numpy.indices(dimensions, dtype=<class 'int'>, sparse=False)
[source]
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: 


Returns: 

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, ..., rN1)
of length N
, the output shape is (N, r0, ..., rN1)
.
The subarrays grid[k]
contains the ND array of indices along the kth
axis. Explicitly:
grid[k, i0, i1, ..., iN1] = ik
>>> 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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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.indices.html