Reorder spike vectors with a flexible-dtype numba kernel - #4784
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The counting-sort kernel from SpikeInterface#4695 moved rows through a flat (N, num_fields) int64 view and addressed unit_index and segment_index as columns 1 and 2, essentially assuming minimum_spike_dtype. Samuel suggest that we might want to change the spike vector dtype in the future, so this is a more flexible variant that can take narrow index dtypes (e.g. int8). Also updated the numpy fallback to work for narrow index fields. Measured on ~400M spikes, 342 units: - 11.6 -> 12.0 s (3x int64 fields) - 13.1 -> 13.0 s (4x int64 fields) - 9.1 s (1 each of int64/int32/int8 fields)
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Follows @samuelgarcia 's comment on #4695 and #4781.
The numba kernel from #4695 moved rows through a flat
(N, num_fields)int64view and addressedunit_indexandsegment_indexas columns 1 and 2, assumingminimum_spike_dtype.Samuel suggest that we might want to change the spike vector dtype in the future, so this is a more flexible variant that can take narrow index dtypes (e.g. int8).
Also updated the numpy fallback to work for narrow index fields.
Measured on ~400M spikes, 342 units: