inpaint
Inpaint over missing data in a two-dimensional array using a penalized least square method based on discrete cosine transforms
Calling Sequence
import spatial_interpolators as spi
output = spi.inpaint(xs, ys, zs)
- spatial_interpolators.inpaint(xs, ys, zs, n=100, s0=3, z0=None, power=2, epsilon=2)[source]
Inpaint over missing data in a two-dimensional array using a penalized least square method based on discrete cosine transforms [Garcia2010] [Wang2012]
- Parameters
- xs: float
input x-coordinates
- ys: float
input y-coordinates
- zs: float
input data
- n: int, default 100
Number of iterations Use 0 for nearest neighbors interpolation
- s0: int, default 3
Smoothing
- z0: float or NoneType, default None
Initial guess for input data
- power: int, default 2
power for lambda function
- epsilon: int, default 2
relaxation factor
References
- Garcia2010
D. Garcia, Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), 1167–1178 (2010). doi: 10.1016/j.csda.2009.09.020
- Wang2012
G. Wang, D. Garcia, Y. Liu, R. de Jeu, and A. J. Dolman, A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations, Environmental Modelling & Software, 30, 139–142 (2012). doi: 10.1016/j.envsoft.2011.10.015