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

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

Notes

Keywords other than dtype are passed to :None:None:`function`.

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 :None:None:`function`.
dtype : data-type, optional: Data-type of the coordinate arrays passed to :None:None:`function`. By default, dtype is float.
like : array_like: 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

Returns

fromfunction : any: The result of the call to :None:None:`function` is passed back directly. Therefore the shape of fromfunction is completely determined by :None:None:`function`. If :None:None:`function` returns a scalar value, the shape of fromfunction would not match the shape parameter.

Construct an array by executing a function over each coordinate.

Examples

>>> 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]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.asarray numpy.asarray_chkfinite dask.array.creation.fromfunction numpy.fromfunction numpy.asanyarray

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