ENH: __array_function__ support for np.lib, part 1/2
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33 tasks
np.lib.arraypad through np.lib.nanfunctions
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hameerabbasi
approved these changes
Oct 9, 2018
| @@ -36,6 +37,11 @@ | |||
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| def _ediff1d_dispatcher(ary, to_end=None, to_begin=None): | |||
| return (ary, to_end, to_begin) | |||
There was a problem hiding this comment.
Needs a is not None check.
| @@ -3294,6 +3405,12 @@ def _ureduce(a, func, **kwargs): | |||
| return r, keepdim | |||
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| def _median_dispatcher( | |||
| a, axis=None, out=None, overwrite_input=None, keepdims=None): | |||
| return (a, out) | |||
There was a problem hiding this comment.
Needs a None check for out.
| @@ -3583,6 +3706,12 @@ def percentile(a, q, axis=None, out=None, | |||
| a, q, axis, out, overwrite_input, interpolation, keepdims) | |||
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| def _quantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, | |||
| interpolation=None, keepdims=None): | |||
| return (a, q, out) | |||
There was a problem hiding this comment.
Needs None check for out.
I opted against this style mostly because it can make the dispatchers significantly longer, e.g., def _ediff1d_dispatcher(ary, to_end=None, to_begin=None):
return (ary, to_end, to_begin)vs def _ediff1d_dispatcher(ary, to_end=None, to_begin=None):
yield ary
if to_end is not None:
yield to_end
if to_begin is not None:
yield to_beginIf this actually made a difference in speed I would go for it, but I suspect they will actually be quite similar in performance. |
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Thanks Stephan. |
__array_function__ support for np.lib, part 1/2
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xref #12028
np.lib.arraypad through np.lib.nanfunctions