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.
The shape of the grid.
Data type of the result.
Return a sparse representation of the grid instead of a dense representation. Default is False.
Returns one array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions).
Returns a tuple of arrays, with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1) with dimensions[i] in the ith place
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
Examples
>>> import numpy as np
>>> grid = np.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0] # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid[1] # column indices
array([[0, 1, 2],
[0, 1, 2]])
The indices can be used as an index into an array.
>>> x = np.arange(20).reshape(5, 4)
>>> row, col = np.indices((2, 3))
>>> x[row, col]
array([[0, 1, 2],
[4, 5, 6]])
Note that it would be more straightforward in the above example to extract the required elements directly with x[:2, :3].
If sparse is set to true, the grid will be returned in a sparse representation.
>>> i, j = np.indices((2, 3), sparse=True)
>>> i.shape
(2, 1)
>>> j.shape
(1, 3)
>>> i # row indices
array([[0],
[1]])
>>> j # column indices
array([[0, 1, 2]])
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