Add IBL's "noise cutoff" quality metric #3984
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| sorting_analyzer=sorting_analyzer | ||
| high_quantile=0.25, | ||
| low_quantile=0.10, | ||
| n_bins=100, |
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There is no discussion of what number of bins means for this metric. Might be worth adding. Why not do 10, or 1000 bins?
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I have added a comment to the docs. This is also the default value used in IBL's implementation.
| if peak_sign == "both": | ||
| raise TypeError('peak_sign should either be "pos" or "neg"!') |
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Shouldn't this error occur right after peak sign so we don't waste time doing the _get_amplitudes_by_units function?
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Also how does a user know what the peak sign is? Where did it come from. I would consider making this note refer to the peak sign of the spike_amplitudes extension.
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I added a comment about where to change the peak_sign in the error message. I moved the error before _get_amplitudes_by_units.
| amps : array-like | ||
| Spike amplitudes. | ||
| high_quantile : float, default: 0.25 | ||
| Quantile of the amplitude range above which values are treated as "high" (e.g. 0.25 = top 25%), the reference region. |
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optionally thinking about this should we also put a note about the top 25% is really the 75 percentile? Not sure about this.
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I think 'eg. 0.25 = top 25%' makes this clear.
Co-authored-by: Zach McKenzie <[email protected]>
Co-authored-by: Zach McKenzie <[email protected]>
for more information, see https://pre-commit.ci
Co-authored-by: Zach McKenzie <[email protected]>
for more information, see https://pre-commit.ci
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From a profiling perspective is there any benefit to thinking about doing a numba/numpy dual strategy? It seems like |
| '`peak_sign` should either be "pos" or "neg". You can set `peak_sign` as an argument when you compute spike_amplitudes.' | ||
| ) | ||
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| amplitudes_by_units = _get_amplitudes_by_units(sorting_analyzer, unit_ids, peak_sign) |
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lets have in mind that this _get_amplitudes_by_units is not efficient because it internal use mask on all units.
amplitude_extension.get_data(outputs="by_units") is really faster for this. We should use it here and also inside
_get_amplitudes_by_units the only pain is that amplitude will be splited over segments but concatenate is fast.
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We just tried to implement amplitude_extension.get_data(outputs="by_units") and then concatenated, but it became very complex. We propose that there should be another PR to make _get_amplitudes_by_units to use the approach suggested.
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Thank you for this, this is really nice. |
related to this, I don't think I've ever got |
Add a new metric "noise cutoff" based on IBL https://figshare.com/articles/online_resource/Spike_sorting_pipeline_for_the_International_Brain_Laboratory/19705522?file=49783080 in a new function
compute_noise_cutoffs.The original implementation is here https://github.com/int-brain-lab/ibllib/blob/2e1f91c622ba8dbd04fc53946c185c99451ce5d6/brainbox/metrics/single_units.py.
The "noise cutoff" metric assesses whether a unit’s spike‐amplitude distribution is truncated
at the low-end, which may be due to the high amplitude detection threhold in the deconvolution step,
i.e., if low‐amplitude spikes were missed. It does not assume a Gaussian shape;
instead, it directly compares counts in the low‐amplitude bins to counts in high‐amplitude bins (
cutoff) and the peak bin (ratio).Here are examples with no truncation and truncation, which give low and high values (
cutoffandratio), respectively.I have added documentation and tests.