JezK
Edit File: _statistical_functions.py
from __future__ import annotations from ._dtypes import ( _floating_dtypes, _numeric_dtypes, ) from ._array_object import Array from ._creation_functions import asarray from ._dtypes import float32, float64 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 _numeric_dtypes: raise TypeError("Only 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 _floating_dtypes: raise TypeError("Only 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 _numeric_dtypes: raise TypeError("Only 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 float32 to float64 for dtype=None # We need to do so here before computing the product to avoid overflow if dtype is None and x.dtype == float32: dtype = float64 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 _floating_dtypes: raise TypeError("Only 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 integers to (u)int64 and float32 to # float64 for dtype=None. `np.sum` does that too for integers, but not for # float32, so we need to special-case it here if dtype is None and x.dtype == float32: dtype = float64 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 _floating_dtypes: raise TypeError("Only floating-point dtypes are allowed in var") return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))