cupynumeric.indices

cupynumeric.indices(dimensions: Sequence[int], dtype: npt.DTypeLike = <class 'int'>, sparse: bool = False) → ndarray | tuple[ndarray, ...]

Return an array representing the indices of a grid. Compute an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis.

Parameters:

• dimensions (Sequence[int]) – The shape of the grid.

• dtype (data-type, optional) – Data type of the result.

• sparse (bool, optional) – Return a sparse representation of the grid instead of a dense representation. Default is False.

Returns:

grid – If sparse is False returns one array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions). If sparse is True returns a tuple of arrays, with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1) with dimensions[i] in the ith place

Return type:

ndarray or tuple[ndarray, …]

See also

numpy.indices

Notes

The output shape in the dense case is obtained by prepending the number of dimensions in front of the tuple of dimensions, i.e. if dimensions is a tuple (r0, ..., rN-1) of length N, the output shape is (N, r0, ..., rN-1). The subarrays grid[k] contains the N-D array of indices along the k-th axis. Explicitly:

grid[k, i0, i1, …, iN-1] = ik

Availability:

Multiple GPUs, Multiple CPUs