dist¶

dist(mat: ndarray, axis: int = None, mode: str = 'sem', ddof: int = 0, where: ndarray = None, keepdims: bool = False, xp=None) → tuple[float, float] | list[float, float] | ndarray[2, float][source][source]¶

Calculate the mean and standard deviation of a matrix.

This function calculates the mean and standard deviation of a matrix along a given axis. If a mask is provided, the mean and standard deviation are calculated only for the elements of the matrix that are not masked.

Parameters:

mat (np.ndarray) – Matrix to calculate mean and standard deviation of.
axis (int) – Axis of matrix to calculate mean and standard deviation along.
mode (str) – Mode of standard deviation to calculate. Can be ‘sem’ for standard error of the mean or ‘std’ for standard deviation.
where (np.ndarray) – Mask of elements to include in mean and standard deviation calculation.
ddof (int)
keepdims (bool)

Returns:

Tuple containing the mean and standard deviation of the matrix.

Return type:

Doubles

Examples

>>> import numpy as np
>>> mat = np.arange(24).reshape(4,6)
>>> dist(mat, 0)[1]
array([3.35410197, 3.35410197, 3.35410197, 3.35410197, 3.35410197,
       3.35410197])
>>> dist(mat, 0, mode='std')[1]
array([6.70820393, 6.70820393, 6.70820393, 6.70820393, 6.70820393,
       6.70820393])