shepard_interpolant
Interpolates data by evaluating Shepard Interpolants based on inverse distances
Calling Sequence
import spatial_interpolators as spi
ZI = spi.shepard_interpolant(xs, ys, zs, XI, YI, power=2.0)
- spatial_interpolators.shepard_interpolant(xs, ys, zs, XI, YI, power=0.0, eps=1e-07, modified=False, D=25000.0, L=500000.0)[source]
Evaluates Shepard interpolants to 2D data based on inverse distance weighting
- Parameters
- xs: float
input x-coordinates
- ys: float
input y-coordinates
- zs: float
input data
- XI: float
output x-coordinates for data grid
- YI: float
output y-coordinates for data grid
- power: float, default 0.0
Power used in the inverse distance weighting
- eps: float, default 1e-7
minimum distance value for valid points
- modified: boo, default False
use declustering modified Shepard’s interpolants [Schnell2014]
- D: float, default 25e3
declustering distance
- L: float, default 500e3
maximum distance to be included in weights
- Returns
- ZI: float
interpolated data grid
References
- Schnell2014
J. Schnell, C. D. Holmes, A. Jangam, and M. J. Prather, “Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model,” Atmospheric Physics and chemistry, 14(15), 7721–7739, (2014). doi: 10.5194/acp-14-7721-2014
- Shepard1968
D. Shepard, “A two-dimensional interpolation function for irregularly spaced data,” ACM68: Proceedings of the 1968 23rd ACM National Conference, 517–524, (1968). doi: 10.1145/800186.810616