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jax.numpy.nansum — JAX documentation

jax.numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=False, initial=None, where=None)[source]#

Return the sum of the array elements along a given axis, ignoring NaNs.

JAX implementation of numpy.nansum().

Parameters:

• a (ArrayLike) – Input array.

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

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

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

• initial (ArrayLike | None) – int or array, default=None. Initial value for the sum.

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

• out (None) – Unused by JAX.

Returns:

An array containing the sum of array elements along the given axis, ignoring NaNs. If all elements along the given axis are NaNs, returns 0.

Return type:

Array

See also

• jax.numpy.nanmin(): Compute the minimum of array elements along a given axis, ignoring NaNs.

• jax.numpy.nanmax(): Compute the maximum of array elements along a given axis, ignoring NaNs.

• jax.numpy.nanprod(): Compute the product of array elements along a given axis, ignoring NaNs.

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

Examples

By default, jnp.nansum computes the sum of elements along the flattened array.

>>> nan = jnp.nan
>>> x = jnp.array([[3, nan, 4, 5],
...                [nan, -2, nan, 7],
...                [2, 1, 6, nan]])
>>> jnp.nansum(x)
Array(26., dtype=float32)

If axis=1, the sum will be computed along axis 1.

>>> jnp.nansum(x, axis=1)
Array([12.,  5.,  9.], dtype=float32)

If keepdims=True, ndim of the output will be same of that of the input.

>>> jnp.nansum(x, axis=1, keepdims=True)
Array([[12.],
       [ 5.],
       [ 9.]], dtype=float32)

To include only specific elements in computing the sum, you can use where.

>>> where=jnp.array([[1, 0, 1, 0],
...                  [0, 0, 1, 1],
...                  [1, 1, 1, 0]], dtype=bool)
>>> jnp.nansum(x, axis=1, keepdims=True, where=where)
Array([[7.],
       [7.],
       [9.]], dtype=float32)

If where is False at all elements, jnp.nansum returns 0 along the given axis.

>>> where = jnp.array([[False],
...                    [False],
...                    [False]])
>>> jnp.nansum(x, axis=0, keepdims=True, where=where)
Array([[0., 0., 0., 0.]], dtype=float32)

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