Implement select_sorting_periods in metrics
#4302
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| self._segment_slices = None | ||
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| @property | ||
| def segment_slices(self): |
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I want to have this in NumpySorting since long time. We could move it there no ?
And also you can do a np.searchsorted for segment at once. without any loop.
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I would make it in base sorting then, since it applies to all sorting objects (after to_spike_vector). No?
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I would make it in base sorting then, since it applies to all sorting objects (after to_spike_vector). No?
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This is OK for me. |
| rng = np.random.default_rng() | ||
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| # number of random periods | ||
| n_periods = 10_000 |
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duration, num_units and n_periods are all quite large for a test. Is it slow??
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I agree we should put a small number
Co-authored-by: Chris Halcrow <[email protected]>
| ("unit_index", "int64"), | ||
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| base_period_dtype = [ |
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move all dtypes to base
Co-authored-by: Chris Halcrow <[email protected]>
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@chrishalcrow I refactored a few metrics to make sure durations, spike counts, and bins are properly accounted for when slicing with periods. Happy to discuss about this! |
alejoe91
changed the title
select_sorting_periodsselect_sorting_periods in metrics
3 participants
Depends on #4316
Slices a sorting object based on an array ov valid periods. Periods are defined as a structured dtype as:
EDIT:
Refactored computation of spike train metrics, to make sure that periods are consistently taken into account. Added 2 utils functions to compute durations per unit and bin edges per unit, that optionally use the provided periods