xorbits.numpy.fromfunction#
- xorbits.numpy.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)
.- 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 bearray([[0, 0], [1, 1]])
andarray([[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.New in version 1.20.0(numpy).
- Returns
fromfunction – 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.
- Return type
any
Notes
Keywords other than dtype and like are passed to function.
Examples
>>> 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]])
Warning
This method has not been implemented yet. Xorbits will try to execute it with numpy.
This docstring was copied from numpy.