Rbf#
- class scipy.interpolate.Rbf(*args, **kwargs)[source]#
- Class for radial basis function interpolation of functions from N-D scattered data to an M-D domain (legacy). - Legacy - This class is considered legacy and will no longer receive updates. While we currently have no plans to remove it, we recommend that new code uses more modern alternatives instead. - Rbfis legacy code, for new usage please use- RBFInterpolatorinstead.- Parameters:
- *argsarrays
- x, y, z, …, d, where x, y, z, … are the coordinates of the nodes and d is the array of values at the nodes 
- functionstr 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. 
- epsilonfloat, optional
- Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start). 
- smoothfloat, 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. 
- normstr, 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 - x1to each point in- x2. For more options, see documentation of scipy.spatial.distances.cdist.
- modestr, 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. 
 
- Attributes:
- Nint
- The number of data points (as determined by the input arrays). 
- dindarray
- The 1-D array of data values at each of the data coordinates xi. 
- xindarray
- The 2-D array of data coordinates. 
- functionstr or callable
- The radial basis function. See description under Parameters. 
- epsilonfloat
- Parameter used by gaussian or multiquadrics functions. See Parameters. 
- smoothfloat
- Smoothing parameter. See description under Parameters. 
- normstr or callable
- The distance function. See description under Parameters. 
- modestr
- Mode of the interpolation. See description under Parameters. 
- nodesndarray
- A 1-D array of node values for the interpolation. 
- Ainternal property, do not use
 
 - Methods - __call__(*args)- Call self as a function. - See also - Examples - >>> import numpy as np >>> from scipy.interpolate import Rbf >>> rng = np.random.default_rng() >>> x, y, z, d = rng.random((4, 50)) >>> rbfi = Rbf(x, y, z, d) # radial basis function interpolator instance >>> xi = yi = zi = np.linspace(0, 1, 20) >>> di = rbfi(xi, yi, zi) # interpolated values >>> di.shape (20,)