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correlograms with swapped indices are not mirrored versions of each other

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
45/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Quiet
Tech stack
python
Domain
data

Research direction

Start at the sorting_analyzer.compute("correlograms") and postprocessing.compute_correlograms entry points, then reproduce the supplied symmetry check on a saved SortingAnalyzer with SpikeInterface 0.104.0. Inspect how CCG events are assigned to bins and compare swapped-index results. Done means the documented mirrored relationship holds, or the documentation accurately describes the observed behavior.

Written by the indexing model from the issue text.

Description

For correlograms computed with either sorting_analyzer.compute("correlograms") or postprocessing.compute_correlograms, correlograms[i, j, :] != correlograms[j, i, ::-1]. For both functions, ccgs were recomputed on saved SortingAnalyzers with SpikeInterface 0.104.0.

I assume this is some sort of artifact of how ccg events are assigned to bins, but the documentation for the compute_correlograms function says that the equality should be satisfied. I used the code below to test both functions with 4 different recordings, for all recordings 90+% of cross-correlograms were not perfect mirrors.

count = 0
max_diff = 0
max_diff_inds = None
for i in range(correlograms.shape[0]):
    for j in range(i, correlograms.shape[0]):
        if not (np.array_equal(correlograms[i, j],correlograms[j, i, ::-1])):
            print(i, j, np.abs(correlograms[i, j] - correlograms[j, i, ::-1]).max())
            count += 1
            if max_diff <  np.abs(correlograms[i, j] - correlograms[j, i, ::-1]).max():
                max_diff_inds = (i, j)
                max_diff = np.abs(correlograms[i, j] - correlograms[j, i, ::-1]).max()


print(count/(correlograms.shape[0]*(correlograms.shape[0]-1)/2))
print(max_diff, max_diff_inds)

Most of the differences are small (1-5 maximum difference between bins across a ccg), but for some unit pairs it can be quite large (see below)

correlograms[71, 97]
Image

correlograms[97, 71]
Image

Dominant language
Python
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Avg merge
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Merged PRs (30d)
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