indices(dimensions, dtype=<class 'int'>, sparse=False)
Compute an array where the subarrays contain index values 0, 1, ... varying only along the corresponding axis.
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
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.
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 an array representing the indices of a grid.
>>> 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]])See :
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numpy.take
skimage.segmentation._watershed.watershed
numpy.ma.core.MaskedArray.put
numpy.fromfunction
numpy.put_along_axis
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