About conditioned Latin Hypercube Sampling (cLHS) in Python

This code is based on the cLHS method of Minasny & McBratney (2006). It follows some of the code from the R package clhs of Roudier et al.

For cLHS the problem is: given \(N\) sites with ancillary variables (\(X\)), select \(x\) a sub-sample of size \(n \ll N\) in order that \(x\) forms a Latin hypercube, or the multivariate distribution of \(X\) is maximally stratified.

In short, this code attempts to create a Latin Hypercube sample by selecting only from input data. It uses simulated annealing to force the sampling to converge more rapidly, and also allows for setting a stopping criterion on the objective function described in Minasny & McBratney (2006).

Credits: Erika Wagoner (wagoner47) and Zhonghua Zheng (zzheng93)