Rbf(*args, **kwargs)The number of data points (as determined by the input arrays).
The 1-D array of data values at each of the data coordinates :None:None:`xi`.
The 2-D array of data coordinates.
The radial basis function. See description under Parameters.
Parameter used by gaussian or multiquadrics functions. See Parameters.
Smoothing parameter. See description under Parameters.
The distance function. See description under Parameters.
Mode of the interpolation. See description under Parameters.
A 1-D array of node values for the interpolation.
:None:None:`Rbf` is legacy code, for new usage please use :None:None:`RBFInterpolator` instead.
x, y, z, ..., d, where x, y, z, ... are the coordinates of the nodes and d is the array of values at the nodes
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.
Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
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.
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 :None:None:`scipy.spatial.distances.cdist`.
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.
A class for radial basis function interpolation of functions from N-D scattered data to an M-D domain.
>>> from scipy.interpolate import RbfSee :
... 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,)
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.interpolate._rbf.Rbf
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