numpy.fromfunction — NumPy v1.15 Manual (original) (raw)

numpy. fromfunction(function, shape, **kwargs)[source]¶

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 ofshape. Each parameter represents the coordinates of the array varying along a specific axis. For example, if shapewere (2, 2), then the parameters would bearray([[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.
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 offromfunction would match the shape parameter.

Parameters:

function : callable The function is called with N parameters, where N is the rank ofshape. Each parameter represents the coordinates of the array varying along a specific axis. For example, if shapewere (2, 2), then the parameters would bearray([[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.

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 offromfunction would match the shape parameter.

Notes

Keywords other than dtype are passed to function.

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]])