ENH: add support for array-likes in polynomial evaluation #18662
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mhvk
reviewed
Mar 22, 2021
| @@ -479,6 +479,7 @@ def __setstate__(self, dict): | |||
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| def __call__(self, arg): | |||
| off, scl = pu.mapparms(self.domain, self.window) | |||
| arg = np.asarray(arg) | |||
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With this, previously working calls using masked arrays would stop working - e.g.,
np.polynomial.Polynomial([1, 2])(np.ma.MaskedArray([3, 4], mask=[True, False]))
That particular part could be solved by making this np.asanyarray - though I guess it is possible the polynomials are also used by, e.g., dask, in which case this would still be a problem.
| @@ -479,6 +479,7 @@ def __setstate__(self, dict): | |||
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| def __call__(self, arg): | |||
| off, scl = pu.mapparms(self.domain, self.window) | |||
| arg = np.asarray(arg) | |||
| arg = off + scl*arg | |||
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A simpler way to do the same thing might be to make sure scl is an array scalar rather than a regular scalar, since array * list already has the right behaviour of interpreting this list as an array. One can do it here by writing this as off + scl[...] * arg - though it might make more sense to ensure scl is an array scalar inside mapparms.
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ENH: add support for array-likes in polynomial evaluation
Closes #17949.
Adds an
asarray()call in the polybase__call__method so that evaluation works with array-likes too. See #17949 for the initial reproduced code example; after the above change, evaluation works as expected: