{
    "info": {
        "author": "Francois-Michel De Rainville",
        "author_email": "f.derainville@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
            "Topic :: Scientific/Engineering"
        ],
        "description": "Python Nearly Orthogonal Latin Hypercube Generator\r\n==================================================\r\n\r\nThis library allows to generate Nearly Orthogonal Latin Hypercubes (NOLH) according to\r\nCioppa (2007) and De Rainville et al. (2012) and reference therein.\r\n\r\nInstallation\r\n------------\r\nClone the repository ::\r\n\r\n    $ git clone http://github.com/fmder/pynolh.git\r\n\r\nand from the cloned directory type ::\r\n\r\n    $ python setup.py install\r\n\r\nPyNOLH requires Numpy.\r\n\r\nUsage\r\n-----\r\nThe library contains a single generator and a function to retrieve the necessary parameters\r\nfrom a desired dimensionality. To generate a 6 dimension NOLH from the indentity permutation::\r\n\r\n    import pynolh\r\n\r\n    dim = 6\r\n    m, q, r = pynolh.params(dim)\r\n    conf = range(q)\r\n    remove = range(dim - r, dim)\r\n    nolh = pynolh.nolh(conf, remove)\r\n\r\nThe NOLH returned is a numpy array with one row being one sample.\r\n\r\nYou can also produce a NOLH from a random permutation configuration vector and remove random columns::\r\n\r\n    import pynolh\r\n    import random\r\n\r\n    dim = 6\r\n    m, q, r = pynolh.params(dim)\r\n    conf = random.sample(range(q), q)\r\n    remove = random.sample(range(q), r)\r\n    nolh = pynolh.nolh(conf, remove)\r\n\r\nThe `nolh()` function accepts configurations with either numbers in [0 q-1] or [1 q]. ::\r\n\r\n    import pynolh\r\n\r\n    dim = 6\r\n    m, q, r = pynolh.params(dim)\r\n    conf = range(1, q + 1)\r\n    remove = range(dim - r + 1, dim + 1)\r\n    nolh = pynolh.nolh(conf, remove)\r\n\r\nSome prebuilt configurations are given within the library. The `CONF` module attribute is a dictionary with the dimension as key and a configuration, columns to remove pair as value. ::\r\n\r\n    import pynolh\r\n\r\n    conf, remove = pynolh.CONF[6]\r\n    nolh = pynolh.nolh(conf, remove)\r\n\r\nThe configurations for dimensions 2 to 7 are from Cioppa (2007) and 8 to 29 are from De Rainville et al. 2012.\r\n\r\nConfiguration Repository\r\n------------------------\r\nSee the `Quasi Random Sequences Repository <http://qrand.gel.ulaval.ca>`_ for more configurations. \r\n\r\nReferences\r\n----------\r\nCioppa, T. M., & Lucas, T. W. (2007). Efficient nearly orthogonal and space-filling Latin hypercubes. *Technometrics*, 49(1).\r\n\r\nDe Rainville, F.-M., Gagn\u00e9, C., Teytaud, O., & Laurendeau, D. (2012). Evolutionary optimization of low-discrepancy sequences. *ACM Transactions on Modeling and Computer Simulation (TOMACS)*, 22(2), 9.",
        "description_content_type": null,
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        "keywords": "",
        "license": "LICENSE.txt",
        "maintainer": "",
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        "name": "pynolh",
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