jax.numpy.take#

jax.numpy.take(a, indices, axis=None, out=None, mode=None)[source]#

Take elements from an array along an axis.

LAX-backend implementation of numpy.take().

In the JAX version, the mode argument defaults to a special mode ("fill") that returns invalid values (e.g., NaN) for out-of-bounds indices. See jax.numpy.ndarray.at for more discussion of out-of-bounds indexing in JAX.

Original docstring below.

When axis is not None, this function does the same thing as “fancy” indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. A call such as np.take(arr, indices, axis=3) is equivalent to arr[:,:,:,indices,...].

Explained without fancy indexing, this is equivalent to the following use of ndindex, which sets each of ii, jj, and kk to a tuple of indices:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
Nj = indices.shape
for ii in ndindex(Ni):
    for jj in ndindex(Nj):
        for kk in ndindex(Nk):
            out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
Parameters

  • a (array_like (Ni..., M, Nk...)) – The source array.

  • indices (array_like (Nj...)) – The indices of the values to extract.

  • axis (int, optional) – The axis over which to select values. By default, the flattened input array is used.

  • mode ({'raise', 'wrap', 'clip'}, optional) –

    Specifies how out-of-bounds indices will behave.

    • ’raise’ – raise an error (default)

    • ’wrap’ – wrap around

    • ’clip’ – clip to the range

    ’clip’ mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers.

Returns

out – The returned array has the same type as a.

Return type

ndarray (Ni…, Nj…, Nk…)