Documentation Status

Conditioned Latin Hypercube Sampling in Python.

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

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).

Installation instructions

Currently, the only way to install this package is from source.

  1. Clone the github repository:

    git clone

    Or using SSH clone:

    git clone
  2. Move into the new directory:

    cd clhs_py
  3. Run the setup script:

    python install

You may also supply the –user option to install for a single user (which is helpful if you don’t have admin/root privledges, for instance):

python install --user

Other options are also available for the setup script. To see all of them with documentation, use:

python install --help

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