Reorder spike vectors with a flexible-dtype numba kernel

[grahamfindlay]

Follows @samuelgarcia 's comment on #4695 and #4781.

The numba kernel from #4695 moved rows through a flat (N, num_fields) int64 view and addressed unit_index and segment_index as columns 1 and 2, 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)

[grahamfindlay]

The 2 failing tests don't appear to have anything to do with this PR. They're tolerance tests failing on test_decimate_antialias_by_chunks which funnily enough passed in its own PR (#4621) with the same scipy and numpy versions, so they must be nondeterministic. Maybe create_sinusoidal_traces just needs a seed. It looks like they're still tiny tiny single-sample discrepancies. It must be a rounding thing on Apple Silicon.

[grahamfindlay]

Yes, I know it was kinda naughty of me to push a fix for that to this PR... forgive me.

EDIT: No longer applicable, see below.

[grahamfindlay]

Removed the fix for the unrelated failing tests since another one was already just merged in #4792

[grahamfindlay]

@samuelgarcia the failing Python 3.14 tests are unrelated and should be fixed in #4845 so this is still ready for your review anytime