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 ofnumpy.nanstd().

Parameters:

• a (ArrayLike) – input array.

• axis (Axis) – optional, int or sequence of ints, default=None. Axis along which thestandard deviation is computed. If None, standard deviaiton is computedalong 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 deviationcomputation isN-ddof,N is number of elements along given axis.

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

• where (ArrayLike |None) – optional, boolean array, default=None. The elements to be used in thestandard 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 ofcomputing it from the input array. If specified, mean must be broadcast-compatiblewith the input array. In the general case, this can be achieved by computing the mean withkeepdims=True andaxis matching this function’saxis argument.

• out (None) – Unused by JAX.

Returns:

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

Return type:

Array

See also

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

• jax.numpy.nanvar(): Compute the variance along the given axis, ignoringNaNs 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)

Ifaxis=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 setkeepdims=True.

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

Ifddof=1:

>>>withjnp.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, youcan usewhere.

>>>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)