explicitly cast the dtype of where's condition parameter to bool
#10087
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keewis
commented
Mar 2, 2025
| @@ -373,7 +373,7 @@ def sum_where(data, axis=None, dtype=None, where=None): | |||
| def where(condition, x, y): | |||
| """Three argument where() with better dtype promotion rules.""" | |||
| xp = get_array_namespace(condition, x, y) | |||
| return xp.where(condition, *as_shared_dtype([x, y], xp=xp)) | |||
| return xp.where(xp.astype(condition, xp.bool), *as_shared_dtype([x, y], xp=xp)) | |||
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looks like it is not that simple... at some point we're trying to call
duck_array_ops.where(True, np.arange(5), np.float64(np.nan))where numpy implements astype by forwarding to the ndarray method (and our own wrapper does the same).
To resolve this, we probably need to make cond a 0d array:
Suggested change
| return xp.where(xp.astype(condition, xp.bool), *as_shared_dtype([x, y], xp=xp)) | |
| try: | |
| cond_ = astype(condition, xp.bool) | |
| except AttributeError: | |
| cond_ = xp.asarray(condition, dtype=xp.bool) | |
| return xp.where(cond_, *as_shared_dtype([x, y], xp=xp)) |
however, I'm not sure I like the way this looks. Another option would be to make astype work for this case
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(this is necessary because
array-api-strictraises an error otherwise)