jax.numpy.nanstd

jax.numpy.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, where=None)

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.

• 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

• jax.numpy.nanmean(): Compute the mean of array elements over a given axis, ignoring NaNs.

• jax.numpy.nanvar(): Compute the variance along the given axis, ignoring NaNs values.

• jax.numpy.std(): Computed the standard deviation along the given axis.

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)