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115 lines
3.3 KiB
115 lines
3.3 KiB
from __future__ import annotations
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from ._dtypes import (
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_floating_dtypes,
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_numeric_dtypes,
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)
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from ._array_object import Array
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from ._creation_functions import asarray
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from ._dtypes import float32, float64
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from typing import TYPE_CHECKING, Optional, Tuple, Union
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if TYPE_CHECKING:
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from ._typing import Dtype
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import numpy as np
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def max(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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keepdims: bool = False,
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) -> Array:
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if x.dtype not in _numeric_dtypes:
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raise TypeError("Only numeric dtypes are allowed in max")
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return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
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def mean(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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keepdims: bool = False,
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) -> Array:
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if x.dtype not in _floating_dtypes:
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raise TypeError("Only floating-point dtypes are allowed in mean")
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return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims))
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def min(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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keepdims: bool = False,
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) -> Array:
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if x.dtype not in _numeric_dtypes:
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raise TypeError("Only numeric dtypes are allowed in min")
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return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
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def prod(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype: Optional[Dtype] = None,
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keepdims: bool = False,
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) -> Array:
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if x.dtype not in _numeric_dtypes:
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raise TypeError("Only numeric dtypes are allowed in prod")
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# Note: sum() and prod() always upcast float32 to float64 for dtype=None
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# We need to do so here before computing the product to avoid overflow
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if dtype is None and x.dtype == float32:
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dtype = float64
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return Array._new(np.prod(x._array, dtype=dtype, axis=axis, keepdims=keepdims))
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def std(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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correction: Union[int, float] = 0.0,
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keepdims: bool = False,
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) -> Array:
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# Note: the keyword argument correction is different here
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if x.dtype not in _floating_dtypes:
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raise TypeError("Only floating-point dtypes are allowed in std")
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return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims))
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def sum(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype: Optional[Dtype] = None,
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keepdims: bool = False,
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) -> Array:
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if x.dtype not in _numeric_dtypes:
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raise TypeError("Only numeric dtypes are allowed in sum")
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# Note: sum() and prod() always upcast integers to (u)int64 and float32 to
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# float64 for dtype=None. `np.sum` does that too for integers, but not for
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# float32, so we need to special-case it here
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if dtype is None and x.dtype == float32:
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dtype = float64
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return Array._new(np.sum(x._array, axis=axis, dtype=dtype, keepdims=keepdims))
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def var(
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x: Array,
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/,
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*,
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axis: Optional[Union[int, Tuple[int, ...]]] = None,
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correction: Union[int, float] = 0.0,
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keepdims: bool = False,
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) -> Array:
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# Note: the keyword argument correction is different here
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if x.dtype not in _floating_dtypes:
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raise TypeError("Only floating-point dtypes are allowed in var")
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return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))
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