ENH: Add proper dtype-support to np.flatiter
#17981
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BvB93
commented
Dec 11, 2020
| if TYPE_CHECKING or HAVE_PROTOCOL: | ||
| class _SupportsArray(Protocol): | ||
| @overload |
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Turns out the protocol already works fine for both positional-only and positional-or-keyword arguments, even without these overloads.
| @overload | ||
| def __array__(self: flatiter[ndarray[Any, _DType]], __dtype: None = ...) -> ndarray[Any, _DType]: ... | ||
| @overload | ||
| def __array__(self, __dtype: DTypeLike) -> ndarray[Any, dtype[Any]]: ... |
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Adding overloads for all DTypeLike objects is quite frankly too much work.
For now just return dtype[Any] if a dtype-like object (besides None) is provided.
BvB93
changed the title
np.flatiternp.flatiter
charris
reviewed
Dec 11, 2020
Addresses numpy#17981 (comment) Co-Authored-By: Charles Harris <charlesr.harris@gmail.com>
|
Thanks Bas. |
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This PR adds proper dtype-support to
np.flatiter.The methods of the latter now successfully return an array/scalar of the appropriate dtype (when applicable).
Note that In order to get
__array__to work, thenpt._SupportsArrayprotocol needed some minor restructuring,the latter now only caring about the default value of its
dtypeparameter (i.e.dtype=None).Concrete implementations of the protocol are responsible for adding any and all
DTypeLike-based overloads.Examples
Given the
np.int64arrayi8_array:next(i8_array.flat)andi8_array.flat[0]are now inferred asnp.int64.i8_array.flat.__array__()is now inferred asnp.ndarray[Any, np.dtype[np.int64]].