compact_radial_basis
Interpolates data using compactly supported radial basis functions of minimal degree and sparse matrix algebra
Calling Sequence
import spatial_interpolators as spi
ZI = spi.compact_radial_basis(xs, ys, zs, XI, YI, dimension, order, method='wendland')
- spatial_interpolators.compact_radial_basis(xs, ys, zs, XI, YI, dimension, order, smooth=0.0, radius=None, method='wendland')[source]
Interpolates a sparse grid using compactly supported radial basis functions of minimal degree and sparse matrix algebra
- Parameters
- xs: float
scaled input x-coordinates
- ys: float
scaled input y-coordinates
- zs: float
input data
- XI: float
scaled output x-coordinates for data grid
- YI: float
scaled output y-coordinates for data grid
- dimension: int
spatial dimension of Wendland function (d)
- order: int
smoothness order of Wendland function (k)
- smooth: float, default 0.0
smoothing weights
- radius: float or NoneType, default None
scaling factor for the basis function
- method: str, default `wendland`
compactly supported radial basis function
'wendland'
- Returns
- ZI: float
interpolated data grid
References
- Buhmann2003
M. Buhmann, “Radial Basis Functions”, Cambridge Monographs on Applied and Computational Mathematics, (2003).
- Wendland1995
H. Wendland, “Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree,” Advances in Computational Mathematics, 4, 389–396, (1995). doi: 10.1007/BF02123482
- Wendland2005
H. Wendland, “Scattered Data Approximation”, Cambridge Monographs on Applied and Computational Mathematics, (2005).