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| author | Aaron Meurer <asmeurer@gmail.com> | 2021-07-08 16:56:27 -0600 |
|---|---|---|
| committer | Aaron Meurer <asmeurer@gmail.com> | 2021-07-08 16:56:27 -0600 |
| commit | fc1ff6fc3045482a72c359689ee7bfa7e3299985 (patch) | |
| tree | 0b5668fa928cf428fc83c103a74bdc02238341cd /numpy/_array_api/_statistical_functions.py | |
| parent | 13796236295b344ee83e79c8a33ad6205c0095db (diff) | |
| download | numpy-fc1ff6fc3045482a72c359689ee7bfa7e3299985.tar.gz | |
Capitalize the names of the type hint types in the array API
That way they aren't ambiguous with the attributes with the same names.
Diffstat (limited to 'numpy/_array_api/_statistical_functions.py')
| -rw-r--r-- | numpy/_array_api/_statistical_functions.py | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/numpy/_array_api/_statistical_functions.py b/numpy/_array_api/_statistical_functions.py index 26afd7354..e6a791fe6 100644 --- a/numpy/_array_api/_statistical_functions.py +++ b/numpy/_array_api/_statistical_functions.py @@ -4,29 +4,29 @@ from ._array_object import ndarray from typing import TYPE_CHECKING if TYPE_CHECKING: - from ._types import Optional, Tuple, Union, array + from ._types import Optional, Tuple, Union, Array import numpy as np -def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def max(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: return ndarray._new(np.max(x._array, axis=axis, keepdims=keepdims)) -def mean(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def mean(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: return ndarray._new(np.asarray(np.mean(x._array, axis=axis, keepdims=keepdims))) -def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def min(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: return ndarray._new(np.min(x._array, axis=axis, keepdims=keepdims)) -def prod(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def prod(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: return ndarray._new(np.asarray(np.prod(x._array, axis=axis, keepdims=keepdims))) -def std(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> array: +def std(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array: # Note: the keyword argument correction is different here return ndarray._new(np.asarray(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims))) -def sum(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def sum(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: return ndarray._new(np.asarray(np.sum(x._array, axis=axis, keepdims=keepdims))) -def var(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> array: +def var(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array: # Note: the keyword argument correction is different here return ndarray._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))) |