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Module « numpy.matlib »

Fonction indices - module numpy.matlib

Signature de la fonction indices

def indices(dimensions, dtype=<class 'int'>, sparse=False) 

Description

help(numpy.matlib.indices)

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.
sparse : boolean, optional
    Return a sparse representation of the grid instead of a dense
    representation. Default is False.

Returns
-------
grid : one ndarray or tuple of ndarrays
    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

See Also
--------
mgrid, ogrid, meshgrid

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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