`numpy.ma.core:fromfunction`

Signature

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

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).

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

: fromfunction: MaskedArray: 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.

Keywords other than dtype and like are passed to function.

import numpy as np
np.fromfunction(lambda i, j: i, (2, 2), dtype=float)

np.fromfunction(lambda i, j: j, (2, 2), dtype=float)

np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)

np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)