Speedup template_similarity #4211
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+19
−11
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I think we should not do this in the same PR. The internal sparse representaion of template will have impact at many places, like merging or plotting. Lets do this more carfully. |
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Author
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You are right regaring the sparsity masks. This has been moved in #4212 |
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@samuelgarcia this one is needed for runtimes of both SC2, TDC2, and very large in vitro recordings |
yger
added
bug
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| for old_ind1, unit_ind1 in zip(old_units_inds, sub_units_inds): | ||
| s = self.data["similarity"][old_ind1, old_units_inds] | ||
| similarity[unit_ind1, sub_units_inds] = s | ||
| similarity[sub_units_inds, unit_ind1] = s |
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| connected_mask = np.logical_and(sparsity_mask[i, :], other_sparsity_mask) | ||
| not_connected_mask = np.sum(connected_mask, axis=1) == 0 | ||
| local_mask = np.logical_or( | ||
| sparsity_mask[i, :], other_sparsity_mask | ||
| ) # shape (other_num_templates, num_channels) | ||
| for local_i in np.flatnonzero(not_connected_mask): | ||
| local_mask[local_i] = False | ||
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Member
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Merci pour le bug! |
samuelgarcia
approved these changes
Nov 14, 2025
Member
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I merge this, this is important. |
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While restructuring the components, we introduced a slowdown in template_similarity, by mistake. I don't think this causes bug, but rather a global problem with speed since we are computing distances for all templates, instead of only those that are intersecting.