- jax.numpy.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, where=None)#
Compute the standard deviation along the specified axis, while
LAX-backend implementation of
Original docstring below.
Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.
For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised.
New in version 1.8.0.
a (array_like) – Calculate the standard deviation of the non-NaN values.
dtype (dtype, optional) – Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.
ddof (int, optional) – Means Delta Degrees of Freedom. The divisor used in calculations is
N - ddof, where
Nrepresents the number of non-NaN elements. By default ddof is zero.
keepdims (bool, optional) –
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.
If this value is anything but the default it is passed through as-is to the relevant functions of the sub-classes. If these functions do not have a keepdims kwarg, a RuntimeError will be raised.
where (array_like of bool, optional) – Elements to include in the standard deviation. See ~numpy.ufunc.reduce for details.
standard_deviation – If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.
- Return type:
ndarray, see dtype parameter above.