scipy.interpolate._rbf:Rbf

Parameters

*args : arrays

x, y, z, ..., d, where x, y, z, ... are the coordinates of the nodes and d is the array of values at the nodes

function : str or callable, optional

The radial basis function, based on the radius, r, given by the norm (default is Euclidean distance); the default is 'multiquadric'

'multiquadric': sqrt((r/self.epsilon)**2 + 1)
'inverse': 1.0/sqrt((r/self.epsilon)**2 + 1)
'gaussian': exp(-(r/self.epsilon)**2)
'linear': r
'cubic': r**3
'quintic': r**5
'thin_plate': r**2 * log(r)

If callable, then it must take 2 arguments (self, r). The epsilon parameter will be available as self.epsilon. Other keyword arguments passed in will be available as well.

epsilon : float, optional

Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).

smooth : float, optional

Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points in this case.

norm : str, callable, optional

A function that returns the 'distance' between two points, with inputs as arrays of positions (x, y, z, ...), and an output as an array of distance. E.g., the default: 'euclidean', such that the result is a matrix of the distances from each point in x1 to each point in x2. For more options, see documentation of scipy.spatial.distances.cdist.

mode : str, optional

Mode of the interpolation, can be '1-D' (default) or 'N-D'. When it is '1-D' the data d will be considered as 1-D and flattened internally. When it is 'N-D' the data d is assumed to be an array of shape (n_samples, m), where m is the dimension of the target domain.