jax.numpy.nanstd#

jax.numpy.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, where=None, mean=None)[source]#

Compute the standard deviation along a given axis, ignoring NaNs.

JAX implementation of numpy.nanstd().

Parameters:

  • a (ArrayLike) – input array.

  • axis (Axis) – optional, int or sequence of ints, default=None. Axis along which the standard deviation is computed. If None, standard deviaiton is computed along flattened array.

  • dtype (DTypeLike | None) – The type of the output array. Default=None.

  • ddof (int) – int, default=0. Degrees of freedom. The divisor in the standard deviation computation is N-ddof, N is number of elements along given axis.

  • keepdims (bool) – bool, default=False. If true, reduced axes are left in the result with size 1.

  • where (ArrayLike | None) – optional, boolean array, default=None. The elements to be used in the standard deviation. Array should be broadcast compatible to the input.

  • mean (ArrayLike | None) – optional, mean of the input array, computed along the given axis. If provided, it will be used to compute the standard deviation instead of computing it from the input array. If specified, mean must be broadcast-compatible with the input array. In the general case, this can be achieved by computing the mean with keepdims=True and axis matching this function’s axis argument.

  • out (None) – Unused by JAX.

Returns:

An array containing the standard deviation of array elements along the given axis. If all elements along the given axis are NaNs, returns nan.

Return type:

Array

See also

Examples

By default, jnp.nanstd computes the standard deviation along flattened array.

>>> nan = jnp.nan
>>> x = jnp.array([[3, nan, 4, 5],
...                [nan, 2, nan, 7],
...                [2, 1, 6, nan]])
>>> jnp.nanstd(x)
Array(1.9843135, dtype=float32)

If axis=0, computes standard deviation along axis 0.

>>> jnp.nanstd(x, axis=0)
Array([0.5, 0.5, 1. , 1. ], dtype=float32)

To preserve the dimensions of input, you can set keepdims=True.

>>> jnp.nanstd(x, axis=0, keepdims=True)
Array([[0.5, 0.5, 1. , 1. ]], dtype=float32)

If ddof=1:

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jnp.nanstd(x, axis=0, keepdims=True, ddof=1))
[[0.71 0.71 1.41 1.41]]

To include specific elements of the array to compute standard deviation, you can use where.

>>> where=jnp.array([[1, 0, 1, 0],
...                  [0, 1, 0, 1],
...                  [1, 1, 0, 1]], dtype=bool)
>>> jnp.nanstd(x, axis=0, keepdims=True, where=where)
Array([[0.5, 0.5, 0. , 0. ]], dtype=float32)