DM-43238: Refactor calcDistances.py for a 50-100x speed increase in AnalyzeMatchedVisitCore #216
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erykoff
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cmsaunders
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Mar 11, 2024
| else: | ||
| raRotated = np.array(data[self.raKey]) | ||
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| np.add.at(meanRa, groupId, raRotated) |
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This is cool, and new to me!
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Numpy universal functions at are my favorite thing, and they even made them faster in the version that will be in the next rubin-env.
| bad2 = nSep <= 2 | ||
| sepMean[bad2] = np.nan | ||
| sepResiduals = separations - sepMean[matchedPairInd] | ||
| sepResiduals = sepResiduals[np.isfinite(sepResiduals)] | ||
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| if len(rmsDistances) == 0: |
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I don't think you need this anymore, since you already returned if good.sum() == 0
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This refactors the loops in calcDistances to be vectorized and get a 10x speed increase. In addition, it adds downsampling which gets another 5-10x speed increase in the densest fields.
One algorithmic change from the old version is that now the computation of
sepResidualschanges from fromseparations - median(separations)toseparations - mean(separations)because this is much easier to vectorize.There is also a small fix for empty tracts.