FM / array

/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/array_api/

/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/array_api

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
linalg.py	18221	0644	edit dl rm
setup.py	341	0644	edit dl rm
_array_object.py	43739	0644	edit dl rm
_constants.py	66	0644	edit dl rm
_creation_functions.py	10050	0644	edit dl rm
_data_type_functions.py	6288	0644	edit dl rm
_dtypes.py	4823	0644	edit dl rm
_elementwise_functions.py	25992	0644	edit dl rm
_indexing_functions.py	601	0644	edit dl rm
_manipulation_functions.py	3317	0644	edit dl rm
_searching_functions.py	1715	0644	edit dl rm
_set_functions.py	2948	0644	edit dl rm
_sorting_functions.py	2031	0644	edit dl rm
_statistical_functions.py	3584	0644	edit dl rm
_typing.py	1228	0644	edit dl rm
_utility_functions.py	824	0644	edit dl rm
__init__.py	10355	0644	edit dl rm

Edit: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/array_api/_statistical_functions.py (3584B)

from __future__ import annotations

from ._dtypes import (
    _real_floating_dtypes,
    _real_numeric_dtypes,
    _numeric_dtypes,
)
from ._array_object import Array
from ._dtypes import float32, float64, complex64, complex128

from typing import TYPE_CHECKING, Optional, Tuple, Union

if TYPE_CHECKING:
    from ._typing import Dtype

import numpy as np

def max(
    x: Array,
    /,
    *,
    axis: Optional[Union[int, Tuple[int, ...]]] = None,
    keepdims: bool = False,
) -> Array:
    if x.dtype not in _real_numeric_dtypes:
        raise TypeError("Only real numeric dtypes are allowed in max")
    return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))

def mean(
    x: Array,
    /,
    *,
    axis: Optional[Union[int, Tuple[int, ...]]] = None,
    keepdims: bool = False,
) -> Array:
    if x.dtype not in _real_floating_dtypes:
        raise TypeError("Only real floating-point dtypes are allowed in mean")
    return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims))

def min(
    x: Array,
    /,
    *,
    axis: Optional[Union[int, Tuple[int, ...]]] = None,
    keepdims: bool = False,
) -> Array:
    if x.dtype not in _real_numeric_dtypes:
        raise TypeError("Only real numeric dtypes are allowed in min")
    return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))

def prod(
    x: Array,
    /,
    *,
    axis: Optional[Union[int, Tuple[int, ...]]] = None,
    dtype: Optional[Dtype] = None,
    keepdims: bool = False,
) -> Array:
    if x.dtype not in _numeric_dtypes:
        raise TypeError("Only numeric dtypes are allowed in prod")
    # Note: sum() and prod() always upcast for dtype=None. `np.prod` does that
    # for integers, but not for float32 or complex64, so we need to
    # special-case it here
    if dtype is None:
        if x.dtype == float32:
            dtype = float64
        elif x.dtype == complex64:
            dtype = complex128
    return Array._new(np.prod(x._array, dtype=dtype, 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
    if x.dtype not in _real_floating_dtypes:
        raise TypeError("Only real floating-point dtypes are allowed in std")
    return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims))

def sum(
    x: Array,
    /,
    *,
    axis: Optional[Union[int, Tuple[int, ...]]] = None,
    dtype: Optional[Dtype] = None,
    keepdims: bool = False,
) -> Array:
    if x.dtype not in _numeric_dtypes:
        raise TypeError("Only numeric dtypes are allowed in sum")
    # Note: sum() and prod() always upcast for dtype=None. `np.sum` does that
    # for integers, but not for float32 or complex64, so we need to
    # special-case it here
    if dtype is None:
        if x.dtype == float32:
            dtype = float64
        elif x.dtype == complex64:
            dtype = complex128
    return Array._new(np.sum(x._array, axis=axis, dtype=dtype, 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
    if x.dtype not in _real_floating_dtypes:
        raise TypeError("Only real floating-point dtypes are allowed in var")
    return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))

from __future__ import annotations from ._dtypes import ( _real_floating_dtypes, _real_numeric_dtypes, _numeric_dtypes, ) from ._array_object import Array from ._dtypes import float32, float64, complex64, complex128 from typing import TYPE_CHECKING, Optional, Tuple, Union if TYPE_CHECKING: from ._typing import Dtype import numpy as np def max( x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ) -> Array: if x.dtype not in _real_numeric_dtypes: raise TypeError("Only real numeric dtypes are allowed in max") return Array._new(np.max(x._array, axis=axis, keepdims=keepdims)) def mean( x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ) -> Array: if x.dtype not in _real_floating_dtypes: raise TypeError("Only real floating-point dtypes are allowed in mean") return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims)) def min( x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ) -> Array: if x.dtype not in _real_numeric_dtypes: raise TypeError("Only real numeric dtypes are allowed in min") return Array._new(np.min(x._array, axis=axis, keepdims=keepdims)) def prod( x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optional[Dtype] = None, keepdims: bool = False, ) -> Array: if x.dtype not in _numeric_dtypes: raise TypeError("Only numeric dtypes are allowed in prod") # Note: sum() and prod() always upcast for dtype=None. `np.prod` does that # for integers, but not for float32 or complex64, so we need to # special-case it here if dtype is None: if x.dtype == float32: dtype = float64 elif x.dtype == complex64: dtype = complex128 return Array._new(np.prod(x._array, dtype=dtype, 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 if x.dtype not in _real_floating_dtypes: raise TypeError("Only real floating-point dtypes are allowed in std") return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims)) def sum( x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype: Optional[Dtype] = None, keepdims: bool = False, ) -> Array: if x.dtype not in _numeric_dtypes: raise TypeError("Only numeric dtypes are allowed in sum") # Note: sum() and prod() always upcast for dtype=None. `np.sum` does that # for integers, but not for float32 or complex64, so we need to # special-case it here if dtype is None: if x.dtype == float32: dtype = float64 elif x.dtype == complex64: dtype = complex128 return Array._new(np.sum(x._array, axis=axis, dtype=dtype, 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 if x.dtype not in _real_floating_dtypes: raise TypeError("Only real floating-point dtypes are allowed in var") return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))