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Test Results 24 files 24 suites 3d 23h 46m 22s ⏱️ For more details on these failures, see this check. Results for commit 25978bc. ♻️ This comment has been updated with latest results. |
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This might help in the majority of cases (to be validated), but a way to be sure to fix the original issue is to pre-sort all labels once before the loop, to change the complexity from O(nbins^2) to O(nbins). std::vector<const TObjString *> sortedLabels(labelsX->GetEntries() + labelsY->GetEntries() + labelsZ->GetEntries());
for (const auto *label : TRangeStaticCast<const TObjString>(labelsX))
sortedLabels[label->GetUniqueId()] = label;
for (const auto *label : TRangeStaticCast<const TObjString>(labelsY))
sortedLabels[labelsX->GetEntries() + label->GetUniqueId()] = label;
// ...
// now we can call sortedLabels[binx] instead of hist->GetXaxis()->GetBinLabel(binx) in the loop |
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silverweed
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We're not really avoiding linear scaling (that's pretty much unavoidable given the situation), so the commit message should probably not mention it
| } | ||
| // Cache bins in vectors | ||
| auto getLabelsCache = [](TAxis *axis) { | ||
| std::vector<const char *> labels; |
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We could/should avoid 2 allocations by using a single vector for all 3 axes
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At the cost of readability perhaps?
| EXPECT_DOUBLE_EQ(hist.Interpolate(0.3, 19.9, 5.), 100. * 19 + 10000. * 4.5); | ||
| EXPECT_DOUBLE_EQ(hist.Interpolate(0.8, 19.9, 5.), 0.3 + 100. * 19 + 10000. * 4.5); | ||
| } | ||
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| #include "TList.h" | |
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thanks for the comment, and to debian13 that failed to compile!
and add a unit test.
Before linear search, assume the bin labels are ordered by unique id. Speeds up merging, reported to be slow by root-project#23681
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therefore speeding up identification of the bin label. Fixes root-project#23681
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Good point, thanks. Done. |
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Before linear search, assume the bin labels are ordered by unique id.
Fixes #23681
En passant, also fixes #23716