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| author | Aaron Meurer <asmeurer@gmail.com> | 2021-01-20 17:57:10 -0700 |
|---|---|---|
| committer | Aaron Meurer <asmeurer@gmail.com> | 2021-01-20 17:57:10 -0700 |
| commit | ad19f7f7dfcfe33fd4591f1be3b4d9d30887899a (patch) | |
| tree | 2c442d50657f3141de08eee76b75d023f081e105 /numpy/_array_api/_statistical_functions.py | |
| parent | 5df8ec9673a73e71554c8f53cc6edb60533c5d17 (diff) | |
| download | numpy-ad19f7f7dfcfe33fd4591f1be3b4d9d30887899a.tar.gz | |
Use np.asarray in the array API submodule for any function that can return a scalar
This is needed to pass mypy type checks for the given type annotations.
Diffstat (limited to 'numpy/_array_api/_statistical_functions.py')
| -rw-r--r-- | numpy/_array_api/_statistical_functions.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/numpy/_array_api/_statistical_functions.py b/numpy/_array_api/_statistical_functions.py index 020053896..e62410d01 100644 --- a/numpy/_array_api/_statistical_functions.py +++ b/numpy/_array_api/_statistical_functions.py @@ -8,21 +8,21 @@ def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keep return np.max(x, axis=axis, keepdims=keepdims) def mean(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: - return np.mean(x, axis=axis, keepdims=keepdims) + return np.asarray(np.mean(x, axis=axis, keepdims=keepdims)) def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: return np.min(x, axis=axis, keepdims=keepdims) def prod(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: - return np.prod(x, axis=axis, keepdims=keepdims) + return np.asarray(np.prod(x, 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: # Note: the keyword argument correction is different here - return np.std(x, axis=axis, ddof=correction, keepdims=keepdims) + return np.asarray(np.std(x, axis=axis, ddof=correction, keepdims=keepdims)) def sum(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: - return np.sum(x, axis=axis, keepdims=keepdims) + return np.asarray(np.sum(x, 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: # Note: the keyword argument correction is different here - return np.var(x, axis=axis, ddof=correction, keepdims=keepdims) + return np.asarray(np.var(x, axis=axis, ddof=correction, keepdims=keepdims)) |