Return a tensor representing the indices of a grid.
Compute a tensor where the subtensors contain index values 0,1,…
varying only along the corresponding axis.
dimensions (sequence of ints) – The shape of the grid.
dtype (dtype, optional) – Data type of the result.
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
grid – The tensor of grid indices,
grid.shape = (len(dimensions),) + tuple(dimensions).
grid.shape = (len(dimensions),) + tuple(dimensions)
The output shape 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
(r0, ..., rN-1)
The subtensors grid[k] contains the N-D array of indices along the
k-th axis. Explicitly:
grid[k,i0,i1,...,iN-1] = ik
>>> import mars.tensor as mt
>>> grid = mt.indices((2, 3))
(2, 2, 3)
>>> grid.execute() # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid.execute() # column indices
array([[0, 1, 2],
[0, 1, 2]])
The indices can be used as an index into a tensor.
>>> x = mt.arange(20).reshape(5, 4)
>>> row, col = mt.indices((2, 3))
>>> # x[row, col] # TODO(jisheng): accomplish this if multiple fancy indexing is supported
Note that it would be more straightforward in the above example to
extract the required elements directly with x[:2, :3].