MAINT: stats.epps_singleton_2samp: NaNs/Infs in input -> NaNs in output #23893
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mdhaber
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Oct 30, 2025
j-bowhay
approved these changes
Oct 30, 2025
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| x, y = xp_promote(x, y, force_floating=True, xp=np) | ||
| t = np.asarray(t, dtype=x.dtype) |
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Not worth rerunning CI for but if we are coming back here in the near future then
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| x, y = xp_promote(x, y, force_floating=True, xp=np) | |
| t = np.asarray(t, dtype=x.dtype) | |
| x, y, t = xp_promote(x, y, t, force_floating=True, xp=np) |
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I wrote it this way intentionally because even if t is treated as an array internally, I think the user may think of it like a sequence of separate floats. It is 1D and doesn't follow broadcasting rules with the samples. I have similar thoughts about rng in iqr.
I should double check, but I didn't think we wrote xp_promote to accept non-array array-likes, and we don't want to coerce it to an array before dtypes determination because that could upcast everything to the default float.
That said, I am a bit conflicted about it. I'll think some more.
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Reference issue
Follow-up to gh-23881
What does this implement/fix?
Currently, NaNs in the input of
epps_singleton_2sampproduce NaNs in the output, whereas Infs result in an error. Either the way, the calculation can't produce a valid number (e.g. in the Inf case, we have sines and cosines of infinity), so let's keep the result consistent. Now that the function is vectorized, it is preferable to return normally with NaNs in the output that correspond with affected slices. This PR makes that change.Additional information
@j-bowhay also replaced
axis : Nonewithaxis : 0as appropriate in the documentation ofepps_singleton_2sampandkruskal.