numpy.fromfunction#

numpy.fromfunction(function, shape, *, dtype=<class 'float'>, like=None, **kwargs)[源代码]#

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:

functioncallable: 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.
dtypedata-type, optional: Data-type of the coordinate arrays passed to function. By default, dtype is float.
likearray_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.

在 1.20.0 版本加入.

Returns:

fromfunctionany: 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.

参见

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=np.float64)
array([[0., 0.],
       [1., 1.]])

>>> np.fromfunction(lambda i, j: j, (2, 2), dtype=np.float64)
array([[0., 1.],
       [0., 1.]])

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

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