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Module « numpy.matlib »

Fonction fromfunction - module numpy.matlib

Signature de la fonction fromfunction

def fromfunction(function, shape, *, dtype=<class 'float'>, like=None, **kwargs) 

Description

help(numpy.matlib.fromfunction)

Construct an array by executing a function over each coordinate.

The resulting array therefore has a value ``fn(x, y, z)`` at
coordinate ``(x, y, z)``.

Parameters
----------
function : callable
    The function is called with N parameters, where N is the rank of
    `shape`.  Each parameter represents the coordinates of the array
    varying along a specific axis.  For example, if `shape`
    were ``(2, 2)``, then the parameters would be
    ``array([[0, 0], [1, 1]])`` and ``array([[0, 1], [0, 1]])``
shape : (N,) tuple of ints
    Shape of the output array, which also determines the shape of
    the coordinate arrays passed to `function`.
dtype : data-type, optional
    Data-type of the coordinate arrays passed to `function`.
    By default, `dtype` is float.
like : array_like, optional
        Reference object to allow the creation of arrays which are not
        NumPy arrays. If an array-like passed in as ``like`` supports
        the ``__array_function__`` protocol, the result will be defined
        by it. In this case, it ensures the creation of an array object
        compatible with that passed in via this argument.

    .. versionadded:: 1.20.0

Returns
-------
fromfunction : any
    The result of the call to `function` is passed back directly.
    Therefore the shape of `fromfunction` is completely determined by
    `function`.  If `function` returns a scalar value, the shape of
    `fromfunction` would not match the `shape` parameter.

See Also
--------
indices, meshgrid

Notes
-----
Keywords other than `dtype` and `like` are passed to `function`.

Examples
--------
>>> import numpy as np
>>> np.fromfunction(lambda i, j: i, (2, 2), dtype=float)
array([[0., 0.],
       [1., 1.]])

>>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float)
array([[0., 1.],
       [0., 1.]])

>>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
array([[ True, False, False],
       [False,  True, False],
       [False, False,  True]])

>>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4]])

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