cLHS

Documentation Status https://img.shields.io/badge/GitHub-clhs__py-informational.svg https://img.shields.io/github/license/wagoner47/clhs_py.svg

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

API Reference

https://img.shields.io/badge/GitHub-clhs__py-informational.svg https://img.shields.io/github/license/wagoner47/clhs_py.svg

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

API Documentation

Licensing Info

MIT License

Copyright (c) 2019 Erika Wagoner

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Indices and tables