Conditioned Latin Hypercube Sampling in Python.
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).
Currently, the only way to install this package is from source.
Clone the github repository:
git clone https://github.com/wagoner47/clhs_py.git
Or using SSH clone:
git clone email@example.com:wagoner47/clhs_py.git
Move into the new directory:
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
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.
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