numpy.indices¶
- numpy.indices(dimensions, dtype=<type 'int'>)[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: dimensions : sequence of ints
The shape of the grid.
dtype : dtype, optional
Data type of the result.
Returns: grid : ndarray
The array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions).
Notes
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 (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
>>> 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].