Metadata-Version: 2.4
Name: expandLHS
Version: 1.0.0
Summary: A Python package to expand a Latin Hypercube Sample
Home-page: https://github.com/m-boschini/expandLHS
Author: Matteo Boschini, Davide Gerosa
Author-email: Matteo Boschini <m.boschini1@campus.unimib.it>, Davide Gerosa <davide.gerosa@unimib.it>
License: Copyright (c) 2025 Matteo Boschini
        
        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.
Project-URL: Homepage, https://github.com/m-boschini/expandLHS
Project-URL: Documentation, https://m-boschini.github.io/expandLHS/
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: numba>=0.57
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15076931.svg)](https://doi.org/10.5281/zenodo.15076931)

## expandLHS

`expandLHS` is a Python module that implements a model-free expansion algorithm for a Latin Hypercube sample set.
The Latin Hypercube Sampling (LHS) is a stratified sampling technique that allows to generate $N$ near-random samples 
in the $P$-dimensional hypercube $[0, 1)^P$. It is a space-filling sampling strategy that ensures the one-dimensional 
projection property, i.e. the samples are uniformly distributed in each one-dimension projection. This module extends the usage of this technique by implementing an expansion algorithm. Starting from an initial LHS set of size $N$, `expandLHS` samples $M$ additional points in a LHS-like fashion trying to preserve the LHS properties at most.

This algorithm is introduced in
- *“LHS in LHS”: a new expansion strategy for Latin hypercube sampling in simulation design.* 
M. Boschini, D. Gerosa, A. Crespi, M. Falcone (to be published)

The code is distributed under version control at
- [github.com/m-boschini/expandLHS](https://github.com/m-boschini/expandLHS)

The documentation is available at
 - [m-boschini.github.io/expandLHS](https://m-boschini.github.io/expandLHS)

To install the code simply use

    pip install expandLHS

An example notebook can be found in the [documentation](https://m-boschini.github.io/expandLHS) together with a detailed description of the functions.

`expandLHS` is released under the MIT License. 
