell integration

[Julie-Fabre]

This PR ports bombcell-style unit classification to SpikeInterface.

Template metrics

• Rewrote peak/trough detection with a new get_trough_and_peak_idx() function that uses scipy.signal.find_peaks(). Since SpikeInterface stores templates based on raw data rather than the heavily smoothed templates used in template matching, the waveforms can be noisy—so you can optionally apply Savitzky-Golay smoothing before detection. The function returns dicts for troughs, peaks before, and peaks after, each containing indices, values, prominences, and widths.

from spikeinterface.postprocessing import get_trough_and_peak_idx

troughs, peaks_before, peaks_after = get_trough_and_peak_idx(
    templates,
    sampling_frequency,
    smooth=True,
    min_thresh_detect_peaks_troughs=0.4,
)

• New metrics: peak_before_to_trough_ratio, peak_after_to_trough_ratio, waveform_baseline_flatness, peak_before_width, trough_width, main_peak_to_trough_ratio.

• Renamed peak_to_valley to peak_to_trough_duration.

analyzer.compute("template_metrics", metric_names=[
    "peak_before_to_trough_ratio",
    "waveform_baseline_flatness",
    "trough_width",
])

Quality metrics

• Added snr_bombcell—peak amplitude over baseline MAD.

analyzer.compute("quality_metrics", metric_names=["snr_bombcell"])

• amplitude_cutoff now has parameters for controlling the histogram fitting:

analyzer.compute("quality_metrics", metric_names=["amplitude_cutoff"], qm_params={
    "amplitude_cutoff": {
        "num_histogram_bins": 100,
        "histogram_smoothing_value": 3,
    }
})

Unit classification

• New in spikeinterface.curation:

import spikeinterface.comparison as sc

thresholds = sc.bombcell_get_default_thresholds()
unit_type, unit_type_string = sc.bombcell_classify_units(
    quality_metrics,
    thresholds=thresholds,
    classify_non_somatic=True,
)
summary = sc.get_classification_summary(unit_type, unit_type_string)

Units get classified as NOISE → MUA → GOOD based on successive threshold checks. Optional NON_SOMA category for non-somatic waveforms.

Plots

• Added plots for classification summaries, metric histograms with threshold lines, waveform overlays by category, and UpSet plots.

from spikeinterface.widgets import (
    plot_unit_classification,
    plot_classification_histograms,
    plot_waveform_overlay,
    plot_upset,
)

plot_unit_classification(analyzer, unit_type, unit_type_string)
plot_classification_histograms(quality_metrics, thresholds=thresholds)
plot_waveform_overlay(analyzer, unit_type, unit_type_string)
plot_upset(quality_metrics, unit_type, unit_type_string)

or a wrapper for all plots:

plots = plot_unit_classification_all(
    sorting_analyzer,
    unit_type,
    unit_type_string,
    quality_metrics=quality_metrics,  # optional, will try to get from analyzer
    thresholds=thresholds,            # optional, uses defaults
    split_non_somatic=False,
    include_upset=True,
)

[samuelgarcia]

Salut Julie,
I read this super quickly. This is super impressive what you did during the hackahton!
I was not aware that you also did the widgets stuff. Waou.

I will be back with more carefully reading.

But some main stuff:

• we avoid to push ipynb in the repo because in saturate the history so we use jupytext instead and push only the resulting generated rst,if the notebook is fast to generate (with simulate data) we also have the tutorial way to push doc through notebooks which is a py file run and generated by the documentaion build.
• I would prefer to not have json directly in the code to handle parameters. I think simple python file with the same contents. lets discuss more
• I would be courious to see the correlation between the basic snr and the one median based you did. I will try to make some plot on this.

[alejoe91]

Can you move this to the function?

The core module has minimal dependencies, and all additional imports should be local :)

[alejoe91]

Suggested change

	Unit labelling based on quality metrics (Bombcell).
	Unit labeling based on quality metrics (Bombcell).

In general, we adopted american english (@chrishalcrow is not happy about it!).

Could you rename this and the files to labeling?