cLHS

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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 https://github.com/wagoner47/clhs_py.git
    

    Or using SSH clone:

    git clone git@github.com:wagoner47/clhs_py.git
    
  2. Move into the new directory:

    cd clhs_py
    
  3. Run the setup script:

    python setup.py 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 setup.py install --user

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

python setup.py install --help

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