BUG: histogram small range robust #24161
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* Fixes numpy#23110 * the histogram `norm` variable is used to determine the bin index of input values, and `norm` is calculated in some cases by dividing `n_equal_bins` by the range of the data; when the range of the data is extraordinarily small, the `norm` can become floating point infinity * in this patch, we delay calculating `norm` to increase resistance to the generation of infinite values--for example, a really small input value divided by a really small range is more resistant to generating infinity, so we effectively just change the order of operations a bit * however, I haven't considered whether this is broadly superior for resisting floating point non-finite values for other `histogram` input/extreme value permutations--one might speculate that this is just patching one extreme case that happened to show up in the wild, but may increase likelihood of some other extreme case that isn't in our testsuite yet * the main logic for this patch is that it fixes an issue that occurred in the wild and adds a test for it--if another extreme value case eventually pops up, at least this case will have a regression guard to keep guiding us in the right direction
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Let's try things, thanks Tyler. Not sure I care for backporting, since Tyler says he isn't quite sure about it being generally better for robubstness (and I guess also identical). |
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Fixes BUG: IndexError when using np.histogram with small values #23110
the histogram
normvariable is used to determine the bin index of input values, andnormis calculated in some cases by dividingn_equal_binsby the range of the data; when the range of the data is extraordinarily small, thenormcan become floating point infinityin this patch, we delay calculating
normto increase resistance to the generation of infinite values--for example, a really small input value divided by a really small range is more resistant to generating infinity, so we effectively just change the order of operations a bithowever, I haven't considered whether this is broadly superior for resisting floating point non-finite values for other
histograminput/extreme value permutations--one might speculate that this is just patching one extreme case that happened to show up in the wild, but may increase likelihood of some other extreme case that isn't in our testsuite yetthe main logic for this patch is that it fixes an issue that occurred in the wild and adds a test for
it--if another extreme value case eventually pops up, at least this case will have a regression guard to keep guiding us in the right direction